AIMC Topic: Connectome

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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...

Deep reinforcement learning guided graph neural networks for brain network analysis.

Neural networks : the official journal of the International Neural Network Society
Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recentl...

Deep learning models of cognitive processes constrained by human brain connectomes.

Medical image analysis
Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity ...

Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.

Human brain mapping
Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold-based geometric neural network for function...