AIMC Topic: Connectome

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Connectome-based reservoir computing with the conn2res toolbox.

Nature communications
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
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
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...

Increased functional connectivity coupling with supplementary motor area in blepharospasm at rest.

Brain research
OBJECTIVE: To explore the abnormalities of brain function in blepharospasm (BSP) and to illustrate its neural mechanisms by assuming supplementary motor area (SMA) as the entry point.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Frontiers in neural circuits
Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited ...

A Survey on Brain Effective Connectivity Network Learning.

IEEE transactions on neural networks and learning systems
Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of...

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population.

NeuroImage
In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connec...

Computing personalized brain functional networks from fMRI using self-supervised deep learning.

Medical image analysis
A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural n...

A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction.

Scientific reports
The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is de...

Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data.

Sensors (Basel, Switzerland)
Brain structural morphology varies over the aging trajectory, and the prediction of a person's age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an indi...