There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD). Elucidating distinct subtypes of diseases allows a better understanding of neuropathogenesis and enables the possibility of developing targeted treatment programs. Recent semi-supervised clustering techniques have provided a data-driven way to understand disease heterogeneity. However, existing methods do not take into account that subtypes of the disease might present themselves at different spatial scales across the brain. Here, we introduce a novel method, MAGIC, to uncover disease heterogeneity by leveraging multi-scale clustering. We first extract multi-scale patterns of structural covariance (PSCs) followed by a semi-supervised clustering with double cyclic block-wise optimization across different scales of PSCs. We validate MAGIC using simulated heterogeneous neuroanatomical data and demonstrate its clinical potential by exploring the heterogeneity of AD using T1 MRI scans of 228 cognitively normal (CN) and 191 patients. Our results indicate two main subtypes of AD with distinct atrophy patterns that consist of both fine-scale atrophy in the hippocampus as well as large-scale atrophy in cortical regions. The evidence for the heterogeneity is further corroborated by the clinical evaluation of two subtypes, which indicates that there is a subpopulation of AD patients that tend to be younger and decline faster in cognitive performance relative to the other subpopulation, which tends to be older and maintains a relatively steady decline in cognitive abilities.
Machine learning methods applied to complex biomedical data has enabled the construction of disease signatures of diagnostic/prognostic value. However, less attention has been given to understanding disease heterogeneity. Semi-supervised clustering methods can address this problem by estimating multiple transformations from a (e.g. healthy) control (CN) group to a patient (PT) group, seeking to capture the heterogeneity of underlying pathlogic processes. Herein, we propose a novel method, Smile-GANs (SeMi-supervIsed cLustEring via GANs), for semi-supervised clustering, and apply it to brain MR…
University of Pennsylvania（宾夕法尼亚大学）；University of Texas San Antonio Health Science Center（德克萨斯大学圣安东尼奥健康科学中心）；National Institute on Aging（美国国家老龄化研究所）
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a novel semi-supervised deep-clustering method, which dissects neuroanatomical heterogeneity, enabling identification of disease subtypes via their imaging signatures relative to controls. When applied to MRIs (2 studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified 4 neurodegenerative patterns/axes: P1, normal anatomy a…
Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline
University of Pretoria（比勒陀利亚大学）；Ferdowsi University of Mashhad（马什哈德菲尔多西大学）
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院）；School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China（山东师范大学计算机科学与人工智能学院）；AiShiWeiLai AI Research, Beijing, China（艾世维来人工智能研究所）；Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China（南京医科大学附属南京脑科医院早期干预科）；Amii, University of Alberta, Edmonton, AB, Canada（阿尔伯塔大学Amii）
FetMRQC: a robust quality control system for multi-centric fetal brain MRI
CIBM – Center for Biomedical Imaging（生物医学成像中心（CIBM））；BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu)（BCNatal胎儿医学研究中心（克利尼克医院与圣茹安德迪乌医院））；Universitat de Barcelona（巴塞罗那大学）；Aix-Marseille Université（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；Institut de Neurosciences de La Timone（拉蒂讷神经科学研究所）；Hôpital Timone, AP-HM（Assistance Publique – Hôpitaux de Marseille 旗下的蒂莫讷医院）；University Children’s Hospital Zurich, University of Zurich（苏黎世大学儿童医院、苏黎世大学）；Neuroscience Center Zurich, University of Zurich（苏黎世大学神经科学中心）
University of Pennsylvania（宾夕法尼亚大学）；University of East London（东伦敦大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；Columbia University（哥伦比亚大学）；Washington University School of Medicine（华盛顿大学医学院）；Indiana University School of Medicine（印第安纳大学医学院）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；National Institute on Aging（国家老龄化研究所）
Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to precision and individualized medicine. We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles. Multimodal data from a multicentre sample (N=996) were analyzed. A semi-supervised clustering method (HYDRA) was applied to regional grey matter (GM) brain volumes to d…
Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）；Advanced Telecommunications Research Institutes International（国际先进电信研究所）；RIKEN（理化学研究所）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）
In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clustering of subjects based on FC recently draws much attention, which can potentially reveal important heterogeneity in subjects such as subtypes of psychiatric disorders. In particular, a multiple-view clustering method is a powerful analytical tool, which…
