Cortical State Research Articles

Cortex-wide characterization of decision-making neural dynamics during spatial navigation.

Decision-making during freely moving behaviors involves complex interactions among many cortical and subcortical regions. However, the spatiotemporal coordination across regions to generate a decision is less understood. Using a head-mounted widefield microscope, cortex-wide calcium dynamics were recorded in mice expressing GCaMP7f as they navigated an 8-maze using two paradigms. The first was an alternating pattern that required short term memory of the previous trial to make the correct decision and the second after a rule change to a fixed path in which rewards were delivered only on the left side. Identification of cortex-wide activation states revealed differences between the two paradigms. There was a higher probability for a visual/retrosplenial cortical state during the alternating paradigm and higher probability of a secondary motor and posterior parietal state during left-only. Three state sequences (motifs) illustrated both anterior and posterior activity propagations across the cortex. The anterior propagating motifs had the highest probability around the decision and propagating motifs peaked following the decision. The latter, likely reflecting internal feedback to influence future actions, were more common in the left-only paradigm. Therefore, the probabilities and sequences of cortical states differ when working memory is required versus a fixed trajectory reward paradigm.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex.

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary-rather than additive-effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

Lateral prefrontal cortex controls interplay between working memory and actions.

Humans must often keep multiple task goals in mind, at different levels of priority and immediacy, while also interacting with the environment. We might need to remember information for an upcoming task while engaged in more immediate actions. Consequently, actively maintained working memory (WM) content may bleed into ongoing but unrelated motor behavior. Here, we experimentally test the impact of WM maintenance on action execution, and we transcranially stimulate lateral prefrontal cortex (PFC) to parse its functional contributions to WM-motor interactions. We first created a task scenario wherein human participants (both sexes) executed cued hand movements during WM maintenance. We manipulated the compatibility between WM and movement goals at the trial level and the statistical likelihood that the two would be compatible at the block level. We found that remembering directional words (e.g., 'left', 'down') biased the trajectory and speed of hand movements that occurred during the WM delay, but the bias was dampened in blocks when WM content predictably conflicted with movement goals. Then we targeted left lateral PFC with two different transcranial magnetic stimulation (TMS) protocols before participants completed the task. We found that an intermittent theta-burst protocol, which is thought to be excitatory, dampened sensitivity to block-level control demands (i.e., proactive control), while a continuous theta-burst protocol, which is thought to be inhibitory, dampened adaptation to trial-by-trial conflict (i.e., reactive control). Therefore, lateral PFC is involved in controlling the interplay between WM content and manual action, but different PFC mechanisms may support different time-scales of adaptive control.

Neuronal rhythmicity and cortical arousal in a mouse model of absence epilepsy

Objectives: Absence seizures impair psychosocial function, yet their detailed neuronal basis remains unknown. Recent work in a rat model suggests that cortical arousal state changes prior to seizures and that single neurons show diverse firing patterns during seizures. Our aim was to extend these investigations to a mouse model with studies of neuronal activity and arousal state to facilitate future fundamental investigations of absence epilepsy.Methods: We performed in vivo extracellular single unit recordings on awake head-fixed C3H/HeJ mice. Mice were implanted with tripolar electrodes for cortical electroencephalography (EEG). Extracellular single unit recordings were obtained with glass micropipettes in the somatosensory barrel cortex, while animals ambulated freely on a running wheel. Signals were digitized and analyzed during seizures and at baseline.Results: Neuronal activity was recorded from 36 cortical neurons in 19 mice while EEG showed characteristic 7–8 Hz spike-wave discharges. Different single neurons showed distinct firing patterns during seizures, but the overall mean population neuronal firing rate during seizures was no different from pre-seizure baseline. However, the rhythmicity of neuronal firing during seizures was significantly increased (p < 0.001). In addition, beginning 10s prior to seizure initiation, we observed a progressive decrease in cortical high frequency (>40 Hz) EEG and an increase in lower frequency (1–39 Hz) activity suggesting decreased arousal state.Significance: We found that the awake head-fixed C3H/HeJ mouse model demonstrated rhythmic neuronal firing during seizures, and a decreased cortical arousal state prior to seizure onset. Unlike the rat model we did not observe an overall decrease in neuronal firing during seizures. Similarities and differences across species strengthen the ability to investigate fundamental key mechanisms. Future work in the mouse model will identify the molecular basis of neurons with different firing patterns, their role in seizure initiation and behavioral deficits, with ultimate translation to human absence epilepsy.

