Biology subjects

Kosciessa, J. Q.

Publications and source records attributed to Kosciessa, J. Q..

7 recordsLinked to original sources

bioRxiv2024

Neural variability compresses with increasing belief precision during Bayesian inference

To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that moment-to-moment neural variability should scale with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured with functional MRI) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in Default Mode Network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that moment-to-moment neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.

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bioRxiv2023

Broadscale dampening of uncertainty adjustment in the aging brain

0.The ability to prioritize among input features according to relevance enables adaptive behaviors across the human lifespan. However, relevance often remains ambiguous, and such uncertainty increases demands for dynamic control. While both cognitive stability and flexibility decline during healthy ageing, it is unknown whether aging alters how uncertainty impacts perception and decision-making, and if so, via which neural mechanisms. Here, we assess uncertainty adjustment across the adult lifespan (N = 100; cross-sectional) via behavioral modelling and a theoretically informed set of EEG-, fMRI-, and pupil-based signatures. On the group level, older adults show a broad dampening of uncertainty adjustment relative to younger adults. At the individual level, older individuals with more young-like neural responses also showed better maintained cognitive control. Our results highlight neural mechanisms whose maintenance plausibly enables flexible task-set, perception, and decision computations across the adult lifespan.

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bioRxiv2022

Dynamic regulation of neural variability during working memory reflects dopamine, functional integration, and decision-making

The regulation of moment-to-moment neural variability may permit effective responses to changing cognitive demands. However, the mechanisms that support variability regulation are unknown. In the context of working memory, we leverage the largest available PET and fMRI dataset to jointly consider three lenses through which neural variability regulation could be understood: dopamine capacity, network-level functional integration, and flexible decision processes. We show that with greater working memory load, upregulation of variability was associated with elevated dopamine capacity and heightened functional integration, effects dominantly expressed in the striato-thalamic system rather than cortex. Strikingly, behavioral modeling revealed that working memory load evoked substantial decision biases during evidence accumulation, and those who jointly expressed a more optimal decision bias and higher dopamine capacity were most likely to upregulate striato-thalamic variability under load. We argue that the ability to align striato-thalamic variability to level of demand may be a hallmark of a well-functioning brain.

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bioRxiv2020

Thalamocortical excitability adjustments guide human perception under uncertainty

Adaptive human behavior builds on prior knowledge about stimulus relevance. Some environments cue such knowledge more than others. To behave adaptively, observers need to flexibly adjust sensory processing to the degree of contextual uncertainty. We hypothesize that the neural basis for these perceptual adjustments consists in the ability of the cortical network to switch back and forth between a rhythmic state that serves selective processing, and a state of elevated asynchronous neural activity that boosts sensitivity. To test this hypothesis, we recorded non-invasive EEG and fMRI BOLD dynamics while 47 healthy young adults performed a parametric visual attention task with varying numbers of relevant stimulus features. Drift-diffusion modeling of response behavior and electrophysiological signatures revealed that greater contextual uncertainty lowered the rate of evidence accumulation while increasing thalamocortical engagement, with concomitant increments in cortical excitability and pupil dilation. As predicted, uncertainty-related processing adjustments were expressed as switches between a state of phase-dependent excitability modulation in the alpha band and a state of increased irregularity of brain dynamics. We conclude that humans dynamically adjust sensory excitability according to the processing fidelity afforded by an upcoming choice, and that neuromodulatory processes involving the thalamus play a key role in adjusting excitability in the human brain. HighlightsO_LIWith increasing contextual uncertainty, human cortical networks shift from a state of phase-dependent excitability modulation in the alpha band into a state of elevated excitatory tone and asynchronous neural activity C_LIO_LIEvidence based on joint modeling of behavior, EEG, and BOLD suggests that neuromodulatory processes involving the thalamus regulate these shifts C_LIO_LITheoretical and empirical considerations suggest contributions of both frequency-specific and aperiodic neural dynamics to human behavior C_LI

