Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia

Haleh Falakshahi; Hooman Rokham; Zening Fu; Daniel H. Mathalon; Judith M. Ford; James Voyvodic; Bryon A. Mueller; Aysenil Belger; Sarah McEwen; Steven G. Potkin; Adrian Preda; Armin Iraji; Jessica A. Turner; Sergey Plis; Vince D. Calhoun

doi:10.1109/BIBE50027.2020.00074

Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia

Haleh Falakshahi, Hooman Rokham, Zening Fu, Daniel H. Mathalon, Judith M. Ford, James Voyvodic, Bryon A. Mueller, Aysenil Belger, Sarah McEwen, Steven G. Potkin, Adrian Preda, Armin Iraji, Jessica A. Turner, Sergey Plis, Vince D. Calhoun

Psychiatry & Behavioral Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Objective: A graph theoretical approach provides a powerful framework for discovering potential biomarkers of psychotic disorders. Comparing the brain graphs of the control and patient groups can help us to discover changes in mental disorders in a more convenient way. In this paper, we propose a novel tool to identify missing links associated with blocked paths (segregation) and new links associated with additional paths (abnormal integration) in estimated patient group's time-varying graphs. We highlight the approach in an example application to the resting-state functional magnetic resonance imaging (fMRI) data of schizophrenia (SZ) patients. Methods: We first estimated whole-brain functional connectivity dynamics using a combination of spatial independent component analysis (ICA), sliding time window, and k-means clustering of windowed correlation matrices on resting-state fMRI data. The clusters are regarded as functional connectivity states, and each of them includes time intervals exhibiting similar connectivity patterns. We then estimated a Gaussian graphical model (GGM) for each state for both groups. To evaluate this approach, we compared different paths between brain components (nodes) of SZ and control groups' graphs within each state by using the concept of connected components in graph theory. Results: We identified missing edges associated with disconnectivity (disconnectors) and showed there are additional edges in the SZ group graph that contribute to creating new paths in brain graphs. Conclusion: The proposed approach provides a tool for extracting time-varying graphs and identifying disconnectors associated with disconnectivity (absence of paths) and also connectors associated with abnormal integration (additional paths) in patient group graphs. Significance: We detected several missing links in SZ, both within and between functional domains, in particular within the subcortical (3 links) and somatomotor (4 links) domains. Interestingly, our proposed method identified new links within the somatomotor domain which may be related to a compensatory response in patients that warrants future study.

Original language	English (US)
Title of host publication	Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	417-424
Number of pages	8
ISBN (Electronic)	9781728195742
DOIs	https://doi.org/10.1109/BIBE50027.2020.00074
State	Published - Oct 2020
Event	20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States Duration: Oct 26 2020 → Oct 28 2020

Publication series

Name	Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

Conference

Conference	20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
Country/Territory	United States
City	Virtual, Cincinnati
Period	10/26/20 → 10/28/20

Bibliographical note

Funding Information:
This work was supported in part by NIH under grants R01EB020407, R01EB006841, P20GM103472, P30GM122734 and NSF under grant 1539067.

Publisher Copyright:
© 2020 IEEE.

Keywords

Brain graph
Gaussian graphical model
joint estimation
partial correlation
resting state fMRI
schizophrenia

Publisher link

10.1109/BIBE50027.2020.00074

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Falakshahi, H., Rokham, H., Fu, Z., Mathalon, D. H., Ford, J. M., Voyvodic, J., Mueller, B. A., Belger, A., McEwen, S., Potkin, S. G., Preda, A., Iraji, A., Turner, J. A., Plis, S., & Calhoun, V. D. (2020). Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 (pp. 417-424). Article 9288042 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE50027.2020.00074

Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia. / Falakshahi, Haleh; Rokham, Hooman; Fu, Zening et al.
Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 417-424 9288042 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Falakshahi, H, Rokham, H, Fu, Z, Mathalon, DH, Ford, JM, Voyvodic, J, Mueller, BA, Belger, A, McEwen, S, Potkin, SG, Preda, A, Iraji, A, Turner, JA, Plis, S & Calhoun, VD 2020, Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia. in Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020., 9288042, Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 417-424, 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020, Virtual, Cincinnati, United States, 10/26/20. https://doi.org/10.1109/BIBE50027.2020.00074

Falakshahi H, Rokham H, Fu Z, Mathalon DH, Ford JM, Voyvodic J et al. Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 417-424. 9288042. (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020). doi: 10.1109/BIBE50027.2020.00074

Falakshahi, Haleh ; Rokham, Hooman ; Fu, Zening et al. / Time-varying Graphs : A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia. Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 417-424 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020).

