Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases. Degeneration accumulates during brain aging and other cerebral activities, causing structural atrophy and dysfunction. At the same time, adaptation allows brain network reorganize to compensate for structural loss to maintain cognition function. Although hidden resilience mechanism is critical and fundamental to uncover the brain aging law, due to the lack of datasets and appropriate methodology, it remains essentially unknown how these two processes interact dynamically across brain networks. To quantitatively investigate this complex process, we analyze aging brains based on 6-year follow-up multimodal neuroimaging database from 63 persons. We reveal the critical mechanism of network resilience that various perturbation may cause fast brain structural atrophy, and then brain can reorganize its functional layout to lower its operational efficiency, which helps to slow down the structural atrophy and finally recover its functional efficiency equilibrium. This empirical finding could be explained by our theoretical model, suggesting one universal resilience dynamical function. This resilience is achieved in the brain functional network with evolving percolation and rich-club features. Our findings can help to understand the brain aging process and design possible mitigation methods to adjust interaction between degeneration and adaptation from resilience viewpoint.
Department of Electrical Engineering, Stanford University, Stanford, CA, USA Dept. of Psychiatry \& Behavioral Sciences, Stanford University, Stanford, CA, USA Department of Radiation Oncology, Stanford University, Stanford, CA, USA Department of Biomedical Data Science, Stanford University, Stanford, CA, USA Department of Computer Science, Stanford University, Stanford, CA, USA
Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants
Southwest University（西南大学）；Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University（北京师范大学基础教育质量评价协同创新中心）
重度抑郁症（MDD）是最常见的精神健康状况之一，其与脑萎缩和死亡率的关系已被深入研究。最近的研究表明，预测年龄与实际年龄之间的偏差可以作为大脑加速老化的标志来表征MDD。然而，目前的结论通常是基于从白人参与者中收集的结构MRI信息得出的。该生物标志物的普遍性需要通过不同种族/种族背景的受试者和不同类型的数据进一步验证。在这里，我们使用的REST元MDD，一个大规模的静息态fMRI数据集收集了多个队列参与者在中国。我们开发了一个基于1101名健康对照的堆叠机器学习模型，该模型可以从fMRI中估计受试者的实际年龄，具有很好的准确性。然后将训练好的模型应用于来自24个研究中心的1276名MDD患者。我们观察到，与对照组相比，MDD患者表现出$+4.43$年（$\text{$ p $} < 0.0001$，$\text{Cohen's $ d $} = 0.35$，$\text{95\% CI}:1.86 - 3.91$）更高的脑预测年龄差（脑PAD）。在MDD亚组中，我们观察到与未用药患者相比，抗抑郁药使用者的$+2.09$年（$\text{$ p $} < 0.05$，$\text{Cohen's $ d $} = 0.134483$）脑PAD具有统计学显著性。通过三种不同的机器学习算法进一步检查观察到的统计关系。在中国参与者中观察到的阳性脑PAD证实了MDD患者中存在加速的脑老化。利…
Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease
University of Southern California（南加州大学）；University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；King's College London（伦敦国王学院）
sMRI-based Brain Age Estimation in MCI using Persistent Homology
Dept.of Artificial Intelligence, School of Artificial Intelligence, Bengaluru, Amrita Vishwa Vidyapeetham, India（人工智能部门，人工智能学院，班加罗尔，阿米塔维莎维达佩يث姆，印度）；Centre for Brain Research, Indian Institute of Science, Bangalore, India（脑研究中心，印度科学研究院，班加罗尔，印度）
Association of Timing and Duration of Moderate-to-Vigorous Physical Activity with Cognitive Function and Brain Aging: A Population-Based Study Using the UK Biobank
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（朱·克雷顿·普瑞特生物医学工程系，佛罗里达大学）；Department of Health Services Research, Management and Policy, University of Florida（健康服务研究、管理与政策系，佛罗里达大学）；Department of Radiation Oncology, Stanford University（放射肿瘤学系，斯坦福大学）；Norman Fixel Institute for Neurological Diseases, University of Florida（诺曼·菲克斯神经疾病研究所，佛罗里达大学）；Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida（认知衰老与记忆中心，麦金尼脑研究所，佛罗里达大学）；Department of Electrical and Computer Engineering, University of Florida（电气与计算机工程系，佛罗里达大学）
Subcortical Shape Variations and Their Associations with Cognition Across the 8th Decade of Life. A Study in the Lothian Birth Cohort 1936
Department of Neuroimaging Sciences（神经影像科学系）；University of Edinburgh（爱丁堡大学）；Computer Graphics and Visualization Laboratory（计算机图形与可视化实验室）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Department of Psychology（心理学系）；Edinburgh Futures Institute（爱丁堡未来研究所）
Modeling Causal Interactions Across Brain Functional Subnetworks for Population-specific Disease Analysis
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；Washington University in St. Louis（华盛顿大学圣路易斯分校）；North Carolina State University（北卡罗来纳州立大学）；Temple University（天普大学）；University of Pittsburgh（匹兹堡大学）
Structural Connectome Validation Using Pairwise Classification
Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；University of Southern California（南加州大学）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）
