Frontiers in Human Neuroscience

Visual wayfinding is essential for safe navigation but remains poorly characterized in people with ultra-low vision (ULV). Because assessing complex environments in the real world carries safety risks, this study utilized a calibrated virtual reality (VR) platform to safely quantify navigation. Participants with ULV, normal vision (NV), and simulated ULV (sULV) completed tasks across three environments (street crossing, cafeteria, and metro station) of increasing complexity to determine which metrics best capture task difficulty. Navigation metrics included motion onset latency, walking speed, path efficiency, and turn deviation derived from head position data. Participants with ULV showed longer onset latency, slower walking speed, reduced path efficiency, and greater turn deviation compared with NV, while sULV showed intermediate performance. These metrics successfully reflected increasing task difficulty across environments, with the metro station posing the greatest challenge. Path efficiency consistently detected differences between environments across groups, whereas turn deviation provided insight into complex tasks. Findings indicate that diverse virtual environments capture distinct aspects of navigation that cannot be safely studied in the real world, and trajectory-based metrics capture navigation behavior more effectively than conventional measures. VR-based assessment offers a useful approach for evaluating functional navigation and guiding rehabilitation strategies in profound vision loss.

Background: Chronic pain affects hundreds of millions worldwide and remains a major clinical challenge, despite numerous available treatments. Advances in brain imaging offer a promising path toward identifying neural signatures of chronic pain, potentially enhancing diagnosis and guiding treatment. However, while a core set of brain regions, including the insula, cingulate, and somatosensory cortices, has been repeatedly implicated, findings regarding other regions and connectivity patterns involved remain inconsistent, with limited robust replication. Objective: To address these gaps, the present work characterizes resting-state functional connectivity and gray matter volume differences between chronic pain patients and pain-free controls. Methods: In this secondary analysis of publicly available data, anatomical and resting-state functional MRI were analyzed from 56 patients with chronic knee pain due to osteoarthritis and 20 pain-free controls. Group comparisons used Network-Based Statistic (NBS) and Bayesian multivariate regression models, controlling for demographic covariates. Results: In the pain group, about 75% of parcellated brain regions exhibited increased functional connectivity compared to controls. The 30 highest degree centrality regions in the NBS network were concentrated in regions consistent with prior pain neuroimaging findings. Additionally, chronic pain patients exhibited reduced gray matter volume (-3.98%; SD 1.2%) across 33% of parcellated brain regions, including key regions implicated in pain processing. Conclusions: These findings demonstrate widespread functional and anatomical neural alterations in chronic pain, revealing a global pattern of reorganization extending beyond previously reported network-pair effects. Characterizing such alterations may contribute to ongoing efforts to identify neuroimaging markers of chronic pain, with potential translational relevance.

PurposeSpeech perception is a fundamental part of everyday communication that relies on more than simple identification of words and sentences. Attention and listening engagement both contribute to speech perception, while representing distinct aspects of the listening experience. Attention is typically associated with cognitive focus, whereas listening engagement additionally involves cognitive and affective immersion in sound. Despite their importance, these states remain difficult to disentangle, behaviorally and physiologically. Both have been linked to interpersonal synchronization (the synchronization of biobehavioral signals across individuals), raising questions about what this synchronization actually reflects. MethodIn this study, we disentangled attention and listening engagement by independently manipulating both factors within a single experiment. Thirty participants listened to two simultaneously presented streams of meaningful speech and were instructed to focus on only one. Both attended and unattended stimuli were designed to be either engaging or non-engaging. Neural activity was recorded using EEG, while physiological responses were measured using heart rate and electrodermal activity. ResultsInterpersonal synchronization was computed from neural and bodily signals, alongside a self- report measure of listening engagement and auditory attention decoding (AAD), a neural measure of selective attention. Interpersonal synchronization of all three modalities significantly predicted listening engagement, whereas neural interpersonal synchronization was the only measure that significantly predicted attention. These findings suggest that attention is primarily driven by cognitive processes represented in the brain, while listening engagement additionally involves affective processes that are more strongly reflected in bodily responses. ConclusionsOverall, this study demonstrates that different forms of interpersonal synchronization reflect distinct dimensions of the listening experience and supports interpersonal synchronization as a potential objective marker of listening engagement.

