Dyadic neurofeedback offers a socially grounded approach for modulating neural processes that unfold between interacting individuals. While recent work demonstrates that dyadic neurofeedback can enhance inter-brain synchrony during interaction, whether its effects extend beyond the trained dyad remains unclear. Here, we examined whether dyadic neurofeedback targeting inter-brain synchrony in the inferior frontal gyrus (IFG) is associated with changes in empathy-for-pain toward unfamiliar social targets. One hundred and eighty-six participants, randomly assigned to dyads, completed three neurofeedback training sessions within one week. Feedback was contingent on real-time IFG inter-brain synchrony in the neurofeedback group or on prerecorded signals in the sham-feedback control group. Empathy-for-pain was assessed before and after training. IFG inter-brain synchrony increased significantly across sessions in the neurofeedback group but not in the control group. Empathy-for-pain ratings showed different trajectories across conditions in the primary raw-data analysis, decreasing significantly in the sham-feedback group while remaining relatively stable in the neurofeedback group. A secondary analysis adjusting for a previously observed task-specific test-retest decline showed a significant increase in the neurofeedback group. Individuals with lower baseline trait empathy showed larger training-related increases in IFG inter-brain synchrony. Network analyses suggested broader changes involving IFG-IPL connectivity. Structural equation modeling further identified significant associations among training-related intra-brain synchrony, inter-brain synchrony, and empathy-for-pain change, consistent with the proposed relationship among these processes. Together, the findings indicate that dyadic neurofeedback can modulate IFG inter-brain synchrony across repeated training and may influence the trajectory of empathy-for-pain responding beyond the immediate training partner.

A prevailing assumption in systems neuroscience is that long-range phase coupling between cortical regions requires locally synchronized oscillatory activity within each interacting area. Here we ask whether distal phase coupling can be detected from MEG data when both interacting regions undergo concurrent event-related desynchronization (ERD). Using controlled simulations with progressively disrupted local phase coherence, we first establish that the Context-Dependent PSIICOS (CD-PSIICOS) framework - a projection-based connectivity estimator that separates power-dominated spatial leakage from interaction-specific cross-spectral structure - can recover distal coupling under increasing local phase dispersion, whereas estimators based on the imaginary part of the cross-spectrum fail at near-zero lags. We then analyzed recordings of a center-out reaching dataset from nine participants. A group decomposition of the coupling time courses identifies four long-range networks in alpha (8-13 Hz) and two in beta (15-25 Hz), each with its own time window. For each network we follow the local connections between its own sources as the power-dominated subspace is projected out, separating leakage-driven from leakage-resistant local coupling. In the earliest alpha network the leakage-resistant pairs stay at or below their pre-stimulus level at every rank while the power of the same sources falls, so this interhemispheric interaction may have no stable local counterpart. In the beta networks such a counterpart does survive, but the coupling it carries is an order of magnitude weaker than the long-range coupling of the same sources. Only in the latest alpha window, by which time power has returned to baseline, do local and long-range coupling rise together. Factorising the coupling into the amplitude of the single-trial cross-terms and the consistency of their phases separates the same cases: in the networks without a local counterpart, phase consistency increases inside the window while the amplitude of the cross-terms declines, whereas in the network whose local coupling rises with it, the increase reflects a growth of the cross-terms themselves rather than any improvement in their phase agreement. Long-range phase coupling can therefore persist, and in places strengthen, between regions undergoing macroscopic ERD, and noninvasive MEG can resolve this dissociation when leakage-aware estimation is employed.

Similarities in cognitive processing have been reported between individuals with autism spectrum disorder (ASD) and non-clinical individuals with autistic traits. However, previous research on autistic traits has primarily focused on social-emotional domains, with limited attention to abstract logical reasoning. Given that sensitivity to rules and patterns is a notable characteristic of the autism spectrum, logical rule processing may be particularly informative for understanding cognitive differences associated with autistic traits. The present research aimed to investigate the behavioral and neural responses of students with autistic traits to logical and illogical number sequences through two event-related potential studies. Based on Autism Spectrum Quotient-10 (AQ-10) scores, participants were categorized into High-AQ and Low-AQ groups. In Study 1, both groups judged whether visually presented logical and illogical three number sequences followed a logical incremental rule. In Study 2, both groups performed the same task as in Study 1, except number sequences were presented in the auditory modality. No significant group difference was found in response to logical number sequences in either study. However, relative to the Low-AQ group, the High-AQ group exhibited larger P2 and P3 amplitudes and more negative emotional reactions to illogical number sequences in the visual modality (Study 1), and more positive N1 and LPP amplitudes, and higher arousal ratings to illogical number sequences in the auditory modality (Study 2). These findings suggest that students with autistic traits may exhibit altered neural responses and heightened emotional reactivity specifically to stimuli that violate logical rules in separate visual and auditory studies.

