Frank Scharnowski

Interbrain synchrony (IBS), also referred to as interpersonal neural synchrony (INS), has become a central construct in social neuroscience and beyond. It describes the temporal alignment of neural activity across individuals and has been studied in a range of paradigms, including sequential interactions, shared-stimulus settings, and hyperscanning, that is, the simultaneous neuroimaging of two or more individuals during real-time interaction. Functional near-infrared spectroscopy (fNIRS) is increasingly used for such studies because it supports portable, naturalistic, face-to-face paradigms. A central goal in these experiments is to quantify IBS between interacting individuals. Wavelet transform coherence (WTC) has become the most widely used method for estimating IBS in fNIRS studies. However, its application involves several non-trivial signal-processing assumptions and time–frequency trade-offs that are often not explicitly addressed, making it difficult to apply and interpret the method consistently. Reliable estimation of IBS is crucial not only for experimental design but also for the interpretation and comparison of results within and across studies. Here, we aim to support this endeavor by providing a principled overview of the relevant signal-processing foundations. To enable researchers to make informed methodological choices, the paper first introduces key concepts in time–frequency analysis and clarifies their relation to WTC. These principles are then connected to the analysis of IBS in fNIRS datasets, with particular emphasis on the time–frequency constraints inherent to wavelet-based methods. Building on these foundations, we provide practical recommendations for experiment design, preprocessing, and frequency targeting. By making resolution limits and analysis assumptions explicit, this primer aims to support improved study planning and more consistent interpretation of IBS in the rapidly growing field of fNIRS-based investigations and beyond. Establishing a shared methodological foundation for IBS analysis is essential for advancing toward more rigorous, transparent, and reproducible measurement of interpersonal brain dynamics in naturalistic neuroscience.

Spider phobia is one of the most common specific phobias and can be highly detrimental to one’s quality of life. To evaluate the efficacy of treatments, reliable pre- and post-treatment assessments are needed. Behavioral avoidance tests (BATs), where one is asked to approach a spider, are commonly used as such measures. With the increasing popularity of computerized interventions, there is a demand for quick assessments for fear of spiders, that can be completed using computers or smartphones. Here, we tested the validity of a novel computerized BAT (cBAT) that we introduced in our previous work. In this cBAT, participants unblurred three images of spiders up to a point where they still felt comfortable viewing them. We expected that the cBAT would be moderately to strongly correlated with established measures for fear of spiders. Our analysis revealed that the cBAT showed moderate convergent and concurrent validity through correlations with self-report questionnaires indicating spider fear, and with a real-life BAT, respectively. However, the latter was no longer observed in a follow-up. While the cBAT facilitates ease of application and has a quick, straightforward participant task, we conclude that the cBAT is a limited measure for fear of spiders and might not fully capture the complexity of spider-related avoidance behavior compared to tasks that simulate real-life scenarios more closely. We propose the development of a refined cBAT that maintains an easily applicable participant task.

Background: Substance use disorders (SUD) are associated with a high global burden of disease, with 5.4% of all disability-adjusted life years lost due to alcohol and illicit drugs. Highly prevalent multimorbidity includes polysubstance use, mental health conditions, and other non-communicable and infectious diseases. Where traditional treatments are insufficient alone, music therapy (MT) is highly engaging and improves motivation and reduces craving; however, its long-term effects are unknown. The present study aims to examine long-term effects of active music groups (AMG) and music listening groups (MLG) versus treatment as usual (TAU) on addiction severity, recovery, and other outcomes in people with SUD Immediate and short-term effects, as well as mechanisms of these interventions, will also be examined. Methods: In individuals with SUD across a wide range of age, gender, socioeconomic, and cultural backgrounds, a parallel 3-arm assessor-blinded pragmatic multinational randomised controlled trial (RCT) with embedded exploratory trials and mechanistic studies will determine long-term effects of AMG and MLG versus TAU on addiction severity (primary endpoint: 1 year), recovery, and other outcomes. Embedded trials will examine immediate effects of AMG or MLG combined with individual components of TAU combined to determine the best combinations of interventions. Experimental studies will examine mechanisms using cognitive testing and brain imaging. With 600 participants in 7 countries randomised, the trial will have 80% power on the primary outcome. Patient representatives, health technology assessment (HTA) bodies, and interventionists have been involved from conception and will ensure feasibility and applicability of the intervention across Europe. Discussion: This document describes the FALCO RCT, the main part of the FALCO project, which aims to reduce disease burden through innovative, effective, and affordable treatment, and will strengthen research and innovation expertise. Recommendations from FALCO will inform intervention delivery across Europe and beyond, leading to increased safety, effectiveness, and cost-effectiveness, and improved quality of life for individuals with SUD. Stakeholders will be involved in communicating findings across all European countries and regions and ensuring that findings are effectively implemented. Trial registration: ClinicalTrials.gov , NCT07028983 , registered 11 ^th of June 2025. https://clinicaltrials.gov/study/NCT07028983