Although dopaminergic therapies in Parkinson's disease primarily restore dopamine within nigrostriatal circuits, symptoms are better indexed by whole-brain dynamics than by local activity. In this manuscript, we hypothesize that L-Dopa therapy affects whole-brain dynamics alike, which in turn relate to clinical improvement. To test this hypothesis, we conducted a repeated-measures, source-reconstructed MEG study in 13 bradykinetic-dominant PD patients, recording resting-state cortical activity OFF medication and ~1 hour after levodopa administration (ON). We characterize brain dynamics using a microstate framework, in which transition probabilities between microstates are used to contrast pathological OFF-state dynamics with those in the ON-state. Microstate dynamics were stable within medication states but reconfigured by L-Dopa, with greater departures from the OFF-state pattern associated, at the individual level, with larger clinical improvements. Our results suggest that individualized changes in microstate dynamics may serve as a neurophysiological marker of dopaminergic responsiveness.

Theft, including shoplifting, extorts a pervasive societal and economic burden. However, the neurobehavioral mechanisms underlying recurrent theft remain sparsely understood. In this study, we investigated social cognition deficits in theft recidivists with kleptomania (TR+K) and instrumental theft recidivists without kleptomania (TR-K) compared to control subjects without criminal records (CT), for which the Social Norms Questionnaire (SNQ-22) to assess explicit moral knowledge, alongside the Dictator Game (DG) and Hawk-Dove Game (HDG) to evaluate discretionary and competitive resource allocation with others, respectively, were administered. Bayesian statistical analyses revealed that all groups demonstrated comparable social norm recognition in SNQ-22 and prosociality in the DG. However, distinct behavioral profiles emerged in specific contexts, such that TR+K exhibited more unfairness than CT and TR-K at discretionary resource allocations in the DG, whereas in the HDG, TR-K demonstrated more aggressive, resource-monopolizing responses, particularly when against an aggressive opponent, than CT and TR+K. These results suggest that theft recidivism may stem from contextual failures rather than general deficits in moral knowledge, which are distinct between TR+K rooted in the internal factor, such as heightened loss aversion, and TR-K characterized by impulsivity over the external factor, such as social conflicts with others.

The human face is central to social interactions, supporting the ability to interpret others mental states using theory of mind (ToM). We examined whether functional near-infrared spectroscopy (fNIRS) would reveal brain-activation differences between live and pre-recorded social conversations in brain regions implicated in ToM. Furthermore, we examined whether activation depended on the visual realism of a social partner - viewed as a human or an animated avatar. By one view, social interactions may be dependent on how natural the social partner appears; by another view, social interactions may depend only upon the attribution of responses to a real human regardless of visual appearance. Neural activation for pre-recorded compared to live interactions was prolonged, consistent with extended cognitive effort. Activation patterns in the right temporoparietal junction differed between interacting with humans versus avatars, along with a stronger preference for looking at the eyes when interacting with a human (vs. avatar), underscoring the social relevance of real faces. Findings highlight the importance of both live interactions and facial realism in shaping social-cognitive processing, a finding with relevance for optimizing online social interactions.

Dietary iron deficiency in the absence of anemia (IDNA) affects numerous people worldwide, with a wide range of negative effects on brain functioning and cognition. Although studies employing electroencephalography (EEG) have revealed a number of negative effects of IDNA in both the time- and frequency domains, to date there have been no attempts to characterize the effects of IDNA on the temporal dynamics of whole brain interactions. To address this issue, we applied multiscale entropy (MSE) analysis to resting-state EEG data collected from IDNA (n = 21) and iron sufficient (IS, n = 21) women. The MSE analysis on this data revealed that entropy was higher overall for the IS than the IDNA group, with significant differences appearing primarily at longer time scales and under right frontal and left and right parietal electrodes. These results suggest that IDNA may negatively affect long-distance interactions among brain regions and that this could conceivably be a source of diminished cognitive function and neural resilience in IDNA.

