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Li Y, Wei Q, Adeli E, Pohl KM, Zhao Q. Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2022; 13431:231-240. [PMID: 36321855 PMCID: PMC9620868 DOI: 10.1007/978-3-031-16431-6_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As one of the state-of-the-art approaches for network analysis, graph convolutional networks (GCN) have been separately used to analyze functional and structural networks, but have not been applied to explore inter-network relationships. In this work, we propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions, so that the joint structure-function graph can be directly analyzed by a single GCN. The weights of inter-network edges are learnable, reflecting non-uniform structure-function coupling strength across the brain. We apply our Joint-GCN to predict age and sex of 662 participants from the public dataset of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional and micro-structural white-matter networks. Our results support that the proposed Joint-GCN outperforms existing multi-modal graph learning approaches for analyzing structural and functional networks.
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Affiliation(s)
- Yueting Li
- Stanford University, Stanford, CA 94305, USA
| | - Qingyue Wei
- Stanford University, Stanford, CA 94305, USA
| | - Ehsan Adeli
- Stanford University, Stanford, CA 94305, USA
| | - Kilian M Pohl
- Stanford University, Stanford, CA 94305, USA
- SRI International, Menlo Park, CA 94025, USA
| | - Qingyu Zhao
- Stanford University, Stanford, CA 94305, USA
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Ouyang J, Zhao Q, Sullivan EV, Pfefferbaum A, Tapert SF, Adeli E, Pohl KM. Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs. IEEE J Biomed Health Inform 2021; 25:2082-2092. [PMID: 33270567 PMCID: PMC8221531 DOI: 10.1109/jbhi.2020.3042447] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Many neurological diseases are characterized by gradual deterioration of brain structure andfunction. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits. To avoid this issue, we propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer and enforce consistency of the classification across visits in line with disease progression. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset composed of 274 normal controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). In allthree experiments our method is superior to other widely used approaches for longitudinal classification thus making a unique contribution towards more accurate tracking of the impact of conditions on the brain. The code is available at https://github.com/ouyangjiahong/longitudinal-pooling.
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Zhao Q, Sullivan EV, Műller‐Oehring EM, Honnorat N, Adeli E, Podhajsky S, Baker FC, Colrain IM, Prouty D, Tapert SF, Brown SA, Meloy MJ, Brumback T, Nagel BJ, Morales AM, Clark DB, Luna B, De Bellis MD, Voyvodic JT, Nooner KB, Pfefferbaum A, Pohl KM. Adolescent alcohol use disrupts functional neurodevelopment in sensation seeking girls. Addict Biol 2021; 26:e12914. [PMID: 32428984 DOI: 10.1111/adb.12914] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 02/20/2020] [Accepted: 04/17/2020] [Indexed: 01/11/2023]
Abstract
Exogenous causes, such as alcohol use, and endogenous factors, such as temperament and sex, can modulate developmental trajectories of adolescent neurofunctional maturation. We examined how these factors affect sexual dimorphism in brain functional networks in youth drinking below diagnostic threshold for alcohol use disorder (AUD). Based on the 3-year, annually acquired, longitudinal resting-state functional magnetic resonance imaging (MRI) data of 526 adolescents (12-21 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) cohort, developmental trajectories of 23 intrinsic functional networks (IFNs) were analyzed for (1) sexual dimorphism in 259 participants who were no-to-low drinkers throughout this period; (2) sex-alcohol interactions in two age- and sex-matched NCANDA subgroups (N = 76 each), half no-to-low, and half moderate-to-heavy drinkers; and (3) moderating effects of gender-specific alcohol dose effects and a multifactorial impulsivity measure on IFN connectivity in all NCANDA participants. Results showed that sex differences in no-to-low drinkers diminished with age in the inferior-occipital network, yet girls had weaker within-network connectivity than boys in six other networks. Effects of adolescent alcohol use were more pronounced in girls than boys in three IFNs. In particular, girls showed greater within-network connectivity in two motor networks with more alcohol consumption, and these effects were mediated by sensation-seeking only in girls. Our results implied that drinking might attenuate the naturally diminishing sexual differences by disrupting the maturation of network efficiency more severely in girls. The sex-alcohol-dose effect might explain why women are at higher risk of alcohol-related health and psychosocial consequences than men.
