Biology subjects

Troendle, M.

Publications and source records attributed to Troendle, M..

5 recordsLinked to original sources

bioRxiv2026

The Impact of Non-Neural Sources on Aperiodic EEG Activity

Aperiodic, 1/f-like EEG activity has emerged as a key index of neural population dynamics, offering insights into excitation/inhibition balance, cognitive states, and clinical conditions. However, the foundational assumption that these parameters reflect neural dynamics rather than non-neural sources remains largely untested. Here, we systematically quantify how data quality and physiological artifacts influence aperiodic parameter estimation across two independent datasets (N=99 and N=103). Findings converged across data sets and two complementary analytical approaches: experimental artifact manipulation and selective signal decomposition. We demonstrate that ocular artifacts and poor data quality systematically inflate aperiodic offsets and exponents, while muscular artifacts exert the opposite effects. These spatially widespread influences persist after state-of-the-art preprocessing and reach magnitudes comparable to reported neurophysiologically meaningful group differences. Our results reveal that differential rates of non-neural sources can drive spurious neural interpretations; consequently, we introduce and validate an approach to mitigate these biases, guiding valid inference in basic and clinical aperiodic research.

neuroscience↗

bioRxiv2023

Emotion-induced frontal α asymmetry predictsrelapse after discontinuation of antidepressantmedication

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSOne in three patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of Major Depressive Disorder (MDD). However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by fMRI has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor. MethodsPatients (n=56) who had remitted from a depressive episode while taking antidepressants underwent EEG recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a six-months follow-up period. 35 healthy controls were also tested. Current source density of the EEG power in the band (8-13Hz) was extracted and alpha-asymmetry was computed by comparing the power across two hemispheres at frontal electrodes (F5 and F6). OutcomesSad mood induction was robust across all groups. Reactivity of -asymmetry to sad mood did not distinguish healthy controls from patients with remitted MDD on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in -asymmetry compared to patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy). InterpretationA simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation. FundingSwiss National Science Foundation project grant 320030L_153449 / 1 to QJMH, Stiftung Deutsche Depressionshilfe to HW and QJMH, a Deutsche Forschungsgemeinschaft (DFG) grant (WA 1539/5-1) to HW, EMDO Stiftung to QJMH and the Rene and Susanne Braginsky Foundation and Clinical Research Priority Programme "Molecular Imaging" at the University of Zurich to KES.

neuroscience↗

bioRxiv2022

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

BackgroundMemory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patients ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinicians experience, motivation and tiredness. MethodsHere, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network. ResultsThe model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. ConclusionsOur AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

neuroscience↗

bioRxiv2020

Decomposing the role of alpha oscillations during brain maturation

Childhood and adolescence are critical stages of the human lifespan, in which fundamental neural reorganizational processes take place. A substantial body of literature investigated neurophysiological changes during brain maturation by focusing on the most dominant feature of the human EEG signal: the alpha oscillation. Ambiguous results were reported for the developmental trajectory of the power of the alpha oscillation. Simulations in this study show that conventional measures of alpha power are confounded by various factors and need to be decomposed into periodic and aperiodic components, which represent distinct underlying brain mechanisms. It is therefore unclear how each part of the signal relates to changes during brain maturation. Using multivariate Bayesian generalized linear mixed models, we examined aperiodic and periodic parameters of alpha activity in the largest openly available pediatric dataset (N=2529, age range 5-21 years) and replicated these findings in a preregistered analysis of an independent validation sample (N=369, age range 6-21yrs). First, the well documented age-related decrease in total alpha power was replicated. However, when controlling for the aperiodic signal component, our findings provide strong evidence for a reversed developmental trajectory of the periodic alpha power, whereas the aperiodic signal components, slope and offset, decreased. Consequently, earlier interpretations on age related changes of alpha power need to be fundamentally reconsidered, incorporating changes in the aperiodic signal. The interpretation of decreased total alpha power as elimination of active synapses rather links to decreases in the aperiodic intercept. Instead, additional analyses of diffusion tensor imaging data indicate that the maturational increase in periodic alpha power is related to increased thalamocortical connectivity. Functionally, our results suggest that increased thalamic control of cortical alpha power is linked to improved attentional performance during brain maturation.

neuroscience↗

bioRxiv2020

Synchrony in the periphery: inter-subject correlation of physiological responses during live music concerts

A concert is a common event at which people gather to share a musical experience. While techniques are increasingly offering insights into naturalistic stimuli perception, this study extended methods to a more ecological context in order to explore real-world music listening within a concert setting. Cardiorespiratory, skin conductance, and facial muscle responses were measured from participants attending one of three concerts with live chamber music performances of works of varying Western Classical styles (Viennese Classical, Contemporary, and Romantic). Collective physiological synchronisation of audience members was operationalised via inter-subject correlation (ISC). By assessing which musical features (obtained via Music Information Retrieval and music-theoretical analyses) evoked moments of high synchrony, logistic regressions revealed that tempo consistently predicted physiological synchrony across all concerts in Classical and Romantic styles, but not the Contemporary style. Highly synchronised responses across all three concert audiences seemed to occur during structural transitional passages, boundaries, and at phrase repetitions. The results support the idea that group synchronisation is linked to musical arousal, structural coherence, and familiarity. By employing physiological ISC and an inter-disciplinary musical analysis, the current study demonstrates a novel approach to gain valuable insight into experiences of naturalistic stimuli in an ecological context.

neuroscience↗