Recognizing others emotions is central to social interaction. Traditional biological psychology infers emotional responding via laboratory measures, whereas contemporary computer vision algorithms claim to identify emotions unobtrusively from facial video. However, the validity of such algorithms for classifying spontaneous emotional responses occurring without explicit communicative intent remains debated. We compared established psychophysiological measures (EEG, facial EMG, EDA activity) with the open-source facial behavior toolkit OpenFace for classifying participants spontaneous responses during free viewing of happiness-inducing, disgust-inducing, and neutral pictures. Participants provided valence and arousal ratings and later selected the basic emotion that best matched their reaction which served as the classification criterion. Using within-participants single-trial support vector machine (SVM) classification, EEG achieved the highest accuracy (40%), followed by facial EMG (37%); OpenFace reached 36%. All methods except EDA exceeded chance performance (33.3%) and were lower compared to human raters (48%). Predictions declined slightly for across-participants SVMs, being at chance for OpenFace and EDA. The results indicate that in principle both, psychophysiological measures and video-derived facial action units, can capture diagnostically relevant aspects of emotional responding during picture viewing, but that their performance is limited when expressions are spontaneous and not produced for communicative purposes. Inter-individual variability in expressivity and physiological responding likely contributes to these limitations and should be considered when deploying automatic emotion recognition in research or applied settings.

Why does an angry face feel uncomfortable? The answer is that it signals a threat. However, a face is only part of an encounter, and distance, facial stimulus type, and gaze may shape discomfort regardless of perceived anger. To separate these cues, we conducted three within-subjects virtual reality experiments. In each experiment, 24 adults viewed avatars at intimate, personal, and social distances (30, 100, and 300 cm, respectively) and rated the faces perceived anger and their own discomfort; head movement was recorded in Experiments 2 and 3. In Experiment 1, the expression (angry, neutral) and facial color (natural, red) were crossed with distance; in Experiment 2, a featureless mannequin served as a nonface comparison; and in Experiment 3, the gaze direction (direct, averted) was manipulated. Expression primarily determined perceived anger, whereas distance predominantly determined discomfort: A nearby neutral face was uncomfortable despite low perceived anger (Experiment 1). A neutral human face was more uncomfortable than a mannequin, although both received similarly low perceived-anger ratings (Experiment 2). Direct gaze increased the discomfort without changing perceived anger (Experiment 3). Backward head movement exhibited a similar pattern, with participants leaning back more from human faces than from the mannequin. These results indicate that the discomfort associated with an angry face is not merely explained by perceived anger. Instead, social discomfort was differentially associated with interpersonal distance and gaze direction and differed between the human-face and mannequin conditions.

In this study, we investigated the neural and behavioral basis of motivationally objective versus subjective value-based decisions. Using a within-subject fMRI design, healthy participants performed a risky decision-making task that elicited different levels of subjectivity in decision-making in two task conditions. In the Best or objective condition, choices were rewarded only when they were objectively best on a given trial, incentivizing decisions based on externally specified per-trial point maximization. In the Choice or subjective condition, participants received the reward associated with the chosen option, irrespective of how it compared to the unchosen option, allowing greater freedom to exercise subjective preferences in decision policy. Behaviorally, participants relied more on objectively optimal policy in the Best than the Choice condition. There was also a greater consensus across participants in behaviorally displayed and self-reported policies in the Best condition as well as a greater commitment to a single policy by individual participants in this condition, further confirming more objective behavior in the Best condition, compared to Choice. Moreover, behavioral inferences showed a greater agreement with self-report in the Best condition. Our fMRI results showed that the decision-making in Choice, relative to the Best condition, was associated with greater BOLD response in mid-cingulum/posterior cingulate cortex and dorsal anterior cingulate cortex, suggesting their involvement in less externally constrained, or motivationally subjective, decision-making.

