White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project

Zekelman, Leo; Zhang, Fan; Makris, Nikos; He, Jianzhong; Chen, Yuqian; Xue, Tengfei; Liera, Daniela; Drane, Daniel L.; Rathi, Yogesh; Golby, Alexandra J.; O’Donnell, Lauren J.

doi:10.1016/j.neuroimage.2021.118739

Cited by 33 publications

(19 citation statements)

References 187 publications

(234 reference statements)

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“…After regressing out the cross-subject registration deformation map, the functional variability pattern and principal structural variability pattern were still significantly correlated (r = 0.43, p < 0.0001; Fig S11). Fourth, a recent study excluded one of all twins in HCP dataset while estimating a brain-behavior association (Zekelman et al, 2022). To exclude the influence of the related subjects, we excluded 17 subjects (one of the twins) from the TRT dataset and found that the individual variability pattern of both FC and SC were almost unchanged (r > 0.97 for all variability patterns).…”

Section: Resultsmentioning

confidence: 99%

Structural insight into the individual variability architecture of the functional brain connectome

Sun

Zhao

Liang

et al. 2022

Preprint

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Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher prediction accuracy in primary regions and lower accuracy in heteromodal regions. We also decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

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Section: Resultsmentioning

confidence: 99%

Structural insight into the individual variability architecture of the functional brain connectome

Sun

Zhao

Liang

et al. 2022

Preprint

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“…It was recently shown that structural neuroimaging modalities (such as dMRI) strongly contribute to prediction of cognitive performance measures, where measures related to language and reading were among the most successfully predicted [10]. Other works have shown that measurements of white matter tract microstructure derived from diffusion MRI tractography relate to neuropsychological measures of language performance [31,29]. However, we believe that there are no studies to predict individual cognitive performance based on microstructure measurements of the white matter fiber tracts.…”

Section: Introductionmentioning

confidence: 88%

“…For each individual's tract, two measurements are calculated: the tract-specific fractional anisotropy (FA) of the first tensor, which corresponds to the tract being traced, and the number of streamlines (NoS). These measurements of the left AF tract have been found to be significantly related to TPVT scores [31]. Tracts are visualized in 3D Slicer via the SlicerDMRI project [15].…”

Section: Dmri Dataset Neuropsychological Assessment and Tractographymentioning

confidence: 99%

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White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

Chen¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF). We directly utilize information from all points in a fiber tract, without the need to average data along the fiber as is traditionally required by diffusion MRI tractometry methods. Specifically, we represent the AF as a point cloud with microstructure measurements at each point, enabling adoption of point-based neural networks. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores. Finally, we propose a Critical Region Localization (CRL) algorithm to localize informative anatomical regions containing points with strong contributions to the prediction results. Our method is evaluated on data from 806 subjects from the Human Connectome Project dataset. Results demonstrate superior neuropsychological score prediction performance compared to baseline methods. We discover that critical regions in the AF are strikingly consistent across subjects, with the highest number of strongly contributing points located in frontal cortical regions (i.e., the rostral middle frontal, pars opercularis, and pars triangularis), which are strongly implicated as critical areas for language processes.

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“…This method is significantly improved compared to previous automated fiber tracking methods, which can only extract the main fiber tracts, failing to cover the entire brain's white matter (i.e., including the cerebellum and superficial tracts). In addition, it was successfully applied in several recent studies [85][86][87][88][89][90][91][92][93][94], demonstrating high test-retest reproducibility [95] and robustness to anatomical variability [96].…”

Section: Wm Fiber Clusteringmentioning

confidence: 99%

Sleep Quality Disturbances Are Associated with White Matter Alterations in Veterans with Post-Traumatic Stress Disorder and Mild Traumatic Brain Injury

Rojczyk

Seitz-Holland

Kaufmann

et al. 2023

JCM

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Sleep disturbances are strongly associated with mild traumatic brain injury (mTBI) and post-traumatic stress disorder (PTSD). PTSD and mTBI have been linked to alterations in white matter (WM) microstructure, but whether poor sleep quality has a compounding effect on WM remains largely unknown. We evaluated sleep and diffusion magnetic resonance imaging (dMRI) data from 180 male post-9/11 veterans diagnosed with (1) PTSD (n = 38), (2) mTBI (n = 25), (3) comorbid PTSD+mTBI (n = 94), and (4) a control group with neither PTSD nor mTBI (n = 23). We compared sleep quality (Pittsburgh Sleep Quality Index, PSQI) between groups using ANCOVAs and calculated regression and mediation models to assess associations between PTSD, mTBI, sleep quality, and WM. Veterans with PTSD and comorbid PTSD+mTBI reported poorer sleep quality than those with mTBI or no history of PTSD or mTBI (p = 0.012 to <0.001). Poor sleep quality was associated with abnormal WM microstructure in veterans with comorbid PTSD+mTBI (p < 0.001). Most importantly, poor sleep quality fully mediated the association between greater PTSD symptom severity and impaired WM microstructure (p < 0.001). Our findings highlight the significant impact of sleep disturbances on brain health in veterans with PTSD+mTBI, calling for sleep-targeted interventions.

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White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project

Cited by 33 publications

References 187 publications

Structural insight into the individual variability architecture of the functional brain connectome

Structural insight into the individual variability architecture of the functional brain connectome

White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

Sleep Quality Disturbances Are Associated with White Matter Alterations in Veterans with Post-Traumatic Stress Disorder and Mild Traumatic Brain Injury

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