Starting from childhood, the human brain restructures and rewires throughout life. Characterizing such complex brain development requires effective analysis of longitudinal and multi-modal neuroimaging data. Here, we propose such an analysis approach named Longitudinal Correlation Analysis (LCA). LCA couples the data of two modalities by first reducing the input from each modality to a latent representation based on autoencoders. A self-supervised strategy then relates the two latent spaces by jointly disentangling two directions, one in each space, such that the longitudinal changes in latent representations along those directions are maximally correlated between modalities. We applied LCA to analyze the longitudinal T1-weighted and diffusion-weighted MRIs of 679 youths from the National Consortium on Alcohol and Neurodevelopment in Adolescence. Unlike existing approaches that focus on either cross-sectional or single-modal modeling, LCA successfully unraveled coupled macrostructural and microstructural brain development from morphological and diffusivity features extracted from the data. A retesting of LCA on raw 3D image volumes of those subjects successfully replicated the findings from the feature-based analysis. Lastly, the developmental effects revealed by LCA were inline with the current understanding of maturational patterns of the adolescent brain.
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）
Prediction of Gender from Longitudinal MRI data via Deep Learning on Adolescent Data Reveals Unique Patterns Associated with Brain Structure and Change over a Two-year Period
University of Hong Kong（香港大学）；Shenzhen Loop Area Institute（深圳河套学院）；Chinese University of Hong Kong（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Harvard University（哈佛大学）；Columbia University（哥伦比亚大学）；University of Washington（华盛顿大学）；Carnegie Mellon University（卡内基梅隆大学）
Evaluating deep transfer learning for whole-brain cognitive decoding
Technische Universität Berlin（柏林工业大学）；Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所）；Stanford University（斯坦福大学）；Max Planck UCL Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与老龄化研究中心）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Towards Unified Neural Decoding with Brain Functional Network Modeling
Westlake University（西湖大学）；Zhejiang University School of Medicine First Affiliated Hospital（浙江大学医学院附属第一医院）；Westlake Institute for Optoelectronics（西湖光电研究院）
Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding
Université Paris-Saclay（巴黎-萨克雷大学）；Inria（法国国家信息与自动化技术研究院）；CEA（法国原子能委员会）；Cognitive Neuroimaging Unit（认知神经成像单位）；Institut National de la Santé et de la Recherche Médicale（法国国家卫生与医学研究院）；Commissariat à l’Energie Atomique et aux énergies alternatives（法国原子能委员会及替代能源委员会）；NeuroSpin center（神经科学中心）；Institut de neuromodulation（神经调制研究所）；Université Paris Cité（巴黎-城市大学）
Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
A Pre-trained Framework for Multilingual Brain Decoding Using Non-invasive Recordings
Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Baptist University（香港 Baptist大学）
A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions（深港脑科学研究院-深圳基础研究机构）；Shenzhen Technological Research Center for Primate Translational Medicine（深圳灵长类转化医学技术研究中心）；University of Chinese Academy of Sciences（中国科学院大学）
Retrieval-Based Brain Decoding by Alignment, not Complexity
University of Rome Tor Vergata（罗马托尔维加塔大学）；Tether Evo；A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院 A.A. 马丁诺斯生物医学成像中心）
CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Sun Yat-sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Karlsruher Institut fur Technologie（卡尔斯鲁厄理工学院）
EmoMind: Decoding Affective Captions from Human Brain fMRI
Department of Biomedical Engineering（生物医学工程系）；Vanderbilt University（范德比大学）；Research Institute of Electrical Communication（电气通信研究所）；Tohoku University（东北大学）；University of Florida（佛罗里达大学）
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
University of Rome, Tor Vergata（罗马第二大学）；Istituto di Bioimmagini e Fisiologia Molecolare, CNR（意大利国家研究委员会生物成像与分子生理学研究所）；Martinos Center for Biomedical Imaging（马丁诺斯生物医学成像中心）；MGH（麻省总医院）；Harvard Medical School（哈佛医学院）
Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing
Ministry of Education Center for Studies of Psychological Application（教育部心理应用研究中心）；Center for Studies of Psychological Application（心理应用研究中心）；Key Laboratory of Brain, Cognition and Education Sciences（脑认知与教育科学重点实验室）；School of Psychology（心理学学院）；Guangdong Key Laboratory of Mental Health and Cognitive Science（广东省心理健康与认知科学重点实验室）
