INTRODUCTION:Integrating functional MRI (fMRI) and diffusion MRI (dMRI) advances neuroimaging by merging complementary perspectives on brain networks, with fMRI measuring functional activity and dMRI capturing structural connectivity (SC). However, combining them dynamically remains challenging due to their drastically different data characteristics. METHODS:We propose a novel framework, "dynamic fusion," which extends joint component analysis via connectivity-matrix ICA to integrate static SC with dynamic functional connectivity (FC). It evaluates how joint components relate across temporal states to capture both static and time-varying connectivity features. As a proof-of-concept, and in contrast to more systematic approaches for modeling true temporal dynamics, we defined two temporal states from the first and last thirds of the fMRI time series. We applied this to fMRI and dMRI from control subjects previously analyzed with a static-only model and to a comparable schizophrenia group from the same study. RESULTS:Results revealed heterogeneous temporal dynamics in FC and, importantly, showed that SC representations differ across the two functional time segments, reflecting functional-context-dependent decomposition rather than anatomical change. It also detected joint dynamic SC-FC differences between schizophrenia and control groups, indicating sensitivity to group-level effects that may be missed in static analyses. DISCUSSION:Although the current time segments are not intended to represent canonical or recurring brain states, the results suggest that multimodal fusion outcomes can depend on the functional context with which SC is fused. Overall, our dynamic fusion offers a promising approach for integrating SC and dynamic FC to better understand brain organization and neuropsychiatric disorders, while motivating more systematic definitions of dynamic states in future work.
BACKGROUND:Event-related potentials (ERPs) provide implicit feedback and error-correction signals that are valuable for brain-computer interfaces (BCIs). However, models trained on source-domain subject data are vulnerable to inter-subject variability and acquisition noise, which substantially degrades generalization to unseen subjects. OBJECTIVE:We propose a multi-view contrastive learning domain generalization (MVCLDG) method to improve cross-subject generalization in ERP recognition by jointly exploiting discriminative feature extraction and domain-invariant representation learning. METHODS:MVCLDG employs a multi-view feature-extraction module that fuses raw electroencephalography with phase information derived from the Hilbert transform via multi-scale inception blocks, thereby capturing both amplitude and phase features. The model then applies domain-alignment and contrastive-learning constraints to reduce distributional discrepancy across domains, compact within-class representations, and enlarge between-class separability. The approach was evaluated on a public Error-Related Negativity (ERN) dataset and a self-collected semantic-syntactic violation dataset; performance was assessed in cross-subject settings, and ablation and visualization analyses were conducted to probe the contributions of components and neurophysiological interpretability. RESULTS:MVCLDG outperformed baseline and representative domain generalization methods in cross-subject ERP recognition without requiring additional target-domain adaptation. Ablation experiments confirmed the effectiveness of each component. Eigen-Class Activation Maps visualizations indicate consistency between the model-attended electrodes and known neurophysiological scalp patterns, supporting both the model's generalization mechanism and its biological interpretability. CONCLUSIONS:MVCLDG offers an effective strategy for integrating phase-aware multi-view feature mining with contrastive domain generalization, yielding improved and interpretable cross-subject ERP recognition. The method advances the feasibility of ERP-based closed-loop BCIs that generalize across users.
BACKGROUND:Investigation of the neural substrates of post-traumatic stress disorder (PTSD) in military personnel using whole-brain approaches remains scarce, hindering the development of circuit-based neuromodulatory interventions. OBJECTIVES:This study aimed to identify potential associations between clinical symptoms and whole-brain resting-state functional connectivity with magnetic resonance imaging in military personnel with adulthood-onset war-related PTSD. METHODS:Thirty-seven soldiers from the Canadian Armed Forces with moderate to severe treatment-resistant PTSD participated in this study. We assessed PTSD, anxiety and depressive symptoms, quality of life, and time since trauma. We characterized the whole-brain functional connectome using independent component analysis and regions of interest (ROI)-to-ROI connectivity, as well as its topology using graph theory. RESULTS:Greater severity of PTSD and anxiety symptoms was associated with lower connectivity (r < 0) between the default mode network (DMN) and frontoparietal network. Greater severity of PTSD symptoms was also associated with a higher nodal clustering coefficient of the inferior parietal lobule from the DMN. Greater severity of anxiety symptoms and longer time since trauma was the only clinical variables that correlated with higher connectivity patterns, all involving the visual networks (the frontoparietal-visual, the visual-DMN, and within-visual networks). CONCLUSIONS:This work contributes to identifying brain targets for the development of personalized neuromodulatory interventions. In particular, the DMN may be a promising target to alleviate PTSD symptoms, and the visual network may be a target to treat comorbid anxiety symptoms.
