Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets

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dc.contributor.authorLiu, Meimeien
dc.contributor.authorZhang, Zhengwuen
dc.contributor.authorDunson, David B.en
dc.date.accessioned2022-09-13T17:57:27Zen
dc.date.available2022-09-13T17:57:27Zen
dc.date.issued2021-12-15en
dc.description.abstractThere has been a huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationships with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connection strengths between ROIs. Due to the high-dimensionality and non-Euclidean nature of networks, it is challenging to depict their population distribution and relate them to human traits. Current approaches focus on summarizing the network using either pre-specified topological features or principal components analysis (PCA). In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer their relationships to human traits. We refer to our method as Graph AuTo-Encoding (GATE). We applied GATE to two large-scale brain imaging datasets, the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) for adults, to study the structural brain connectome and its relationship with cognition. Numerical results demonstrate huge advantages of GATE over competitors in terms of prediction accuracy, statistical inference, and computing efficiency. We found that the structural connectome has a stronger association with a wide range of human cognitive traits than was apparent using previous approaches.en
dc.description.notesData used in the preparation of this article were obtained from the Human Connectome Project (https://www.humanconnectome.org/) and Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org).The HCP WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) were funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The ABCD study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html.A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/scientists/workgroups/.ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This paper reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.; Dunson and Zhang want to acknowledge support from the National Institutes of Health (NIH) of the United States under award number MH118927. We also want to acknowledge the support from the NVIDIA Corporation with their donation of the Titan-V GPU.en
dc.description.sponsorship16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research [1U54MH091657]; McDonnell Center for Systems Neuroscience at Washington University; National Institutes of Health [U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, U01DA041025]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.neuroimage.2021.118750en
dc.identifier.eissn1095-9572en
dc.identifier.issn1053-8119en
dc.identifier.other118750en
dc.identifier.pmid34823023en
dc.identifier.urihttp://hdl.handle.net/10919/111812en
dc.identifier.volume245en
dc.language.isoenen
dc.publisherAcademic Press-Elsevieren
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectBrain networksen
dc.subjectNon-linear factor analysisen
dc.subjectGraph CNNen
dc.subjectReplicated networksen
dc.subjectVariational auto-encoderen
dc.titleGraph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasetsen
dc.title.serialNeuroimageen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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