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Interpretable Multimodality Embedding Of Cerebral Cortex Using Attention Graph Network For Identifying Bipolar Disorder [article]

Huzheng Yang, Xiaoxiao Li, Yifan Wu, Siyi Li, Su Lu, James S. Duncan, James C. Gee, Shi Gu
2019 bioRxiv   pre-print
In this paper, we developed an Edge-weighted Graph Attention Network (EGAT) with Dense Hierarchical Pooling (DHP), to better understand the underlying roots of the disorder from the view of structure-function  ...  patterns among Default Mode, Fronto-parietal and Cingulo-opercular networks contribute to identifying BP.  ...  Conclusion In this work, we proposed a novel graph-attention based method for cerebral cortex analysis that integrates sMRI and fMRI using GNN to classify BP v.s. HC.  ... 
doi:10.1101/671339 fatcat:stplj4hwvjasppet7engh3njue

Artificial intelligence applications in psychoradiology

Fei Li, Huaiqiang Sun, Bharat B Biswal, John A Sweeney, Qiyong Gong
2021 Psychoradiology  
path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be  ...  In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the  ...  Wenjing Zhang, Lekai Luo, Wanfang You, and Yuxia Wang, who are from Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, for performing aspects of the work  ... 
doi:10.1093/psyrad/kkab009 fatcat:7z3shbc4vzfg7pwmfyumqsgtia

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  The model receives a mesh as input and produces one output label for each node of the mesh, and parcellates the cerebral cortex into three parcels using a graph attention-based model (GAT) [78] .  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Regional Homogeneity

Lili Jiang, Xi-Nian Zuo
2016 The Neuroscientist  
Functional connectivity (FC) with rfMRI is the most widely used method to describe remote or long-distance relationships in studies of cerebral cortex parcellation, interindividual variability, and brain  ...  as a network centrality to characterize multimodal local features of the brain connectome; (3) render a neurobiological perspective on local functional homogeneity by linking its temporal, spatial, and  ...  Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially supported by the National Key Basic  ... 
doi:10.1177/1073858415595004 pmid:26170004 pmcid:PMC5021216 fatcat:lslruxe7dnfyllfdgtsh7rkrqy

Graph Neural Networks in Network Neuroscience [article]

Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
2021 arXiv   pre-print
We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration.  ...  Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance  ...  Therefore, (67) another attention-based network, proposed a combination of different GNN layers (Edge-Weighted GAT layer, followed by Diffpool layers) to learn the graph embedding and identify the Bipolar  ... 
arXiv:2106.03535v1 fatcat:jx7ixd7xjngthaq6qhb25gssm4

Deep learning in mental health outcome research: a scoping review

Chang Su, Zhenxing Xu, Jyotishman Pathak, Fei Wang
2020 Translational Psychiatry  
mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data.  ...  Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health  ...  Laksshman et al. 71 used exome sequencing data to predict bipolar disorder outcomes of patients.  ... 
doi:10.1038/s41398-020-0780-3 pmid:32532967 pmcid:PMC7293215 fatcat:gbdjszebnndt3j4todyw5k2scq

Machine learning models identify multimodal measurements highly predictive of transdiagnostic symptom severity for mood, anhedonia, and anxiety [article]

Monika S. Mellem, Yuelu Liu, Humberto Gonzalez, Matthew Kollada, William J Martin, Parvez Ahammad
2018 bioRxiv   pre-print
The top performing multimodal models retained a high level of interpretability which enabled several clinical and scientific insights.  ...  contains clinical scale assessments, resting-state functional-MRI (rs-fMRI) and structural-MRI (sMRI) imaging measures from patients with schizophrenia, bipolar disorder, attention deficit and hyperactivity  ...  Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. (2006): An 21 automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral 22 based regions of  ... 
doi:10.1101/414037 fatcat:q4woleaperhupb6zxiekfbfz4e

Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications [article]

Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas
2022 arXiv   pre-print
We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice.  ...  To foster cross-disciplinary research, we present GCNs technical advancements, emerging medical applications, identify common challenges in the use of image-based GCNs and their extensions in model interpretation  ...  introduced an edge-weighted graph attention network (EGAT) (Veličković et al., 2017) with a diffPooling (Ying et al., 2018) to classify Bipolar disorder (BP) and HC from sMRI and fMRI in cerebral cortex  ... 
arXiv:2202.08916v3 fatcat:zskcqvgjpnb6vdklmyy5rozswq

