Biology subjects

Sorger, B.

Publications and source records attributed to Sorger, B..

9 recordsLinked to original sources

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

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

Inclusivity in fNIRS Studies: Quantifying the Impact of Hair and Skin Characteristics on Signal Quality with Practical Recommendations for Improvement

Functional Near-Infrared Spectroscopy (fNIRS) holds transformative potential for research and clinical applications in neuroscience due to its non-invasive nature and adaptability to real-world settings. However, despite its promise, fNIRS signal quality is sensitive to individual differences in biophysical factors such as hair and skin characteristics, which can significantly impact the absorption and scattering of near-infrared light. If not properly addressed, these factors risk biasing fNIRS research by disproportionately affecting signal quality across diverse populations. Our results quantify the impact of various hair properties, skin pigmentation as well as head size, sex and age on signal quality, providing quantitative guidance for future hardware advances and methodological standards to help overcome these critical barriers to inclusivity in fNIRS studies. We provide actionable guidelines for fNIRS researchers, including a suggested metadata table and recommendations for cap and optode configurations, hair management techniques, and strategies to optimize data collection across varied participants. This research paves the way for the development of more inclusive fNIRS technologies, fostering broader applicability and improved interpretability of neuroimaging data in diverse populations.

neuroscience↗

bioRxiv2024

Riemannian Geometry for the Classification of Brain States with fNIRS

BackgroundFunctional near-infrared spectroscopy (fNIRS) has recently gained momentum as a reliable and accurate tool for assessing brain states. This increase in popularity is due to its robustness to movement, non-invasive nature, portability, and user-friendly application. However, compared to functional magnetic resonance imaging (fMRI), fNIRS is less sensitive to deeper brain activity and offers less coverage. Additionally, due to fewer advancements in method development, the performance of fNIRS-based brain-state classification still lags behind more prevalent methods like fMRI. MethodsWe introduce a novel classification approach grounded in Riemannian geometry for the classification of kernel matrices, leveraging the temporal and spatial channel relationships and inherent duality of fNIRS signals--more specifically, oxygenated and deoxygenated hemoglobin. For the Riemannian geometry-based models, we compared different kernel matrix estimators and two classifiers: Riemannian Support Vector Classifier and Tangent Space Logistic Regression. These were benchmarked against four models employing traditional feature extraction methods. Our approach was tested in two brain-state classification scenarios based on the same fNIRS dataset: an 8-choice classification, which includes seven established plus an individually selected imagery task, and a 2-choice classification of all possible 28 2-task combinations. ResultsThe novel approach achieved a mean 8-choice classification accuracy of 65%, significantly surpassing the mean accuracy of 42% obtained with traditional methods. Additionally, the best-performing model achieved an average accuracy of 96% for 2-choice classification across all possible 28 task combinations, compared to 78% with traditional models. ConclusionTo our knowledge, we are the first to demonstrate that the proposed Riemannian geometry-based classification approach is both powerful and viable for fNIRS data, considerably increasing the accuracy in binary and multi-class classification of brain activation patterns.

neuroscience↗

bioRxiv2023

Towards fNIRS Hyperfeedback: A Feasibility Study on Real-Time Interbrain Synchrony

Social interaction is of fundamental importance to humans. Prior research has highlighted the link between interbrain synchrony and positive outcomes in human social interaction. Neurofeedback is an established method to train ones brain activity and might offer a possibility to increase interbrain synchrony. Consequently, it would be advantageous to determine the feasibility of creating a neurofeedback system for enhancing interbrain synchrony to benefit human interaction. In this study, we investigated whether the most widely employed metric for interbrain synchrony, namely wavelet transform coherence, can be assessed accurately in near real-time using functional near-infrared spectroscopy (fNIRS), which is recognized for its mobility and ecological suitability for interactive research. To this end, we have undertaken a comprehensive approach encompassing simulations and a re-evaluation of two human-interaction datasets. Our findings indicate the potential for a stable near real-time measurement of wavelet transform coherence for integration durations of about one minute. This would align well with the methodology of an intermittent neurofeedback procedure. Our investigation lays the technical foundation for an fNIRS-based system to measure interbrain synchrony in near real-time. This advancement is crucial for the future development of a neurofeedback training system tailored to enhance interbrain synchrony to potentially benefit human interaction.

neuroscience↗

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2020

Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: Effects on signal quality and sensitivity

Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Available resources often dictate the approach researchers use for optode-layout design. Here we compared four approaches that incrementally incorporated subject-specific magnetic resonance imaging (MRI) information while participants performed mental-calculation, mental-rotation and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB), employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional and vascular information of the same subject (fVASC). The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI and fVASC approaches resulted in a similar outcome. We conclude that additional individual MRI data leads to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗

bioRxiv2018

Somatosensory imagery induces topographically specific activation patterns instrumental to fMRI-based Brain Computer Interfacing

Real-time functional magnetic resonance imaging (fMRI) is a promising non-invasive method for brain computer interfaces (BCIs). BCIs translate brain activity into signals that allow communication with the outside world. Visual and motor imagery are often used as information-encoding strategies, but can be challenging if not grounded in recent experience in these modalities, e.g. in patients with locked-in-syndrome (LIS). In contrast, somatosensory imagery might constitute a more suitable information-encoding strategy as somatosensory function is often very robust. Somatosensory imagery has been shown to activate somatotopic cortex, but it has been unclear so far whether it can be reliably detected on a single-trial level and successfully classified according to specific somatosensory imagery content.\n\nUsing ultra-high field 7-T fMRI, we show reliable and high-accuracy single-trial decoding of left-foot vs. right-hand somatosensory imagery. Correspondingly, higher decoding accuracies were associated with greater spatial separation of hand and foot decoding-weight patterns in primary somatosensory cortex (S1). Exploiting these novel neuroscientific insights, we developed - and provide a proof of concept for - basic BCI communication by showing that binary (yes/no) answers encoded by somatosensory imagery can be decoded with high accuracy not only offline but also in real-time.\n\nThis study demonstrates that body part-specific somatosensory imagery differentially activates somatosensory cortex in a topographically specific manner; evidence which was surprisingly still lacking in the literature. It is also offers a promising novel somatosensory imagery based fMRI-BCI control strategy, with particularly high potential for visually and motor-impaired patients. The strategy could also be transferred to lower MRI field strengths and to mobile functional near-infrared spectroscopy. Finally, given that communication BCIs provide the BCI user with a form of feedback based on their brain signals and can thus be considered as a specific form of neurofeedback, and that repeated use of a BCI has been shown to enhance underlying representations, we expect that the current BCI could also offer an interesting new approach for somatosensory rehabilitation training in the context of stroke and phantom limb pain.

neuroscience↗