Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms

This study assesses the connectivity alterations caused by Alzheimer's disease (AD) and mild cognitive impairment (MCI) in magnetoencephalogram (MEG) background activity. Moreover, a novel methodology to adaptively extract brain rhythms from the MEG is introduced. This methodology relies on the ability of empirical mode decomposition to isolate local signal oscillations and constrained blind source separation to extract the activity that jointly represents a subset of channels. Inter-regional MEG connectivity was analysed for 36 AD, 18 MCI and 26 control subjects in δ, θ, α and β bands over left and right central, anterior, lateral and posterior regions with magnitude squared coherence—c(f). For the sake of comparison, c(f) was calculated from the original MEG channels and from the adaptively extracted rhythms. The results indicated that AD and MCI cause slight alterations in the MEG connectivity. Computed from the extracted rhythms, c(f) distinguished AD and MCI subjects from controls with 69.4% and 77.3% accuracies, respectively, in a full leave-one-out cross-validation evaluation. These values were higher than those obtained without the proposed extraction methodology

Proteomic analysis of the cerebrospinal fluid of patients with Creutzfeldt-Jakob disease

So far, only the detection of 14-3-3 proteins in cerebrospinal fluid (CSF) has been accepted as diagnostic criterion for Creutzfeldt-Jakob disease (CJD). However, this assay cannot be used for screening because of the high rate of false-positive results, whereas patients with variant CJD are often negative for 14-3-3 proteins. The aim of this study was to compare the spot patterns of CSF by 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) to search for a CJD-specific spot pattern. We analyzed the CSF of 28 patients {[}11 CJD, 9 Alzheimer's disease ( AD), 8 nondemented controls (NDC)] employing 2D-PAGE which was optimized for minimal volumes of CSF (0.1 ml; 7-cm strips). All samples were run at least three times, gels were silver stained and analyzed by an analysis software and manually revised. We could consistently match 268 spots which were then compared between all groups. By the use of 5 spots, we were able to differentiate CJD from AD or NDC with a sensitivity of 100%. CJD could also be distinguished from both groups by using a heuristic clustering algorithm of 2 spots. We conclude that this proteomic approach can differentiate CJD from other diseases and may serve as a model for other neurodegenerative diseases. Copyright (C) 2007 S. Karger AG, Basel

Tau protein, A beta 42 and S-100B protein in cerebrospinal fluid of patients with dementia with Lewy bodies

The intra vitam diagnosis of dementia with Lewy bodies (DLB) is still based on clinical grounds. So far no technical investigations have been available to support this diagnosis. As for tau protein and beta-amyloid((1-42)) (Abeta42), promising results for the diagnosis of Alzheimer's disease ( AD) have been reported; we evaluated these markers and S-100B protein in cerebrospinal fluid (CSF), using a set of commercially available assays, of 71 patients with DLB, 67 patients with AD and 41 nondemented controls (NDC) for their differential diagnostic relevance. Patients with DLB showed significantly lower tau protein values compared to AD but with a high overlap of values. More prominent differences were observed in the comparison of DLB patients with all three clinical core features and AD patients. Abeta42 levels were decreased in the DLB and AD groups versus NDC, without significant subgroup differences. S-100B levels were not significantly different between the groups. Tau protein levels in CSF may contribute to the clinical distinction between DLB and AD, but the value of the markers is still limited especially due to mixed pathology. We conclude that more specific markers have to be established for the differentiation of these diseases. Copyright (C) 2005 S. Karger AG, Basel

Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method

There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice

Genetic association of CDC2 with cerebrospinal fluid tau in Alzheimer's disease

We have recently reported that a polymorphism in the cell division cycle (CDC2) gene, designated Ex6 + 7I/D, is associated with Alzheimer's disease (AD). The CDC2 gene is located on chromosome 10q21.1 close to the marker D10S1225 linked to AD. Active cdc2 accumulates in neurons containing neurofibrillary tangles (NFT), a process that can precede the formation of NFT. Therefore, CDC2 is a promising candidate susceptibility gene for AD. We investigated the possible effects of the CDC2 polymorphism on cerebrospinal fluid (CSF) biomarkers in AD patients. CDC2 genotypes were evaluated in relation to CSF protein levels of total tau, phospho-tau and beta-amyloid (1-42) in AD patients and control individuals, and in relation to the amount of senile plaques and NFT in the frontal cortex and in the hippocampus in patients with autopsy-proven AD and controls. The CDC2 Ex6 + 7I allele was associated with a gene dose-dependent increase of CSF total tau levels (F-2,F- 626 = 7.0, p = 0.001) and the homozygous CDC2Ex6 +7II genotype was significantly more frequent among AD patients compared to controls (p = 0.006, OR = 1.57, 95% CI 1.13-2.17). Our results provide further evidence for an involvement of cdc2 in the pathogenesis of AD. Copyright (C) 2005 S. Karger AG, Basel

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at MICCAI International Conference 202