AIMC Topic: Brain

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Neurons throughout the brain embed robust signatures of their anatomical location into spike trains.

eLife
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical location wit...

Speech imagery brain-computer interfaces: a systematic literature review.

Journal of neural engineering
Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whos...

Kcc-ReHo and Cohe-ReHo in bipolar disorder: their associated genes and potential for diagnosis and treatment prediction.

Neuropharmacology
The neural mechanisms underlying resting-state cerebral functional activity in bipolar disorder (BD) and the effects of pharmacotherapy on it remain unclear. This study investigated changes in local brain activity in BD patients (BDPs) following trea...

A lightweight spiking neural network for EEG-based motor imagery classification.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) aim to simulate the human brain neural network, using sparse spike event streams for effective and energy-efficient spatio-temporal signal processing. This paper proposes a lightweight SNN model for electroencephalogram...

Disrupted functional topology of the white matter connectome in rhegmatogenous retinal detachment: insights from graph theory and machine learning.

Neuroreport
BACKGROUND: Rhegmatogenous retinal detachment (RRD) is known to induce functional alterations in the gray matter regions associated with vision. However, the impact of RRD on the white matter (WM) connectome remains largely unexplored.

BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.

Brain topography
Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not a routine acquisition data, which limits its popularity in clinical applications. Therefore, it is ...

Covarying gray and white matter networks characterize schizophrenia and bipolar disorders on a continuum: A data fusion machine learning approach and a brain network analysis.

Journal of affective disorders
Schizophrenia (SZ) and Bipolar disorder (BD) share genetic and cerebral abnormalities, supporting an expanded continuum hypothesis. In this paper, we aim to better characterize differences and commonalities of gray and white matter features between S...

Unsupervised single-image super-resolution for infant brain MRI.

NeuroImage
Acquiring high-resolution (HR) MR images of infant brains is challenging due to lengthy scan times and limited subject compliance. Image super-resolution (SR) techniques can generate HR images from low-resolution (LR) inputs, reducing the need for ex...

Selection of representative electrodes for stereoscopic visual comfort studies in conjunction with brain mechanism analysis.

Brain research
The widespread use of 3D stereoscopic technology has drawn attention to the problem of visual discomfort, several studies have used EEG to assess visual comfort and discomfort phenomena, but there is a lack of scientific basis for the selection of el...

Three-dimensional U-Net with transfer learning improves automated whole brain delineation from MRI brain scans of rats, mice, and monkeys.

Computers in biology and medicine
BACKGROUND: Automated whole-brain delineation (WBD) techniques often struggle to generalize across pre-clinical studies due to variations in animal models, magnetic resonance imaging (MRI) scanners, and tissue contrasts. We developed a 3D U-Net neura...