Neuroinflammation and Anhedonia: A Large-Scale DBSI Study of Reward-Processing Neurocircuits
Poster No:
408
Submission Type:
Abstract Submission
Authors:
Wei Zhang1, Aristeidis Sotiras1, Deanna Barch2, Janine Bijsterbosch3
Institutions:
1Washington University in St. Louis, Saint Louis, MO, 2Washington University, Saint Louis, MO, 3Washington University in St Louis, St Louis, MO
First Author:
Co-Author(s):
Introduction:
Anhedonia, a core symptom of major depressive disorder (MDD), is also a common feature of several other psychiatric conditions1. Previous research has linked anhedonia to peripheral inflammation markers, such as CRP2 and IL-63. Motivational deficits associated with anhedonia are closely tied to dysfunction within the cortico-striatal neurocircuit, which has been identified as particularly susceptible to the effects of inflammation4. Despite these findings, the relationship between anhedonia symptoms and neuroinflammation-the presence of inflammation within the brain-remains poorly understood. This study aims to bridge this gap by leveraging a large-scale imaging dataset and a well-established non-invasive technique to quantify neuroinflammation markers and investigate their association with anhedonia.
Methods:
We used neuroimaging data, demographic, and anhedonia symptom measures from 14,253 participants in the UK Biobank (Mage=63.4 [SD=7.46], n=7,451 female). Using Diffusion Basis Spectrum Imaging (DBSI), we derived neuroinflammation markers (restricted fraction [RF], hindered fraction [HF], and restricted and hindered apparent diffusion coefficients [RADC, HADC]) from DWI data for brain structures in the cortico-striatal circuit (Fig.1A). These DBSI-derived markers have previously been linked to recent depressive symptoms5, Alzheimer's Disease6, and COVID infection status7. In this study, we focused on the reward-processing neurocircuits, and defined regions of interest (ROIs) using the Destrieux cortical8 and CIT168 subcortical atlases9, resulting in 19 ROIs. For each participant, we calculated the average values of the four neuroinflammation markers within each ROI.
Anhedonia symptoms were assessed using a single item from the Recent Depressive Symptom Scale (RDS10), asking about loss of interest or pleasure over the past two weeks. Participants were grouped into two classes: those reporting no symptoms and those reporting moderate-to-high frequency of symptoms.
To examine the association between neuroinflammation markers and anhedonia, we used an extreme gradient boosting (XGBoost) model for classification. Neuroinflammation markers across 19 ROIs (76 features) were included, and the dataset, was split into training (80%) and testing (20%) sets and stratified by class to ensure proportional representation. Due to class imbalance (Fig.1B), we employed the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN)11 to oversample the minority class, which balanced sample sizes between classes, and refine the training data. A 10-fold cross-validation was applied during training to optimize the hyperparameters. Optimal parameters were selected based on the ROC AUC to fit the final training model on the entire training set. Classification was evaluated on the testing data, with performance assessed using a confusion matrix and ROC AUC metrics.
Anhedonia symptoms were assessed using a single item from the Recent Depressive Symptom Scale (RDS10), asking about loss of interest or pleasure over the past two weeks. Participants were grouped into two classes: those reporting no symptoms and those reporting moderate-to-high frequency of symptoms.
To examine the association between neuroinflammation markers and anhedonia, we used an extreme gradient boosting (XGBoost) model for classification. Neuroinflammation markers across 19 ROIs (76 features) were included, and the dataset, was split into training (80%) and testing (20%) sets and stratified by class to ensure proportional representation. Due to class imbalance (Fig.1B), we employed the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN)11 to oversample the minority class, which balanced sample sizes between classes, and refine the training data. A 10-fold cross-validation was applied during training to optimize the hyperparameters. Optimal parameters were selected based on the ROC AUC to fit the final training model on the entire training set. Classification was evaluated on the testing data, with performance assessed using a confusion matrix and ROC AUC metrics.
·Fig.1A/B
Results:
The XGBoost classification model achieved a test set accuracy of 73.4%, with precision of 97.6%, recall of 74.6%, and an F1 score of 84.6%. However, ROC-AUC for the classification model was notably low in the testing data (0.52), contrasting with the best ROC-AUC during training (0.99). Feature importance analysis on the testing data suggested that neuroinflammation markers in primarily subcortical regions as relatively important contributing features (Fig.2).
·Fig.2
Conclusions:
These preliminary findings provide tentative evidence for a null association between neuroinflammation markers and anhedonia. However, the model's predictive performance highlights the need for further investigation and validation. Planned analyses will integrate brain structural and functional measures to refine the classification model, and the final presentation is expected to include findings on whether combining neuroinflammation markers with multimodal brain features improves classification performance.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Multivariate Approaches
Keywords:
Other - Diffusion MRI, DBSI, neuroinflammation, anheodnia
1|2Indicates the priority used for review
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2. Lu S, et al. Increased plasma levels of IL-6 are associated with striatal structural atrophy in major depressive disorder patients with anhedonia. Front Psychiatry. 2022;13. doi:10.3389/fpsyt.2022.1016735
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