Brain signal variability and gene expression profiles underlying anxiety
Poster No:
522
Submission Type:
Abstract Submission
Authors:
Priyanka Jaipal Sigar1, Zachary T. Goodman2, Jason Nomi1, Katie Bessette1, Taylor Bolt1, Lucina Uddin1
Institutions:
1Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 2Department of Psychology, University of Miami, Coral Gables, FL
First Author:
Priyanka Jaipal Sigar
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Co-Author(s):
Jason Nomi
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Katie Bessette
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Taylor Bolt
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Lucina Uddin, Ph.D.
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA
Introduction:
Anxiety disorder (AD) is the most common mental health condition across all ages, typically emerging around age 11 (Krygsman & Vaillancourt, 2022; Kessler et al., 2005). About 32% of adolescents experience subclinical anxiety, often preceding AD in adulthood, with pediatric and adolescent anxiety strongly predicting adulthood anxiety (Bittner et al., 2007). Emerging research links brain signal variability (BSV) to neuropsychiatric disorders, but its relationship with anxiety symptoms across the lifespan remains uncharacterized despite its greater predictive power compared to conventional mean signal-based methods.
Understanding anxiety requires examining the interplay between genes, neural networks, and behavior, yet the link between gene expression and brain function across the lifespan remains unexplored. This cross-sectional study used neuroimaging data from the Enhanced Nathan Kline Institute Rockland Sample (NKI; Nooner et al., 2012; 312 individuals aged 8–85) and transcriptional data from the Allen Human Brain Atlas (AHBA; Hawrylycz et al., 2012; n = 6, aged 25–47) to explore transcriptional correlations with anxiety-related imaging phenotypes, aiming to uncover neurobiological mechanisms underlying anxiety across the lifespan.
Understanding anxiety requires examining the interplay between genes, neural networks, and behavior, yet the link between gene expression and brain function across the lifespan remains unexplored. This cross-sectional study used neuroimaging data from the Enhanced Nathan Kline Institute Rockland Sample (NKI; Nooner et al., 2012; 312 individuals aged 8–85) and transcriptional data from the Allen Human Brain Atlas (AHBA; Hawrylycz et al., 2012; n = 6, aged 25–47) to explore transcriptional correlations with anxiety-related imaging phenotypes, aiming to uncover neurobiological mechanisms underlying anxiety across the lifespan.
Methods:
Neuroimaging Analysis:
Resting-state fMRI data from 574 participants (60% female, aged 8–85 years) in the NKI Sample were analyzed. Inclusion criteria required neuroimaging and behavioral data availability and head motion < 0.5 mm. Anxiety levels were measured using the z-scored Multidimensional Anxiety Scale for Children (MASC) for participants under 18 and the z-scored State-Trait Anxiety Index (STAI-trait) for adults. Preprocessing included frame removal, despiking, realignment, normalization, smoothing (6 mm FWHM), ICA-FIX (Griffanti et al., 2014), and bandpass filtering (0.01–0.10 Hz). Voxel-wise root mean square of successive differences (rMSSD) maps were calculated using MATLAB scripts and analyzed with ordinary least squares (OLS) regression in FSL. Covariates included linear age, gender, and head motion, with quadratic age added in secondary analyses.
Transcriptomics Analysis:
Gene expression data from the AHBA comprising 20,737 genes across 3,702 brain tissue samples from six donors (aged 24–57) were processed using the Abagen Python toolbox (Markello et al., 2021) to map microarray probe locations to neuroimaging atlases. The pipeline included probe-to-gene verification, filtering, mapping to brain regions, and normalization. Samples were mirrored bilaterally, and missing voxels were interpolated. Spatial correlation analysis between AHBA gene expression and voxel-wise corrected MSSD maps (p > 0.001, uncorrected) was conducted, with significance evaluated using 10,000 permutation steps on BSV regional labels.
Resting-state fMRI data from 574 participants (60% female, aged 8–85 years) in the NKI Sample were analyzed. Inclusion criteria required neuroimaging and behavioral data availability and head motion < 0.5 mm. Anxiety levels were measured using the z-scored Multidimensional Anxiety Scale for Children (MASC) for participants under 18 and the z-scored State-Trait Anxiety Index (STAI-trait) for adults. Preprocessing included frame removal, despiking, realignment, normalization, smoothing (6 mm FWHM), ICA-FIX (Griffanti et al., 2014), and bandpass filtering (0.01–0.10 Hz). Voxel-wise root mean square of successive differences (rMSSD) maps were calculated using MATLAB scripts and analyzed with ordinary least squares (OLS) regression in FSL. Covariates included linear age, gender, and head motion, with quadratic age added in secondary analyses.
Transcriptomics Analysis:
Gene expression data from the AHBA comprising 20,737 genes across 3,702 brain tissue samples from six donors (aged 24–57) were processed using the Abagen Python toolbox (Markello et al., 2021) to map microarray probe locations to neuroimaging atlases. The pipeline included probe-to-gene verification, filtering, mapping to brain regions, and normalization. Samples were mirrored bilaterally, and missing voxels were interpolated. Spatial correlation analysis between AHBA gene expression and voxel-wise corrected MSSD maps (p > 0.001, uncorrected) was conducted, with significance evaluated using 10,000 permutation steps on BSV regional labels.
Results:
Our analysis revealed significant increases in BSV associated with higher anxiety levels. Controlling for linear age effects, increased BSV was observed in the precentral and postcentral gyri (z > 3.3, uncorrected, cluster p < 0.05), with top genes including SGK3, DNER, and SOWAHC (Fig. 1). For quadratic age effects, increased BSV in the cingulate, precentral, and postcentral gyri was linked to genes such as MAPK8, KIFI3A, and KLRC3 (Fig. 2). In older adults, BSV increases were observed in the same regions (z > 2.3, uncorrected, cluster p < 0.05), with strong correlations to SGK3 and DNER, while no significant results were found for younger individuals.
Conclusions:
This study explores the neurobiological mechanisms of anxiety by linking BSV and anxiety symptoms to gene expression. Significant BSV-anxiety associations were found in sensorimotor and cingulate-based networks, reflecting symptoms like muscle tension and emotional dysregulation (Li et al., 2019; Picó-Pérez et al., 2017). Genes involved in synapse formation, signaling, and ion transport (Ackermann et al., 2008; Ahmed et al., 2016; Hirokawa et al., 2009) likely modulate neuronal excitability and connectivity underlying anxiety.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Transcriptomics
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Anxiety
FUNCTIONAL MRI
Other - Lifespan; Brain Signal Variability; Subclinical anxiety; Transcriptomics;
1|2Indicates the priority used for review
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