Altered brain state dynamics in children with a family history of substance use disorder vary by sex
Poster No:
435
Submission Type:
Abstract Submission
Authors:
Louisa Schilling1, Parker Singleton2, Ceren Tozlu3, Keith Jamison1, Amy Kuceyeski1
Institutions:
1Weill Cornell Medicine, New York City, NY, 2Weill Cornell Medicine, New York, NY, 3Weill Cornell Medicine, NYC, NY
First Author:
Co-Author(s):
Introduction:
Identifying factors that elevate an individual's risk of substance use disorder (SUD) is vital to public health. An individual's risk of SUD is shaped by a complex interplay of biosocial factors, with genetics being a particularly potent factor. As such, family history of SUD is a strong predictor of an individual's susceptibility (Bogdan et al., 2023). Current developmental models describe individual vulnerability to SUD as being due to an aberrant reward system, reduced inhibitory control, or a combination of these (Heitzeg et al., 2015). Yet, few studies have explored how family history affects brain function and structure prior to substance use. Herein, we used a network control theory approach (NCT) to quantify sex-specific differences in brain state dynamics in youth with (FHP) and without (FHN) a family history of SUD.
Methods:
We analyzed a subset of 1244 youth (675 females, aged 9-11) from the baseline visit of the Adolescent Brain Cognitive Development (ABCD) study who were scanned on a Siemens MRI (Casey et al., 2018). Parent-reported family history of SUD was used to categorize subjects as FHP (1+ parent and/or 2+ grandparents with SUD) or FHN (no parental nor grandparental SUD). We analyzed pre-processed rsfMRI and structural connectivity data – as described in Chen et al. (2022) – parcellated into a FreeSurfer-based atlas of 86 cortical and subcortical regions. Following previous work (Singleton et al., 2022 & Cornblath et al., 2020), we performed k-means clustering (k=4) of brain activity into recurring brain states. For all transitions between states, we calculated the transition probability (TP; i.e., likelihood of transition) and the NCT-derived transition energy (TE) required to drive the brain towards a given transition (Gu et al., 2015). All p-values were Benjamini–Hochberg (BH) corrected.
Results:
For each state (k=4), we calculated the cosine similarity of its high and low-amplitude activity to a priori resting-state networks (Figs 1A-B; Yeo et al., 2011). States were identified as 2 pairs of anti-correlated states: default mode (DMN+/-) and visual (VIS+/-). First, to assess whole-brain energetics, we averaged across all pairwise TEs to calculate a global TE for each subject. Two-sample t-tests revealed lower global TE in FHP males (M-FHP) compared to M-FHN (pFDR= 0.049; Fig 1C). In contrast, FHP females (F-FHP) exhibited a trend towards higher global TE compared to F-FHN, although not statistically significant (pFDR= 0.14). An ANOVA conducted on global TE, controlling for variables including sex, age, family history of SUD, motion (mean framewise displacement), study site, and sex*family history of SUD, revealed significant effects for sex (pFDR < 0.0001), motion (pFDR=0.04), study site (pFDR < 0.0001), and sex*family history of SUD (pFDR=0.013). For pairwise transitions, M-FHP individuals had significantly decreased TE for all transitions to DMN+/- (Fig 2A & F), lower TP from DMN- to DMN+, and increased TP from VIS+ to DMN+ (Fig 2C) compared to M-FHN. F-FHP youth had significantly increased TE when persisting in VIS+/-states (Figs 2B & E) but no significant differences in TP (Fig 2D) compared to F-FHN.
Conclusions:
The effect of family history of SUD on brain function and structure is modulated by biological sex. M-FHP youth exhibit lower TE, particularly when transitioning to or persisting in DMN. This suggests, given that transitions from VIS to DMN reflect bottom-up processing, there is a decreased energy barrier for bottom-up transitions in M-FHP. Indeed, M-FHP youth exhibit an increased probability of transitioning from VIS+ to DMN+. F-FHP, on the other hand, exhibit increased TE when persisting in VIS. This may reflect a higher energy barrier to persist in the VIS state and thus a tendency to shift bottom-up. Together, these results indicate a sex-specific mechanism by which FHP individuals may be biased towards bottom-up transitions and therefore more prone to heightened reward sensitivity and reduced inhibitory control.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Task-Independent and Resting-State Analysis
Keywords:
Addictions
Computational Neuroscience
Development
Dopamine
FUNCTIONAL MRI
PEDIATRIC
Pediatric Disorders
Psychiatric Disorders
Sexual Dimorphism
STRUCTURAL MRI
1|2Indicates the priority used for review
Provide references using author date format
2. Heitzeg, M. M., Cope, L. M., Martz, M. E. & Hardee, J. E. Neuroimaging Risk Markers for Substance Abuse: Recent Findings on Inhibitory Control and Reward System Functioning. Curr. Addict. Rep. 2, 91–103 (2015).
3. Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., ... & Dale, A. M. (2018). The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Developmental cognitive neuroscience, 32, 43-54.
4. Chen, J., Tam, A., Kebets, V., Orban, C., Ooi, L. Q. R., Asplund, C. L., ... & Yeo, B. T. (2022). Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nature communications, 13(1), 2217.
5. Singleton, S. P., Luppi, A. I., Carhart-Harris, R. L., Cruzat, J., Roseman, L., Nutt, D. J., ... & Kuceyeski, A. (2022). Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape. Nature communications, 13(1), 5812.
6. Cornblath, E. J., Ashourvan, A., Kim, J. Z., Betzel, R. F., Ciric, R., Adebimpe, A., ... & Bassett, D. S. (2020). Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands. Communications biology, 3(1), 261.
7. Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., ... & Bassett, D. S. (2015). Controllability of structural brain networks. Nature communications, 6(1), 8414.
8. Yeo, BT Thomas, et al. (2011). "The organization of the human cerebral cortex estimated by intrinsic functional connectivity." Journal of neurophysiology.