Biology subjects

Gerster, M.

Publications and source records attributed to Gerster, M..

6 recordsLinked to original sources

bioRxiv2026

Stimulus identity rather than emotion drives EEG classification on the FACED dataset

Reliable benchmark datasets are critical for advancing EEG-based emotion recognition. The Finer-grained Affective Computing EEG Dataset (FACED) is the largest publicly available EEG emotion dataset (123 subjects, nine emotion categories) and a widely adopted benchmark. We demonstrate that both intra-subject and cross-subject classification on FACED primarily reflects stimulus identity rather than emotion. Using a linear classifier (LinearSVC) and a deep learning model (CLISA), we show that (1) classification performance is comparable for trials where subjects reported feeling versus not feeling the assigned emotion; (2) accuracy drops when stimulus-assigned labels are replaced with individual self-reports; and (3) accuracy increases when reducing to one video per emotion despite discarding two-thirds of the data. These results reflect three design choices in FACED: few stimuli per category, stimulus-assigned labels, and within-video temporal splits for cross-validation. Together, these make the dataset susceptible to temporal autocorrelation and stimulus-identity confounds. To guide future work, we propose five recommendations -- spanning stimulus diversity, temporal independence, and label validation -- for emotion-decoding study designs that mitigate these confounds.

neuroscience↗

bioRxiv2025

Change in motor state equilibrium explains prokinetic effect of apomorphine on locomotion in experimental Parkinsonism

Gait impairments remain a major therapeutic challenge in Parkinsons disease (PD). Apomorphine is gaining renewed clinical attention with the expanding use of pump infusion systems. Yet, the specific role of apomorphine on the neural regulation of gait has remained poorly characterized, limiting its targeted use for symptom-specific therapy in PD. Here, we examined the neurobehavioral effects of apomorphine on runway locomotion in the unilateral 6-hydroxydopamine (6-OHDA) rat model. Therapeutic drug doses significantly increased total walking distance, related to reduced akinesia and prolonged gait episodes. Conversely, 3D kinematic analysis revealed reduced limb velocities under medication. At the neural level, therapy doses selectively enhanced cortical high-gamma rhythms without substantially altering beta or low-gamma activity. Instead, beta and low-gamma oscillations were consistently suppressed during motor activity in both medication ON and OFF conditions. Neurobehavioral correlations showed that transitions into gait were facilitated by reductions in beta and low-gamma activity, whereas transitions to akinesia were primarily suppressed when high-gamma activity was elevated. Our findings suggest that modulating cortical activity can aid ameliorating gait deficits in PD. We further propose that the complex therapy effects of apomorphine are best explained by a shift in motor-state equilibrium that is defined by the transitions of akinesia, stationary movements and gait. Together, these insights establish a mechanistic framework to guide the development of targeted gait therapies in PD.

neuroscience↗

bioRxiv2025

Beyond beta: Aperiodic broadband power reflects Parkinson's disease severity - a multicenter study

Parkinsons disease is linked to increased beta oscillations in the subthalamic nucleus, which correlate with motor symptoms. However, findings across studies have varied. Our standardized analysis of multicenter datasets shows that small sample sizes contributed to these discrepancies--a challenge we address by pooling datasets into one large cohort (n=119). Moving beyond beta power, we disentangled spectral components reflecting distinct neural processes. Combining aperiodic offset, low beta, and low gamma oscillations explained significantly more variance in symptom severity than beta alone. Moreover, interhemispheric within-patient analyses showed that, unlike beta oscillations, aperiodic broadband power-likely reflecting spiking activity-was increased in the more affected hemisphere. These findings identify aperiodic broadband power as a potential biomarker for adaptive deep brain stimulation and provide novel insights into the relationship between subthalamic hyperactivity and motor symptoms in human Parkinsons disease.

neuroscience↗

bioRxiv2024

Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of Parkinson's disease

Deep brain stimulation is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinsons disease, subthalamic deep brain stimulation clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex - basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully invasive neural multisite recordings in patients undergoing deep brain stimulation surgery with normative MRI-based whole-brain connectomics. Our findings demonstrate that dopamine and stimulation exert distinct mesoscale effects through modulation of local neural population activity. In contrast, at the macroscale, stimulation mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex - basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and deep brain stimulation, laying the foundation for advanced closed-loop neurostimulation therapies.

neuroscience↗

bioRxiv2021

Separating neural oscillations from aperiodic 1/f activity: challenges and recommendations

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law P{propto}1/f{beta}and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent {beta}. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.

neuroscience↗

bioRxiv2021

Patient-specific network connectivitycombined with a next generation neural massmodel to test clinical hypothesis of seizurepropagation

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine the mathematical modelling with structural information from non-invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along the example of diffusion-weighted magnetic resonance imaging (MRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e. the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

neuroscience↗