Biology subjects

Man, I.

Publications and source records attributed to Man, I..

3 recordsLinked to original sources

bioRxiv2026

Single-Session HRV Biofeedback Alters Neural Activity but Not Subjective Anxiety

Anxiety and stress are among the most prevalent mental health concerns worldwide, yet barriers to effective treatment remain high. Biofeedback-based approaches offer a promising, low-cost complement to traditional interventions. The present study examined whether real-time heart rate variability (HRV) biofeedback enhances the efficacy of paced breathing for reducing state anxiety, and whether self-reported and neural measures of anxiety relief converge. Healthy adults completed a speeded Stroop task for stress induction, followed by a ten-minute paced breathing exercise. Participants were randomly assigned to an HRV biofeedback condition, in which real-time cardiac variability was displayed, or a control condition with the same breathing cue and no physiological feedback. State anxiety was assessed using the Spielberger State-Trait Anxiety Inventory (STAI-S), and neural activity was measured via EEG spectral power across delta, theta, alpha, and beta frequency bands. The Stroop task reliably elevated state anxiety, and paced breathing reduced STAI-S scores equivalently in both groups; HRV biofeedback did not confer additional subjective benefit. In contrast, EEG analyses revealed that the biofeedback group showed increases in delta and bilateral theta power relative to controls, and spatially widespread increases in alpha and beta power. Time-frequency analyses further showed more extensive and sustained spectral modulation in the biofeedback condition. These findings reveal a dissociation between self-reported and neural responses to a brief HRV biofeedback intervention: paced breathing alone was sufficient to reduce acute state anxiety, while HRV biofeedback produced distinct changes in neural oscillatory activity without enhancing self-reported anxiety relief. These results show that subjective anxiety recovery and neural recovery can diverge during acute HRV biofeedback, suggesting that physiological engagement with biofeedback emerges before conscious benefits.

neuroscience↗

bioRxiv2021

Reconstructing heterogeneous pathogen interactions from co-occurrence data via statistical network inference

Infectious diseases often involve multiple pathogen species or multiple strains of the same pathogen. As such, knowledge of how different pathogen species or pathogen strains interact is key to understand and predict the outcome of interventions that target only a single pathogen or subset of strains involved in disease. While population-level data have been used to infer pathogen strain interactions, most previously used inference methods only consider uniform interactions between all strains, or focus on marginal interactions between pairs of strains (without correction for indirect interactions through other strains). Here, we evaluate whether statistical network inference could be useful for reconstructing heterogeneous interaction networks from cross-sectional surveys tracking co-occurrence of multi-strain pathogens. To this end, we applied a suite of network models to data simulating endemic infection states of pathogen strains. Satisfactory performance was demonstrated by unbiased estimation of interaction parameters for large sample size. Accurate reconstruction of networks may require regularization or penalizing for sample size. Of note, performance deteriorated in the presence of host heterogeneity, but this could be overcome by correcting for individual-level risk factors. Our work demonstrates how statistical network inference could prove useful for detecting pathogen interactions and may have implications beyond epidemiology.

ecology↗

bioRxiv2019

Capturing multiple-type interactions into practical predictors of type replacement following HPV vaccination

Current HPV vaccines target a subset of the oncogenic human papillomavirus (HPV) types. If HPV types compete during infection, vaccination may trigger replacement by the non-targeted types. Existing approaches to assess the risk of type replacement have focussed on detecting competitive interactions between pairs of vaccine and non-vaccine types. However, methods to translate any inferred pairwise interactions into predictors of replacement have been lacking. In this paper, we develop practical predictors of type replacement in a multi-type setting, readily estimable from pre-vaccination longitudinal or cross-sectional prevalence data. The predictors we propose for replacement by individual non-targeted types take the form of weighted cross hazard ratios of acquisition versus clearance, or aggregate odds ratios of coinfection with the vaccine types. We elucidate how the hazard-based predictors incorporate potentially heterogeneous direct and indirect type interactions by appropriately weighting type-specific hazards and show when they are equivalent to the odds-based predictors. Additionally, pooling type-specific predictors proves to be useful for predicting increase in the overall non-vaccine-tvpe prevalence. Using simulations, we demonstrate good performance of the predictors under different interaction structures. We discuss potential applications and limitations of the proposed methodology in predicting type replacement, as compared to existing approaches.

epidemiology↗