Distinct cognitive states might be supported either by dedicated, task-specific brain networks or by flexible modulation within shared ones, a distinction central to understanding how the brain allocates neural resources. Guided by the principle of parsimony, we hypothesized that both language and numerical cognition, when matched for sensory modality and attentional demand, would engage a common network architecture differentiated primarily through targeted modulation of connectivity strength and temporal dynamics rather than wholesale reconfiguration. We applied a jackknife-based dynamic functional connectivity approach to examine time-varying network engagement during auditory language comprehension and auditory math problem-solving in 172 age- and sex-matched participants from the Human Connectome Project-Young Adult cohort, using the Desikan-Killiany atlas and an 85% participation threshold. Of the significant connections identified, many were retained across both tasks, with 87 connections common to story and math, compared with 26 story-associated and 11 math-associated connections. Rather than indicating a novel shared-core architecture, this overlap is consistent with prior evidence that task functional connectivity is strongly constrained by a common intrinsic connectivity backbone. We therefore used the shared edge set as a common network framework for testing how two modality-matched, but cognitively distinct auditory tasks differ in connectivity strength and temporal variability. Among the 87 shared connections, 84 showed significant task-related differences in mean connectivity strength, indicating that story-math differentiation was expressed primarily through quantitative modulation of shared connections rather than wholesale changes in edge membership. Exploratory rolling-correlation analyses further suggested that some edges may show temporal variability in math-story connectivity correspondence even when mean connectivity strength is similar, highlighting a potential dynamic feature not captured by static connectivity estimates alone. Together, these findings suggest that story and math processing rely on a broadly shared connectivity backbone, while task differentiation emerges through graded modulation of connection strength and, in some cases, temporal variability.

Progressive deterioration of semantic memory is a defining characteristic of the semantic variant of primary progressive aphasia or semantic dementia (SD). Semantic deficits in SD are classically attributed to temporal lobe atrophy, in particular in the anterior temporal lobe. Recent neuroimaging studies have associated SD with disruptions of intrinsic connectivity networks during task and at rest, predominantly implicating regions of the semantic and language networks. Yet, anterior temporal lobe atrophy disrupts connectivity within the Default Mode Network (DMN), particularly in the anterior portions associated with semantic memory. Few neuroimaging studies have focussed on DMN connectivity in SD and links to semantic memory impairments. Using whole-brain seed-based connectivity analyses during resting state fMRI, we examined DMN functional connectivity in eight bilateral seeds and the association with semantic impairments in 16 SD compared to 20 age-matched healthy participants. Patients exhibited within DMN hypoconnectivity between the anterior hippocampus and medial orbito-frontal cortex, on the right, and between the posterior cingulate cortex and middle temporal gyrus, on the left. Furthermore, functional connectivity of the left posterior cingulate cortex with the middle temporal gyrus correlated positively with left lateral temporal atrophy in SD patients. Correlations with semantic performances showed that connectivity of the right anterior hippocampus and left middle/superior frontal gyri correlated positively with animal fluency in controls, but not in patients. In SD, connectivity between the left posterior cingulate cortex and left cerebellum area 9 correlated positively with semantic knowledge. These findings provide evidence that semantic SD impairments are subserved by altered DMN functional connectivity, linked to atrophy in the anterior temporal lobes and anterior hippocampi.

The photon measurement density function (PMDF) plays a fundamental role in both pre-experimental optode arrangement and post-experimental data analysis in functional near-infrared spectroscopy (fNIRS). Conventionally, PMDFs are derived from structural MR images through tissue segmentation and photon propagation modeling (PPM), which are computationally demanding and time-consuming, thereby limiting their practical use. In this study, we propose a novel deep learning-based framework to estimate PMDFs directly from MR images and channel configurations. The proposed method supports flexible source-detector distances and eliminates the need for explicit tissue segmentation and repeated photon simulations. Specifically, a convolutional neural network is trained to predict photon fluence distributions, from which PMDFs are subsequently derived using the adjoint formulation. The trained model is evaluated on channels placed in both trained and unseen scalp regions across commonly used source-detector distances. The results demonstrate that the proposed method achieves PMDF estimations comparable to those obtained from PPM. Overall, this approach significantly reduces computational cost and has the potential to facilitate broader adoption of PMDF-based methods in the fNIRS community.

Uncovering the mechanisms underlying cognition and brain disorders requires accurate regional source imaging and reliable characterization of inter-regional statistical dependencies. Traditional methods typically follow a two-stage pipeline that first performs source imaging and then calculates inter-regional dependence measures separately. Such a decoupled framework can propagate source-reconstruction errors to covariance-based network analyses, leading to biased source localization and distorted dependency patterns. To address these limitations, we propose a joint estimation method for regional brain source activity and source covariance via full-structure covariance learning, termed Joint Activity-Covariance Estimation via Full-Structure Covariance Learning (JAC-FS). JAC-FS constructs a Gaussian probabilistic model at the brain-region level and employs a full-structure source covariance matrix to represent regional source variance and inter-regional dependencies. In this covariance representation, diagonal entries encode marginal regional source variance, whereas off-diagonal entries quantify symmetric zero-lag covariance-based dependencies. These estimates are statistical associations and do not by themselves establish direct, directional, or causal physiological communication. A median-weighted covariance statistic suppresses high-energy artifacts, and convex surrogate optimization provides closed-form updates for source activity and source covariance. In addition, constrained noise estimation with diagonal floor protection, low-rank correlation constraints, and phased damped updates limits signal absorption and spurious covariance structure. Simulations showed that JAC-FS outperformed conventional algorithms in regional source localization, temporal recovery, source-covariance recovery against the simulated ground truth, and recovery of predefined short- and long-range covariance edges. In real MEG analyses, JAC-FS produced focal evoked source distributions and representative time courses together with evoked source-covariance patterns. These results establish JAC-FS as a unified framework for regional source imaging and zero-lag source covariance estimation.

