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Editorial: Designing a Protocol Adopting an Artificial Intelligence (AI)–Driven Approach for Early Diagnosis of Late-Onset Alzheimer’s Disease
... More recently, the landscape of AD drug-repurposing has rapidly evolved given the advancement in AIdriven computational methods 38,39 . Rodriguez et al. (2021) developed a machine learning framework to predict a list of genes that associate with different stages of AD, based on gene expression data from multiple datasets, for drug repurposing. ...
Alzheimer’s Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.
... Given the lack of ground truth, most of those papers have not reported their exact accuracy for identifying AD-related genes, while some of them have claimed that previous studies/analyses (related to AD/neurodegenerative diseases or gene functions) could support some genes they identified [65][66][67][68] . Besides, in 2021, Li et al. have published a plan on designing an AI-driven causal graph model to identify the HGMs for AD in the future 69 . Moreover, utilizing data on 83 diseases, a feed-forward neural network has been designed for disease diagnosis based on information of gene expression and disease pathways, and sensitivity analysis has been performed to identify associations between diseases and genes 70 . ...
Post-traumatic Stress Disorder (PTSD) is a common debilitating mental disorder, that occurs in some individuals following extremely traumatic events. Traditional identification of Genetic Markers (GM) for PTSD is mainly based on a statistical clinical approach by comparing PTSD patients with normal controls. However, these statistical studies present limitations, often generating inconsistent results. Few studies have yet examined thoroughly the role of somatic mutations, PTSD disease pathways and their relationships. Capitalizing on deep learning techniques, we have developed a novel hierarchical graph attention network to identify highly correlational GM (HGMs) of PTSD. The network presents the following novelties: First, both a hierarchical graph structure and a graph attention mechanism have been integrated into a model to develop a graph attention network (GAtN) model. Second, domain-specific knowledge, including somatic mutations, genes, PTSD pathways and their correlations have been incorporated into the graph structures. Third, 12 somatic mutations having high or moderate impacts on proteins or genes have been identified as the potential HGMs for PTSD. Fourth, our study is carefully guided by prominent PTSD literature or clinical experts of the field; any high saliency HGMs generated from our model are further verified by existing PTSD-related authoritative medical journals. Our study illustrates the utility and significance of a hybrid approach, integrating both AI and expert-guided/domain-specific knowledge for thorough identification of biomarkers of PTSD, while building on the nature of convergence and divergence of PTSD pathways. Our expert-guided AI-driven methodology can be extended to other pathological-based HGM identification studies; it will transform the methodology of biomarker identification for different life-threatening diseases to speed up the complex lengthy procedures of new biomarkers identification.
Elucidating Alzheimer's disease (AD) prodromal symptoms can resolve the outstanding challenge of early diagnosis. Based on intrinsically related substrates of olfaction and spatial navigation, we propose a novel experimental framework for their conjoint study. Artificial intelligence-driven multimodal study combining self-collected olfactory and motion data with available big clinical datasets can potentially promote high-precision early clinical screenings to facilitate timely interventions targeting neurodegenerative progression.
Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.
The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field.
Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature ( n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.
Alzheimer’s disease (AD) represents a global health challenge, with an estimated 55 million people suffering from the non-curable disease across the world. While amyloid-β plaques and tau neurofibrillary tangles in the brain define AD proteinopathy, it has become evident that diverse coding and non-coding regions of the genome may significantly contribute to AD neurodegeneration. The diversity of factors associated with AD pathogenesis, coupled with age-associated damage, suggests that a series of triggering events may be required to initiate AD. Since somatic mutations accumulate with aging, and aging is a major risk factor for AD, there is a great potential for somatic mutational events to drive disease. Indeed, recent data from the Gozes team/laboratories as well as other leading laboratories correlated the accumulation of somatic brain mutations with the progression of tauopathy. In this review, we lay the current perspectives on the principal genetic factors associated with AD and the potential causes, highlighting the contribution of somatic mutations to the pathogenesis of late onset Alzheimer’s disease. The roles that artificial intelligence and big data can play in accelerating the progress of causal somatic mutation markers/biomarkers identification, and the associated drug discovery/repurposing, have been highlighted for future AD and other neurodegenerative studies, with the aim to bring hope for the vulnerable aging population.