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Affiliation(s)
- Qingyu Zhao
- Department of Psychiatry & Behavioral Sciences Stanford University School of Medicine Stanford CA USA
| | - Edith V. Sullivan
- Department of Psychiatry & Behavioral Sciences Stanford University School of Medicine Stanford CA USA
| | - Eva M. Műller‐Oehring
- Department of Psychiatry & Behavioral Sciences Stanford University School of Medicine Stanford CA USA
- Center for Health Sciences SRI International Menlo Park CA USA
| | | | - Ehsan Adeli
- Department of Psychiatry & Behavioral Sciences Stanford University School of Medicine Stanford CA USA
| | - Simon Podhajsky
- Center for Health Sciences SRI International Menlo Park CA USA
| | - Fiona C. Baker
- Center for Health Sciences SRI International Menlo Park CA USA
| | - Ian M. Colrain
- Center for Health Sciences SRI International Menlo Park CA USA
| | - Devin Prouty
- Center for Health Sciences SRI International Menlo Park CA USA
| | - Susan F. Tapert
- Department of Psychiatry University of California San Diego CA USA
| | - Sandra A. Brown
- Department of Psychiatry University of California San Diego CA USA
- Department of Psychology University of California San Diego CA USA
| | - Mary J. Meloy
- Department of Psychiatry University of California San Diego CA USA
| | - Ty Brumback
- Department of Psychological Science Northern Kentucky University Highland Heights KY USA
| | - Bonnie J. Nagel
- Departments of Psychiatry and Behavioral Neuroscience Oregon Health & Sciences University Portland OR USA
| | - Angelica M. Morales
- Departments of Psychiatry and Behavioral Neuroscience Oregon Health & Sciences University Portland OR USA
| | - Duncan B. Clark
- Department of Psychiatry University of Pittsburgh Pittsburgh PA USA
| | - Beatriz Luna
- Department of Psychiatry University of Pittsburgh Pittsburgh PA USA
| | - Michael D. De Bellis
- Department of Psychiatry & Behavioral Sciences Duke University School of Medicine Durham NC USA
| | - James T. Voyvodic
- Department of Radiology Duke University School of Medicine Durham NC USA
| | - Kate B. Nooner
- Department of Psychology University of North Carolina Wilmington Wilmington NC USA
| | - Adolf Pfefferbaum
- Department of Psychiatry & Behavioral Sciences Stanford University School of Medicine Stanford CA USA
- Center for Health Sciences SRI International Menlo Park CA USA
| | - Kilian M. Pohl
- Department of Psychiatry & Behavioral Sciences Stanford University School of Medicine Stanford CA USA
- Center for Health Sciences SRI International Menlo Park CA USA
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Gadgil S, Zhao Q, Pfefferbaum A, Sullivan EV, Adeli E, Pohl KM. Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12267:528-538. [PMID: 33257918 PMCID: PMC7700758 DOI: 10.1007/978-3-030-59728-3_52] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. Simultaneously, the model learns the importance of graph edges within ST-GCN to gain insight into the functional connectivities contributing to the prediction. In analyzing the rs-fMRI of the Human Connectome Project (HCP, N = 1,091) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N = 773), ST-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals. Furthermore, the brain regions and functional connections significantly contributing to the predictions of our model are important markers according to the neuroscience literature.
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Affiliation(s)
- Soham Gadgil
- Computer Science Department, Stanford University, Stanford, USA
| | - Qingyu Zhao
- School of Medicine, Stanford University, Stanford, USA
| | - Adolf Pfefferbaum
- School of Medicine, Stanford University, Stanford, USA
- Center of Health Sciences, SRI International, Menlo Park, USA
| | | | - Ehsan Adeli
- Computer Science Department, Stanford University, Stanford, USA
- School of Medicine, Stanford University, Stanford, USA
| | - Kilian M Pohl
- School of Medicine, Stanford University, Stanford, USA
- Center of Health Sciences, SRI International, Menlo Park, USA
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Seghier ML, Fahim MA, Habak C. Educational fMRI: From the Lab to the Classroom. Front Psychol 2019; 10:2769. [PMID: 31866920 PMCID: PMC6909003 DOI: 10.3389/fpsyg.2019.02769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022] Open
Abstract
Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.
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Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Mohamed A Fahim
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Claudine Habak
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
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Zhao Q, Kwon D, Müller-Oehring EM, Le Berre AP, Pfefferbaum A, Sullivan EV, Pohl KM. Longitudinally consistent estimates of intrinsic functional networks. Hum Brain Mapp 2019; 40:2511-2528. [PMID: 30806009 PMCID: PMC6497087 DOI: 10.1002/hbm.24541] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/08/2019] [Accepted: 02/04/2019] [Indexed: 12/17/2022] Open
Abstract
Increasing numbers of neuroimaging studies are acquiring data to examine changes in brain architecture by investigating intrinsic functional networks (IFN) from longitudinal resting-state functional MRI (rs-fMRI). At the subject level, these IFNs are determined by cross-sectional procedures, which neglect intra-subject dependencies and result in suboptimal estimates of the networks. Here, a novel longitudinal approach simultaneously extracts subject-specific IFNs across multiple visits by explicitly modeling functional brain development as an essential context for seeking change. On data generated by an innovative simulation based on real rs-fMRI, the method was more accurate in estimating subject-specific IFNs than cross-sectional approaches. Furthermore, only group-analysis based on longitudinally consistent estimates identified significant developmental effects within IFNs of 246 adolescents from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The findings were confirmed by the cross-sectional estimates when the corresponding group analysis was confined to the developmental effects. Those effects also converged with current concepts of neurodevelopment.
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Affiliation(s)
- Qingyu Zhao
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Dongjin Kwon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California.,Center for Health Sciences, SRI International, Menlo Park, California
| | - Eva M Müller-Oehring
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California.,Center for Health Sciences, SRI International, Menlo Park, California
| | - Anne-Pascale Le Berre
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Adolf Pfefferbaum
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California.,Center for Health Sciences, SRI International, Menlo Park, California
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Kilian M Pohl
- Center for Health Sciences, SRI International, Menlo Park, California
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