Article

Comparison of Neuroimaging Modalities for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Dementia

January 2014
Neurobiology of Aging 35(1):143–151

DOI:10.1016/j.neurobiolaging.2013.06.018

Authors:

Paula T Trzepacz

Indiana University School of Medicine

Peng jin yu

Dalian University of Technology

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Clinical utility of principal components analysis on PET data in the prediction of Alzheimer’s disease dementia

Thesis

Jan 2021

Ganna Blazhenets

In this study, the voxel-wise principal components analysis (PCA) was applied to [18F]FDG PET and [18F]AV-45 PET data to identify metabolic patterns related to conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia. Methods: [18F]FDG PET scans of 544 MCI patients were included for analysis. Voxel-based PCA was implemented to identify AD dementia conversion-related pattern (FDG-ADCRP). PCA was additionally applied to the amyloid PET data to construct the Aβ-ADCRP. Cox proportional hazard regression models assessed the predictive value of the pattern expression score (PES) of FDG-ADCRP and Aβ-ADCRP alone and in combination with non-imaging variables. The models were compared in their abilities to stratify subjects according to their conversion risks employing Kaplan-Meier survival analyses. Those subjects who had also CSF measures of phosphorylated tau available were categorised following the AT(N) classification scheme. PES of FDG-ADCRP was compared among groups and its prognostic value was assessed within the group of subjects with biologically defined AD. Then, constructed patterns were validated against neuropathological staging schemes of Braak and Thal. Results: PCA applied to [18F]FDG PET revealed the FDG-ADCRP that involved regions with a relative decrease in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus) and a relative increase in metabolism (sensorimotor and occipital, cerebellum, and putamen). The constructed Aβ-ADCRP showed high amyloid load in the posterior cingulate cortex and precuneus, the mesial frontal, the insular and ventral striatum. The PES of Aβ-ADCRP yielded significantly lower predictive value than PES of FDG-ADCRP, while both were improved when combined with non-imaging variables. Best prediction accuracy was reached when the PES of Aβ-ADCRP, the PES of FDG-ADCRP, and non-imaging variables were combined into one model. In subjects categorised according to the AT(N) classification scheme, PES of FDG-ADCRP was significantly higher only in the group with biologically defined AD. Finally, the PES of FDG-ADCRP and Aβ-ADCRP showed a highly significant association with the neuropathological examinations. Conclusions: The PCA applied to [18F]FDG PET resulted in FDG-ADCRP, which is a valuable biomarker of conversion in subjects with MCI and biologically defined AD. It shows great potential for stratifying subjects according to the predicted risk. The strong association with post-mortem data promotes its use in research and clinical settings.

Amyloid biomarkers as predictors of conversion from mild cognitive impairment to Alzheimer's dementia: a comparison of methods

Article

Full-text available

Nov 2020

Background Amyloid-β (Aβ) PET is an established predictor of conversion from mild cognitive impairment (MCI) to Alzheimer’s dementia (AD). We compared three PET (including an approach based on voxel-wise Cox regression) and one cerebrospinal fluid (CSF) outcome measures in their predictive power. Methods Datasets were retrieved from the ADNI database. In a training dataset (N = 159), voxel-wise Cox regression and principal component analyses were used to identify conversion-related regions (Cox-VOI and AD conversion-related pattern (ADCRP), respectively). In a test dataset (N = 129), the predictive value of mean normalized ¹⁸F-florbetapir uptake (SUVR) in AD-typical brain regions (composite SUVR) or the Cox-VOI and the pattern expression score (PES) of ADCRP and CSF Aβ42/Aβ40 as predictors were compared by Cox models (corrected for age and sex). Results All four Aβ measures were significant predictors (p < 0.001). Prediction accuracies (Harrell’s c) showed step-wise significant increases from Cox-SUVR (c = 0.71; HR = 1.84 per Z-score increase), composite SUVR (c = 0.73; HR = 2.18), CSF Aβ42/Aβ40 (c = 0.75; HR = 3.89) to PES (c = 0.77; HR = 2.71). Conclusion The PES of ADCRP is the most predictive Aβ PET outcome measure, comparable to CSF Aβ42/Aβ40, with a slight but statistically significant advantage.

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Article

Full-text available

Mar 2020

Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

Added value of amyloid PET in individualized risk predictions for MCI patients

Article

Full-text available

Jul 2019

Introduction: To construct a prognostic model based on amyloid positron emission tomography (PET) to predict clinical progression in individual patients with mild cognitive impairment (MCI). Methods: We included 411 MCI patients from the Alzheimer's Disease Neuroimaging Initiative. Prognostic models were constructed with Cox regression with demographics, magnetic resonance imaging, and/or amyloid PET to predict progression to Alzheimer's disease dementia. The models were validated in the Amsterdam Dementia Cohort. Results: The combined model (Harrell's C = 0.82 [0.78-0.86]) was significantly superior to demographics (β = 0.100, P < .001), magnetic resonance imaging (β = 0.037, P = .011), and PET only models (β = 0.053, P = .003).The models can be used to calculate individualized risk, for example, a female MCI patient (age = 60, APOE ε4 positive, Mini-Mental State Examination = 25, hippocampal volume = 5.8 cm3, amyloid PET positive) has 35% (19-57) risk in one year and 85% (64-97) risk in three years. Model performances in the Amsterdam Dementia Cohort were reasonable. Discussion: The present study facilitates the interpretation of an amyloid PET result in the context of a patient's own characteristics and clinical assessment.

β-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline

Article

Full-text available

Jul 2020

Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ and tau better predicting early dementia status, and biomarkers of neurodegeneration, especially glucose hypometabolism, better predicting later dementia status. Our results suggest that AD treatments may also need to be disease stage-oriented with Aβ and tau as targets in early AD and glucose metabolism as a target in later AD. Here the authors analyze the Alzheimer’s Disease Neuroimaging Initiative dataset using random forest machine learning methods and determine that Aβ and tau biomarkers are better predictors of early dementia status, while glucose hypometabolism is a better predictor of later dementia status. These results suggest the need for stage-oriented Alzheimer’s disease treatments.

Pineal volume reduction in patients with mild cognitive impairment who converted to Alzheimer's disease

Article

Jul 2020

Aim Pineal parenchymal volume (PPV) reduction is one of the predisposing factors for Alzheimer's disease (AD). Therefore, PPV could be used as a predictor of developing AD in clinical settings. We investigated whether PPV in patients with mild cognitive impairment (MCI) was correlated with conversion of these patients to AD. Methods A total of 237 patients with MCI underwent brain magnetic resonance imaging (MRI). A two‐sample t‐test was used to compare PPV at baseline in MCI patients who converted to AD (MCI‐C) and those who did not convert (MCI‐NC). Logistic regression analysis with forced entry was used to identify predictors of AD, with variables of PPV, age, gender, education, APOE‐ε4 alleles, Mini Mental State Examination (MMSE) score, and total intracranial volume at baseline. Two‐way repeated measures analysis of variance (ANOVA) was conducted to compare PPV at baseline and at the last examination in the MCI‐C and MCI‐NC groups. Results PPV in the MCI‐C group was significantly lower than that in the MCI‐NC group. In logistic regression analysis, two independent predictors of AD were identified: MMSE and PPV. Two‐way repeated measures ANOVA revealed a significant group effect, but no time effect. Conclusion The pineal volume is a predictor of AD conversion, and pineal volume reduction in AD starts early when patients are still in the MCI stage. Thus, pineal volume reduction might be useful as a predictor of developing AD in clinical settings. This article is protected by copyright. All rights reserved.

Companion animal models of neurological disease

Article

Full-text available

Nov 2019
J NEUROSCI METH

Clinical translation of novel therapeutics that improve the survival and quality of life of patients with neurological disease remains a challenge, with many investigational drug and device candidates failing in advanced stage clinical trials. Naturally occurring inherited and acquired neurological diseases, such as epilepsy, inborn errors of metabolism, brain tumors, spinal cord injury, and stroke occur frequently in companion animals, and many of these share epidemiologic, pathophysiologic and clinical features with their human counterparts. As companion animals have a relatively abbreviated lifespan and genetic background, are immunocompetent, share their environment with human caregivers, and can be clinically managed using techniques and tools similar to those used in humans, they have tremendous potential for increasing the predictive value of preclinical drug and device studies. Here, we review comparative features of spontaneous neurological diseases in companion animals with an emphasis on neuroimaging methods and features, illustrate their historical use in translational studies, and discuss inherent limitations associated with each disease model. Integration of companion animals with naturally occurring disease into preclinical studies can complement and expand the knowledge gained from studies in other animal models, accelerate or improve the manner in which research is translated to the human clinic, and ultimately generate discoveries that will benefit the health of humans and animals.

Predictive Value of 18 F-Florbetapir and 18 F-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia

Article

Full-text available

Oct 2019

The present study examines the predictive values of amyloid PET, 18F-fluorodeoxyglucose (FDG) PET and non-imaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: 319 patients with MCI from the Alzheimer's disease neuroimaging initiative database were included. In a derivation dataset (n = 159), the following Cox proportional hazard models were constructed, each adjusted for age and sex: i) amyloid PET using 18F-Florbetapir: pattern expression score (PES) of an amyloid-beta AD conversion-related pattern (Aβ-ADCRP) constructed by principle components analysis (PCA); ii) 18F-FDG PET: PES of a previously defined 18F-FDG-based ADCRP, constructed by PCA; iii) non-imaging model: functional activities questionnaire, apolipoprotein E and mini-mental state examination score; iv) 18F-FDG PET + amyloid PET model, v) amyloid PET + non-imaging model, vi) 18F-FDG PET + non-imaging model, and vii) amyloid PET + 18F-FDG PET + non-imaging model. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: Based on the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the non-imaging model and significantly improved by adding non-imaging variables. Best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and non-imaging variables. The combined model yielded five-year free-of-conversion rates of the low-, medium- and high-risk groups of 100%, 64%, and 24%, respectively. Conclusions:18F-FDG PET, amyloid PET, and non-imaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks which is of great interest for patient care and clinical trials.

Evaluating Frontoparietal Network Topography for Diagnostic Markers of Alzheimer’s Disease

Preprint

Full-text available

Apr 2024

Bayard Rogers

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer’s disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

The use of individual-based FDG-PET volume of interest in predicting conversion from mild cognitive impairment to dementia

Article

Full-text available

Mar 2024
BMC Med Imag

Background Based on a longitudinal cohort design, the aim of this study was to investigate whether individual-based ¹⁸F fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) regional signals can predict dementia conversion in patients with mild cognitive impairment (MCI). Methods We included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with Alzheimer’s disease with dementia, and 40 cognitively normal controls. Data from annual cognitive measurements, 3D T1 magnetic resonance imaging (MRI) scans, and ¹⁸F-FDG-PET scans were used for outcome analysis. An individual-based FDG-PET approach was applied using seven volumes of interest (VOIs), Z transformed using a normal FDG-PET template. Hypometabolism was defined as a Z score < -2 of regional standard uptake value ratio. For the longitudinal cognitive test scores, generalized estimating equations were used. A linear mixed-effects model was used to compare the temporal impact of cortical hypometabolism and cortical thickness degeneration. Results The clinical follow-up period was 6.6 ± 3.8 years (range 3.1 to 16.0 years). The trend of cognitive decline could differentiate MCI-C from MCI-NC after 3 years of follow-up. In the baseline 18F-FDG-PET scan of the patients with MCI, medial temporal lobe (MTL; 94.7% sensitivity, 80.5% specificity) and posterior cingulate cortex (PCC; 89.5% sensitivity, 73.1% specificity) hypometabolism predicted conversion with high accuracy. ¹⁸F-FDG-PET hypometabolism preceded dementia conversion at an interval of 3.70 ± 1.68 years and was earlier than volumetric changes, with the exception of the MTL. Conclusions Our finding supports the use of individual-based ¹⁸F-FDG-PET analysis to predict MCI conversion to dementia. Reduced FDG-PET metabolism in the MTL and PCC were strongly associated with future cognitive decline in the MCI-C group. Changes in ¹⁸F-FDG-PET occurred 1 to 8 years prior to conversion to dementia. Progressive hypometabolism in the PCC, precuneus and lateral temporal lobe, but not MTL, preceded MRI findings at the MCI stage.

Cortical folding correlates to aging and Alzheimer’s Disease’s cognitive and CSF biomarkers

Article

Full-text available

Feb 2024

This manuscript presents the quantification and correlation of three aspects of Alzheimer’s Disease evolution, including structural, biochemical, and cognitive assessments. We aimed to test a novel structural biomarker for neurodegeneration based on a cortical folding model for mammals. Our central hypothesis is that the cortical folding variable, representative of axonal tension in white matter, is an optimal discriminator of pathological aging and correlates with altered loadings in Cerebrospinal Fluid samples and a decline in cognition and memory. We extracted morphological features from T1w 3T MRI acquisitions using FreeSurfer from 77 Healthy Controls (age = 66 ± 8.4, 69% females), 31 Mild Cognitive Impairment (age = 72 ± 4.8, 61% females), and 13 Alzheimer’s Disease patients (age = 77 ± 6.1, 62% females) of recruited volunteers in Brazil to test its discriminative power using optimal cut-point analysis. Cortical folding distinguishes the groups with reasonable accuracy (Healthy Control-Alzheimer’s Disease, accuracy = 0.82; Healthy Control-Mild Cognitive Impairment, accuracy = 0.56). Moreover, Cerebrospinal Fluid biomarkers (total Tau, A 1-40, A 1-42, and Lipoxin) and cognitive scores (Cognitive Index, Rey’s Auditory Verbal Learning Test, Trail Making Test, Digit Span Backward) were correlated with the global neurodegeneration in MRI aiming to describe health, disease, and the transition between the two states using morphology.