Abstract
This paper contains an analysis and comparison of different classifiers on different datasets of Psychiatric Disorders- Personality Disorder, Depression, Anxiety, Schizophrenia and Alzheimer's disease. Psychiatric disorders are also referred to as mental disorders, abnormalities of the mind that result in persistent behavior which can seriously cause day to day function and life. Stochastic in AI refers to if there is any uncertainty or randomness involved in results and are used during optimization; Using this process also helps to provide precise results. The study of stochastic process in AI uses mathematical knowledge and techniques from probability, set theory, calculus, linear algebra and mathematical analysis like Fourier analysis, real analysis, and functional analysis. this technique is used to construct neural network for making artificial intelligent mode for processing and minimizing human effort. This paper contains classifiers like SVM, MLP, LR, KNN, DT, and RF. Several types of attributes are used and have been trained by Weka tool, MATLAB, and Python. The results show that the SVM classifier showed the best performance for all the attributes and disorders researched in this paper.
Aim
Mild behavioural impairment (MBI) is a neurobehavioural syndrome characterized by emergent neuropsychiatric symptoms in later life. There has been no systematic review or meta‐analysis on the prevalence of MBI. The main aim of the study is to calculate the pooled prevalence of MBI.
Methods
A search of the literature on MBI in mild cognitive impairment (MCI), cognitively normal (CN), and subjective cognitive impairment (SCI) and CN but at risk (CN‐AR) subjects published between 1 January 2003 and 28 September 2020 was conducted. Meta‐analysis using a random effects model was performed to determine the pooled estimate of the prevalence of MBI. Meta‐regression was performed to identify factors contributing to the variance of prevalence rate. A systematic review was also performed to study the impact of MBI in cognitive outcomes and its correlation to the pathology and genetics of Alzheimer's disease.
Results
Eleven studies conducted among 15 689 subjects underwent meta‐analysis, revealing the pooled prevalence of MBI to be 33.5% (95% confidence interval (CI): 22.6%–46.6%). Seven studies conducted among 1358 MCI subjects underwent meta‐analysis, revealing the pooled prevalence to be 45.5% (95%CI: 36.1%–55.3%). Four studies conducted among 13 153 CN subjects underwent meta‐analysis, revealing the pooled prevalence to be 17.0% (95%CI: 7.2%–34.9%). Five studies conducted among 1158 SCI or CN‐AR subjects underwent meta‐analysis, revealing the pooled prevalence to be 35.8% (95%CI: 21.4%–53.2%). A systematic review of 13 studies showed that MBI has a significant impact on cognitive deterioration and is associated with the pathology and genetics of Alzheimer's disease.
Conclusions
In MCI, CN, and SCI and CN‐AR subjects, MBI is common. Our finding is potentially useful in planning future clinical trials.