Poster Session I

2013 Neuropsychopharmacology  
(B30%) in the prefrontal cortex (PFC), and behaviorally results in an impairment of the prefrontallymediated attentional set shifting task.  ...  Cognitive deficits (attention, working memory, and cognitive flexibility) are considered a core symptom cluster in schizophrenia (SZ); predictive of functional outcome yet not alleviated by current drug  ...  cortex of youth with bipolar disorder early in their illness course.  ... 
doi:10.1038/npp.2013.279 fatcat:54ipecxjarcvljrvn5fgtgif5u

Unraveling the Miswired Connectome: A Developmental Perspective

Adriana Di Martino, Damien A. Fair, Clare Kelly, Theodore D. Satterthwaite, F. Xavier Castellanos, Moriah E. Thomason, R. Cameron Craddock, Beatriz Luna, Bennett L. Leventhal, Xi-Nian Zuo, Michael P. Milham
2014 Neuron  
The recent maturation of pediatric in vivo brain imaging is bringing the identification of clinically meaningful brain-based biomarkers of developmental disorders within reach.  ...  The vast majority of mental illnesses can be conceptualized as developmental disorders of neural interactions within the connectome, or developmental miswiring.  ...  , Tourette's, or bipolar disorders).  ... 
doi:10.1016/j.neuron.2014.08.050 pmid:25233316 pmcid:PMC4169187 fatcat:mhuqbmrhhvbd7nopixhytgx2k4

Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy [article]

Ganne Chaitanya, Walter Hinds, James Kragel, Xiaosong He, Noah Sideman, Youssef Ezzyat, Michael R Sperling, Ashwini Sharan, Joseph I Tracy
2019 bioRxiv   pre-print
Yet, the functional connectivity characteristics of networks subserving these HFA memory linkages remains uncertain.  ...  Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.  ...  Dorian Pustina for his help with the machine learning strategy. The authors thank all the healthy controls and patients with epilepsy, whose data was used for this study.  ... 
doi:10.1101/660696 fatcat:f23w3wqbs5dzdcaclhlsxispm4

Introduction to JINS Special Issue on Human Brain Connectivity in the Modern Era: Relevance to Understanding Health and Disease

Deanna M. Barch, Mieke Verfaellie, Stephen M. Rao
2016 Journal of the International Neuropsychological Society  
Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159. Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V.J., Meuli, R., & Thiran, J.P. (2007).  ...  Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function.  ...  Although much work is needed to further validate the use of graph theory for interpreting brain connectivity data, network science represents an exciting new field of research that is increasingly showing  ... 
doi:10.1017/s1355617716000047 fatcat:f2preenihbes5ftkrxbo7tgt64

Benchmarking functional connectome-based predictive models for resting-state fMRI

Kamalaker Dadi, Mehdi Rahim, Alexandre Abraham, Darya Chyzhyk, Michael Milham, Bertrand Thirion, Gaël Varoquaux
2019 NeuroImage  
Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices.  ...  We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric  ...  In particular we use readily-preprocessed rest-fMRI data from Multimodal treatment study of Attention Deficit Hyperactivity Disorder (MTA).  ... 
doi:10.1016/j.neuroimage.2019.02.062 pmid:30836146 fatcat:gyc6jxopp5gihn3alwgrf7zcge

Stratified medicine for mental disorders

Gunter Schumann, Elisabeth B. Binder, Arne Holte, E. Ronald de Kloet, Ketil J. Oedegaard, Trevor W. Robbins, Tom R. Walker-Tilley, Istvan Bitter, Verity J. Brown, Jan Buitelaar, Roberto Ciccocioppo, Roshan Cools (+32 others)
2014 European Neuropsychopharmacology  
There is recognition that biomedical research into the causes of mental disorders and their treatment needs to adopt new approaches to research.  ...  Novel biomedical techniques have advanced our understanding of how the brain develops and is shaped by behaviour and  ...  Network analysis is being used to identify neural changes in human neurological or psychiatric disorders which may provide novel endophenotypes (or 'biomarkers') for the purposes described above (Fornito  ... 
doi:10.1016/j.euroneuro.2013.09.010 pmid:24176673 fatcat:5u4n44l2trcydkcumhauf7l6bu
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