Biology subjects

Lagisz, M.

Publications and source records attributed to Lagisz, M..

10 recordsLinked to original sources

bioRxiv2026

Music exposure reduces anxiety- and depression-like behavior in rodents: a systematic review and multilevel meta-analysis

Anxiety and depressive disorders impose a major global burden, prompting interest in non-pharmacological interventions that may influence affective processes. Music exposure has often been reported to affect anxiety- and depression-like behaviors, but preclinical findings remain heterogeneous and have not been quantitatively synthesized. Prior work has also focused almost entirely on mean behavioral responses, largely overlooking inter-individual variability as a biologically meaningful dimension. We conducted a preregistered systematic review and multilevel meta-analysis of experimental studies testing music exposure in laboratory rodents. Following PRISMA and PRISMA-EcoEvo guidelines, we synthesized 298 effect sizes from 20 studies using multilevel models to account for non-independence among effect sizes. We quantified effects on mean behavior with the log response ratio (lnRR) and effects on variability with the log variability ratio (ln V R). Overall, music exposure was associated with a statistically significant reduction in anxiety- and depression-like behaviors, corresponding to an average decrease of about 18% relative to controls. This mean effect was detected across outcome types and life stages despite substantial heterogeneity. By contrast, music exposure did not produce a statistically significant overall change in inter-individual behavioral variability. Instead, variability responses were context dependent: behavioral assay type and music meta-genre significantly moderated ln V R, with anxiety-like assays tending to show increased variability and depression-like assays tending to show reduced variability under music exposure. These results suggest that music exposure reliably shifts average affect-related behavior without uniformly changing behavioral stability across individuals. Because the evidence comes mainly from short-term exposures in young adult laboratory rodents, generalization beyond similar contexts should remain cautious.

animal behavior and cognition↗

bioRxiv2025

Underappreciated role of environmental enrichment in alleviating depression and anxiety: Quantitative evidence synthesis of rodent models

Environmental enrichment has long been recognized as a non-pharmacological intervention to mitigate mental health issues, yet its efficacy, and heterogeneity of treatment effects across experimental contexts remain underexplored. Heterogeneity of treatment effects, which reflects variability in individual responses to interventions, is a critical factor in determining the generalizability and personalization needs of treatments. Here, we conducted a registered meta-analysis of 62 studies and 1,112 comparisons in rodent models to evaluate the impact of environmental enrichment on depressive and anxiety-like behaviours. We found that environmental enrichment reduced these behaviours of animal models by 16% on average and decreased inter-individual variability by 12%, indicating not only effectiveness but also low heterogeneity of treatment effects, which suggests consistent effects across individuals. Environmental enrichment further nullified the adverse effects of stressors, demonstrating a significant antagonistic interaction. These effects were robust across multiple sensitivity analyses, including model-based predictions, post-stratification, multi-model inference, publication bias correction, and critical appraisal of study quality. Moderator analyses highlighted the importance of exposure timing and the inclusion of social enrichment components. Taken together, our pre-clinical evidence on rodent models supports environmental enrichment as a low-cost, scalable, and biologically grounded intervention with translational relevance for developing equitable and accessible treatments for depression and anxiety. Given the importance of innovation and personalization in mental health care, the low heterogeneity of treatment effects of environmental enrichment positions it as a promising avenue for non-pharmacological therapeutic strategies that can be broadly applied without extensive tailoring.

animal behavior and cognition↗

bioRxiv2024

Interpreting prediction intervals and distributions for decoding biological generality in meta-analyses

Despite the importance of identifying predictable regularities for knowledge transfer across contexts, the generality of ecological and evolutionary findings is yet to be systematically quantified. We present the first large-scale evaluation of generality using new metrics. By focusing on biologically relevant study levels, we show that generalization is not uncommon. Overall, 20% of meta-analyses will produce a non-zero effect 95% of the time in future replication studies with a 70% probability of observing meaningful effects in study-level contexts. We argue that the misconception that generalization is exceedingly rare is due to conflating within-study and between-study variances in ecological and evolutionary meta-analyses, which results from focusing too much on total heterogeneity (the sum of within-study and between-study variances). We encourage using our proposed approach to elucidate general patterns underpinning ecological and evolutionary phenomena.

ecology↗

bioRxiv2024

Bivariate multilevel meta-analysis of log response ratio and standardized mean difference for robust and reproducible environmental and biological sciences

Meta-analytic modelling plays a pivotal role in synthesizing research and informing relevant policies. Yet researchers face many analytical challenges. In environmental and biological sciences, one of the most common yet unrecognised issues is the selection between two common effect size metrics, log response ratio (lnRR) and standardized mean difference (SMD); these two are the most popular and alternative effect sizes. Having to choose between them creates room for analytical flexibility, which is susceptible to researcher degrees of freedom. Another common issue is failure to deal with statistical dependence between effect sizes, resulting in invalid inferences on evidence. We propose addressing these two issues through the joint synthesis (dual use) of lnRR and SMD. Using 75 meta-analyses, including 3,887 environmental/biological primary studies ([~]20,000 effect sizes), we show a high false positive rate (40%) in conventional meta-analytic practices (random-effects model) compared to the proposed bivariate multilevel meta-analysis of lnRR and SMD along with robust variance estimation. Relying solely on either lnRR or SMD results in non-trivial discrepancies in detecting statistically significant effects (18%) and occasional inconsistencies in sign (9%). Discrepancies in interpreting effect size, heterogeneity, and publication bias are prevalent between models using lnRR and SMD (e.g., 52% for publication bias). In contrast, bivariate synthesis of lnRR and SMD yields substantial information gain, reducing standard error in effect size estimates by 29%, equivalent to adding 40 additional effect sizes. We present a user-friendly website with a step-by-step implementation guide. Our proposed robust approach aspires to improve meta-analytic modelling using lnRR and SMD in environmental and biological evidence synthesis, amplifying their reproducibility and credibility.