Brain disease can be screened using eye movements. Degenerative brain disorders change eye movement because they affect not only memory and cognition but also the cranial nervous system involved in eye movement. We compared the facial and eye movement patterns of patients with mild Alzheimer’s disease and cognitively normal people to analyze the neurological signs of dementia. After detecting the facial landmarks, the coordinate values for the movements were extracted. We used Spearman’s correlation coefficient to examine associations between horizontal and vertical facial and eye movements. We analyzed the correlation between facial and eye movements without using special eye-tracking equipment or complex conditions in order to measure the behavioral aspect of the natural human gaze. As a result, we found differences between patients with Alzheimer’s disease and cognitively normal people. Patients suffering from Alzheimer’s disease tended to move their face and eyes simultaneously in the vertical direction, whereas the cognitively normal people did not, as confirmed by a Mann–Whitney–Wilcoxon test. Our findings suggest that objective and accurate measurement of facial and eye movements can be used to screen such patients quickly. The use of camera-based testing for the early detection of patients showing signs of neurodegeneration can have a significant impact on the public care of dementia. View Full-Text
With increasing public demands for timely and accurate air pollution reporting, more air quality monitoring stations have been deployed by the governments in urban metropolises to increase the coverage of urban air pollution monitoring. However, due to systematic or accidental failures, some air pollution measurements obtained from these stations are found to have missing values, which will adversely affect the accuracy of any follow-up air pollution analyses and the quality of environmental decisionmakings. In this study, the mathematical property of air quality measurements is investigated to recover the missing air pollution values. A new algorithm, which matches meteorology data with air pollution data from different locations, to reconstruct the data matrix and recover missing entries, is proposed. Next, a Low Rank Matrix Completion problem is used to reconstruct the missing values, by transforming the data recovery problem to a sub-gradient primal-dual problem, based on the duality theory, with Singular Value Thresholding (SVT) employed to develop sub-optimal solutions. Next, an Interpolation-SVT (ISVT) approach is adopted to handle the sparsity of observed measurements. Comprehensive case studies are conducted to evaluate the performance of the proposed methods. The simulation results have demonstrated that the proposed SVT and ISVT methods can effectively recover the missing air pollution data and outperform existing interpolation methods and data imputation techniques. The proposed study can improve air pollution estimation and prediction whenever the low-rank data types that are used as proxies for air pollution estimation contain a lot of missing values and require data recovery.
With Alzheimer's disease (AD) exhibiting reduced ability of neural stem cell renewal, we hypothesized that de novo mutations controlling embryonic development, in the form of brain somatic mutations instigate the disease. A leading gene presenting heterozygous dominant de novo autism-intellectual disabilities (ID) causing mutations is activity-dependent neuroprotective protein (ADNP), with intact ADNP protecting against AD-tauopathy. We discovered a genomic autism ADNP mutation (c.2188C>T) in postmortem AD olfactory bulbs and hippocampi. RNA-Seq of olfactory bulbs also identified a novel ADNP hotspot mutation, c.2187_2188insA. Altogether, 665 mutations in 596 genes with 441 mutations in AD patients (389 genes, 38% AD-exclusive mutations) and 104 genes presenting disease-causing mutations (OMIM) were discovered. OMIM AD mutated genes converged on cytoskeletal mechanisms, autism and ID causing mutations (about 40% each). The number and average frequencies of AD-related mutations per subject were higher in AD subjects compared to controls. RNA-seq datamining (hippocampus, dorsolateral prefrontal cortex, fusiform gyrus and superior frontal gyrus-583 subjects) yielded similar results. Overlapping all tested brain areas identified unique and shared mutations, with ADNP singled out as a gene associated with autism/ID/AD and presenting several unique aging/AD mutations. The large fusiform gyrus library (117 subjects) with high sequencing coverage correlated the c.2187_2188insA ADNP mutation frequency to Braak stage (tauopathy) and showed more ADNP mutations in AD specimens. In cell cultures, the ADNP-derived snippet NAP inhibited mutated-ADNP-microtubule (MT) toxicity and enhanced Tau-MT association. We propose a paradigm-shifting concept in the perception of AD whereby accumulating mosaic somatic mutations promote brain pathology.
The ubiquity and remarkable technological progress of wearable consumer devices and mobile-computing platforms (smart phone, smart watch, tablet), along with the multitude of sensor modalities available, have enabled continuous monitoring of patients and their daily activities. Such rich, longitudinal information can be mined for physiological and behavioral signatures of cognitive impairment and provide new avenues for detecting MCI in a timely and cost-effective manner. In this work, we present a platform for remote and unobtrusive monitoring of symptoms related to cognitive impairment using several consumer-grade smart devices. We demonstrate how the platform has been used to collect a total of 16TB of data during the Lilly Exploratory Digital Assessment Study, a 12-week feasibility study which monitored 31 people with cognitive impairment and 82 without cognitive impairment in free living conditions. We describe how careful data unification, time-alignment, and imputation techniques can handle missing data rates inherent in real-world settings and ultimately show utility of these disparate data in differentiating symptomatics from healthy controls based on features computed purely from device data.
