Biology subjects

Naas, A.

Publications and source records attributed to Naas, A..

4 recordsLinked to original sources

bioRxiv2026

The hedonic evaluation of neurofeedback stimuli is fast, automatic and implicit: An ERP study on stimulus design.

Introduction - Neurofeedback (NFB) has demonstrated efficacy in treating various disorders, often achieving substantial symptom reductions. Despite its effectiveness, a significant percentage of users (non-responders), fail to benefit from NFB. Addressing this issue, the study at hand investigates the role of NFB design on neuro-physiological responses. Method - event related potentials (ERPs) are examined in response to the application of different aesthetic principles in the context of NFB stimulus design. Drawing from Self-Determination Theory and ERP studies in the field of web design, 16 feedback stimuli were developed according to specific design principles. Stimulus design effects were inspected by means of ERPs allowing for the assessment of implicit and fast electroencephalogram (EEG) reactions. Results of n = 38 participants indicated distinct ERP response patterns at time window of interest 1 (TWOI-1; 100-200 ms) and TWOI-2 (200-300 ms), predicted by beholder-based liking and complexity of stimuli. The findings align with the proposed hypotheses suggesting that aesthetic evaluation of NFB stimuli occurs rapidly and implicitly. In response to the aesthetic vs. non-aesthetic categories, the findings were mixed. The results underscore the importance of further exploration of aesthetic design guidelines in the context of NFB applications. It is discussed how NFB aesthetics relate to Processing Fluency and Affective Prediction Error Theory, while the theoretical methodological issue of the Fixed Effect Fallacy is taken into consideration. The study contributes to the broader understanding of how design elements can affect therapeutic efficacy and engagement in NFB and Human Computer Interaction in therapeutic contexts in general.

neuroscience↗

bioRxiv2025

Frequency-Specific Resting-State MEG Network Characteristics of Tinnitus Patients Revealed by Graph Learning

Tinnitus, the perception of sound without an external source, affects a significant portion of the population, yet its impact on brain communication diagram known as the functional connectome, remains limited. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index and coherence rely on pairwise comparisons and are therefore limited in providing a holistic encoding of FC. Here, we employ an alternative approach to estimate the entire connectivity structure by analyzing all time-courses simultaneously. This approach is robust even for short-duration recordings, facilitating faster functional connectome identification and real-time applications. Using resting-state MEG recordings from controls and individuals with tinnitus, we demonstrated that the learned connectomes outperform correlation-based connectomes in fingerprinting, that is, identifying an individual from test-retest acquisitions. Group-level analysis revealed distinct altered FC in tinnitus across multiple frequency bands, affecting the default mode, auditory, visual, and salience networks, suggesting a reorganization of these large-scale networks beyond auditory areas. Our study reveals that tinnitus presents highly individualized and heterogeneous whole-brain connectome profiles, highlighting the need to focus on individual variability rather than group-level differences to gain a more nuanced understanding of tinnitus. Personalized FC could enable patient-specific tinnitus models, optimizing treatment strategies for individualized care.

neuroscience↗

bioRxiv2024

Prediction of acoustic tinnitus suppression using resting state EEG: An explainable AI approach