ecology↗

bioRxiv2023

Best Paper awards lack transparency, inclusivity, and support for Open Science

Awards can propel academic careers. They also reflect the culture and values of the scientific community. But, do awards incentivise greater transparency, diversity, and openness in science? Our cross-disciplinary survey of 222 awards for the "best" journal articles across all 27 SCImago subject areas revealed that journals and learned societies administering such awards generally publish little detail on their procedures and criteria. Award descriptions are brief, rarely including contact details or information on the nominations pool. Nominations of underrepresented groups are not explicitly encouraged and concepts that align with Open Science are almost absent from the assessment criteria. Instead, such awards increasingly rely on article-level impact metrics. USA-affiliated researchers dominated the winners pool (48%), while researchers from the Global South and developing countries were uncommon (11%). Sixty-one-percent of individual winners were men. Thus, Best Paper awards miss the global calls for greater transparency and equitable access to academic recognition.

scientific communication and education↗

bioRxiv2023

Keywords to success: a practical guide to maximise the visibility and impact of academic papers

In a growing digital landscape, enhancing the discoverability and resonance of scientific articles is essential. Here, we offer ten recommendations to amplify the discoverability of studies in scientific databases. Particularly, we argue that the strategic use and placement of key terms in the title, abstract, and keyword sections can boost indexing and appeal. By surveying 237 journals in ecology and evolutionary biology, we found that current author guidelines may unintentionally limit article discoverability. Our survey of 5842 studies revealed that authors frequently exhaust abstract word limits -- particularly those capped under 250 words. This suggests that current guidelines may be overly restrictive and not optimised to increase the dissemination and discoverability of digital publications. Additionally, 91.9% of studies used redundant keywords in the title or abstract, undermining optimal indexing in databases. We encourage adopting structured abstracts to maximise the incorporation of key terms in titles, abstracts, and keywords. In addition, we encourage the relaxation of abstract and keyword limitations in journals with strict guidelines, and the inclusion of multilingual abstracts to broaden global accessibility. These evidence-based recommendations to editors are designed to improve article engagement and facilitate evidence synthesis, thereby aligning scientific publishing with the modern needs of academic research.

scientific communication and education↗

bioRxiv2022

Sex differences in allometry for phenotypic traits indicate that females are not scaled males

Sex differences in the lifetime risk and expression of disease are well-known. Preclinical research targeted at improving treatment, increasing health span, and reducing the financial burden of health care, has mostly been conducted on male animals and cells. The extent to which sex differences in phenotypic traits are explained by sex differences in body weight remains unclear. We quantify sex differences in the allometric relationship between trait value and body weight for 375 phenotypic traits in male and female mice, recorded in >2.1 million measurements from the International Mouse Phenotyping Consortium. We find sex differences in allometric parameters (slope, intercept, residual SD) are common (76% traits). Body weight differences do not explain all sex differences in trait values but scaling by weight may be useful for some traits. Our results support a trait-specific patterning of sex differences in phenotypic traits, promoting case-specific approaches to drug dosage scaled by body weight.

evolutionary biology↗

bioRxiv2020

Sex and Power: sexual dimorphism in trait variability and its eco-evolutionary and statistical implications

Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioral traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the estrus-mediated variability hypothesis); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the greater male variability hypothesis. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex-bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance.

evolutionary biology↗

bioRxiv2019

Meta-analysis reveals that the provision of multiple ecosystem services requires a diversity of land covers

Sustaining multiple ecosystem services across a landscape requires an understanding of how consistently services are shaped by different categories of land uses. Yet, this understanding is generally constrained by the availability of fine-resolution data for multiple services across large areas and the spatial variability of land-use effects on services. We systematically surveyed published literature for New Zealand (1970 - 2015) to quantify the supply of 17 services across 25 land covers (as a proxy for land use). We found a consistent trade-off in the services supplied by anthropogenic land covers with a high production intensity (e.g., cropping) versus those with extensive or no production. In contrast, forest cover was not associated with any distinct patterns of service supply. By drawing on existing research findings we reveal complementarity and redundancy (potentially influencing resilience) in service supply from different land covers. This can guide practitioners in shaping land systems that sustainably support human well-being.

ecology↗

bioRxiv2019

Pharmacological manipulations of judgement bias: a systematic review and meta-analysis

Validated measures of animal affect are crucial to research spanning a number of disciplines including neuroscience, psychopharmacology, and animal welfare science. Judgement bias, which assesses decision-making under ambiguity, is a promising measure of animal affect. One way of validating this measure is to induce affective states using pharmacological manipulations and determine whether the predicted judgement biases are observed. Here, we conducted a systematic review and meta-analysis using data from 19 published research articles that use this approach from which 440 effect sizes were extracted. The results of the meta-analysis suggest that pharmacological manipulations overall altered judgement bias as predicted. However, there were several moderating factors including the neurobiological target of the drug, whether the drug was hypothesised to induce a relatively positive or negative affective state, dosage, and the presented cue. This may partially reflect interference from adverse effects of the drug, such as sedation. Thus, while judgement bias can be used to measure pharmacologically-induced affective states, potential adverse effects of the drug should be considered when interpreting results.

animal behavior and cognition↗