Alzheimer’s Disease (AD) represents a major and rapidly growing burden to the healthcare ecosystem. A growing body of evidence indicates that cognitive, behavioral, sensory, and motor changes may precede clinical manifestations of AD by several years. Existing tests designed to diagnose neurodegenerative diseases, while well-validated, are often less effective in detecting deviations from normal cognitive decline trajectory in the earliest stages of the disease. In the quest for gold standards for AD assessment, there is a growing interest in the identification of readily accessible digital biomarkers, which harness advances in consumer grade mobile and wearable technologies. Topics examined include a review of existing early clinical manifestations of AD and a path to the respective sensor and mobile/wearable device usage to acquire domain-centric data towards objective, high frequency and passive digital phenotyping.
Mild cognitive impairment (MCI) has been extensively investigated in recent decades to identify groups with a high risk of dementia and to establish effective prevention methods during this period. Neuropsychological performance and cortical thickness are two important biomarkers used to predict progression from MCI to dementia. This study compares the cortical thickness and neuropsychological performance in people with MCI and cognitively healthy older adults. We further focus on the relationship between cortical thickness and neuropsychological performance in these two groups. Forty-nine participants with MCI and 40 cognitively healthy older adults were recruited. Cortical thickness was analysed with semiautomatic software, Freesurfer. The analysis reveals that the cortical thickness in the left caudal anterior cingulate (p=0.041), lateral occipital (p=0.009) and right superior temporal (p=0.047) areas were significantly thinner in the MCI group after adjustment for age and education. Almost all neuropsychological test results (with the exception of forward digit span) were significantly correlated to cortical thickness in the MCI group after adjustment for age, gender and education. In contrast, only the score on the Category Verbal Fluency Test and the forward digit span were found to have significant inverse correlations to cortical thickness in the control group of cognitively healthy older adults. The study results suggest that cortical thinning in the temporal region reflects the global change in cognition in subjects with MCI and may be useful to predict progression of MCI to Alzheimer’s disease. The different pattern in the correlation of cortical thickness to the neuropsychological performance of patients with MCI from the healthy control subjects may be explained by the hypothesis of MCI as a disconnection syndrome.
Background:
The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.
Results:
Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).
Conclusions:
Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.
Background: Although memory impairment is the main symptom of Alzheimer’s disease (AD), language impairment can be an important marker. Relatively few studies of language in AD quantify the impairments in connected speech using computational techniques.
Objective: We aim to demonstrate state-of-the-art accuracy in automatically identifying Alzheimer’s disease from short narrative samples elicited with a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis.
Methods: Data are derived from the DementiaBank corpus, from which 167 patients diagnosed with “possible” or “probable” AD provide 240 narrative samples, and 97 controls provide an additional 233. We compute a number of linguistic variables from the transcripts, and acoustic variables from the associated audio files, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. To examine the degree of heterogeneity of linguistic impairments in AD, we follow an exploratory factor analysis on these measures of speech and language with an oblique promax rotation, and provide interpretation for the resulting factors.
Results: We obtain state-of-the-art classification accuracies of over 81% in distinguishing individuals with AD from those without based on short samples of their language on a picture description task. Four clear factors emerge: semantic impairment, acoustic abnormality, syntactic impairment, and information impairment.
Conclusion: Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.
Background. We describe the trends in prevalence and mortality of dementia among older people in Hong Kong over time. Projections of the number of older people with dementia through 2039 and estimation of the disease burden are also included. Methods. Prevalence data were extracted from previous studies in Hong Kong. Mortality data were obtained from the Department of Health of Hong Kong. Projections of the number of people with dementia were calculated by applying the prevalence rates of dementia obtained from previous studies to Hong Kong population projections. The burden of dementia was measured by Disability-Adjusted Life Years (DALYs). Results. The number of people aged 60 and above with dementia is projected to increase by 222%, from 103,433 in 2009 to 332,688 in 2039, with a large proportion of those living in institutions. The number of deaths due to dementia among people aged 60 and above has more than doubled between 2001 and 2009. Mortality rates for dementia have also risen. In 2006, about 286,313 DALYS were lost due to dementia. Conclusions. The information presented may be used to formulate a long-term care strategy for dementia of the ageing population in Hong Kong.
