51) Temporal Prediction in Non-Deterministic Continuous Environments: investigating the role of oscillatory entrainment and interval learning
Elmira Hosseini1
1Max Planck Institute for Biological Cybernetics, Max Planck Society, Tübingen, Germany
Interaction with our continuously changing environment relies on anticipating timing of events, enhancing information processing efficiency. Abundant research has investigated temporal prediction in deterministic environments such as isochronous rhythms, where the presumed mechanism is Oscillatory Entrainment (OE) to external rhythms. However, in everyday life, continuous streams lack fully-deterministic temporal regularities. Previous research of temporal prediction in uncertain environments has focused on isolated intervals, suggesting a Distributional-Learning (DL) model. However, in non-deterministic streams, if and under which conditions either of these mechanisms drives prediction is unclear. To address this, we combined computational modeling of the two mechanisms (OE and DL) and human behavioral experiments. In our simulation modelling, we found that while models are affected differently by the degree of variability in the environment, they lead to more overlapping predictions in lower degrees of variability. In our behavioral experiment (speeded response task), we presented specific streams from our simulation in which the predictions of the two mechanisms were differentiated the most, with targets happening at either timepoint of the predictions. Participants’ behavior followed predictions of either of the models, depending on the degree of variability and context of the environment. Overall, our results highlight the role of inherent differences between OE and DL mechanisms and the environmental context in dealing with temporal prediction in uncertain environments.
52) From Movement to Mind - Physical Activity does not affect Cognitive Functioning in Older Age
Sabrina Diny1, Luca Franzen1
1University of Salzburg, Austria
Background: The World Health Organization highlights the importance of physical activity for physical and mental health, indicating its essential role in preventing age-related cognitive decline in aging populations. Understanding how and to what extent physical activity influences cognition and aging is crucial. However, most existing studies are cross-sectional, providing only limited and short-term insights into the relationship between physical activity and cognitive health. This study aimed to investigate long-term effects by examining various measures of physical activity and their impact on cognitive functioning in older adults using longitudinal data.
Methods: Data from the Berlin Aging Study (BASE), a multidisciplinary investigation involving psychological and medical examinations of older adults in West Berlin, were used. Multiple regression analyses were conducted to predict cognitive performance (memory and intelligence). Analyses included current activity status, total duration of physical activity across the lifespan, frequency, past physical activity, activity at different age phases, and control variables such as baseline cognitive ability, gender, BMI, education, and sleep. The final sample included N = 206 participants, aged 68-97 at baseline (M = 78.68) and 73-103 at follow-up (M = 83.67).
Results: After controlling for covariates, none of the physical activity measures significantly predicted cognitive functioning at follow-up. While some cross-sectional and minor longitudinal associations were found between years of exercise and cognitive performance in different life phases, these preliminary results indicate no definite causal, long-term effects of physical activity on cognitive functioning in older age.
Discussion: The relationship between physical activity and cognitive functioning is complex. Future research should include diverse samples and begin in earlier life stages to better understand the long-term effects of physical activity on cognitive functioning.
53) Action-Related Hub in the Left Lateral Occipitotemporal Cortex (LOTC): Spatial Arrangement and Gradual Abstraction
Franziska Pfannerstill1,2, Paul Downing3, Angelika Lingnau2,4
1University of Salzburg, Austria, 2Royal Holloway University of London, University of London, Egham, United Kingdom, 3Bangor University, United Kingdom, 4University of Regensburg, Germany
Various types of action-related information were found to be represented in the left lateral occipitotemporal cortex (LOTC). Studies on basic and biological motion, bodies, body parts, tools, action perception, performance, and action verbs demonstrate selective LOTC recruitment. However, differences in tasks, participants and methods between studies prevent direct task comparisons. We aimed to directly compare several tasks within the same participants to identify a consistent spatial arrangement of action-related selectivity and the similarity of representations across LOTC in this study.
FMRI data for eight localizer experiments (Motion, Biological Motion, Bodies & Tools, Body Parts, Action Observation, Action Performance, Verbs, and Higher-Level Retinotopic Mapping) using visual stimuli in 21 participants were collected. Participants viewed pictures, videos, or words and performed tasks appropriate for the different localizers. We analyzed the data with eleven random-effects general linear model (RFX GLM) contrasts and threshold-free cluster enhancement (FSL). Peak t-values were determined in the occipital and temporal lobes for each participant and contrast, and mean peak coordinates and confidence intervals for peak location reliability across participants were calculated.
Results indicated that basic motion and body perception primarily activated posterior areas of the LOTC, while tools and action observation were localized in more central regions, and verbs and action performance activated anterior regions. Peak distributions were consistent across participants, with confidence intervals indicating overlap among different functional localizers. Action observation and verbs elicited focal activation, whereas biological motion showed greater variability across the occipital and temporal lobes.
