AIMC Topic: Brain

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Causal importance of low-level feature selectivity for generalization in image recognition.

Neural networks : the official journal of the International Neural Network Society
Although our brain and deep neural networks (DNNs) can perform high-level sensory-perception tasks, such as image or speech recognition, the inner mechanism of these hierarchical information-processing systems is poorly understood in both neuroscienc...

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.

BMC bioinformatics
BACKGROUND: MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional da...

Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therap...

Towards deep learning for connectome mapping: A block decomposition framework.

NeuroImage
We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated i...

Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural...

Cellular and Molecular Probing of Intact Human Organs.

Cell
Optical tissue transparency permits scalable cellular and molecular investigation of complex tissues in 3D. Adult human organs are particularly challenging to render transparent because of the accumulation of dense and sturdy molecules in decades-age...

Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method.

BMC neuroscience
BACKGROUND: It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network funct...

Logarithmic Fuzzy Entropy Function for Similarity Measurement in Multimodal Medical Images Registration.

Computational and mathematical methods in medicine
Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To integrate multimodal information, multimodal registration is significant. The entropy-based registration applies a structure descriptor set to replac...

Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest.

IEEE journal of biomedical and health informatics
Alzheimer's disease (AD) has become a severe medical challenge. Advances in technologies produced high-dimensional data of different modalities including functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP). Understan...

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics
Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the worlds population. Most of the current seizure detection approaches strongly rely on patient history records a...