In this report, the results of a household survey were used to examine the prevalence of very mild and mild dementia in Chinese older persons in Hong Kong.
The study adopted a two-phase design. At Phase 1, 6100 subjects were screened using the Cantonese version of the Mini-mental State Examination (MMSE) and a short memory inventory. At Phase 2, 2073 subjects were screened positive and 737 were evaluated by psychiatrists. Clinical Dementia Rating (CDR) and cognitive assessment were used for diagnosis of dementia. Very mild dementia (VMD) was defined as a global CDR of 0.5, with memory and non-memory subscale scores of 0.5 or more. Mild dementia was classified for subjects with a CDR of 1.
The overall prevalence of VMD and mild dementia for persons aged 70 years or above was 8.5% (95%CI: 7.4-9.6) and 8.9% (95%CI: 7.8-10.0) respectively. Among subjects with clinical dementia, 84.6% had mild (CDR1) dementia. Logistic regression analyses revealed that older age, lower educational level and significant cerebrovascular risk factors were risk factors for dementia, while regular physical exercise was a protective factor for dementia.
A sizable proportion of community-living subjects suffered from milder forms of dementia. They represent a high risk for early intervention to reduce potential physical and psychiatric morbidity.
Background: This paper investigates the statistical relationship between bilingualism and the Onset Age (OA) of AD and MCI across a clinical sample, consisting of 580 Alzheimer's Disease (AD) subjects and 1264 Mild Cognitive Impairment (MCI) subjects, via a statistical analysis conducted on the sample retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Method: To investigate whether bilingualism has any correlation with the OAs of AD or MCI subjects, our study leverages the full potential of the ADNI dataset, a dataset that covers both the OA and the bilingualism status of both the AD and MCI subjects. Prior to performing any meaningful statistical analysis, a regression model and a probabilistic model were developed in parallel to fill in the missing OA and bilingualism values. A simple least-square regression model that consists of an independent variable of registered age for Mini-Mental State Examination (MMSE) score was used to estimate the OA of the AD and MCI subjects in the ADNI dataset. After filling in the missing OA values, the number of subjects relevant for the statistical analysis increased from 816 (AD: 371, MCI: 445) to 1844 (AD: 580, MCI: 1264), which greatly enlarged the representation of the AD and MCI sample in the ADNI population. With increased sample size, a novel probabilistic classification model was introduced to infer an ADNI subject’s bilingualism when relevant demographic information and deterministic outcome were not readily available from the ADNI dataset. The weighted average OA for the bilinguals and the monolinguals was then computed, where the weights for the probabilistic labels were assigned based on the percentage of bilingualism in the general US population. Finally, a statistical analysis was performed to test whether any statistically significant correlation exists between the OA and the bilingualism of the AD and MCI subjects within the ADNI dataset. Findings: Our preliminary study demonstrates no significant statistical difference between the OA of the bilinguals and the monolinguals within the ADNI dataset. Thus, the monolingual speakers within the ADNI dataset do not statistically manifest earlier onset, as compared to the bilingual speakers, which is slightly inconsistent with some earlier statistical findings that bilingual speakers enjoy certain distinctive advantages, such as late onset of AD, as compared to monolingual counterparts.
Background:
We recently discovered autism/intellectual disability somatic mutations in postmortem brains, presenting higher frequency in Alzheimer's disease subjects, compared with the controls. We further revealed high impact cytoskeletal gene mutations, coupled with potential cytoskeleton-targeted repair mechanisms.
Objective:
The current study was aimed at further discerning if somatic mutations in brain diseases are presented only in the most affected tissue (the brain), or if blood samples phenocopy the brain, toward potential diagnostics.
Methods:
Variant calling analyses on an RNA-seq database including peripheral blood samples from 85 soldiers (58 controls and 27 with symptoms of post-traumatic stress disorder, PTSD) was performed.
Results:
High (e.g., protein truncating) as well as moderate impact (e.g., single amino acid change) germline and putative somatic mutations in thousands of genes were found. Further crossing the mutated genes with autism, intellectual disability, cytoskeleton, inflammation, and DNA repair databases, identified the highest number of cytoskeletal-mutated genes (187 high and 442 moderate impact). Most of the mutated genes were shared and only when crossed with the inflammation database, more putative high impact mutated genes specific to the PTSD-symptom cohorts versus the controls (14 versus 13) were revealed, highlighting tumor necrosis factor specifically in the PTSD-symptom cohorts.
