Biology subjects

Gilbert, F.

Publications and source records attributed to Gilbert, F..

8 recordsLinked to original sources

bioRxiv2025

Bayesian multilevel modeling of group and participant level effect sizes: Stronger inter-individual differences in pupil dilation compared to EEG alpha power during aversive conditioning

Aversive conditioning prompts reliable changes in the power of EEG alpha-band oscillations and pupil dilation. Both variables have been used to test hypotheses on the acquisition, generalization, and extinction of conditioned threat. Existing studies have largely relied on trial averages and group-level analyses. Thus, the variability of these physiological markers to aversive learning at the subject level is currently unknown. Comparisons of group-level analyses in prior studies suggest that pupil dilation and EEG-alpha activity capture complementary information. However, to date, no study has directly compared these two markers in terms of their effect sizes at the level of individual participants. The present study employed Bayesian multilevel modeling to quantify the variability of conditioning effect estimates for alpha-band power and pupillometry. Estimates were examined at the group level and at the participant level, across two conditioning paradigms, involving visual and auditory cues. Although the two metrics shared similar effect sizes at the group level, participant-level variability in these effect sizes was substantially higher for pupil-dilation compared to alpha-power, and this finding was replicated across both paradigms. These findings have important implications for clinical and inter-individual difference research which requires both the quantification of effects at the participant-level as well as meaningful variability between-participants that can be linked to relevant differences such as anxiety.

neuroscience↗

bioRxiv2025

Emotional Responses to Naturalistic and AI-generated Affective Pictures: A Systematic Comparison

Pictures depicting naturalistic scenes are widely used in studies of human emotion. However, the practical use of affective pictures is limited by several factors, including the difficulty of obtaining content-diverse, high-quality, openly accessible, and standardized stimuli that are necessary for specific research questions. The use of artificially generated (AI) pictures could address this limitation, but it is unclear if AI-generated pictures evoke reliable emotional responses. The present study sought to address these challenges by comparing emotional responses to AI-generated pictures with responses to original, standardized, pictures. In two study iterations, standardized pictures containing pleasant, neutral, and unpleasant content were selected from the International Affective Picture System (IAPS) and other sources. Then, a matched AI-counterpart was created for each original picture using generative deep neural networks. A total of 109 participants viewed the picture sets while pupil diameter and electroencephalogram (EEG) were recorded. Evaluative ratings of hedonic valence and emotional arousal were also collected. For both AI and original exemplars, pictures depicting emotional content elicited stronger responses than neutral content for ratings and EEG-derived variables, with weaker effect sizes for the AI-generated pictures. Furthermore, picture-level analyses found that ratings and EEG measures were strongly correlated between matched AI and original pictures. Pupil data also showed the expected content effects in study iteration 2, but not in iteration 1. Together, this initial study suggests that AI-generated picture sets can effectively elicit well-established self-reported affect and physiological responses, presenting a promising avenue for future studies of human emotion.

neuroscience↗

bioRxiv2025

Long-Term Intestinal Epithelial Remodeling Induced by Acute Protein-Energy Malnutrition

Protein-energy malnutrition (PEM) is a global health burden with lasting effects that extend well beyond the initial nutrient deficiency. To systematically investigate the long-term effects of a single episode of PEM on the structure and function of the intestinal epithelium and its associated microbiota, we employed a comprehensive multi-omics approach, including (spatial) transcriptomics, DNA methylation analysis, fecal metagenomics, and metabolomics. Our findings show that PEM persistently alters the intestinal epithelium by depleting Paneth cells and suppressing antimicrobial gene expression - changes linked to DNA methylation that persist despite dietary recovery. In germ-free mice, the sustained epithelial phenotype after was absent. We identified the microbial lipid metabolite 9-HODE and epigenetically deregulated PPAR-driven GDF15 expression as key molecular drivers of the persistent PEM-induced Paneth cell dysfunction. Targeting microbial lipid production and its link to the host GDF15 pathway could offer novel therapeutic strategies for long-term consequences of malnutrition and other Paneth cell-associated diseases.

immunology↗

bioRxiv2025

Concept2Brain: An AI model for predicting subject-level neurophysiological responses to text and pictures

The current growth of artificial intelligence (AI) tools provides an unprecedented opportunity to extract deeper insights from neurophysiological data while also enabling the reproduction and prediction of brain responses to a wide range of events and situations. Here, we introduce the Concept2Brain model, a deep network architecture designed to generate synthetic electrophysiological responses to semantic/emotional information conveyed through pictures or text. Leveraging AI solutions like CLIP from OpenAI, the model generates a representation of pictorial or language input and maps it into an electrophysiological latent space. We demonstrate that this openly available resource generates synthetic neural responses that closely resemble those observed in studies of naturalistic scene perception. The Concept2Brain model is provided as a web service tool for creating open and reproducible EEG datasets, allowing users to predict brain responses to any semantic concept or picture. Beyond its applied functionality, it also paves the way for AI-driven modeling of brain activity, offering new possibilities for studying how the brain represents the world.

neuroscience↗

bioRxiv2025

Social Anxiety Increases Autonomic and Visuocortical Generalization of Conditioned Aversive Responses to Faces

Aversive generalization learning is an adaptive trait that is necessary for survival in a dynamic environment. However, this process is exaggerated in persons with anxiety disorders, leading to overgeneralization of learned threat associations, hyperreactive fight-or-flight responses, and persistent avoidance. Patients with social anxiety disorder (SAD) exhibit impaired conditioned threat discrimination particularly with respect to social stimuli, such as faces. The present study examined the relationship between social anxiety and generalization of visuocortical and pupil dilation responses to a series of facial morphs, one of which was always paired with a noxious sound. Steady-state visual evoked potentials (ssVEPs; N = 65) increasingly fit a model of generalization, and pupil dilation responses (N = 62) also decreasingly discriminated the CS+ as a function of social anxiety. These results contribute to a growing body of work suggesting that SAD dysregulates the ability of autonomic responses to specifically target social threat. The finding of widened visuocortical tuning in SAD implicates a role of the visual system in driving attentional biases in anxiety disorders, including increased visual processing of safety signals similar to threat cues.

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↗