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Updated: Jan 11, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Published on: September 12, 2011
Transdiagnostic similarities and distinctions in brain networks associated with autistic social impairments: a
Jennifer L Bruno1, Julia R Plank2, Samantha Leder2
1Department of Psychiatry and Behavioral Sciences, Stanford University, 1520 Page Mill Road Palo Alto, Stanford, CA, 94305, USA. jenbruno@stanford.edu.
Background:
Despite high rates of autism spectrum disorder (ASD), understanding of pathophysiology is limited. The RAS-mitogen-activated protein kinase (RAS-MAPK) pathway plays a crucial role in ASD and is altered in children with Noonan syndrome (NS). Children with NS offer a unique model to disentangle genetic and neurological underpinnings of ASD.
Methods:
This study aimed to examine functional brain network anatomy underlying social impairments in children with NS (n = 28, mean age = 8.24), and tested generalizability of models developed in a non-syndromic cohort enriched for ASD (Autism Brain Imaging Data Exchange (ABIDE), n = 352, mean age = 11.0). Connectome-based predictive modeling (CPM) was applied to fMRI data to predict the severity of autism symptoms, indexed by the Social Responsive Scale (SRS), in children with NS. Next, we tested if a model developed to predict autism symptoms in an autism-enriched sample of children without NS (ABIDE) could predict autism symptoms in children with NS.
Results:
Predicted SRS scores were significantly associated with observed SRS scores in NS (rs = 0.43, p = 0.011). Application of the predictive model generated in the autism-enriched cohort (ABIDE) significantly predicted observed SRS scores in NS (rs = 0.46, p = 0.018). Predictive brain networks in both NS and the non-syndromic cohorts included subcortical-cerebellar networks and visual processing networks.
Limitations:
The size of our NS cohort is small, given the rarity of NS. However, the significant cross-dataset comparison yielded in this study suggests that use of large publicly available datasets can be useful in contextualizing smaller and harder to collect datasets in rare genetic syndromes.
Conclusions:
The presence of shared brain networks suggests converging patterns of functional connectivity underlying autism symptoms across diagnoses. These findings point to potential overlap between non-syndromic autism and NS and highlight the value of human genetic models for studying ASD. Future work investigating RAS-MAPK pathway dysregulation may further elucidate its contribution to autism-related brain function.
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