Chinese ADNI study:Characteristic AD biomarkers precisely predicts Alzheimer’s dementia: China Aging and Neurodegenerative Initiative (CANDI) study

Article

Full-text available

Dec 2023

Background To test the utility of the “A/T/N” system in the Chinese population, we study core Alzheimer’s disease (AD) biomarkers in a newly established Chinese cohort. Method A total of 411 participants were selected, including 96 cognitively normal individuals, 94 patients with mild cognitive impairment (MCI) patients, 173 patients with AD, and 48 patients with non‐AD dementia. Fluid biomarkers were measured with single molecule array. Amyloid beta (A β ) deposition was determined by 18F‐Flobetapir positron emission tomography (PET), and brain atrophy was quantified using magnetic resonance imaging (MRI). Result A β 42/A β 40 was decreased, whereas levels of phosphorylated tau (p‐tau) were increased in cerebrospinal fluid (CSF) and plasma from patients with AD. CSF A β 42/A β 40, CSF p‐tau, and plasma p‐tau showed a high concordance in discriminat‐ ing between AD and non‐AD dementia or elderly controls. A combination of plasma p‐tau, apolipoprotein E ( APOE ) genotype, and MRI measures accurately predicted amyloid PET status. Conclusion These results revealed a universal applicability of the “A/T/N” framework in a Chinese population and established an optimal diagnostic model consisting of cost‐ effective and non‐invasive approaches for diagnosing AD.

Sleep Biomarkers for Predicting Cognitive Decline and Alzheimer’s Disease: A Systematic Review of Longitudinal Studies

Article

Nov 2023
J ALZHEIMERS DIS

Background: Sleep disturbances are considered a hallmark of dementia, and strong evidence supports the association between alterations in sleep parameters and cognitive decline in patients with mild cognitive impairment and Alzheimer’s disease (AD). Objective: This systematic review aims to summarize the existing evidence on the longitudinal association between sleep parameters and cognitive decline, with the goal of identifying potential sleep biomarkers of AD-related neurodegeneration. Methods: Literature search was conducted in PubMed, Web of Science, and Scopus databases from inception to 28 March 2023. Longitudinal studies investigating the association between baseline objectively-measured sleep parameters and cognitive decline were assessed for eligibility. Results: Seventeen studies were included in the qualitative synthesis. Sleep fragmentation, reduced sleep efficiency, reduced REM sleep, increased light sleep, and sleep-disordered breathing were identified as predictors of cognitive decline. Sleep duration exhibited a U-shaped relation with subsequent neurodegeneration. Additionally, several sleep microstructural parameters were associated with cognitive decline, although inconsistencies were observed across studies. Conclusions: These findings suggest that sleep alterations hold promise as early biomarker of cognitive decline, but the current evidence is limited due to substantial methodological heterogeneity among studies. Further research is necessary to identify the most reliable sleep parameters for predicting cognitive impairment and AD, and to investigate interventions targeting sleep that can assist clinicians in the early recognition and treatment of cognitive decline. Standardized procedures for longitudinal studies evaluating sleep and cognition should be developed and the use of continuous sleep monitoring techniques, such as actigraphy or EEG headband, might be encouraged.

Evaluation of Brain Perfusion with Radionuclide Imaging in Dementia Patients

Article

Full-text available

Aug 2023

Sule Ceylan

Objective: This study aims to evaluate the perfusion in the affected brain lobes with radionuclide imaging in patients with dementia and to contribute to the literature by determining the relationship between the degree of dementia. Methods: In this retrospective study, 44 adult patients were included. The mean age of the patients was 73 (57-87). Twenty-three (52.3%) of these were female. All patients underwent mini-mental state test, neurological examinations and radionuclide brain perfusion imaging. The areas of interest of the frontal, parietal, temporal, and occipital lobes and the cerebellum were drawn, respectively, and the cerebellum ratios of the relevant lobes were compared. In this way, brain perfusion index values of lobes were obtained. Results: Brain perfusion in group 1 was higher than in other groups. Groups were also compared in pairs. There was no significant difference in perfusion values in the occipital lobe. There was a significant difference in perfusion between groups 1 and 2 in lobes other than the occipital lobe. This was also the case for Groups 1 and 3. When group 2 with early dementia symptoms, and group 3 with moderate dementia were compared, there was a significant difference between the perfusion values of only the right and left frontal lobes. Conclusion: Early diagnosis is very important in progressive dementia. Clinical tests are successfully applied to categorize patients. Evaluation of brain perfusion and metabolism with functional radionuclide imaging is significant in guiding the treatment and evaluating the success of the treatment.

Cerebral amyloid deposition predicts long‐term cognitive decline in hemorrhagic small vessel disease

Article

Full-text available

Aug 2023

Background To investigate the association between cerebral amyloid deposition and long‐term cognitive outcomes in patients with hemorrhagic small vessel disease (SVD) and survivors of intracerebral hemorrhage (ICH). Methods Patients experiencing an ICH without overt dementia were prospectively recruited (n = 68) for brain MRI and Pittsburgh compound B (PiB) positron emission tomography scans at baseline. Cognitive function was assessed using the mini‐mental status examination (MMSE) and clinical dementia rating after an overall median follow‐up of 3.8 years. A positive amyloid scan was defined as a global PiB standardized uptake value ratio >1.2. Associations between follow‐up cognitive outcomes and neuroimaging markers were explored using multivariable Cox regression models. Results PiB(+) patients were older (72.1 ± 7.8 vs. 59.9 ± 11.7, p = .002) and more frequently had cerebral amyloid angiopathy (CAA) (63.6% vs. 15.8%, p = .002) than PiB(−) patients. PiB(+) was associated with a higher risk of dementia conversion (32.9 vs. 4.0 per 100‐person‐years, hazard ratio [HR] = 15.7 [3.0–80.7], p = .001) and MMSE score decline (58.8 vs. 9.9 per 100‐person‐years, HR = 6.2 [1.9–20.0], p = .002). In the non‐CAA subgroup (n = 52), PiB(+) remained an independent predictor of dementia conversion, p = .04). In the Cox models, PiB(+) was an independent predictor of dementia conversion (HR = 15.8 [2.6–95.4], p = .003) and MMSE score decline (HR = 5.7 [1.6–20.3], p = .008) after adjusting for confounders. Conclusions Cerebral amyloid deposition potentially contributes to long‐term cognitive decline in SVD‐related ICH.

Comprehensive Review of Task-Based Neuroimaging Studies of Cognitive Deficits in Alzheimer’s Disease using Electrophysiological Methods

Article

May 2023
AGEING RES REV

With an aging population, cognitive decline and neurodegenerative disorders are emerging public health crises with enormous yet still under-recognized burdens. Alzheimer's disease (AD) is the most common type of dementia, and the number of cases is expected to dramatically rise in the upcoming decades. Substantial efforts have been placed into understanding the disease. One of the primary avenues of research is neuroimaging, and while positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are most common, crucial recent advancements in electrophysiological methods such as magnetoencephalography (MEG) and electroencephalography (EEG) have provided novel insight into the aberrant neural dynamics at play in AD pathology. In this review, we outline task-based M/EEG studies published since 2010 using paradigms probing the cognitive domains most affected by AD, including memory, attention, and executive functioning. Furthermore, we provide important recommendations for adapting cognitive tasks for optimal use in this population and adjusting recruitment efforts to improve and expand future neuroimaging work.

Amyloid‐β PET in Alzheimer's disease: A systematic review and Bayesian meta‐analysis

Article

Full-text available

Dec 2022

Background In recent years, longitudinal studies of Alzheimer's disease (AD) have been successively concluded. Our aim is to determine the efficacy of amyloid‐β (Aβ) PET in diagnosing AD and early prediction of mild cognitive impairment (MCI) converting to AD. By pooling studies from different centers to explore in‐depth whether diagnostic performance varies by population type, radiotracer type, and diagnostic approach, thus providing a more comprehensive theoretical basis for the subsequent widespread application of Aβ PET in the clinical setting. Methods Relevant studies were searched through PubMed. The pooled sensitivities, specificities, DOR, and the summary ROC curve were obtained based on a Bayesian random‐effects model. Results Forty‐eight studies, including 5967 patients, were included. Overall, the pooled sensitivity, specificity, DOR, and AUC of Aβ PET for diagnosing AD were 0.90, 0.80, 35.68, and 0.91, respectively. Subgroup analysis showed that Aβ PET had high sensitivity (0.91) and specificity (0.81) for differentiating AD from normal controls but very poor specificity (0.49) for determining AD from MCI. The pooled sensitivity and specificity were 0.84 and 0.62, respectively, for predicting the conversion of MCI to AD. The differences in diagnostic efficacy between visual assessment and quantitative analysis and between ¹¹C‐PIB PET and ¹⁸F‐florbetapir PET were insignificant. Conclusions The overall performance of Aβ PET in diagnosing AD is favorable, but the differentiation between MCI and AD patients should consider that some MCI may be at risk of conversion to AD and may be misdiagnosed. A multimodal diagnostic approach and machine learning analysis may be effective in improving diagnostic accuracy.

Discriminating Aging Cognitive Decline Spectrum Using PET and Magnetic Resonance Image Features

Article

Aug 2022
J ALZHEIMERS DIS

Background: The population aging increased the prevalence of brain diseases, like Alzheimer's disease (AD), and early identification of individuals with higher odds of cognitive decline is essential to maintain quality of life. Imaging evaluation of individuals at risk of cognitive decline includes biomarkers extracted from brain positron emission tomography (PET) and structural magnetic resonance imaging (MRI). Objective: We propose investigating ensemble models to classify groups in the aging cognitive decline spectrum by combining features extracted from single imaging modalities and combinations of imaging modalities (FDG+AMY+MRI, and a PET ensemble). Methods: We group imaging data of 131 individuals into four classes related to the individuals' cognitive assessment in baseline and follow-up: stable cognitive non-impaired; individuals converting to mild cognitive impairment (MCI) syndrome; stable MCI; and Alzheimer's clinical syndrome. We assess the performance of four algorithms using leave-one-out cross-validation: decision tree classifier, random forest (RF), light gradient boosting machine (LGBM), and categorical boosting (CAT). The performance analysis of models is evaluated using balanced accuracy before and after using Shapley Additive exPlanations with recursive feature elimination (SHAP-RFECV) method. Results: Our results show that feature selection with CAT or RF algorithms have the best overall performance in discriminating early cognitive decline spectrum mainly using MRI imaging features. Conclusion: Use of CAT or RF algorithms with SHAP-RFECV shows good discrimination of early stages of aging cognitive decline, mainly using MRI image features. Further work is required to analyze the impact of selected brain regions and their correlation with cognitive decline spectrum.

A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study

Article

Full-text available

Jun 2022

Introduction: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort. Methods: A total of 411 participants were selected, including 96 cognitively normal individuals, 94 patients with mild cognitive impairment (MCI) patients, 173 patients with AD, and 48 patients with non-AD dementia. Fluid biomarkers were measured with single molecule array. Amyloid beta (Aβ) deposition was determined by 18 F-Flobetapir positron emission tomography (PET), and brain atrophy was quantified using magnetic resonance imaging (MRI). Results: Aβ42/Aβ40 was decreased, whereas levels of phosphorylated tau (p-tau) were increased in cerebrospinal fluid (CSF) and plasma from patients with AD. CSF Aβ42/Aβ40, CSF p-tau, and plasma p-tau showed a high concordance in discriminating between AD and non-AD dementia or elderly controls. A combination of plasma p-tau, apolipoprotein E (APOE) genotype, and MRI measures accurately predicted amyloid PET status. Discussion: These results revealed a universal applicability of the "A/T/N" framework in a Chinese population and established an optimal diagnostic model consisting of cost-effective and non-invasive approaches for diagnosing AD.

Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification

Article

Full-text available

Apr 2022

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

Association of Homocysteine Levels With Medial Temporal Lobe Atrophy Among Carriers and Non-carriers of APOE ε4 in MCI Subjects

Article

Full-text available

Apr 2022

Background Different clinical subtypes of mild cognitive impairment (MCI) involve heterogeneous underlying etiologies. This study investigated the association between demographics, neuropsychological performance, apolipoprotein E (APOE) genotype and magnetic resonance imaging (MRI) measures in patients with MCI (amnestic [aMCI] and non-amnestic [naMCI]).Methods This case–control study included 130 aMCI patients, 58 naMCI patients, and 1,106 healthy controls (HCs). APOE genotypes, medial temporal lobe atrophy (MTA), neurological evaluation results, and white matter hyperintensities (WMH) were investigated. Serum folate and vitamin B12 concentrations were analyzed by radioimmunoassay, and plasma hyperhomocysteinemia (Hcy) was assessed by a high-performance liquid chromatography-fluorescence method.ResultsSerum folate levels were significantly lower, but plasma Hcy levels were higher, in patients with aMCI and naMCI than in healthy controls. There were significantly higher MTA scores in the aMCI group than the healthy control group. Multiple linear regression showed that serum Hcy and folate concentrations were positively associated with MTA (p < 0.05), while APOE4 showed a significant negative association with MTA in the aMCI group (p < 0.01). In addition, moderate/severe WMH showed a significant negative association with MTA in the naMCI and HC groups (p < 0.01).Conclusion The combined presence of APOE4 and Hcy is associated with aMCI in elderly individuals, while moderate/severe WMH is related to naMCI, which suggests etiological differences across MCI subtypes.

