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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Related Experiment Video

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Combining functional, structural, and morphological networks for multimodal classification of developing autistic

Changchun He1,2,3,4, Jesus M Cortes5,6,7, Yi Ding8

  • 1College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China. changchunhh@gmail.com.

Brain Imaging and Behavior
|June 4, 2025
PubMed
Summary

Multimodal neuroimaging reveals distinct brain connectivity patterns in autism spectrum disorder (ASD). Combining functional, structural, and diffusion MRI data improves classification accuracy, highlighting key brain regions involved in ASD pathophysiology.

Keywords:
AutismBrain networkClassificationMagnetic resonance imagingMulti-modality

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Area of Science:

  • Neuroscience
  • Radiology
  • Genetics

Background:

  • Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social interaction deficits and repetitive behaviors.
  • Abnormalities in brain connectivity are increasingly recognized as a core feature in the pathophysiology of ASD.
  • Neuroimaging techniques like fMRI, DTI, and sMRI provide valuable insights into brain structure and function.

Purpose of the Study:

  • To investigate the role of multimodal brain connectivity in the pathophysiology of autism spectrum disorder (ASD).
  • To assess the classification accuracy of combined functional, structural, and diffusion MRI data in differentiating individuals with ASD from typically developing controls (TDC).
  • To identify specific brain regions and connectivity patterns associated with ASD and their correlation with social behaviors.

Main Methods:

  • Utilized neuroimaging data (fMRI, DTI, sMRI) from 50 individuals with ASD and 47 TDC (aged 5-18 years) from the Autism Brain Image Data Exchange database.
  • Constructed brain networks based on functional, structural, and morphological connectivity.
  • Employed machine learning algorithms to classify individuals with ASD and TDC based on connectivity features.

Main Results:

  • The combination of fMRI, sMRI, and DTI data achieved an 82.69% classification accuracy for differentiating individuals with ASD from TDC.
  • This multimodal approach outperformed single-modality or dual-modality combinations previously studied.
  • Significant distinguishing connectivity features were identified in the temporal, parietal, occipital, and prefrontal lobes across the different imaging modalities.
  • These connectivity patterns were found to predict abnormal social interaction behaviors in individuals with ASD.

Conclusions:

  • Multimodal brain connectivity analysis offers complementary information crucial for understanding ASD pathophysiology.
  • Integrated analysis of functional, structural, and diffusion MRI data enhances the accuracy of ASD identification.
  • Connectivity patterns in specific brain regions are pivotal in ASD and are linked to social deficits, underscoring the importance of multimodal approaches.