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to reflect the underlying neural mechanisms during information processing and are therefore also called the "building blocks" of human thought. In this study, we examined, if EEG microstate sequences can serve as potential triggers for a Brain-Computer Interface (BCI). To this end, a semi-supervised deep learning model architecture consisting of an LSTM-based autoencoder and a dense neural network was utilized to classify between left- and right-hand motor imagery EEG data, with the resulting classification output serving as the BCI trigger. On the one hand, this was done in a 2-step approach, in which the autoencoder and classifer have been trained separately. On the other hand, an end-to-end approach was employed, where training was performed by combining reconstruction and classification losses. Results show that the proposed model architecture was able to extract relevant features from microstate sequences and exploit them for within subjects and sessions classification. Applying transfer learning to session-to-session or across-subject transfer resulted in peak classification accuracies around 89%. We also investigated to what extent transfer learning has to be applied to reach considerable classification accuracies serving as the calibration time representative. We found that on average around 400s are needed for BCI calibration when emplyoing our approach to reach 80% classification accuracy. The present study signifies that the investigation of EEG microstate trajectories can be a promising approach for extracting BCI triggers, as it reduces the dimensionality of multi-channel recorded EEG signals to a distinct number of brain states over time. Deep learning methods, especially transfer learning, applied to EEG microstate trajectories seem promising regarding user-convenient and calibration-free BCIs in real-world applications.

Analysis does not always enhance aesthetic experience. Philosophical accounts have long suggested that decomposing an aesthetic experience into determinate components may weaken it, yet this possibility has rarely been tested experimentally. To examine whether, when, and how analysis produces divergent effects on aesthetic experience, we conducted two experiments manipulating analysis depth. Experiment 1 showed that, during affective analysis of visual art, deep analysis produced a significantly weaker increase in aesthetic ratings than shallow analysis. In Experiment 2, we selected this condition to investigate the underlying mechanism. The behavioral effect was replicated: deep analysis removed the increase produced by shallow analysis without reducing ratings below the image baseline. Frequency-resolved brain network analysis further revealed a stronger task-related component and higher spatial entropy within the default mode network under deep analysis. Network-behavior correlations observed under shallow analysis were absent under deep analysis, suggesting reduced correspondence between the default-mode network (DMN) organization and aesthetic experience. Exploratory analyses further showed that spatial weights in the lateral temporal cortex and inferior parietal lobule were associated with smaller increases in aesthetic ratings. Together, these findings indicate that deeper analysis can selectively weaken improvements in aesthetic experience by altering how affective information is organized within the DMN.

Effortful cognition is typically associated with controlled, task-constrained neural processing, whereas effortless awareness may require a more flexible, internally driven mode of brain organization. Critical brain dynamics provide a principled framework for characterizing this shift, as systems near criticality are thought to balance stability and flexibility, allowing efficient information processing without excessive control. Transcendental Meditation (TM), characterized by a shift from effortful mental engagement to effortless awareness, offers a natural model for testing this possibility. Here, we investigated whether critical brain dynamics track TM as a global meditative state, or instead reflect moment-to-moment fluctuations in subjective effortlessness. We combined high-density electroencephalography (EEG) with time-resolved phenomenological reports using Temporal Experience Tracing (TET). Experienced TM practitioners (N = 33) and matched controls (N = 33) completed resting-state recordings before and after a 30-minute TM or silent counting control task. Long-range temporal correlations (LRTCs) were quantified using detrended fluctuation analysis, while functional excitation/inhibition (fEI) balance was used to estimate directional deviations from criticality. State-based analyses showed that TM increased alpha and beta LRTCs relative to pre- and post-resting state within meditators, but revealed no robust between-group differences in either LRTCs or fEI balance. In contrast, neurophenomenological analyses showed that subjective effortlessness was robustly associated with increased theta, alpha, beta, and broadband LRTCs, with significantly stronger relationships in meditators than controls. Restricting analyses to low-effort periods further revealed higher beta LRTCs in meditators, a difference missed by conventional state comparisons. These findings identify scale-free neural dynamics as a candidate marker of "letting go" during meditation.

Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with development underlying reading comprehension. This study aims to address this issue by combining functional magnetic resonance imaging (fMRI) with Hidden Markov Model (HMM) to explore brain state dynamics. A total of 35 typically developing children and 31 adults were scanned while reading a story. Our results demonstrated a tripartite brain state organization, characterized respectively by high activities in the visual (State #1), language (State #2), and default mode network (DMN, State #3) regions. Children exhibited significantly longer dwell time in the DMN state (State #3) compared to adults, along with a higher probability of transitioning from the language state (State #2) to the DMN state (State #3). In addition, adults exhibited greater flexibility in state transitions during reading comprehension. Finally, the alignment between the dynamic states of children and the average states of adults was a significant positive predictor of their reading comprehension performance. This study provides a novel, intuitive perspective on how brain state dynamics evolve during the development of reading comprehension.

