NREM sleep improves behavioral performance by desynchronizing cortical circuits.

Sleep improves cognitive performance, yet little is known about the neural mechanisms of this improvement. We performed multielectrode recording in macaque visual and dorsolateral prefrontal cortex while animals performed a visual discrimination task before and after non-rapid eye movement (NREM) sleep. Although sleep induces synchronized fluctuations in population activity across cortical areas, the post-sleep population activity became more desynchronized relative to the pre-sleep state.

High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas.

One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown.

Heterogeneous side effects of cortical inactivation in behaving animals.

Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns.

Neurophysiological brain mapping of human sleep-wake states.

Objective: We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018).

Functional Connectivity in Dorsolateral Frontal Cortex: Intracranial Electroencephalogram Study.

Mechanisms underlying the variation in the appearance of electroencephalogram (EEG) over human head are not well characterized. We hypothesized that spatial variation of the EEG, being ultimately linked to variations in cortical neurobiology, was dependent on cortical connectivity patterns. Specifically, we explored the relationship of resting-state functional connectivity derived from intracranial EEG (iEEG) data in seven ( = 7) human epilepsy patients with the intrinsic dynamic variability of the local iEEG.