Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations

Allaman, Leslie; Mottaz, Anaïs; Kleinschmidt, Andreas; Guggisberg, Adrian G.

doi:10.1523/jneurosci.1166-20.2020

Cited by 16 publications

(13 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aligned with our hypothesis, we found that lower anticipatory a FSS was associated with greater efficiency. We do not believe this finding is at odds with studies showing that frontoparietal a synchronization, in contrast to desynchronization, is associated with top-down control, since a synchronization is predominantly observed during resting wakefulness (Sadaghiani et al, 2012;Allaman et al, 2020) and in poststimulus evoked activity, particularly for attended stimuli Michalareas et al, 2016;Lobier et al, 2018). Indeed topdown control via a oscillations is suggested to be flexible, with synchronization observed during internal processing/rest, and desynchronization prevailing during anticipatory task preparation (Palva and Palva, 2007;Klimesch, 2012).…”

Section: Discussionmentioning

confidence: 62%

“…We hypothesized desynchronization of anticipatory FSS as it has been related to cortical disinhibition, signifying increased excitability to facilitate the processing of impending stimuli (Haegens et al, 2011;Wang et al, 2016). Notably, frontoparietal a synchronization, i.e., network coupling has also been associated with top-down control, but this occurs in contexts different from phasic stimulus anticipation, i.e., during resting wakefulness (Sadaghiani et al, 2012;Allaman et al, 2020) and in poststimulus sensory evoked activity Michalareas et al, 2016;Lobier et al, 2018). Indeed a oscillations show flexible signatures, with greater synchronization during internal task-related processing and rest, but desynchronization during preparation for upcoming task-relevant information (Palva and Palva, 2007;Klimesch, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Closed-Loop Neurofeedback of α Synchrony during Goal-Directed Attention

et al. 2021

View full text Add to dashboard Cite

a Oscillations in sensory cortex, under frontal control, desynchronize during attentive preparation. Here, in a selective attention study with simultaneous EEG in humans of either sex, we first demonstrate that diminished anticipatory a synchrony between the midfrontal region of the dorsal attention network and ventral visual sensory cortex [frontal-sensory synchrony (FSS)] significantly correlates with greater task performance. Then, in a double-blind, randomized controlled study in healthy adults, we implement closedloop neurofeedback (NF) of the anticipatory a FSS signal over 10 d of training. We refer to this closed-loop experimental approach of rapid NF integrated within a cognitive task as cognitive NF (cNF). We show that cNF results in significant trial-by-trial modulation of the anticipatory a FSS measure during training, concomitant plasticity of stimulus-evoked a/h responses, as well as transfer of benefits to response time (RT) improvements on a standard test of sustained attention. In a third study, we implement cNF training in children with attention deficit hyperactivity disorder (ADHD), replicating trial-by-trial modulation of the anticipatory a FSS signal as well as significant improvement of sustained attention RTs. These first findings demonstrate the basic mechanisms and translational utility of rapid cognitive-task-integrated NF.

show abstract

Section: Discussionmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Closed-Loop Neurofeedback of α Synchrony during Goal-Directed Attention

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Here, instead of a concern with changes in neural activity "triggered" by external events, the focus is on intrinsic physiological features, such as functional connectivity, power or phase-coupling of frequency-specific oscillations (e.g., alpha rhythm 3 ). The putative cognitive role of these features are sometimes inferred based on their relationship to behavioural or psychological measures 3,4 , or by evaluating an anatomical overlap with task evoked activity patterns reported in other studies 5,6 . This approach aims to find a correspondence between intrinsic and task evoked neural patterns (sometimes termed the brain's 'functional architecture') 7 .…”

Section: A Dichotomy In Human Neurosciencementioning

confidence: 99%

Decoding cognition from spontaneous neural activity

et al. 2022

View full text Add to dashboard Cite

In human neuroscience, studies of cognition are rarely grounded in non-task evoked, "spontaneous", neural activity. Indeed, studies of "spontaneous" activity tend to focus predominantly on intrinsic neural patterns, for example, resting-state networks. Taking a "representation rich" approach bridges an apparent gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing for quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such approach advances cognitive research beyond a focus on immediate task demand and provide insight into the functional relevance of intrinsic neural pattern (e.g., default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.

show abstract

“…Interestingly, resting state source-space phase-coupling and amplitude-coupling constitute spatially distinct associations with behavioural task performance (Guggisberg et al, 2015). Further, resting state e-phys connectivity not only predicts individuals' perceptual performance but also stimulus-related activation (oscillation power) on tasks (Allaman et al, 2020).…”

Section: Association Of Resting State Activity and Connectivity With Behavioural Traits Across Individualsmentioning

confidence: 99%

Connectomics of Human Electrophysiology

Sadaghiani¹,

Brookes²,

Baillet³

2021

Preprint

View full text Add to dashboard Cite

We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and signal processing methods available for deriving both static and dynamic connectomes using electrophysiological data. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.

show abstract

Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations

Cited by 16 publications

References 75 publications

Closed-Loop Neurofeedback of α Synchrony during Goal-Directed Attention

Closed-Loop Neurofeedback of α Synchrony during Goal-Directed Attention

Decoding cognition from spontaneous neural activity

Connectomics of Human Electrophysiology

Contact Info

Product

Resources

About