Stiffness in Nonlinear Brain Connectome: Understanding Brain Mechanisms of ASD
Poster No:
327
Submission Type:
Abstract Submission
Authors:
SIQI YANG1, Sida Chen1, Qianyuan Tang1, Changsong Zhou1
Institutions:
1Hong Kong Baptist University, Hong Kong, Hong Kong
First Author:
Co-Author(s):
Introduction:
Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by deficits in social interaction and communication, as well as restricted and repetitive patterns of behavior (RRB). Despite extensive research, due to the complexity and heterogeneity of ASD, the mechanism of ASD remains discussion. What we emphasize here is that ASD is typically diagnosed in early childhood, a critical period for brain development. During this early brain development process, which brain regions or inter-brain connections are most susceptible to the underlying neurobiological pathological changes of ASD that influence brain dynamics? And how to identify these atypical developmental abnormalities, differing from typical development controls (TCD)? To address these challenges, a new theoretical framework, stiff-sloppy method as a promising method can analysis the brain from neurobiological parameters to brain dynamics. Stiff means that some neurobiological parameters cause significant changes to the dynamics, whereas those insensitive parameters are considered sloppy. We find and analyze the developing-related and cognitive-related sensitive brain regions and connections in both ASD and TCD group.
Methods:
The data used in this study are from the ABIDE I and ABIDE II datasets (Di Martino et al., 2017; Di Martino et al., 2014). fMRI preprocessing was conducted following standard guidelines and applied AAL atlas (Tzourio-Mazoyer et al., 2002). To analyze individual differences in ASD participants, we applied the stiff-sloppy framework (Panas et al., 2015; Ponce-Alvarez et al., 2020), treating these differences as perturbations around the group-averaged ASD brain in parameter space. Using the pairwise Maximum Entropy Model (p-MEM), equivalent to the Ising model with a Boltzmann distribution, we modeled brain dynamics with parameters hi (regional excitability) and Jij (connection strength) (Schneidman et al., 2006; Watanabe et al., 2013). Z-scored fMRI data were binarized at a threshold of 0.4 to fit the model. The Fisher Information Matrix was computed for group-averaged h and J parameters, and eigen-decomposition identified stiffer eigenvectors (larger eigenvalues). An elastic model was then used to optimize eigenvector combinations, identifying age- and behavior-related directions. Five-fold cross-validation ensured generalizability by splitting data into training and test sets to determine optimal model parameters.
Results:
Using this method, we identified the development-related sensitive brain regions in both group. In individuals with ASD, the identified sensitive regions-such as the Putamen, left Hippocampus, and left Insula-are known to play critical roles in social cognition. Additionally, the sensitivity in the left posterior Cingulum and right inferior Occipital Gyrus may point to disruptions in default mode network activity and visual processing, respectively. In contrast, the TCD group shows sensitivity in regions such as the right middle orbital frontal gyrus, left olfactory cortex, middle temporal pole and rectus gyrus suggest that the TCD group may exhibit atypical neurodevelopment in regions more directly tied to these functions, differing from the broader sensorimotor and social-cognitive disruptions observed in ASD.
Conclusions:
Overall, these results underscore the heterogeneity of brain region sensitivity across neurodevelopmental conditions. Explore the overlap between development-related sensitive brain regions and those associated with social abilities and RRB. This could provide insights into how certain cognitive impairments may lessen with age, while others-particularly those related to executive function and memory-may worsen over time. Collectively, the stiff-sloppy method is a promising method to analysis the brain from neurobiological parameters to brain dynamics and a new way to discuss individual differences of heterogenous clinical behaviors.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Keywords:
Autism
Cognition
Computational Neuroscience
Development
Informatics
Modeling
1|2Indicates the priority used for review
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Provide references using APA citation style.
Di Martino, A. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data, 4, 170010.
McPartland, J. C. (2021). Looking back at the next 40 years of ASD neuroscience research. Journal of Autism and Developmental Disorders, 51(12), 4333-4353.
Panas, D. (2015). Sloppiness in spontaneously active neuronal networks. The Journal of Neuroscience, 35(22), 8480-8492.
Ponce-Alvarez, A. (2020). Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively. eLife, 9, e53268.
Schneidman, E. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440(7087), 1007-1012.
Tzourio-Mazoyer, N. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273-289.
Watanabe, T. (2013). A pairwise maximum entropy model accurately describes resting-state human brain networks. Nature Communications, 4(1), 1370.