Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which diffusion measures are the most informative with regard to cognitive functions in both the healthy state as well as after injury. In this study, we use diffusion MRI to formulate a predictive model for performance on working memory based on the most relevant MRI features. The key challenge is to identify relevant features over a large feature space with high accuracy in an efficient manner. To tackle this challenge, we propose a novel improvement of the best first search approach with crossover operators inspired by genetic algorithm. Compared against other heuristic feature selection algorithms, the proposed method achieves significantly more accurate predictions and yields clinically interpretable selected features.
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）
The Charles Stark Draper Laboratory, Inc.（查尔斯·斯塔克·德雷珀实验室有限公司）
Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patient's cognitive state such as learning, memory, and language production/comprehension. Although computer-aided diagnostic techniques for classification of a patient's current disease state exist, they provide little insight into the relationship between changes in brain structure and different aspects of a patient's cogniti…
Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In …
University of Eastern Finland（东芬兰大学）；Tampere University（坦佩雷大学）
The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in timing therapeutic interventions in dementia and at-risk populations. In the present work, we compared single and multitask learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based prediction methods ADAS-Cog changes, we stratified t…
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University（维克森林大学）；University of British Columbia（不列颠哥伦比亚大学）；Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；University of Nottingham（诺丁汉大学）
Background: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). Objective: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. Methods: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used:…
CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction
Medical AI Research Centre, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院医学人工智能研究中心）；School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院Paul C. Lauterbur生物医学成像研究中心）
Faculty of Science, Northern Border University（北部边境大学理学院）；SEU Saudi Electronic University（沙特电子大学）
Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from Magnetic Resonance Imaging (MRI) images in order to detect the Alzheimer's disease at the MCI stage. The proposed method is evaluated using a big database from …
Prediction of the cognitive evolution of a person susceptible to develop a neurodegenerative disorder is crucial to provide an appropriate treatment as soon as possible. In this paper we propose a 3D siamese network designed to extract features from whole-brain 3D MRI images. We show that it is possible to extract meaningful features using convolution layers, reducing the need of classical image processing operations such as segmentation or pre-computing features such as cortical thickness. To lead this study we used the Alzheimer's Disease Neuroimaging Initiative (ADNI), a public data base of…
Alzheimer's disease (AD) is the most common age-related dementia. Mild cognitive impairment (MCI) is the early stage of cognitive decline before AD. It is crucial to predict the MCI-to-AD conversion for precise management, which remains challenging due to the diversity of patients. Previous evidence shows that the brain network generated from diffusion MRI promises to classify dementia using deep learning. However, the limited availability of diffusion MRI challenges the model training. In this study, we develop a self-supervised contrastive learning approach to generate structural brain netwo…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai Mental Health Center（上海市精神卫生中心）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
Subjective cognitive decline (SCD) is a preclinical stage of Alzheimer's disease (AD) which occurs even before mild cognitive impairment (MCI). Progressive SCD will convert to MCI with the potential of further evolving to AD. Therefore, early identification of progressive SCD with neuroimaging techniques (e.g., structural MRI) is of great clinical value for early intervention of AD. However, existing MRI-based machine/deep learning methods usually suffer the small-sample-size problem which poses a great challenge to related neuroimaging analysis. The central question we aim to tackle in this p…
Technical University Munich（慕尼黑工业大学）；Ludwig Maximilians University Munich（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagnosis, FDG-PET is not always available. The reliable detection of Alzheimer's disease with only MRI could be beneficial, especially in regions where FDG-PET might not be affordable for all patients. To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classif…
A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
University of Calcutta（加尔各答大学）；Institute of Engineering and Management（工程与管理学院）；University of Engineering and Management（工程与管理大学）；San Francisco State University（旧金山州立大学）
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。
Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification
Department of Neurology, Medical University of Graz（格拉茨医科大学神经科）；Institute of Biomedical Imaging, Graz University of Technology（格拉茨技术大学生物医学成像研究所）；BioTechMed-Graz（格拉茨BioTechMed）
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）
The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Massachusetts General Hospital（麻省总医院）；The University of Sydney（悉尼大学）；Beijing Institute of Technology（北京理工大学）；Boston University（波士顿大学）；University of Electronic Science and Technology of China（电子科技大学）；Harvard University（哈佛大学）；Harvard-MIT Health Sciences and Technology（哈佛-麻省理工健康科学与技术学部）
DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography
Eindhoven University of Technology（埃因霍温理工大学）；Brigham and Women’s Hospital（布莱尔妇女医院）；Harvard Medical School（哈佛医学院）；The University of Sydney（悉尼大学）；University of Electronic Science and Technology of China（电子科技大学）；Harvard-MIT Health Sciences and Technology（哈佛-麻省理工健康科学与技术）
