AIMC Topic: Connectome
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Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.
Journal of neurotrauma
Apr 29, 2024
Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinatio...
Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning.
Medical image analysis
Apr 6, 2024
Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and cli...
NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.
Neuroradiology
Apr 2, 2024
PURPOSE: Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-le...
Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis.
Neuroscience and biobehavioral reviews
Mar 26, 2024
Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising pre...
Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.
GeroScience
Mar 18, 2024
Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing...
DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.
IEEE transactions on medical imaging
Mar 5, 2024
Parcellation of anatomically segregated cortical and subcortical brain regions is required in diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical specificity of tractography. Most current dMRI parcellation approache...
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.
Medical image analysis
Feb 23, 2024
We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud repres...
Connectome-based reservoir computing with the conn2res toolbox.
Nature communications
Jan 22, 2024
The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial...
A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks.
Behavioural brain research
Jul 27, 2023
BACKGROUND: It has been recently shown that deep learning models exhibited remarkable performance of representing functional Magnetic Resonance Imaging (fMRI) data for the understanding of brain functional activities. With hierarchical structure, dee...
Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: Evidence from deep learning and stereotactic electroencephalography.
Neural networks : the official journal of the International Neural Network Society
Jul 23, 2023
Cognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved...
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