[Show abstract][Hide abstract] ABSTRACT: The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.
[Show abstract][Hide abstract] ABSTRACT: The synaptic plasticity hypothesis of major depressive disorder (MDD) posits that alterations of synaptic plasticity represent a final common pathway underlying the clinical symptoms of the disorder. This study tested the hypotheses that patients with MDD show an attenuation of cortical synaptic long-term potentiation (LTP) like plasticity in comparison to healthy controls, and that this attenuation recovers after remission. Cortical synaptic LTP-like plasticity was measured using a transcranial magnetic stimulation protocol, i.e. paired associative stimulation (PAS), in 27 inpatients with MDD according to ICD-10 criteria and 27 sex- and age-matched healthy controls. The amplitude of motor evoked potentials was measured before and after PAS. Patients were assessed during the acute episode and at follow-up to determine the state- or trait-character of LTP-like changes. LTP-like plasticity, the PAS-induced increase in motor evoked potential amplitudes was significantly attenuated in patients with an acute episode of MDD compared to healthy controls. Patients with remission showed a restoration of synaptic plasticity, whereas the deficits persisted in patients without remission, indicative for a state-character of impaired LTP-like plasticity. The results provide first evidence for a state-dependent partial occlusion of cortical LTP-like plasticity in MDD. This further identifies impaired LTP-like plasticity as a potential pathomechanism and treatment target of the disorder.Neuropsychopharmacology accepted article preview online, 07 October 2015. doi:10.1038/npp.2015.310.
Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 10/2015; DOI:10.1038/npp.2015.310 · 7.05 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: LTP-like plasticity measured by visual evoked potentials (VEP) can be induced in the intact human brain by presenting checkerboard reversals. Also associated with LTP-like plasticity, around two third of participants respond to transcranial magnetic stimulation (TMS) with a paired-associate stimulation (PAS) protocol with a potentiation of their motor evoked potentials. LTP-like processes are also required for verbal and motor learning tasks. We compared effect sizes, responder rates and intercorrelations as well as the potential influence of attention between these four assessments in a group of 37 young and healthy volunteers. We observed a potentiation effect of the N75 and P100 VEP component which positively correlated with plasticity induced by PAS. Subjects with a better subjective alertness were more likely to show PAS and VEP potentiation. No correlation was found between the other assessments. Effect sizes and responder rates of VEP potentiation were higher compared to PAS. Our results indicate a high variability of LTP-like effects and no evidence for a system-specific nature. As a consequence, studies wishing to assess individual levels of LTP-like plasticity should employ a combination of multiple assessments.
Frontiers in Human Neuroscience 09/2015; 9(506). DOI:10.3389/fnhum.2015.00506 · 3.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background:
Hippocampal grey matter (GM) atrophy predicts conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Pilot data suggests that mean diffusivity (MD) in the hippocampus, as measured with diffusion tensor imaging (DTI), may be a more accurate predictor of conversion than hippocampus volume. In addition, previous studies suggest that volume of the cholinergic basal forebrain may reach a diagnostic accuracy superior to hippocampal volume in MCI.
The present study investigated whether increased MD and decreased volume of the hippocampus, the basal forebrain and other AD-typical regions predicted time to conversion from MCI to AD dementia.
79 MCI patients with DTI and T1-weighted magnetic resonance imaging (MRI) were retrospectively included from the European DTI Study in Dementia (EDSD) dataset. Of these participants, 35 converted to AD dementia after 6-46 months (mean: 21 months). We used Cox regression to estimate the relative conversion risk predicted by MD values and GM volumes, controlling for age, gender, education and center.
Decreased GM volume in all investigated regions predicted an increased risk for conversion. Additionally, increased MD in the right basal forebrain predicted increased conversion risk. Reduced volume of the right hippocampus was the only significant predictor in a stepwise model combining all predictor variables.
Volume reduction of the hippocampus, the basal forebrain and other AD-related regions was predictive of increased risk for conversion from MCI to AD. In this study, volume was superior to MD in predicting conversion.
[Show abstract][Hide abstract] ABSTRACT: Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.98. Multi-class separation of 138 patients with either AD or FTD from other included groups was good on the training set (AUC >0.9) but substantially less accurate (AUC = 0.76 for AD and 0.78 for FTD) on data from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies.
[Show abstract][Hide abstract] ABSTRACT: Objective:
Paired associative stimulation (PAS) is a widely used transcranial magnetic stimulation (TMS) paradigm to induce synaptic long-term potentiation (LTP)-like plasticity in the intact human brain. The PAS effect is reduced in Alzheimer's dementia (AD) but has not yet been assessed in patients with mild cognitive impairment (MCI).
PAS was assessed in a group of 24 MCI patients and 24 elderly controls. MCI patients were further stratified by their cognitive profile as well as hippocampal atrophy and Apolipoprotein E (ApoE) genotype.
There was no difference in PAS effects between MCI patients and healthy controls. MCI patients tended to show a higher response rate and an average PAS effect. PAS effects were not correlated with markers of disease severity or ApoE genotype but were more pronounced in individuals with shorter sleep duration and in MCI subjects with higher ratings of subjective alertness.
Contrary to our initial hypothesis, there was no clear difference in PAS between MCI patients and healthy controls.
Our results argue against a continuous reduction of LTP-like plasticity along the spectrum of clinical MCI when stratified by MCI-subtype, APOE genotype or hippocampus atrophy.
Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 08/2015; DOI:10.1016/j.clinph.2015.08.010 · 3.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Objectives:
The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI).
Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects.
Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics.
Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features.
