Connectomic Abnormalities in Co-morbid MDD and AUD Elucidated via SDD in the HCP-YA Dataset
Poster No:
1196
Submission Type:
Abstract Submission
Authors:
Braeden Rodriguez1, Liang Zhan2, Alex Leow3, Olusola Ajilore4, Paul Thomas1
Institutions:
1University of Illinois at Chicago, Chicago, IL, 2University of Pittsburgh, Pittsburgh, PA, 3University of Illinois Chicago, Chicago, IL, 4University of Illinois - Chicago, Chicago, IL
First Author:
Co-Author(s):
Introduction:
When comorbid, Major Depressive Disorder (MDD) and Alcohol Use Disorder (AUD) produce a dangerous mutually reinforcing cycle. With lifetime comorbidity rates as high as 21%, these mutually deleterious disorders constitute a major public health concern (Lindgren, 2019)(Boschloo, 2011).
The negative urgency (NU) model of addiction suggests that negative emotions catalyze impulsivity, promoting maladaptive coping strategies like alcohol consumption (Zorilla, 2019). As MDD is characterized by chronic affective distress and diminished executive function, it is an ideal disorder to study NU (Cyders, 2015).
Our goal is to identify NU-consistent connectivity changes within the Human Connectome Project (HCP) dataset. We hypothesize that subjects in this study with both disorders will exhibit interoceptive, cognitive control, and affective deficits, and corresponding alterations in insular, amygdalar, and OFC connectivity. Subjects in the 'Both' group will exhibit: higher somatic distress and insular hyperconnectivity; higher externalizing scores and OFC dysconnectivity; greater anxiety with higher amygdalar connectivity and centrality.
The negative urgency (NU) model of addiction suggests that negative emotions catalyze impulsivity, promoting maladaptive coping strategies like alcohol consumption (Zorilla, 2019). As MDD is characterized by chronic affective distress and diminished executive function, it is an ideal disorder to study NU (Cyders, 2015).
Our goal is to identify NU-consistent connectivity changes within the Human Connectome Project (HCP) dataset. We hypothesize that subjects in this study with both disorders will exhibit interoceptive, cognitive control, and affective deficits, and corresponding alterations in insular, amygdalar, and OFC connectivity. Subjects in the 'Both' group will exhibit: higher somatic distress and insular hyperconnectivity; higher externalizing scores and OFC dysconnectivity; greater anxiety with higher amygdalar connectivity and centrality.
Methods:
MDD subjects were defined as HCP subjects with at least one prior depressive episode (Langeneker, 2018). AUD subjects were defined based on DSM-IV diagnosis of alcohol abuse or dependence. A total of 32 subjects met both conditions. Propensity Score Matching selected similar single-diagnosis and control subjects, creating four groups: Both, MDD, AUD, Control (n=128).
Network reconstruction was performed via CONN and FSL probtrackx for fMRI and DTI imaging, followed by Freesurfer parcellation to generate 82 ROI functional (FC) and structural (SC) connectivity matrices for each subject. These were used to produce Heat Kernel (HK) and Structural Diffusion Distance (SDD) connectomes, which estimate efficiency of information flow (Chung, 2016)(Thomas, 2022).
Groupwise differences with respect to connectomic properties will be identified via 2x2 ANOVA. Chosen metrics will be: local (ROI-specific) and global (whole-brain) mean edge strengths, local betweenness and eigenvector centrality. Linear mixed effect modeling (LMM) will elucidate relationships between psychiatric variables (anxiety, somatic distress, externalizing symptoms) and ROI-specific connectivity.
Network reconstruction was performed via CONN and FSL probtrackx for fMRI and DTI imaging, followed by Freesurfer parcellation to generate 82 ROI functional (FC) and structural (SC) connectivity matrices for each subject. These were used to produce Heat Kernel (HK) and Structural Diffusion Distance (SDD) connectomes, which estimate efficiency of information flow (Chung, 2016)(Thomas, 2022).
Groupwise differences with respect to connectomic properties will be identified via 2x2 ANOVA. Chosen metrics will be: local (ROI-specific) and global (whole-brain) mean edge strengths, local betweenness and eigenvector centrality. Linear mixed effect modeling (LMM) will elucidate relationships between psychiatric variables (anxiety, somatic distress, externalizing symptoms) and ROI-specific connectivity.
Results:
For all imaging modalities, significant main effects of MDD and AUD on global mean edge strength were detected by 2x2 ANOVA. There was also a significant effect of interaction (Fig. 1a). Post-hoc Tukey HSD showed that MDD subjects had significantly less global connectivity in all imaging modalities relative to other groups (p<0.001, BH corrected) (Fig 1b).
Significant main effects of MDD and AUD, as well as significant interaction effects, on seed ROI SDD were detected via 2x2 ANOVA (Fig. 2a). Post-hoc Tukey HSD showed that the Both group had significantly lower bilateral amygdalar, insular, and OFC SDD relative to other groups (Fig. 2b).
No significant effects were seen with respect to behavioral or topological variables.
Significant main effects of MDD and AUD, as well as significant interaction effects, on seed ROI SDD were detected via 2x2 ANOVA (Fig. 2a). Post-hoc Tukey HSD showed that the Both group had significantly lower bilateral amygdalar, insular, and OFC SDD relative to other groups (Fig. 2b).
No significant effects were seen with respect to behavioral or topological variables.
·Figure 1. Results of global edge strength comparison. Global mean edge strength was compared between groups via 2x2 ANOVA. F and p-values are reported in A; normalized mean values are reported in B.
·Figure 2. Local edge strength comparison. Edge strength at seed ROIs was compared between groups via 2x2 ANOVA. F and p-values are reported in A, while normalized ROI means are shown in B.
Conclusions:
Our global analyses confirmed that MDD evokes global hypoconnectivity. Significant interaction effects indicate that combined presentation has emergent properties which warrant further study.
SDD was the best tool at finding ROI-level differences, supporting its predictive utility. Significant reductions in insular and amygdalar SDD is in line with our hypotheses, implying greater influence of these ROIs.
Reduced OFC SDD contradicted our hypothesis, as was the lack of behavioral and topological differences.
The 82 ROI parcellation used limits our ability to see activity in functionally heterogeneous brain regions such as the insula (Naqvi, 2014). We are also limited by psychiatric definitions; another clinical phenotype may be a better candidate for NU research.
Future efforts include use of the Network Based Statistic (Zalesky, 2010) to identify altered clusters in an unbiased fashion, and investigation of temporal properties.
SDD was the best tool at finding ROI-level differences, supporting its predictive utility. Significant reductions in insular and amygdalar SDD is in line with our hypotheses, implying greater influence of these ROIs.
Reduced OFC SDD contradicted our hypothesis, as was the lack of behavioral and topological differences.
The 82 ROI parcellation used limits our ability to see activity in functionally heterogeneous brain regions such as the insula (Naqvi, 2014). We are also limited by psychiatric definitions; another clinical phenotype may be a better candidate for NU research.
Future efforts include use of the Network Based Statistic (Zalesky, 2010) to identify altered clusters in an unbiased fashion, and investigation of temporal properties.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Learning and Memory:
Learning and Memory Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Task-Independent and Resting-State Analysis
Keywords:
Addictions
Affective Disorders
Anxiety
DISORDERS
FUNCTIONAL MRI
Machine Learning
Open Data
Psychiatric Disorders
Systems
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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