AIMC Topic: Brain Mapping

Clear Filters Showing 161 to 170 of 513 articles

MEG Source Localization via Deep Learning.

Sensors (Basel, Switzerland)
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole s...

Using distance on the Riemannian manifold to compare representations in brain and in models.

NeuroImage
Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner ...

Representational Content of Oscillatory Brain Activity during Object Recognition: Contrasting Cortical and Deep Neural Network Hierarchies.

eNeuro
Numerous theories propose a key role for brain oscillations in visual perception. Most of these theories postulate that sensory information is encoded in specific oscillatory components (e.g., power or phase) of specific frequency bands. These theori...

Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning.

Nature communications
Commonly used for Parkinson's disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here,...

EEG microstate features for schizophrenia classification.

PloS one
Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes ...

Generalizable dimensions of human cortical auditory processing of speech in natural soundscapes: A data-driven ultra high field fMRI approach.

NeuroImage
Speech comprehension in natural soundscapes rests on the ability of the auditory system to extract speech information from a complex acoustic signal with overlapping contributions from many sound sources. Here we reveal the canonical processing of sp...

Whole-brain functional MRI registration based on a semi-supervised deep learning model.

Medical physics
PURPOSE: Traditional registration of functional magnetic resonance images (fMRI) is typically achieved through registering their coregistered structural MRI. However, it cannot achieve accurate performance in that functional units which are not neces...

A graph neural network framework for causal inference in brain networks.

Scientific reports
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully compreh...

Volume Reduction Techniques for the Classification of Independent Components of rs-fMRI Data: a Study with Convolutional Neural Networks.

Neuroinformatics
In the last decade, neurosciences have had an increasing interest in resting state functional magnetic resonance imaging (rs-fMRI) as a result of its advantages, such as high spatial resolution, compared to other brain exploration techniques. To impr...