Phenotypic regional fMRI activation patterns during memory encoding in MCI and AD

Reliable blood-oxygen-level-dependent (BOLD) fMRI phenotypic biomarkers of Alzheimer's disease (AD) or mild cognitive impairment (MCI) are likely to emerge only from a systematic, quantitative, and aggregate examination of the functional neuroimaging research literature

Factor Structure of the National Alzheimerʼs Coordinating Centers Uniform Dataset Neuropsychological Battery: An Evaluation of Invariance Between and Within Groups Over Time

The neuropsychological battery from the National Alzheimer’s Disease Coordinating Center (NACC) is designed to provide a sensitive assessment of mild cognitive disorders for multicenter investigations. Comprised of eight common neuropsychological tests (12 measures), the battery assesses cognitive domains affected early in the course of Alzheimer’s disease (AD). We examined the factor structure of the battery across levels of cognition (normal, mild cognitive impairment (MCI), dementia) based on Clinical Dementia Rating (CDR) scores to determine cognitive domains tapped by the battery. Using data pooled from 29 NIA funded Alzheimer’s Disease Centers, exploratory factor analysis was used to derive a general model using half of the sample; four factors representing memory, attention, executive function, and language were identified. Confirmatory factor analysis (CFA) was used on the second half of the sample to evaluate invariance between groups and within groups over one year. Factorial invariance testing included systematic addition of constraints and comparisons of nested models. The general CFA model had a good fit. As constraints were added, model fit deteriorated slightly. Comparisons within groups demonstrated stability over one year. In a range of cognition from normal to dementia, factor structures and factor loadings will vary little. Further work is needed to determine if domains become more or less distinct in severely cognitively compromised individuals

Enriched white matter connectivity networks for accurate identification of MCI patients

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer’s disease (AD), is frequently considered to be good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ1, λ2, λ3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using a SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients

Identification of MCI individuals using structural and functional connectivity networks

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer’s disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity

Event-Related Functional Magnetic Resonance Imaging Changes during Relational Retrieval in Normal Aging and Amnestic Mild Cognitive Impairment

Abstract The earliest cognitive deficits observed in amnestic mild cognitive impairment (aMCI) appear to center on memory tasks that require relational memory (RM), the ability to link or integrate unrelated pieces of information. RM impairments in aMCI likely reflect neural changes in the medial temporal lobe (MTL) and posterior parietal cortex (PPC). We tested the hypothesis that individuals with aMCI, as compared to cognitively normal (CN) controls, would recruit neural regions outside of the MTL and PPC to support relational memory. To this end, we directly compared the neural underpinnings of successful relational retrieval in aMCI and CN groups, using event-related functional magnetic resonance imaging (fMRI), holding constant the stimuli and encoding task. The fMRI data showed that the CN, compared to the aMCI, group activated left precuneus, left angular gyrus, right posterior cingulate, and right parahippocampal cortex during relational retrieval, while the aMCI group, relative to the CN group, activated superior temporal gyrus and supramarginal gyrus for this comparison. Such findings indicate an early shift in the functional neural architecture of relational retrieval in aMCI, and may prove useful in future studies aimed at capitalizing on functionally intact neural regions as targets for treatment and slowing of the disease course. (JINS, 2012, 18, 886-897

Event-Related Functional Magnetic Resonance Imaging Changes during Relational Retrieval in Normal Aging and Amnestic Mild Cognitive Impairment

The earliest cognitive deficits observed in amnestic mild cognitive impairment (aMCI) appear to center on memory tasks that require relational memory (RM), the ability to link or integrate unrelated pieces of information. RM impairments in aMCI likely reflect neural changes in the medial temporal lobe (MTL) and posterior parietal cortex (PPC). We tested the hypothesis that individuals with aMCI, as compared to cognitively normal (CN) controls, would recruit neural regions outside of the MTL and PPC to support relational memory. To this end, we directly compared the neural underpinnings of successful relational retrieval in aMCI and CN groups, using event-related functional magnetic resonance imaging (fMRI), holding constant the stimuli and encoding task. The fMRI data showed that the CN, compared to the aMCI, group activated left precuneus, left angular gyrus, right posterior cingulate, and right parahippocampal cortex during relational retrieval, while the aMCI group, relative to the CN group, activated superior temporal gyrus and supramarginal gyrus for this comparison. Such findings indicate an early shift in the functional neural architecture of relational retrieval in aMCI, and may prove useful in future studies aimed at capitalizing on functionally intact neural regions as targets for treatment and slowing of the disease course

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients