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Structurofunctional resting-state networks correlate with motor function in chronic stroke

التفاصيل البيبلوغرافية
العنوان: Structurofunctional resting-state networks correlate with motor function in chronic stroke
المؤلفون: Benjamin T. Kalinosky, Reivian Berrios Barillas, Brian D. Schmit
المصدر: NeuroImage: Clinical, Vol 16, Iss , Pp 610-623 (2017)
بيانات النشر: Elsevier, 2017.
سنة النشر: 2017
المجموعة: LCC:Computer applications to medicine. Medical informatics
LCC:Neurology. Diseases of the nervous system
مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
الوصف: Purpose: Motor function and recovery after stroke likely rely directly on the residual anatomical connections in the brain and its resting-state functional connectivity. Both structural and functional properties of cortical networks after stroke are revealed using multimodal magnetic resonance imaging (MRI). Specifically, functional connectivity MRI (fcMRI) can extract functional networks of the brain at rest, while structural connectivity can be estimated from white matter fiber orientations measured with high angular-resolution diffusion imaging (HARDI). A model that marries these two techniques may be the key to understanding functional recovery after stroke. In this study, a novel set of voxel-level measures of structurofunctional correlations (SFC) was developed and tested in a group of chronic stroke subjects. Methods: A fully automated method is presented for modeling the structure-function relationship of brain connectivity in individuals with stroke. Brains from ten chronic stroke subjects and nine age-matched controls were imaged with a structural T1-weighted scan, resting-state fMRI, and HARDI. Each subject's T1-weighted image was nonlinearly registered to a T1-weighted 152-brain MNI template using a local histogram-matching technique that alleviates distortions caused by brain lesions. Fractional anisotropy maps and mean BOLD images of each subject were separately registered to the individual's T1-weighted image using affine transformations. White matter fiber orientations within each voxel were estimated with the q-ball model, which approximates the orientation distribution function (ODF) from the diffusion-weighted measurements. Deterministic q-ball tractography was performed in order to obtain a set of fiber trajectories. The new structurofunctional correlation method assigns each voxel a new BOLD time course based on a summation of its structural connections with a common fiber length interval. Then, the voxel's original time-course was correlated with this fiber-distance BOLD signal to derive a novel structurofunctional correlation coefficient. These steps were repeated for eight fiber distance intervals, and the maximum of these correlations was used to define an intrinsic structurofunctional correlation (iSFC) index. A network-based SFC map (nSFC) was also developed in order to enhance resting-state functional networks derived from conventional functional connectivity analyses. iSFC and nSFC maps were individually compared between stroke subjects and controls using a voxel-based two-tailed Student's t-test (alpha=0.01). A linear regression was also performed between the SFC metrics and the Box and Blocks Score, a clinical measure of arm motor function. Results: Significant decreases (p
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2213-1582
Relation: http://www.sciencedirect.com/science/article/pii/S2213158217301675; https://doaj.org/toc/2213-1582
DOI: 10.1016/j.nicl.2017.07.002
URL الوصول: https://doaj.org/article/0e996570a764414ca2b004edc9b2e694
رقم الأكسشن: edsdoj.0e996570a764414ca2b004edc9b2e694
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:22131582
DOI:10.1016/j.nicl.2017.07.002