Parkinson's disease (PD) is a progressive neurodegenerative disorder in which patients show progressively worsening motor symptoms, often followed by cognitive impairment and dementia. Brain MRI can be used to identify patterns of neurodegeneration that are characteristic of PD, but the spatial pattern of brain abnormalities is still not well understood. “Sulcus-based morphometry” provides measures of the cortical fissures of the brain that reflect degenerative changes in relation to neuropsychiatric disease. Extracting sulci requires good contrast between the gray matter and the CSF, and less well-defined sulci may be difficult to extract reliably. Before embarking on a study of sulcal abnormalities in PD, we set out to determine the reliability of measures from 123 sulci, defined by an existing atlas, using publicly available test-retest data from 8 cohorts. Of the 123 atlas-defined sulci, several major sulci were broken down into smaller regions (e.g., the superior temporal sulcus was divided into the main STS, the anterior terminal ascending branch of STS and the posterior terminal ascending branch of STS); we assessed reliability in each individually, and after merging the portions of the sulci together, in a newly defined, concatenated atlas. For 467 subjects from the PPMI cohort (http://www.ppmiinfo. org ;age range: 61.5 ± 10.1 years), we segmented and labeled major sulci and extracted 4 shape descriptors for each: length, depth, surface area, and width. We then aimed to establish the profile of case-control differences for 3 candidate sulci of interest: the central sulcus, superior temporal sulcus and the calcarine fissure. These sulci were among the more robust in terms of reproducibility; we found that the calcarine width was associated with PD, offering new features for genetic and interventional studies of PD.
The quest to identify genetic factors that shape the human brain has been greatly accelerated by imaging. The human brain functions as a complex network of integrated systems and connected processes, and a vast number of features can be observed and extracted from structural brain images -- including regional volume, shape, and other morphological features of given brain structures. This feature set can be considered as part of the structural network of the brain, which is under strong genetic control. However, it is unclear which of the imaging derived features serve as the most promising traits for discovering specific genes that affect brain structure. Here, we aim to create the first ever network of genetically correlated cortical sulcal features, and through a twin model, determine the degree of genetic correlation across the entire network. Building on functional brain network analysis, we consider the high-dimensional genetic correlation structure as a undirected graph with a complex network of multi-weighted hubs to uncover the underlying genetic core of sulcal morphometry.
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