Neuronal Variability as a Proxy for Network State

Nogueira, Ramon; Lawrie, Sofía; Moreno-Bote, Rubén

doi:10.1016/j.tins.2018.02.003

Cited by 16 publications

(16 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The functional role of the observed neural and behavioral variability in repetitions of the same task is a fundamental question in systems neuroscience [1][2][3][4][5][6][7][8][9][10][11][12][13] and is currently under extensive discussion. The focus has been mainly on the analysis of trial-to-trial variability dynamics in motor [2,[14][15][16][17] or in sensory [4,8,16,[18][19][20] cortical areas; and more recently in frontal regions during decision-making tasks [2,3,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

et al. 2020

Self Cite

View full text Add to dashboard Cite

Shared neuronal variability has been shown to modulate cognitive processing. However, the relationship between shared variability and behavioral performance is heterogeneous and complex in frontal areas such as the orbitofrontal cortex (OFC). Mounting evidence shows that single-units in OFC encode a detailed cognitive map of task-space events, but the existence of a robust neuronal ensemble coding for the predictability of choice outcome is less established. Here, we hypothesize that the coding of foreseeable outcomes is potentially unclear from the analysis of units activity and their pairwise correlations. However, this code might be established more conclusively when higher-order neuronal interactions are mapped to the choice outcome. As a case study, we investigated the trial-to-trial shared variability of neuronal ensemble activity during a two-choice interval-discrimination task in rodent OFC, specifically designed such that a lose-switch strategy is optimal by repeating the rewarded stimulus in the upcoming trial. Results show that correlations among triplets are higher during correct choices with respect to incorrect ones, and that this is sustained during the entire trial. This effect is not observed for pairwise nor for higher than third-order correlations. This scenario is compatible with constellations of up to three interacting units assembled during trials in which the task is performed correctly. More interestingly, a state-space spanned by such constellations shows that only correct outcome states that can be successfully predicted are robust over 100 trials of the task, and thus they can be accurately decoded. However, both incorrect and unpredictable outcome representations were unstable and thus non-decodeable, due to spurious negative correlations. Our results suggest that predictability of successful outcomes, and hence the optimal behavioral strategy, can be mapped out in OFC ensemble states reliable over trials of the task, and revealed by sufficiency complex neuronal interactions.

show abstract

Section: Introductionmentioning

confidence: 99%

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The crucial point to dissolve the apparent contradiction is the distinction between the two forms of neural variability – the quenching of stimulus-induced variability (i.e., TTV reduction), and the variability of ongoing neural activity across time (i.e., TV during resting-state or entire task state). As alluded to above, the high TV of ongoing activity can be considered as the brain’s exploration of possible states within a region of neuronal activity space [Churchland et al, 2010; Deco and Hugues, 2012; He, 2013; Nogueira et al, 2018; Ponce-Alvarez et al, 2015; Tononi et al, 2016], whereas a superimposed stimulation would shrink the available neuronal activity space to a subset with high stimulus-relevance; thus reducing the neural states from “exploratory” to “certainty”.…”

Section: Introductionmentioning

confidence: 99%

Disrupted neural variability during propofol‐induced sedation and unconsciousness

Huang

Zhang

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

Variability quenching is a widespread neural phenomenon in which trial-to-trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability-quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting-state and stimulus-induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial-to-trial mean, TTM) and variability (TTV) of auditory stimulus-induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting-state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus-induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting-state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic-induced unconsciousness.

show abstract

“…The SSE at each time point is lower during task state than during resting state, suggesting that the spatial pattern for the amplitude of spontaneous neural activity is more variegated than that of task-evoked neural activity at each time point. The great difference between task state and resting state makes SSE a stable proxy of brain state [33].…”

Section: Discussionmentioning

confidence: 99%

“…In the present study, we highlighted the importance of spatial variability of neural activity in distinguishing brain states and put forward spatial SE (SSE) to evaluate the spatial variability of fMRI signal during task state and resting state. It has been suggested that spontaneous activity reflects the stochastic exploration of the high-dimensional space [33,34]. In contrast, the cognitive processing invoked by external stimuli transfers this space into functionally relevant subspaces [35], causing responses to exhibit lower dimensionality and lower variability [36].…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of low frequency brain signal differentiates brain states

Wang

Yang

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

show abstract

Neuronal Variability as a Proxy for Network State

Cited by 16 publications

References 14 publications

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

Disrupted neural variability during propofol‐induced sedation and unconsciousness

Spatial variability of low frequency brain signal differentiates brain states

Contact Info

Product

Resources

About