AIMC Topic: Brain

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Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging.

Neurobiology of aging
Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on...

Deep learning-based target metabolite isolation and big data-driven measurement uncertainty estimation in proton magnetic resonance spectroscopy of the brain.

Magnetic resonance in medicine
PURPOSE: The aim of this study was to develop a method for metabolite quantification with simultaneous measurement uncertainty estimation in deep learning-based proton magnetic resonance spectroscopy ( H-MRS).

Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning.

Brain connectivity
Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human b...

NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation.

Neural networks : the official journal of the International Neural Network Society
Spatial navigation depends on the combination of multiple sensory cues from idiothetic and allothetic sources. The computational mechanisms of mammalian brains in integrating different sensory modalities under uncertainty for navigation is enlighteni...

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we pro...

Multimodal Image Analysis for Assessing Multiple Sclerosis and Future Prospects Powered by Artificial Intelligence.

Seminars in ultrasound, CT, and MR
The purpose of this paper is to serve as a template for greater understanding for the practicing radiologist about key steps to perform multimodality computer analysis of MRI images, specifically in multiple sclerosis patients. With this understandin...

A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.

Asian journal of psychiatry
BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measures is reported to have higher accuracy as a biomarker in Alzheimer's dementia. However, such an approach has not been examined to differentiate healthy...

End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation.

Neural networks : the official journal of the International Neural Network Society
Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for the treatment of several nervous system disorders. In particular, transcranial direct current stimulation (tDCS) is widely used as an affordable clini...

Supervised learning in spiking neural networks: A review of algorithms and evaluations.

Neural networks : the official journal of the International Neural Network Society
As a new brain-inspired computational model of the artificial neural network, a spiking neural network encodes and processes neural information through precisely timed spike trains. Spiking neural networks are composed of biologically plausible spiki...

ψ-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation.

IEEE transactions on medical imaging
Sub-cortical brain structure segmentation is of great importance for diagnosing neuropsychiatric disorders. However, developing an automatic approach to segmenting sub-cortical brain structures remains very challenging due to the ambiguous boundaries...