Biology subjects

Lührs, M.

Publications and source records attributed to Lührs, M..

7 recordsLinked to original sources

bioRxiv2026

Searchlight Optimization Using Representational Similarity Analysis for Subject-Level Voxel Selection in Emotional State Decoding

Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be insufficient for capturing fine-grained representational differences. This challenge becomes particularly relevant in experimental settings and interventions such as neurofeedback training, where voxels are not only measured as neural responses but also used as targets for intervention based on their previously observed activity patterns. In this study, we propose a subject-level searchlight optimization framework that integrates voxel-wise general linear model (GLM)-based univariate analysis with representational similarity analysis (RSA)-based multivariate refinement to identify voxels that are both task-relevant and condition-sensitive. To enhance practical applicability, the framework further incorporates a data-driven hyperparameter tuning step based on Bayesian optimization, enabling efficient identification of high-performing configurations from small pilot datasets, with consistent performance when applied to larger samples. The proposed framework was evaluated using an emotion imagery fMRI dataset with four affective conditions. Results demonstrate that the multivariate refinement improves alignment between empirical and target representational structures compared with univariate selection alone. Compared with a classifier-based voxel selection approach, the RSA-based approach better preserves the representational geometry of emotional states while maintaining discriminative capacity. These findings highlight the effectiveness, efficiency, and robustness of the proposed RSA framework, providing a practical solution for identifying condition-sensitive voxels and supporting more precise multivariate investigation of affective brain states in multi-condition fMRI studies.

neuroscience↗

bioRxiv2026

All signals considered: Data quality partially explains inter-individual task differences in a large, open fNIRS dataset

Significance: High inter-subject variability and limited reproducibility in functional near-infrared spectroscopy (fNIRS) research may partly reflect global systemic physiology and signal quality differences, possibly distorting task-evoked hemodynamic responses. Aim: We investigate how signal quality relates to inter-subject variability in motor-task fNIRS responses and introduce a large, open, multi-task, near whole-head fNIRS dataset with extensive peripheral physiology and short-channel recordings. Approach: Fifty-seven participants completed resting-state, motor action, motor imagery, emotion recognition, visual, and auditory tasks during fNIRS recording. Peripheral measures included pulse oximetry, heart rate, blood oxygen saturation, respiration, room temperature, galvanic skin response, electrocardiogram, and electromyography. Signal quality was assessed using the scalp coupling index (SCI), coefficient of variation (CV), signal-to-noise ratio (SNR) and a spectral measure here coined the coupling SNR (cSNR). Results: Quality metrics were weakly to moderately correlated, except SNR and CV, which showed the expected inverse relationship. All quality metrics were significantly related to channel length and associated with task-related activation estimates. Group-level analyses validated activation in expected task-related regions. Conclusions: The assessed metrics capture complementary features of fNIRS signal quality and may help explain individual activation differences. The dataset provides a comprehensive, open resource enabling future evaluation of physiological correction methods and confound mitigation.

neuroscience↗

bioRxiv2026

Light on Broken Networks: Resting-State fNIRS as a Tool for Connectivity Mapping

Resting-state functional connectivity (RSFC) and networks (RSNs) provide insight into large-scale brain organization and its disruption in neurological disease. RSNs are most commonly assessed using fMRI, yet its translational use is constrained by high cost, motion sensitivity, and limited feasibility for repeated measurements. Functional near-infrared spectroscopy (fNIRS) offers a portable alternative, but its reliability for RSFC and RSN mapping remains insufficiently established. Near whole-head fNIRS data and fMRI-BOLD signals of corresponding cortical regions were extracted, based on which RSN organization was compared across two independent cohorts of 31 participants each. Cross-modal convergence and divergence were assessed using bivariate and partial correlations across multiple network levels. Edgewise analyses revealed substantial modality differences with bivariate correlations (50-61% of edges), which were markedly reduced using partial correlations (<3%). Group-level connectivity patterns showed moderate cross-modal similarity (r {approx} 0.37). At nodal level, net strength, local efficiency, and path-length differed substantially between modalities, while normalized strength and assortativity were largely comparable. Across nodes, group-level graph-metric distributions were broadly similar for normalized strength, assortativity, local efficiency, and path length (rho {approx} 0.27-0.5). At network-level, fNIRS-derived modules significantly overlapped with fMRI modules, particularly based on bivariate correlations, identifying default mode, attentional, executive, salience, sensorimotor, and visual networks (Jaccard {approx} 0.27-0.5). Overall, fNIRS captured key features of large-scale RSFC and RSN organization observed with fMRI, supporting meaningful cross-modal correspondence and translational utility. While partial correlations enhanced edge-level agreement, they attenuated nodal and modular recovery, suggesting greater suitability for targeted connectivity analyses rather than whole-network characterization.

neuroscience↗

bioRxiv2026

Independent Component Analysis Outperforms Seed-Based Approach in Detecting fNIRS-based Resting-State Functional Connectivity

