Brain research bulletin · 2025
Mapping the cerebral structural changes related to the multi-dimensional neuropsychiatric deficits in patients with ischemic thalamic stroke.
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Li Congjun, Ye Chen, Sun Rui, Pan Ruosu, Cao Le, Jiang Shuai, Yan Yuyign, Yang Tang, Wang Hang, Liu Junfeng, Tao Wendan, Hu Na, Lui Su, Wu Bo
Аннотация
BACKGROUND: The thalamus serves as a central hub in the brain's neural network, playing a crucial role in sensory processing, cognitive functions, and emotional regulation. Although isolated thalamic strokes are relatively rare, their profound impact on cognitive and emotional outcomes highlights the importance of comprehensive investigation into thalamic involvement in post-stroke neuropsychiatric deficits.
METHOD: This study enrolled 44 patients with first-ever unilateral thalamic ischemic stroke. All participants underwent MRI acquisition and neuropsychological evaluation, including Beijing version of the Montreal Cognitive Assessment (MoCA-BJ), Symbol Trails Test (STT), Stroop Color-Word Test (Stroop), Chinese Auditory Verbal Learning Test (CAVLT), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Depression Rating Scale (HAMD). Gray matter volume (GMV) was extracted using CAT12 across 170 ROIs. Partial least squares (PLS) regression was applied to examine the association between GMV and 11 neuropsychological scores. Variable Importance in Projection (VIP) and loading analysis were conducted to identify key contributing regions. Additionally, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed using 80 lesioned-side and 80 contralateral GMV features to distinguish lesion laterality, further validating the discriminative contribution of selected ROIs.
RESULTS: PLS regression with three components achieved the optimal balance between explanatory power and model performance. Several cerebellar, temporal, and limbic structures showed high VIP scores and strong loadings, indicating their contribution to neuropsychological impairment. PLS-DA yielded satisfactory classification performance (mean AUC = 0.928 ± 0.093), highlighting robust lateralized structural differences.
CONCLUSION: This study shows that structural alterations in cerebello-thalamo-cortical circuits are significantly associated with cognitive and emotional deficits following thalamic infarction. The integration of PLS and PLS-DA approaches provides robust evidence for identifying relevant biomarkers, Future research should validate these findings.
METHOD: This study enrolled 44 patients with first-ever unilateral thalamic ischemic stroke. All participants underwent MRI acquisition and neuropsychological evaluation, including Beijing version of the Montreal Cognitive Assessment (MoCA-BJ), Symbol Trails Test (STT), Stroop Color-Word Test (Stroop), Chinese Auditory Verbal Learning Test (CAVLT), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Depression Rating Scale (HAMD). Gray matter volume (GMV) was extracted using CAT12 across 170 ROIs. Partial least squares (PLS) regression was applied to examine the association between GMV and 11 neuropsychological scores. Variable Importance in Projection (VIP) and loading analysis were conducted to identify key contributing regions. Additionally, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed using 80 lesioned-side and 80 contralateral GMV features to distinguish lesion laterality, further validating the discriminative contribution of selected ROIs.
RESULTS: PLS regression with three components achieved the optimal balance between explanatory power and model performance. Several cerebellar, temporal, and limbic structures showed high VIP scores and strong loadings, indicating their contribution to neuropsychological impairment. PLS-DA yielded satisfactory classification performance (mean AUC = 0.928 ± 0.093), highlighting robust lateralized structural differences.
CONCLUSION: This study shows that structural alterations in cerebello-thalamo-cortical circuits are significantly associated with cognitive and emotional deficits following thalamic infarction. The integration of PLS and PLS-DA approaches provides robust evidence for identifying relevant biomarkers, Future research should validate these findings.