Conclusion:
With microtubules and neuro-immune interactions playing essential roles in brain neuroprotection and Alzheimer-related neurodegeneration, the current mutation discoveries contribute to mechanistic understanding of PTSD and brain protection, as well as provide future diagnostics toward personalized military deployment strategies and drug design.
Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.
Air pollution has become a global challenge, and obtaining real-time air quality information is urgently needed. Although the governments have been trying their best in delivering accurate air quality reports, missing air pollution data remains a key challenge. Based on the temporal-spatial correlation of the data, we propose a novel long-short term context encoder (LSCE) structure for recovering missing air pollution data. The original context encoder approach based on image completion focuses on reconstructing rectangular missing regions. Differing from traditional methods, our fully convolutional neural network architecture enjoys the following novelties. First, LSCE can recover irregular missing data patterns. Second, we devise two data pre-processing strategies to produce two types of context encoders, namely, the long-short term cutting context encoder (LSCCE) and the long-short term sliding context encoder (LSSCE). Compared with LSCCE, LSSCE increases the number of training data matrixes. Finally, we investigate the significance of adaptive training in addressing different types of missing data. Our simulation results have demonstrated that our approach, especially, LSSCE, can outperform existing missing data recovery methods. Besides, our techniques can be widely applicable for recovering other temporally and spatially correlated missing data, such as vehicular traffic or meteorology data.
Providing support for ageing and frail populations to extend their personal autonomy is desirable for their well-being as it is for the society at large, since it can ease the economic and social challenges caused by ever-ageing developed societies. Ambient-assisted living (AAL) technologies and services might be a solution to address those challenges. Recent improved capabilities in both ambient and wearable technologies, especially those related with video and lifelogging data, and huge advances in the accuracy of intelligent systems for AAL are leading to more valuable and trustworthy services for older people and their caregivers. These advances have been particularly relevant in the last years due to the appearance of RGB-D devices and the development of deep learning systems. This article reviews these latest developments in the intersection of AAL, intelligent systems, lifelogging, and computer vision. This paper provides a study of previous reviews in these fields, and later analyses newer intelligent techniques employed with different video-based lifelogging technologies in order to offer lifelogging services for AAL. Additionally, privacy and ethical issues associated with these technologies are discussed. This review aims at facilitating the understanding of the multiple fields involved.
In this article, we discuss the new requirements for standards for policy and mechanism to retain privacy when analyzing users data. More and more information is gathered about all of us, and used for a variety of reasonable commercial goals -- recommendations, targetted advertising, optimising product reliability or service delivery: the list goes on and on. However, the risks of leakage or misuse also grow. Recent years have seen the development of a number of tools and techniques limit these risks, ranging from improved security for processing systems, through to control over what is disclosed in the results. Most of these tools and techniques will require agreements on when and how they are used and how they inter-operate.
Objective. Several studies have demonstrated saccadic eye movement (SEM) abnormalities in Alzheimer’s disease (AD) when patients performed prosaccade (PS) and antisaccade (AS) tasks. Some studies have also showed that SEM abnormalities were correlated with dementia rating tests such as the MMSE. Therefore, it has been suggested that SEMs could provide useful information for diagnosis. However, little is known about predictive saccades (PreS) – saccades triggered before or very quickly after stimuli appearance – and their relationships with cognition in AD. Here, we aimed to examine the relationships between our usual dementia screening tests and SEM parameters in PS, AS, and also PreS task.
Method. We compared SEMs in 20 patients suffering from AD and in 35 healthy older adults (OA) in PS, AS and PreS task. All participants also completed a neuropsychological evaluation.
Results. We showed that AD patients had higher latency and latency variability regardless the tasks, and also higher AS cost, in comparison with OA. Moreover, AD patients made more uncorrected AS and took more time to correct incorrect AS. In PreS task, AD patients showed higher gain and gain variability than OA when they made anticipated saccades. Close relationships were found between the majority of SEM variables in PS, AS and PreS tasks and dementia screening tests, especially the MMSE and episodic memory measures.