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 one’s brain activity and might offer a possibility to increase interbrain synchrony, too. Consequently, it would be advantageous to determine the feasibility of creating a neurofeedback system for enhancing interbrain synchrony to benefit human interaction. One vital step toward developing a neurofeedback setup is to determine whether the target metric can be determined in relatively short time windows. In this study, we investigated whether the most widely employed metric for interbrain synchrony, wavelet transform coherence, can be assessed accurately in short time windows 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 where we created artificial data of different noise levels of a dyadic interaction and re-processed two human-interaction datasets. For both artificial and in vivo data, we computed short windows of interbrain synchrony of varying size and assessed significance at each window size. Our findings indicate that relatively short windows of wavelet transform coherence of integration durations of about 1 minute are feasible. This would align well with the methodology of an intermittent neurofeedback procedure. Our investigation lays a foundational step toward an fNIRS-based system to measure interbrain synchrony in real time and provide participants with information about their interbrain synchrony. This advancement is crucial for the future development of a neurofeedback training system tailored to enhance interbrain synchrony to potentially benefit human interaction.

Posttraumatic stress disorder (PTSD) is a debilitating psychiatric illness, experienced by approximately 10% of the population. Heterogeneous presentations that include heightened dissociation, comorbid anxiety and depression, and emotion dysregulation contribute to the severity of PTSD, in turn, creating barriers to recovery. There is an urgent need to use data-driven approaches to better characterize complex psychiatric presentations with the aim of improving treatment outcomes. We sought to determine if machine learning models could predict PTSD-related illness in a real-world treatment-seeking population using self-report clinical data. Secondary clinical data from 2017 to 2019 included pretreatment measures such as trauma-related symptoms, other mental health symptoms, functional impairment, and demographic information from adults admitted to an inpatient unit for PTSD in Canada (n = 393). We trained two nonlinear machine learning models (extremely randomized trees) to identify predictors of (a) PTSD symptom severity and (b) functional impairment. We assessed model performance based on predictions in novel subsets of patients. Approximately 43% of the variance in PTSD symptom severity (R²_avg = .43, R²_median = .44, p = .001) was predicted by symptoms of anxiety, dissociation, depression, negative trauma-related beliefs about others, and emotion dysregulation. In addition, 32% of the variance in functional impairment scores (R²_avg = .32, R²_median = .33, p = .001) was predicted by anxiety, PTSD symptom severity, cognitive dysfunction, dissociation, and depressive symptoms. Our results reinforce that dissociation, cooccurring anxiety and depressive symptoms, maladaptive trauma appraisals, cognitive dysfunction, and emotion dysregulation are critical targets for trauma-related interventions. Machine learning models can inform personalized medicine approaches to maximize trauma recovery in real-world inpatient populations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Neuroimaging has greatly improved our understanding of phobic mechanisms. To expand on these advancements, we present data on the heterogeneity of neural patterns in spider phobia combined with various psychological dimensions of spider phobia, using spider-relevant stimuli of various intensities. Specifically, we have created a database in which 49 spider-fearful individuals viewed 225 spider-relevant images in the fMRI scanner and performed behavioral avoidance tasks before and after the fMRI scan. For each participant, the database consists of the neuroimaging part, which includes an anatomical scan, 5 passive-viewing and 2 resting-state functional runs in both raw and preprocessed form, along with associated quality control reports. Additionally, a behavioral section includes self-report questionnaires and avoidance tasks collected in pre- and post-sessions. The dataset is well suited for investigating neural mechanisms of phobias, brain-behavior correlations, and also contributes to the existing phobic neuroimaging datasets with spider-fearful samples.

