Altered cerebral connectivity is one of the core pathophysiological mechanism underlying the development and progression of information-processing deficits in schizophrenia. To date, most diffusion tensor imaging (DTI) studies used fractional anisotropy (FA) to investigate disrupted white matter connections. However, a quantitative interpretation of FA changes is often impeded by the inherent limitations of the underlying tensor model. A more fine-grained measure of white matter alterations could be achieved by measuring fiber density (FD) - a novel non-tensor-derived diffusion marker. This study investigates, for the first time, FD alterations in schizophrenia patients. FD and FA maps were derived from diffusion data of 25 healthy controls (HC) and 21 patients with schizophrenia (SZ). Using tract-based spatial statistics (TBSS), group differences in FD and FA were investigated across the entire white matter. Furthermore, we performed a region of interest (ROI) analysis of frontal fasciculi to detect potential correlations between FD and positive symptoms. As a result, whole brain TBSS analysis revealed reduced FD in SZ patients compared to HC in several white matter tracts including the left and right thalamic radiation (TR), superior longitudinal fasciculus (SLF), corpus callosum (CC), and corticospinal tract (CST). In contrast, there were no significant FA differences between groups. Further, FD values in the TR were negatively correlated with the severity of positive symptoms and medication dose in SZ patients. In summary, a novel diffusion-weighted data analysis approach enabled us to identify widespread FD changes in SZ patients with most prominent white matter alterations in the frontal and subcortical regions. Our findings suggest that the new FD measure may be more sensitive to subtle changes in the white matter microstructure compared to FA, particularly in the given population. Therefore, investigating FD may be a promising approach to detect subtle changes in the white matter microstructure of altered connectivity in schizophrenia.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6420505 | PMC |
http://dx.doi.org/10.1038/s41598-019-40070-2 | DOI Listing |
Sci Rep
December 2024
Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia.
Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road 88th, Hangzhou, 310009, China.
Chronic ischemia in moyamoya disease (MMD) impaired white matter microstructure and neural functional network. However, the coupling between cerebral blood flow (CBF) and functional connectivity and the association between structural and functional network are largely unknown. 38 MMD patients and 20 sex/age-matched healthy controls (HC) were included for T1-weighted imaging, arterial spin labeling imaging, resting-state functional MRI and diffusion tensor imaging.
View Article and Find Full Text PDFPediatr Rheumatol Online J
December 2024
Section of Rheumatology, Department of Pediatrics, Alberta Children's Hospital, University of Calgary, Calgary, Canada.
Background: Primary small vessel CNS vasculitis (sv-cPACNS) is a challenging inflammatory brain disease in children. Brain biopsy is mandatory to confirm the diagnosis. This study aims to develop and validate a histological scoring tool for diagnosing small vessel CNS vasculitis.
View Article and Find Full Text PDFBiol Psychiatry Cogn Neurosci Neuroimaging
December 2024
Department of Psychiatry, University of Cambridge, Cambridge, UK; Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany. Electronic address:
Background: A preference for sooner-smaller over later-larger rewards, known as delay discounting, is a candidate transdiagnostic marker of waiting impulsivity and a research domain criterion. While abnormal discounting rates have been associated with many psychiatric diagnoses and abnormal brain structure, the underlying neuropsychological processes remain largely unknown. Here, we deconstruct delay discounting into choice and rate processes by testing different computational models and investigate their associations with white matter tracts.
View Article and Find Full Text PDFNeuroimage
December 2024
Institute of Population Health, University of Liverpool, United Kingdom; Hanse Wissenschaftskolleg, Delmenhorst, Germany. Electronic address:
Recent work has shown rapid microstructural brain changes in response to learning new tasks. These cognitive tasks tend to draw on multiple brain regions connected by white matter (WM) tracts. Therefore, behavioural performance change is likely to be the result of microstructural, functional activation, and connectivity changes in extended neural networks.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!