Independent morphological variables correlate with Aging, Mild Cognitive Impairment, and Alzheimer's Disease

Preprint

Full-text available

Jan 2022

This manuscript presents a study with recruited volunteers that comprehends three sorts of events present in Alzheimer's Disease (AD) evolution (structural, biochemical, and cognitive) to propose an update in neurodegeneration biomarkers for AD. The novel variables, K, I, and S, suggested based on physics properties and empirical evidence, are defined by power-law relations between cortical thickness, exposed and total area, and natural descriptors of brain morphology. Our central hypothesis is that variable K, almost constant in healthy human subjects, is a better discriminator of a diseased brain than the current morphological biomarker, Cortical Thickness, due to its aggregated information. We extracted morphological features from 3T MRI T1w images of 123 elderly subjects: 77 Healthy Cognitive Unimpaired Controls (CTL), 33 Mild Cognitive Impairment (MCI) patients, and 13 Alzheimer's Disease (AD) patients. Moreover, Cerebrospinal Fluid (CSF) biomarkers and clinical data scores were correlated with K, intending to characterize health and disease in the cortex with morphological criteria and cognitive-behavioral profiles. K distinguishes Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Cognitive Unimpaired Controls globally and locally with reasonable accuracy (CTL-AD, 0.82; CTL-MCI, 0.58). Correlations were found between global and local K associated with clinical behavioral data (executive function and memory assessments) and CSF biomarkers (t-Tau, Aβ-40, and Aβ-42). The results suggest that the cortical folding component, K, is a premature discriminator of healthy aging, Mild Cognitive Impairment, and Alzheimer's Disease, with significant differences within diagnostics. Despite the non-concomitant events, we found correlations between brain structural degeneration (K), cognitive tasks, and biochemical markers.

Nanoarchitectured Bioconjugates and Bioreceptors Mediated Surface Plasmon Resonance Biosensor for In Vitro Diagnosis of Alzheimer’s Disease: Development and Future Prospects

Article

Jan 2021

Alzheimer’s disease (AD) is an obvious neurological disorder characterized by progressive brain cell death that resulted in memory loss, cognitive decline, and finally dementia. Besides, AD is also affected by a multifunctional pathway, which leads to alteration in the biomolecular level as AD steps forward. Notwithstanding numerous diagnosis techniques, the conventionally engaged technology permits the detection of AD biomarkers with low sensitivity and poor selectivity. Concerning this, in recent years bioconjugates and bioreceptors based AD biomarkers recognition is gaining huge prospective to improved selectivity and sensitivity of AD at the molecular level. The present review deals with the recent progress in bioreceptors and bioconjugates mediated surface plasmon resonance (SPR) biosensor for in vitro diagnosis of AD. Fascinatingly, this review inculcates the information of assorted important AD biomarkers viz. beta-amyloid (Ab), Tau protein, apolipoprotein (apoE4), 17-b-hydroxysteroid dehydrogenase type 10 (17b-HSD-10), acetylcholine, etc. In addition, this review sheds light on the utmost and unique methods of bioconjugates synthesis, which is holding the huge attention of researchers for AD biomarker detection and contributed to the development of simplistic, rapid, and socioeconomic sensitivity enhancement methods. Concisely, this review gives insight into the analytical performance of nanoarchitectured bioconjugate and bioreceptor-mediated SPR biosensor and their revolutionary benefits in terms of selectivity and sensitivity for in vitro diagnosis of AD biomarkers. Overall, this review gives a detailed overview of research done to date in the meadow of SPR biosensors in the in vitro diagnosis of AD, which paves the new pathway for futuristic biomedical applications.

Multimodal MRI Based Classification and Prediction of Alzheimer’s Disease Using Random Forest Ensemble

Conference Paper

Jul 2020

Random Forest Feature Selection, Fusion and Ensemble Strategy: Combining Multiple Morphological MRI Measures to Discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer’s disease neuroimaging initiative (ADNI) database

Preprint

Full-text available

Dec 2017

Background In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer’s disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. New Method Based on preprocessed MRI images from the organizers of a neuroimaging challenge ² , we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 AD were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. Results In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. Comparison with Existing Method(s) The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Conclusions Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD. HIGHLIGHTS 1 st place in International Challenge for Automated Prediction of MCI from MRI Data Multi-class classification of normal control, MCI, converting MCI, and Alzheimer’s disease Morphometric measures from 3D T1 brain MRI images have been analysed (ADNI1 cohort). A Random Forest Feature Selection, Fusion and Ensemble Strategy was applied to classification and prediction of AD. Accuracy and robustness have been assessed in a blind dataset

Machine Learning on Population Imaging for Mental Health

Thesis

Sep 2020

Kamalaker Dadi

Mental disorders display a vast heterogeneity across individuals. A fundamental challenge to studying their manifestations or risk factors is that the diagnosis of mental pathological conditions are seldom available in large public health cohorts. Here, we seek to develop brain signatures, biomarkers, of mental disorders. For this, we use ma-chine learning to predict mental-health outcomes through population imaging i. e. with brain imaging (Magnetic Resonance Imaging ( MRI )).Given behavioral or clinical assessments, population imaging can relate unique features of the brain variations to these non-brain self-reported measures based on questionnaires. These non-brain measurements carry a unique description of each individual’s psychological differences which can be linked to psychopathology using statistical methods. This PhD thesis investigates the potential of learning such imaging-based outcomes to analyze mental health. Using machine-learning methods, we conduct an evaluation, both a comprehensive and robust, of population measures to guide high-quality predictions of health outcomes. This thesis is organized into three main parts: first, we present an in-depth study of connectome biomarkers, second, we propose a meaningful data reduction which facilitates large-scale population imaging studies, and finally we introduce proxy measures for mental health. We first set up a thorough benchmark for imaging-connectomes to predict clinical phenotypes. With the rise in the high-quality brain images acquired without tasks, there is an increasing demand in evaluation of existing models for predictions. We performed systematic comparisons relating these images to clinical assessments across many cohorts to evaluate the robustness of population imaging methods for mental health. Our benchmarks emphasize the need for solid foundations in building brain networks across individuals. They outline clear methodological choices. Then, we contribute a new generation of brain functional atlases to facilitate high-quality predictions for mental health. Brain functional atlases are indeed the main bottleneck for prediction. These atlases are built by analyzing large-scale functional brain volumes using scalable statistical algorithm, to have better grounding for outcome prediction. After comparing them with state-of-the-art methods, we show their usefulness to mitigate large-scale data handling problems. The last main contribution is to investigate the potential surrogate measures for health outcomes. We consider large-scale model comparisons using brain measurements with behavioral assessments in an imaging epidemiological cohort, the United Kingdom ( UK ) Biobank. On this complex dataset, the challenge lies in finding the appropriate covariates and relating them to well-chosen outcomes. This is challenging, as there are very few available pathological outcomes. After careful model selection and evaluation, we identify proxy measures that display distinct links to socio-demographics and may correlate with non-pathological conditions like the condition of sleep, alcohol consumption and physical fitness activity. These can be indirectly useful for the epidemiological study of mental health.

ACR Appropriateness Criteria® Dementia

Article

May 2020
J Am Coll Radiol

Degenerative disease of the central nervous system is a growing public health concern. The primary role of neuroimaging in the workup of patients with probable or possible Alzheimer disease has typically been to exclude other significant intracranial abnormalities. In general, the imaging findings in structural studies, such as MRI, are nonspecific and have limited potential in differentiating different types of dementia. Advanced imaging methods are not routinely used in community or general practices for the diagnosis or differentiation of forms of dementia. Nonetheless, in patients who have been evaluated by a dementia expert, FDG-PET helps to distinguish Alzheimer disease from frontotemporal dementia. In patients with suspected dementia with Lewy bodies, functional imaging of the dopamine transporter (ioflupane) using SPECT may be helpful. In patients with suspected normal-pressure hydrocephalus, DTPA cisternography and HMPAO SPECT/CT brain may provide assessment. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Transcranial Doppler could help to differentiate the types of dementia? A pilot study when CSF biomarkers are not available

Article

Full-text available

Jun 2020
J NEURAL TRANSM

Our objective was to find a mean flow velocity (MFV) cut-off point to differentiate between normal and cognitive impaired patients using Clinical Dementia Rating (CDR) as a comparison method. To evaluate MFV (in cm/s) and pulsatility index (PI) from the left middle cerebral artery (MCA) and basilar artery using transcranial Doppler in a pilot study from an outpatient cognition unit and compare with cognitively normal older adults (at the age of sixty or older) from the Geriatric Ambulatory of Fluminense Federal University. We hypothesized that there is a MFV and PI cut-off point to potentially distinguish between normal and impaired cognition. Sixty-one patients with cognitive decline, including 18 with amnestic mild cognitive impairment (aMCI), 31 with probable Alzheimer disease (AD), 12 with vascular dementia (VD), and 10 cognitively normal older adults were included in the study. Patients with dementia (both AD and VD, p < 0.01) and aMCI (p < 0.05) had lower MFV than the control group in the MCA (32.2 cm/s, 31.9 cm/s, and 36.6 cm/s, respectively) and dementia patients had higher PI compared to control (AD and VD, both p < 0.05). Basilar MFV showed to be no difference between the patients and the control group. A cut off value of 39.1 cm/s was found in a ROC curve (area under de curve value 0.85, 95% CI 0.75–0.95) for mean MCA MFV to be predictive of cognitive impairment (CDR ≥ 0.5). In this study, the values of MCA MFV below 39.1 cm/s were predictive of cognitive impairment according to CDR. TCD is an inexpensive method that could be used in a clinical scenario to help differentiate normal cognition from cognitive decline. Multicentric and longitudinal studies should be done to validate that.

Behavioral and Psychological Symptoms of Dementia in Alzheimer\'s Dementia: The Neuroimaging Correlates

Article

Full-text available

Dec 2019

Diagnostic Biomarkers of Alzheimer’s Disease: A State-of-the-Art Review

Article

Full-text available

Nov 2019

In 2018, there was a recent shift towards a biological definition of Alzheimer's disease (AD), based on biomarkers measured in vivo even before the onset of clinical dementia symptoms. No single biomarker can by itself accurately diagnose AD. A combination of biomarkers assessed through imaging and cerebrospinal fluid (CSF) yields better diagnostic accuracy. Although amyloid PET imaging and CSF levels of amyloid and tau deposits are increasingly used in AD clinical trials to increase diagnostic confidence in enrolled subjects, routine use of these biomarkers in clinical settings is still premature because of the risk of overdiagnosis, increased cost and/or invasiveness of the assessment method. Also, standardization of measures across studies is needed to assure biomarker regulatory approval. Exploring novel biomarkers beyond the amyloid and tau pathologies, and their longitudinal change across the AD continnum are important research avenues for the future.

Controls-based denoising, a new approach for medical image analysis, improves prediction of conversion to Alzheimer’s disease with FDG-PET

Article

Full-text available

Oct 2019
EUR J NUCL MED MOL I

Objective The pattern expression score (PES), i.e., the degree to which a pathology-related pattern is present, is frequently used in FDG-brain-PET analysis and has been shown to be a powerful predictor of conversion to Alzheimer’s disease (AD) in mild cognitive impairment (MCI). Since, inevitably, the PES is affected by non-pathological variability, our aim was to improve classification with the simple, yet novel approach to identify patterns of non-pathological variance in a separate control sample using principal component analysis and removing them from patient data (controls-based denoising, CODE) before calculating the PES. Methods Multi-center FDG-PET from 220 MCI patients (64 non-converter, follow-up ≥ 4 years; 156 AD converter, time-to-conversion ≤ 4 years) were obtained from the ADNI database. Patterns of non-pathological variance were determined from 262 healthy controls. An AD pattern was calculated from AD patients and controls. We predicted AD conversion based on PES only and on PES combined with neuropsychological features and ApoE4 genotype. We compared classification performance achieved with and without CODE and with a standard machine learning approach (support vector machine). Results Our model predicts that CODE improves the signal-to-noise ratio of AD-PES by a factor of 1.5. PES-based prediction of AD conversion improved from AUC 0.80 to 0.88 (p= 0.001, DeLong’s method), sensitivity 69 to 83%, specificity 81% to 88% and Matthews correlation coefficient (MCC) 0.45 to 0.66. Best classification (0.93 AUC) was obtained when combining the denoised PES with clinical features. Conclusions CODE, applied in its basic form, significantly improved prediction of conversion based on PES. The achieved classification performance was higher than with a standard machine learning algorithm, which was trained on patients, explainable by the fact that CODE used additional information (large sample of healthy controls). We conclude that the proposed, novel method is a powerful tool for improving medical image analysis that offers a wide spectrum of biomedical applications, even beyond image analysis.

Cerebral metabolism in patients with cognitive disorders: A combined MRS and PET study

Article

Jan 2019
ZH NEVROL PSIKHIATR

Aim: Magnetic resonance spectroscopy (MRS) allows studying the content of many metabolites in neural tissue in vivo. There are numerous studies devoted to the MRS data analysis in Alzheimer's disease (AD), but their results are contradictory. Thus, it is rational to compare the data obtained with MRS and positron emission tomography (PET) with fluorodeoxyglucose (FDG), which allows evaluating the brain functional state. In this paper, the authors compared MRS data in AD and mild cognitive impairment (MCI) with the cerebral glucose metabolism changes according to FDG PET. Material and methods: Multivoxel proton MRS of the supraventricular area was performed in patients with AD (n=16) and MCI (n=14). The following metabolites ratios were evaluated: NAA/Cr, Cho/Cr, NAA/Cho (NAA - N-acetylaspartate, Cr - creatine, Cho - choline). All patients underwent neurological examination, assessment of cognitive status and PET with FDG. Results: A decrease in NAA/Cr and Cho/Cr ratios in the supraventricular white matter and medial cortex in both hemispheres was observed in AD patients. In the MCI group, NAA/Cr ratio were decreased only in left white matter adjusting to the parietal cortex. Positive correlations of NAA/Cr and Cho/Cr ratios with cognitive status, as well as the cerebral glucose metabolism rate according to the PET data in frontal, parietal, temporal and cingulate cortex were revealed. Conclusion: The decrease in the NAA/Cr ratio in the supraventricular white matter and the medial cortex in AD and the correlation of this index with the results of cognitive tests and cerebral glucose metabolism suggest that it can be of diagnostic significance, reflecting the severity of cognitive impairment. In this case, the NAA/Cr ratio should be evaluated taking into account the changes in concentrations of both metabolites (NAA and Cr) in dementia.

