Immune Factors Affect Reward Network Graph Theory Metrics in Adolescents with Psychiatric Symptoms
Poster No:
494
Submission Type:
Abstract Submission
Authors:
Benjamin Ely1, Tram Nguyen2, Manishkumar Patel3, Seunghee Kim-Schulze3, Vilma Gabbay4
Institutions:
1University of Miami, Miami, NY, 2Albert Einstein College of Medicine, Bronx, NY, 3Icahn School of Medicine at Mount Sinai, New York, NY, 4University of Miami, Miami, FL
First Author:
Co-Author(s):
Introduction:
Adolescence a critical period in brain development marked by rapid maturation of cortical areas important to self-control and emotion regulation. Symptoms of mood and anxiety disorders also frequently emerge in adolescence, suggesting disruptions in maturing brain networks contribute to mental illness (Walker 2017). Although the biological mechanisms driving or preventing symptom onset in adolescence are poorly understood, our group (Bradley 2019, Freed 2019) and others (Byrne 2015) have consistently found elevated immune and inflammatory biomarkers in depressed adolescents. Building on this work, we examined associations between immune factors, derived from detailed assays of blood samples, and reward network properties, derived using graph theory, in adolescents with diverse psychiatric symptoms.
Methods:
We recruited 122 adolescents (age=15.0±2.2, 78 female), including 23 healthy controls and 99 with primarily mood and anxiety symptoms. Diagnoses were confirmed by clinician via semi-structured interview. All subjects completed questionnaires to assess depression (BDI), anxiety (MASC), anhedonia (TEPS), and suicidality (BSSI) severity.
MRI was collected on a 3T Siemens Skyra with sequences similar to Human Connectome Project (HCP) Lifespan (Harms 2018), including 10min of resting-state fMRI (2.3mm isotropic, TR=1s, 5x multiband). Data were preprocessed via HCP pipelines, including MSMAll cortical alignment (Robinson 2014); denoised using ICA-FIX, CompCor, and bandpass (0.1-0.01Hz); and parcellated into 750 nodes encompassing the entire 32k CIFTI cortex and subcortex (Ji 2019). Reward Anticipation, Reward Attainment, and Reward Prediction Error (RPE) network masks were generated based on a separate task fMRI study in the same cohort (Ely 2021). Graph theory metrics of Strength Centrality (Cstr), Eigenvector Centrality (Ceig), and Local Efficiency (Eloc) were computed via Brain Connectivity Toolbox.
Immune activity was measured under three conditions. Olink multiplex assays measured 68 circulating biomarkers. Whole-blood samples were cultured for 6hr on standard growth medium or with 0.1µg/mL lipopolysaccharide (LPS) to stimulate immune response, after which 41 analytes were measured via Luminex Milliplex florescence assays. Median fluorescence intensity was measured twice for each sample and averaged. Exploratory factor analysis with varimax rotation identified four primary immune factors under each condition.
Correlations were calculated between graph theory metrics and immune factors, controlled for age and sex, using non-parametric permutation tests (pFWE<0.05) in FSL PALM.
MRI was collected on a 3T Siemens Skyra with sequences similar to Human Connectome Project (HCP) Lifespan (Harms 2018), including 10min of resting-state fMRI (2.3mm isotropic, TR=1s, 5x multiband). Data were preprocessed via HCP pipelines, including MSMAll cortical alignment (Robinson 2014); denoised using ICA-FIX, CompCor, and bandpass (0.1-0.01Hz); and parcellated into 750 nodes encompassing the entire 32k CIFTI cortex and subcortex (Ji 2019). Reward Anticipation, Reward Attainment, and Reward Prediction Error (RPE) network masks were generated based on a separate task fMRI study in the same cohort (Ely 2021). Graph theory metrics of Strength Centrality (Cstr), Eigenvector Centrality (Ceig), and Local Efficiency (Eloc) were computed via Brain Connectivity Toolbox.
Immune activity was measured under three conditions. Olink multiplex assays measured 68 circulating biomarkers. Whole-blood samples were cultured for 6hr on standard growth medium or with 0.1µg/mL lipopolysaccharide (LPS) to stimulate immune response, after which 41 analytes were measured via Luminex Milliplex florescence assays. Median fluorescence intensity was measured twice for each sample and averaged. Exploratory factor analysis with varimax rotation identified four primary immune factors under each condition.
Correlations were calculated between graph theory metrics and immune factors, controlled for age and sex, using non-parametric permutation tests (pFWE<0.05) in FSL PALM.
Results:
Figure 1 shows associations between circulating biomarker factor 4 and ELoc within the Reward Anticipation network, including nodes within the left nucleus accumbens and medial thalamus (blue-green voxels, circled).
Figure 2 shows associations between factor 3 of the medium-cultured biomarkers and CStr within the Reward Prediction Error network, including multiple cortical nodes within the dorsolateral prefrontal and lateral parietal cortices (blue-green borders, circled). Similar findings were observed using LPS-cultured biomarker factors as well as using the ELoc within the same network, with the latter also identifying a large node in the left thalamus.
Figure 2 shows associations between factor 3 of the medium-cultured biomarkers and CStr within the Reward Prediction Error network, including multiple cortical nodes within the dorsolateral prefrontal and lateral parietal cortices (blue-green borders, circled). Similar findings were observed using LPS-cultured biomarker factors as well as using the ELoc within the same network, with the latter also identifying a large node in the left thalamus.
Conclusions:
Both circulating and cultured immune biomarkers were associated with altered resting-state properties within task-derived reward networks, including the ventral striatum, thalamus, dorsolateral prefrontal cortex, and lateral parietal cortex. These findings suggest inflammatory processes early in the course of mental illness may interfere with the function and development of reward networks. Follow-up analyses will examine the relationship between immune factors, reward network properties, and clinical symptomatology using multiple regression techniques.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Reward and Punishment 2
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other
Keywords:
Affective Disorders
Blood
FUNCTIONAL MRI
Infections
Multivariate
PEDIATRIC
Psychiatric
Other - Immune
1|2Indicates the priority used for review
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Provide references using APA citation style.
Byrne ML et al. Adolescent-Onset Depression: Are Obesity and Inflammation Developmental Mechanisms or Outcomes? Child Psychiatry Hum Dev 2015, 46(6):839-850.
Ely BA et al. Data-Driven Parcellation and Graph Theory Analyses to Study Adolescent Mood and Anxiety Symptoms. Trans Psychiatry 2021, 11(1):266.
Freed RD et al. Anhedonia as a Clinical Correlate of Inflammation in Adolescents Across Psychiatric Conditions. World J Biol Psychiatry 2019, 20(9):712-722.
Harms MP et al. Extending the Human Connectome Project Across Ages: Imaging Protocols for the Lifespan Development and Aging Projects. NeuroImage 2018, 183:972-984.
Ji JL et al. Mapping the Human Brain's Cortical-Subcortical Functional Network Organization. NeuroImage 2019, 185:35-57.
Robinson EC et al. MSM: a New Flexible Framework for Multimodal Surface Matching. NeuroImage 2014, 100:414-426.
Walker DM et al. Adolescence and Reward: Making Sense of Neural and Behavioral Changes Amid the Chaos. J Neurosci 2017, 37(45):10855-10866.