Brain connectivity最新文献

Introduction: Ultrasound is a promising new approach for noninvasive brain stimulation. Low-intensity focused ultrasound (LIFU) allows targeting the deep brain with high spatial and temporal resolution. For clinical use, ultrasound systems must fulfill specific requirements. Three-dimensional (3D) steering and focusing either requires mechanical displacement of (focused) transducers or multielement arrays and corresponding multichannel electronics. Since the waveform has an impact of the induced neurostimulation effect, electronics need sufficient flexibility for generating arbitrary temporal signal patterns. For compensation of skull aberration artifacts, elements must be excited with defined phase resulting of phase aberration correction (PAC) algorithms. Finally, for being clinically usable, systems must be combined with planning hardware and software. Methods: A versatile system for 3D steered LIFU based on two-dimensional matrix arrays was designed, fabricated, and characterized in terms of focusing, steering, and output of temporal patterns. Our PAC algorithm was validated on an ex vivo skull. The system was tested for compliance with defined medical device standard by accredited laboratories, and an initial Magnetic resonance imaging (MRI) phantom study was performed. Results: Our system allows 3D beam steering and focusing with lateral focus sizes down to 4 mm, which is less than the size of a human gyrus, such that detailed targeting is possible. Arbitrary temporal signal patterns (different wave forms, pulse length, duty cycle, and ramping) were generated. Different software interfaces allow patient-specific planning with a Magnetic resonance Tomograph (MR)- or neuronavigation-based workflow, in which a custom-developed PAC algorithm allows compensation of the skull bone. The absence of transducer susceptibility artifacts was shown in the MRI phantom study, and the acoustic focus was localized using magnetic resonance acoustic radiation force imaging. Discussion: Our new versatile ultrasound neuromodulation platform represents a compromise between conformal helmet-like systems and single element transducer setups. It is flexible in terms of spatiotemporal stimulation patterns and can be accommodated to different workflows. Impact Statement Progress in the field of ultrasound neurostimulation is depending on the availability of suitable hardware fulfilling a range of practical, technical, safety, and regulatory requirements. Systems must fit in established clinical workflows (e.g., usable with MR and/or neuronavigation systems), allow accessing deep brain regions, and generate defined spatiotemporal ultrasound patterns. Furthermore, basic regulatory constraints (e.g., IEC 60601-1) must be fulfilled. Our new low-intensity focused ultrasound (LIFU) system addresses these requirements and is flexible enough for use in a research environment. It was developed for facilitati

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.

Background: The multilayer network framework has emerged as an innovative approach for analyzing electrophysiological networks, providing insights into complex neuronal interactions by integrating connectivity across different frequency bands in electroencephalography (EEG) and magnetoencephalography (MEG) data. Current Limitations: Traditionally, multilayer networks have treated canonical frequency bands (e.g., delta, theta, alpha, beta, gamma) as distinct layers. Recent findings could raise potential concerns regarding this approach, emphasizing the need to incorporate the distinction between periodic (oscillatory) and aperiodic (broadband) signal components. Conceptual Advance: Aperiodic signals may reflect excitation-inhibition balance and scale-free dynamics, while periodic signals capture oscillatory rhythms, both contributing uniquely to brain network interactions. A multilayer network framework in the current context could be applicable in the case of genuine coupling between these components, termed "aperiodic-to-periodic coupling." This necessitates novel connectivity metrics and analytical methods that can handle broadband data. Furthermore, challenges remain in decomposing these components in the time domain and developing robust metrics for broadband connectivity that account for signal leakage. Outlook: Addressing these issues will enhance multilayer frameworks, enabling better insights into brain network integrity, cognitive dysfunction, and neurological conditions.

