AIM:Non-suicidal self-injury (NSSI) is a prevalent behavior among adolescents with major depressive disorder (MDD), yet the precise neural mechanisms remain unclear. This study aimed to investigate the temporal dynamics of brain connectivity associated with NSSI in adolescents with MDD using dynamic functional connectivity (dFC) analysis. METHODS:Resting-state fMRI data from 204 adolescents (154 with NSSI, 50 without) were analyzed. dFC variability within the fronto-limbic network was assessed using a seed-based dynamic conditional correlation approach. Group differences in dFC variability were examined, and a machine-learning model was used to predict NSSI based on dFC features. Mediation analysis explored the dFC's role in the relationship between depressive symptoms and NSSI. RESULTS:Adolescents with NSSI exhibited reduced dFC variability, which mediated the relationship between depressive severity and NSSI behavior (a*b = 0.144; p = 0.001). Key connections-insula, anterior cingulate cortex, orbitofrontal cortex, and hippocampus-were critical in distinguishing NSSI from non-NSSI groups. Machine learning models based on these connections achieved robust and stable performance with mean AUC of 0.84 and PR-AUC of 0.94 in predicting NSSI. CONCLUSIONS:Altered dFC within the fronto-limbic network may underlie NSSI in adolescents with MDD, identifying preliminary neural features for targeted interventions and highlighting neurobiological heterogeneity associated with NSSI in adolescents with MDD.

Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of naturalistic stimuli is rich and continuous. The fMRI signals of naturalistic stimuli are complex and include different components. A major challenge is isolate the stimuli-induced signals while simultaneously tracking the brain's responses to these stimuli in real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic brain network analysis methods and their improved versions. The main features of NaDyNet are: 1) extracting signals of interest from naturalistic fMRI signals; 2) incorporating six commonly used dynamic analysis methods and three static analysis methods; 3) improved versions of these dynamic methods by adopting inter-subject analysis to eliminate the effects of non-interest signals; 4) performing K-means clustering analysis to identify temporally reoccurring states along with their temporal and spatial attributes; 5) Visualization of spatiotemporal results. We then introduced the rationale for incorporating inter-subject analysis to improve existing dynamic brain network analysis methods and presented examples by analyzing naturalistic fMRI data. We hope that this toolbox will promote the development of naturalistic neuroscience. The toolbox is available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of naturalistic stimuli is rich and continuous. The fMRI signals of naturalistic stimuli are complex and include different components. A major challenge is isolate the stimuli-induced signals while simultaneously tracking the brain's responses to these stimuli in real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic brain network analysis methods and their improved versions. The main features of NaDyNet are: 1) extracting signals of interest from naturalistic fMRI signals; 2) incorporating six commonly used dynamic analysis methods and three static analysis methods; 3) improved versions of these dynamic methods by adopting inter-subject analysis to eliminate the effects of non-interest signals; 4) performing K-means clustering analysis to identify temporally reoccurring states along with their temporal and spatial attributes; 5) Visualization of spatiotemporal results. We then introduced the rationale for incorporating inter-subject analysis to improve existing dynamic brain network analysis methods and presented examples by analyzing naturalistic fMRI data. We hope that this toolbox will promote the development of naturalistic neuroscience. The toolbox is available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