Evaluating Frontoparietal Network Topography for Diagnostic Markers of Alzheimer’s Disease

Preprint

Full-text available

Feb 2024

Bayard Rogers

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer’s Disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. These event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of ( N = 63) mild untreated AD patients and a control group of ( N = 73) healthy age-matched adults. A concurrent cross-modal associative memory test and 128-channel high-density EEG facilitated data collection. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity; our findings demonstrate statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing, though there was no interaction effect. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude by visually mapping FPN integration existent in healthy controls, yet absent in AD patients during cross-modal memory tasks. The implications and limitations of these findings are discussed, as are foundations for future research in exploring processes and strategies that lead to identifying clinically useful biomarkers for the detection and treatment of AD.

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Article

Dec 2023

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment using qEEG

Article

Full-text available

Dec 2023

Background Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain‐related disorders such as mild cognitive impairment (MCI), and dementia. Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and dementia from qEEG time‐frequency (TF) images of the subjects in an eyes‐closed resting state (ECR). Method The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 dementia. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event‐rated changes of frequency sub‐bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed‐forward neural network (FFN). Result The trained models’, HC vs. MCI, HC vs. dementia, and HC vs. CASE (MCI + dementia), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs. MCI was 83%, 93%, and 73%, HC vs. dementia was 81%, 80%, and 83%, and HC vs. CASE (MCI + dementia) was 88%, 80%, and 90%, respectively. Conclusion The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.

Can neurocognitive assessment be a lower-cost substitute for biomarkers in predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD)? A narrative review

Article

Dec 2023

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Preprint

Full-text available

Jul 2023

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Article

Full-text available

Apr 2023

Purpose: Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR). Participants and Methods: The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN). Results: The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively. Conclusion: The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.

The use of an individual-based FDG-PET volume of interest approach in mild cognitive impairment: a multi-modality longitudinal follow-up study

Preprint

Full-text available

Dec 2022

Background Based on a longitudinal cohort design, the aim of this study was to investigate whether individual-based ¹⁸F fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) regional signals can predict dementia conversion in patients with mild cognitive impairment (MCI). Methods We included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with amyloid-positive Alzheimer disease, and 40 cognitively normal controls. Data from annual cognitive measurements, 3D T1 magnetic resonance imaging (MRI) scans, and ¹⁸F-FDG-PET scans were used for outcome analysis. An individual-based FDG-PET approach was defined using seven volumes of interest (VOIs), Z transformed using a normal FDG-PET template. Hypometabolism was defined as a Z score < -2 of regional standard uptake value ratio. For the longitudinal cognitive test scores, generalized estimating equations were used. A linear mixed effect model was used to compare the time effect of cortical hypometabolism and cortical thickness degeneration. Results The clinical follow-up period was 6.6 ± 3.8 years (range 3.1 to 16.0 years). The cognitive decline trends could differentiate MCI-C from MCI-NC after 3 years of follow-up. In MCI at the first ¹⁸F-FDG-PET scan, medial temporal lobe (94.7% sensitivity, 80.5% specificity) and posterior cingulate cortex (89.5% sensitivity, 73.1% specificity) hypometabolism predicted conversion with high accuracy. ¹⁸F-FDG-PET hypometabolism preceded dementia conversion at an interval of 3.70 ± 1.68 years and was earlier than volumetric changes. Conclusions Our analysis support the use of individual-based ¹⁸F-FDG-PET analysis to predict MCI conversion. Changes in ¹⁸F-FDG-PET occurred 1 to 8 years prior to dementia conversion, and hypometabolism occurred 2 years before MRI findings.

Maladaptive Changes in Delay Discounting in Males during the Covid-19 Pandemic: The Predictive Role Of Functional Connectome

Article

Jan 2022
CEREB CORTEX

The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.

Alzheimer’s disease: a scoping review of biomarker research and development for effective disease diagnosis

Article

Jul 2022

Introduction: Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD. Areas covered: This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments. Expert opinion: It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.

Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation

Article

Jun 2022
IEEE T MED IMAGING

Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal distributions between different domains in a class-agnostic fashion. In this paper, we present a dual-attention domain-adaptative segmentation network (DADASeg-Net) for cross-modality medical image segmentation. The key contribution of DADASeg-Net is a novel dual adversarial attention mechanism, which regularizes the domain adaptation module with two attention maps respectively from the space and class perspectives. Specifically, the spatial attention map guides the domain adaptation module to focus on regions that are challenging to align in adaptation. The class attention map encourages the domain adaptation module to capture class-specific instead of class-agnostic knowledge for distribution alignment. DADASeg-Net shows superior performance in two challenging medical image segmentation tasks.

An overview of Alzheimer's disease and its diagnosis using conventional and novel methods

Conference Paper

Oct 2021

Xuanning Zhao

Segmenting Bones from Brain MRI via Generative Adversarial Learning

Chapter

Jul 2021

Computed tomography (CT) is the standard imaging modality for capturing bony structures that are used in craniomaxillofacial (CMF) surgical planning. Unfortunately, CT emits radiation and is not a safe imaging modality, especially for infant patients. Thus, there is a clinical need of using alternative safer modalities, e.g., magnetic resonance imaging (MRI), for those patient populations. Although MRI provides good image quality for soft tissue, it lacks bony boundary information that can be used for segmentation. In this work, we introduce a novel method to automatically segment bony structures from MRI. Our method is based on a convolutional neural network composed of an image synthesis sub-network and a segmentation sub-network. The image synthesis sub-network jointly learns the cycle-consistent mappings both from MRI-to-CT and from CT-to-MRI via generative adversarial learning. Given an image from either modality, this sub-network generates the image of the other modality, resulting in a new MRI-CT pair of which the anatomical structure information are supposed to be consistent. In this way, the bone annotations (labels) from CT modality are implicitly transferred to the MRI modality to train the segmentation sub-network. We train the model in a semi-supervised manner (i.e., make use of both paired and unpaired MRI-CT data) to solve the problem with limited number of paired MRI-CT images. Moreover, a neighbor-based anchoring method and a feature-matching-based semantic consistency regularization are proposed to ameliorate the ambiguity problem of cycle-consistent cross-modality image synthesis. Experimental results demonstrate that the proposed method can effectively boost the generalizability of the segmentation sub-network. Compared with other state-of-the-art methods using only limited paired MRI-CT data, the proposed method successfully improves the segmentation performance by using both the paired and unpaired data in a semi-supervised manner.

Diverse Data Augmentation for Learning Image Segmentation with Cross-Modality Annotations

Article

Apr 2021
MED IMAGE ANAL

The dearth of annotated data is a major hurdle in building reliable image segmentation models. Manual annotation of medical images is tedious, time-consuming, and significantly variable across imaging modalities. The need for annotation can be ameliorated by leveraging an annotation-rich source modality in learning a segmentation model for an annotation-poor target modality. In this paper, we introduce a diverse data augmentation generative adversarial network (DDA-GAN) to train a segmentation model for an unannotated target image domain by borrowing information from an annotated source image domain. This is achieved by generating diverse augmented data for the target domain by one-to-many source-to-target translation. The DDA-GAN uses unpaired images from the source and target domains and is an end-to-end convolutional neural network that (i) explicitly disentangles domain-invariant structural features related to segmentation from domain-specific appearance features, (ii) combines structural features from the source domain with appearance features randomly sampled from the target domain for data augmentation, and (iii)train the segmentation model with the augmented data in the target domain and the annotations from the source domain. The effectiveness of our method is demonstrated both qualitatively and quantitatively in comparison with the state of the art for segmentation of craniomaxillofacial bony structures via MRI and cardiac substructures via CT.

Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation

Article

Sep 2020
IEEE T MED IMAGING

An increasing number of studies are leveraging unsupervised cross-modality synthesis to mitigate the limited label problem in training medical image segmentation models. They typically transfer ground truth annotations from a label-rich imaging modality to a label-lacking imaging modality, under an assumption that different modalities share the same anatomical structure information. However, since these methods commonly use voxel/pixel-wise cycle-consistency to regularize the mappings between modalities, high-level semantic information is not necessarily preserved. In this paper, we propose a novel anatomy-regularized representation learning approach for segmentation-oriented cross-modality image synthesis. It learns a common feature encoding across different modalities to form a shared latent space, where 1) the input and its synthesis present consistent anatomical structure information, and 2) the transformation between two images in one domain is preserved by their syntheses in another domain. We applied our method to the tasks of cross-modality skull segmentation and cardiac substructure segmentation. Experimental results demonstrate the superiority of our method in comparison with state-of-the-art cross-modality medical image segmentation methods.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

Article

Mar 2020

Background: Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. Objectives: To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. Search methods: On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. Selection criteria: We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. Data collection and analysis: Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. Main results: We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate. Authors' conclusions: The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Design of data driven decision support systems for the early detection of subjects at risk to develop Alzheimer’s disease

Thesis

Nov 2019

Manon Ansart

The goal of this thesis is to design data-driven methods to identify subjects at risk to develop Alzheimer's disease. As it is a progressive disease, subtle signs can appear several years before the first clinical symptoms. Identifying subjects who show these signs, and who are likely to develop the disease in the coming years, is a crucial point that could allow researchers to better study the disease mechanism, select patients for clinical trials and tailor patient care.In the first chapter, we conduct a review of methods predicting the future diagnosis of subjects suffering from mild cognitive impairment. We quantitatively and qualitatively study these methods, and take a critical view point by identifying several methodological issues.In the second chapter, we propose our own method to predict the future diagnosis by using a two-step approach: we first predict the future subject characteristics, and then use this result to predict the corresponding diagnosis.In the third chapter, we propose an automatic method to select subjects with a positive biomarker for clinical trials, so as to minimize the recruitment cost.In the last chapter, we analyze prescription patterns before and after diagnosis using a medical record database. We use them to predict if a patient will develop Alzheimer's disease in the next five or ten years.Across these works, we show the importance to take into account the adoption of these methods and the settings in which they can be used, especially regarding the test cohort, the data types and the interpretability of the method.

Cerebral Metabolism in Patients with Cognitive Disorders: a Combined Magnetic Resonance Spectroscopy and Positron Emission Tomography Study

Article

Nov 2019

Objectives. Magnetic resonance spectroscopy (MRS) allows the contents of many metabolites in living tissues to be assessed. There is a good number of studies analyzing MRS data in Alzheimer’s disease (AD), though their results are contradictory. In this regard, there is value in comparing MRS data with fluorodeoxyglucose (FDG) positron emission tomography (PET) results, which assess the functional state of nervous tissue. The present study provides a comparison of MRI scan data in AD and moderate cognitive impairment (MCI) with the characteristics of cerebral glucose metabolism assessed from FDG-PET data. Materials and methods. Multivoxel proton MRS of the supraventricular region was carried out in patients with AD (n = 16) and MCI (n = 14). The following metabolite ratios were determined: NAA/Cr, Cho/Cr, and NAA/Cho (NAA is N-acetylaspartate, Cr is creatine, and Cho is choline). Patients underwent neurological investigation, assessment of cognitive status, and PET scans with FDG. Results. Patients with AD showed decreases in NAA/Cr and Cho/Cr in the white matter of the medial cortex of the supraventricular areas of both hemispheres. The MCI group showed a decrease in the NAA/Cr ratio in only one area of the white matter of the left hemisphere, adjacent to the parietal cortex. Positive correlations were found between NAA/Cr and Cho/Cr with measures of cognitive status and with the rate of glucose metabolism measured from PET data in the frontal, parietal, and temporal areas and the cingulate cortex. Conclusions. The decrease in the NAA/Cr ratio in the supraventricular white matter and the medial cortex in AD and the correlation of this parameter with cognitive test results and cerebral glucose metabolism constitute evidence that it may have diagnostic value, reflecting the severity of cognitive impairments. Assessment of the NAA/Cr ratio should be carried out with consideration of the fact that dementia alters the concentrations of both metabolites (NAA and Cr).

One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures

Article

Aug 2019

Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single pairedMRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.

Enriching Amnestic Mild Cognitive Impairment Populations for Clinical Trials: Optimal Combination of Biomarkers to Predict Conversion to Dementia

Article

Full-text available

Jul 2012
J ALZHEIMERS DIS

The goal of this study was to identify the optimal combination of magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) biomarkers to predict conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) dementia within two years, for enriching clinical trial populations. Data from 63 subjects in the Alzheimer's Disease Neuroimaging Initiative aMCI cohort who had MRI and FDG-PET imaging along with CSF data at baseline and at least two years clinical follow-up were used. A Bayesian classification method was used to determine which combination of 31 variables (MRI, FDG-PET, CSF measurements, apolipoprotein E (ApoE) genotype, and cognitive scores) provided the most accurate prediction of aMCI to AD conversion. The cost and time trade-offs for the use of these biomarkers as inclusion criteria in clinical trials were evaluated. Using the combination of all biomarkers, ApoE genotype, and cognitive scores, we achieved an accuracy of 81% in predicting aMCI to AD conversion. With only ApoE genotype and cognitive scores, the prediction accuracy decreased to 62%. By comparing individual modalities, we found that MRI measures had the best predictive power (accuracy = 78%), followed by ApoE, FDG-PET, CSF, and the Alzheimer's disease assessment scale-cognitive subscale. The combination of biomarkers from different modalities, measuring complementary aspects of AD pathology, provided the most accurate prediction of aMCI to AD conversion within two years. This was predominantly driven by MRI measures, which emerged as the single most powerful modality. Overall, the combination of MRI, ApoE, and cognitive scores provided the best trade-off between cost and time compared with other biomarker combinations for patient recruitment in clinical trial.

