P115 Structural and Functional Brain Differences in Autistic Aging Using Graph Theoretic Analysis
Dominique Hughes*1, B. Blair Braden2, Sharon Crook1
1School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, United States of America 2College of Health Solutions, Arizona State University, Tempe, Arizona, United States of America
*Email: dhughe13@asu.edu
Introduction
Recent research indicates that people with autism (ASD) have increased risk for early-onset dementia and other neurodegenerative diseases [1,2,3]. Prior research has found brain differences related to age between ASD and neurotypical (NT) populations, but the ways these differences contribute to increased risk during aging remain unclear [4,5,6]. Our work employs graph theory to analyze structural and functional brain scans from ASD and NT individuals. We use linear regression to identify brain graph measures where age by diagnosis interaction (ADI) is a significant factor in determining graph measure values.
Methods We obtained T1, diffusion and functional MRI scans from 96 individuals aged 40-75, (n=48 ASD, mean age = 56.4, n=48 NT, mean age = 57.3). The TVB-UKBB and CONN data processing pipelines extract white matter tract weights and functional connectivity values, respectively, between regions listed in the Regional Map 96 brain parcellation [7,8,9]. We conduct 50% consensus thresholding to remove spurious weights. Strength values are found using the Brain Connectivity Toolbox on the structural and functional connectivity matrices [10]. We conduct linear regression to determine if age by diagnosis interaction is a significant predictor of the strength values. Results For the structural graphs, ADI was a significant predictor (p<0.01) for strength values for areas of the right and left prefrontal cortex. For the functional graphs, ADI was a significant predictor for strength values for areas of the right prefrontal, parahippocampal, auditory, sensory, and premotor cortices, and the left prefrontal, gustatory and visual cortices. Discussion ADI significantly predicted functional strengths over a range of cortices, while structural measures were more selective and varied. Strength values quantifying the prefrontal cortex particularly are significantly predicted by ADI in both structural and functional graph measures. The difference between functional and structural results demonstrate the complexity of identifying ASD specific aging trajectories. To better understand how these measures may affect increased cognitive decline in ASD, future work will analyze the relationship between these graph measures and cognition measures recorded from the same 96 individuals.
Acknowledgements We would like to acknowledge funding sources for our project, theNational Institute on Aging [P30 AG072980], theNational Institute of Mental Health [R01MH132746; K01MH116098], theDepartment of Defense [AR140105], and theArizona Biomedical Research Commission [ADHS16-162413]. References
