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Get Free AccessAutistic meltdowns are involuntary fits of intense frustration, rage, and often physical violence elicited by sensory and cognitive stressors easily tolerated by neurotypicals. While nearly 70% of autistic individuals display the “crisis behaviors” associated with meltdowns, the neural mechanisms that underlie this maladaptive response are not yet well understood. This has thus far hampered progress towards a dedicated therapeutic intervention–beyond traditional medications—that limits their frequency and severity. Here, we aim to initiate an interdisciplinary dialogue on the etiology of meltdowns. In doing so, we frame meltdowns as a consequence of underlying chronic hypervigilance and acute hyperreactivity to objectively benign stressors driven by differences in the insular cortex—a multimodal integration hub that adapts autonomic state and behavior to meet environmental demands. We first discuss meltdowns through the lens of neurophysiology and argue that intra-insular hypoconnectivity engenders vagal withdrawal and sympathetic hyperarousal in autism, driving chronic hypervigilance and reducing the threshold of stressors those with autism can tolerate before experiencing a meltdown. Next, we turn to neuropsychology and present evidence that meltdowns reflect an inability to properly integrate contextual evidence, particularly social cues, when acutely assessing ambiguous signs of danger in the environment—a process termed neuroception. Finally, we build on contemporary predictive coding accounts of autism to argue that meltdowns are ultimately driven by chronic failures of sensory attenuation and coherent deep inference within the interoceptive hierarchy, possibly linked to oxytocin deficiency during infancy. Throughout, we synthesize each perspective to construct a multidisciplinary, insula-based model of meltdowns.
Paul A. Soden, Anjali Bhat, Adam K. Anderson, Karl Friston (2024). The Meltdown Pathway: A Multidisciplinary Account of Autistic Meltdowns. , DOI: https://doi.org/10.31234/osf.io/27w9p.
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Type
Preprint
Year
2024
Authors
4
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.31234/osf.io/27w9p
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