Nonuniform High-Gamma (60–500 Hz) Power Changes Dissociate Cognitive Task and Anatomy in Human Cortex

Gaona, Charles M.; Sharma, Mohit; Freudenburg, Zachary V.; Breshears, Jonathan D.; Bundy, David T.; Roland, Jarod L.; Barbour, Dennis L.; Schalk, Gerwin; Leuthardt, Eric C.

doi:10.1523/jneurosci.4722-10.2011

Cited by 84 publications

(75 citation statements)

References 48 publications

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“…Finally, task-related changes in synchronization were frequencyspecific, in agreement with work showing that different frequency scales in the cortical physiology are associated with different cognitive processes or brain states (6,44). The δ-frequency modulations were associated with endogenous maintenance of spatial attention in the DAN and SMN, whereas more transient shifts of attention involved θ-phase modulations in both the DAN and VAN.…”

Section: Discussionsupporting

confidence: 85%

Frequency-specific mechanism links human brain networks for spatial attention

Daitch¹,

Sharma²,

Roland

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Selective attention allows us to filter out irrelevant information in the environment and focus neural resources on information relevant to our current goals. Functional brain-imaging studies have identified networks of broadly distributed brain regions that are recruited during different attention processes; however, the dynamics by which these networks enable selection are not well understood. Here, we first used functional MRI to localize dorsal and ventral attention networks in human epileptic subjects undergoing seizure monitoring. We subsequently recorded cortical physiology using subdural electrocorticography during a spatialattention task to study network dynamics. Attention networks become selectively phase-modulated at low frequencies (δ, θ) during the same task epochs in which they are recruited in functional MRI. This mechanism may alter the excitability of task-relevant regions or their effective connectivity. Furthermore, different attention processes (holding vs. shifting attention) are associated with synchrony at different frequencies, which may minimize unnecessary cross-talk between separate neuronal processes.O ne of the hallmarks of effective behavior is the ability to flexibly attend to particular stimuli in the environment. Selective attention can be driven endogenously by one's current goals or by salient external stimuli. Human neuroimaging studies have identified two sets of fronto-parietal regions that are recruited during these two types of attention. A set of dorsal fronto-parietal regions (dorsal attention network or DAN) shows sustained activity during endogenous or goal-driven attention (1), and reorienting to unexpected targets transiently activates both the DAN and a second set of regions, the ventral attention network (VAN) (2). Although functional MRI (fMRI) has identified the brain regions that are involved in these attentional operations (3, 4), the slow nature of the hemodynamic response has severely limited the study of network dynamics at behaviorally relevant time scales (5). Here, we report results obtained by cortical surface (electrocorticography or ECoG) recordings in epilepsy patients undergoing clinical monitoring to identify seizure foci. Electrode locations for each subject were colocalized with functional brain networks, including the DAN and VAN identified in the same subjects using fMRI. This experimental paradigm allowed us to objectively link fast electrophysiological dynamics, during performance of an attention task, to well-studied functional brain networks.ECoG measures nonspiking, local field potential oscillations across a range of frequencies, which are thought to reflect fluctuations in local neuronal excitability (6, 7). Phase modulations of activity within a region and between regions may therefore affect, respectively, their ability to respond to inputs and to transfer information between one another (8, 9). In support of this theory, previous studies in both animals and humans have shown that either local or long-distance synchrony change in a tas...

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Section: Discussionsupporting

confidence: 85%

Frequency-specific mechanism links human brain networks for spatial attention

Daitch¹,

Sharma²,

Roland

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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“…4 B and C. Across all subjects, distinguishing between the general task and rest largely required a single FSN, whereas distinguishing subtasks had a mean of two FSNs. Also, consistent with previous studies demonstrating that amplitude modulation in narrow-band higher-frequency oscillations was associated with different aspects of a speech task (12), these more subtask-specific networks were also associated with more narrow subbands in the high gamma range. Finally, the regions involved with parsing subtasks also appear to be widely distributed across the cortex ( Fig.…”

Section: Timing Of Functional Spectral Network Activation Relative Tosupporting

confidence: 90%

Fast-scale network dynamics in human cortex have specific spectral covariance patterns

Freudenburg¹,

Gaona²,

Sharma³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Whether measured by MRI or direct cortical physiology, infraslow rhythms have defined state invariant cortical networks. The time scales of this functional architecture, however, are unlikely to be able to accommodate the more rapid cortical dynamics necessary for an active cognitive task. Using invasively monitored epileptic patients as a research model, we tested the hypothesis that faster frequencies would spectrally bind regions of cortex as a transient mechanism to enable fast network interactions during the performance of a simple hear-and-repeat speech task. We term these short-lived spectrally covariant networks functional spectral networks (FSNs). We evaluated whether spectrally covariant regions of cortex, which were unique in their spectral signatures, provided a higher degree of task-related information than any single site showing more classic physiologic responses (i.e., single-site amplitude modulation). Taken together, our results showing that FSNs are a more sensitive measure of task-related brain activation and are better able to discern phonemic content strongly support the concept of spectrally encoded interactions in cortex. Moreover, these findings that specific linguistic information is represented in FSNs that have broad anatomic topographies support a more distributed model of cortical processing. T he brain's intrinsic functional architecture of correlated fluctuations in resting state metabolic and electrophysiologic activity has been well established (1, 2). This functional architecture has been shown to be present in the absence of a task, during all stages of sleep, and even under anesthesia (3). How anatomically distributed regions of cortex interact during the performance of a cognitive task is less understood. Due to slower time scales associated with the hemodynamic response of current neuroimaging techniques (4) and their electrophysiologic correlates (1), the more static networks are not adequate to accommodate the more rapid dynamics associated with many behavioral tasks. Given the limitations of the described time scales, we hypothesized that faster frequencies would "spectrally bind" regions of cortex as a transient mechanism to enable fast network interactions that accommodate the flexible use of neuronal resources. Beyond previously described notions that single higher-frequency synchronization enables neuronal interactions (5), we postulated that dynamic networks are represented by a multitude of spectral characteristics. These transient spectrally covariant networks, which we term functional spectral networks (FSNs), would enable a higher level of fidelity in the transmission of cortical-cortical information.Using invasively monitored epileptic patients as a research model, we tested this hypothesis in the setting of a simple hearand-repeat task. Given that human speech processing involves a widely distributed area located predominantly in perisylvian regions (6), this provided a robust model to evaluate networkderived behavior. We evaluated whether spectrally covariant...

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“…However, recent evidence shows the importance of high-frequency network oscillations (Canolty et al, 2006;Gaona et al, 2011), which remained largely unexplored in previous studies. Here we show that diazepam (DZ) causes an overall slowing of slow and fast field oscillations.…”

Section: Discussionmentioning

confidence: 99%