Comparison of Imaging Biomarkers in the Alzheimer Disease Neuroimaging Initiative and the Mayo Clinic Study of Aging

Article

Full-text available

May 2012

Objective To determine whether magnetic resonance imaging measurements observed in the Alzheimer Disease Neuroimaging Initiative (ADNI) convenience sample differ from those observed in the Mayo Clinic Study of Aging (MCSA) population-based sample. Design Comparison of 2 samples. Setting Fifty-nine recruiting sites for the ADNI in the United States and Canada and the MCSA, a population-based cohort in Olmsted County, Minnesota. Patients Cognitively normal subjects and amnestic subjects with mild cognitive impairment were selected from the ADNI convenience cohort and MCSA population-based cohort. A simple random sample of subjects from both cohorts in the same age range was selected, and a second sample applied matching for age, sex, educational level, apolipoprotein E genotype, and Mini-Mental State Examination score. Main Outcome Measures Baseline hippocampal volumes and annual percentage of decline in hippocampal volume. Results In the population-based sample, MCSA subjects were older, had less education, performed worse on the Mini-Mental State Examination, and had a family history of Alzheimer disease less often than did ADNI subjects. Baseline hippocampal volumes were larger in ADNI compared with MCSA cognitively normal subjects in the random sample, although no differences were observed after matching. Rates of decline in hippocampal volume were greater in the ADNI compared with the MCSA for cognitively normal subjects and those with amnestic mild cognitive impairment, even after matching. Conclusions Rates of decline in hippocampal volume suggest that ADNI subjects have a more aggressive brain pathologic process than MCSA subjects and hence may not be representative of the general population. These findings have implications for treatment trials that use ADNI-like recruitment mechanisms and for studies validating new diagnostic criteria for Alzheimer disease in its various stages.

Utility of Combinations of Biomarkers, Cognitive Markers, and Risk Factors to Predict Conversion From Mild Cognitive Impairment to Alzheimer Disease in Patients in the Alzheimer's Disease Neuroimaging Initiative

Article

Full-text available

Sep 2011

Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors. To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease. Longitudinal study. We analyzed the Alzheimer's Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses. The Alzheimer's Disease Neuroimaging Initiative public database. Primary outcome measures were odds ratios, pseudo- R(2)s, and effect sizes. In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease. Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic trajectory of the disease than by a sharp decline in functional ability and, to a lesser extent, by declines in executive function.

Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors

Article

Full-text available

Jul 2011
PLOS ONE

Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

Clinical features and multidisciplinary approaches to dementia care

Article

Full-text available

May 2011

Dementia is a clinical syndrome of widespread progressive deterioration of cognitive abilities and normal daily functioning. These cognitive and behavioral impairments pose considerable challenges to individuals with dementia, along with their family members and caregivers. Four primary dementia classifications have been defined according to clinical and research criteria: 1) Alzheimer's disease; 2) vascular dementias; 3) frontotemporal dementias; and 4) dementia with Lewy bodies/Parkinson's disease dementia. The cumulative efforts of multidisciplinary healthcare teams have advanced our understanding of dementia beyond basic descriptions, towards a more complete elucidation of risk factors, clinical symptoms, and neuropathological correlates. The characterization of disease subtypes has facilitated targeted management strategies, advanced treatments, and symptomatic care for individuals affected by dementia. This review briefly summarizes the current state of knowledge and directions of dementia research and clinical practice. We provide a description of the risk factors, clinical presentation, and differential diagnosis of dementia. A summary of multidisciplinary team approaches to dementia care is outlined, including management strategies for the treatment of cognitive impairments, functional deficits, and behavioral and psychological symptoms of dementia. The needs of individuals with dementia are extensive, often requiring care beyond traditional bounds of medical practice, including pharmacologic and non-pharmacologic management interventions. Finally, advanced research on the early prodromal phase of dementia is reviewed, with a focus on change-point models, trajectories of cognitive change, and threshold models of pathological burden. Future research goals are outlined, with a call to action for social policy initiatives that promote preventive lifestyle behaviors, and healthcare programs that will support the growing number of individuals affected by dementia.

Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CHThe diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:270-279

Article

Full-text available

May 2011
ALZHEIMERS DEMENT

The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of developing criteria for the symptomatic predementia phase of Alzheimer's disease (AD), referred to in this article as mild cognitive impairment due to AD. The workgroup developed the following two sets of criteria: (1) core clinical criteria that could be used by healthcare providers without access to advanced imaging techniques or cerebrospinal fluid analysis, and (2) research criteria that could be used in clinical research settings, including clinical trials. The second set of criteria incorporate the use of biomarkers based on imaging and cerebrospinal fluid measures. The final set of criteria for mild cognitive impairment due to AD has four levels of certainty, depending on the presence and nature of the biomarker findings. Considerable work is needed to validate the criteria that use biomarkers and to standardize biomarker analysis for use in community settings.

Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer's disease

Article

Full-text available

Oct 2010
BRAIN

Biomarkers of brain Aβ amyloid deposition can be measured either by cerebrospinal fluid Aβ42 or Pittsburgh compound B positron emission tomography imaging. Our objective was to evaluate the ability of Aβ load and neurodegenerative atrophy on magnetic resonance imaging to predict shorter time-to-progression from mild cognitive impairment to Alzheimer's dementia and to characterize the effect of these biomarkers on the risk of progression as they become increasingly abnormal. A total of 218 subjects with mild cognitive impairment were identified from the Alzheimer's Disease Neuroimaging Initiative. The primary outcome was time-to-progression to Alzheimer's dementia. Hippocampal volumes were measured and adjusted for intracranial volume. We used a new method of pooling cerebrospinal fluid Aβ42 and Pittsburgh compound B positron emission tomography measures to produce equivalent measures of brain Aβ load from either source and analysed the results using multiple imputation methods. We performed our analyses in two phases. First, we grouped our subjects into those who were 'amyloid positive' (n = 165, with the assumption that Alzheimer's pathology is dominant in this group) and those who were 'amyloid negative' (n = 53). In the second phase, we included all 218 subjects with mild cognitive impairment to evaluate the biomarkers in a sample that we assumed to contain a full spectrum of expected pathologies. In a Kaplan-Meier analysis, amyloid positive subjects with mild cognitive impairment were much more likely to progress to dementia within 2 years than amyloid negative subjects with mild cognitive impairment (50 versus 19%). Among amyloid positive subjects with mild cognitive impairment only, hippocampal atrophy predicted shorter time-to-progression (P < 0.001) while Aβ load did not (P = 0.44). In contrast, when all 218 subjects with mild cognitive impairment were combined (amyloid positive and negative), hippocampal atrophy and Aβ load predicted shorter time-to-progression with comparable power (hazard ratio for an inter-quartile difference of 2.6 for both); however, the risk profile was linear throughout the range of hippocampal atrophy values but reached a ceiling at higher values of brain Aβ load. Our results are consistent with a model of Alzheimer's disease in which Aβ deposition initiates the pathological cascade but is not the direct cause of cognitive impairment as evidenced by the fact that Aβ load severity is decoupled from risk of progression at high levels. In contrast, hippocampal atrophy indicates how far along the neurodegenerative path one is, and hence how close to progressing to dementia. Possible explanations for our finding that many subjects with mild cognitive impairment have intermediate levels of Aβ load include: (i) individual subjects may reach an Aβ load plateau at varying absolute levels; (ii) some subjects may be more biologically susceptible to Aβ than others; and (iii) subjects with mild cognitive impairment with intermediate levels of Aβ may represent individuals with Alzheimer's disease co-existent with other pathologies.

Subregional neuroanatomical change as a biomarker for Alzheimer's disease

Article

Full-text available

Dec 2009
P NATL ACAD SCI USA

Regions of the temporal and parietal lobes are particularly damaged in Alzheimer's disease (AD), and this leads to a predictable pattern of brain atrophy. In vivo quantification of subregional atrophy, such as changes in cortical thickness or structure volume, could lead to improved diagnosis and better assessment of the neuroprotective effects of a therapy. Toward this end, we have developed a fast and robust method for accurately quantifying cerebral structural changes in several cortical and subcortical regions using serial MRI scans. In 169 healthy controls, 299 subjects with mild cognitive impairment (MCI), and 129 subjects with AD, we measured rates of subregional cerebral volume change for each cohort and performed power calculations to identify regions that would provide the most sensitive outcome measures in clinical trials of disease-modifying agents. Consistent with regional specificity of AD, temporal-lobe cortical regions showed the greatest disease-related changes and significantly outperformed any of the clinical or cognitive measures examined for both AD and MCI. Global measures of change in brain structure, including whole-brain and ventricular volumes, were also elevated in AD and MCI, but were less salient when compared to changes in normal subjects. Therefore, these biomarkers are less powerful for quantifying disease-modifying effects of compounds that target AD pathology. The findings indicate that regional temporal lobe cortical changes would have great utility as outcome measures in clinical trials and may also have utility in clinical practice for aiding early diagnosis of neurodegenerative disease.

Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort

Article

Full-text available

Aug 2009
CURR ALZHEIMER RES

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multi-center study assessing neuroimaging in diagnosis and longitudinal monitoring. Amnestic Mild Cognitive Impairment (MCI) often represents a prodromal form of dementia, conferring a 10-15% annual risk of converting to probable AD. We analyzed baseline 1.5T MRI scans in 693 participants from the ADNI cohort divided into four groups by baseline diagnosis and one year MCI to probable AD conversion status to identify neuroimaging phenotypes associated with MCI and AD and potential predictive markers of imminent conversion. MP-RAGE scans were analyzed using publicly available voxel-based morphometry (VBM) and automated parcellation methods. Measures included global and hippocampal grey matter (GM) density, hippocampal and amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. The overall pattern of structural MRI changes in MCI (n=339) and AD (n=148) compared to healthy controls (HC, n=206) was similar to prior findings in smaller samples. MCI-Converters (n=62) demonstrated a very similar pattern of atrophic changes to the AD group up to a year before meeting clinical criteria for AD. Finally, a comparison of effect sizes for contrasts between the MCI-Converters and MCI-Stable (n=277) groups on MRI metrics indicated that degree of neurodegeneration of medial temporal structures was the best antecedent MRI marker of imminent conversion, with decreased hippocampal volume (left > right) being the most robust. Validation of imaging biomarkers is important as they can help enrich clinical trials of disease modifying agents by identifying individuals at highest risk for progression to AD.

CSF Biomarkers and Incipient Alzheimer Disease in Patients With Mild Cognitive Impairment

Article

Full-text available

Aug 2009
J Am Med Assoc

Small single-center studies have shown that cerebrospinal fluid (CSF) biomarkers may be useful to identify incipient Alzheimer disease (AD) in patients with mild cognitive impairment (MCI), but large-scale multicenter studies have not been conducted. To determine the diagnostic accuracy of CSF beta-amyloid(1-42) (Abeta42), total tau protein (T-tau), and tau phosphorylated at position threonine 181 (P-tau) for predicting incipient AD in patients with MCI. The study had 2 parts: a cross-sectional study involving patients with AD and controls to identify cut points, followed by a prospective cohort study involving patients with MCI, conducted 1990-2007. A total of 750 individuals with MCI, 529 with AD, and 304 controls were recruited by 12 centers in Europe and the United States. Individuals with MCI were followed up for at least 2 years or until symptoms had progressed to clinical dementia. Sensitivity, specificity, positive and negative likelihood ratios (LRs) of CSF Abeta42, T-tau, and P-tau for identifying incipient AD. During follow-up, 271 participants with MCI were diagnosed with AD and 59 with other dementias. The Abeta42 assay in particular had considerable intersite variability. Patients who developed AD had lower median Abeta42 (356; range, 96-1075 ng/L) and higher P-tau (81; range, 15-183 ng/L) and T-tau (582; range, 83-2174 ng/L) levels than MCI patients who did not develop AD during follow-up (579; range, 121-1420 ng/L for Abeta42; 53; range, 15-163 ng/L for P-tau; and 294; range, 31-2483 ng/L for T-tau, P < .001). The area under the receiver operating characteristic curve was 0.78 (95% confidence interval [CI], 0.75-0.82) for Abeta42, 0.76 (95% CI, 0.72-0.80) for P-tau, and 0.79 (95% CI, 0.76-0.83) for T-tau. Cut-offs with sensitivity set to 85% were defined in the AD and control groups and tested in the MCI group, where the combination of Abeta42/P-tau ratio and T-tau identified incipient AD with a sensitivity of 83% (95% CI, 78%-88%), specificity 72% (95% CI, 68%-76%), positive LR, 3.0 (95% CI, 2.5-3.4), and negative LR, 0.24 (95% CI, 0.21-0.28). The positive predictive value was 62% and the negative predictive value was 88%. This multicenter study found that CSF Abeta42, T-tau, and P-tau identify incipient AD with good accuracy, but less accurately than reported from single-center studies. Intersite assay variability highlights a need for standardization of analytical techniques and clinical procedures.

