AIMC Topic: Brain

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Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces.

IEEE transactions on cybernetics
Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This...

Development of a spontaneous pain indicator based on brain cellular calcium using deep learning.

Experimental & molecular medicine
Chronic pain remains an intractable condition in millions of patients worldwide. Spontaneous ongoing pain is a major clinical problem of chronic pain and is extremely challenging to diagnose and treat compared to stimulus-evoked pain. Although extens...

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation.

BioMed research international
Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor's exact location in the brain. Magnetic resonance imaging (MRI) is an effi...

Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features.

Scientific reports
Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior. Recent approaches that incorporate brain functional radiomic features and machine learning have shown po...

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

Proceedings of the National Academy of Sciences of the United States of America
There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challengin...

Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection.

Statistics in medicine
Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of psychiatric disorders and the selection of potential biomarkers. Due to the high dimensionality of imaging data and heterogeneous subtypes of psychiat...

Recall Network: A Simple Brain-Inspired Algorithm for Classification.

Computational intelligence and neuroscience
The latest development of neuroscience has deepened the understanding of the information-processing mechanisms in the human brain and inspired a couple of sophisticated computational methods, such as deep learning, memory networks, and hierarchical t...

Gating Attractor Dynamics of Frontal Cortex Under Acupuncture via Recurrent Neural Network.

IEEE journal of biomedical and health informatics
Acupuncture can regulate the functions of human body and improve the cognition of brain. However, the mechanism of acupuncture manipulations remains unclear. Here, we hypothesis that the frontal cortex plays a gating role in information routing of br...

eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We her...

Brain-inspired chaotic backpropagation for MLP.

Neural networks : the official journal of the International Neural Network Society
Backpropagation (BP) algorithm is one of the most basic learning algorithms in deep learning. Although BP has been widely used, it still suffers from the problem of easily falling into the local minima due to its gradient dynamics. Inspired by the fa...