This study characterizes how people combine visual and tactile directional cues while acting in a fully immersive 360{degrees} virtual environment. Participants used a vibrotactile belt and VR headset to localize targets while we manipulated visual reliability and the spatial discrepancy between visual and tactile signals. Behaviorally, degraded visual input made visual responses slower, less precise, and more susceptible to tactile pull, whereas tactile-guided responses remained comparatively stable. We then asked whether these behavioral changes reflected a change in multisensory binding or a change in sensory uncertainty. A Bayesian Causal Inference (BCI) framework captured the structure of behavior under high visual reliability and continued to track individual differences under low visual reliability, even though its absolute goodness-of-fit decreased. Under extreme visual noise, Bayesian Information Criterion sometimes favored a simpler Maximum Likelihood Estimation (MLE) model, but MLE showed poor absolute fit and did not capture meaningful behavioral variability. This dissociation shows that statistical parsimony and explanatory validity can diverge when behavior becomes highly variable. BCI-derived parameters further indicated that degraded vision increased visual uncertainty, while the prior tendency to bind visual and tactile cues remained stable. Kinematic analyses added a complementary insight: early movement trajectories were strongly shaped by tactile signals, even when final localization was visually guided. Together, these findings suggest that visual-tactile integration in 360{degrees} environments depends on sensory reliability and task demands, with tactile cues providing fast body-centered guidance when visual information is limited.

Despite our best efforts to stay focused on a task, our arousal waxes and wanes over time. Lower levels of arousal are typically associated with drowsiness, whereas higher levels are often associated with stress. These changes in arousal move us away from ideal task performance and manifest as fluctuations in neural activity. We asked whether moment-by-moment neurofeedback could be used to counteract neural fluctuations and thereby regulate arousal levels. Here, we developed an intracortical brain-computer interface (BCI) in which animals used visual neurofeedback to maintain neural population activity in prefrontal cortex near a pre-specified activity target. We found animals used moment-to-moment neurofeedback to reduce neural fluctuations on timescales of seconds to hundreds of milliseconds, and that arousal-related regulation of neural activity was associated with BCI use. Our findings suggest that neurofeedback may enhance or restore regulation of neural activity, with potential clinical applications in conditions where such regulation is impaired.

Background: Homeless individuals are disproportionately affected by limited access to primary care and pain management services. While community-based organizations frequently conduct outreach, few have ethical, standardized, or replicable methods for assessing pain and distributing referrals in field settings. Existing models range from passive, meals-only outreach, to costly mobile clinics with limited reach. This leaves a critical gap that a low-resource, agile framework is designed to address. Objective: The aim of this quality-improvement initiative was to implement and iteratively refine a standardized, field-based pain assessment and response pathway for adults experiencing homelessness and to evaluate its feasibility, fidelity, safety, and operational barriers during routine outreach. Methods: A cross-sectional quality improvement needs assessment was conducted during homeless outreach activities in San Francisco and Sacramento using convenience sampling. The intervention framework incorporated volunteer training, cognitive capacity screening, verbal informed consent, vital sign collection, and predefined criteria for emergency escalation. Participants were unsheltered adults with adequate decisional capacity to provide voluntary informed consent. Results: The dataset included 193 encounters, with valid pain scores for 175 participants. Mean pain was 3.79 plus or minus 2.78, with a median of 3. Cold packs were used for acute discomfort and foot or ankle pain, while hot packs were used for joint pain. Some participants declined comfort measures. No supply shortages, referral confusion, or emergency-escalation delays were documented. Telephone access remained a barrier to referral completion. Conclusion: This framework demonstrates a replicable and ethically grounded model for field-based pain assessment and referral during homeless outreach. The pathway was feasible and safe to implement, expanded care options beyond default emergency-department referral by directing stable nonemergent pain toward primary care, and identified limited telephone access as a major barrier to completing follow-up.