INTRODUCTION:Brain networks and meditation have recently gained attention, with studies suggesting that more efficiently organized meditation brain networks are linked to better cognitive performance. This efficiency is exemplified in small-world brain networks, which combine local segregation with global integration, facilitating optimal information processing. This study examines the small-world propensity (SWP), a marker of neural efficiency, among three functional brain networks: the default mode network (DMN), fronto-parietal network (FPN), and attention network (AN) in three groups: advanced meditators (AM), beginner meditators (BM), and control meditators (CM). METHODS:Using magnetoencephalography (MEG), we recorded 10-min meditation sessions from AM and BM groups practicing Surat-Shabda-Yoga meditation at different stages. The CM (baseline group), with no formal training in meditation, had introductory exposure to "four chakra meditation" and practiced the same. SWP was computed using coherence-based connectivity measures across frequencies ranging from 4 to 45 Hz during stable meditative states. RESULTS:Significant differences were observed between meditators and non-meditators, with AM and BM groups compared with the CM group. Specifically, the AN in the AM group compared with CM exhibited higher SWP at the beta frequency range (19 Hz), while the FPN in the BM group compared with the CM showed increased SWP at the theta frequency range (8 Hz). DISCUSSION:These findings highlight how meditation engages the brain's intrinsic network architecture in a frequency- and stage-specific manner, supporting efficient information processing and offering a scientific basis for its cognitive and regulatory benefits.
Background: Tinnitus is an auditory phantom perception in the absence of any corresponding acoustic stimulus whose pathophysiology remains poorly understood. This study aimed to investigate alterations in the functional organization of the brain in individuals with tinnitus using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory analysis.Methods: We conducted a study including 44 individuals with tinnitus and 32 healthy controls. Using rs-fMRI and graph theory measures, we characterized whole-brain topological properties, including network segregation, integration, small-worldness, and global efficiency. In addition, regional segregation and integration were assessed using clustering coefficient and participation coefficient analyses to identify alterations in brain hub regions.Results: Our findings revealed altered topological properties in the tinnitus brain, particularly in the balance between cerebral segregation and integration, leading to deviations from optimal small-world architecture. We also observed alterations in the topology of specific auditory and nonauditory brain regions associated with phantom sound perception. Notably, patients with tinnitus exhibited a decreased nodal participation coefficient in the thalamus, suggesting reduced connectivity between this region and different functional modules as well as long-range connections.Conclusions: These results suggest that tinnitus is associated with alterations in the functional organization of the brain, leading to disrupted information processing and sensory integration.
INTRODUCTION:Prior visual neuroscience research has contributed ample evidence on functional anatomy of two long-range systemic visual networks, dorsal (DVN) and ventral (VVN). Their developmental course of functional connectivity was rarely studied. METHODS:We examined within- and between-network connectivity using cortical periodic alpha band 8-13 Hz, a well-elaborated developmental marker of cognitive inhibitory control. Resting state magnetoencephalography (rsMEG) investigated age differences in functional network connectivity between carefully screened male participants: younger group (YG, 6:10-12 years) and older group (OG, 18:7-29 years). The morphology of cortical network nodes was informed a priori by pilot resting state functional magnetic resonance imaging (rsfMRI) and MRI morphometry studies. Phase Lag Index was employed to compute within- and between-network connectivity. We summarized the age differences in connectivity using graph theory metrics. RESULTS:The power spectral density across cortical areas was comparable between YG and OG, indicating similar signal-to-noise ratios across the age groups. The dorsal brain in YG showed higher within-network connectivity for the inferior parietal/occipital (DVN) and medial posterior nodes (cingulate/precuneus) of the default mode network (DMN), functionally/anatomically linked to DVN. A significantly reduced anterior brain connectivity for VVN in YG suggested its protracted maturation. The topography of alpha connectivity between age groups displayed no statistically significant differences in the posterior dorsal nodes of DVN/DMN but significantly lower connectivity in the anterior dorsal/medial cortex in YG as compared with OG. DISCUSSION:The current rsMEG finding on intrinsic alpha-band oscillatory connectivity in child participants is consistent with prior neuroimaging evidence in humans and primates securing an early maturational course of posterior dorsal brain networks.
