Increased resting-state thalamocortical functional connectivity in children and young adults with autism spectrum disorder.
Bengi BaranQuynh Trang Huong NguyenDimitrios MylonasSusan L SantangeloDara S ManoachPublished in: Autism research : official journal of the International Society for Autism Research (2022)
There is converging evidence that abnormal thalamocortical interactions contribute to attention deficits and sensory sensitivities in autism spectrum disorder (ASD). However, previous functional MRI studies of thalamocortical connectivity in ASD have produced inconsistent findings in terms of both the direction (hyper vs. hypoconnectivity) and location of group differences. This may reflect, in part, the confounding effects of head motion during scans. In the present study, we investigated resting-state thalamocortical functional connectivity in 8-25 year-olds with ASD and their typically developing (TD) peers. We used pre-scan training, on-line motion correction, and rigorous data quality assurance protocols to minimize motion confounds. ASD participants showed increased thalamic connectivity with temporal cortex relative to TD. Both groups showed similar age-related decreases in thalamic connectivity with occipital cortex, consistent with a process of circuit refinement. Findings of thalamocortical hyperconnectivity in ASD are consistent with other evidence that decreased thalamic inhibition leads to increase and less filtered sensory information reaching the cortex where it disrupts attention and contributes to sensory sensitivity. This literature motivates studies of mechanisms, functional consequences, and treatment of thalamocortical circuit dysfunction in ASD.
Keyphrases
- functional connectivity
- resting state
- autism spectrum disorder
- attention deficit hyperactivity disorder
- intellectual disability
- young adults
- deep brain stimulation
- working memory
- computed tomography
- magnetic resonance imaging
- high speed
- systematic review
- traumatic brain injury
- contrast enhanced
- oxidative stress
- high resolution
- machine learning
- big data
- mass spectrometry
- health information