Background The Incentive-Sensitization Theory postulates that addiction is primarily driven by the sensitization of the brain’s reward system to addictive substances, such as nicotine. According to this theory, exposure to such substances leads to an increase in ‘wanting’, while ‘liking’ the experience remains relatively unchanged. Although this candidate mechanism has been well substantiated through animal brain research, its translational validity for humans has only been partially demonstrated so far, with evidence from human neuroscience data being very limited. Methods From fMRI data of N=31 individuals with Nicotine Use Disorder, we created multivoxel patterns capable of capturing wanting and liking-related dimensions from a smoking cue-reactivity task. Using these patterns, we then designed a novel resting-state ‘reading’ method to evaluate how much wanting or liking still persist as a neural trace after watching the cues. Results We found that the persistence of wanting-related brain patterns at rest increases with longer smoking history but this was not the case for liking-related patterns. Interestingly, such behavior has not been observed for non-temporal measures of smoking intensity. Conclusion This study provides basic human neuroscience evidence that the dissociation between liking and wanting escalates over time, further substantiating the Incentive-Sensitization Theory, at least for Nicotine Use Disorder. These results suggest that treatment approaches could be personalized to account for the variability in individuals’ neural adaptation to addiction by considering how individuals differ in the extent to which their incentive salience system is sensitized.

Exposure therapy is an established treatment for anxiety disorders but the mechanisms underpinning its effectiveness remain unclear. Two theories offer contrasting perspectives: the traditional habituation model posits that a form of stimulus desensitization is required during exposure, while the inhibitory learning model emphasizes the formation of new non-fearful associations. Crucially, while the former may manifest as amygdala habituation, the latter may not. To distinguish between the two models, this study uses functional magnetic resonance imaging to examine the amygdala responses of spider-fearful participants during fear exposure. We hypothesized that intervention success might align with stable or even increased amygdala activation – an indicator of active engagement and re-learning as proposed by the inhibitory learning model. Conversely, decreasing amygdala activity might not be a sign of reduced fear memory as proposed by the habituation model, but could signal mental detachment, leading to suboptimal treatment outcomes. Our results corroborated our hypotheses: individuals with escalating amygdala responses during exposure exhibited better clinical progress, while those showing amygdala ‘habituation’ benefited less. Our results strengthen the case for the inhibitory learning model and highlight that therapy may not aim to diminish fear per se but rather to engage patients in active processing and association formation.

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 one’s 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.

Introduction: Neurofeedback based on functional magnetic resonance imaging allows for learning voluntary control over one's own brain activity, aiming to enhance cognition and clinical symptoms. We previously reported improved sustained attention temporarily by training healthy participants to up‐regulate the differential activity of the sustained attention network minus the default mode network (DMN). However, the long‐term brain and behavioral effects of this training have not yet been studied. In general, despite their relevance, long‐term learning effects of neurofeedback training remain under‐explored.Methods: Here, we complement our previously reported results by evaluating the neurofeedback training effects on functional networks involved in sustained attention and by assessing behavioral and brain measures before, after, and 2 months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self‐regulation runs without feedback (i.e., transfer runs) and during resting‐state runs from 15 healthy individuals.Results: Neurally, we found that participants maintained their ability to control the differential activity during follow‐up sessions. Further, exploratory analyses showed that the training increased the functional connectivity between the DMN and the occipital gyrus, which was maintained during follow‐up transfer runs but not during follow‐up resting‐state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow‐up.Conclusion: The discrepancy between lasting regulation‐related brain changes but transient behavioral and resting‐state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior in the long term, a better understanding of the brain–behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool.