Imran H. Quraishi

The paper investigates impaired consciousness in temporal lobe seizures, finding that bilateral spread is not required, onset side has no effect, and specific EEG patterns predict impaired consciousness with high accuracy.

This study analyzes intracranial EEG power changes in frontal lobe seizures to determine how they relate to impaired consciousness, finding distinct patterns across seizure types.

The paper investigates how disease-causing gain-of-function mutations in the Slack potassium channel affect excitability differently in excitatory and inhibitory neurons.

This study shows that the human hippocampus encodes real-world navigation through local field potentials in ambulatory patients.

The paper shows that neural entrainment during statistical learning reveals online abstraction by detecting both exemplar and category-level regularities in intracranial recordings.

The paper presents a computational model of nonquantal transmission at the vestibular hair cell-calyx synapse, showing how electrical potential in the synaptic cleft affects transmission latency and speed.

The paper uses intracranial EEG to study how prediction from statistical learning interferes with episodic memory in human visual cortex, showing that predicted items are more likely to be forgotten.

This study investigates how statistical learning in spatial navigation shifts from hippocampal to medial prefrontal cortex representation over time using longitudinal iEEG data from an epilepsy patient.