Magnetic Resonance Imaging Improves Cerebrospinal Fluid Biomarkers in the Early Detection of Alzheimer's Disease

Article

Full-text available

Feb 2009

Little is known of combined utility of magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) biomarkers for prediction of Alzheimer's disease (AD) and longitudinal data is scarce. We examined these biomarkers at baseline and longitudinally in incipient AD. Forty-five subjects [21 controls (NL-NL), 16 stable MCI (MCI-MCI), 8 MCI who declined to AD (MCI-AD)] received MRI and lumbar puncture at baseline and after 2 years. CSF measures included total and phosphorylated tau (T-tau, P-tau(231)), amyloid-beta (Abeta(42)/Abeta(40)) and isoprostane. Voxel-based morphometry identified gray matter concentration (GMC) differences best distinguishing study groups and individual GMC values were calculated. Rate of medial temporal lobe (MTL) atrophy was examined using regional boundary shift (rBS) method. At baseline, for MRI, MCI-AD showed reduced GMC-MTL, and for CSF higher CSF T-tau, P-tau(231), IP and lower Abeta(42)/Abeta(40) as compared with MCI-MCI or NL-NL. Longitudinally, rBS-MTL atrophy was higher in MCI-AD than in either MCI-MCI or NL-NL, particularly in the left hemisphere. CSF data showed longitudinally greater increases of isoprostane in MCI-AD as compared with NL-NL. Combining baseline CSF-P-tau(231) and GMC-MTL significantly increased overall prediction of AD from 74% to 84% (p(step)<0.05). These results provide support for including multiple modalities of biomarkers in the identification of memory clinic patients at increased risk for dementia.

Family members’ attitudes towards telling the patient with AD their diagnosis

Article

Full-text available

Sep 1996

3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease

Article

Full-text available

Aug 2007
BRAIN

Mild cognitive impairment (MCI), particularly the amnestic subtype (aMCI), is considered as a transitional stage between normal aging and a diagnosis of clinically probable Alzheimer's disease (AD). The aMCI construct is particularly useful as it provides an opportunity to assess a clinical stage which in most subjects represents prodromal AD. The aim of this study was to assess the progression of cerebral atrophy over multiple serial MRI during the period from aMCI to progression to AD. Thirty-three subjects were selected that fulfilled clinical criteria for aMCI and had three serial MRI scans: the first scan approximately 3 years before the diagnosis of AD, the second scan approximately 1 year before, and the third scan at the time of the diagnosis of AD. A group of 33 healthy controls were age and gender-matched to the study cohort. Voxel-based morphometry (VBM) was used to assess patterns of grey matter atrophy in the aMCI subjects at each time-point compared to the control group. Customized templates and prior probability maps were used to avoid normalization and segmentation bias. The pattern of grey matter loss in the aMCI subject scans that were 3 years before the diagnosis of AD was focused primarily on the medial temporal lobes, including the amygdala, anterior hippocampus and entorhinal cortex, with some additional involvement of the fusiform gyrus, compared to controls. The extent and magnitude of the cerebral atrophy further progressed by the time the subjects were 1 year before the diagnosis of AD. At this point atrophy in the temporal lobes spread to include the middle temporal gyrus, and extended into more posterior regions of the temporal lobe to include the entire extent of the hippocampus. The parietal lobe also started to become involved. By the time the subjects had progressed to a clinical diagnosis of AD the pattern of grey matter atrophy had become still more widespread with more severe involvement of the medial temporal lobes and the temporoparietal association cortices and, for the first time, substantial involvement of the frontal lobes. This pattern of progression fits well with the Braak and Braak neurofibrillary pathological staging scheme in AD. It suggests that the earliest changes occur in the anterior medial temporal lobe and fusiform gyrus, and that these changes occur at least 3 years before progression to the diagnosis of AD. These results also suggest that 3D patterns of grey matter atrophy may help to predict the time to the first diagnosis of AD in subjects with aMCI.

Beta-amyloid imaging and memory in non-demented individuals: Evidence for preclinical Alzheimer's disease

Article

Full-text available

Dec 2007
BRAIN

Beta-amyloid (Abeta) deposition is pathognomic for Alzheimer's disease (AD), but may occur in normal elderly people without apparent cognitive effect. Episodic memory impairment is an early and prominent sign of AD, but its relationship with Abeta burden in non-demented persons and in AD patients is unclear. We examined this relationship using 11C-PIB-PET as a quantitative marker of Abeta burden in vivo in healthy ageing (HA), mild cognitive impairment (MCI) and AD. Thirty-one AD, 33 MCI and 32 HA participants completed neuropsychological assessment and a 11C-PIB-PET brain scan. Multiple linear regression analyses were conducted relating episodic memory performance and other cognitive functions to Abeta burden. Ninety-seven percent of AD, 61% of MCI and 22% of HA cases had increased cortical PIB binding, indicating the presence of Abeta plaques. There was a strong relationship between impaired episodic memory performance and PIB binding, both in MCI and HA. This relationship was weaker in AD and less robust for non-memory cognitive domains. Abeta deposition in the asymptomatic elderly is associated with episodic memory impairment. This finding, together with the strong relationship between PIB binding and the severity of memory impairment in MCI, suggests that individuals with increased cortical PIB binding are on the path to Alzheimer's disease. The data also suggests that early intervention trials for AD targeted to non-demented individuals with cerebral Abeta deposition are warranted.

Regularization and Variable Selection via the Elastic Nets

Article

Jan 2015

The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging and Alzheimer's Association workgroup

Article

Jan 2011

Brain amyloid deposition and metabolism in early and late mild cognitive impairment

Article

Jul 2012

Background: The hypothetical model of dynamic biomarkers for Alzheimer's disease (AD) describes high amyloid deposition and hypometabolism at the mild cognitive impairment (MCI) stage. However, it remains unknown whether brain amyloidosis and hypometabolism follow the same trajectories in MCI individuals. We used the concept of early MCI (EMCI) and late MCI (LMCI) as defined by the Alzheimer's disease Neuroimaging Initiative (ADNI)-Go in order to compare the biomarker profile between EMCI and LMCI.

Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers

Article

Feb 2013

In 2010, we put forward a hypothetical model of the major biomarkers of Alzheimer's disease (AD). The model was received with interest because we described the temporal evolution of AD biomarkers in relation to each other and to the onset and progression of clinical symptoms. Since then, evidence has accumulated that supports the major assumptions of this model. Evidence has also appeared that challenges some of our assumptions, which has allowed us to modify our original model. Refinements to our model include indexing of individuals by time rather than clinical symptom severity; incorporation of interindividual variability in cognitive impairment associated with progression of AD pathophysiology; modifications of the specific temporal ordering of some biomarkers; and recognition that the two major proteinopathies underlying AD biomarker changes, amyloid β (Aβ) and tau, might be initiated independently in sporadic AD, in which we hypothesise that an incident Aβ pathophysiology can accelerate antecedent limbic and brainstem tauopathy.

Apolipoprotein ε4-Allele as a Significant Risk Factor for Conversion from Mild Cognitive Impairment to Alzheimer’s disease: a Meta-analysis of Prospective Studies

Article

Dec 2012
J MOL NEUROSCI

Recent studies of the relationship between progression from mild cognitive impairment (MCI) to Alzheimer disease (AD) and APOE ε4-allele revealed inconsistent results. To estimate the risk posed by APOE ε4-allele for developing AD in MCI subjects using meta-analysis and identify possible sources of heterogeneity between studies, we reviewed longitudinal epidemiological studies of the presence of APOE ε4-allele on risk for progression to dementia in MCI subjects, and conducted meta-analyses of the results from these studies. Our study was derived from 315 positive-outcome events and 461 negative-outcome events from 8 prospective studies. The pooled RR was statistically significant (pooled RR = 2.09; 95 % CI, 1.52–2.88). The Q statistics indicated high heterogeneity across studies (Q = 14.21, p = 0.003). RR were significantly related to the ethnicity of the sample (z = 3.58, p = 0.024). No significant heterogeneity was observed after stratification in four European-population studies (χ 2 = 0.67, p = 0.880), but it remained in four American-population studies (χ 2 = 18.52, p = 0.003). Heterogeneity markedly reduced after excluding one specific study (Q = 4.9, p = 0.009; I 2 = 39 %). APOE ε4-allele conferred increased risk for progression to dementia in MCI subjects. Ethnicity is a critical factor to yield heterogeneity. Further studies using larger sample sizes are required.

The experience of research participation and the value of diagnosis in dementia: Implications for practice

Article

Jul 2009

AMY SMITH ELIZABETH KING NICHOLAS HINDLEY LIN BARNETSON JEAN BARTON KIM A.JOBST

Abstract Aims : To examine the effect on patients and carers of being told the diagnosis of a dementing illness such as Alzheimer's disease, to explore the perceptions of carers and, as far as possible, of patients regarding their experience of research participation, and to evaluate our own practice in relation to meeting the information needs of patients and families. Methods : Postal survey with quantitative and qualitative elements sent to the main carers of 90 patients participating in The Oxford Project to Investigate Memory and Ageing (OPTIMA). Results : Seventy-nine questionnaires were completed. Analysis of data revealed an overall positive response from patients and carers to research participation. Carers were eager for all information. Looking at brain scans was helpful in the explanation of the organic changes caused by the disease. Despite mixed reactions to the diagnosis of a dementing illness, most carers preferred to know. Conclusions : Carers indicated that patients and families can benefit from early disclosure of diagnosis. This requires early referral, sensitive and ongoing support and further research into patients' reactions.

Amyloid imaging in Alzheimer's disease: comparison of florbetapir and Pittsburgh Compound-B positron emission tomography

Article

Jul 2012
J NEUROL NEUROSUR PS

Amyloid imaging provides in vivo detection of the fibrillar amyloid-β (Aβ) plaques of Alzheimer's disease (AD). The positron emission tomography (PET) ligand, Pittsburgh Compound-B (PiB-C11), is the most well studied amyloid imaging agent, but the short half-life of carbon-11 limits its clinical viability. Florbetapir-F18 recently demonstrated in vivo correlation with postmortem Aβ histopathology, but has not been directly compared with PiB-C11. Fourteen cognitively normal adults and 12 AD patients underwent PiB-C11 and florbetapir-F18 PET scans within a 28-day period. Both ligands displayed highly significant group discrimination and correlation of regional uptake. These data support the hypothesis that florbetapir-F18 provides comparable information with PiB-C11.

Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Statist Soc B. 2005;67(2):301-20

Article

Apr 2005
J R STAT SOC B

We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p≫n case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso.

The prognostic value of amyloid imaging

Article

Apr 2012
EUR J NUCL MED MOL I

Mild cognitive impairment is characterized by a decline in cognitive performance without interference with activities of daily living. The amnestic subtype of mild cognitive impairment progresses to Alzheimer's disease at a rate of 10-15% per year and in the majority the neuropathology is intermediate between the neuropathological changes of typical ageing and Alzheimer's disease. Amyloid deposition occurs over a decade before the development of noticeable cognitive symptoms in a continuous process that starts in healthy elderly individuals. Newly developed PET amyloid imaging agents provide noninvasive biomarkers for the early in vivo detection of Alzheimer's pathology in healthy elderly individuals and those with mild cognitive impairment. Exclusion of amyloid pathology should allow a more accurate prognosis to be given and ensure appropriate recruitment into clinical trials testing the efficacy of new putative antiamyloid agents at an earlier disease stage. The development of (18)F-labelled amyloid imaging agents has increased the availability of this new technology for clinical and research use since they can be used in PET centres where a cyclotron and radiochemistry are not available. This review discusses the role of PET imaging for assessing the amyloid load in cognitively normal elderly subjects and subjects with mild cognitive impairment at risk of conversion to Alzheimer's disease.

Longitudinal imaging of Alzheimer pathology using [C-11]PIB, [F-18]FDDNP and [F-18]FDG PET

Article

Mar 2012
EUR J NUCL MED MOL I

[(11)C]PIB and [(18)F]FDDNP are PET tracers for in vivo detection of the neuropathology underlying Alzheimer's disease (AD). [(18)F]FDG is a glucose analogue and its uptake reflects metabolic activity. The purpose of this study was to examine longitudinal changes in these tracers in patients with AD or mild cognitive impairment (MCI) and in healthy controls. Longitudinal, paired, dynamic [(11)C]PIB and [(18)F]FDDNP (90 min each) and static [(18)F]FDG (15 min) PET scans were obtained in 11 controls, 12 MCI patients and 8 AD patients. The mean interval between baseline and follow-up was 2.5 years (range 2.0-4.0 years). Parametric [(11)C]PIB and [(18)F]FDDNP images of binding potential (BP(ND)) and [(18)F]FDG standardized uptake value ratio (SUVr) images were generated. A significant increase in global cortical [(11)C]PIB BP(ND) was found in MCI patients, but no changes were observed in AD patients or controls. Subsequent regional analysis revealed that this increase in [(11)C]PIB BP(ND) in MCI patients was most prominent in the lateral temporal lobe (p < 0.05). For [(18)F]FDDNP, no changes in global BP(ND) were found. [(18)F]FDG uptake was reduced at follow-up in the AD group only, especially in frontal, parietal and lateral temporal lobes (all p < 0.01). Changes in global [(11)C]PIB binding (ρ = -0.42, p < 0.05) and posterior cingulate [(18)F]FDG uptake (ρ = 0.54, p < 0.01) were correlated with changes in Mini-Mental-State Examination score over time across groups, whilst changes in [(18)F]FDDNP binding (ρ = -0.18, p = 0.35) were not. [(11)C]PIB and [(18)F]FDG track molecular changes in different stages of AD. We found increased amyloid load in MCI patients and progressive metabolic impairment in AD patients. [(18)F]FDDNP seems to be less useful for examining disease progression.

