Brain connectivity最新文献

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: 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.

Background: Emerging video services (EVS) offer users various multimedia presentations, and satisfaction assessment is crucial for enhancing their user experience and competitiveness. However, existing research methods are unable to provide a quantitative satisfaction assessment. Electroencephalogram (EEG), as a popular signal source in brain-computer interface (BCI), with the advantage of being difficult to disguise and containing rich brain activity information, has gained increasing attention from researchers. This article aims to investigate the advantages of employing EEG for modeling satisfaction in EVS. Unlike the subjective metrics assessment in traditional video services, generating satisfaction in EVS involves a range of cognitive functions, including cognitive load, emotion, and audiovisual perception, which are difficult to characterize using a single feature. The representation of brain states for complex cognitive functions has been a major challenge for EEG modeling approaches.

Methods: To address this challenge, we propose an EEG-based EVS satisfaction assessment BCI by raising a Point-to-Global graph representation learning strategy (P2G) that efficiently identifies satisfaction level through a parallel coding module and a graph-based brain region perception module. P2G captures satisfaction-sensitive graph representations in EEG samples based on coding and integrating point features and the global topography.

Results: We validate the effectiveness of introducing a P2G learning strategy in EVS satisfaction modeling using a self-constructed dataset and a relevant public dataset, and our method outperforms existing methods. Additionally, we provide a detailed visual analysis to unveil neural markers associated with EVS satisfaction, thereby laying a scientific foundation for the optimization and development of video services.

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.

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.

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 (μ_age = 12.2 years). In addition, MEG scans were acquired at baseline and follow-up for 17 control participants (μ_age = 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.

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.

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.