Normative modeling of thalamocortical connectivity reveals specific links to autism symptoms
Poster No:
359
Submission Type:
Late-Breaking Abstract Submission
Authors:
Shinwon Park1, Phoebe Thomson1, Page Freeman1, Boris Bernhardt2, Seok-Jun Hong3, Michael Milham1, Adriana Di Martino1
Institutions:
1Child Mind Institute, New York, NY, 2McGill University, Montreal, Quebec, 3IBS Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, Korea, Republic of
First Author:
Co-Author(s):
Seok-Jun Hong
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University
Suwon, Korea, Republic of
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University
Suwon, Korea, Republic of
Introduction:
Thalamocortical connectivity plays a crucial role in the development of large-scale cortical functional networks and thus their behavioral processes they underlie [1]. While atypical thalamocortical connectivity has been reported in autism spectrum disorder (ASD) [2], prior studies have not directly examined the role of its age related pattern in relation to inter-individual variability in autism symptoms. Normative modeling provides an approach to examine the association of individual deviations from age-related normative trajectories with autism symptom severity [3].
Methods:
We analyzed resting-state fMRI data from the Autism Brain Imaging Data Exchange (ABIDE I & II) [4], including 1,117 participants (5-22 yrs), after quality control. Preprocessing was consistent with the CPAC-Reproducible Brain Charts pipeline [5]. Thalamic gradients and their neocortical projection maps (NEOMAP) were derived from connectopic mapping [6]. Generalized additive models for location, scale, and shape (GAMLSS) modelled age-related normative trajectories in neurotypicals (NT), applying the SHASH distribution and cubic splines [7-9]. For computational efficiency, we used the Schaefer 100-region atlas [10] and created region-specific normative curves of NEOMAP values as a function of age. Normative z-scores, representing deviations from age-expected values in ASD individuals, were correlated with clinical measures from the Autism Diagnostic Observation Schedule (ADOS-G), including total, social, communication, and repetitive behavior scores.
Results:
The group averaged NEOMAP revealed a thalamocortical gradient extending from the salience network to externally-oriented regions, including the visual, somatomotor and dorsal attention networks, consistent with prior work (Figure 1A). In NTs, non-linear age-related trends of the median showed significant effects across the visual, somatomotor, dorsal attention and default mode networks (Figure 1B). Sensory regions, including visual, somatomotor and auditory areas, showed an initial increase around age 10, followed by a steady decrease. In contrast, regions in the control and default mode networks showed a consistent decrease, while those in the dorsal attention networks increased with age. In ASD behavioral associations with deviation scores revealed distinct patterns by brain regions and clinical domains (Figure 1C). For example, age-related thalamo-cortical gradient deviations in the somatomotor regions were positively associated with communication scores, while social scores were correlated with the z scores from the anterior insula, a hub of the salience network, and the superior frontal regions within the control network. Finally, repetitive behavior scores were negatively linked with deviations in the medial prefrontal cortex, known for its role in inhibitory control and habit formation.
Conclusions:
Identifying age-related deviations of thalamocortical connectivity revealed region specific associations with autistic symptom domains. Follow-up planned analyses will 1) assess the specific age related patterns characterizing individuals with elevated deviation scores in the regions with significant symptoms association, 2) leverage seed based analyses to delineate the functional connectivity patterns underlying gradient deviation, 3) assess replicability of results. This approach may point towards age sensitive windows that can inform clinical interventions at the individual level.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence
Lifespan Development Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 2
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Autism
Development
FUNCTIONAL MRI
Open Data
Sub-Cortical
Thalamus
1|2Indicates the priority used for review
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