Background: In anti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis, the cerebellum, a characteristic brain region, exhibits abnormal functioning and structure. However, the relationship between resting-state activities in the cerebellar subregions and core symptoms of cognitive dysfunction is unclear. Methods: In this study, a total of 23 patients with anti-NMDAR encephalitis and 23 healthy controls were included, and 19 patients (mean age 30.05 ± 13.03 years) and 21 healthy controls (mean age 28.42 ± 9.47 years) were enrolled based on image quality and head movement. Seed-based functional connectivity (FC) analysis was used to investigate changes in FC of cerebellar subregions, and the association between changes in cerebellar subregion FC and cognitive dysfunction was explored in conjunction with cognitive scales. Results: Patients with anti-NMDAR encephalitis exhibited poorer cognitive performance than the healthy controls did. In the patient group, the FC between the right cerebellar Crus I and the left thalamus was significantly reduced and showed a negative correlation with disease duration (p < 0.05); however, it showed positive correlations with attention and information processing speed according to Symbol Digit Modalities Test (p < 0.01), as well as with verbal learning and memory (according to California Verbal Learning Test (CVLT; p < 0.05). The FCs between the left cerebellar Crus Ia and the right medial superior frontal gyrus, as well as between the left cerebellar Crus Ib and the right middle temporal gyrus, were decreased. The connectivity between the cerebellar vermis Crus II and the left putamen, along with the opercular part of the inferior frontal gyrus, showed a significant reduction. Decreased FC between the left cerebellar X lobule and the left putamen showed positive correlation with CVLT (p < 0.01). Conclusion: Cerebellar subregion and brain abnormalities FC in patients with anti-NMDAR encephalitis are linked to cognitive deficits. These results provide insights into the neurophysiological mechanisms underlying cognitive impairment in these patients.

Objective: Repetitive transcranial magnetic stimulation (rTMS) uses electromagnetic fields to induce electrical currents in the superficial cortex, and this electric signal is believed to propagate to functionally connected distal brain regions. We previously reported that rTMS targeting the postcentral gyrus affected resting-state functional connectivity with the posterior insula. The current study investigated whether rTMS targeting the postcentral gyrus would affect task-based functional connectivity (TBFC) with the posterior insula during a cognitive-affective distress task. Methods: Twenty-five healthy participants were assigned to 10 Hertz (Hz) (n = 13) or 1 Hz (n = 12) rTMS groups. Participants received five consecutive days of once-daily rTMS and underwent pre- and post-rTMS magnetic resonance imaging (MRI) scans while completing a cognitive-affective distress task with negative auditory feedback. rTMS coil placement over the right postcentral gyrus was guided with neuronavigation, and TBFC analysis of the MRI data was performed using the bilateral auditory cortex as a seed region-of-interest. Results: There was an false discovery rate (FDR)-corrected significant group-by-session-by-condition interaction in a right putamen/posterior insula cluster: in the distress condition, the 1 Hz rTMS group had significantly weaker (i.e., smaller absolute value) negative TBFC following rTMS (p = 0.005), while the 10 Hz group had no significant effect. Conclusion: This preliminary, proof-of-concept study suggests that rTMS can modulate TBFC in distal brain regions implicated in the neural response to cognitive-affective negative feedback. Future research should investigate whether rTMS can both modulate insula-associated TBFC and improve cognitive-affective task performance or mood outcomes, potentially by increasing the number of rTMS sessions or using different rTMS pulse sequences. Impact Statement Clinical application of repetitive transcranial magnetic stimulation (rTMS) may exert a therapeutic effect by modulating the strength of functional connectivity between superficial cortical areas and deeper brain regions. These effects on functional connectivity are typically measured while participants are at rest. This proof-of-concept study suggests that rTMS can have a measurable effect on task-based functional connectivity as well. In the future, this could be an important means of understanding how rTMS exerts effects on cognitive-affective task performance and mood.

