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.
