Brain connectivity最新文献

{"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":"<p><p><b><i>Objectives:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusion:</i></b> 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}

{"title":"Dynamic Task-Related Changes in Electroencephalography Brain Connectivity During a Button-Press Task in Children with and Without Bilateral Cerebral Palsy.","authors":"Sang Wook Lee, Thomas C Bulea, Julia E Kline, Diane L Damiano","doi":"10.1089/brain.2024.0096","DOIUrl":"https://doi.org/10.1089/brain.2024.0096","url":null,"abstract":"<p><p><b><i>Background:</i></b> 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. <b><i>Methods:</i></b> Children with CP (<i>n</i> = 15) and TD (<i>n</i> = 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 (<i>α</i>-band: 8-12 Hz, <i>β</i>-band: 13-35 Hz, <i>γ</i>-band: 36-85 Hz). <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"15 4","pages":"162-174"},"PeriodicalIF":2.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972415","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":"Distinct Brain Connectivity Patterns in Sickle Cell Disease: A Biomarker for Chronic Pain Severity.","authors":"Jamille E R S Santana, Maria Luiza Carvalho, Tiago da Silva Lopes, José G V Miranda, Pedro Montoya, Abrahão F Baptista, André Fonseca","doi":"10.1089/brain.2024.0087","DOIUrl":"10.1089/brain.2024.0087","url":null,"abstract":"<p><p><b><i>Background:</i></b> 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. <b><i>Method:</i></b> 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. <b><i>Results:</i></b> Individuals with SCD and chronic pain exhibited a distinct brain connectivity signature compared to healthy individuals and within pain sublevels. <b><i>Conclusion:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"125-138"},"PeriodicalIF":2.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143662465","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":"Task-radMBNet: An Improved Task-Driven Dynamic Graph Sparsity Pattern Radiomics-Based Morphological Brain Network for Alzheimer's Disease Characterization.","authors":"Limei Song, Zhiwei Song, Pengzhi Nan, Qiang Zheng","doi":"10.1089/brain.2024.0053","DOIUrl":"10.1089/brain.2024.0053","url":null,"abstract":"<p><p><b><i>Background:</i></b> The study of task-driven dynamic adaptive graph sparsity patterns in Alzheimer's disease (AD) analysis is of great importance, as it allows for better focus on regions and connections of interest and enhances task sensitivity. <b><i>Methods:</i></b> In this study, we introduced a task-driven dynamic adaptive graph sparsity model (called task-driven radiomics-based morphological brain network [Task-radMBNet]) for AD diagnosis based on radiomics-based morphological brain network (radMBN). Specifically, the Task-radMBNet was established by devising a connectivity feature-based graph convolutional network (GCN) channel (called a connectivity-GCN channel) and a radiomics feature-based GCN channel (called a radiomics-GCN channel), where the two GCN channels shared a same dynamic sparse brain network in graph convolution but worked for different aims separately. The connectivity-GCN channel dynamically learned the graph's sparse topology that best suits the target task, while the radiomics-GCN channel combined radiomics node features and dynamic topology to improve AD diagnostic accuracy. <b><i>Results:</i></b> The Task-radMBNet achieved superior classification accuracy of 87.8% and 86.0% in early AD diagnosis across a total of 1273 subjects within the AD Neuroimaging Initiative and European Diffusion Tensor Imaging (DTI) Study on Dementia databases. We also visualized the topology heat map and important connectivity under different network sparse settings. <b><i>Conclusions:</i></b> The results demonstrated significant promise in the diagnosis of neurological disorders by integrating Task-radMBNet with radMBN.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"139-149"},"PeriodicalIF":2.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802495","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":"From Dyadic to Higher-Order Interactions: Enhanced Representation of Homotopic Functional Connectivity Through Control of Intervening Variables.","authors":"Behdad Khodabandehloo, Payam Jannatdoust, Babak Nadjar Araabi","doi":"10.1089/brain.2024.0056","DOIUrl":"10.1089/brain.2024.0056","url":null,"abstract":"<p><p><b><i>Background:</i></b> The brain's complex functionality emerges from network interactions that go beyond dyadic connections, with higher-order interactions significantly contributing to this complexity. Homotopic functional connectivity (HoFC) is a key neurophysiological characteristic of the human brain, reflecting synchronized activity between corresponding regions in the brain's hemispheres. <b><i>Materials and Methods:</i></b> Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we evaluate dyadic and higher-order interactions of three functional connectivity (FC) parameterizations-bivariate correlation, partial correlation, and tangent space embedding-in their effectiveness at capturing HoFC through the inter-hemispheric analogy test. <b><i>Results:</i></b> Higher-order feature vectors are generated through node2vec, a random walk-based node embedding technique applied to FC networks. Our results show that higher-order feature vectors derived from partial correlation most effectively represent HoFC, while tangent space embedding performs best for dyadic interactions. <b><i>Discussion:</i></b> These findings validate HoFC and underscore the importance of the FC construction method in capturing intrinsic characteristics of the human brain.