Voxel-based correlation of 18F-THK5351 accumulation with gray matter structural networks in cognitively normal older adults

Background No previous studies have examined the correlations between tau and gray matter network alterations in cognitively normal (CN) older adults. Here, we investigated the correlations between 18F-THK5351 and local network measures at the voxel level. Material and methods We recruited 47 amyloid-negative CN older adults (65.0 ± 7.9 years, 55% women). All participants underwent structural magnetic resonance imaging (MRI) and 11C-Pittsburgh compound-B and 18F-THK5351 positron emission tomography (PET) scans. Single-subject gray matter networks extracted from T1-weighted MRI data based on cortical similarities were analyzed using the graph theoretical approach. The 18F-THK5351 PET and four local network measures (betweenness centrality, clustering coefficient, characteristic path length, and degree) were evaluated to calculate voxel-wise correlations among the imaging modalities. Result Significant positive correlations between 18F-THK5351 and local network measures were detected in the bilateral caudate. Conclusion Our findings suggest that tau and neuroinflammation in CN older adults may influence local gray matter network in the caudate.

Gray Matter Volume Covariance Networks, Social Support, and Cognition in Older Adults

Objective We examined the neural substrates of social support in older adults. Social support is associated with better outcomes in many facets of aging—including cognitive and functional health—but the underlying neural substrates remain largely unexplored. Methods Voxel-based morphometry and multivariate statistics were used to identify gray matter volume covariance networks associated with social support in 112 older adults without dementia (M age = 74.6 years, 50% female), using the Medical Outcomes Study Social Support Survey. Results A gray matter network associated with overall social support was identified and included prefrontal, hippocampal, amygdala, cingulate, and thalamic regions. A gray matter network specifically associated with tangible social support (e.g., someone to help you if you were confined to bed) was also identified, included prefrontal, hippocampal, cingulate, insular, and thalamic regions, and correlated with memory and executive function. Discussion Gray matter networks associated with overall and tangible social support in this study were composed of regions previously associated with memory, executive function, aging, and dementia. Longitudinal research of the interrelationships between social support, brain structure, and cognition is needed, but strengthening social support may represent a new path toward improving cognition in aging that should be explored.

Gray matter networks and cognitive impairment in multiple sclerosis

Background: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS). Objective: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS. Methods: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female). Seven network parameters were computed and compared within MS between cognitively normal and impaired subjects, and associated with performance on neuropsychological tests in six cognitive domains with regression models. Analyses were controlled for age, gender, whole-brain gray matter volumes, and education level. Results: Compared to MS subjects with normal cognition, MS subjects with cognitive impairment showed a more random network organization as indicated by lower lambda values (all p < 0.05). Worse average cognition and executive function were associated with lower lambda values. Impaired information processing speed, working memory, and attention were associated with lower clustering values. Conclusion: Our findings indicate that MS subjects with a more randomly organized gray matter network show worse cognitive functioning, suggesting that single-subject gray matter graphs may capture neurological dysfunction due to MS.