[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125643-en":3,"doc-seo-125643-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125643,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI","New brain connectivity measures are needed to close gaps in existing functional MRI connectivity metrics and to support studies of brain function, cognitive capacity, and early disease markers. Traditional functional connectivity methods based on pairwise correlations and partial correlations miss nonlinear regional relationships. The work proposes a machine-learning functional connectivity measure (ML.FC) and two effective connectivity measures (ML.EC and structurally projected Granger causality, SP.GC) to capture nonlinear and directionally informed whole-brain connectivity efficiently.","Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI  \nCooper J. Mellema, PhD1,2,5  \nAlbert A. Montillo, PhD1,2,3,4,5  \n1Lyda Hill Department of Bioinformatics  \n2Biomedical Engineering Department  \n3Advanced Imaging Research Center  \n4Radiology Department  \n5University of Texas Southwestern Medical Center  \n[Cooper.Mellema@UTSouthwestern.edu](Cooper.Mellema@UTSouthwestern.edu)  \n[Albert.Montillo@UTSouthwestern.edu](Albert.Montillo@UTSouthwestern.edu)  \nAbstract  \nObjective: New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI (fMRI), such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of functional connectivity (ML.FC) which efficiently captures linear and nonlinear aspects.  \nApproach: To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling (DCM) and structural equation modeling (SEM) have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms of reproducibility and the ability to predict individual traits in order to demonstrate these measures’ internal validity. We use four repeatscans of the same individuals from the Human Connectome Project (HCP) and measure the ability of the measures to predict individual subject physiologic and cognitive traits.  \nMain results: The proposed new FC measure of ML.FC attains high reproducibility (mean intra-subject R2 of 0 .44), while the proposed EC measure of SP.GC attains the highest predictive power (mean R2 across prediction tasks of 0 . 66) .  \nSignificance: The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.  \nKeywords: fMRI, connectivity, functional connectivity, effective connectivity, reproducibility, reliability, patterns, causality  \n1 Introduction  \nThe connectivity of the human brain is integral to cognitive capacity, can be an early marker for human disease, and underlies the fundamental functioning of the central nervous system (Ashburner et al., 2004) . However, measuring connectivity in vivo has proven problematic (Andellini et al., 2015; Fiecas et al., 2013; Rowe, 2010) . Functional magnetic resonance imaging (fMRI 1) of the brain measures the blood-oxygen-  \n1 All abbreviations used in this manuscript are described in detail in Supplemental Table S1  \nlevel-dependent (BOLD) signal and serves as an indirect measure of neural activity. The brain scan can be parcellated into neuroanatomical regions and the mean regional time series can be computed from the voxels in each region. By measuring temporal relationships between the mean BOLD signal from two or more regions of the brain, the underlying direct and indirect connectivity and communication within the brain can be probed. The connections between regions can then be used to represent the subject-specific connectome as a connectivity graph with each region represented as a node in the graph, while the edges between nodes are assigned an edge strength proportion to the pairw","cbCaiuXonEvCsdQl","https://ap.wps.com/l/cbCaiuXonEvCsdQl","pdf",2299647,1,29,"English","en",105,"# Abstract\n## Objective and gap in existing measures\n## Proposed machine learning measures\n## Approach and evaluation\n## Main results and significance\n# Keywords\n# Introduction\n## Brain connectivity and relevance to cognition and disease\n## Definitions: functional vs effective connectivity\n## Limitations of traditional measures","[{\"question\":\"What problem do the authors aim to address in functional MRI connectivity measures?\",\"answer\":\"Existing functional and effective connectivity measures have gaps, including limited ability to model nonlinear relationships and challenges in computation across whole-brain region sets. The paper targets improved reproducibility and reliability.\"},{\"question\":\"How does ML.FC differ from traditional functional connectivity approaches?\",\"answer\":\"ML.FC is a machine learning based measure designed to efficiently capture both linear and nonlinear aspects of associations between regional time series, beyond pairwise correlation and partial correlation.\"},{\"question\":\"Why do the authors introduce effective connectivity measures like ML.EC and SP.GC?\",\"answer\":\"Effective connectivity is directional and model-dependent, but common approaches are computationally impractical for whole-brain analyses. The paper proposes ML.EC for nonlinear whole-brain effective connectivity and SP.GC to adapt Granger causal connectivity while respecting underlying structural connectivity.\"}]","Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI | PDF",1785900377,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"novel-machine-learning-approaches-for-improving-the-reproducibility-and-reliability-of-functional-and-effective-connectivity-from-functional-mri","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/novel-machine-learning-approaches-for-improving-the-reproducibility-and-reliability-of-functional-and-effective-connectivity-from-functional-mri/125643/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem do the authors aim to address in functional MRI connectivity measures?","Question",{"text":75,"@type":76},"Existing functional and effective connectivity measures have gaps, including limited ability to model nonlinear relationships and challenges in computation across whole-brain region sets. The paper targets improved reproducibility and reliability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does ML.FC differ from traditional functional connectivity approaches?",{"text":80,"@type":76},"ML.FC is a machine learning based measure designed to efficiently capture both linear and nonlinear aspects of associations between regional time series, beyond pairwise correlation and partial correlation.",{"name":82,"@type":73,"acceptedAnswer":83},"Why do the authors introduce effective connectivity measures like ML.EC and SP.GC?",{"text":84,"@type":76},"Effective connectivity is directional and model-dependent, but common approaches are computationally impractical for whole-brain analyses. 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