Modeling functional connectivity changes during an auditory language task using line graph neural networks.

Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional correlation as edges. Graph neural networks (GNNs) are machine learning architectures used to analyze FC graphs.

Neuronal sequences in population bursts encode information in human cortex.

Neural coding has traditionally been examined through changes in firing rates and latencies in response to different stimuli. However, populations of neurons can also exhibit transient bursts of spiking activity, wherein neurons fire in a specific temporal order or sequence. The human brain may utilize these neuronal sequences within population bursts to efficiently represent information, thereby complementing the well-known neural code based on spike rate or latency.

Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability.

For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue.

Focal seizures induce spatiotemporally organized spiking activity in the human cortex.

Epileptic seizures are debilitating because of the clinical symptoms they produce. These symptoms, in turn, may stem directly from disruptions in neural coding. Recent evidence has suggested that the specific temporal order, or sequence, of spiking across a population of cortical neurons may encode information.