Conclusion. Our findings, in agreement with previous studies, demonstrated that AD affects several SEM parameters. SEM abnormalities may reflect selective and executive attention impairments in AD.
Keywords: Dementia; Attention; Executive functions; Eye movement
Importance:
Studies have established the importance of physical activity and fitness, yet limited data exist on the associations between objective, real-world physical activity patterns, fitness, sleep, and cardiovascular health.
Objectives:
To assess the feasibility of obtaining measures of physical activity, fitness, and sleep from smartphones and to gain insights into activity patterns associated with life satisfaction and self-reported disease.
Design, setting, and participants:
The MyHeart Counts smartphone app was made available in March 2015, and prospective participants downloaded the free app between March and October 2015. In this smartphone-based study of cardiovascular health, participants recorded physical activity, filled out health questionnaires, and completed a 6-minute walk test. The app was available to download within the United States.
Main outcomes and measures:
The feasibility of consent and data collection entirely on a smartphone, the use of machine learning to cluster participants, and the associations between activity patterns, life satisfaction, and self-reported disease.
Results:
From the launch to the time of the data freeze for this study (March to October 2015), the number of individuals (self-selected) who consented to participate was 48 968, representing all 50 states and the District of Columbia. Their median age was 36 years (interquartile range, 27-50 years), and 82.2% (30 338 male, 6556 female, 10 other, and 3115 unknown) were male. In total, 40 017 (81.7% of those who consented) uploaded data. Among those who consented, 20 345 individuals (41.5%) completed 4 of the 7 days of motion data collection, and 4552 individuals (9.3%) completed all 7 days. Among those who consented, 40 017 (81.7%) filled out some portion of the questionnaires, and 4990 (10.2%) completed the 6-minute walk test, made available only at the end of 7 days. The Heart Age Questionnaire, also available after 7 days, required entering lipid values and age 40 to 79 years (among 17 245 individuals, 43.1% of participants). Consequently, 1334 (2.7%) of those who consented completed all fields needed to compute heart age and a 10-year risk score. Physical activity was detected for a mean (SD) of 14.5% (8.0%) of individuals' total recorded time. Physical activity patterns were identified by cluster analysis. A pattern of lower overall activity but more frequent transitions between active and inactive states was associated with equivalent self-reported cardiovascular disease as a pattern of higher overall activity with fewer transitions. Individuals' perception of their activity and risk bore little relation to sensor-estimated activity or calculated cardiovascular risk.
Conclusions and relevance:
A smartphone-based study of cardiovascular health is feasible, and improvements in participant diversity and engagement will maximize yield from consented participants. Large-scale, real-world assessment of physical activity, fitness, and sleep using mobile devices may be a useful addition to future population health studies.
Biomarkers for Alzheimer's disease (AD) are vital for disease detection in the clinical setting. Discovered in our laboratory, activity-dependent neuroprotective protein (ADNP) is essential for brain formation and linked to cognitive functions. Here, we revealed that blood borne expression of ADNP and its paralog ADNP2 is correlated with premorbid intelligence, AD pathology, and clinical stage. Age adjustment showed significant associations between: 1) higher premorbid intelligence and greater serum ADNP, and 2) greater cortical amyloid and lower ADNP and ADNP2 mRNAs. Significant increases in ADNP mRNA levels were observed in patients ranging from mild cognitive impairment (MCI) to AD dementia. ADNP2 transcripts showed high correlation with ADNP transcripts, especially in AD dementia lymphocytes. ADNP plasma/serum and lymphocyte mRNA levels discriminated well between cognitively normal elderly, MCI, and AD dementia participants. Measuring ADNP blood-borne levels could bring us a step closer to effectively screening and tracking AD.
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers’ questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
Background:
This study evaluates which cognitive measure is best for predicting incident dementia in a population-based random sample of Chinese older adults without dementia over a five-year period.