INTRODUCTION:The widespread participation of children in contact sports raises public interest and concern regarding neurological conditions later in life that may be related to repetitive head impacts (RHIs). Advanced neuroimaging techniques are advantageous for understanding functional brain changes. Particularly, magnetoencephalography (MEG) has shown promise as a clinical tool for concussion diagnosis and prognosis as well as understanding of RHI. METHODOLOGY:In this study, we utilized preseason and postseason eyes-open resting state MEG data to evaluate changes in functional connectivity correlated with RHI in 72 football players (μage = 12.2 years). In addition, MEG scans were acquired at baseline and follow-up for 17 control participants (μage = 11.5 years). Standard preprocessing techniques were followed, and coherence values were computed for regions of interest defined via the Desikan-Killiany atlas. The network-based statistic toolbox was used, and standard analysis of covariance (ANCOVAs) were implemented with corrections for multiple comparisons. RESULTS:Postseason comparisons between football players and controls showed global hypoconnectivity in the delta frequency band for football players and hyperconnectivity in the theta and beta frequency bands in left cortical regions. No significant differences were found in preseason versus postseason comparisons within the football and control groups or between the two groups during preseason. DISCUSSION:The combination of hypo- and hyperconnectivity may reflect compensatory mechanisms activated during postseason that deviate from typical cognitive development in this critical developmental age group. Further research is needed to explore the long-term effects of RHI on brain connectivity and cognitive development.
BACKGROUND:Intracranial dural arteriovenous fistula (DAVF) disrupts cerebral hemodynamics and can lead to widespread alterations in brain network connectivity and cognitive function. This study aimed to evaluate spontaneous brain activity and cognitive changes in DAVF patients using resting-state functional MRI (rsfMRI) and neuropsychological assessment, with evaluations conducted at baseline, 1 month, and 1 year postembolization to capture dynamic recovery-related changes in brain function and cognition. METHODS:Fifty DAVF patients and 50 age and sex-matched healthy controls underwent rsfMRI. Amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) metrics were computed at both whole-brain and network levels. Cognitive performance was assessed using Addenbrooke's Cognitive Examination (ACE). All patients underwent embolization, followed by rsfMRI and ACE evaluations at 1 month and 1 year. ACE scores were included as covariates to explore cognitive-network associations. RESULTS:Compared with controls, DAVF patients showed significantly increased ALFF in cerebellar regions and decreased ALFF/fALFF in frontal, insular, and parietal areas, especially within the Default Mode Network (DMN) and Dorsal Attention Network (DAN). Postembolization, rsfMRI metrics showed normalization trends, especially in DMN and DAN, mirroring improvements in ACE scores. ACE-based covariate analysis revealed domain-specific correlations: memory scores correlated with ALFF in the DMN (r = 0.62), and visuospatial scores with DAN (r = 0.55). CONCLUSIONS:This study provides longitudinal evidence that DAVF disrupts brain network integrity and cognition, with partial recovery following treatment. rsfMRI-derived ALFF and fALFF measures, particularly when analyzed alongside cognitive scores, may provide preliminary support for future clinical applications in DAVF prognosis and monitoring.