Semantic dementia (SD) is a neurodegenerative disorder marked by a progressive decline in semantic memory, mainly due to focal atrophy in the anterior temporal lobe (ATL). As the disease advances, atrophy spreads to the perisylvian regions, accompanied by non-semantic language impairments. Despite these clinical findings, the network mechanisms behind cross-domain linguistic deficits remain poorly understood. In this study, we used our recently developed meta-networking framework of cortical language network dynamics to systematically examine domain-specific network disruptions in SD. Using resting-state functional MRI (fMRI) and comprehensive neuropsychological tests across several language domains, we analyzed data from SD patients at two timepoints: baseline (n = 42) and a 2-year follow-up (n = 24). Our findings showed that the framework successfully identified domain-specific language network degeneration. Beyond the ATL, progressive atrophy disrupted the dynamic separation of language networks involved in semantic processing, phonological processing, and speech production. These disruptions were characterized by state-specific hypo- and hyper-connectivity patterns that related to distinct language impairments. At follow-up, atrophy extended to posterior temporal and prefrontal regions, worsening network function. Importantly, the patterns of language network disruption predicted individual language deficits, providing a mechanistic link between structural degeneration, functional network changes, and clinical symptoms. ### Competing Interest Statement The authors have declared no competing interest. National Social Science Foundation of China, 20&ZD296 Key-Area Research and Development Program of Guangdong Province, 2019B030335001 Research Center for Brain Cognition and Human Development, Guangdong, China, 2024B0303390003 Shanghai Medical Innovation and Development Foundation “Brain Health Youth Fund - Precision Diagnosis and Treatment Research on Alzheimer’s Disease”, SMIDF-150-2025A30 Basic Scientific Research Project of Shanghai Sixth People’s Hospital, ynqn202222 Special Project for Clinical Research of Shanghai Municipal Health Commission, 202440009 National Natural Science Foundation of China, 82501892, 32400862

Semantic control refers to the ability to flexibly retrieve and manipulate stored knowledge to support context-appropriate behavior. A left-lateralized network comprising the left inferior frontal gyrus (IFG), posterior middle temporal gyrus (pMTG), and dorsal medial prefrontal cortex (dmPFC) has been consistently implicated in this process. While previous studies have established the necessity of the IFG and pMTG in semantic control, the causal role of the left dmPFC remains unclear. Additionally, it is unknown whether each of these three regions exhibits internal functional differentiation and how they interact to support semantic control. To address these questions, we combined task-based functional magnetic resonance imaging (fMRI) with fMRI-guided transcranial magnetic stimulation (TMS). We found that dmPFC, like IFG and pMTG, is causally involved in semantic control. All three regions exhibited a consistent anterior–posterior functional gradient: anterior subregions were selectively engaged during high-demand semantic processing, whereas posterior subregions responded to both easy and hard tasks. Furthermore, combined activation patterns of these regions better predicted the behavioral differences between hard and easy semantic tasks compared to the activation patterns of any single region. Semantic control modulated both the autoinhibition within individual regions and the functional connectivity among them, suggesting these regions operate in a coordinated network rather than in isolation. These findings advance our understanding of the neural architecture supporting flexible semantic behavior. Significance Statement Understanding how the brain supports flexible semantic behavior is critical for both cognitive neuroscience and clinical neuropsychology. While prior studies have consistently implicated the left IFG, pMTG, and dmPFC in semantic control, the causal contribution of the dmPFC and the functional dynamics among these regions have remained unclear. This study provides the first causal evidence for the dmPFC’s role in semantic control, reveals functional differentiation within the IFG and pMTG, and shows that these regions interact as an integrated network. These findings challenge the notion of functionally homogeneous control nodes and highlight a topographically organized, interactive system underlying controlled semantic retrieval. This work refines our mechanistic understanding of semantic control and may inform clinical models of language and conceptual deficits. ### Competing Interest Statement The authors have declared no competing interest. the National Social Science Foundation of Chinathe National Social Science Foundation of China, , 20&ZD296 the Key-Area Research and Development Program of Guangdong Provincethe Key-Area Research and Development Program of Guangdong Province, , 2019B030335001 the National Natural Science Foundation of Chinathe National Natural Science Foundation of China, , 32100889, 32300881 Research Center for Brain Cognition and Human Development, Guangdong, ChinaResearch Center for Brain Cognition and Human Development, Guangdong, China, , 2024B0303390003

