Biology subjects

Auer, T.

Publications and source records attributed to Auer, T..

9 recordsLinked to original sources

bioRxiv2026

Cognitive exertion reshapes resting-state EEG markers across the adult lifespan

Resting-state electroencephalography (rsEEG) yields robust indices of ageing, notably individual alpha peak frequency (iAPF), alpha power, and the aperiodic exponent. Whether these markers reflect stable traits or shift with cognitive exertion remains unresolved, with direct consequences for lifespan and clinical research. We parameterised periodic and aperiodic rsEEG activity before and after cognitive tasks in a lifespan cohort (N = 390, aged 20-70), a five-year longitudinal follow-up (N = 100), and an independent older-adult dataset completing a different task (N = 71). Across datasets, cognitive exertion produced lifespan-wide iAPF slowing and increase in associated power. Notably, aperiodic shifts were age-dependent, with post-task steepening of the exponent in younger adults that progressively flattened with advancing age, resulting in a stronger effect of age on post-task exponents. Our results demonstrate that widely used spectral metrics exhibit acute state-dependency and post-task recordings offer a promising translational framework for indexing individual differences in healthy ageing and pathology. Trial registrationClinicaltrials.gov: NCT05155397

neuroscience↗

bioRxiv2025

Non-linear effects of evening light exposure on cognitive performance

In humans, exposure to light can impact alertness and cognitive performance. These cognitive effects of light are mediated by the intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing the photopigment melanopsin, which signals environmental light in addition to the cone- and rod-mediated pathways. Most studies investigating the cognitive effects of light have focused on alertness, raising the question how higher-level cognitive tasks such as working memory are modulated by light. This study investigated the dose-response relationship for alertness, cognitive performance and mental workload. Each level of melanopic illuminance (ranging from 1 lx to 595 lx melanopic EDI) was evaluated over separate days with a six-hour exposure in a controlled climate chamber with artificial lighting. Participants (n=16, 10 female, 27.4{+/-}2.5 years), completed the Psychomotor Vigilance Test (PVT) and n-back task every 30 minutes to assess reaction time, attention, and working memory, alongside subjective evaluations through questionnaires. The results suggest an inverted U-shaped correlation between cognitive functions and melanopic EDI and a U-shaped relationship between subjective assessments and melanopic EDI. Extreme lighting conditions in our stimulus set - both dim (1 lx melanopic EDI) and bright (595 lx melanopic EDI) - were associated with increased sleepiness and perceived workload, quicker reaction times, and diminished cognitive performance. Conversely, moderate illuminance levels (10 lx melanopic EDI and 70 lx melanopic EDI) positively influenced cognitive performance and mental workload but resulted in slower reaction times. This study illustrates that the relationship between melanopic EDI levels and cognitive performance does not follow a linear dose-response pattern, indicating a complex strategy for resource allocation in cognition.

neuroscience↗

bioRxiv2023

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing.

neuroscience↗

bioRxiv2022

Retrieving fMRI data in real-time: difficulties and pitfalls

One of the significant challenges in real-time fMRI environments is to ensure that the functional images are exported in real-time. The prerequired ability to reconstruct these images immediately after the acquisition has already been resolved in 2004. Nowadays, more sophisticated sequences allow for higher resolution and faster repetition times and thereby challenging the ability to export this data in real-time. In this article, we tackle the potentially arising problem of sending the reconstructed data from the MRI to an external PC to perform the real-time fMRI analysis. We show that depending on the implementation of the data transfer, long delays can occur that can differ drastically in time and how often they occur. In addition, we propose a solution for SIEMENS MRI devices which was tested and applied already on multiple MRI devices including 3T and 7T machines on different vendor software versions. This new technique can be used as a blueprint that can be directly applied to other manufacturers. We also provide the source code of the described solution and show that the delay in the data transfer can be significantly reduced to a tolerable level using our proposed procedure. Finally, we integrate measurement options for the data transfer times to improve quality measures in real-time fMRI environments (e.g., clinical) that can implement the proposed solution. Efforts should be taken by the real-time community and MRI manufacturers to employ a standardized real-time export e.g., similar to the lab streaming layer which is used as a standard export method in EEG environments.

neuroscience↗

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗

bioRxiv2019

Best practices for making reliable inferences from citizen science data: case study using eBird to estimate species distributions

Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort. To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes. Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes. Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

ecology↗

bioRxiv2018

Optimizing conservation of migratory species over their full annual cycle in the Western Hemisphere

Limited knowledge of the distribution, abundance, and habitat associations of migratory species introduces uncertainty about the most effective conservation actions. We used Neotropical migratory birds as a model group to evaluate contrasting approaches to land prioritization to support [≥]30% of the global abundances of 117 species throughout the annual cycle in the Western hemisphere. Conservation targets were achieved in 43% less land area in plans based on annual vs. weekly optimizations. Plans agnostic to population structure required comparatively less land area to meet targets, but at the expense of representation. Less land area was also needed to meet conservation targets when human-dominated lands were included rather than excluded from solutions. Our results point to key trade-offs between efforts minimizing the opportunity costs of conservation vs. those ensuring spatiotemporal representation of populations, and demonstrate a novel approach to the conservation of migratory species based on leading-edge abundance models and linear programming to identify portfolios of priority landscapes and inform conservation planners.

ecology↗

bioRxiv2018

Modeling Avian Full Annual Cycle Distribution and Population Trends with Citizen Science Data

Information on species distributions and abundances, and how these change over time are central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at relevant scales across range-wide extents for two main reasons. First, local and regional processes that affect populations vary throughout the year and across species ranges, requiring fine-scale, year-round information across broad -- sometimes hemispheric -- spatial extents. Second, while citizen science projects can collect data at these scales, using these data requires appropriate analysis to address known sources of bias. Here we present an analytical framework to address these challenges and generate year-round, range-wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long-distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and relative abundance with enough spatiotemporal resolution to support inference across a range of spatial scales throughout the annual cycle. Additionally, we generate intra-annual estimates of the range, intra-annual estimates of the associations between species and the local environment, and inter-annual trends in relative abundance. This is the first example of an analysis to capture intra- and inter-annual distributional dynamics across the entire range of a broadly distributed, highly mobile species.

ecology↗