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abstract = "Objective: A graph theoretical approach provides a powerful framework for discovering potential biomarkers of psychotic disorders. Comparing the brain graphs of the control and patient groups can help us to discover changes in mental disorders in a more convenient way. In this paper, we propose a novel tool to identify missing links associated with blocked paths (segregation) and new links associated with additional paths (abnormal integration) in estimated patient group's time-varying graphs. We highlight the approach in an example application to the resting-state functional magnetic resonance imaging (fMRI) data of schizophrenia (SZ) patients. Methods: We first estimated whole-brain functional connectivity dynamics using a combination of spatial independent component analysis (ICA), sliding time window, and k-means clustering of windowed correlation matrices on resting-state fMRI data. The clusters are regarded as functional connectivity states, and each of them includes time intervals exhibiting similar connectivity patterns. We then estimated a Gaussian graphical model (GGM) for each state for both groups. To evaluate this approach, we compared different paths between brain components (nodes) of SZ and control groups' graphs within each state by using the concept of connected components in graph theory. Results: We identified missing edges associated with disconnectivity (disconnectors) and showed there are additional edges in the SZ group graph that contribute to creating new paths in brain graphs. Conclusion: The proposed approach provides a tool for extracting time-varying graphs and identifying disconnectors associated with disconnectivity (absence of paths) and also connectors associated with abnormal integration (additional paths) in patient group graphs. Significance: We detected several missing links in SZ, both within and between functional domains, in particular within the subcortical (3 links) and somatomotor (4 links) domains. Interestingly, our proposed method identified new links within the somatomotor domain which may be related to a compensatory response in patients that warrants future study.",

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note = "Funding Information: This work was supported in part by NIH under grants R01EB020407, R01EB006841, P20GM103472, P30GM122734 and NSF under grant 1539067. Publisher Copyright: {\textcopyright} 2020 IEEE.; 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 ; Conference date: 26-10-2020 Through 28-10-2020",

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AU - Mathalon, Daniel H.

AU - Ford, Judith M.

AU - Voyvodic, James

AU - Mueller, Bryon A.

AU - Belger, Aysenil

AU - McEwen, Sarah

AU - Potkin, Steven G.

AU - Preda, Adrian

AU - Iraji, Armin

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N2 - Objective: A graph theoretical approach provides a powerful framework for discovering potential biomarkers of psychotic disorders. Comparing the brain graphs of the control and patient groups can help us to discover changes in mental disorders in a more convenient way. In this paper, we propose a novel tool to identify missing links associated with blocked paths (segregation) and new links associated with additional paths (abnormal integration) in estimated patient group's time-varying graphs. We highlight the approach in an example application to the resting-state functional magnetic resonance imaging (fMRI) data of schizophrenia (SZ) patients. Methods: We first estimated whole-brain functional connectivity dynamics using a combination of spatial independent component analysis (ICA), sliding time window, and k-means clustering of windowed correlation matrices on resting-state fMRI data. The clusters are regarded as functional connectivity states, and each of them includes time intervals exhibiting similar connectivity patterns. We then estimated a Gaussian graphical model (GGM) for each state for both groups. To evaluate this approach, we compared different paths between brain components (nodes) of SZ and control groups' graphs within each state by using the concept of connected components in graph theory. Results: We identified missing edges associated with disconnectivity (disconnectors) and showed there are additional edges in the SZ group graph that contribute to creating new paths in brain graphs. Conclusion: The proposed approach provides a tool for extracting time-varying graphs and identifying disconnectors associated with disconnectivity (absence of paths) and also connectors associated with abnormal integration (additional paths) in patient group graphs. Significance: We detected several missing links in SZ, both within and between functional domains, in particular within the subcortical (3 links) and somatomotor (4 links) domains. Interestingly, our proposed method identified new links within the somatomotor domain which may be related to a compensatory response in patients that warrants future study.

AB - Objective: A graph theoretical approach provides a powerful framework for discovering potential biomarkers of psychotic disorders. Comparing the brain graphs of the control and patient groups can help us to discover changes in mental disorders in a more convenient way. In this paper, we propose a novel tool to identify missing links associated with blocked paths (segregation) and new links associated with additional paths (abnormal integration) in estimated patient group's time-varying graphs. We highlight the approach in an example application to the resting-state functional magnetic resonance imaging (fMRI) data of schizophrenia (SZ) patients. Methods: We first estimated whole-brain functional connectivity dynamics using a combination of spatial independent component analysis (ICA), sliding time window, and k-means clustering of windowed correlation matrices on resting-state fMRI data. The clusters are regarded as functional connectivity states, and each of them includes time intervals exhibiting similar connectivity patterns. We then estimated a Gaussian graphical model (GGM) for each state for both groups. To evaluate this approach, we compared different paths between brain components (nodes) of SZ and control groups' graphs within each state by using the concept of connected components in graph theory. Results: We identified missing edges associated with disconnectivity (disconnectors) and showed there are additional edges in the SZ group graph that contribute to creating new paths in brain graphs. Conclusion: The proposed approach provides a tool for extracting time-varying graphs and identifying disconnectors associated with disconnectivity (absence of paths) and also connectors associated with abnormal integration (additional paths) in patient group graphs. Significance: We detected several missing links in SZ, both within and between functional domains, in particular within the subcortical (3 links) and somatomotor (4 links) domains. Interestingly, our proposed method identified new links within the somatomotor domain which may be related to a compensatory response in patients that warrants future study.

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Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

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