Brain connectivity最新文献

{"title":"Electroencephalogram-Based Satisfaction Assessment Brain-Computer Interface in Emerging Video Service by Using Graph Representation Learning.","authors":"Yifan Niu, Ziyu Li, Gangyan Zeng, Yuan Zhang, Li Yao, Xia Wu","doi":"10.1177/21580014251359107","DOIUrl":"https://doi.org/10.1177/21580014251359107","url":null,"abstract":"<p><p><b><i>Background:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144625375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Functional Connectivity of Executive Functions and Attention Networks During Reading Versus Narrative Comprehension in Dyslexia.","authors":"Rola Farah, Raya Meri, Tzipi Horowitz-Kraus","doi":"10.1177/21580014251358735","DOIUrl":"https://doi.org/10.1177/21580014251358735","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Executive functions (EF) are cognitive processes supporting language and reading. Children with dyslexia show reading difficulties primarily due to phonological processing, with additional reported deficits in EF. This study aimed to determine the differences in EF involvement during written (reading) versus oral language (narrative) comprehension in children with dyslexia versus typical readers neurobiologically and behaviorally. <b><i>Methods:</i></b> Reading, language, and EF behavioral measures and functional MRI data were collected from 55 typical readers (TR) and 65 English-speaking children with dyslexia ages 8-12 years during reading and narrative comprehension tasks. Differences within and between functional connectivity of EF and attention networks were calculated and then compared between groups and tasks using Fisher Z-transformation. <b><i>Results:</i></b> Children with dyslexia showed higher functional connectivity values in EF and attention networks in both reading and narrative comprehension tasks, whereas TR showed higher functional connectivity in narrative versus reading comprehension. Within groups, analysis showed higher functional connectivity within dorsal attention functional brain network (DAN) and between DAN-fronto-parietal (FP), cingulo-opercular (CO)-FP, and ventral attention functional brain network (VAN)-DAN, in the reading versus narrative comprehension task in the dyslexia group. TR showed higher functional connectivity within VAN, and between VAN-FP in the narrative compared to the reading comprehension tasks. <b><i>Discussion:</i></b> Children with dyslexia seem to greatly utilize EF and attention-related networks in narrative and reading comprehension tasks and demonstrate a greater network integration for the written versus oral comprehension task. TR, however, utilize these networks only during oral comprehension, which may point to a greater reliance on memory and processing effort in the absence of written information.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneity of Degree Centrality Revealed Different Subtypes in Children with Autism Spectrum Disorder.","authors":"Xiaonan Guo, Yingnan Xing, Dong Cui, Rongjuan Zhou, Le Gao","doi":"10.1089/brain.2024.0033","DOIUrl":"https://doi.org/10.1089/brain.2024.0033","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Autism spectrum disorder (ASD) is a neurodevelopmental condition that exhibits a wide range of clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on deviations in brain functional networks. <b><i>Methods:</i></b> Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were analyzed in 105 children with ASD and 102 demographically matched typical controls (TC) children. Heterogeneity through discriminative analysis (HYDRA) was utilized to identify subtypes of ASD based on the degree centrality (DC) maps. Voxel-wise group comparisons were then performed between ASD subtypes and the TC group. The relationship between the altered DC and the symptom severity was finally analyzed for ASD subtypes using the multivariate support vector regression approach. <b><i>Results:</i></b> HYDRA identified three subtypes of ASD. Distinct DC alteration patterns were observed in brain regions including the fusiform gyrus, insula, and inferior frontal gyrus in ASD subtypes. Moreover, the altered DC values for ASD subtype 1 and subtype 3 can predict the restricted and repetitive behavior and social communication impairments in ASD, respectively. <b><i>Conclusions:</i></b> Our findings demonstrated the heterogeneity of brain functional networks in ASD and provided a promising way to explain the high heterogeneity of clinical symptoms and outcomes.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Tasks on Functional Brain Connectivity Derived from Interindividual Correlations: Insights from Regional Homogeneity of Functional MRI Data.","authors":"Xin Di, Pratik Jain, Bharat B Biswal","doi":"10.1089/brain.2024.0044","DOIUrl":"10.1089/brain.2024.0044","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Research on brain functional connectivity often relies on intraindividual moment-to-moment correlations of functional activity, typically using functional MRI (fMRI). Interindividual correlations are also employed in data from fMRI and positron emission tomography. Many studies have not specified tasks during scanning, keeping participants in an implicit \"resting\" condition. This lack of task specificity raises questions about how different tasks impact interindividual correlation estimates. <b><i>Methods and Results:</i></b> In our analysis of fMRI data from 100 unrelated participants scanned during seven tasks and in a resting state, we calculated regional homogeneity (ReHo) for each task as a regional measure of brain function. We found that changes in ReHo due to tasks were relatively small compared with its variations across brain regions. Cross-region variations of ReHo were highly correlated among tasks. Similarly, whole-brain interindividual correlation patterns were remarkably consistent across the tasks, showing correlations greater than 0.78. Changes in interindividual correlations between tasks were primarily driven by connectivity in the visual, somatomotor, and default mode networks, as well as the interactions between them. <b><i>Conclusions:</i></b> These subtle yet statistically significant differences in functional connectivity may be linked to specific brain regions associated with the studied tasks. Future studies should consider task design when exploring interindividual connectivity in specific brain systems.