Objective: To evaluate the clinical efficacy of non-invasive electrical pulsed radiofrequency (PRF) stimulation on diagnostic thresholds and subjective pain in chronic, pedal diabetic peripheral neuropathy (DPN). Methods: A randomized, single-blind, placebo-controlled trial (ClinicalTrials.gov: NCT07725419) enrolled 92 patients with pedal DPN naive to PRF and scoring [&ge;] 4/10 on the Douleur Neuropathique 4 (DN4) test. Participants received either active PRF stimulation (n = 46) or a non-stimulating placebo (n = 46) applied bilaterally to the sciatic nerve in the popliteal fossa for 10 minutes per limb, once weekly for three weeks. The primary outcome was clinical neuropathic resolution (DN4 < 4). Secondary outcomes included subjective pain tracking via the Brief Pain Inventory-Short Form (BPI-SF) Worst Pain scale over a 6-month follow-up window. Missing data were handled via Non-Responder Imputation (NRI). Longitudinal continuous trajectories were modeled using Linear Mixed-Effects Models (LMMs) adjusted for age, gender, and baseline medication use. Results: In the Intention-to-Treat population (N = 92), a significant diagnostic responder effect occurred at 3 months, with 39.1% of active patients dropping below the diagnostic threshold for neuropathy (DN4 < 4) versus 19.6% of placebo controls (p = 0.039). For subjective pain, 47.7% of active patients achieved a Minimally Clinically Important Difference ([&ge;] 3-point reduction) in BPI Worst Pain at 1 month compared to 19.4% of placebo controls (p = 0.008). Multivariable logistic regression identified active treatment as a significant independent predictor of clinical response (Adjusted OR = 4.86; 95% CI: 1.56 to 17.53; p = 0.010). Continuous LMM tracking confirmed a statistically significant treatment-by-timepoint interaction for BPI Worst Pain at 1 month (p = 0.046). Conclusion: A brief, three-week course of non-invasive PRF stimulation serves as a safe, effective, non-pharmacological adjunct that aids in managing the diagnostic presentation of neuropathic pain and mitigates worst pain experiences in patients suffering from pedal DPN.

Intelligible speech disrupts selective auditory attention more than an unintelligible stream. However, low-level acoustic features of intelligible speech are relatively similar to target speech, confounding results. While controlling acoustic similarity and limiting energetic masking, we examined how masker intelligibility affects behavior and electroencephalography (EEG). Normal hearing listeners detected color words within a target stream of randomly timed words while ignoring an ongoing masker. Maskers were either spoken by the same or a different talker and comprised either isochronous sequences of intelligible words or temporally scrambled versions. Scrambled maskers either lacked broadband energy changes over time (Experiment 1) or were amplitude modulated to have the same energy profiles as intelligible, isochronous maskers (Experiment 2). In both experiments, scrambled maskers yielded better performance than intelligible maskers. For intelligible maskers, performance was better for different compared to identical talkers. EEG responses paralleled behavior: target-evoked onset responses were larger for scrambled than for intelligible maskers, particularly for identical talkers. Later target recognition responses were larger for color than other target words but unaffected by masker type or talker. Even when low-level acoustic features were carefully matched, intelligible maskers impaired auditory attention and reduced target-evoked neural responses more than scrambled maskers, implicating early sensory filtering.

Although a substantial body of evidence has demonstrated that emotional interference affects cognitive performance, relatively little is known about its temporal evolution and the respective brain regions underlying its regulation. The present study investigated the temporal dynamics of emotional interference and the contribution of prefrontal regions involved in its regulation. Forty-eight participants completed a 2-back task and a Set-switching task under neutral and negative emotional conditions while receiving sham, dorsolateral prefrontal cortex (dlPFC), or ventromedial prefrontal cortex (vmPFC) stimulation. Stimulation was administered either online during task performance or after a 5 min pre-task period. Consistent with previous findings, negative emotions impaired executive performance, particularly during high-demand updating conditions. Critically, time-resolved analyses revealed that emotional interference evolved dynamically throughout task performance and was differentially modulated by prefrontal stimulation. The most consistent stimulation effects emerged after approximately 10 minutes of cumulative stimulation exposure and varied as a function of the stimulation site, executive-control demands, and stimulation timing. Notably, online stimulation produced more consistent modulation than pre-task stimulation. Together, these findings indicate that both emotional interference and its neuromodulation are dynamic processes. More broadly, they suggest that the contribution of prefrontal control systems to emotion-cognition interactions may be better understood through their temporal evolution rather than through static measures of performance alone.