Introduction to the Recommendations from the National Institute on Aging and the Alzheimer's Association Workshop on Diagnostic Guidelines for Alzheimer's Disease

Article

May 2011
ALZHEIMERS DEMENT

Criteria for the clinical diagnosis of Alzheimer's disease (AD) were established in 1984. A broad consensus now exists that these criteria should be revised to incorporate state-of-the-art scientific knowledge. The National Institute on Aging (NIA) and the Alzheimer's Association sponsored a series of advisory round table meetings in 2009 whose purpose was to establish a process for revising diagnostic and research criteria for AD. The recommendation from these advisory meetings was that three separate work groups should be formed with each assigned the task of formulating diagnostic criteria for one phase of the disease: the dementia phase; the symptomatic, pre-dementia phase; and the asymptomatic, preclinical phase of AD. Two notable differences from the AD criteria published in 1984 are incorporation of biomarkers of the underlying disease state and formalization of different stages of disease in the diagnostic criteria. There was a broad consensus within all three workgroups that much additional work is needed to validate the application of biomarkers for diagnostic purposes. In the revised NIA-Alzheimer's Association criteria, a semantic and conceptual distinction is made between AD pathophysiological processes and clinically observable syndromes that result, whereas this distinction was blurred in the 1984 criteria. The new criteria for AD are presented in three documents. The core clinical criteria of the recommendations regarding AD dementia and MCI due to AD are intended to guide diagnosis in the clinical setting. However, the recommendations of the preclinical AD workgroup are intended purely for research purposes.

Longitudinal Assessment of A beta and Cognition in Aging and Alzheimer Disease

Article

Jan 2011
ANN NEUROL

Assess Aβ deposition longitudinally and explore its relationship with cognition and disease progression. Clinical follow-up was obtained 20 ± 3 months after [¹¹C]Pittsburgh compound B (PiB)-positron emission tomography in 206 subjects: 35 with dementia of the Alzheimer type (DAT), 65 with mild cognitive impairment (MCI), and 106 age-matched healthy controls (HCs). A second PiB scan was obtained at follow-up in 185 subjects and a third scan after 3 years in 57. At baseline, 97% of DAT, 69% of MCI, and 31% of HC subjects showed high PiB retention. At 20-month follow-up, small but significant increases in PiB standardized uptake value ratios were observed in the DAT and MCI groups, and in HCs with high PiB retention at baseline (5.7%, 2.1%, and 1.5%, respectively). Increases were associated with the number of apolipoprotein E ε4 alleles. There was a weak correlation between PiB increases and decline in cognition when all groups were combined. Progression to DAT occurred in 67% of MCI with high PiB versus 5% of those with low PiB, but 20% of the low PiB MCI subjects progressed to other dementias. Of the high PiB HCs, 16% developed MCI or DAT by 20 months and 25% by 3 years. One low PiB HC developed MCI. Aβ deposition increases slowly from cognitive normality to moderate severity DAT. Extensive Aβ deposition precedes cognitive impairment, and is associated with ApoE genotype and a higher risk of cognitive decline in HCs and progression from MCI to DAT over 1 to 2 years. However, cognitive decline is only weakly related to change in Aβ burden, suggesting that downstream factors have a more direct effect on symptom progression.

Prediction of conversion from mild cognitive impairment to Alzheimer's disese dementia based upon biomarkers and neuropsychological test performance

Article

Dec 2010

The current study tested the accuracy of primary MRI and cerebrospinal fluid (CSF) biomarker candidates and neuropsychological tests for predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. In a cross-validation paradigm, predictor models were estimated in the training set of AD (N = 81) and elderly control subjects (N = 101). A combination of CSF t-tau/Aβ(1-4) ratio and MRI biomarkers or neuropsychological tests (free recall and trail making test B (TMT-B)) showed the best statistical fit in the AD vs. HC comparison, reaching a classification accuracy of up to 64% when applied to the prediction of MCI conversion (3.3-year observation interval, mean = 2.3 years). However, several single-predictor models showed a predictive accuracy of MCI conversion comparable to that of any multipredictor model. The best single predictors were right entorhinal cortex (prediction accuracy = 68.5% (95% CI (59.5, 77.4))) and TMT-B test (prediction accuracy 64.6% (95% CI (55.5, 73.4%))). In conclusion, short-term conversion to AD is predicted by single marker models to a comparable degree as by multimarker models in amnestic MCI subjects.

The new Alzheimer's criteria in a naturalistic series of patients with mild cognitive impairment

Article

Dec 2010
J NEUROL

To test the validity of the new diagnostic criteria for Alzheimer's disease (AD) in a naturalistic series of patients with mild cognitive impairment (MCI). Ninety consecutive MCI patients were enrolled in a longitudinal study on the natural history of cognitive impairment. Medial temporal (MT) atrophy on MRI was defined as hippocampal volume below the fifth percentile of the distribution in healthy elders, abnormal CSF was based on Sjogren's cutoffs for Abeta42 and tau, and temporoparietal hypometabolism on 18F-FDG PET based on Herholz's t sum score. Patients were followed clinically to detect conversion to AD (MCI-AD), non-AD dementia (MCI-nAD), or no conversion (MCI-NC). The 24 MCI-AD and 15 MCI-nAD patients had sociodemographic, clinical, and neuropsychological baseline features similar to the 51 MCI-NC patients. All MCI patients with MT atrophy converted to AD, as did all those with abnormal CSF, but only 48 and 35% of those without MT atrophy or abnormal CSF converted (p on logrank test = 0.0007 and 0.001). Prediction of AD conversion was enhanced when positivity to either MT atrophy or abnormal CSF was considered, with only 15% of those MCI patients negative on both converting to AD (p < 0.0005). Markers were not predictive of non-AD dementia conversion. The accuracy of either MT atrophy or abnormal CSF in discriminating MCI-AD from MCI-NC was good (AUC 0.82, 95% CI 0.70-0.95). MT atrophy and abnormal CSF are the single most robust predictors of conversion to AD in MCI patients, and their combination enhances prediction. AD markers are not predictive of conversion to non-AD dementia.

Twelve-Month Metabolic Declines in Probable Alzheimer's Disease and Amnestic Mild Cognitive Impairment Assessed Using an Empirically Pre-Defined Statistical Region-of-Interest: Findings from the Alzheimer's Disease Neuroimaging Initiative

Article

Mar 2010
NEUROIMAGE

Alzheimer's disease (AD) is characterized by specific and progressive reductions in fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the cerebral metabolic rate for glucose (CMRgl), some of which may precede the onset of symptoms. In this report, we describe twelve-month CMRgl declines in 69 probable AD patients, 154 amnestic mild cognitive impairment (MCI) patients, and 79 cognitively normal controls (NCs) from the AD Neuroimaging Initiative (ADNI) using statistical parametric mapping (SPM). We introduce the use of an empirically pre-defined statistical region-of-interest (sROI) to characterize CMRgl declines with optimal power and freedom from multiple comparisons, and we estimate the number of patients needed to characterize AD-slowing treatment effects in multi-center randomized clinical trials (RCTs). The AD and MCI groups each had significant twelve-month CMRgl declines bilaterally in posterior cingulate, medial and lateral parietal, medial and lateral temporal, frontal and occipital cortex, which were significantly greater than those in the NC group and correlated with measures of clinical decline. Using sROIs defined based on training sets of baseline and follow-up images to assess CMRgl declines in independent test sets from each patient group, we estimate the need for 66 AD patients or 217 MCI patients per treatment group to detect a 25% AD-slowing treatment effect in a twelve-month, multi-center RCT with 80% power and two-tailed alpha=0.05, roughly one-tenth the number of the patients needed to study MCI patients using clinical endpoints. Our findings support the use of FDG PET, brain-mapping algorithms and empirically pre-defined sROIs in RCTs of AD-slowing treatments.

Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade

Article

Jan 2010

Currently available evidence strongly supports the position that the initiating event in Alzheimer's disease (AD) is related to abnormal processing of beta-amyloid (Abeta) peptide, ultimately leading to formation of Abeta plaques in the brain. This process occurs while individuals are still cognitively normal. Biomarkers of brain beta-amyloidosis are reductions in CSF Abeta(42) and increased amyloid PET tracer retention. After a lag period, which varies from patient to patient, neuronal dysfunction and neurodegeneration become the dominant pathological processes. Biomarkers of neuronal injury and neurodegeneration are increased CSF tau and structural MRI measures of cerebral atrophy. Neurodegeneration is accompanied by synaptic dysfunction, which is indicated by decreased fluorodeoxyglucose uptake on PET. We propose a model that relates disease stage to AD biomarkers in which Abeta biomarkers become abnormal first, before neurodegenerative biomarkers and cognitive symptoms, and neurodegenerative biomarkers become abnormal later, and correlate with clinical symptom severity.

Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI

Article

Sep 2009

The Functional Activities Questionnaire (FAQ) and Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) are frequently used indices of cognitive decline in Alzheimer's disease (AD). The goal of this study was to compare FDG-PET and clinical measurements in a large sample of elderly subjects with memory disturbance. We examined relationships between glucose metabolism in FDG-PET regions of interest (FDG-ROIs), and ADAS-cog and FAQ scores in AD and mild cognitive impairment (MCI) patients enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Low glucose metabolism at baseline predicted subsequent ADAS-cog and FAQ decline. In addition, longitudinal glucose metabolism decline was associated with concurrent ADAS-cog and FAQ decline. Finally, a power analysis revealed that FDG-ROI values have greater statistical power than ADAS-cog to detect attenuation of cognitive decline in AD and MCI patients. Glucose metabolism is a sensitive measure of change in cognition and functional ability in AD and MCI, and has value in predicting future cognitive decline.

Conversion of amyloid positive and negative MCI to AD over 3 years: an 11C-PIB PET study

Article

Aug 2009
NEUROLOGY

Patients with amnestic mild cognitive impairment (MCI) represent an important clinical group as they are at increased risk of developing Alzheimer disease (AD). (11)C-PIB PET is an in vivo marker of brain amyloid load. To assess the rates of conversion of MCI to AD during a 3-year follow-up period and to compare levels of amyloid deposition between MCI converters and nonconverters. Thirty-one subjects with MCI with baseline (11)C-PIB PET, MRI, and neuropsychometry have been clinically followed up for 1 to 3 years (2.68 +/- 0.6 years). Raised cortical (11)C-PIB binding in subjects with MCI was detected with region of interest analysis and statistical parametric mapping. Seventeen of 31 (55%) subjects with MCI had increased (11)C-PIB retention at baseline and 14 of these 17 (82%) clinically converted to AD during follow-up. Only one of the 14 PIB-negative MCI cases converted to AD. Of the PIB-positive subjects with MCI, half (47%) converted to AD within 1 year of baseline PIB PET, these faster converters having higher tracer-retention values than slower converters in the anterior cingulate (p = 0.027) and frontal cortex (p = 0.031). Seven of 17 (41%) subjects with MCI with known APOE status were epsilon4 allele carriers, this genotype being associated with faster conversion rates in PIB-positive subjects with MCI (p = 0.035). PIB-positive subjects with mild cognitive impairment (MCI) are significantly more likely to convert to AD than PIB-negative patients, faster converters having higher PIB retention levels at baseline than slower converters. In vivo detection of amyloid deposition in MCI with PIB PET provides useful prognostic information.

Amyloid Imaging in Mild Cognitive Impairment Subtypes

Article

May 2009
ANN NEUROL

We utilized the amyloid imaging ligand Pittsburgh Compound B (PiB) to determine the presence of Alzheimer's disease (AD) pathology in different mild cognitive impairment (MCI) subtypes and to relate increased PiB binding to other markers of early AD and longitudinal outcome. Twenty-six patients with MCI (13 single-domain amnestic-MCI [a-MCI], 6 multidomain a-MCI, and 7 nonamnestic MCI) underwent PiB imaging. Twenty-three had clinical follow-up (21.2 +/- 16.0 [standard deviation] months) subsequent to their PiB scan. Using cutoffs established from a control cohort, we found that 14 (54%) patients had increased levels of PiB retention and were considered "amyloid-positive." All subtypes were associated with a significant proportion of amyloid-positive patients (6/13 single-domain a-MCI, 5/6 multidomain a-MCI, 3/7 nonamnestic MCI). There were no obvious differences in the distribution of PiB retention in the nonamnestic MCI group. Predictors of conversion to clinical AD in a-MCI, including poorer episodic memory, and medial temporal atrophy, were found in the amyloid-positive relative to amyloid-negative a-MCI patients. Longitudinal follow-up demonstrated 5 of 13 amyloid-positive patients, but 0 of 10 amyloid-negative patients, converted to clinical AD. Further, 3 of 10 amyloid-negative patients "reverted to normal." These data support the notion that amyloid-positive patients are likely to have early AD, and that the use of amyloid imaging may have an important role in determining which patients are likely to benefit from disease-specific therapies. In addition, our data are consistent with longitudinal studies that suggest a significant percentage of all MCI subtypes will develop AD.

Cerebrospinal Fluid Biomarker Signature in Alzheimer's Disease Neuroimaging Initiative Subjects

Article

Apr 2009
ANN NEUROL

Develop a cerebrospinal fluid biomarker signature for mild Alzheimer's disease (AD) in Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects. Amyloid-beta 1 to 42 peptide (A beta(1-42)), total tau (t-tau), and tau phosphorylated at the threonine 181 were measured in (1) cerebrospinal fluid (CSF) samples obtained during baseline evaluation of 100 mild AD, 196 mild cognitive impairment, and 114 elderly cognitively normal (NC) subjects in ADNI; and (2) independent 56 autopsy-confirmed AD cases and 52 age-matched elderly NCs using a multiplex immunoassay. Detection of an AD CSF profile for t-tau and A beta(1-42) in ADNI subjects was achieved using receiver operating characteristic cut points and logistic regression models derived from the autopsy-confirmed CSF data. CSF A beta(1-42) was the most sensitive biomarker for AD in the autopsy cohort of CSF samples: receiver operating characteristic area under the curve of 0.913 and sensitivity for AD detection of 96.4%. In the ADNI cohort, a logistic regression model for A beta(1-42), t-tau, and APO epsilon 4 allele count provided the best assessment delineation of mild AD. An AD-like baseline CSF profile for t-tau/A beta(1-42) was detected in 33 of 37 ADNI mild cognitive impairment subjects who converted to probable AD during the first year of the study. The CSF biomarker signature of AD defined by A beta(1-42) and t-tau in the autopsy-confirmed AD cohort and confirmed in the cohort followed in ADNI for 12 months detects mild AD in a large, multisite, prospective clinical investigation, and this signature appears to predict conversion from mild cognitive impairment to AD.

