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Brain Imaging

Brain Imaging

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Related Experiment Video

Updated: Dec 27, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Transdiagnostic Brain Mapping in Developmental Disorders.

Roma Siugzdaite¹, Joe Bathelt², Joni Holmes¹

¹MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK.

Current Biology : CB

|February 29, 2020

Summary

This summary is machine-generated.

Machine learning revealed distinct brain and cognitive profiles in children. Brain network organization, particularly hub dependence, influences cognitive impairments, suggesting a new framework for understanding brain-to-cognition relationships.

Keywords:

cognitive skills connectomics cortical morphology developmental disorders diffusion weighted imaging graph theory learning difficulties machine learning

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Last Updated: Dec 27, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Published on: September 12, 2011

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

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Published on: June 13, 2025

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Area of Science:

Neuroscience
Developmental Psychology
Computational Biology

Background:

Childhood learning difficulties and developmental disorders are prevalent but poorly understood at the brain mechanism level.
Progress in linking brain structure and function to cognitive abilities in children has been limited.

Purpose of the Study:

To investigate the relationship between brain structure, cognitive profiles, and learning abilities in children.
To explore how brain network organization influences cognitive outcomes and susceptibility to impairments.

Main Methods:

Utilized machine learning on structural neuroimaging, cognitive, and learning data from 479 children.
Applied machine learning to cortical morphology and diffusion-weighted imaging (DWI) to construct white-matter connectomes.
Simulated network attacks on connectomes to assess network resilience and hub dependence.

Main Results:

Identified distinct cognitive and brain profiles significantly associated with learning ability and cognitive function.
Found that brain-to-cognition mappings were not one-to-one; similar neural profiles could relate to different cognitive impairments.
Demonstrated that children with highly connected brain hubs showed selective or no cognitive impairments, while those less dependent on hubs had more severe impairments.

Conclusions:

Propose a new framework where brain-to-cognition relationships are moderated by the organizational context of the overall neural network.
Brain network organization, particularly the role of hubs, plays a critical role in cognitive resilience and the manifestation of learning difficulties.
Understanding network topology is crucial for deciphering the mechanisms underlying childhood learning and developmental disorders.