Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses
Abstract Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area u...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34210-y |
| _version_ | 1797827583047368704 |
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| author | Madison Lewis Tales Santini Nicholas Theis Brendan Muldoon Katherine Dash Jonathan Rubin Matcheri Keshavan Konasale Prasad |
| author_facet | Madison Lewis Tales Santini Nicholas Theis Brendan Muldoon Katherine Dash Jonathan Rubin Matcheri Keshavan Konasale Prasad |
| author_sort | Madison Lewis |
| collection | DOAJ |
| description | Abstract Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal “attacks” (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge. |
| first_indexed | 2024-04-09T12:50:38Z |
| format | Article |
| id | doaj.art-8d29b8c9abdd43488abd4761b7f4f274 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T12:50:38Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8d29b8c9abdd43488abd4761b7f4f2742023-05-14T11:15:17ZengNature PortfolioScientific Reports2045-23222023-05-0113111410.1038/s41598-023-34210-yModular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychosesMadison Lewis0Tales Santini1Nicholas Theis2Brendan Muldoon3Katherine Dash4Jonathan Rubin5Matcheri Keshavan6Konasale Prasad7Department of Bioengineering, Swanson School of Engineering, University of PittsburghDepartment of Bioengineering, Swanson School of Engineering, University of PittsburghDepartment of Psychiatry, University of Pittsburgh School of MedicineDepartment of Psychiatry, University of Pittsburgh School of MedicineDepartment of Bioengineering, Swanson School of Engineering, University of PittsburghDepartment of Mathematics, University of PittsburghDepartment of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical SchoolDepartment of Bioengineering, Swanson School of Engineering, University of PittsburghAbstract Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal “attacks” (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.https://doi.org/10.1038/s41598-023-34210-y |
| spellingShingle | Madison Lewis Tales Santini Nicholas Theis Brendan Muldoon Katherine Dash Jonathan Rubin Matcheri Keshavan Konasale Prasad Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses Scientific Reports |
| title | Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses |
| title_full | Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses |
| title_fullStr | Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses |
| title_full_unstemmed | Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses |
| title_short | Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses |
| title_sort | modular architecture and resilience of structural covariance networks in first episode antipsychotic naive psychoses |
| url | https://doi.org/10.1038/s41598-023-34210-y |
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