Behavior needs neural variability

Waschke, Leonhard; Kloosterman, Niels A.; Obleser, Jonas; Garrett, Douglas D.

doi:10.1016/j.neuron.2021.01.023

Cited by 212 publications

(213 citation statements)

References 155 publications

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“…Here we approach these challenges through the variability of neural activity, which has been hypothesized to enable the formation of the connectome 5,6,8,29,37 , and which is increasingly recognized to carry important functional links to age and behavior 30,31,[38][39][40] . Using timeresolved complexity estimation of resting-state BOLD signals, we uncover the universal phenomenon of "complexity drops" that represent spontaneous episodes of increased pattern regularity in the brain.…”

Section: Mainmentioning

confidence: 99%

“…Lastly, we turn to the behavioral implications of our findings, given that the variability of neural activity is increasingly recognized to be functionally relevant [29][30][31][38][39][40][51][52][53] . Even over the narrow range of early adulthood, complexity dynamics were highly sensitive to age and reflected inter-individual differences in cognition and motor performance, suggesting that an increased capacity for complexity drops represents a beneficial functional profile of the brain.…”

mentioning

confidence: 99%

“…The organization of these connectivity networks is increasingly well understood, including the temporal dynamics 14-20 , network economy [21][22][23] , and functional hierarchy 24-27 within the connectome. However, it remains a fundamental challenge in neuroscience to understand how these spatially distributed properties are embedded in the local activity of individual brain regions 8,29 .Recent work suggests that functional connections between regions arise from only a few critical episodes in the blood oxygen level-dependent (BOLD) brain signals 17,35,36 , yet the nature of such critical instants remains unclear.Here we approach these challenges through the variability of neural activity, which has been hypothesized to enable the formation of the connectome 5,6,8,29,37 , and which is increasingly recognized to carry important functional links to age and behavior 30,31,[38][39][40] . Using timeresolved complexity estimation of resting-state BOLD signals, we uncover the universal phenomenon of "complexity drops" that represent spontaneous episodes of increased pattern regularity in the brain.…”

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confidence: 99%

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A spatiotemporal complexity architecture of human brain activity

Krohn

Schwanenflug

Waschke

et al. 2021

Preprint

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The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

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Section: Mainmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A spatiotemporal complexity architecture of human brain activity

Krohn

Schwanenflug

Waschke

et al. 2021

Preprint

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“…This study took an individual differences approach, relating individual differences in behavior (i.e, individual DRB effects in RT and accuracy) to individual differences in neural oscillations (i.e., individual postmovement beta ERS effects). Neural variability both within and between subjects is considered a key dimension for understanding brain-behavior correlations ( Waschke, Kloosterman, Obleser, & Garrett, 2021). Theoretically, individual (DRB and beta ERS) effects in within-subject designs are composed of mean group effects, meaningful individual deviations from the mean group effect, and measurement error (see Seghier & Price, 2018).…”

Section: This Studymentioning

confidence: 99%

Watching the Brain as It (Un)Binds: Beta Synchronization Relates to Distractor–Response Binding

Pastötter

Moeller

Frings

2021

Journal of Cognitive Neuroscience

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Human action control relies on event files, that is, short-term stimulus–response bindings that result from the integration of perception and action. The present EEG study examined oscillatory brain activities related to the integration and disintegration of event files in the distractor–response binding (DRB) task, which relies on a sequential prime–probe structure with orthogonal variation of distractor and response relations between prime and probe. Behavioral results indicated a DRB effect in RTs, which was moderated by the duration of the RSI between prime response and probe stimulus onset. Indeed, a DRB effect was observed for a short RSI of 500 msec but not for a longer RSI of 2000 msec, indicating disintegration of event files over time. EEG results revealed a positive correlation between individual DRB in the RSI-2000 condition and postmovement beta synchronization after both prime and probe responses. Beamformer analysis localized this correlation effect to the middle occipital gyrus, which also showed highest coherency with precentral and inferior parietal brain regions. Together, these findings suggest that postmovement beta synchronization is a marker of event file disintegration, with the left middle occipital gyrus being a hub region for stimulus–response bindings in the visual DRB task.

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“…M. Churchland & Abbott, 2012;Deco & Hugues, 2012;Goris et al, 2014;Shadlen & Newsome, 1998). In particular, neural response variability is believed to change during development and to be correlated with behavioral performance, stimuli conditions and to vary across different neural states (Lombardo et al, 2018;McIntosh et al, 2008;Mišić et al, 2010;Voytek et al, 2015;Waschke et al, 2021). Several studies have shown that spike count variability changes across different conditions in several cortical, subcortical, and cerebellar regions (A. K. Churchland et al, 2011;M.…”

Section: Introductionmentioning

confidence: 99%

Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

Ghazizadeh

2021

Preprint

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Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contribution of within trial spike irregularity (nψ) and trial to trial rate variability (nRV) to FF reduction has remained elusive. Here, we introduce a principled approach for accurate estimation of variability components for a doubly stochastic point process which unlike previous methods allows for a time varying nψ (aka φ). Notably, analysis across multiple subcortical and cortical areas showed across the board reduction in rate variability. However, unlike what was previously thought, spiking irregularity was not constant in time and was even enhanced in some regions abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying nψ arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.

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Behavior needs neural variability

Cited by 212 publications

References 155 publications

A spatiotemporal complexity architecture of human brain activity

A spatiotemporal complexity architecture of human brain activity

Watching the Brain as It (Un)Binds: Beta Synchronization Relates to Distractor–Response Binding

Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

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