State-dependent complexity of the local field potential in the primary visual cortex.

The local field potential (LFP) is as a measure of the combined activity of neurons within a region of brain tissue. While biophysical modeling schemes for LFP in cortical circuits are well established, there is a paramount lack of understanding regarding the LFP properties along the states assumed in cortical circuits over long periods. Here we use a symbolic information approach to determine the statistical complexity based on Jensen disequilibrium measure and Shannon entropy of LFP data recorded from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice. Using these information quantifiers, we find consistent relations between LFP recordings and measures of cortical states at the neuronal level. More specifically, we show that LFP's statistical complexity is sensitive to cortical state (characterized by spiking variability), as well as to cortical layer. In addition, we apply these quantifiers to characterize behavioral states of freely moving mice, where we find indirect relations between such states and spiking variability.

An Unpredictable Brain Is a Conscious, Responsive Brain.

Severe traumatic brain injuries typically result in loss of consciousness or coma. In deeply comatose patients with traumatic brain injury, cortical dynamics become simple, repetitive, and predictable. We review evidence that this low-complexity, high-predictability state results from a passive cortical state, represented by a stable repetitive attractor, that hinders the flexible formation of neuronal ensembles necessary for conscious experience. Our data and those from other groups support the hypothesis that this cortical passive state is because of the loss of thalamocortical input. We identify the unpredictability and complexity of cortical dynamics captured by local field potential as a sign of recovery from this passive coma attractor. In this Perspective article, we discuss how these electrophysiological biomarkers of the recovery of consciousness could inform the design of closed-loop stimulation paradigms to treat disorders of consciousness.

Neocortical long-range inhibition promotes cortical synchrony and sleep.

Behavioral states such as sleep and wake are highly correlated with specific patterns of rhythmic activity in the cortex. During low arousal states such as slow wave sleep, the cortex is synchronized and dominated by low frequency rhythms coordinated across multiple regions. Although recent evidence suggests that GABAergic inhibitory neurons are key players in cortical state modulation, the in vivo circuit mechanisms coordinating synchronized activity among local and distant neocortical networks are not well understood. Here, we show that somatostatin and chondrolectin co-expressing cells (Sst-Chodl cells), a sparse and unique class of neocortical inhibitory neurons, are selectively active during low arousal states and are largely silent during periods of high arousal. In contrast to other neocortical inhibitory neurons, we show these neurons have long-range axons that project across neocortical areas. Activation of Sst-Chodl cells is sufficient to promote synchronized cortical states characteristic of low arousal, with increased spike co-firing and low frequency brain rhythms, and to alter behavioral states by promoting sleep. Contrary to the prevailing belief that sleep is exclusively driven by subcortical mechanisms, our findings reveal that these long-range inhibitory neurons not only track changes in behavioral state but are sufficient to induce both sleep-like cortical states and sleep behavior, establishing a crucial circuit component in regulating behavioral states.

