Understanding distinct neurological aging patterns across various populations is vital in the context of a globally aging populace. This study seeks to unravel the structural variations in the aging brain, taking into consideration different ethnic backgrounds. MRI data from Indian, Chinese, Japanese, and Caucasian populations were analyzed using a two-pronged approach. Initially, a group analysis was performed involving tissue segmentation through FSL-FAST, examining gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Subsequently, a continuous model-based analysis was employed, defining aging as a diffeomorphic transformation, which facilitated a detailed intra- and inter-population analysis, and examined both global anatomy and age-dependent distances of each population in comparison to the Indian population. Detailed insights into the spatial distribution of brain deformations over time were obtained, with particular focus on anatomical changes relative to a reference time point. The proposed comprehensive structural comparison framework, applied to a sample dataset encompassing four distinct populations, represents a pioneering effort to compare structural differences related to brain aging on a global scale. Subsequent studies utilizing larger datasets and applying the proposed analysis across groups based on various criteria will further advance our understanding of aging-related changes.
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
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）
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…
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）
Unveiling Normative Trajectories of Lifespan Brain Maturation Using Quantitative MRI
University of Basel（巴塞尔大学）；University Hospital Basel（巴塞尔大学医院）；Donders Institute for Brain, Cognition and Behaviour, Radboud University（拉德堡德大学唐德斯脑、认知与行为研究所）；University of Genova（热那亚大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿蒂努拉·A·马丁诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）
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）
Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease
Southern University of Science and Technology（南方科技大学）；Shenzhen People’s Hospital, The Second Affiliated Hospital of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology（深圳市人民医院，暨南大学附属第二医院，南方科技大学附属第一医院）；Tianjin Huanhu Hospital（天津市环湖医院）；Shenzhen Bay Laboratory（深圳湾实验室）
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Electrical and Information Engineering, Zhengzhou University（郑州大学电气工程学院）
