Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model.
Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls
University of Bojnord（博季努尔德大学）；Munster Technological University（芒斯特理工大学）
A Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment
Universiti Teknologi PETRONAS（国油科技大学）；Hospital Universiti Sains Malaysia（马来西亚理科大学医院）；Universiti Sains Malaysia（马来西亚理科大学）；Ulster University（阿尔斯特大学）；University of Calgary（卡尔加里大学）；University of Bath（巴斯大学）
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
Cornell University（康奈尔大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Brain and Mind Research Institute, Weill Cornell Medicine（威尔康奈尔医学院脑与心智研究所）；Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University（康奈尔大学南希·E·与彼得·C·梅尼格生物医学工程学院）
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）
Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank
University of Cambridge（剑桥大学）；Cambridge University（剑桥大学）
Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI
National Tsing Hua University（国立清华大学）；MOST Joint Research Center for AI Technology and All Vista Healthcare（科技部人工智能技术与全视野医疗联合研究中心）；National Health Research Institutes（国家卫生研究院）
准确检测痴呆症发作的能力在疾病的治疗中是重要的。临床上，阿尔茨海默病（AD）和轻度认知障碍（MCI）患者的诊断基于心理测试和脑成像（例如正电子发射断层扫描（PET）和解剖磁共振成像（MRI））的综合评估。在这项工作中，使用两个不同的数据集，我们提出了一个行为分数嵌入编码器网络（BSEN），集成定期管理的心理测试信息到编码过程中表示主题的静息状态fMRI数据的自动分类任务。BSEN基于3D卷积自动编码器结构，具有对比度损失，使用MiniMental State Examination（MMSE）和临床痴呆评级（CDR）的行为评分进行联合优化。我们提出的使用BSEN的分类框架实现了59.44%的总体识别准确率（3类分类：AD，MCI和健康对照），我们进一步提取了健康对照（HC）和AD患者之间最具区分力的区域。
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
Faculty of Electrical and Computer Engineering, National Technical University of Athens（雅典国立技术大学电气与计算机工程学院）；Center for Biomedical Image Computing and Analytics, University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Maebashi Institute of Technology（前桥工业大学）
Machine Learning for Neuroimaging with Scikit-Learn
CEA（法国原子能和替代能源委员会）；University of Bonn（波恩大学）；Imperial College London（伦敦帝国理工学院）；Institut Mines-Telecom（法国矿业-电信联盟）；Telecom ParisTech（巴黎电信学院）；CNRS LTCI（法国国家科学研究中心信息与通信技术实验室）
Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines
Universiti Teknologi PETRONAS（马来西亚国油大学）；Hospital Universiti Sains Malaysia（马来西亚理科大学医院）；Universiti Sains Malaysia（马来西亚理科大学）；Ulster University（阿尔斯特大学）；University of Calgary（卡尔加里大学）；University of Bath（巴斯大学）
本文基于 324 名 BioFIND 参与者的 MEG 与 MRI 数据，通过交叉验证机器学习分类识别 MCI，发现融合未校正 MEG 与 z-score 校正 MRI 特征效果最佳。
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
LPIXEL Inc.（LPIXEL公司）；University of Cambridge（剑桥大学）；National Institute of Informatics（国立信息学研究所）；National Center for Global Health and Medicine（国立国际医疗研究中心）；Juntendo University（顺天堂大学）；ELTE Eötvös Loránd University（厄特沃什·罗兰大学）；The University of Tokyo（东京大学）
Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks
Tri-institutional Center for Translational Research in Neuroimaging & Data Science (TReNDS)（三机构神经影像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Faculty of Medicine Siriraj Hospital, Mahidol University（玛希隆大学诗里拉吉医学院）；Neurocognitive Disorders & Neural Computing Research Group（神经认知障碍与神经计算研究组）
Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps
Columbia University（哥伦比亚大学）；Zuckerman Institute（祖克曼研究所）；Taub Institute for Research on Alzheimer’s Disease and the Aging Brain（陶布阿尔茨海默病与衰老大脑研究所）；Vagelos College of Physicians and Surgeons（瓦格洛斯内科与外科医学院）；New York State Psychiatric Institute（纽约州精神病学研究所）
A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks
McGovern Institute for Brain Research, Massachusetts Institute of Technology（麻省理工学院麦戈文脑研究所）；Complutense University of Madrid（马德里康普顿斯大学）；Harvard Medical School（哈佛医学院）
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI
School of Computer Science and Engineering, Vellore Institute of Technology（韦洛尔理工学院计算机科学与工程学院）；College of Engineering, Guindy, Anna University（安娜大学金迪工程学院）；Institute of Neurosurgery, Madras Medical College（马德拉斯医学院神经外科研究所）
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；University of Sheffield（谢菲尔德大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Department of Pediatric, Qilu Hospital of Shandong University（山东大学齐鲁医院儿科）；Department of Radiology, Qilu Hospital of Shandong University（山东大学齐鲁医院放射科）；Cheeloo College of Medicine, Shandong University（山东大学齐鲁医学院）