Background Protein expression asymmetry between brain hemispheres is hypothesized to influence functional connectivity, yet its role in language-related networks remains poorly understood. Additionally, how such molecular differences relate to brain reorganization in glioma requires further exploration.Methods We performed label-free tandem mass spectrometry on 13 left-hemispheric language-related Brodmann areas (BAs) and their right-hemispheric counterparts from 10 donor brains, identifying protein signatures across 6 language-related functional modules. We then compared these proteomic profiles with resting-state structural and functional connectivity data from 26 BAs across 90 subjects from the Human Connectome Project (HCP). Finally, we examined functional compensation in 13 glioma patients with tumors in Wernicke's area, correlating gray matter volume in contralateral homologs with linguistic performance.Results Protein expression heterogeneity was greater within hemispheres than between homologous contralateral BAs. Hierarchical clustering revealed interactions between core language areas (Broca's, Wernicke's, Geschwind's) and auditory/motor regions. Functional connectivity strength correlated with protein expression similarity, particularly in symmetric BA4 (primary motor cortex). Excitatory/inhibitory (E/I) neuronal markers (GRIA1/GRIA4) showed a left-positive, right-negative correlation with connectivity, suggesting hemispheric differences in synaptic regulation. Glioma patients exhibited right-hemispheric compensation, with gray matter volume in Wernicke's homolog correlating with linguistic function.Conclusion Our findings support the hypothesis of a homophilic mixing effect between protein expression similarity and connectome architecture, and help explain brain rearrangement in glioma patients.Key points Protein expression differs more within hemispheres than across homologous regions, with distinct signatures in language-related brain areas. Functional connectivity strength correlates with protein expression similarity, showing left-right asymmetry in excitatory/inhibitory synaptic regulation (GRIA1/GRIA4). Right-hemispheric homologs compensate for left-hemispheric language-area damage in glioma patients, linking molecular profiles to functional reorganization.

Stroke affecting the basal ganglia and thalamus can lead to language deficits. In addition to the lesion's direct impact on language processing, connectional diaschisis involving cortical-subcortical interactions also plays a critical role. This study investigated connectional diaschisis using the dynamic meta-networking framework of language in patients with basal ganglia and thalamus stroke, analyzing longitudinal resting-state fMRI data collected at 2 weeks (n = 32), 3 months (n = 19), and 1 year post-stroke (n = 23). As expected, we observed dynamic cortico-subcortical interactions between cortical language regions and subcortical regions in healthy controls (HCs; n = 25). The cortical language network exhibited dynamic domain-segregation patterns in HCs, severely disrupted in the acute phase following stroke. The connectional diaschisis manifested as dual effects characterized by both hypo- and hyper-connectivity, which positively and negatively correlated with language deficits, respectively. State-specific changes in nodal and topological properties were also identified. Throughout language recovery, cortical language network dynamics gradually normalized toward suboptimal domain-segregation patterns, accompanied by the normalization of nodal and topological properties. These findings underscore the crucial role of cortico-subcortical interactions in language processing.

The human language network undergoes reorganization across different spatiotemporal scales, limited by its fixed structural neurobiological foundations. Although stroke often damages white matter, its effect on temporary language network reorganization is not fully understood. This study examined longitudinal behavioral and resting-state fMRI data from post-stroke patients with exclusive subcortical lesions at three time points: two weeks (n = 38), three months (n = 29), and one year (n = 27). Patients showed mild to moderate language impairments during the acute phase, which improved within three months. The extent of disconnection in several left-hemispheric white matter tracts was negatively associated with language deficits. Healthy controls (HC, n = 25) exhibited domain-segregation dynamics in the cortical language network, limited by the underlying white matter pathways. In patients, severe, state- and track-specific network disruptions were observed in the acute phase, characterized by hypo- and hyper-connectivity and abnormal topological features. As language recovery progressed, connectivity patterns began to return to normal, resembling those of HC, and the domain-segregation dynamics reappeared. These results deepen our understanding of structure-function coupling and indicate significant post-lesional plasticity within the cortical language network. ### Competing Interest Statement The authors have declared no competing interest.

