Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model
Imaging Genetics Center, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California（南加州大学凯克医学院USC Stevens神经影像与信息学研究所影像遗传学中心）；BCN NeuroImaging Center and Department of Neuroscience of the University of Groningen, University Medical Center Groningen（格罗宁根大学医学中心格罗宁根大学BCN神经影像中心与神经科学系）；University of Cape Town（开普敦大学）；Stanford University（斯坦福大学）；City, University of London（伦敦城市大学）；Imperial College London（伦敦帝国理工学院）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；Trinity College Dublin（都柏林圣三一学院）；Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；VU University Medical Center（阿姆斯特丹自由大学医学中心）；Orygen, The National Centre of Excellence in Youth Mental Health（Orygen国家青少年心理健康卓越中心）；The University of Melbourne（墨尔本大学）
Dajiang Zhu, Brandalyn C. Riedel, Neda Jahanshad, Nynke A. Groenewold, Dan J. Stein, Ian H. Gotlib, Matthew D. Sacchet, Danai Dima, James H. Cole, Cynthia H. Y.… 展开作者
Dajiang Zhu, Brandalyn C. Riedel, Neda Jahanshad, Nynke A. Groenewold, Dan J. Stein, Ian H. Gotlib, Matthew D. Sacchet, Danai Dima, James H. Cole, Cynthia H. Y. Fu, Henrik Walter, Ilya M. Veer, Thomas Frodl, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson
Large-scale collaborative analysis of brain imaging data, in psychiatry and neu-rology, offers a new source of statistical power to discover features that boost ac-curacy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the fea-tures that help to improve the classification accuracy are preserved. In tests on da-ta from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an ef-fective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data.
Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study
McGill University（麦吉尔大学）；Aifred Health（Aifred Health公司）；Queens College, CUNY（纽约市立大学皇后学院）；University of Michigan（密歇根大学）；University of Arizona（亚利桑那大学）；Google（谷歌公司）
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
COMSATS University Islamabad（伊斯兰堡COMSATS大学）；University of Okara（奥卡拉大学）；Ca’ Foscari University of Venice（威尼斯大学）；Ege University（埃格大学）；Université Paris-Est Créteil (UPEC)（巴黎东克雷泰伊大学（UPEC））
QuanvNeXt: An end-to-end quanvolutional neural network for EEG-based detection of major depressive disorder
The University of Queensland（昆士兰大学）；BRAC University（布拉克大学）；Bangladesh Medical University（孟加拉医学大学）；University of Southern Queensland（南方昆士兰大学）；Charles Sturt University（查尔斯·斯特劳特大学）
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Monash University Malaysia（莫纳什大学马来西亚校区）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
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…
MDD-Thinker: Towards Large Reasoning Models for Major Depressive Disorder Diagnosis
Macao Polytechnic University（澳门理工大学）；North China University of Science and Technology（华北理工大学）；Yantai Yuhuangding Hospital, Qingdao University（青岛大学烟台毓璜顶医院）
EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression
University of Belgrade（贝尔格莱德大学）；Amsterdam Health and Technology Institute, HealthInc（阿姆斯特丹健康与技术研究所）；Delaware State University（特拉华州立大学）；Life Activities Advancement Center（生命活动促进中心）；Institute for Experimental Phonetics and Speech Pathology（实验语音学与言语病理学研究所）；Institute for Mental Health（精神卫生研究所）；College of Medicine and Health Sciences, UAE University（阿联酋大学医学与健康科学学院）
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
Booth School of Business（布斯商学院）；The University of Chicago（芝加哥大学）；Cornell University（康奈尔大学）
We consider the problem of estimating the difference between two undirected functional graphical models with shared structures. In many applications, data are naturally regarded as a vector of random functions rather than as a vector of scalars. For example, electroencephalography (EEG) data are treated more appropriately as functions of time. In such a problem, not only can the number of functions measured per sample be large, but each function is itself an infinite-dimensional object, making estimation of model parameters challenging. This is further complicated by the fact that curves are u…
Speech Analysis for Automatic Mania Assessment in Bipolar Disorder
Boğaziçi University（博阿齐奇大学）；Utrecht University（乌得勒支大学）；University of Alberta（阿尔伯塔大学）；Erenköy Psychiatric and Neurological Diseases Training and Research Hospital（埃伦科伊精神病与神经病学疾病培训和研究医院）
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（南加州大学凯克医学院马克与玛丽史蒂文斯神经成像与信息学研究所成像遗传学中心）；University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心、格罗宁根大学）；University of Cape Town（开普敦大学）
Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Computer Science and Engineering, Indian Institute of Technology（印度理工学院计算机科学与工程系）
Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；Key Laboratory of Cognition and Personality(SWU), Ministry of Education（教育部认知与人格重点实验室（西南大学））；School of Psychology, Southwest University (SWU)（西南大学心理学部）；Southwest University Branch, 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西南大学分中心）