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting the Multilayer Network Framework for Electrophysiological Networks.","authors":"Prejaas K B Tewarie, Steven Laureys, Rikkert Hindriks","doi":"10.1089/brain.2025.0010","DOIUrl":"10.1089/brain.2025.0010","url":null,"abstract":"<p><p><b><i>Background:</i></b> 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. <b><i>Current Limitations:</i></b> 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. <b><i>Conceptual Advance:</i></b> 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. <b><i>Outlook:</i></b> Addressing these issues will enhance multilayer frameworks, enabling better insights into brain network integrity, cognitive dysfunction, and neurological conditions.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"189-194"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in the Functional Connectivity of Cerebellar Subregions in Anti-<i>N</i>-Methyl-d-Aspartate Receptor Encephalitis.","authors":"Muzi Li, Zijun Liu, Jingwen Li, Guang Xu, Junzhang Tian, Xiaofen Ma","doi":"10.1089/brain.2024.0102","DOIUrl":"10.1089/brain.2024.0102","url":null,"abstract":"<p><p><b><i>Background:</i></b> In anti-<i>N</i>-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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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 (<i>p</i> < 0.05); however, it showed positive correlations with attention and information processing speed according to Symbol Digit Modalities Test (<i>p</i> < 0.01), as well as with verbal learning and memory (according to California Verbal Learning Test (CVLT; <i>p</i> < 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 (<i>p</i> < 0.01). <b><i>Conclusion:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"195-203"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder-Neurofeedback Research: A Systematic Review and Statistical Analysis.","authors":"Peng Ding, Lize Tan, He Pan, Anming Gong, Wenya Nan, Yunfa Fu","doi":"10.1089/brain.2024.0084","DOIUrl":"https://doi.org/10.1089/brain.2024.0084","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;b&gt;&lt;i&gt;Objectives:&lt;/i&gt;&lt;/b&gt; Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (PTSD). However, existing research lacks comprehensive methodologies and experimental designs. There are concerns in the field regarding the effectiveness and mechanistic interpretability of NF, prompting this study to conduct a systematic analysis of primary NF techniques and research outcomes in PTSD modulation. The study aims to explore reasons behind these concerns and propose directions for addressing them. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; A search conducted in the Web of Science database up to December 1, 2023, yielded 111 English articles, of which 80 were excluded based on predetermined criteria irrelevant to this study. The remaining 31 original studies were included in the literature review. A checklist was developed to assess the robustness and credibility of these 31 studies. Subsequently, these original studies were classified into electroencephalogram-based NF (EEG-NF) and functional magnetic resonance imaging-based NF (fMRI-NF) based on BCI type. Data regarding target brain regions, target signals, modulation protocols, control group types, assessment methods, data processing strategies, and reported outcomes were extracted and synthesized. Consensus theories from existing research and directions for future improvements in related studies were distilled. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Analysis of all included studies revealed that the average sample size of PTSD patients in EEG and fMRI NF studies was 17.4 (SD 7.13) and 14.6 (SD 6.37), respectively. Due to sample and neurofeedback training protocol constraints, 93% of EEG-NF studies and 87.5% of fMRI-NF studies used traditional statistical methods, with minimal utilization of basic machine learning (ML) methods and no studies utilizing deep learning (DL) methods. Apart from approximately 25% of fMRI NF studies supporting exploratory psychoregulatory strategies, the remaining EEG and fMRI studies lacked explicit NF modulation guidance. Only 13% of studies evaluated NF effectiveness methods involving signal classification, decoding during the NF process, and lacking in process monitoring and assessment means. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; In summary, NF holds promise as an adjunctive intervention technique for PTSD, potentially aiding in symptom alleviation for PTSD patients. However, improvements are necessary in the process evaluation mechanisms for PTSD-NF, clarity in NF modulation guidance, and development of ML/DL methods suitable for PTSD-NF with small sample sizes. To address these challenges, it is crucial to adopt more rigorous methodologies for monitoring NF, and future research should focus on the integration of advanced data analysis techniques to enhance the effectiveness and precision of PTSD-NF interventions. Impact Statement The implications of this study are to address the limited application of Neurofeedback tr","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Transcranial Magnetic Stimulation on Cognitive-Affective Task-Based Functional Connectivity.","authors":"Merideth A Addicott, Jonathan R Young, L Gregory Appelbaum","doi":"10.1089/brain.2024.0095","DOIUrl":"https://doi.org/10.1089/brain.2024.0095","url":null,"abstract":"<p><p><b><i>Objective:</i></b> 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. <b><i>Methods:</i></b> Twenty-five healthy participants were assigned to 10 Hertz (Hz) (<i>n</i> = 13) or 1 Hz (<i>n</i> = 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. <b><i>Results:</i></b> 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 (<i>p</i> = 0.005), while the 10 Hz group had no significant effect. <b><i>Conclusion:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"15 4","pages":"153-161"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct Comparison of EEG Resting State and Task Functional Connectivity Patterns for Predicting Working Memory Performance Using Connectome-Based Predictive Modeling.","authors":"Anton Pashkov, Ivan Dakhtin","doi":"10.1089/brain.2024.0059","DOIUrl":"https://doi.org/10.1089/brain.2024.0059","url":null,"abstract":"<p><p><b><i>Background:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusion:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"15 4","pages":"175-187"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143975072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connecting the Dots: How Adaptive Brain Networks Guide the Future of Clinical Neuroscience.","authors":"Roxane Hoyer, Steven Laureys","doi":"10.1089/brain.2025.0055","DOIUrl":"https://doi.org/10.1089/brain.2025.0055","url":null,"abstract":"","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"15 4","pages":"151-152"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143975115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}