BACKGROUND:Novel therapies are needed to improve smoking cessation outcomes in people with opioid use disorder (OUD), as they are far more likely to smoke cigarettes (70-90%) compared to the general population (11.6%) and demonstrate a poorer response to smoking cessation interventions. METHODS:This pilot study, intended to be a hypothesis-generating mechanistic investigation, is the first to explore the impact of a single day (four sessions) of accelerated intermittent theta burst stimulation (iTBS) (1800 pulses/session) versus sham iTBS applied to the left dorsolateral prefrontal cortex (L-dlPFC) in people with OUD who smoke tobacco cigarettes (n = 8 received iTBS, n = 7 received sham iTBS). Resting-state functional connectivity was acquired at baseline and after the fourth session. Attentional bias for cigarette and opioid cues, and craving assessments, were completed at baseline, and after the first and fourth sessions. RESULTS:Connectivity between the L-dlPFC seed and a cluster comprising the left anterior supramarginal gyrus showed a significant group × time interaction, with planned comparisons showing a greater increase at follow-up in the iTBS compared with sham iTBS group (t12 = 6.37, β = 0.40, p < 0.001). Cigarette cue attentional bias showed a significant group × session interaction (t80 = 2.34, p = 0.02), with planned comparisons showing a decrease after iTBS and an increase following sham iTBS. No effect of iTBS was observed for opioid cue attentional bias. Cigarette craving decreased in both iTBS and sham groups but did not show a significant group × session interaction. CONCLUSIONS:These results are promising but should be interpreted with caution, given the limited sample size, which precluded analyses adjusting for sex or medications for OUD. This pilot study aims to identify neural and behavioral targets for future studies of accelerated iTBS in people with OUD who smoke cigarettes. Future trials could examine the effects of increased doses of iTBS (e.g., more days of accelerated iTBS) to identify dosing to promote smoking cessation among individuals with OUD effectively.
INTRODUCTION:Resting-state functional connectivity (FC) has distinct, personalized patterns that could serve as a unique fingerprint of each individual's brain. While previous brain fingerprinting methods have used FC maps over a scanning session (static method), it has been shown that the brain is a dynamic system that switches between several metastable states, each of which has a different FC map. Taking the dynamic nature of brain connectivity into account will likely lead to more subject-specific information and better individual identification. METHODS:In this article, we derived the state-specific FCs using sliding window correlation and clustering and evaluated their performance in individual identification and cognitive score prediction. RESULTS:The resultant dynamic fingerprints outperformed the static fingerprints in identification accuracy. Furthermore, some of the brain states were more accurate in predicting cognitive scores, indicating that connectivity in some brain states is informative of cognitive abilities, possibly useful as biomarkers for brain disorders. DISCUSSION:These findings suggest that incorporating dynamic information captures subject-specific connectivity features that are not present in static FC alone. The observation that specific states contribute more to cognitive prediction further highlights their potential utility as biomarkers for brain disorders.
Introduction: Brain-computer interfaces (BCIs) translate brain activity into commands, enabling applications in communication, control, and neurorehabilitation. A major challenge in noninvasive BCIs is balancing classification performance with interpretability, as many approaches prioritize accuracy while overlooking the neural mechanisms underlying their predictions.Methods: In this study, we conduct a meta-analysis of feature interpretability across widely used methods in motor imagery (MI)-based BCIs, including power spectral density, common spatial patterns (CSP), Riemannian geometry, and functional connectivity. Specifically, we explore how network topology and spatial organization contribute to MI decoding by investigating brain network lateralization.Results: Through evaluations on multiple EEG-based BCI datasets, our results confirm the superior classification performance of CSP and Riemannian methods. However, network lateralization provides stronger neurophysiological plausibility, revealing robust lateralization patterns in sensorimotor and frontal regions contralateral to imagined movements.Discussion: These findings underscore the potential of connectivity-based features as a complementary tool for enhancing interpretability, supporting the development of more transparent and clinically relevant MI-based BCIs.Impact Statement This study addresses a critical gap in motor imagery-based brain-computer interfaces (BCIs) by systematically evaluating and comparing the interpretability of widely used methods, including power spectral density, common spatial pattern, Riemannian geometry, and functional connectivity. By analyzing these approaches across wide-ranging datasets, we offer valuable insights into the underlying neural mechanisms driving their performance. Our findings contribute to enhancing the transparency and biological relevance of BCI systems, ultimately advancing the development of more clinically meaningful and neurophysiologically interpretable BCIs.