Mental imagery is a hallmark of human cognition, yet the neural mechanisms underlying these internal states remain poorly understood. Speech imagery—the internal simulation of speech without overt articulation—has been proposed to partially share neural substrates with actual speech articulation. However, the precise feature encoding and spatiotemporal dynamics of this neural architecture remain controversial, constraining the understanding of mental states and the development of reliable speech imagery decoders. Here, we leveraged high-resolution electrocorticography recordings to investigate the shared and modality-specific cortical coding of articulatory kinematic trajectories (AKTs) during speech imagery and articulation. Applying a linear model, we identified robust neural dynamics in frontoparietal cortex that encoded AKTs across both modalities. Shared neural populations across the middle premotor cortex, subcentral gyrus, and postcentral-supramarginal junction exhibited consistent spatiotemporal stability during the integrative articulatory planning. In contrast, modality-specific populations for speech imagery and articulation were somatotopically interleaved along the primary sensorimotor cortex, revealing a hierarchical spatiotemporal organization distinct from shared encoding regions. We further developed a generalized neural network to decode multi-population neural dynamics. The model achieved high syllable prediction accuracy for speech imagery (79% median accuracy), closely matching the performance of speech articulation (81%). This model robustly extrapolated AKT decoding to untrained syllables within each modality while demonstrating cross-modal generalization across shared populations. These findings uncover a somato-cognitive hierarchy linking high-level supramodal planning with modality-specific neural manifestation, revolutionizing an imagery-based brain-computer interface that directly decodes thoughts for synthetic telepathy. ### Competing Interest Statement The authors have declared no competing interest. Ministry of Science and Technology of the People's Republic of China, https://ror.org/027s68j25, 2022ZD0212300 National Natural Science Foundation of China, 32371146, 32371154 China Postdoctoral Science Foundation, https://ror.org/0426zh255, GZB20240661 Shanghai Municipal Education Commission, 2023ZKZD13 Science and Technology Commission of Shanghai Municipality, https://ror.org/03kt66j61, 24QA2705500, 22PJ1410500 Shanghai Municipal People's Government, LG-GG-202402-06

Formal thought disorder (FTD) is a core symptom of schizophrenia, yet the neural network mechanisms underlying this phenotype remain poorly understood. In this study, we applied a dynamic meta-networking framework, which captures temporally recurring functional network states, to investigate alterations in the language and executive control networks and their associations with positive and negative FTD. Resting-state fMRI data were collected from three independent cohorts: a discovery cohort comprising 150 first-episode, drug-naïve patients with schizophrenia and 175 healthy controls (HCs); a replication cohort including 183 first-episode, drug-naïve patients and 109 HCs; and a third cohort consisting of 71 patients who had received two weeks of antipsychotic treatment and 71 HCs. Meta-networking analysis identified four distinct resting-state meta-states within both the language and executive control networks. Connectivity-behavior correlation analyses and machine-learning regression models revealed that positive FTD was associated with aberrant connectivity across specific meta-states in both networks. In contrast, negative FTD was linked exclusively to dysfunction within two meta-states of the executive control network. Notably, these polarity-specific, multi-state connectivity disruptions normalized following short-term antipsychotic treatment, highlighting their potential as clinically relevant neuroimaging biomarkers. ### Competing Interest Statement The authors have declared no competing interest. the National Social Science Foundation of China, Grant No. 20&ZD296 the Key-Area Research and Development Program of Guangdong Province, Grant No. 2019B030335001 the National Natural Science Foundation of China, Grant No. 32400862, Grant No. 82151314, Grant No. 82472092 the Research Center for Brain Cognition and Human Development, Guangdong, China, Grant No. 2024B0303390003 the Clinical Research Plan of Shanghai Hospital Development Center, Grant No. SHDC12022113 grants from Guangzhou Science and Technology Bureau, Municipal (enterprise) and Institute, Grant No. 2024A03J0298, Grant No. 2024A03J0219 the STI 2030-Major Projects, Grant No. 2021ZD020050