Methods:
A total of 787 community-dwelling Chinese older adults without dementia in Hong Kong were assessed at baseline (T0), at two years (T1), and at five years (T2).
Results:
The annual conversion rate to dementia was 1.6% and 6.3% for baseline normal participants and baseline mild cognitive impairment (MCI) participants, respectively. The Cantonese version of Mini-mental State Examination (CMMSE) scores declined significantly over time. In participants who progressed to dementia, Category Verbal Fluency Test (CVFT) scores dropped significantly from T0 to T1. A 1-SD drop of either CMMSE or CVFT in two years predicted dementia at five years with 91.5% sensitivity and 62.0% specificity. A stable CMMSE and CVFT at two years predicted a 91% chance of not progressing to clinical dementia at five years.
Conclusion:
In this community sample of Chinese older adults, a decline in cognitive screening tests in short term (two years) offered useful information in predicting dementia conversion over a longer period.
The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.
studies have suggested that neuropsychiatric (NP) symptoms influence the development of dementia among older adults. But, the results are inconsistent and there is limited information about NP symptoms in population-based samples.
to explore the association between NP symptoms and risk of cognitive decline in Chinese older persons residing in the community.
prospective study.
community sample.
a total of 321 community-dwelling Chinese older persons aged 60 or over with mild cognitive impairment participated in the study.
at baseline, each subject was assessed with Clinical Dementia Rating (CDR), Mini-Mental State Examination, list learning and delayed recall, and Category Verbal Fluency Test. Severity of NP symptoms was evaluated with Neuropsychiatric Inventory (NPI). Global cognitive status at the end of 2-year study period was determined by CDR.
at baseline, 40.5% of participants exhibited one or more NP symptoms (NPI total score ≥ 1). Night-time behaviours (22.1%), depression (16.8%), apathy (14.0%) and anxiety (12.8%) were the most common NP symptoms. At the end of 2-year follow-up, 27.5% of participants with depression at baseline developed dementia, compared with 14.8% of those without depression (χ² = 4.90, P= 0.03). Aberrant motor behaviour was also significantly associated with deterioration in cognition (χ² = 5.84, P= 0.02), although it was an infrequent occurrence. On logistic regression analysis, only depression at baseline was shown to be a risk factor for progression to dementia (OR= 2.40, 95% CI 1.05-5.46, P= 0.04).
depression in non-demented older persons may represent an independent dimension reflecting early neuronal degeneration. Further studies should be conducted to assess whether effective management of NP symptoms exerts beneficial effects on cognitive function.
Feasibility of obtaining measures of lifestyle from a smartphone app: the MyHeart Counts Cardiovascular Health Study
- M V Mcconnell
- A Shcherbina
- A Pavlovic
- J R Homburger
- R L Goldfeder
- D Waggot
- E A Ashley
- MV McConnell
Correlation between the bilingual status and the onset age of AD and MCI subjects: evidence from the ADNI dataset
- J Li
- Y Han
- Jck Lam
- Vok Li
- S Matthews
- Lyl Cheung
- V Yip
- J Downey
- D Chan
- I Gozes
A novel Interpolation-SVT approach for recovering missing low-rank air quality data
- Y W Yu
- J Q Yu
- Vok Li
- Jck Lam
Missing air pollution data recovery based on Long-Short Term Context Encoder
- Y W Yu
- Vok Li
- Jck Lam
Prevalence of mild behavioural impairment: a systematic review and meta-analysis
- Y Pan
- YF Shea
- S Li
- R Chen
- HKF Mak
- PKC Chiu
- YQ Song
Pan Y, Shea YF, Li S, Chen R, Mak HKF, Chiu PKC, Song YQ (2021)
Prevalence of mild behavioural impairment: a systematic review
and meta-analysis. Psychogeriatrics 21(1):100-111
Cambridge University Press RADAR-AD (n.d.) Remote assessment of disease and relapse -Alzheimer's disease
- J Pearl
Pearl J (2009) Causality. Cambridge University Press
RADAR-AD (n.d.) Remote assessment of disease and relapse -Alzheimer's
disease. Available at: https:// cordis. europa. eu/ proje ct/ id/ 806999