AIMC Topic: Connectome
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Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains.
NeuroImage
Apr 7, 2021
The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary ma...
Identification of focal epilepsy by diffusion tensor imaging using machine learning.
Acta neurologica Scandinavica
Mar 18, 2021
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness.
Deep learning based segmentation of brain tissue from diffusion MRI.
NeuroImage
Mar 16, 2021
Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weigh...
Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network.
Epilepsy & behavior : E&B
Mar 16, 2021
PURPOSE: Focal epilepsy is a risk factor for language impairment in children. We investigated whether the current state-of-the-art deep learning network on diffusion tractography connectome can accurately predict expressive and receptive language sco...
Functional annotation of human cognitive states using deep graph convolution.
NeuroImage
Feb 12, 2021
A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is "brain decoding", which consists of inferring a set of experimental conditions performed by a participant, using pattern classification of br...
Reliability modelling of resting-state functional connectivity.
NeuroImage
Feb 11, 2021
Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliabili...
Resting-state functional network models for posttraumatic stress disorder.
Journal of neurophysiology
Feb 10, 2021
Four recent articles were examined for their use of resting-state functional magnetic resonance imaging on participants with posttraumatic symptoms. Theory-driven computations were complemented by the novel use of network metrics, which revealed redu...
Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis.
Neuroinformatics
Feb 5, 2021
Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions....
Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation.
Neuroradiology
Jan 22, 2021
PURPOSE: To investigate whether Parkinson's disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)-based stru...
Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.
Human brain mapping
Jan 15, 2021
Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active-duty military personnel. Accurate diagnosis of cmTBI is challenging since the sympto...
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