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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

96
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Structural connectome alterations between individuals with autism and neurotypical controls using feature

Yurim Jang1, Hyoungshin Choi2,3, Seulki Yoo4

  • 1Artificial Intelligence Convergence Research Center, Inha University, Incheon, Republic of Korea.

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This study reveals unique brain structural connectome patterns in autism spectrum disorder (ASD) using advanced AI. These patterns correlate with communication abilities, offering potential biomarkers for autistic connectopathy.

Keywords:
Autism spectrum disorderAutoencoderFeature representation learningIntegrated gradientStructural connectivity

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Artificial Intelligence

Background:

  • Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by sensory and social communication impairments.
  • Previous neuroimaging studies linked atypical brain organization in individuals with ASD to autistic behaviors.
  • Analyzing whole-brain structural connectome abnormalities in a low-dimensional latent space remains underinvestigated.

Purpose of the Study:

  • To investigate whole-brain structural connectome abnormalities in autism using autoencoder-based feature representation learning.
  • To identify potential biomarkers for autistic connectopathy by analyzing low-dimensional latent features of brain connectivity.
  • To explore the relationship between structural connectome features and communication abilities in individuals with ASD.

Main Methods:

  • Utilized diffusion magnetic resonance imaging (dMRI) to assess structural connectivity in 80 individuals with ASD and 61 neurotypical controls.
  • Employed autoencoder models to generate low-dimensional latent features from whole-brain structural connectomes.
  • Applied an integrated gradient approach to determine the contribution of input data to latent feature prediction.

Main Results:

  • Observed significant differences in integrated gradient values between individuals with ASD and controls within transmodal regions and between sensory and limbic systems.
  • Identified significant associations between integrated gradient values and communication abilities in individuals with ASD.
  • Demonstrated the utility of autoencoder-based feature learning for uncovering subtle structural connectome differences.

Conclusions:

  • The study provides novel insights into the whole-brain structural connectome in autism spectrum disorder.
  • Findings suggest that specific patterns of structural connectivity may serve as potential biomarkers for autistic connectopathy.
  • This approach highlights the potential of advanced machine learning techniques in understanding neurodevelopmental conditions.