Fluorodeoxyglucose-Positron-Emission Tomography, Single-Photon Emission Tomography, and Structural MR Imaging for Prediction of Rapid Conversion to Alzheimer Disease in Patients with Mild Cognitive Impairment: A Meta-Analysis

Article

Dec 2008
AM J NEURORADIOL

Patients with mild cognitive impairment (MCI) are at risk for developing Alzheimer disease (AD). To diagnose AD at an early stage, one must develop highly specific and sensitive tools to identify it among at-risk subjects. The purpose of this study was to evaluate and compare the ability of fluorodeoxyglucose-positron-emission tomography (FDG-PET), single-photon emission tomography (SPECT), and structural MR imaging to predict conversion to AD in patients with MCI. Relevant studies were identified with MEDLINE from January 1990 to April 2008. Meta-analysis and meta-regression were done on the diagnostic performance data for each technique from eligible studies. We estimated and compared the weighted summary sensitivities, specificities, likelihood ratios (LRs), and summary receiver operating characteristic curves of each imaging technique. Twenty-four eligible studies were included, with a total of 1112 patients. FDG-PET performed statistically better in LR+ and odds ratio (OR), whereas no statistical difference was found in pooled sensitivity, specificity, and LR- for each technique. No statistical difference was confirmed between SPECT and MR imaging. The Q* index estimates for FDG-PET, SPECT, and structural MR imaging were respectively 0.86, 0.75, and 0.76. In meta-regression, statistical significance was found only between technique and log OR, with a regression coefficient of -0.575. This meta-analysis showed that FDG-PET performs slightly better than SPECT and structural MR imaging in the prediction of conversion to AD in patients with MCI; parallel performance was found between SPECT and MR imaging.

Truth-telling and the diagnosis of dementia

Article

Jul 2000

Gill Pinner

The change of attitude towards disclosure of the diagnosis of medical conditions, most notably cancer, has been dramatic in the past 40 years. However, the progression of openness towards patients suffering from Alzheimer's disease or other dementias may be less apparent. Most of the research on

Can FDG-PET predict who is to rapidly convert to Alzheimer's disease?

Article

May 2003
NEUROLOGY

Patients with mild cognitive impairment (MCI) were assessed, and a metabolic profile associated with conversion to AD at 18-month follow-up was sought. As compared with nonconverters (n = 10), converters (n = 7) had lower fluorodeoxyglucose uptake in the right temporoparietal cortex (p = 0.02, corrected for cluster size), without individual overlap. Awaiting replication in an independent sample, these findings suggest that among patients with MCI, fluorodeoxyglucose PET may accurately identify rapid converters.

Dementia diagnosis and disclosure: A dilemma in practice

Article

Apr 2004

Providing the individual with the correct information about their diagnosis can help maximize the patient's autonomy, however empirical evidence identifies inconsistencies in the practice of diagnosis disclosure in dementia. Within health care, ethical problems arise frequently and these present a challenge for health care professionals. This challenge can also give rise to conflict when professionals are torn between respecting autonomy, doing good and avoiding paternalism. The aim of this paper is to highlight the need for interprofessional collaboration when faced with ethical dilemmas such as diagnosis disclosure in the care of adults with dementia. The use of a dialogue and supplementary notes are used to explore crucial ethical points raised by health care professionals. The increasing rate of dementia and the need for patients to have access to timely and appropriate information about their diagnosis has prompted much debate about disclosing the diagnosis with the individual who has dementia. This paper may also be useful for educational purposes when used as a framework for discussion/debate in student nurse education. Ethical theories are of benefit in assisting the members of the multidisciplinary team to reach a morally defensible decision. Making ethical decisions in practice can cause the nurse concern. While there is no complete set of 'rules' that can provide an answer to each ethical dilemma, it is of immense value to nurses working within the multidisciplinary team to possess a sound knowledge of ethical positions in order to analyse the many complex situations that nurses encounter. The pivotal role of the health care professional is to work in collaboration and engage in sharing the diagnosis with the patient. It is hoped that this paper will stimulate and encourage further debate and study regarding the individual with dementia and diagnosis disclosure. Recommendations for practice, education, policy and research will also be highlighted.

Mild cognitive impairment - Beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment

Article

Oct 2004

The First Key Symposium was held in Stockholm, Sweden, 2-5 September 2003. The aim of the symposium was to integrate clinical and epidemiological perspectives on the topic of Mild Cognitive Impairment (MCI). A multidisciplinary, international group of experts discussed the current status and future directions of MCI, with regard to clinical presentation, cognitive and functional assessment, and the role of neuroimaging, biomarkers and genetics. Agreement on new perspectives, as well as recommendations for management and future research were discussed by the international working group. The specific recommendations for the general MCI criteria include the following: (i) the person is neither normal nor demented; (ii) there is evidence of cognitive deterioration shown by either objectively measured decline over time and/or subjective report of decline by self and/or informant in conjunction with objective cognitive deficits; and (iii) activities of daily living are preserved and complex instrumental functions are either intact or minimally impaired.

Mild cognitive impairment as a diagnostic entity

Article

Oct 2004

Ronald C Petersen

The concept of cognitive impairment intervening between normal ageing and very early dementia has been in the literature for many years. Recently, the construct of mild cognitive impairment (MCI) has been proposed to designate an early, but abnormal, state of cognitive impairment. MCI has generated a great deal of research from both clinical and research perspectives. Numerous epidemiological studies have documented the accelerated rate of progression to dementia and Alzheimer's disease (AD) in MCI subjects and certain predictor variables appear valid. However, there has been controversy regarding the precise definition of the concept and its implementation in various clinical settings. Clinical subtypes of MCI have been proposed to broaden the concept and include prodromal forms of a variety of dementias. It is suggested that the diagnosis of MCI can be made in a fashion similar to the clinical diagnoses of dementia and AD. An algorithm is presented to assist the clinician in identifying subjects and subclassifying them into the various types of MCI. By refining the criteria for MCI, clinical trials can be designed with appropriate inclusion and exclusion restrictions to allow for the investigation of therapeutics tailored for specific targets and populations.

Prediction of Individual Clinical Outcome in MCI by Means of Genetic Assessment and 18F-FDG PET

Article

Nov 2005

Patients with mild cognitive impairment (MCI) represent a risk population for progressing to dementia of the Alzheimer type (DAT). However, clinical criteria do not ensure reliable individual prognosis in these patients. The objective of this longitudinal, prospective study was to examine the value of (18)F-FDG PET of cerebral glucose metabolism and of genetic susceptibility, as defined by an APOEepsilon4-positive genotype, with regard to the early diagnosis of DAT in patients with MCI. In 30 patients with the diagnosis of MCI (16 female, 14 male; age, 70 +/- 8 y), baseline and follow-up examinations (mean observation period, 16 mo) were performed. In all patients, the APOE genotype was assessed and cerebral glucose metabolism was evaluated at baseline using cranial (18)F-FDG PET. Individual PET data were screened for findings suggestive of Alzheimer's disease (AD), with the help of an automated computer program. After stereotactical normalization of the PET images, this program performs an observer-independent statistical comparison with an age-matched reference database (n = 22). In 43% of all MCI subjects, a PET scan suggestive of AD pathology according to our predefined criteria was observed at baseline (PET+); 57% of all MCI patients were carriers of the APOE epsilon4 allele (e4+). In 40% of all patients, progression of symptoms within the observation period justified the clinical diagnosis of probable DAT at the time of follow-up reevaluation. Statistical evaluation revealed the best results for PET with regard to early diagnosis of DAT in MCI patients (sensitivity, 92%; specificity, 89%). Classification according to the APOE genotype was significantly less successful (sensitivity, 75%; specificity, 56%). However, a combination of both diagnostic tests allowed early diagnosis with either very high specificity (PET+ AND e4+: sensitivity, 67%; specificity, 100%) or very high sensitivity (PET+ OR e4+: sensitivity, 100%; specificity, 44%). (18)F-FDG PET of cerebral glucose metabolism is a valuable diagnostic tool for the prediction of clinical outcome in individual MCI patients. Results are superior to the exclusive assessment of the APOE genotype. A combination of both functional imaging and genotyping may allow an early high-risk or low-risk stratification of patients with either very high sensitivity or very high specificity. This may be valuable, for example, for patient selection in scientific studies.

Inverse Relation between In Vivo Amyloid Imaging Load and Cerebrospinal Fluid A 42 in Humans

Article

Mar 2006

Amyloid-beta(42) (Abeta(42)) appears central to Alzheimer's disease (AD) pathogenesis and is a major component of amyloid plaques. Mean cerebrospinal fluid (CSF) Abeta(42) is decreased in dementia of the Alzheimer's type. This decrease may reflect plaques acting as an Abeta(42) "sink," hindering transport of soluble Abeta(42) between brain and CSF. We investigated this hypothesis. We compared the in vivo brain amyloid load (via positron emission tomography imaging of the amyloid-binding agent, Pittsburgh Compound-B [PIB]) with CSF Abeta(42) and other measures (via enzyme-linked immunosorbent assay) in clinically characterized research subjects. Subjects fell into two nonoverlapping groups: those with positive PIB binding had the lowest CSF Abeta(42) level, and those with negative PIB binding had the highest CSF Abeta(42) level. No relation was observed between PIB binding and CSF Abeta(40), tau, phospho-tau(181), plasma Abeta(40), or plasma Abeta(42). Importantly, PIB binding and CSF Abeta(42) did not consistently correspond with clinical diagnosis; three cognitively normal subjects were PIB-positive with low CSF Abeta(42), suggesting the presence of amyloid in the absence of cognitive impairment (ie, preclinical AD). These observations suggest that brain amyloid deposition results in low CSF Abeta(42), and that amyloid imaging and CSF Abeta(42) may potentially serve as antecedent biomarkers of (preclinical) AD.

PET imaging of amyloid deposition in patients with mild cognitive impairment

Article

May 2007

It is of great clinical value to identify subjects at a high risk of developing AD. We previously found that the amyloid positron emission tomography (PET) tracer PIB showed a robust difference in retention in the brain between AD patients and healthy controls (HC). Twenty-one patients diagnosed with MCI (mean age 63.3+/-7.8 (S.D.) years) underwent PET studies with (11)C-PIB, and (18)F-fluoro-deoxy-glucose (FDG) to measure cerebral glucose metabolism, as well as assessment of cognitive function and CSF sampling. Reference group data from 27 AD patients and 6 healthy controls, respectively, were used for comparison. The mean cortical PIB retention for the MCI patients was intermediate compared to HC and AD. Seven MCI patients that later at clinical follow-up converted to AD (8.1+/-6.0 (S.D.) months) showed significant higher PIB retention compared to non-converting MCI patients and HC, respectively (ps<0.01). The PIB retention in MCI converters was comparable to AD patients (p>0.01). Correlations were observed in the MCI patients between PIB retention and CSF Abeta(1-42), total Tau and episodic memory, respectively.

Imaging Brain Amyloid in Alzheimer's Disease with Pittsburgh Compound-B

Article

Apr 2004

This report describes the first human study of a novel amyloid-imaging positron emission tomography (PET) tracer, termed Pittsburgh Compound-B (PIB), in 16 patients with diagnosed mild AD and 9 controls. Compared with controls, AD patients typically showed marked retention of PIB in areas of association cortex known to contain large amounts of amyloid deposits in AD. In the AD patient group, PIB retention was increased most prominently in frontal cortex (1.94-fold, p = 0.0001). Large increases also were observed in parietal (1.71-fold, p = 0.0002), temporal (1.52-fold, p = 0.002), and occipital (1.54-fold, p = 0.002) cortex and the striatum (1.76-fold, p = 0.0001). PIB retention was equivalent in AD patients and controls in areas known to be relatively unaffected by amyloid deposition (such as subcortical white matter, pons, and cerebellum). Studies in three young (21 years) and six older healthy controls (69.5 +/- 11 years) showed low PIB retention in cortical areas and no significant group differences between young and older controls. In cortical areas, PIB retention correlated inversely with cerebral glucose metabolism determined with 18F-fluorodeoxyglucose. This relationship was most robust in the parietal cortex (r = -0.72; p = 0.0001). The results suggest that PET imaging with the novel tracer, PIB, can provide quantitative information on amyloid deposits in living subjects.

Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI Family members' attitudes toward telling the patient with Alzheimer's disease their diagnosis

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Squibb Company; Eisai Inc

Bristol-Myers

Bristol-Myers Squibb Company; Eisai Inc; Elan Pharmaceuticals Inc;

The new Alzheimer's criteria in a naturalistic series of patients with mild cognitive impairment The prognostic value of amyloid imaging Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive

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S L Risacher
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Mild cognitive impairment as a diagnostic entity b-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease

Jan 2000
514-518

R C P T Petersen
Trzepacz

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The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging and Alzheimer's Association workgroup on diagnostic guidelines for Alzheimer's disease

Jan 2011
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Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., Gamst, A., Holtzman, D.M., Jagust, W.J., Petersen, R.C., Snyder, P.J., Carrillo, M.C., Thies, B., Phelps, C.H., 2011. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging and Alzheimer's Association workgroup on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7, 270e279.