Objective: Here we aim to search for stable intra- and inter-band cross-correlations during the peri-ictal transition of focal onset seizures. Furthermore, we search for dynamic features by analyzing relative eigenvalues of the cross-correlation matrix. Methods: In this study, we analyze 50 extracranial electroencephalographic recordings from 24 patients with different types of focal epilepsy, separating the data into different frequency bands. Thereby we construct a multiband cross-correlation matrix, evaluate stability of the correlation structures and the time evolution of relative eigenvalues using a running window approach. Results: We find a consistent, pronounced average cross-correlation pattern that is independent of the physiological state, is subject-independent, and is highly similar across different frequency bands. In contrast, dynamic features of brain activity are encoded in deviations from this baseline pattern, expressed by relative eigenvalues along the whole spectrum. Conclusion: We associate the stable background pattern as the dynamics upon (or close to) the attractor dynamics, necessary to maintain the brain in an efficient operational mode. Transient dynamical features are expressed by temporal deviations from this pattern. Our results are congruent with the hypothesis that the brain is a complex system operating close to a critical point of a phase transition.
Introduction: To date, brain–computer interfaces (BCIs) have not achieved reliable real-time communication through auditory or tactile modalities. Such interfaces would be crucial for brain-injured patients with severe motor impairments who are also blind or deaf. This study validates the functionality of the NeuroCommTrainer, a mobile and easy-to-use multimodal BCI with flex-printed electrode strips that does not require vision and adapts to users’ attentiveness levels to initiate stimulation. Methods: In a study of 20 healthy participants, we evaluated auditory and vibrotactile oddball paradigms to train the system to differentiate rare and frequent event-related potentials (ERPs). In real-time online sessions, the system detected participants’ mental focus to adaptively initiate stimulation through attentiveness monitoring. Results: The NeuroCommTrainer successfully captured auditory and tactile ERPs, achieving a classification accuracy of 75% for stimuli in the calibration session, which is not yet reflected in the online session with 34% of found targets (chance level = 16.7%). Discussion: The presented early-stage prototype of the NeuroCommTrainer requires several improvements before clinical application in brain-damaged patients, which include refined algorithms to reduce classification variance across participants, and enhanced attentiveness detection specifically tuned to brain activity of the targeted patient group. The present study makes a critical step in this direction and shows that a transition into a practicable communication system for brain-damaged patients may be achievable in the future.
Background: Chemotherapy-related cognitive impairment (CRCI), commonly known as "chemobrain," frequently occurs during breast cancer treatment and has been linked to altered brain function. This resting-state functional magnetic resonance imaging study examined chemotherapy-related changes in functional brain activity, network connectivity, and associations with cognitive outcomes. Methods: Twenty-eight patients with breast cancer were assessed prechemotherapy (BB) and postchemotherapy (BBF), alongside 27 healthy controls of comparable age at baseline (BH) and follow-up (BHF). Mean fractional amplitude of low-frequency fluctuations (mfALFF) and mean regional homogeneity (mReHo) quantified functional brain activity. Graph theoretical analysis (GTA) assessed network topology; network-based statistics (NBS) evaluated interregional connectivity. Cognitive performance was evaluated through standardized assessments. Results: Postchemotherapy patients exhibited reduced anxiety and lower FACT-Cog scores. Voxel-wise analyses showed increased mfALFF in frontal regions and mReHo in superior temporal and inferior frontal gyri, alongside decreases in postcentral, lingual, and parahippocampal areas. Healthy controls showed increased activity in medial frontal and cingulate regions, with reductions in the temporal lobe and putamen. GTA revealed higher global efficiency and reduced modularity, path length, and network complexity in the BBF group compared with BHF. NBS showed weaker structural connectivity in motor and occipital regions prechemotherapy and decreased parietal and insular connectivity postchemotherapy. Multiple regression showed brain-behavior correlations: declines in FACT-Cog, Digit Symbol Substitution, and mood scores were linked to altered activity in frontal, parietal, cingulate, and occipital areas, while positive correlations suggested compensatory activation. Conclusions: Chemotherapy was associated with longitudinal alterations in brain activity, network organization, and connectivity in breast cancer survivors. Brain-behavior associations suggest disrupted neural networks may underlie CRCI.