Overlapping clusters of multiple action-related functions were found within the LOTC. The distribution of peaks suggests a gradient of abstraction, where posterior regions encode concrete perceptual representations, while anterior areas are involved in more abstract cognitive tasks. We propose that subregions of the LOTC specialize in evaluating specific aspects of action, while the broader region integrates diverse information sources to form a more abstract representation of action understanding.
54) The Austrian NeuroCloud - FAIR data operations for Cognitive Neuroscience
Barbara Strasser-Kirchweger and the ANC Team1
1University of Salzburg, Austria
The Austrian NeuroCloud (ANC) is a FAIR-enabling platform for sustainable research data management in Cognitive Neuroscience. The ANC offers tools and services to archive, manage, and share neurocognitive data flexibly and according to community standards. Scientists have full control over what they share (e.g., full original datasets or data derivatives), how they share it (by choosing from a selection of licensing models), and with whom (e.g., by using the ANC’s adjustable User Agreement templates).
The ANC provides persistent DOIs for data releases and operates in accordance with European GDPR. Moreover, the ANC fully supports the mission of the EOSC and is committed to the EU’s open science policy, legal standards, and best open science practices. Accordingly, the ANC aspires to facilitate FAIR data operations along the entire data lifecycle, actively supporting the ongoing shift in research culture towards increased transparency, data reusability, and result reproducibility.
55) PyRASA - Spectral parameterization in python based on IRASA
Fabian Schmidt1, Thomas Hartmann1, Nathan Weisz1
1University of Salzburg, Austria
The electric signals generated by physiological activity exhibit both activity patterns that are regularly repeating over time (i.e. periodic) and activity patterns that are temporally irregular (i.e. aperiodic). In recent years several algorithms have been proposed to separate the periodic from the aperiodic parts of the signal, such as the irregular-resampling auto-spectral analysis (IRASA; Wen & Liu, 2016). IRASA separates periodic and aperiodic components by up-/downsampling time domain signals and computing their respective auto-power spectra. Finally, the aperiodic component is isolated by averaging over the resampled auto-power spectra removing any frequency specific activity. The aperiodic component can then be subtracted from the original power spectrum yielding the residual periodic component. Here, we present a new Python toolbox that is building upon and extends the IRASA algorithm. The toolbox allows the user not only to separate power spectra, but also contains functionality to further parametrize the periodic and aperiodic spectra, by means of peak detection and several slope fitting options (eg. spectral knees). We can show that the tool performs as well in detecting variations in aperiodic components as the current gold-standard (specparam; Donoghue et al. 2020), while requiring less parameter tuning and being more robust in detecting “true” oscillatory activity. Furthermore, we extend the IRASA algorithm to the time-frequency domain allowing for a time-resolved spectral parameterization using IRASA.
56) Neuromagnetic Dynamics in Migraine: Comparing Periodic and Aperiodic Activity in Patients and Controls
Vanessa Frey1, Stefan Leis1, Eugen Trinka1,2, Nathan Weisz1,2, Gianpaolo Demarchi2
1Paracelsus Medical University, Salzburg, Austria, 2University of Salzburg, Austria
Migraine is a highly prevalent neurological disorder that significantly impacts both individuals and society. This study investigates the differences in periodic and aperiodic brain activity between migraine patients and healthy controls using magnetoencephalography (MEG). The research involves 25 migraine patients and 25 healthy controls. Within the patient group, further analysis differentiates between those with migraine with aura (MA, n=15) and without aura (MoA, n=10).
Traditional MEG analyses often focus solely on periodic oscillations. However, recent advancements highlight the potential significance of aperiodic activity, which may provide valuable insights into the brain's excitation-inhibition (E-I) balance. Aperiodic components, often excluded from traditional analyses, can reveal underlying neural dynamics that are not captured by periodic oscillations alone. By utilizing advanced methods such as FOOOF (Fitting Oscillations and One Over F) and IRASA (Irregular-Resampling Auto-Spectral Analysis), our study performs a comprehensive full power spectrum analysis, incorporating these often-neglected aperiodic components.
We hypothesize that migraine patients exhibit a higher imbalance in the E-I proportion, reflecting increased cortical excitability, compared to healthy controls. Additionally, we expect this imbalance to be more pronounced in MA patients compared to MoA patients. This exploratory project involved MEG data collected on healthy subjects, and patients without migraine at the time of the measurements. The results could reveal crucial differences in the neuromagnetic characteristics between these groups, advancing our understanding of migraine pathophysiology and potentially guiding the development of more effective treatment strategies. This study has the potential to uncover novel neurophysiological information that could lead to more targeted and effective therapeutic interventions for migraine sufferers.