Brain connectivity最新文献

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.

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.

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.

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: 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.

Background: Brain-computer interfaces (BCIs) can provide alternative, motor-independent means of communication for people who have lost motor function. A promising variant is the functional magnetic resonance imaging (fMRI)-based BCI, which exploits information on hemodynamic brain activity evoked by performing different mental tasks. However, due to the sluggish nature of the hemodynamic response, a current challenge is to make these BCIs as efficient and fast as possible to allow useful clinical application. Furthermore, there is yet no consensus on optimal mental-task selection for multi-voxel pattern analysis-based decoding, nor whether certain tasks generalize well across users, or if individualized task selection would yield a higher decoding accuracy. Methods: To increase BCI efficiency, we tested whether distributed patterns of 3T-fMRI brain activation evoked by two-second mental tasks could be reliably discriminated in 2- to 7-class classification. In addition, we identified optimal mental-task combinations for high-accuracy classification across all classes. Finally, we examined whether individualized task selection-based on subjects' previous decoding performance (accuracy-based tasks) or their subjective preference (preference-based tasks)-was superior to the other in a yes/no communication paradigm. Results: The 2-class decoding resulted in a mean accuracy of 78% and 3- to 7-class accuracies were above chance level. Mental calculation and spatial navigation were most frequently associated with the highest decoding accuracy. Furthermore, subjects could encode yes/no answers using their accuracy-based and preference-based tasks with mean accuracies of 83% and 81%, respectively. This implies that this paradigm, using short encoding durations, is well-suited to the diversity of patients and could greatly increase BCI efficiency.

Background: Time-resolved functional network connectivity (trFNC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFNC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchrony (PS), a phase-based technique. Methods: To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project with 827 subjects [repetition time (TR): 0.7 sec] and the Function Biomedical Informatics Research Network with 311 subjects (TR: 2 sec), which included 151 schizophrenia (SZ) patients and 160 controls. Results: Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, whereas PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (∼30 sec), but larger windows (∼88 sec) sacrifice clinically relevant information. Both methods identify a SZ-associated brain network state but show different patterns: SWPC highlights low anticorrelations between visual, subcortical, auditory, and sensory-motor networks, whereas PS shows reduced positive synchronization among these networks. Conclusion: In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.