Background: The integration of machine learning with advanced neuroimaging has emerged as a powerful approach for uncovering the relationship between neuronal activity patterns and behavioral traits. While resting-state neuroimaging has significantly contributed to understanding the neural basis of cognition, recent fMRI studies suggest that task-based paradigms may offer superior predictive power for cognitive outcomes. However, this hypothesis has never been tested using electroencephalography (EEG) data. Methods: We conducted the first experimental comparison of predictive models built on high-density EEG data recorded during both resting-state and an auditory working memory task. Multiple data processing pipelines were employed to ensure robustness and reliability. Model performance was evaluated by computing the Pearson correlation coefficient between predicted and observed behavioral scores, supplemented by mean absolute error and root mean square error metrics for each model configuration. Results: Consistent with prior fMRI findings, task-based EEG data yielded slightly better modeling performance than resting-state data. Both conditions demonstrated high predictive accuracy, with peak correlations between observed and predicted values reaching r = 0.5. Alpha and beta band functional connectivity were the strongest predictors of working memory performance, followed by theta and gamma bands. Additionally, the choice of parcellation atlas and connectivity method significantly influenced results, highlighting the importance of methodological considerations. Conclusion: Our findings support the advantage of task-based EEG over resting-state data in predicting cognitive performance, aligning with. The study underscores the critical role of frequency-specific functional connectivity and methodological choices in model performance. These insights should guide future experimental designs in cognitive neuroscience. Impact Statement This study provides the first direct comparison of EEG-based functional connectivity during rest and task conditions for predicting working memory performance using connectome-based predictive modeling (CPM). It demonstrates that task-based EEG data slightly outperforms resting-state data, with alpha and beta bands being the most predictive. The findings highlight the critical influence of methodological choices, such as parcellation atlases and connectivity metrics, on model outcomes. By bridging gaps in EEG research and validating CPM's applicability, this work advances the optimization of neuroimaging protocols for cognitive assessment, offering insights for future studies in cognitive neuroscience.

Background: Cerebral palsy (CP) often affects function of one or both arms. Resting-state magnetic resonance imaging studies identified abnormal neuronal connectivity related to functional deficits in CP, with few studies on dynamic, task-related changes in connectivity. Here, we compare connectivity in participants with CP and typical development (TD) during an upper limb task and relate these to motor performance. Methods: Children with CP (n = 15) and TD (n = 15) performed a button-press task with both arms, while recording 64-channel electroencephalography. Inter- and intrahemispheric connectivity between dominant and nondominant premotor, motor, and sensory regions were examined during rest, movement preparation, and execution using a normalized magnitude squared time-frequency coherence analysis (α-band: 8-12 Hz, β-band: 13-35 Hz, γ-band: 36-85 Hz). Results: The only group differences were in intrahemispheric connectivity during nondominant arm trials, with CP having higher frontal to central connectivity than TD in all frequency bands in the dominant hemisphere and higher central to parietal beta connectivity in the nondominant hemisphere. Significant main effects for period showed most differences between rest and movement phases. Group by period interactions were also only found during nondominant arm trials (interhemispheric: CP coherence increased more during execution in frontal, central, and parietal regions; intrahemispheric: CP coherence decreased less during execution in nondominant and dominant frontal to parietal regions). Clinical and movement scores were moderately related to connectivity in CP, with poorer nondominant arm function significantly correlated with higher inter- and intrahemispheric coherence. Conclusions: Group differences emerged mainly during intrahemispheric nondominant arm trials across frequency bands with higher coherence in CP associated with greater functional limitation. Impact Statement In contrast to assessing brain connectivity with MRI in children with CP, the use of EEG enables the investigation of this during a functional task, and the sample is not limited by head movements that preclude the attainment of high-quality MRI data in many with CP. The finding of increased task-specific intrahemispheric brain connectivity in bilateral CP, the magnitude of which was related to the degree of functional limitations, suggests a new target for rehabilitation as well as a sensitive outcome measure for clinical trials aimed at improving brain and motor function in CP.

Background: Central nervous system complications are common in sickle cell disease (SCD), and the defining associated biomarkers are becoming increasingly relevant for physicians in diagnostic and prognostic contexts. Recent studies have reported altered brain connectivity in pain processing, highlighting a new avenue for developing sensitive measures of SCD severity. Method: This cross-sectional study used graph theory concepts to analyze effective connectivity in individuals with SCD and healthy controls during rest and motor imagery tasks. The SCD group was further divided into two subgroups based on pain intensity (less pain or more pain) during the evaluation. Results: Individuals with SCD and chronic pain exhibited a distinct brain connectivity signature compared to healthy individuals and within pain sublevels. Conclusion: Chronic pain in SCD shows a unique brain connectivity pattern when compared to healthy subjects and across different pain levels. The results support the hypothesis that chronic pain condition is associated with decreased interhub connections and increased intrahub connections for specific brain rhythms. Furthermore, the small-world parameter can distinguish SCD individuals from controls and differentiate pain levels within SCD individuals, offering a promising biomarker for clinical assessment.