Brain structural covariance network features are robust markers of early heavy alcohol use

Jonatan Ottino-González; Renata B Cupertino; Zhipeng Cao; Sage Hahn; Devarshi Pancholi; Matthew D Albaugh; Ty Brumback; Fiona C Baker; Sandra A Brown; Duncan B Clark; Massimiliano de Zambotti; David B Goldston; Beatriz Luna; Bonnie J Nagel; Kate B Nooner; Kilian M Pohl; Susan F Tapert; Wesley K Thompson; Terry L Jernigan; Patricia Conrod; Scott Mackey; Hugh Garavan

doi:10.1111/add.16330

Brain structural covariance network features are robust markers of early heavy alcohol use

Addiction. 2024 Jan;119(1):113-124. doi: 10.1111/add.16330. Epub 2023 Sep 19.

Authors

Jonatan Ottino-González^{1

2}, Renata B Cupertino³, Zhipeng Cao², Sage Hahn², Devarshi Pancholi², Matthew D Albaugh², Ty Brumback⁴, Fiona C Baker⁵, Sandra A Brown⁶, Duncan B Clark⁷, Massimiliano de Zambotti⁵, David B Goldston⁸, Beatriz Luna⁷, Bonnie J Nagel⁹, Kate B Nooner¹⁰, Kilian M Pohl^{5

11}, Susan F Tapert¹², Wesley K Thompson¹³, Terry L Jernigan¹⁴, Patricia Conrod¹⁵, Scott Mackey², Hugh Garavan²

Affiliations

¹ Division of Endocrinology, The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, USA.
² Department of Psychiatry, University of Vermont Larner College of Medicine, Burlington, VT, USA.
³ Department of Genetics, University of California San Diego, San Diego, CA, USA.
⁴ Department of Psychological Science, Northern Kentucky University, Highland Heights, KY, USA.
⁵ Center for Health Sciences, SRI International, Menlo Park, CA, USA.
⁶ Departments of Psychology and Psychiatry, University of California, San Diego, La Jolla, CA, USA.
⁷ Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
⁸ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
⁹ Departments of Psychiatry and Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA.
¹⁰ Department of Psychology, University of North Carolina Wilmington, Wilmington, NC, USA.
¹¹ Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
¹² Department of Psychiatry, University of California San Diego, San Diego, CA, USA.
¹³ Department of Radiology, University of California San Diego, San Diego, CA, USA.
¹⁴ Center for Human Development, University of California, San Diego, CA, USA.
¹⁵ Department of Psychiatry, Université de Montreal, CHU Ste Justine Hospital, Montreal, Québec, Canada.

Abstract

Background and aims: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

Design and setting: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).

Cases: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

Measurements: Graph theory metrics of segregation and integration were used to summarize SCN.

Findings: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

Conclusion: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Keywords: Alcohol; cortical thickness; early marker; graph theory; neurodevelopment; structural covariance networks.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Adolescent
Adult
Alcoholism* / pathology
Brain / pathology
Child
Connectome* / methods
Cross-Sectional Studies
Humans
Magnetic Resonance Imaging / methods
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding