Park Gilsoon, Khan Mahir H, Andrushko Justin W, Banaj Nerisa, Borich Michael R, Boyd Lara A, Brodtmann Amy, Brown Truman R, Buetefisch Cathrin M, Conforto Adriana B, Cramer Steven C, Dimyan Michael, Domin Martin, Donnelly Miranda R, Egorova-Brumley Natalia, Ermer Elsa R, Feng Wuwei, Geranmayeh Fatemeh, Hanlon Colleen A, Hordacre Brenton, Jahanshad Neda, Kautz Steven A, Salah Khlif Mohamed, Liu Jingchun, Lotze Martin, MacIntosh Bradley J, Mohamed Feroze B, Nordvik Jan E, Piras Fabrizio, Revill Kate P, Robertson Andrew D, Schranz Christian, Schweighofer Nicolas, Seo Na Jin, Soekadar Surjo R, Srivastava Shraddha, Tavenner Bethany P, Thielman Gregory T, Thomopoulos Sophia I, Vecchio Daniela, Werden Emilio, Westlye Lars T, Winstein Carolee J, Wittenberg George F, Yu Chunshui, Thompson Paul M, Liew Sook-Lei, Kim Hosung
Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.
Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
medRxiv. 2024 Oct 28:2024.10.26.24316190. doi: 10.1101/2024.10.26.24316190.
Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain-predicted age difference (brain-PAD) has emerged as a sensitive biomarker. Our previous study showed higher global brain-PAD associated with poorer motor function post-stroke. However, the relationship between local stroke lesion load, regional brain age, and motor impairment remains unclear.
We studied 501 individuals with chronic unilateral stroke (>180 days post-stroke) from the ENIGMA Stroke Recovery Working Group dataset (34 cohorts). Structural T1-weighted MRI scans were used to estimate regional brain-PAD in 18 predefined functional subregions via a graph convolutional network algorithm. Lesion load for each region was calculated based on lesion overlap. Linear mixed-effects models assessed associations between lesion size, local lesion load, and regional brain-PAD. Machine learning classifiers predicted motor outcomes using lesion loads and regional brain-PADs. Structural equation modeling examined directional relationships among corticospinal tract lesion load (CST-LL), ipsilesional brain-PAD, motor outcomes, and contralesional brain-PAD.
Larger total lesion size was positively associated with higher ipsilesional regional brain-PADs (older brain age) across most regions (p < 0.05), and with lower contralesional brain-PAD, notably in the ventral attention-language network (p < 0.05). Higher local lesion loads showed similar patterns. Specifically, lesion load in the salience network significantly influenced regional brain-PADs across both hemispheres. Machine learning models identified CST-LL, salience network lesion load, and regional brain-PAD in the contralesional frontoparietal network as the top three predictors of motor outcomes. Structural equation modeling revealed that larger stroke damage was associated with poorer motor outcomes (β = -0.355, p < 0.001), which were further linked to younger contralesional brain age (β = 0.204, p < 0.001), suggesting that severe motor impairment is linked to compensatory decreases in contralesional brain age.
Our findings reveal that larger stroke lesions are associated with accelerated aging in the ipsilesional hemisphere and paradoxically decelerated brain aging in the contralesional hemisphere, suggesting compensatory neural mechanisms. Assessing regional brain age may serve as a biomarker for neuroplasticity and inform targeted interventions to enhance motor recovery after stroke.
Micheal J Fox Foundation, National Institutes of Health, Canadian Institutes of Health Research, National Health and Medical Research Council, Australian Brain Foundation, Wicking Trust, Collie Trust, and Sidney and Fiona Myer Family Foundation, National Heart Foundation, Hospital Israelita Albert Einstein, Australian Research Council Future Fellowship, Wellcome Trust, National Institute for Health Research Imperial Biomedical Research Centre, European Research Council, Deutsche Forschungsgemeinschaft, REACT Pilot, National Resource Center, Research Council of Norway, South-Eastern Norway Regional Health Authority, Norwegian Extra Foundation for Health and Rehabilitation, Sunnaas Rehabilitation Hospital HT, University of Oslo, and VA Rehabilitation Research and Development.
中风会导致复杂的慢性大脑结构和功能变化，这些变化会特别影响运动结果。脑预测年龄差异（brain-PAD）已成为一种敏感的生物标志物。我们之前的研究表明，中风后整体脑-PAD越高，运动功能越差。然而，局部中风病灶负荷、区域脑年龄与运动障碍之间的关系仍不清楚。
我们研究了来自ENIGMA中风恢复工作组数据集（34个队列）的501例慢性单侧中风患者（中风后>180天）。使用结构T1加权MRI扫描通过图卷积网络算法估计18个预定义功能子区域的区域脑-PAD。根据病灶重叠计算每个区域的病灶负荷。线性混合效应模型评估病灶大小、局部病灶负荷与区域脑-PAD之间的关联。机器学习分类器使用病灶负荷和区域脑-PAD预测运动结果。结构方程模型研究皮质脊髓束病灶负荷（CST-LL）、患侧脑-PAD、运动结果和对侧脑-PAD之间的方向关系。
在大多数区域，总病灶越大与患侧区域脑-PAD越高（脑年龄越大）呈正相关（p<0.05），与对侧脑-PAD越低呈正相关，尤其是在腹侧注意-语言网络中（p<0.05）。较高的局部病灶负荷显示出类似的模式。具体而言，突显网络中的病灶负荷显著影响两侧半球的区域脑-PAD。机器学习模型将CST-LL、突显网络病灶负荷和对侧额顶网络中的区域脑-PAD确定为运动结果的前三大预测因子。结构方程模型显示，较大的中风损伤与较差的运动结果相关（β=-0.355，p<0.001），这进一步与对侧脑年龄较小相关（β=0.204，p<0.001），表明严重的运动障碍与对侧脑年龄的代偿性降低有关。
我们的研究结果表明，较大的中风病灶与患侧半球的加速衰老相关，而与对侧半球的脑衰老减速相关，提示存在代偿性神经机制。评估区域脑年龄可能作为神经可塑性的生物标志物，并为中风后增强运动恢复的靶向干预提供依据。
迈克尔·J·福克斯基金会、美国国立卫生研究院、加拿大卫生研究院、澳大利亚国家卫生与医学研究委员会、澳大利亚脑基金会、威金信托基金、科利信托基金、悉尼和菲奥娜·迈尔家族基金会、澳大利亚国家心脏基金会、以色列艾伯特·爱因斯坦医院、澳大利亚研究理事会未来奖学金、惠康信托基金、英国国家卫生研究院帝国生物医学研究中心、欧洲研究理事会、德国研究基金会、REACT试点项目、国家资源中心、挪威研究理事会、挪威东南部地区卫生局、挪威健康与康复额外基金会、松纳斯康复医院HT、奥斯陆大学和美国退伍军人事务部康复研究与发展部。
