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

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

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

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

Can lifestyle modification improve neurocognition? Rationale and design of the ENLIGHTEN clinical trial

Risk factors for cardiovascular disease (CVD) not only increase the risk for clinical CVD events, but also are associated with a cascade of neurophysiologic and neuroanatomic changes that increase the risk of cognitive impairment and dementia. Although epidemiological studies have shown that exercise and diet are associated with lower CVD risk and reduced incidence of dementia, no randomized controlled trial (RCT) has examined the independent effects of exercise and diet on neurocognitive function among individuals at risk for dementia. The ENLIGHTEN trial is a RCT of patients with CVD risk factors who also are characterized by subjective cognitive complaints and objective evidence of neurocognitive impairment without dementia (CIND

Association of traumatic brain injury with subsequent neurological and psychiatric disease: a meta-analysis

Mild traumatic brain injury (TBI) has been proposed as a risk factor for development of Alzheimer’s disease, Parkinson’s disease, depression, and other illnesses. This study’s objective was to determine the association of prior mild TBI with subsequent diagnosis (i.e., at least one year post-injury) of neurologic or psychiatric disease

Tissue-Specific Genetic Control of Splicing: Implications for the Study of Complex Traits

Numerous genome-wide screens for polymorphisms that influence gene expression have provided key insights into the genetic control of transcription. Despite this work, the relevance of specific polymorphisms to in vivo expression and splicing remains unclear. We carried out the first genome-wide screen, to our knowledge, for SNPs that associate with alternative splicing and gene expression in human primary cells, evaluating 93 autopsy-collected cortical brain tissue samples with no defined neuropsychiatric condition and 80 peripheral blood mononucleated cell samples collected from living healthy donors. We identified 23 high confidence associations with total expression and 80 with alternative splicing as reflected by expression levels of specific exons. Fewer than 50% of the implicated SNPs however show effects in both tissue types, reflecting strong evidence for distinct genetic control of splicing and expression in the two tissue types. The data generated here also suggest the possibility that splicing effects may be responsible for up to 13 out of 84 reported genome-wide significant associations with human traits. These results emphasize the importance of establishing a database of polymorphisms affecting splicing and expression in primary tissue types and suggest that splicing effects may be of more phenotypic significance than overall gene expression changes

Characterization of the Poly-T Variant in the TOMM40 Gene in Diverse Populations

We previously discovered that a polymorphic, deoxythymidine-homopolymer (poly-T, rs10524523) in intron 6 of the TOMM40 gene is associated with age-of-onset of Alzheimer's disease and with cognitive performance in elderly. Three allele groups were defined for rs10524523, hereafter ‘523’, based on the number of ‘T’-residues: ‘Short’ (S, T≤19), ‘Long’ (L, 20≤T≤29) and ‘Very Long’ (VL, T≥30). Homopolymers, particularly long homopolymers like ‘523’, are difficult to genotype because ‘slippage’ occurs during PCR-amplification. We initially genotyped this locus by PCR-amplification followed by Sanger-sequencing. However, we recognized the need to develop a higher-throughput genotyping method that is also accurate and reliable. Here we describe a new ‘523’ genotyping assay that is simple and inexpensive to perform in a standard molecular genetics laboratory. The assay is based on the detection of differences in PCR-fragment length using capillary electrophoresis. We discuss technical problems, solutions, and the steps taken for validation. We employed the novel assay to investigate the ‘523’ allele frequencies in different ethnicities. Whites and Hispanics have similar frequencies of S/L/VL alleles (0.45/0.11/0.44 and 0.43/0.09/0.48, respectively). In African-Americans, the frequency of the L-allele (0.10) is similar to Whites and Hispanics; however, the S-allele is more prevalent (0.65) and the VL-allele is concomitantly less frequent (0.25). The allele frequencies determined using the new methodology are compared to previous reports for Ghanaian, Japanese, Korean and Han Chinese cohorts. Finally, we studied the linkage pattern between TOMM40-‘523’ and APOE alleles. In Whites and Hispanics, consistent with previous reports, the L is primarily linked to ε4, while the majority of the VL and S are linked to ε3. Interestingly, in African-Americans, Ghanaians and Japanese, there is an increased frequency of the ‘523’S-APOEε4 haplotype. These data may be used as references for ‘523’ allele and ‘523’-APOE haplotype frequencies in diverse populations for the design of research studies and clinical trials