White matter connectivity by diffusion distance on 3T MRI

2004 ◽  
Author(s):  
T. Sato ◽  
K. Kashikura ◽  
H. Kabasawa ◽  
Y. Yonekura ◽  
K. Minato
Keyword(s):  
White Matter ◽  
Diffusion Distance ◽  
3T Mri ◽  
White Matter Connectivity

scholarly journals White matter network damage mediates association between cerebrovascular disease and cognition

2021 ◽  
pp. 0271678X2199098
Author(s):  
Saima Hilal ◽  
Siwei Liu ◽  
Tien Yin Wong ◽  
Henri Vrooman ◽  
Ching-Yu Cheng ◽  
...  
Keyword(s):  
White Matter ◽  
Cerebrovascular Disease ◽  
Verbal Memory ◽  
Visual Memory ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Domain Specific ◽  
Epidemiology Of Dementia ◽  
Z Scores ◽  
White Matter Connectivity

To determine whether white matter network disruption mediates the association between MRI markers of cerebrovascular disease (CeVD) and cognitive impairment. Participants (n = 253, aged ≥60 years) from the Epidemiology of Dementia in Singapore study underwent neuropsychological assessments and MRI. CeVD markers were defined as lacunes, white matter hyperintensities (WMH), microbleeds, cortical microinfarcts, cortical infarcts and intracranial stenosis (ICS). White matter microstructure damage was measured as fractional anisotropy and mean diffusivity by tract based spatial statistics from diffusion tensor imaging. Cognitive function was summarized as domain-specific Z-scores. Lacunar counts, WMH volume and ICS were associated with worse performance in executive function, attention, language, verbal and visual memory. These three CeVD markers were also associated with white matter microstructural damage in the projection, commissural, association, and limbic fibers. Path analyses showed that lacunar counts, higher WMH volume and ICS were associated with executive and verbal memory impairment via white matter disruption in commissural fibers whereas impairment in the attention, visual memory and language were mediated through projection fibers. Our study shows that the abnormalities in white matter connectivity may underlie the relationship between CeVD and cognition. Further longitudinal studies are needed to understand the cause-effect relationship between CeVD, white matter damage and cognition.

White matter connectivity networks predict levodopa-induced dyskinesia in Parkinson’s disease

2021 ◽  
Author(s):  
Jin Ho Jung ◽  
Yae Ji Kim ◽  
Seok Jong Chung ◽  
Han Soo Yoo ◽  
Yang Hyun Lee ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
White Matter Connectivity

scholarly journals Differences in white matter connectivity between treatment-resistant and treatment-responsive subtypes of schizophrenia

2018 ◽  
Vol 282 ◽  
pp. 47-54 ◽  
Author(s):  
Carolyn Beth McNabb ◽  
Rob Kydd ◽  
Frederick Sundram ◽  
Ian Soosay ◽  
Bruce Roy Russell
Keyword(s):  
White Matter ◽  
Treatment Resistant ◽  
White Matter Connectivity

scholarly journals The convergence and divergence of episodic and semantic functions across lateral parietal cortex

2020 ◽  
Author(s):  
Gina F. Humphreys ◽  
JeYoung Jung ◽  
Matthew A. Lambon Ralph
Keyword(s):  
White Matter ◽  
Semantic Memory ◽  
Parietal Cortex ◽  
Memory Retrieval ◽  
Task Difficulty ◽  
Structural Connectivity ◽  
Angular Gyrus ◽  
Episodic Retrieval ◽  
Semantic Retrieval ◽  
White Matter Connectivity

AbstractSeveral decades of neuropsychological and neuroimaging research have highlighted the importance of lateral parietal cortex (LPC) across a myriad of cognitive domains. Yet, despite the prominence of this region the underlying function of LPC remains unclear. Two domains that have placed particular emphasis on LPC involvement are semantic memory and episodic memory retrieval. From each domain, sophisticated models have been proposed as to the underlying function, as well as the more domain-general that LPC is engaged by any form of internally-directed cognition (episodic and semantic retrieval both being examples if this process). Here we directly address these alternatives using a combination of fMRI and DTI white-matter connectivity data. The results show that ventral LPC (angular gyrus) was positively engaged during episodic retrieval but disengaged during semantic memory retrieval. In addition, the level of activity negatively varied with task difficulty in the semantic task whereas episodic activation was independent of difficulty. In contrast, dorsal LPC (intraparietal sulcus) showed domain general activation that was positively correlated with task difficulty. In terms of structural connectivity, a dorsal-ventral and anterior-posterior gradient of connectivity was found to different processing networks (e.g., mid-angular gyrus (AG) connected with episodic retrieval). We propose a unifying model in which LPC as a whole might share a common underlying function (e.g., multimodal buffering) and variations across subregions arise due to differences in the underlying white matter connectivity.

scholarly journals Exploring the laminar connectome

2021 ◽  
Author(s):  
Ittai Shamir ◽  
Omri Tomer ◽  
Ronnie Krupnik ◽  
Yaniv Assaf
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Structural Description ◽  
Mri Imaging ◽  
Healthy Human ◽  
Cortical Grey Matter ◽  
Cortical Laminar ◽  
White Matter Connectivity ◽  
Human Brains

The human connectome is the complete structural description of the network of connections and elements that form the wiring diagram of the brain. Because of the current scarcity of information regarding laminar end points of white matter tracts inside cortical grey matter, tractography remains focused on cortical partitioning into regions, while ignoring radial partitioning into laminar components. To overcome this biased representation of the cortex as a single homogenous unit, we use a recent data-derived model of cortical laminar connectivity, which has been further explored and corroborated in the macaque brain by comparison to published studies. The model integrates multimodal MRI imaging datasets regarding both white matter connectivity and grey matter laminar composition into a laminar-level connectome. In this study we model the laminar connectome of healthy human brains (N=20) and explore them via a set of neurobiologically meaningful complex network measures. Our analysis demonstrates a subdivision of network hubs that appear in the standard connectome into each individual component of the laminar connectome, giving a fresh look into the role of laminar components in cortical connectivity and offering new prospects in the fields of both structural and functional connectivity.

Changes in White Matter Connectivity Following Therapy for Anomia Post stroke

2013 ◽  
Vol 28 (4) ◽  
pp. 325-334 ◽  
Author(s):  
Sophia van Hees ◽  
Katie McMahon ◽  
Anthony Angwin ◽  
Greig de Zubicaray ◽  
Stephen Read ◽  
...  
Keyword(s):  
White Matter ◽  
Post Stroke ◽  
White Matter Connectivity

scholarly journals Neural correlates of polygenic risk score for autism spectrum disorders in general population

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Budhachandra Khundrakpam ◽  
Uku Vainik ◽  
Jinnan Gong ◽  
Noor Al-Sharif ◽  
Neha Bhutani ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
White Matter ◽  
General Population ◽  
Cortical Thickness ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Cortical Regions ◽  
White Matter Connectivity

Abstract Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case–control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.

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