AIMC Topic: Magnetoencephalography

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Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

Human brain mapping
Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active-duty military personnel. Accurate diagnosis of cmTBI is challenging since the sympto...

Neural dynamics of perceptual inference and its reversal during imagery.

eLife
After the presentation of a visual stimulus, neural processing cascades from low-level sensory areas to increasingly abstract representations in higher-level areas. It is often hypothesised that a reversal in neural processing underlies the generatio...

BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial.

Neurology
OBJECTIVE: To determine whether training with a brain-computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.

Classification of cognitive reserve in healthy older adults based on brain activity using support vector machine.

Physiological measurement
OBJECTIVE: Cognitive reserve (CR) refers to the capacity of the brain to actively cope with damage via the implementation of remedial cognitive processes. Traditional CR measurements focus on static proxies, which may not be able to appropriately est...

Neural activity underlying the detection of an object movement by an observer during forward self-motion: Dynamic decoding and temporal evolution of directional cortical connectivity.

Progress in neurobiology
Relatively little is known about how the human brain identifies movement of objects while the observer is also moving in the environment. This is, ecologically, one of the most fundamental motion processing problems, critical for survival. To study t...

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

NeuroImage
Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, m...

Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography.

Journal of neuroscience methods
BACKGROUND: When many features and a small number of clinical data exist, previous studies have used a few top-ranked features from the Fisher's discriminant ratio (FDR) for feature selection. However, there are many similarities between selected fea...

Somatosensory evoked fields predict response to vagus nerve stimulation.

NeuroImage. Clinical
There is an unmet need to develop robust predictive algorithms to preoperatively identify pediatric epilepsy patients who will respond to vagus nerve stimulation (VNS). Given the similarity in the neural circuitry between vagus and median nerve affer...

EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes.

IEEE transactions on medical imaging
Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical asses...

Two Distinct Neural Timescales for Predictive Speech Processing.

Neuron
During speech listening, the brain could use contextual predictions to optimize sensory sampling and processing. We asked if such predictive processing is organized dynamically into separate oscillatory timescales. We trained a neural network that us...