Poster No:
407
Submission Type:
Abstract Submission
Authors:
Stephanie Pedrahita1, Annika Linke2, Michaela Cordova2, Molly Wilkinson2, Janice Hau2, Gioia Toro2, Kalekirstos Alemu2, Jiwandeep Kohli2, Ralph-Axel Mueller2, Ruth Carper2
Institutions:
1San Diego State University, San diego, CA, 2Brain Development Imaging Labs, Department of Psychology, San Diego State University, San Diego, CA
First Author:
Co-Author(s):
Annika Linke
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Michaela Cordova, MS
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Molly Wilkinson
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Janice Hau
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Gioia Toro
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Kalekirstos Alemu
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Jiwandeep Kohli
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Ralph-Axel Mueller
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Ruth Carper, PhD
Brain Development Imaging Labs, Department of Psychology, San Diego State University
San Diego, CA
Introduction:
Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder. Preliminary evidence suggests an increased risk for accelerated or early-onset cognitive and neurological decline5,8,9. While it is well established that brain development in children, adolescents and young adults with ASD diverges from neurotypical (NT) peers, it is unknown how brain function is impacted in older adults with ASD and what consequences this may have for cognition and behavioral abilities. Better understanding of age-related changes of brain function in ASD is crucial to establish best practices for cognitive and health screenings in adults with ASD and develop preventions that might reduce the risk of accelerated decline. Decreases in blood-oxygenation-level-dependent (BOLD) signal variability in typical aging have been shown across multiple studies2,3,10, likely reflecting declining Gamma-Aminobutyric Acid (GABA) activity4,7, and are associated with poorer cognitive performance1,3. We hypothesized that adults with ASD would show reduced BOLD signal variability compared to the NT group with steeper negative age associations in the ASD than NT group, potentially reflecting accelerated aging in this cross-sectional sample.
Methods:
This study assessed BOLD signal variability in a cohort of adults (40-70 years), 26 with ASD and 37 age-matched typical controls, who participated in a multimodal longitudinal study of aging in ASD. All participants completed two eyes-open 6-minute resting-state fMRI scans acquired on a 3T GE MRI using a fast multiband EPI sequence (TR=0.8s, 2mm iso. voxel size). There were no significant differences between the ASD and NT groups on age, gender, non-verbal IQ, body mass index, co-occurring hypertension, or head motion (RMSD) during the scan. fMRI data underwent standard pre-processing using SPM12 and the CONN toolbox, including rigid-body realignment, normalization to the MNI template, bandpass filtering, and nuisance regression to remove physiological and motion confounds. Average BOLD signal time series were extracted from the Harvard-Oxford anatomical parcellation and BOLD signal variability calculated as the standard deviation of the timeseries for each region of interest (ROI). ROIs were those identified by Lalwani et al.4 to show significant age-related reductions in BOLD signal variability and included frontal, temporal, parietal, occipital and insular cortical areas. General linear models tested for main effects of diagnostic group (ASD, NT), age and group-by-age interactions (controlling for RMSD) in each region. Multiple-comparison corrected statistical significance was defined as Benjamini-Hochberg FDR-adjusted p<0.1.
Results:
For all ROIs, BOLD signal variability decreased with age across groups (Figure 1A) with right insular cortex and bilateral IFG showing significant age effects when adjusted for multiple comparisons. Significant group-by-age interactions were observed for right insular, left temporal occipital fusiform and right inferior lateral occipital cortex (Figure 1B) with BOLD signal variability showing strong negative associations with age in the ASD but not NT group (Figure 1C).
Conclusions:
The only previous study assessing BOLD signal variability in ASD was conducted in children and adolescents and found no significant group differences6. When examining older adults with ASD we found cross-sectional age-related changes in BOLD signal variability. Together, these two findings may indicate that decreased BOLD signal variability arises only later in adulthood in ASD, potentially as a result of accelerated aging. However, given limited prior research and evidence from postmortem and animal studies as well as MRI spectroscopy of altered GABA activity across the lifespan in ASD, additional longitudinal analyses will be necessary to determine if the results presented truly reflect accelerated aging or arise from lifelong persistent differences in brain function.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Other Methods
Keywords:
ADULTS
Aging
Autism
FUNCTIONAL MRI
1|2Indicates the priority used for review
Provide references using author date format
1. Burzynska, A. Z., Wong, C. N., Voss, M. W., Cooke, G. E., McAuley, E., & Kramer, A. F. (2015). White matter integrity supports BOLD signal variability and cognitive performance in the aging human brain. PloS one, 10(4), e0120315.
2. Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. The Journal of neuroscience : the official journal of the Society for Neuroscience, 31(12), 4496–4503.
3. Grady, C. L., & Garrett, D. D. (2018). Brain signal variability is modulated as a function of internal and external demand in younger and older adults. NeuroImage, 169, 510–523.
4. Lalwani, P., Garrett, D. D., & Polk, T. A. (2021). Dynamic Recovery: GABA Agonism Restores Neural Variability in Older, Poorer Performing Adults. The Journal of neuroscience : the official journal of the Society for Neuroscience, 41(45), 9350–9360.
5. Mason, D., Ronald, A., Ambler, A., Caspi, A., Houts, R., Poulton, R., Ramrakha, S., Wertz, J., Moffitt, T. E., & Happé, F. (2021). Autistic traits are associated with faster pace of aging: Evidence from the Dunedin study at age 45. Autism Research, 14(8), 1684–1694.
6. Nomi, J. S., Bolt, T. S., Ezie, C. E. C., Uddin, L. Q., & Heller, A. S. (2017). Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan. The Journal of neuroscience : the official journal of the Society for Neuroscience, 37(22), 5539–5548.
7. Shew, W. L., Yang, H., Yu, S., Roy, R., & Plenz, D. (2011). Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. The Journal of neuroscience : the official journal of the Society for Neuroscience, 31(1), 55–63.
8. Torres, E. B., Caballero, C., & Mistry, S. (2020). Aging with Autism Departs Greatly from Typical Aging. Sensors, 20(2), 572. MDPI AG.
9. Walsh, M. J., Ofori, E., Pagni, B. A., Chen, K., Sullivan, G., & Braden, B. B. (2022). Preliminary findings of accelerated visual memory decline and baseline brain correlates in middle-age and older adults with autism: The case for hippocampal free-water. Frontiers in Aging Neuroscience, 14, 1029166.
10. Waschke, L., Kloosterman, N. A., Obleser, J., & Garrett, D. D. (2021). Behavior needs neural variability. Neuron, 109(5), 751–766.