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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

180
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.
180
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
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Related Experiment Video

Updated: Aug 12, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder.

S Qasim Abbas1, Lianhua Chi1, Yi-Ping Phoebe Chen1

  • 1Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia.

Artificial Intelligence in Medicine
|January 29, 2023
PubMed
Summary

This study introduces a Deep Multimodal Neuroimaging Framework (DeepMNF) for improved computer-aided diagnosis of Autism Spectrum Disorder (ASD). The framework enhances diagnostic accuracy by integrating functional and structural MRI data, outperforming existing methods on the ABIDE-1 dataset.

Keywords:
ABIDEAutism spectrum disorderComputer-aided diagnosisConvolutional neural networkMultimodal neuroimaging frameworkfMRIsMRI

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Neurological disorders like Autism Spectrum Disorder (ASD) are increasingly prevalent, necessitating advanced diagnostic tools for early intervention.
  • Existing computer-aided diagnosis (CAD) systems for ASD often struggle with multisite neuroimaging data variability from repositories like ABIDE.
  • A need exists for robust diagnostic methods that can effectively analyze brain structure and function to identify ASD-related patterns.

Purpose of the Study:

  • To propose a novel Deep Multimodal Neuroimaging Framework (DeepMNF) for enhanced ASD diagnosis.
  • To integrate complementary information from functional Magnetic Resonance Imaging (fMRI) and Structural Magnetic Resonance Imaging (sMRI) for improved diagnostic performance.
  • To address data heterogeneity in multisite neuroimaging datasets by fusing cross-modality spatiotemporal information.

Main Methods:

  • Developed a Deep Multimodal Neuroimaging Framework (DeepMNF) integrating fMRI and sMRI data.
  • Utilized 2D time-series data from fMRI and 3D images from sMRI to capture spatiotemporal information.
  • Exploited cross-modality fusion techniques to enhance group differences and homogeneities within the data.

Main Results:

  • The DeepMNF achieved superior validation performance compared to existing state-of-the-art methods on the ABIDE-1 repository.
  • Demonstrated the effectiveness of combining different neuroimaging modalities (fMRI and sMRI) within a single framework.
  • The framework successfully integrated multisite data, mitigating issues of variability and heterogeneity.

Conclusions:

  • The proposed DeepMNF offers a promising approach for accurate and robust computer-aided diagnosis of Autism Spectrum Disorder.
  • Multimodal neuroimaging data fusion is crucial for overcoming limitations of single-modality approaches and multisite data variability.
  • This framework represents a significant advancement in leveraging neuroimaging for neurological disorder diagnostics.