Wanlin Zhu

Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study.

Resting-state functional magnetic resonance imaging (MRI), have revealed coactivation in a distributed network that characterizes the default-mode in the human brain. However, details from resting-state imaging in amnestic type mild cognitive impairment (aMCI) is poorly understand. Regional homogeneity, which characterizes low-frequency blood oxygenation level dependent fluctuation, after statistically controlling for the regional atrophy and age in resting-state, were examined and compared between the two groups. When regional atrophy was controlled, decreased regional homogeneity in posterior cingulate cortex and precuneus still remained significant in aMCI patients. In addition the aMCI subjects displayed several regions of increased homogeneity, typically in right inferior parietal lobule, right fusiform gyrus and bilateral putamen. The impairment of posterior cingulate and precuneus could be an important marker to distinguish aMCI from healthy aging in the resting-state. Moreover, the increased regional homogeneity changes would be consistent with compensation for damage to the medial temporal regions and limbic structures, perhaps by recruitment of alternative regions.

Discrete Neuroanatomical Networks Are Associated with Specific Cognitive Abilities in Old Age

There have been many attempts at explaining age-related cognitive decline on the basis of regional brain changes, with the usual but inconsistent findings being that smaller gray matter volumes in certain brain regions predict worse cognitive performance in specific domains. Additionally, compromised white matter integrity, as suggested by white matter hyperintensities or decreased regional white matter fractional anisotropy, has an adverse impact on cognitive functions. The human brain is, however, a network and it may be more appropriate to relate cognitive functions to properties of the network rather than specific brain regions. We report on graph theory-based analyses of diffusion tensor imaging tract-derived connectivity in a sample of 342 healthy individuals aged 72–92 years. The cognitive domains included processing speed, memory, language, visuospatial, and executive functions. We examined the association of these cognitive assessments with both the connectivity of the whole brain network and individual cortical regions. We found that the efficiency of the whole brain network of cortical fiber connections had an influence on processing speed and visuospatial and executive functions. Correlations between connectivity of specific regions and cognitive assessments were also observed, e.g., stronger connectivity in regions such as superior frontal gyrus and posterior cingulate cortex were associated with better executive function. Similar to the relationship between regional connectivity efficiency and age, greater processing speed was significantly correlated with better connectivity of nearly all the cortical regions. For the first time, regional anatomical connectivity maps related to processing speed and visuospatial and executive functions in the elderly are identified.

Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors

Prediction of conversion from mild cognitive impairment (MCI) to Alzheimers disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimers Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

Thick Visual Cortex in the Early Blind

We investigated the key neurodevelopmental factors that determine cortical thickness, namely synaptogenesis and regression, by analyzing the thickness of the visual cortex in humans with early- and late-onset blindness. The bilateral visual cortices of the early blind were significantly thicker than those of the late blind and the sighted controls, but the latter two groups did not differ significantly. This suggests reduced “pruning” of synapses in the visual cortex, which may be due to a lack of visual experience during a critical developmental period. These findings support the hypothesis that sensory experience is necessary for an appropriate regression and remodeling of neuronal processes and that synaptic regression might be a major determinant of macroscopic anatomical features like cortical thickness.

White matter integrity in mild cognitive impairment: A tract-based spatial statistics study

Mild cognitive impairment (MCI) as a clinical diagnosis has limited specificity, and identifying imaging biomarkers may improve its predictive validity as a pre-dementia syndrome. This study used diffusion tensor imaging (DTI) to detect white matter (WM) structural alterations in MCI and its subtypes, and aimed to examine if DTI can serve as a potential imaging marker of MCI. We studied 96 amnestic MCI (aMCI), 69 non-amnestic MCI (naMCI), and 252 cognitively normal (CN) controls. DTI was performed to measure fractional anisotropy (FA), and tract-based spatial statistics (TBSS) were applied to investigate the characteristics of WM changes in aMCI and naMCI. The diagnostic utility of DTI in distinguishing MCI from CN was further evaluated by using a binary logistic regression model. We found that FA was significantly reduced in aMCI and naMCI when compared with CN. For aMCI subjects, decreased FA was seen in the frontal, temporal, parietal, and occipital WM, together with several commissural, association, and projection fibres. The best discrimination between aMCI and controls was achieved by combining FA measures of the splenium of corpus callosum and crus of fornix, with accuracy of 74.8% (sensitivity 71.0%, specificity 76.2%). For naMCI subjects, WM abnormality was more anatomically widespread, but the temporal lobe WM was relatively spared. These results suggest that aMCI is best characterized by pathology consistent with early Alzheimers disease, whereas underlying pathology in naMCI is more heterogeneous, and DTI analysis of white matter structural integrity can serve as a potential biomarker of MCI and its subtypes.

Changing topological patterns in normal aging using large-scale structural networks.