Introduction: Paroxysmal depolarization shifts (PDSs), correlated with interictal epileptiform discharges, involve significant membrane potential changes and action potentials. While synchronicity is crucial in paroxysmal activity, the precise function of PDSs and their propagation mechanisms, especially non-synaptic pathways like ephaptic coupling, remains poorly understood. This study investigates the role of ephaptic coupling in PDS propagation in hippocampal cultures, focusing on voltage-gated calcium channel (VGCC) subtypes. Methods: PDSs were induced in hippocampal neurone-glial cultures using bicuculline. The outside-out patch-clamp technique was used to record PDS activity at varying distances from the neuronal network. The effects of L-type (nifedipine) and T-type (ML-218) VGCC inhibitors on PDS amplitude and frequency were assessed. Membrane capacitance and resistance were monitored to verify the outside-out configuration. Results: PDSs could be recorded up to 16 µm from the network, with amplitude decreasing exponentially with distance. PDS frequency remained constant. Blocking L-type VGCCs completely abolished PDS activity at a distance, while T-type VGCC inhibition significantly reduced PDS amplitude. The transition from whole-cell to outside-out configuration was confirmed by a significant decrease in membrane capacitance. Discussion: The findings suggest that ephaptic coupling contributes to PDS propagation in vitro, with L-type VGCCs playing a critical role in field-mediated signal transmission. Constant PDS frequency with varying amplitude at a distance highlights a potential synchronization mechanism during epileptiform activity. Further research should investigate the interplay between ion channels and the extracellular environment during ephaptic coupling, paving the way for brain stimulation-based therapies. Conclusion: Research demonstrates that ephaptic coupling can propagate PDSs in hippocampal neurone-glial cultures, highlighting a promising mechanism for understanding epileptiform foci. This finding is critical for comprehending how these foci form and expand, and it also opens avenues for developing brain stimulation-based therapies.
Design: As the cerebellum has reciprocal communications with the frontal cortex, this retrospective cohort study examined the effects of dual-site repetitive transcranial magnetic stimulation (ds-rTMS: dorsolateral prefrontal cortex [DLPFC] + cerebellum) in disorders of consciousness (DoC). Setting: Single-center study in the Department of Rehabilitation of Jinhua Hospital of TCM Affiliated to Zhejiang University of Traditional Chinese Medicine. Participants: Twenty-nine patients with DoC. Intervention: Systematic review of clinical records comparing ds-TMS (DLPFC + cerebellum) with conventional single-site DLPFC-rTMS. Main Measures: Coma Recovery Scale-Revised (CRS-R) scores, mismatch negativity (MMN) latency, P300 latency, Judson grade, and Hall grade. Results: ds-TMS was associated with larger gains in consciousness (CRS-R scores) compared with DLPFC-rTMS in a retrospective cohort. Both interventions had comparable improvement in cognitive and somatosensory outcomes (MMN, P300, and Judson/Hall grades). Higher CRS-R scores correlated with shorter MMN latency and better Hall grades. Conclusions: ds-TMS treatment may represent an effective therapeutic approach for DoC, with potential effects on consciousness recovery.
INTRODUCTION:Advancements in brain-computer interfaces (BCIs) have improved real-time neural signal decoding, enabling adaptive closed-loop neuromodulation. These systems dynamically adjust stimulation parameters based on neural biomarkers, enhancing treatment precision and adaptability. However, existing neuromodulation frameworks often depend on high-power computational platforms, limiting their feasibility for portable, real-time applications. METHODS:We propose RONDO (Recursive Online Neural DecOding), a resource-efficient neural decoding framework that employs dynamic updating schemes in online learning with recurrent neural networks (RNNs). RONDO supports simple RNNs, long short-term memory networks, and gated recurrent units, allowing flexible adaptation to different signal type, accuracy, and real-time constraints. RESULTS:Experimental results show that RONDO's adaptive model updating improves neural decoding accuracy by 35% to 45% compared to offline learning. Additionally, RONDO operates within real-time constraints of neuroimaging devices without requiring cloud-based or high-performance computing. Its dynamic updating scheme ensures high accuracy with minimal updates, improving energy efficiency and robustness in resource-limited settings. CONCLUSIONS:RONDO presents a scalable, adaptive, and energy-efficient solution for real-time closed-loop neuromodulation, eliminating reliance on cloud computing. Its flexibility makes it a promising tool for clinical and research applications, advancing personalized neurostimulation and adaptive BCIs.
