Biology subjects

Kopcanova, M.

Publications and source records attributed to Kopcanova, M..

3 recordsLinked to original sources

bioRxiv2024

Characterising time-on-task effects on oscillatory and aperiodic EEG components and their co-variation with visual task performance.

Research on brain-behaviour relationships often makes the implicit assumption that these derive from a co-variation of stochastic fluctuations in brain activity and performance across trials of an experiment. However, challenging this assumption, oscillatory brain activity, as well as indicators of performance, such as response speed, can show systematic trends with time on task. Here we tested whether time-on-task trends explain a range of relationships between oscillatory brain activity and response speed, accuracy as well as decision confidence. Thirty-six participants performed 900 trials of a two-alternative forced choice visual discrimination task with confidence ratings. Pre- and post-stimulus spectral power (1-40Hz) and aperiodic (i.e., non-oscillatory) components were compared across blocks of the experimental session and tested for relationships with behavioural performance. We found that time-on-task effects on oscillatory EEG activity were primarily localised within the alpha band, with alpha power increasing and peak alpha frequency decreasing over time, even when controlling for aperiodic contributions. Aperiodic, broadband activity on the other hand did not show time-on-task effects in our data set. Importantly, time-on-task effects in alpha frequency and power explained variability in single-trial reaction times, and controlling for time-on-task effectively removed these relationships. However, time-on-task effects did not affect other EEG signatures of behavioural performance, including post-stimulus predictors of single-trial decision confidence. Our results dissociate alpha-band brain-behaviour relationships that can be explained away by time-on-task from those that remain after accounting for it - thereby further specifying the potential functional roles of alpha in human visual perception.

neuroscience↗

bioRxiv2023

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimers disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.

neuroscience↗

bioRxiv2023

Two distinct stimulus-locked EEG signatures reliably encode domain-general confidence during decision formation

Decision confidence, an internal estimate of how accurate our choices are, is essential for metacognitive self-evaluation and guides behaviour. However, it can be suboptimal and hence understanding the underlying neurocomputational mechanisms is crucial. To do so, it is essential to establish the extent to which both behavioural and neurophysiological measures of metacognition are reliable over time and shared across cognitive domains. The evidence regarding domain-generality of metacognition has been mixed, while the test-retest reliability of the most widely used metacognitive measures has not been reported. Here, in human participants of both sexes, we examined behavioural and electroencephalographic (EEG) measures of metacognition across two tasks that engage distinct cognitive domains - visual perception and semantic memory. The test-retest reliability of all measures was additionally tested across two experimental sessions. The results revealed a dissociation between metacognitive bias and efficiency, whereby only metacognitive bias showed strong test-retest reliability and domain-generality whilst metacognitive efficiency (measured by M-ratio) was neither reliable nor domain-general. Hence, overall confidence calibration (i.e., metacognitive bias) is a stable trait-like characteristic underpinned by domain-general mechanisms whilst metacognitive efficiency may rely on more domain-specific computations. Additionally, we found two distinct stimulus-locked EEG signatures related to the trial-by-trial fluctuations in confidence ratings during decision formation. A late event-related potential was reliably linked to confidence across cognitive domains, while evoked spectral power predicted confidence most reliably in the semantic knowledge domain. Establishing the reliability and domain-generality of neural predictors of confidence represents an important step in advancing our understanding of the mechanisms underlying self-evaluation. Significance StatementUnderstanding the mechanisms underlying metacognition is essential for addressing deficits in self-evaluation. Open questions exist regarding the domain-generality and reliability of both behavioural and neural measures of metacognition. We show that metacognitive bias is reliable across cognitive domains and time, whereas the most adopted measure of metacognitive efficiency is domain-specific and shows poor test-retest reliability. Hence, more reliable measures of metacognition, tailored to specific domains, are needed. We further show that decision confidence is linked to two EEG signatures: late event-related potentials and evoked alpha/beta spectral power. While the former predicts confidence in both perception and semantic knowledge domains, the latter is only reliably linked to knowledge confidence. These findings provide crucial insights into the computations underlying metacognition across domains.

neuroscience↗