Engle, H. | Search bioRxiv

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↗