Blindly separated spontaneous network-level oscillations predict corticospinal excitability

Objective. The corticospinal responses of the motor network to transcranial magnetic stimulation (TMS) are highly variable. While often regarded as noise, this variability provides a way of probing dynamic brain states related to excitability. We aimed to uncover spontaneously occurring cortical states that alter corticospinal excitability. Approach. Electroencephalography (EEG) recorded during TMS registers fast neural dynamics—unfortunately, at the cost of anatomical precision. We employed analytic Common Spatial Patterns technique to derive excitability-related cortical activity from pre-TMS EEG signals while overcoming spatial specificity issues. Main results. High corticospinal excitability was predicted by alpha-band activity, localized adjacent to the stimulated left motor cortex, and suggesting a travelling wave-like phenomenon towards frontal regions. Low excitability was predicted by alpha-band activity localized in the medial parietal–occipital and frontal cortical regions. Significance. We established a data-driven approach for uncovering network-level neural activity that modulates TMS effects. It requires no prior anatomical assumptions, while being physiologically interpretable, and can be employed in both exploratory investigation and brain state-dependent stimulation.

In and Out of Criticality? State-Dependent Scaling in the Rat Visual Cortex

The presumed proximity to a critical point is believed to endow the brain with scale-invariant statistics, which are thought to confer various functional advantages in terms of its information processing, storage, and transmission capabilities. To assess the relationship between scaling and cortical states, we apply a phenomenological renormalization group analysis to 3-h spiking data recordings from the urethane-anesthetized rat's visual cortex. Under this type of anesthesia, cortical states dynamically shift across a spectrum of synchronization levels, defined by population spiking rate variability. By developing a scaling criterion based on the kurtosis of the momentum-space activity distribution, our study combines the coarse-graining method with state-dependent analysis. We find that scaling signatures only appear as spiking variability surpasses a specified threshold. Notably, within this regime, scaling exponents show relative stability. Conversely, subthreshold activity is primarily asynchronous and fails to meet the scaling criterion. Our results suggest that a wide range of cortical states corresponds to small deviations around a critical point, with the system fluctuating in and out of criticality, spending roughly three-quarters of the experiment duration within a scaling regime. Published by the American Physical Society 2024

Alteration of cortical functional networks in mood disorders with resting-state electroencephalography

IntroductionThis study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with Bipolar disorder and Major depressive disorder, comparing the neuropathology of these disorders.ObjectivesThis study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with Bipolar disorder and Major depressive disorder, comparing the neuropathology of these disorders.Methods A total of 116 participants (35 patients diagnosed with bipolar disorder(BD), 39 patients diagnosed with Major depressive disorder(MDD), and 42 people who are healthy-control groups(HC)) were enrolled for this study. Depression and anxiety were evaluated with using State-Trait Anxiety Inventory (STAI) and Beck Depression Inventory (BDI). Graph theory‑based source‑level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands.Results At the global level, patients with BD and MDD showed higher strength (p = 0.001) and CC (p = 0.001), and lower PL (p &lt; 0.001) in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus(p &lt; 0.001), left isthmus cingulate(p &lt; 0.001), bilateral paracentral(p &lt; 0.001), and left superior frontal(p &lt; 0.001); however, patients with MDD showed higher nodal CC only in the right precuneus(p &lt; 0.001) compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs.ConclusionsThis study suggest that both patients have similar network changes at the global level, but they have different network changes at the nodal level. Also, the higher nodal CCs in the high beta band might indicate the regions became more connected with their neighbors in accordance with the severity of depressive and anxious states. This study found a significant correlation between cortical network state and anxiety-related psychological measure in BD patients. Our source-level cortical network indices might contribute to the understanding of the neuropathological mechanisms in these two disorders.Disclosure of InterestNone Declared

Transcranial Direct Current Stimulation (tDCS) as a Therapeutic Tool for Chronic Insomnia