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"113-124"},"PeriodicalIF":2.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613335","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":"Distinct Neural Connectivity Patterns During Music Listening and Imagination: An Electroencephalography Study.","authors":"Kiarash Fouladi, Hessam Ahmadi, Ali Motie-Nasrabadi","doi":"10.1089/brain.2024.0042","DOIUrl":"10.1089/brain.2024.0042","url":null,"abstract":"<p><p><b><i>Background:</i></b> The brain's function changes during various activities, and numerous studies have explored this field. An intriguing and significant area of research is the brain's functioning during imagination and periods of inactivity. <b><i>Objective:</i></b> This study explores the differences in brain connectivity during music listening and imagination: by identifying distinct neural connectivity patterns and providing insights into the cognitive mechanisms underlying auditory imagination. <b><i>Methods:</i></b> Effective connectivity matrices were generated using generalized partial directed coherence (GPDC) and directed Directed Transfer Function (dDTF) methods applied to non-invasive electroencephalography data from these two conditions. Statistical tests were performed to illustrate the differences in brain connectivity, followed by the creation of brain graphs and the application of a non-parametric permutation test to demonstrate statistical significance. Data classification between listening to music and imagining it was performed using an Support Vector Machine (SVM) classifier with different feature vectors. <b><i>Results:</i></b> Combining features extracted from GPDC and dDTF achieved an accuracy of 71.3% while using GPDC and dDTF features individually yielded accuracies of 60% and 62.1%, respectively. Among all the graph's global features, only modularity and small-worldness showed statistically significant differences in dDTF and GPDC. Overall, findings reveal that information flows from the left hemisphere to the right hemisphere increases during music imagination compared with listening, highlighting distinct neural connectivity patterns associated with imaginative processes. <b><i>Conclusion:</i></b> The study provides novel insights into the distinct neural connectivity patterns during music listening and imagination, contributing to the broader understanding of cognitive processes associated with auditory imagination and perception.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"59-70"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799394","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":"Developmental Mismatch Across Brain Modalities in Young Children.","authors":"Xiangyu Long, Madison Long, Jamie Roeske, Jess E Reynolds, Catherine Lebel","doi":"10.1089/brain.2024.0046","DOIUrl":"10.1089/brain.2024.0046","url":null,"abstract":"<p><p><b><i>Background:</i></b> Brain development during the preschool period is complex and extensive and underlies ongoing behavioral and cognitive maturation. Increasing understanding of typical brain maturation during this time is critical to early identification of atypical development and could inform treatments and interventions. Previous studies have suggested mismatches between brain structural and functional development in later childhood and adolescence. The current study aimed to delineate the developmental matches and mismatches between brain measures from multiple magnetic resonance imaging modalities in young children. <b><i>Methods:</i></b> Brain volume, cortical thickness, fractional anisotropy, cerebral blood flow (CBF), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and eigenvector centrality mapping (ECM) were included. Multi-modal neuroimages for 159 datasets from 67 typically developing preschoolers (2.0-7.6 years old) were collected and analyzed. <b><i>Results:</i></b> Functional measures (CBF, ECM, ReHo, ALFF) had similar developmental trajectories across regions, whereas development trajectories for brain volumes and cortical thickness were more heterogeneous. Furthermore, within individuals, brain volumes and cortical thickness were very good at predicting individual scans from prior longitudinal scans. <b><i>Conclusions:</i></b> These findings provide a more detailed characterization of the complex interplay of different types of brain development in the early years, laying the foundation for future studies on the impact of environmental factors and neurodevelopmental disorders on the development matches/mismatches patterns between brain areas and modalities.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":" ","pages":"71-83"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871334","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}