Tinnitus, characterized by the perception of sound without an external source, affects a significant portion of the population and can lead to considerable individual suffering, yet understanding of its suppression remains limited. Understanding neural traits of tinnitus suppression may be crucial for developing accurate predictive models in tinnitus research and treatment. This study aims to classify individuals capable of brief acoustic tinnitus suppression (BATS; also known as residual inhibition) based on their independent resting state EEG (n=102), exploring the classifications robustness on various sample splits, and the relevance of resulting specific EEG features in the spirit of explainable AI. A comprehensive set of EEG features, including band power in standard frequency bands, spectral entropy, aperiodic slope and offset of the power spectrum, and connectivity, was included in both sensor and source space. Binary classification of the BATS status was performed using a comprehensive set of standard classifiers and Pearson correlation for feature selection, which addresses multicollinearity, avoiding complex dimensionality reduction techniques. Feature importance was assessed using Gini impurity metrics, allowing interpretation of the directionality of identified neural features. The Random Forest model showed the most consistent performance, with its majority voting mechanism effectively reducing overfitting and providing reliable predictions, and was therefore chosen for subsequent feature interpretation analysis. Our classification task demonstrated high accuracy across the various BATS split thresholds, suggesting that the choice of threshold does not significantly influence the underlying pattern in the data. We achieved classification accuracies of 98% for sensor and source models and 86% for the connectivity model in the main split. Looking at identified important features, our findings align with and extend existing neuroscience research in tinnitus by discovering highly specific and novel neural features in naive resting-state data predictive of BATS. Gamma power is identified as the most important feature in the sensor model, followed by alpha power, which fits current models of sensory processing, prediction, and updating (gamma) as well as inhibitory (alpha) frameworks. The overall spectral shape of the EEG power spectrum tends to be more normal in +BATS individuals, as reflected in the aperiodic offset and slope features. In the source model, important features are lateralized in that the gamma feature is more prominent in the left core auditory network, whereas the alpha feature is distributed more sparsely over the right hemisphere in line with auditory attention data. Furthermore, we identified several hotspots in the temporal, insular, parietal, parahippocampal, medial prefrontal, and (posterior) cingulate cortex implicated in sensory processing, gating, attention, and memory processes. Relevant network features were found in a hyperconnected bilateral auditory network (within the network), while the full auditory network was hyperconnected to limbic regions (between networks), which may reflect an intact sensory gating mechanism aiding tinnitus suppression. This studys implications extend to improving the understanding and prediction of tinnitus loudness perception and tinnitus distress as well as its (acoustic) suppression. Furthermore, our approach underscores the importance of careful feature selection, model choice, and validation strategies in analyzing complex neurophysiological data.

neuroscience↗

bioRxiv2019

Alpha Neurofeedback Training with a portable Low-Priced and Commercially Available EEG Device Leads to Faster Alpha Enhancement.A Single-blind, Sham-feedback Controlled Study & Methodological Review

IntroductionFindings of recent studies have proposed that it is possible to enhance cognitive capacities of healthy individuals by means of individual upper alpha (around 10 to 13.5 Hz) neurofeedback training. Although these results are promising, most of this research was conducted based on high-priced EEG systems developed for clinical and research purposes only. This study addresses the question whether such effects can also be shown with an easy to use and comparably low priced Emotiv Epoc EEG headset available for the average consumer. In addition, critical voices were raised regarding the control group designs of studies addressing the link between neurofeedback training and cognitive performance. Based on an extensive literature review revealing considerable methodological issues in an important part of the existing research, the present study addressed the question whether individual upper alpha neurofeedback has a positive effect on alpha amplitudes (i.e. increases alpha amplitudes) and short-term memory performance focussing on a methodologically sound, single-blinded, sham controlled design. MethodParticipants (N = 33) took part in four test sessions over four consecutive days of either neurofeedback training or sham feedback (control group). In the experimental group, five three-minute periods of visual neurofeedback training were administered each day whereas in the control group, the same amount of sham feedback was presented. Performance on eight digit-span tests as well as participants affective states were assessed before and after each of the daily training sessions. ResultsParticipants in the neurofeedback training (NFT) group showed faster and greater alpha enhancement compared to the control group. Contrary to the authors expectations, alpha enhancement was also observed in the control group. Surprisingly, exploratory analyses showed a significant correlation between the initial alpha level and the alpha improvement during the course of the study. This finding suggests that participants with high initial alpha levels profit more from alpha NFT interventions. digit-span performance increased in both groups over the course of time. However, the increase in individual upper relative alpha did not explain significant variance of digit-span improvement. In the discussion, the authors explore the appearance of the alpha enhancement in the control group and possible reasons for the absence of a connection between NFT and short-term memory.

neuroscience↗