[Show abstract][Hide abstract] ABSTRACT: We present in this paper a method to perform a length parameterization of cortical sulcus meshes. Such parameterization allows morphological features to be localized in a normalized way along the length of the sulcus and can be used to perform population studies and group comparisons. Our method uses the second eigenfunction of the Laplace-Beltrami operator, and the resulting parameterization is quasi-isometric. The process is validated on the central sulci of a set of subjects and its efficiency is demonstrated by quantifying morphological differences between left and right-handed subjects.
IEEE International Symposium on Biomedical Imaging, Brooklyn, USA; 04/2015
[Show abstract][Hide abstract] ABSTRACT: Alzheimer’s disease (AD), the predominant cause of dementia, is characterized by progressive loss of memory and other cognitive functions with advancing age, and both genetic and non-genetic factors modifying disease risk. This chapter provides a summary of the underlying neuropathology, epidemiology, and clinical characteristics of AD. Additionally, recently developed methods of automated diagnosing, novel therapeutic strategies, and possible preventing variables are briefly described.
Brain Mapping: An Encyclopedic Reference, First Edition edited by Arthur W. Toga, 02/2015: chapter 73; Elsevier., ISBN: 9780123970251
[Show abstract][Hide abstract] ABSTRACT: Deterministic dynamic causal modeling (DCM) for fMRI data is a sophisticated approach to analyse effective connectivity in terms of directed interactions between brain regions of interest. To date it is difficult to know if acquired fMRI data will yield precise estimation of DCM parameters. Focusing on parameter identifiability, an important prerequisite for research questions on directed connectivity, we present an approach inferring if parameters of an envisaged DCM are identifiable based on information from fMRI data. With the freely available "attention to motion" dataset, we investigate identifiability of two DCMs and show how different imaging specifications impact on identifiability. We used the profile likelihood, which has successfully been applied in systems biology, to assess the identifiability of parameters in a DCM with specified scanning parameters. Parameters are identifiable when minima of the profile likelihood as well as finite confidence intervals for the parameters exist. Intermediate epoch duration, shorter TR and longer session duration generally increased the information content in the data and thus improved identifiability. Irrespective of biological factors such as size and location of a region, attention should be paid to densely interconnected regions in a DCM, as those seem to be prone to non-identifiability. Our approach, available in the DCMident toolbox, enables to judge if the parameters of an envisaged DCM are sufficiently determined by underlying data without priors as opposed to primarily reflecting the Bayesian priors in a SPM-DCM. Assessments with the DCMident toolbox prior to a study will lead to improved identifiability of the parameters and thus might prevent suboptimal data acquisition. Thus, the toolbox can be used as a preprocessing step to provide immediate statements on parameter identifiability.
Frontiers in Neuroscience 02/2015; 9:43. DOI:10.3389/fnins.2015.00043 · 3.66 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.
[Show abstract][Hide abstract] ABSTRACT: Several models of neural compensation in healthy aging have been suggested to explain brain activity that aids to sustain cognitive function. Applying recently suggested criteria of "attempted" and "successful" compensation, we reviewed existing literature on compensatory mechanisms in preclinical Huntington's disease (HD) and amnestic mild cognitive impairment (aMCI). Both disorders constitute early stages of neurodegeneration ideal for examining compensatory mechanisms and developing targeted interventions. We strived to clarify whether compensation criteria derived from healthy aging populations can be applied to early neurodegeneration. To concentrate on the close coupling of cognitive performance and brain activity, we exclusively addressed task fMRI studies. First, we found evidence for parallels in compensatory mechanisms between healthy aging and neurodegenerative disease. Several studies fulfilled criteria of attempted compensation, while reports of successful compensation were largely absent, which made it difficult to conclude on. Second, comparing working memory studies in preclinical HD and aMCI, we identified similar compensatory patterns across neurodegenerative disorders in lateral and medial prefrontal cortex. Such patterns included an inverted U-shaped relationship of neurodegeneration and compensatory activity spanning from preclinical to manifest disease. Due to the lack of studies systematically targeting all criteria of compensation, we propose an exemplary study design, including the manipulation of compensating brain areas by brain stimulation. Furthermore, we delineate the benefits of targeted interventions by non-invasive brain stimulation, as well as of unspecific interventions such as physical activity or cognitive training. Unambiguously detecting compensation in early neurodegenerative disease will help tailor interventions aiming at sustained overall functioning and delayed clinical disease onset.
Frontiers in Psychiatry 09/2014; 5:132. DOI:10.3389/fpsyt.2014.00132
[Show abstract][Hide abstract] ABSTRACT: Background
Corrupted gradient directions (GD) in diffusion weighted images may seriously affect reliability of diffusion tensor imaging (DTI)-based comparisons at the group level. In the present study we employed a quality control (QC) algorithm to eliminate corrupted gradient directions from DTI data. We then assessed effects of this procedure on comparisons between Huntington disease (HD) subjects and controls at the group level.
Sixty-one HD patients in early stages and forty matched healthy controls were studied in a longitudinal design (baseline and two follow-ups at three time points over 15 months), in a multicenter setting with similar acquisition protocols on four different MR scanners at four European study sites. A QC algorithm was used to identify corrupted GD in DTI data sets. Differences in fractional anisotropy (FA) maps at the group level with and without elimination of corrupted GD were analyzed.
The elimination of corrupted GD had an impact on individual FA maps as well as on cross-sectional group comparisons between HD subjects and controls. Following application of the QC algorithm, less small clusters of FA changes were observed, compared to the analysis without QC. However, the main pattern of regional reductions and increases in FA values with and without QC-based elimination of corrupted GD was unchanged.
An impact on the result patterns of the comparison of FA maps between HD subjects and controls was observed depending on whether QC-based elimination of corrupted GD was performed. QC-based elimination of corrupted GD in DTI scans reduces the risk of type I and type II errors in cross-sectional group comparison of FA maps contributing to an increase in reliability and stability of group comparisons.