We examine normal aging from the perspective of topological patterns of structural brain networks constructed from two healthy age cohorts 20 years apart. Based on graph theory, we constructed structural brain networks using 90 cortical and subcortical regions as a set of nodes and the interregional correlations of grey matter volumes across individual brains as edges between nodes, and further analyzed the topological properties of the age-specific networks. We found that the brain structural networks of both cohorts had small-world architecture, and the older cohort (N = 374; mean age = 66.6 years, range 64-68) had lower global efficiency but higher local clustering in the brain structural networks compared with the younger cohort (N = 428; mean age = 46.7, range 44-48). The older cohort had reduced hemispheric asymmetry and lower centrality of certain brain regions, such as the bilateral hippocampus, bilateral insula, left posterior cingulated, and right Heschl gyrus, but that of the prefrontal cortex (PFC) was not different. These structural network differences may provide the basis for changes in functional connectivity and indeed cognitive function as we grow older.

Regional gray matter changes are associated with cognitive deficits in remitted geriatric depression: An optimized voxel-based morphometry study

BACKGROUND We aimed to investigate structural abnormalities in first-episode remitted geriatric depression (RGD) with optimized voxel-based morphometry (VBM) in closely matched patients and healthy control subjects and examine the relationship of performances on neuropsychological tests with regional gray matter volumes. METHODS Nineteen subjects with first-episode RGD and 16 well-matched healthy control subjects were recruited for this study, and neuropsychological tests and magnetic resonance imaging were conducted on the subjects. The differences in regional gray matter volume were determined between these two groups by optimized VBM. RESULTS The volumes of right superior frontal cortex, left postcentral cortex, and right middle temporal gyrus were significantly smaller in patients with RGD relative to healthy control subjects. However, patients with RGD had larger left cingulate gyrus volume compared with healthy control subjects. There was a significant negative correlation between left cingulate gyrus volume and Rey Auditory Verbal Learning Test delayed recall raw score in patients with RGD. CONCLUSION These results reveal that RGD is associated with gray matter changes of certain brain regions hypothesized to influence cognition and might thus be involved in the psychopathology and pathophysiology of cognitive impairment in RGD.

A longitudinal study of brain atrophy over two years in community-dwelling older individuals

Most previous neuroimaging studies of age-related brain structural changes in older individuals have been cross-sectional and/or restricted to clinical samples. The present study of 345 community-dwelling non-demented individuals aged 70-90years aimed to examine age-related brain volumetric changes over two years. T1-weighted magnetic resonance imaging scans were obtained at baseline and at 2-year follow-up and analyzed using the FMRIB Software Library and FreeSurfer to investigate cortical thickness and shape and volumetric changes of subcortical structures. The results showed significant atrophy across much of the cerebral cortex with bilateral transverse temporal regions shrinking the fastest. Atrophy was also found in a number of subcortical structures, including the CA1 and subiculum subfields of the hippocampus. In some regions, such as left and right entorhinal cortices, right hippocampus and right precentral area, the rate of atrophy increased with age. Our analysis also showed that rostral middle frontal regions were thicker bilaterally in older participants, which may indicate its ability to compensate for medial temporal lobe atrophy. Compared to men, women had thicker cortical regions but greater rates of cortical atrophy. Women also had smaller subcortical structures. A longer period of education was associated with greater thickness in a number of cortical regions. Our results suggest a pattern of brain atrophy with non-demented people that resembles a less extreme form of the changes associated with Alzheimers disease (AD).

The effects of age and sex on cortical sulci in the elderly

A large number of structural brain studies using magnetic resonance imaging (MRI) have reported age-related cortical changes and sex difference in brain morphology. Most studies have focused on cortical thickness or density, with relatively few studies of cortical sulcal features, especially in the elderly. In this paper, we report global sulcal indices (g-SIs) of both cerebral hemispheres and the average sulcal span in six prominent sulci, as observed in T1-weighted scans obtained from a large community cohort of 319 non-demented individuals aged between 70 and 90 years (mean=78.06+/-4.75; male/female=149/170), using automated methods. Our results showed that for both hemispheres, g-SIs had significant negative correlations with age in both men and women. Using an interactive effect analysis, we found that g-SIs for men declined faster with age than that for women. The widths of all six sulcal spans increased significantly with age, with largest span increase occurring in the superior frontal sulcus. Compared to women, men had significantly wider sulcal spans for all sulci that were examined. Our findings suggest that both age and sex contribute to significant cortical gyrification differences and variations in the elderly. This study establishes a reference for future studies of age-related brain changes and neurodegenerative diseases in the elderly.

COMT Val158Met Modulates Association Between Brain White Matter Architecture and IQ

The intelligence quotient (IQ) is typically associated with the architecture of gray and white matter in specific brain regions, and this association appears to be genetically based. However, specific sources of genetic variation for the association have not been studied extensively. Using diffusion tensor imaging in 15 mental retardation patients and 80 healthy volunteers, we studied the association between white matter architecture and IQ and also investigated the effects of COMT val158met on this association. The results showed that fractional anisotropy (FA) values in the prefrontal lobe and the hippocampus formation were associated with IQ and that val158met may affect this association. Subjects who were val homozygous showed steeper slopes for regression of the FA value on IQ than met carriers. Our findings suggest that COMT val158met may contribute to intelligence by affecting the association between IQ and the white matter architecture in the prefrontal lobe and the hippocampal formation.