Biology subjects

Panitz, C.

Publications and source records attributed to Panitz, C..

5 recordsLinked to original sources

bioRxiv2025

Comparing Methods for Mass Univariate Analyses of Human EEG: Empirical Data and Simulations

Electroencephalography (EEG) is a widely used method for investigating human brain dynamics. However, EEG analyses are frequently conducted with limited a priori knowledge regarding locations or latencies of meaningful statistical effects. This makes it difficult for researchers to form regions of interest (ROIs), which are then analyzed using traditional statistical models such as analysis of variance. In addition, exploratory studies, or studies interested in determining the exact temporal and spatial extent of a predicted effect may aim to examine many sensor locations and time points, often jointly. To address this, mass univariate analyses have become a valuable complement to ROI-based approaches. These methods attempt to correct for multiple comparisons while mitigating the risk of false positives and false negatives, thus enabling statistical inference in high-dimensional EEG data. Here, we review and evaluate different approaches for delineating spatial and temporal effect boundaries in three different datasets, focusing on within-subjects comparisons. Specifically, we focus on permutation-based approaches and their Bayesian alternatives to address condition differences in i) steady-state evoked responses, ii) event-related potentials, and iii) time-frequency data. Overall, simulation results indicate that cluster-based permutation tests provide a relatively liberal approach to correct for multiple comparisons across domains, with high sensitivity for detecting large effects. In contrast, the permutation-based tmax procedure yields the most conservative method across datasets. Bayesian approaches inherently are continuous in nature and thus strongly depend on the selection of thresholds for when support for a hypothesis is considered meaningful. HighlightsO_LIDirect comparison of different mass univariate tools applied to real EEG data C_LIO_LIVariability in the number of datapoints showing statistical condition differences C_LIO_LIMass univariate tools alleviate the arbitrary averaging of time windows and electrodes C_LIO_LICaution is warranted when using Bayes factors for mass-univariate comparisons C_LI

neuroscience↗

bioRxiv2025

Beyond Threat: Changes in Visuocortical Engagement and Oscillatory Brain Activity during Non-Aversive Associative Learning

Aversive conditioning produces selectively heightened visuocortical responses to conditioned stimuli stimulus (CS) that predict aversive unconditioned events (US). However, it is unclear whether similar neural signatures emerge as a consequence of mere association formation between a CS-US pair, i.e., when a CS predicts a neutral event. To address this, we paired a soft tone (65 dB) with one of two high-contrast circular gratings (15{degrees} or 75{degrees}; CS+; counterbalanced), while two intermediate orientations (35{degrees}, 55{degrees}) were never paired with the neutral tone, serving as generalization stimuli (GS). A sample of 22 participants viewed each grating for 3000 ms, with the tone presented during the last 1000 ms of each grating presentation. Gratings were flickered (turned on and off) at a temporal rate of 15 Hz, to evoked steady-state Visual Evoked Potentials (ssVEPs), a metric of visuocortical engagement. Time-frequency decomposition via Morlet wavelets quantified changes in the amplitude of alpha-band (8-13 Hz) oscillations--an additional electrophysiological index that has been shown to be sensitive to aversive conditioning. Contrary to findings observed during aversive conditioning, alpha amplitude increased, rather than decreased, during CS+ trials relative to GSs. Likewise, ssVEP amplitude was higher for GSs than for the CS+, which again is the opposite of what is found during aversive conditioning. These findings suggest that a CS paired with non-aversive outcomes engages mechanisms consistent with working memory, anticipation, or imagery processes, reflected in heightened alpha amplitude and attenuated ssVEP, rather than the defensive potentiation observed during aversive conditioning.

neuroscience↗

bioRxiv2024

Decoding in the fourth dimension: Classification of temporal patterns and their generalization across locations