&#x0D; &#x0D; &#x0D; &#x0D; Background and Objective: Insomnia is the most common sleep problem which is associated with cortical over- excitation. Transcranial direct-current stimulation (tDCS) potentially modifies insomnia-related cortical state. There- fore, we tested the hypothesis that insomnia severity can be modulated by tDCS.&#x0D; Materials and Methods: The current study was conducted with a pretest-posttest design and a control group. A total of 32 women with insomnia were randomly categorized into an intervention group (active stimulation) and a control group (sham stimulation). In the intervention group, tDCS ‎was used with an intensity of 2mA for 20 to 30 minutes during 12 sessions (3 times a week). Anodal stimulation was performed‎on the left primary motor cortex (M1) and cathodal stimulation was performed‎on the right dorsal lateral prefrontal cortex (DLPFC). The control group received sham stim- ulation for 20 to 30 minutes during 12 sessions (3 times a week). All participants were evaluated before and after the intervention using the Insomnia Severity Index (ISI) and Positive Affect and Negative Affect Schedule (PANAS).&#x0D; Results: The results of univariate analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) showed a significant difference between the tDCS group and the sham group in terms of reduction in the severity of insomnia. We also observed that positive affect increased and negative affect decreased following insomnia treatment (P ≤ 0.005).&#x0D; Conclusion: The results of our study indicated that performing our designed tDCS protocol for treating insomnia can be effective in treating insomnia and improving positive and negative affect.&#x0D; &#x0D; &#x0D; &#x0D;

Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke.

Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity. Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand. The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age. The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.

Single-cell multiplex chromatin and RNA interactions in ageing human brain

Dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA1–3. Chromatin complex compositions change during cellular differentiation and ageing, and are expected to be highly heterogeneous among terminally differentiated single cells4–7. Here we introduce the multinucleic acid interaction mapping in single cells (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression and RNA–chromatin associations within individual nuclei. When applied to 14 human frontal cortex samples from older donors, MUSIC delineated diverse cortical cell types and states. We observed that nuclei exhibiting fewer short-range chromatin interactions were correlated with both an ‘older’ transcriptomic signature and Alzheimer’s disease pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci and a promoter tends to be that in which these cis expression quantitative trait loci specifically affect the expression of their target gene. In addition, female cortical cells exhibit highly heterogeneous interactions between XIST non-coding RNA and chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploration of chromatin architecture and transcription at cellular resolution in complex tissues.

A machine learning approach for real-time cortical state estimation

Objective. Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as ‘cortical state’. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

The Two-Process Model: Origin of Its Concepts and Their Implications

The two-process model of sleep regulation has served as a conceptual framework in the last four decades for understanding sleep physiology. In the 1970s, long-term recordings of sleep in rats were obtained thanks to EEG telemetry. NonREM sleep and REM sleep were found to differ in their time course and response to light-dark protocols. There were indications for their coupling to the circadian system, in particular the light-dark and the dark-light transitions. With the advent of quantitative EEG analysis, slow-wave activity in nonREM sleep was recognized as a sleep-wake-dependent variable. The term “sleep homeostasis” was coined to specify the regulated balance between sleep and waking. The regulatory homeostatic process was designated as “Process S”. In the two-process model, its interaction with the circadian pacemaker “Process C” can account for sleep duration under various experimental protocols. Local, use-dependent slow-wave activity changes were demonstrated in both humans and rats by the selective, unilateral activation of a cortical region prior to sleep. Finding that rest in invertebrates has sleep-like regulatory properties opened a new realm of animal studies. Comparative sleep studies in a broad variety of animal species confirmed the validity of the basic concepts of the two-process model. Recent studies have addressed sleep-related changes of brain temperature as an indicator of brain metabolism; the application of the model to Drosophila; the divergence of cortical and subcortical states; and sleep in an increasing number of species and taxa.

Brain-state-dependent constraints on claustrocortical communication and function

Neural activity in the claustrum has been associated with a range of vigilance states, yet the activity patterns and efficacy of synaptic communication of identified claustrum neurons have not been thoroughly determined. Here, we show that claustrum neurons projecting to the retrosplenial cortex are most active during synchronized cortical states such as non-rapid eye movement (NREM) sleep and are suppressed during increased cortical desynchronization associated with arousal, movement, and REM sleep. The efficacy of claustrocortical signaling is increased during NREM and diminished during movement due in part to increased cholinergic tone. Finally, claustrum activation during NREM sleep enhances memory consolidation through the phase resetting of cortical delta waves. Therefore, claustrocortical communication is constrained to function most effectively during cognitive processes associated with synchronized cortical states, such as memory consolidation.

