Resting state fMRI yields mechanisms underlying chronic pain and depression in Gulf War Illness
Poster No:
241
Submission Type:
Abstract Submission
Authors:
Guangming Yang1, Carly Ragin1, Gabriell Champion1, Anna Ree1, Thomas Novak1, Chanse Denmon1, Keith McGregor2, Ying Guo1, Anna Woodbury1, Sheila Rauch1, Albert Leung3, Kaundinya Gopinath1
Institutions:
1Emory University, Atlanta, GA, 2University of Alabama at Birmingham, Birmingham, AL, 3UC San Digo, School of Medicine, La Jolla, CA
First Author:
Co-Author(s):
Introduction:
Around 200,000 veterans (up to 32% of those deployed) of the 1991 Gulf War (GW) suffer from GW illness (GWI). GWI is a poorly understood chronic medical condition, characterized by multiple symptoms indicative of brain function deficits in multiple functional domains [1-3]. Among the symptoms of brain impairment chronic pain including headaches, body muscle and joint pain conditions (GWI-HAP) are the most debilitating, affecting around 64% of the GWI veterans. Further, depression carries a very high co-morbid rate (>50%) in patients with chronic pain, including GWI-HAP. In this preliminary study, we explored brain mechanisms underlying GWI-HAP with and without depression through resting state fMRI (rsfMRI) by comparing them with an age-matched cohort of healthy controls. We set to achieve this by employing a combination of connectomics and machine learning methods to extract neuroimaging biomarkers of these disease groups from rsfMRI data.
Methods:
Thirty-five GWI-HAP veterans (mean age 49.4 yrs.) were examined out of which eighteen had depression (Hamilton rating scale for depression (HRSD) ≥ 14). These veterans along with 22 age-matched healthy controls (HC) were scanned in a Siemens 3T MRI Prisma-Fit scanner. Resting fMRI data were acquired with a 10-min whole-brain multiband gradient echo EPI (TR/TE/FA = 2200/27ms/80°, resolution = 2mm x 2mm x 2mm) sequence. The preprocessed rsfMRI data for each subject was parcellated based on the Brainnetome atlas [4] to construct a 274-node graph formed by Pearson correlation between different Brainnetome ROI-averaged time-series. Ordinal and binary support vector machine learning [5] were employed to classify the veterans into GWI-HAP (HRSD ≥ 14), GWI-HAP (HRSD ≤ 14) or HC. The generalizability of the SVM classifications were tested with 5-fold cross-validation [5].
Results:
The SVM classifications were able to predict the participants as belonging to GWI-HAP (HRSD ≥ 14), GWI-HAP (HRSD ≤ 14) or HC with 100% accuracy. The SVM algorithm provided importance scores (SVMimp) to each edge in the 274-node Brainnetome graphs based on its contribution to the classification. The hubness of each node was determined by normalized sum of SVMimp scores of all its edges. Fig.1 shows the between-group FC t-test maps of selected hubs (among top 5 most impaired) of each binary SVM classifier. Fig.2 tabulates the top 10% of the edges of two other selected hubs of each binary SVM classifier. GWI-HAP veterans with depression exhibited impaired FC compared to healthy controls between amygdala and cognitive control areas consistent with affective processing deficits [6] . Both GWI-HAP (HRSD ≥ 14) and GWI-HAP (HRSD ≤ 14) exhibited abnormally high FC compared to HC between pain processing and pain evaluation areas consistent with enhanced expression of chronic pain. For example, GWI-HAP (HRSD ≥ 14) exhibited increased FC compared to HC between dorsal anterior cingulate cortex, insula and cerebellar vermis [7, 8]; and GWI-HAP (HRSD ≤ 14) exhibited increased ventromedial prefrontal cortex FC with insula and cerebellar lobules VIIIa and VIIIb [7, 9]. Relatedly, cerebellum exhibited functional impairments in both GWI-HAP groups, which is consistent with our previous GWI studies [7]. Cerebellum is innervated with cholinergic synapses and highly susceptible to cholinergic neurotoxic nerve agents which are implicated in the etiology of GWI [10].
·Fig.1: Between-group FC t-test maps of selected Brainnetome hub nodes of corresponding SVM classifiers. Only edges in the top-tenth percentile SVMimp scores are shown.
·Fig2: Connectome (FC matrix) edges in the top tenth percentile of SVMimp scores of selected hub nodes for each of the 3 between-group classifiers. The left column indicates which group has higher FC
Conclusions:
The results of this preliminary analysis implicate impairments in cognitive control of emotion and nociception as mechanisms underlying the enhanced chronic pain and depression observed in GWVI-HAP veterans. A fuller picture of deficits in FC in brain function networks is expected to emerge as more GWI-HAP subjects of both groups are examined in this ongoing project. Better understanding of impairments in these networks in GWI-HAP will benefit the rehabilitation of veterans with GWI-HAP.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Classification and Predictive Modeling
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Keywords:
Affective Disorders
Cerebellum
Degenerative Disease
DISORDERS
FUNCTIONAL MRI
Machine Learning
Neurological
Pain
Psychiatric Disorders
Toxins
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Provide references using APA citation style.
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