Structural covariance across the lifespan: Brain development and aging through the lens of inter-network relationships.
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ABSTRACT:
SUBMITTER: Aboud KS
PROVIDER: S-EPMC6478172 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel-based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre-specified motor, working memory, cognitive flexibility, and social-affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre-HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre-HD were observed, but increased positive correlations were evident for mHD, relative to pre-HD and HC. These findings could be explained by a HD-related neuronal loss heterogeneously affecting the examined network at the pre-HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow-up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs.
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Project description:Brain-specific SIRT6-KO mice present increased DNA damage, learning impairments, and neurodegenerative phenotypes, placing SIRT6 as a key protein in preventing neurodegeneration. In the aging brain, SIRT6 levels/activity decline, which is accentuated in Alzheimer's patients. To understand SIRT6 roles in transcript pattern changes, we analyzed transcriptomes of young WT, old WT and young SIRT6-KO mice brains, and found changes in gene expression related to healthy and pathological aging. In addition, we traced these differences in human and mouse samples of Alzheimer's and Parkinson's diseases, healthy aging and calorie restriction (CR). Our results define four gene expression categories that change with age in a pathological or non-pathological manner, which are either reversed or not by CR. We found that each of these gene expression categories is associated with specific transcription factors, thus serving as potential candidates for their category-specific regulation. One of these candidates is YY1, which we found to act together with SIRT6 regulating specific processes. We thus argue that SIRT6 has a pivotal role in preventing age-related transcriptional changes in brains. Therefore, reduced SIRT6 activity may drive pathological age-related gene expression signatures in the brain.
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Project description:Background and aimsRecently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.Design and settingCross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).CasesCases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.MeasurementsGraph theory metrics of segregation and integration were used to summarize SCN.FindingsMirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.ConclusionStructural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.
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