Resting-state prefrontal EEG biomarker in correlation with postoperative delirium in elderly patients.

Postoperative delirium (POD) is associated with adverse outcomes in elderly patients after surgery. Electroencephalography (EEG) can be used to develop a potential biomarker for degenerative cerebral dysfunctions, including mild cognitive impairment and dementia. This study aimed to explore the relationship between preoperative EEG and POD. We included 257 patients aged >70 years who underwent spinal surgery. We measured the median dominant frequency (MDF), which is a resting-state EEG biomarker involving intrinsic alpha oscillations that reflect an idle cortical state, from the prefrontal regions. Additionally, the mini-mental state examination and Montreal cognitive assessment (MoCA) were performed before surgery as well as 5 days after surgery. For long-term cognitive function follow up, the telephone interview for cognitive status™ (TICS) was performed 1 month and 1 year after surgery. Fifty-two (20.2%) patients were diagnosed with POD. A multivariable logistic regression analysis that included age, MoCA score, Charlson comorbidity index score, Mini Nutritional Assessment, and the MDF as variables revealed that the MDF had a significant odds ratio of 0.48 (95% confidence interval 0.27-0.85). Among the patients with POD, the postoperative neurocognitive disorders could last up to 1 year. Low MDF on preoperative EEG was associated with POD in elderly patients undergoing surgery. EEG could be a novel potential tool for identifying patients at a high risk of POD.

Astrocytic calcium waves: unveiling their roles in sleep and arousal modulation.

Neuron-astrocyte interactions are vital for the brain's connectome. Understanding astrocyte activities is crucial for comprehending the complex neural network, particularly the population-level functions of neurons in different cortical states and associated behaviors in mammals. Studies on animal sleep and wakefulness have revealed distinct cortical synchrony patterns between neurons. Astrocytes, outnumbering neurons by nearly fivefold, support and regulate neuronal and synaptic function. Recent research on astrocyte activation during cortical state transitions has emphasized the influence of norepinephrine as a neurotransmitter and calcium waves as key components of ion channel signaling. This summary focuses on a few recent studies investigating astrocyte-neuron interactions in mouse models during sleep, wakefulness, and arousal levels, exploring the involvement of noradrenaline signaling, ion channels, and glutamatergic signaling in different cortical states. These findings highlight the significant impact of astrocytes on large-scale neuronal networks, influencing brain activity and responsiveness. Targeting astrocytic signaling pathways shows promise for treating sleep disorders and arousal dysregulation. More research is needed to understand astrocytic calcium signaling in different brain regions and its implications for dysregulated brain states, requiring future human studies to comprehensively investigate neuron-astrocyte interactions and pave the way for therapeutic interventions in sleep- and arousal-related disorders.

Endogenous fluctuations in cortical state selectively enhance different modes of sensory processing in human temporal lobe

The degree of synchronized fluctuations in neocortical network activity can vary widely during alertness. One influential idea that has emerged over the past few decades is that perceptual decisions are more accurate when the state of population activity is desynchronized. This suggests that optimal task performance may occur during a particular cortical state – the desynchronized state. Here we show that, contrary to this view, cortical state can both facilitate and suppress perceptual performance in a task-dependent manner. We performed electrical recordings from surface-implanted grid electrodes in the temporal lobe while human subjects completed two perceptual tasks. We found that when local population activity is in a synchronized state, network and perceptual performance are enhanced in a detection task and impaired in a discrimination task, but these modulatory effects are reversed when population activity is desynchronized. These findings indicate that the brain has adapted to take advantage of endogenous fluctuations in the state of neural populations in temporal cortex to selectively enhance different modes of sensory processing during perception in a state-dependent manner.