Phubbing, a prevalent interpersonal phenomenon in which individuals divert their attention to their phones while neglecting their interaction partners during face-to-face interactions, has been associated with adverse psychological and relational outcomes, including social pain. However, the same "eyes-on-phone" behavior may not always be experienced as phubbing or evoke feelings of neglect and social pain. The present research examined whether belonging context shapes behavioral and neural responses to "eyes-on-phone" behavior during face-to-face interaction. In Study 1, 163 participants completed a scenario-based task that required them to judge how they felt about several stories, in which belonging context (positive, negative, unrelated) was crossed with attentional state (eyes-on-phone, eyes-on-you). In Study 2, 50 participants completed the Cyberball paradigm to manipulate different belonging contexts (positive, negative, unrelated) with 6 models, followed by an ERP experiment in which they judged whether images of these models displaying different attentional states (eyes-on-phone, eyes-on-you) made them feel neglect-related social pain. Across both studies, within "positive" contexts, "eyes-on-phone" behavior elicited greater social pain, including perceived neglect, emotional unpleasantness, and unwillingness to communicate (Study 1), as well as a greater tendency to endorse neglect-related social pain and larger P3 amplitudes (Study 2) than "eyes-on-you" behavior, whereas this pattern was not consistently observed in the "negative" or "unrelated" contexts. These findings indicate that phubbing is not inherently experienced as social pain. Rather, its social meaning depends on prior belonging context: a mismatch between contextual cues and subsequent attentional behavior may give rise to expectancy violations, which could contribute to social pain when the behavior is interpreted as neglectful.

Magnetic resonance imaging (MRI) super-resolution (SR) focuses on reconstructing high-quality, high-resolution target images from sparsely acquired signals while enhancing reconstruction performance through guidance from a reference image. In clinical practice, MRI SR contributes to reduced scan durations and alleviates patient discomfort, as well as motion artifacts associated with extended immobility. Existing SR approaches, however, exhibit insufficient interaction with texture features from the reference image, which results in pronounced inaccuracies at image edges. In addition, these methods often neglect the issue of noise amplification introduced during the SR process. To overcome these limitations, an implicit dynamic spatial propagation network (IDSPN) is introduced, integrating an implicit neural network with dynamic spatial propagation. Initially, residual dense blocks and residual channel attention networks are employed to extract features from the low-resolution input and reference images, respectively. Subsequently, a texture transformer performs feature alignment between the input and reference images and transfers texture information from the reference image to the low-resolution input, followed by upsampling through an implicit neural network. A backbone network then estimates affinity and propagation offsets, enabling refinement via multi-stage iterative diffusion. The proposed approach is evaluated on three publicly available datasets, with experimental findings demonstrating state-of-the-art performance and indicating its potential for clinical application. Codes are available from https://github.com/whj-proj/IDSPN.git.

Adolescence and young adulthood are critical periods for the emergence of transdiagnostic psychiatric symptoms. Pattern separation (PS) is the ability to discriminate between similar stimuli. Its behavioral proxy is mnemonic discrimination. PS is an important hippocampal function impaired in several mental disorders. Previous research demonstrated that while childhood adversity might lead to impaired PS, acute exercise could enhance it. Using a randomized controlled design, we examined the effect of acute moderate-intensity exercise on PS versus a rest condition in a group of young people. Building on our prior report in a healthy youth sample (Jennen et al., 2025), the present study examines adolescents and young adults with transdiagnostic psychiatric symptoms and childhood adversity, compared to healthy controls. We integrated behavioral and neural PS with an emotional mnemonic discrimination task and functional magnetic resonance imaging. Our analyses did not reveal significant effects of exercise or psychiatric symptoms on mnemonic discrimination and PS. Additionally, PS was not associated with symptom dimensions or childhood adversity. Across groups, we observed decreased discrimination for positive images and increased neural activation in the left centromedial amygdala for emotional images. No correlation between neural activity and mnemonic discrimination could be observed and exploratory analyses revealed no neural activity consistent with PS. We conclude that in our study neither mnemonic discrimination nor PS was associated with psychiatric symptoms or childhood adversity in adolescents and young adults with transdiagnostic psychiatric symptoms. In addition, acute exercise had no effect. Future research should prioritize establishing robust and reliable associations of both acute exercise and psychiatric symptoms with mnemonic discrimination and neural PS.