LTCI, Télécom Paris, IP Paris（巴黎电信学院 LTCI，巴黎理工学院）；University of Turin（都灵大学）；NeuroSpin, CEA, Université Paris-Saclay（巴黎萨克雷大学原子能与替代能源委员会神经自旋中心）
Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers. To estimate accurate and generalizable models, large datasets have been collected, which are often multi-site and multi-scanner. This large heterogeneity negatively affects the generalization performance of DL models since they are prone to overfit site-related noise. Recently, contrastive learning approaches have been shown to be more robust against noise in data or labels. For this reason, we pro…
BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Cambridge（剑桥大学）；University of Sheffield（谢菲尔德大学）；The University of Georgia（佐治亚大学）
Lehigh University（利哈伊大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature sp…
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁大学医学院）
Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information. We demonstrate improved classification on 3 subsets…
Beijing Institute of Technology（北京理工大学）；Yangzhou University（扬州大学）；Northern Jiangsu People’s Hospital（苏北人民医院）；Tsinghua University（清华大学）
Graph neural networks (GNNs) provide powerful insights for brain neuroimaging technology from the view of graphical networks. However, most existing GNN-based models assume that the neuroimaging-produced brain connectome network is a homogeneous graph with single types of nodes and edges. In fact, emerging studies have reported and emphasized the significance of heterogeneity among human brain activities, especially between the two cerebral hemispheres. Thus, homogeneous-structured brain network-based graph methods are insufficient for modelling complicated cerebral activity states. To overcom…
RadboudUMC（拉德堡德大学医学中心）；Donders Institute（唐德斯研究所）；University of Oxford（牛津大学）；King’s College London（伦敦国王学院）
Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge. A promising approach is to use probabilistic regression methods to estimate normative models of brain function using (f)MRI data then use these to map variation across individuals in clinical populations (e.g., via anomaly detection). To fully capture individual differences, it is crucial to statistically model the patterns of correlation across different brain regions and individuals. However, this is very challenging for neuroimagi…
Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohorts
Department of Management, Griffith Business School（管理学院，格里菲斯商学院）；Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania（神经科学系，佩尔曼医学学院，宾夕法尼亚大学）；Marketing Department, Wharton School of Business, University of Pennsylvania（营销系，沃顿商学院，宾夕法尼亚大学）；Department of Psychology, School of Arts and Sciences, University of Pennsylvania（心理学系，艺术与科学学院，宾夕法尼亚大学）
Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
Washington University in St Louis（圣路易斯华盛顿大学）；Washington University School of Medicine in St Louis（圣路易斯华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）
Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Emory University School of Medicine（埃默里大学医学院）；University of Florida School of Medicine（佛罗里达大学医学院）；Brown University（布朗大学）
Automated segmentation of medical imaging is of broad interest to clinicians and machine learning researchers alike. The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image. Image segmentation is a difficult task because of multiparametric heterogeneity within the images, an obstacle that has proven especially challenging in efforts to automate the segmentation of brain lesions from non-contrast head computed tomography (CT). In this research, we have experimented with multiple available deep learning a…
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Medical College of Wisconsin（威斯康星医学院）；University of California, San Diego（加州大学圣地亚哥分校）；Baylor College of Medicine（贝勒医学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of California, San Francisco（加州大学旧金山分校）
Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic represen…
A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods don't explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, unsupervised methods may parse heterogeneity that is driven by nuisance confounding factors that affect brain structure or function, rather than heterogeneity relevant to a pathology of interest. On the other hand, semi-supervised clustering methods s…
Department of Computer Science and Engineering, University of Texas at Arlington, United States（德克萨斯理工大学计算机科学与工程系，美国）；School of Computing, University of Georgia, United States（佐治亚大学计算机学院，美国）
University of Connecticut（康涅狄格大学）；Jiangxi University of Finance and Economics（江西财经大学）；Yale School of Public Health（耶鲁公共卫生学院）；University of California, Riverside（加州大学河滨分校）；University of Connecticut Health Center（康涅狄格大学健康中心）
Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity, i.e., to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differen…