SignificanceResting-state functional connectivity (RSFC) is an important measure in advancing our understanding of brain function and development as well as various neurological and mental disorders. Studying RSFC with functional near-infrared spectroscopy (fNIRS) offers several advantages over functional magnetic resonance imaging (fMRI), especially for clinical and pediatric populations. However, the optimal strategy to estimate RSFC based on fNIRS, particularly in identifying reliable connectivity patterns across chromophores, remains unclear. Establishing robust analysis approaches is essential for reliable and clinically meaningful applications. AimThis study systematically evaluated commonly used analysis methods regarding their effectiveness to detect RSFC patterns within the motor network using both oxygenated (HbO) and deoxygenated (HbR) hemoglobin signals. ApproachNear whole-head resting-state fNIRS data were analyzed from 38 participants. RSFC was estimated with five analytical approaches: three seed-based methods (SBA-GLM, SBA-GLM with respiratory regression, and SBA-correlation) and two independent component analyses (ICA) approaches using two different contrast functions. Performance was assessed via receiver operating characteristic analyses based on both anatomical and functional definitions of motor-related connectivity. Areas under the curves (AUC) were statistically compared with DeLongs test, and the spatial similarity between HbO and HbR RSFC was quantified by correlating RSFC patterns from the two chromophores. ResultsAcross reference definitions and chromophores, ICA consistently achieved higher performance (AUC = 0.82-0.96) in detecting motor-related RSFC than SBA (AUC = 0.63-0.86). Significant differences emerged when functionally defined connectivity references were used, with ICA outperforming SBA across chromophores. Under certain condition, correlational-SBA (AUC = 0.66-0.86) significantly outperformed GLM-based methods (AUC = 0.63-0.85). Finally, ICA results demonstrated greater spatial similarity between obtained HbO and HbR RSFC patterns (r = 0.90-0.92) than SBA (r = 0.84-0.86), indicating higher cross-chromophore consistency. ConclusionsICA provides a robust and consistent framework for estimating fNIRS-based RSFC across both HbO and HbR, outperforming SBA in accuracy and cross-chromophore consistency. While correlational-SBA offers a computationally efficient alternative and outperforms GLM-based methods, ICA should be preferred when reliable and chromophore-consistent RSFC estimates are required. Importantly, these findings demonstrate that HbR contains RSFC information comparable to HbO and highlights the critical role of analytical strategy and reference definition in RSFC evaluation. Collectively, these results contribute to the methodological standardization of fNIRS-based RSFC and support its use in future neuroscientific and clinical applications.

neuroscience↗

bioRxiv2024

Navigating the Maze: Identifying Potential Pitfalls in Attention State Classification from fMRI Brain Patterns

Multi-voxel pattern analysis (MVPA) is a powerful technique to decode brain states from functional magnetic resonance imaging (fMRI) activity patterns. In neurofeedback (NF) applications, it has been used to perform real-time classification of brain activity patterns, establishing a closed-loop system that provides immediate feedback to the participants, enabling them to learn to control a complex mental state. However, MVPA has many potential limitations when applied to fMRI datasets (especially in real-time analysis) arising from small effect sizes, small number of training samples, high dimensionality of the data and, more generally, design choices. All these factors might produce inaccurate classification results. In this work, we followed a previous NF paradigm for sustained attention training. Participants were presented with composite images superimposing faces and scenes. They were instructed to focus on one class (either face or scene) for an extended period. A logistic regression classifier was trained to determine whether participants were adequately focusing on the instructed category based on their fMRI data. We analysed the classification outputs of the no-feedback training runs using various classifier settings, including whole brain data and different masking approaches, combined with different methods for the computation of single-trial fMRI responses. Furthermore, a ventricle mask was used as a control condition for the classification task, and simulations were carried out to assess the influence of the class order on the classification performances. We found inflation of the decoding accuracy for several common design choices and confounders. In particular, motion artefacts and low frequency drifts coupled with the task timing might have artificially increased the accuracy scores. Furthermore, the simulations revealed that fixed order in the presentation of experimental conditions resulted in further inflation of the classification accuracies especially in GLM-based and average-based trial estimate methods. We discuss the drawbacks of applying MVPA using the analysed sustained attention paradigm and provide insights for future improvements.

neuroscience↗

bioRxiv2021

Online self-evaluation of fMRI-based neurofeedback performance

This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level of blood-oxygen-level-dependent (BOLD) activation. This setup offers the possibility to study performance monitoring in the absence of somatosensory feedback. Here, we studied two types of self-evaluation expressed before receiving neurofeedback: performance predictions and perceived confidence in the prediction judgement. We hypothesized that throughout learning, participants would (1) improve the precision of their performance predictions about the actual changes in their BOLD response, and (2) that reported confidence would progressively increase with improved metacognitive precision. Participants completed three sessions of SMA regulation in a 7T fMRI scanner, performing a drawing motor imagery task. During each trial, they modulated their mental drawing strategy to achieve one of two different levels of target fMRI signal change. They then reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not differentiate between the better and worse predictions. In addition to shedding light on mechanisms of internal monitoring during neurofeedback training, these results also point to a dissociation between self-evaluation of performance and corresponding reported confidence in the presence of feedback.

neuroscience↗

bioRxiv2021

Extremely Fast pRF Mapping for Real-Time Applications

Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is build upon hashed-Gaussian encoding of the stimulus, which significantly reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico, as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping.

neuroscience↗