Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex

Abstract

Animal studies have shown robust electrophysiological activity in the sensory cortex in the absence of stimuli or tasks. Similarly, recent human functional magnetic resonance imaging (fMRI) revealed widespread, spontaneously emerging cortical fluctuations. However, it is unknown what neuronal dynamics underlie this spontaneous activity in the human brain. Here we studied this issue by combining bilateral single-unit, local field potentials (LFPs) and intracranial electrocorticography (ECoG) recordings in individuals undergoing clinical monitoring. We found slow (<0.1 Hz, following 1/f-like profiles) spontaneous fluctuations of neuronal activity with significant interhemispheric correlations. These fluctuations were evident mainly in neuronal firing rates and in gamma (40-100 Hz) LFP power modulations. Notably, the interhemispheric correlations were enhanced during rapid eye movement and stage 2 sleep. Multiple intracranial ECoG recordings revealed clear selectivity for functional networks in the spontaneous gamma LFP power modulations. Our results point to slow spontaneous modulations in firing rate and gamma LFP as the likely correlates of spontaneous fMRI fluctuations in the human sensory cortex.The neuronal events occurring in the sensory cortex when no stimulus is presented are not well understood. Contrary to traditional feed-forward models of information processing, a growing body of single-unit, LFP, electroencephalography (EEG), and optical imaging data point to robust levels of spontaneous neuronal activity in sensory areas of the mammalian cortex 1-7 . The modulation of such spontaneous neuronal activity can occur on very slow time scales 8, 9 . These robust spontaneous waves pose a challenge for models linking neuronal activity and sensory perception 10,11 , namely in explaining how the brain distinguishes between spontaneous events and vivid sensory percepts. One possibility is that the precise neuronal dynamics differ substantially between spontaneous and sensory-evoked conditions. This

Mesoscale infraslow spontaneous membrane potential fluctuations recapitulate high-frequency activity cortical motifs

“…Intriguingly, the correlation structure derived from 1-4 Hz spontaneous ECoG activity as well as gamma (50-100 Hz) band-limited power also positively correlated with the BOLD signal 10 . Similar observations of correlative spontaneous fluctuations in gamma LFP power as well as neuronal firing rates have been observed from simultaneous, bilateral recordings in auditory cortex 59 . However, it is the inference of analogous ECoG correlation structure between spontaneous activity derived from the slow cortical potential and from 1-4 Hz frequency activity that is consistent with our observation of the shared functional organization between infraslow and slow spontaneous VSD activity.…”

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

“…This so-called "spontaneous" or "resting-state" brain activity has been shown to covary among functionally related brain regions, for example, those involved with sensorimotor (Biswal et al 1995;Lowe et al 1998), attention (Fox et al 2006), and default-mode (Raichle et al 2001;Greicius et al 2003) functions. The patterns that have been observed with resting-state fMRI are similar to those activated by various tasks (Smith et al 2009), and generally consistent with those based on intracranial electro-optical and electrical recordings in animals (Kenet et al 2003;Leopold et al 2003), and on electrocorticography Nir et al 2008) and magnetoencephalography (MEG;de Pasquale et al 2010;Liu et al 2010;Brookes et al 2011;de Pasquale et al 2012;Hipp et al 2012) in humans. For this reason, fMRI covariation patterns have been interpreted as representing covarying electrical activity in so-called "functional networks.…”

“…Part of this spectral range is accessible with EEG. Over a much longer time scale in the order of several seconds to tens of seconds, network activity presents as comodulations in the power envelop of specific spectral bands, to which neuronal oscillations contribute (Leopold et al 2003;Lu et al 2007;Nir et al 2008) and which have an fMRI correlate (Magri et al 2012). Although extensive efforts have been made to characterize these "fast" and "slow" network interactions separately, their relationship has rarely been studied and remains largely unknown.…”

Learning sculpts the spontaneous activity of the resting human brain

“…This trained behavioral state corresponds to a state of anticorrelation between visual and attention regions. Anticorrelation in spontaneous BOLD activity may be related to antiphase changes in slow cortical potentials and gamma power (41)(42)(43), which may prevent the 2 systems from interfering with each other under task conditions. Analysis of large-scale neural network models indicates that spontaneous network anticorrelation is an efficient computational state to facilitate independent task recruitment and switching (44).…”