AIMC Topic: Brain

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Modularity maximization as a flexible and generic framework for brain network exploratory analysis.

NeuroImage
The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and d...

Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing.

Molecules (Basel, Switzerland)
Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that hum...

CEREBRUM-7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes.

Human brain mapping
Ultra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroi...

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features.

IEEE transactions on pattern analysis and machine intelligence
This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neur...

HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation.

Journal of healthcare engineering
Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generaliza...

A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI.

Human brain mapping
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that ...

Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research.

Neural networks : the official journal of the International Neural Network Society
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given rise to a new generation of in silico neural networ...

Research on Information Visualization Graphic Design Teaching Based on DBN Algorithm.

Computational intelligence and neuroscience
With the advent of the era of big data, how to quickly obtain effective information and efficiently disseminate information technology has become the most popular topic. Studies have shown that the ability of the human brain to process data and infor...

Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO.

Journal of neuroscience methods
BACKGROUND: Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicia...

Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between e...