Theta oscillations shift towards optimal frequency for cognitive control

Senoussi, Mehdi; Verbeke, Pieter; Desender, Kobe; Loof, Esther De; Talsma, Durk; Verguts, Tom

doi:10.1101/2020.08.30.273706

Cited by 12 publications

(21 citation statements)

References 20 publications

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“…Adaptations in theta band dynamics also assist with optimizing this process, as observed with the reduction in peak theta frequency during higher cognitive control loads. Simulated models show the slower theta frequency increases the probability of success in more difficult scenarios [40], better adapting to the task demands. Given the diverse roles of theta band activity and phase synchrony in executive tasks, it is not surprising that it is highly predictive of CEN activity across numerous executive tasks, as seen in Figure 4.…”

Section: Discussionmentioning

confidence: 99%

Tracking the Brain’s Intrinsic Connectivity Networks in EEG

Shaw

Heisz

et al. 2021

Preprint

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Functional magnetic resonance imaging (fMRI) has identified dysfunctional network dynamics underlying a number of psychopathologies, including post-traumatic stress disorder, depression and schizophrenia. There is tremendous potential for the development of network-based clinical biomarkers to better characterize these disorders. However, to realize this potential requires the ability to track brain networks using a more affordable imaging modality, such as Electroencephalography (EEG). Here we present a novel analysis pipeline capable of tracking brain networks from EEG alone, after training on supervisory signals derived from data simultaneously recorded in EEG and fMRI, while people engaged in various cognitive tasks. EEG-based features were then used to classify three cognitively-relevant brain networks with up to 75\% accuracy. These findings could lead to affordable and non-invasive methods to objectively diagnose brain disorders involving dysfunctional network dynamics, and to track and even predict treatment responses.

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Section: Discussionmentioning

confidence: 99%

Tracking the Brain’s Intrinsic Connectivity Networks in EEG

Shaw

Heisz

et al. 2021

Preprint

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“…This view has recently been gaining support. Evidence in favor of a more fine-grained characterization of theta oscillations include variations in theta peak frequency depending on task demands and difficulty (Senoussi et al, 2020), and differences in the functional significance of high (posterior) and low (anterior) hippocampal theta (Goyal et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…To test our predictions, time-frequency spectra were extracted from the CTI (0 − 1800 ms) in the frequency range 3 − 7 Hz, averaged from channels Fz and AFz. The choice of these two channels was guided by both their widespread use in the literature concerning midfrontal theta (Berger et al, 2019; Cooper et al, 2019; Onton et al, 2005; Popov et al, 2018; Senoussi et al, 2020) and a condition-independent channel selection procedure. Namely, we fitted the power spectra from all channels, computed across all trials, regardless of the Load condition and the Task, with the FOOOF toolbox (Haller et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Ample evidence has associated control-related top-down mechanisms with larger theta (3 - 7 Hz) amplitude over mid-frontal scalp electrodes (Cavanagh & Frank, 2014; Cohen & Donner, 2013). Recent computational models attribute to theta a central role in orchestrating long-range neural communication and in binding task-relevant information (Senoussi et al, 2020; Verbeke & Verguts, 2019; Verguts, 2017). At the same time, studies showed increased theta power for WM manipulation (Itthipuripat, Wessel, & Aron, 2013; Onton, Delorme, & Makeig, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Neural oscillations track the maintenance and proceduralization of novel instructions

Formica

González-García

Senoussi

et al. 2020

Preprint

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Humans are extremely efficient in rapidly and flexibly converting complex symbolic instructions into novel behaviors. Previous evidence and theoretical models suggest that the implementation of a novel instruction requires the reformatting of its declarative content into an action-oriented code optimized for the execution of the instructed behavior. While neuroimaging research focused on identifying the brain areas involved in such process, its temporal profile and electrophysiological characteristics remain unknown. In the present study, we recorded EEG while we asked participants to either simply maintain declaratively the content of novel S-R mappings for recognition or to proactively prepare for their implementation. By means of time-frequency analyses, we isolated the oscillatory features specifically associated with the proceduralization of the encoded instruction. Before the onset of the implementation target, we observed stronger delta/low-theta activity over frontal electrodes and a significant suppression in mu and beta activity over central electrodes. On the contrary, activity in the alpha band showed no differences between the two tasks. Together, these results support the crucial role attributed to prefrontal regions in orchestrating complex task setting and further extend on it by characterizing the temporal and frequency profile of this process. Moreover, we highlight the critical involvement of motor activity in the proactive preparation for novel instruction implementation. Significance StatementHumans developed the unique ability of converting novel instructions in effective behavior. This skill is associated with activity in frontoparietal brain regions, whose interplay supports the reformatting of the declarative content of the instruction into an action-guiding representation. However, the time-resolved unfolding of such cognitive processes is still poorly understood. Here, we investigated how oscillatory brain activity differed between simple maintenance and reformatting of novel instructions. In preparation to the target, we observed differences in lower frequencies over frontal regions, and oscillatory features of motor preparation. Together, these results suggest that instruction implementation is mediated by the exertion of top-down cognitive control, reflected in theta dynamics, and motor-related activation.

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“…For simplicity, here, we employed the same control frequency for all trials. However, as illustrated in recent work, frequency might also change as a function of task difficulty (Senoussi et al, 2021). In summary, an important avenue for future work is to further explore the computational role of different frequency bands.…”

Section: Discussionmentioning

confidence: 86%

Neural Synchrony for Adaptive Control

Verbeke

Verguts

2021

Journal of Cognitive Neuroscience

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Cognitive control can be adaptive along several dimensions, including intensity (how intensely do control signal influence bottom–up processing) and selectivity (what information is selected for further processing). Furthermore, control can be exerted along slow or fast time scales. Whereas control on a slow time scale is used to proactively prepare for upcoming challenges, control can also be used on a faster time scale to react to unexpected events that require control. Importantly, a systematic comparison of these dimensions and time scales remains lacking. Moreover, most current models of adaptive control allow predictions only at a behavioral, not neurophysiological, level, thus seriously reducing the range of available empirical restrictions for informing model formulation. The current article addresses this issue by implementing a control loop in an earlier model of neural synchrony. The resulting model is tested on a Stroop task. We observe that only the model that exerts cognitive control on intensity and selectivity dimensions, as well as on two time scales, can account for relevant behavioral and neurophysiological data. Our findings hold important implications for both cognitive control and how computational models can be empirically constrained.

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Theta oscillations shift towards optimal frequency for cognitive control

Cited by 12 publications

References 20 publications

Tracking the Brain’s Intrinsic Connectivity Networks in EEG

Tracking the Brain’s Intrinsic Connectivity Networks in EEG

Neural oscillations track the maintenance and proceduralization of novel instructions

Neural Synchrony for Adaptive Control

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