Mapping the spatial organization of depression structural MRI correlates in multiple datasets
Poster No:
401
Submission Type:
Abstract Submission
Authors:
Kassandra Hamilton1, Xiaoke Luo1, Ty Easley2, Fyzeen Ahmad3, Thomas Guo1, Setthanan Jarukasemkit4, Hailey Modi1, Samuel Naranjo Rincon5, Cabria Shelton1, Lyn Stahl1, Zijian Wang1, Yuling Zhu1, Petra Lenzini4, Kayla Hannon6, Janine Bijsterbosch4
Institutions:
1Washington University in St. Louis, St. Louis, MO, 2Washington University, St Louis, St Louis, MO, 3University of Minnesota, Minneapolis, MN, 4Washington University in St Louis, St Louis, MO, 5Washington University St. Louis, St. Louis, MO, 6Washington University in St Louis, St. Louis, MO
First Author:
Co-Author(s):
Introduction:
Despite substantial research, efforts to establish the neural correlates of depression are plagued by inconsistencies. This study leverages multiple large-scale datasets (Nper study =185-11,000) to comprehensively establish neuroimaging correlates of depression. In particular, we focus on two key questions: 1) How are multivariate neuroimaging correlates of depression distributed across the brain, and 2) How do study design differences impact the observed neuroimaging correlates of depression.
Methods:
Datasets: This study leverages 6 datasets: Adolescent Brain Cognitive Development (Casey et al., 2018), UK Biobank (Miller et al., 2016), Human Connectome Project Young Adult (Van Essen et al., 2013), HCP Developmental, HCP Aging (Harms et al., 2018), and Anxious misery Connectomes Related to Human Disease (Seok et al., 2021).
Variables: In each dataset, we identified depression-related phenotypes including personality-based proxy measures and depression severity measures. For T1-weighted structural MRI variables, we used the Desikan-Killiany-Tourville (DKT) atlas to extract cortical thickness, area, and volume measures and the automatic subcortical segmentation (ASEG) to extract subcortical volume measures.
Spatial mapping: For each parcel in DKT we calculated the proportion of parcel vertices overlapping with each of the seven Yeo networks (Yeo et al., 2011). Parcels were assigned to the Yeo network with the largest proportion overlap. ASEG regions were assigned to an eighth subcortical 'network'.
Analysis: Univariate linear mixed-effect models were fit separately for each combination of depression phenotype and imaging measure within each dataset (Fig. 1). All analyses were controlled for age, sex, head motion, and intracranial volume (fixed effects), and for site and family group (random effects).
Comparisons: The effect sizes (betas) for the depression phenotype resulting from all models were the basis of our comparison analyses. To test the spatial distribution of depression correlates, we performed an ANOVA on the effect sizes with a main effect for networks and a main effect for dataset. To assess the impact of study sample (and potential lifespan effects), we leveraged the main effect for dataset from the prior 2-way ANOVA. To assess the impact of depression phenotype, we performed separate 1-way ANOVAs for each dataset with a factor for depression phenotype.
Variables: In each dataset, we identified depression-related phenotypes including personality-based proxy measures and depression severity measures. For T1-weighted structural MRI variables, we used the Desikan-Killiany-Tourville (DKT) atlas to extract cortical thickness, area, and volume measures and the automatic subcortical segmentation (ASEG) to extract subcortical volume measures.
Spatial mapping: For each parcel in DKT we calculated the proportion of parcel vertices overlapping with each of the seven Yeo networks (Yeo et al., 2011). Parcels were assigned to the Yeo network with the largest proportion overlap. ASEG regions were assigned to an eighth subcortical 'network'.
Analysis: Univariate linear mixed-effect models were fit separately for each combination of depression phenotype and imaging measure within each dataset (Fig. 1). All analyses were controlled for age, sex, head motion, and intracranial volume (fixed effects), and for site and family group (random effects).
Comparisons: The effect sizes (betas) for the depression phenotype resulting from all models were the basis of our comparison analyses. To test the spatial distribution of depression correlates, we performed an ANOVA on the effect sizes with a main effect for networks and a main effect for dataset. To assess the impact of study sample (and potential lifespan effects), we leveraged the main effect for dataset from the prior 2-way ANOVA. To assess the impact of depression phenotype, we performed separate 1-way ANOVAs for each dataset with a factor for depression phenotype.
Results:
Brain regions with relatively strong associations between structural MRI features and depression were observed across all 8 networks. Surprisingly, depression correlates were equally prevalent in early sensorimotor and visual networks as compared to higher-order cognitive networks such as default mode and salience networks (Fig. 2A). This finding suggests an often overlooked role of primary networks in depression. A significant main effect of depression phenotype was observed in all datasets (p<.005), with larger effect sizes in depression-severity measures than personality-proxy measures (Fig. 2B). Furthermore, there was a main effect of dataset (F=136.74, p<.005; Fig. 2C), such that datasets with larger sample sizes resulted in smaller, less variable effect sizes. This finding is consistent with prior work (Marek et al., 2022), and points to the challenge of sampling variability in underpowered studies. Together, these results may explain the observed differences in neuroimaging correlates across previous studies.
Conclusions:
The lack of significant difference in effect size across cortical networks suggests the presence of a more diffuse pattern of depression correlates spread throughout the brain, with notable deviations in the subcortical areas. Study design factors potentially drive inconsistency in findings regarding the neural correlates of depression, underscoring the importance for future work to consider these factors more deliberately and also the importance of study replication.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Univariate Modeling 2
Keywords:
Affective Disorders
Data analysis
MRI
Psychiatric Disorders
STRUCTURAL MRI
1|2Indicates the priority used for review
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