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bioRxiv2019

Boosting Brain Signal Variability Underlies Liberal Shifts in Decision Bias

Strategically adopting decision biases allows organisms to tailor their choices to environmental demands. For example, a liberal response strategy pays off when target detection is crucial, whereas a conservative strategy is optimal for avoiding false alarms. Using conventional time-frequency analysis of human electroencephalographic (EEG) activity, we previously showed that bias setting entails adjustment of evidence accumulation in sensory regions (Kloosterman et al., 2019), but the presumed prefrontal signature of a strategic conservative-to-liberal bias shift has remained elusive. Here, we show that a liberal bias shift relies on frontal regions adopting a more unconstrained neural regime (boosted entropy) that is suited to the detection of unpredictable events. Overall EEG variation, spectral power and event-related potentials could not explain this relationship, highlighting the unique contribution of moment-to-moment neural variability to bias shifts. Neural variability modulation through prefrontal cortex appears instrumental for permitting an organism to tailor its decision bias to environmental demands. Impact statementMoment-to-moment variability is a prominent feature of neural activity. Rather than representing mere noise, this variability might enable us to flexibly adapt our decision biases to the environment.

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bioRxiv2019

Standard multiscale entropy reflects spectral power at mismatched temporal scales: What’s signal irregularity got to do with it?

Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales - that are thought to reflect slow dynamics - by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales - presumed to indicate fast dynamics - is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest. Author SummaryBrain signals exhibit a wealth of dynamic patterns that that are thought to reflect ongoing neural computations. Multiscale sample entropy (MSE) intends to describe the temporal irregularity of such patterns at multiple time scales of brain function. However, the notion of time scales may often be unintuitive. In particular, traditional implementations of MSE are sensitive to slow fluctuations at fine time scales, and fast dynamics at coarse time scales. This conceptual divergence is often overlooked and may lead to difficulties in establishing the unique contribution of MSE to effects of interest over more established spectral power. Using simulations and empirical data, we highlight these issues and provide evidence for their relevance for valid practical inferences. We further highlight that standard MSE and traditional spectral power are highly collinear in our example. Finally, our analyses indicate that spectral filtering can be used to estimate temporal signal irregularity at matching and intuitive time scales. To guide future studies, we make multiple recommendations based on our observations. We believe that following these suggestions may advance our understanding of the unique contributions of neural signal irregularity to neural and cognitive function across the lifespan.

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bioRxiv2018

Single-trial characterization of neural rhythms: potentials and challenges

AbstractThe average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates however, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (extended Better OSCillation detection: "eBOSC"; cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in single trials with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Despite those signal-to-noise-based limitations, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial analyses of neural rhythms in electrophysiological recordings. Using an applied example of working memory retention, rhythm detection indicated load-related increases in the duration of frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta rhythms that was observed exclusively through amplification of rhythmic amplitudes. HighlightsO_LITraditional narrow-band rhythm metrics conflate the power and duration of rhythmic and arrhythmic periods. We extend a state-of-the-art rhythm detection method (eBOSC) to derive rhythmic episodes in single trials that can disambiguate rhythmic and arrhythmic periods. C_LIO_LISimulations indicate that this can be done with high specificity given sufficient rhythmic power, but with strongly impaired sensitivity when rhythmic SNR is low. Empirically, surface EEG recordings exhibit stable inter-individual differences in -rhythmicity in ranges where simulations suggest a gradual bias, leading to high collinearity between narrow-band and rhythm-specific estimates. C_LIO_LIBeyond these limitations, we highlight multiple empirical benefits of characterizing rhythmic episodes in single trials, such as (a) a principled separation of rhythmic and arrhythmic content, (b) an amplification of rhythmic amplitudes, and (c) a specific characterization of sustained and transient events. C_LIO_LIIn an exemplary application, rhythm-specific estimates increase sensitivity to working memory load effects, in addition to indicating a frequency modulation of frontal theta rhythms through the amplification of rhythmic power. C_LI

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