During adolescence, the human cortex undergoes substantial remodeling to support a rapid expansion of behavioral repertoire. Accurately quantifying these changes is a prerequisite for understanding normal brain development, as well as the neuropsychiatric disorders that emerge in this vulnerable period. Past accounts have demonstrated substantial regional heterogeneity in patterns of brain development, but frequently have been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Capitalizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of cortical development in a sample of 934 youths ages 8-20, who completed structural neuroimaging as part of the Philadelphia Neurodevelopmental Cohort. Patterns of structural covariance (PSCs) derived by NMF were highly reproducible over a range of resolutions, and differed markedly from common gyral-based structural atlases. Moreover, PSCs were largely symmetric and showed correspondence to specific large-scale functional networks. The level of correspondence was ordered according to their functional role and position in the evolutionary hierarchy, being high in lower-order visual and somatomotor networks and diminishing in higher-order association cortex. Furthermore, PSCs showed divergent developmental associations, with PSCs in higher-order association cortex networks showing greater changes with age than primary somatomotor and visual networks. Critically, such developmental changes within PSCs were significantly associated with the degree of evolutionary cortical expansion. Together, our findings delineate a set of structural brain networks that undergo coordinated cortical thinning during adolescence, which is in part governed by evolutionary novelty and functional specialization.
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http://dx.doi.org/10.1073/pnas.1620928114 | DOI Listing |
Background: The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method: We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high-dimensional neuroimaging data for AD progression, incorporating age and gender as covariates.
Background: Seizures maybe associated with worse neuropathology findings in people with dementia. However, the role of seizure control and how it may impact post-mortem histopathology findings in people with dementia remains unexplored.
Method: We used the longitudinal, multicenter National Alzheimer Coordinating Center data from 9/2005 to 12/2021 to evaluate the association between seizure control and histopathological neurodegenerative changes in people with dementia.
Alzheimers Dement
December 2024
University of Southern California, Los Angeles, CA, USA.
Background: TDP-43 (TAR DNA-binding protein 43) is one of the most frequently observed co-pathologies in Alzheimer's disease (AD). Recognizing the diversity of pathological features in individuals with AD, including the presence of TDP-43, may lead to more personalized and effective treatment approaches. We investigate ante-mortem cortical microstructural changes in MRI with subsequent autopsy confirmation of Alzheimer's disease neuropathological changes (ADNC) with and without TDP-43 comorbidity.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
Background: South Asian (SA) older adults are one of the fastest growing US populations developing Alzheimer's disease (AD) and related dementias (ADRD). Compared to non-Hispanic white (NHW) Americans, SA are hesitant to enroll in neuropsychological and MRI research. This status complicates accurate assessment and diagnosis.
View Article and Find Full Text PDFBackground: Different patterns of atrophy exist in the dementia stage of AD. However, little is known about the heterogeneity of atrophy patterns and the mechanisms that drive subsequent propagation of the disease in the preclinical stages.
Method: From the AMYPAD-PNHS cohort, we included a total of 1323 non-demented individuals, including 1094 amyloid-negative, and 229 amyloid-positive participants (Table 1).
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