Neuroscience research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and electrode locations. However, the strength of many neurophysiological recording techniques such as electroencephalography or magnetoencephalography is in their rich temporal, rather than spatial, content. The present report proposes a new decoding method that relies on the time information contained in neural time series. This information is then used in a subsequent step, generalization across location (GAL), which characterizes the relationship between sensor locations based on their ability to cross-decode. Two datasets are used to demonstrate usage of this method, referred to as time-GAL, involving (1) event-related potentials in response to affective pictures and (2) steady-state visual evoked potentials in response to aversively conditioned grating stimuli. In both cases, experimental conditions were successfully decoded based on the temporal features contained in the neural time series. Cross-decoding occurred in regions known to be involved in visual and affective processing. We conclude that the time-GAL approach holds promise for analyzing neural time series from a wide range of paradigms and measurement domains providing an assumption-free method to quantifying differences in temporal patterns of neural information processing and whether these patterns are shared across sensor locations. Author summaryDecoding and classification approaches are widely used in computational biology. In the field of neuroscience, pattern classification approaches typically use spatial information. In many instances, neural time series however are best defined by the their temporal, rather than spatial, features. Here, we propose a novel decoding approach taking advantage of the temporal information. Specifically, we utilize the waveform of neural time series as features for decoding experimental conditions and quantify each decoders generalization across locations (GAL) in multi-channel recordings. We illustrate the usage of our open source toolbox using two datasets, showing the sensitivity of the method to systematic condition differences in temporal dynamics along with its ability to capture and quantify spatial dependencies between recording locations.

neuroscience↗

bioRxiv2024

Quantifying Population-level Neural Tuning Functions Using Ricker Wavelets and the Bayesian Bootstrap

Experience changes the tuning of sensory neurons, including neurons in retinotopic visual cortex, as evident from work in humans and non-human animals. In human observers, visuo-cortical re-tuning has been studied during aversive generalization learning paradigms, in which the similarity of generalization stimuli (GSs) with a conditioned threat cue (CS+) is used to quantify tuning functions. This work utilized pre-defined tuning shapes reflecting prototypical generalization (Gaussian) and sharpening (Difference-of-Gaussians) patterns. This approach may constrain the ways in which re-tuning can be characterized, for example if tuning patterns do not match the prototypical functions or represent a mixture of functions. The present study proposes a flexible and data-driven method for precisely quantifying changes in neural tuning based on the Ricker wavelet function and the Bayesian bootstrap. The method is illustrated using data from a study in which university students (n = 31) performed an aversive generalization learning task. Oriented gray-scale gratings served as CS+ and GSs and a white noise served as the unconditioned stimulus (US). Acquisition and extinction of the aversive contingencies were examined, while steady-state visual event potentials (ssVEP) and alpha-band (8-13 Hz) power were measured from scalp EEG. Results showed that the Ricker wavelet model fitted the ssVEP and alpha-band data well. The pattern of re-tuning in ssVEP amplitude across the stimulus gradient resembled a generalization (Gaussian) shape in acquisition and a sharpening (Difference-of-Gaussian) shape in an extinction phase. As expected, the pattern of re-tuning in alpha-power took the form of a generalization shape in both phases. The Ricker-based approach led to greater Bayes factors and more interpretable results compared to prototypical tuning models. The results highlight the promise of the current method for capturing the precise nature of visuo-cortical tuning functions, unconstrained by the exact implementation of prototypical a-priori models. HighlightsO_LITuning functions are a common way for describing sensory responses, primarily in the visual cortex. C_LIO_LIThe quantification and interpretation of tuning functions has faced computational and conceptual problems. C_LIO_LIWe demonstrated how the Ricker function can be used as a simple and interpretable way for measuring tuning functions. C_LIO_LIWe applied a Ricker function together with a Bayesian Bootstrap approach across a gradient of stimulus features in a generalization conditioning task to characterize visual tuning in the human EEG data. C_LI

neuroscience↗

bioRxiv2019

Extinction-resistant attention to long-term conditioned threat is indexed by selective visuocortical alpha suppression in humans

While ERP studies have shown heightened early visual attention to conditioned threat, it is unknown whether this attentional prioritization is sustained throughout later processing stages and whether it is robust to extinction. To investigate sustained visual attention, we assessed visuocortical alpha suppression in response to conditioned and extinguished threat. N = 87 participants underwent a two-day threat conditioning paradigm with acquisition and extinction on one day and a critical recall test one day later. EEG time-frequency analyses revealed that, on Day 2, threat-conditioned vs. safety cues evoked stronger occipital alpha power suppression from 600 to 1200 ms. Notably, this suppression was resistant to extinction. The present study showed for the first time that threat conditioning enhances sustained modulation of visuocortical attention to threat in the long term. The long-term stability and extinction resistance of alpha suppression suggests a crucial role of visuocortical attention mechanisms in the maintenance of learned fears.

neuroscience↗