Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data-driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier. First, we trained an accurate DNN classifier. Then, for detecting the biomarkers, different from the DNN visualization works in computer vision, we take advantage of the anatomical structure of brain fMRI and develop a frequency-normalized sampling method to corrupt images. Furthermore, in the ASD vs. control subjects classification scenario, we provide a new approach to detect and characterize important brain features into three categories. The biomarkers we found by the proposed method are robust and consistent with previous findings in the literature. We also validate the detected biomarkers by neurological function decoding and comparing with the DNN activation maps.
VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Department of Biomedical Engineering and Biotechnology, and the Health Engineering Innovation Group (HEIG), Khalifa University（生物医学工程与生物技术系，以及健康工程创新小组（HEIG），卡利法大学）；Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki（电气与计算机工程系，阿尔基洛斯大学）
scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
School of Energy and Electrical Engineering, Chang'an University（长安大学能源与电气工程学院）；Tulane University（杜兰大学）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
Vrije University Amsterdam（荷兰阿姆斯特丹自由大学）；Tri-institutional Center for Translational Research in Neuroimaging（转化神经影像研究联合中心）；Emory University（埃默里大学）；Key Laboratory of Genetic Evolution and Animal Models（遗传进化与动物模型重点实验室）；Kunming Institute of Zoology（昆明动物研究所）；Chinese Academy of Sciences Kunming（中国科学院昆明分院）；Department of Psychiatry, Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学精神病科）；Department of Physics and Technology, UiT The Arctic University of Norway（北极大学挪威理工学院物理与技术系）
BrainCSD: A Hierarchical Consistency-Driven MoE Foundation Model for Unified Connectome Synthesis and Multitask Brain Trait Prediction
Department of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术系）；School of Intelligence Science and Engineering, College of Artificial Intelligence, Harbin Institute of Technology（哈尔滨工业大学智能科学与工程学院）；School of Biomedical Engineering, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical, Measurements and Ultrasound Imaging, Shenzhen University Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
BrainCSD 提出层次一致性驱动的 MoE 基础模型，通过 ROI 特异性、编码-激活和网络感知细化三个组件联合合成 FC/SC 并支持多任务预测，在缺失模态下取得 SOTA 性能。
Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder
Southern University of Science and Technology（南方科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Central South University（中南大学）
STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis
Laboratory of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University（数字图像与智能计算实验室，信息工程学院，上海 Maritime 大学）；Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University（中英双语研究部，香港理工大学）；Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University（神经科，徐州医学院附属连云港医院）
针对 fMRI 脑疾病分类忽略时空依赖的问题，提出 STARFormer，通过 ROI 空间重组、时间特征重组和时空融合三个模块，在 ASD 和 ADHD 数据集上达到最先进性能。
Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Key Laboratory of Intelligent Bioinformatics（深圳市智能生物信息学重点实验室）；Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院高性能计算中心）
NeuroGAN-3D: Enhancing Intrinsic Functional Brain Networks via High-Fidelity 3D Generative Super-Resolution
Georgia State University（佐治亚州立大学）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（神经影像与数据科学转化研究三机构中心（TReNDS））；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning
Department of Medicine, Danube Private University (DPU)（丹ube私人大学医学系）；Department of Medical Physics and Biomedical Engineering, Medical University of Vienna（维也纳医学大学医学物理与生物医学工程系）；Department of Medical Informatics, Faculty of Medicine, Gonabad University of Medical Sciences（戈纳巴德医学院医学信息学系）；Austrian Center for Medical Innovation and Technology (ACMIT)（奥地利医学创新与技术中心）
Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning
Department of Computing, Hong Kong Polytechnic University（香港理工大学计算学系）；School of Information Engineering, Nanchang University（南昌大学信息工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI
Shanghai Maritime University（上海海事大学）；Affiliated Lianyungang Hospital of Xuzhou Medical University（徐州医科大学附属连云港医院）；ShanghaiTech University（上海科技大学）；The Hong Kong Polytechnic University（香港理工大学）
3D Gait-Based Autism Classification Using Attention-Enhanced Deep Learning with Cross-Fold Statistical Stability Analysis
Begum Rokeya University（贝古姆罗基亚大学）；Texas State University（德克萨斯州立大学）；Asian Institute of Technology（亚洲理工学院）；National Center for Geriatrics and Gerontology（国立长寿医疗研究中心）
Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University（上海 Maritime 大学数字图像与智能计算实验室）
