인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This study investigated alterations in functional connectivity (FC) within cortico-basal ganglia-thalamo-cortical (CBTC) circuits and identified critical connections influencing poststroke motor recovery, offering insights into optimizing brain modulation strategies to address the limitations of traditional single-target stimulation. We delineated individual-specific parallel loops of CBTC through probabilistic tracking and voxel connectivity profiles-based segmentation and calculated FC values in poststroke patients and healthy controls, comparing with conventional atlas-based FC calculation. Support vector machine (SVM) analysis distinguished poststroke patients from controls. Connectome-based predictive modeling (CPM) used FC values within CBTC circuits to predict upper limb motor function. Poststroke patients exhibited decreased ipsilesional connectivity within the individual-specific CBTC circuits. SVM analysis achieved 82.8% accuracy, 76.6% sensitivity, and 89.1% specificity using individual-specific parallel loops. Additionally, CPM featuring positive connections/all connections significantly predicted Fugl-Meyer assessment of upper extremity scores. There were no significant differences in the group comparisons of conventional atlas-based FC values, and the FC values resulted in SVM accuracy of 75.0%, sensitivity of 67.2%, and specificity of 82.8%, with no significant CPM capability. Individual-specific parallel loops show superior predictive power for assessing upper limb motor function in poststroke patients. Precise mapping of the disease-related circuits is essential for understanding poststroke brain reorganization.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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