BACKGROUND AND AIMS:Glymphatic dysfunction may exacerbate post-stroke cognitive impairment (PSCI) via impaired metabolic waste clearance. However, longitudinal dynamics of glymphatic function during the chronic stroke phase (3-12 months) and links to cognitive recovery remain unclear. This study aimed to characterize chronic glymphatic remodeling dynamics using the DTI-ALPS index while exploring its temporal associations with cognitive outcomes and assessing lesion location effects. METHODS:In this retrospective cohort study, 51 chronic stroke patients (3-12 months post-stroke) and 27 matched healthy controls underwent DTI scans and neuropsychological assessments (evaluating language, memory, motor, attention) at 3 months (3 M-S) and 1 year (1Y-S) post-stroke. The DTI-ALPS index was calculated for lesioned/contralateral hemispheres. Patients were stratified by lesion location (cortical [n = 17] vs. subcortical [n = 34]). Group comparisons and Spearman correlations (FDR-corrected) were performed. RESULTS:Stroke patients showed significantly lower DTI-ALPS index versus controls at both 3 M-S and 1Y-S (FDR-p < 0.001). At 3 M-S, the lesioned hemisphere ALPS index was significantly lower than the contralateral hemisphere (FDR-p < 0.05); this difference resolved by 1Y-S. No significant differences existed between cortical/subcortical subgroups. Weak correlations emerged at 3 M-S between lesioned-hemisphere ALPS index and Motor/Memory scores (r = 0.280-0.316, uncorrected p < 0.05), but these did not survive FDR correction and disappeared by 1Y-S. Lesion volume did not correlate with ALPS index. CONCLUSIONS:Chronic stroke patients exhibit persistent glymphatic dysfunction. The affected hemisphere showed more severe impairment at 3 months post-stroke, with partial improvement observed by the 1-year mark. Transient cognitive associations observed at 3 months diminished by the 1-year follow-up, suggesting stabilization of recovery patterns in later stages. Despite study limitations, these findings validate the utility of the DTI-ALPS index for chronic-phase assessments and highlight the importance of targeting glymphatic dysfunction as a therapeutic strategy for PSCI.

Semantic control enables flexible retrieval and manipulation of stored knowledge. A left-lateralized network including the inferior frontal gyrus, posterior middle temporal gyrus, and dorsal medial prefrontal cortex has been implicated in this process. However, the functional differentiation within each region and their interactions remain unclear. Combining functional MRI and transcranial magnetic stimulation, we demonstrate that all three regions are causally involved in semantic control. Anterior subregions are engaged under hard semantic tasks, whereas posterior subregions respond more generally. Machine learning prediction analyses indicate that combined activity across these regions predicts semantic performance better than any region alone. Dynamic causal modeling further reveals that semantic control demands modulate both self-inhibition and interregional connectivity. Bayesian multiple regression shows that stimulation effects in frontal cortex are best explained by an interaction between local activation and electric field strength, while effects in temporal cortex are better predicted by task-dependent network connectivity. These findings highlight the distributed and interactive mechanisms underlying flexible knowledge retrieval. fMRI and TMS reveal that inferior frontal, medial frontal, and posterior temporal cortices are causally involved in semantic control, forming a distributed network through functional interactions and showing heterogeneity within each region.

Inner speech is a silent verbal experience and plays central roles in human consciousness and cognition. Despite impressive studies over the past decades, the neural mechanisms of inner speech remain largely unknown. In this study, we adopted an ecological paradigm called situationally simulated inner speech. Unlike mere imaging speech of words, situationally simulated inner speech involves the dynamic integration of contextual background, episodic and semantic memories, and external events into a coherent structure. We conducted dynamic activation and network analyses on fMRI data, where participants were instructed to engage in inner speech prompted by cue words across 10 different contextual backgrounds. Our seed-based co-activation pattern analyses revealed dynamic involvement of the language network, sensorimotor network, and default mode network in situationally simulated inner speech. Additionally, frame-wise dynamic conditional correlation analysis uncovered four temporal-reoccurring states with distinct functional connectivity patterns among these networks. We proposed a triple network model for deliberate inner speech, including language network for a truncated form of overt speech, sensorimotor network for perceptual simulation and monitoring, and default model network for integration and ‘sense-making’ processing. Highlights 1. In ten contextual backgrounds, subjects were instructed to perform situationally simulated inner speech based on cue words. 2. The ventral parts of the bilateral somatosensory areas and middle superior temporal gyrus were as centers for seed-based co-activation pattern analyses. 3. A triple network model of language network, sensorimotor network, and default mode network was proposed for deliberate inner speech. ### Competing Interest Statement The authors have declared no competing interest.

BACKGROUND: The single-subject morphological network (SSMN) provides a new approach for constructing structural connectome. However, its clinical relevance in post-stroke deficits and recovery remains unexplored. METHODS: This study utilized high-resolution 3D T1-weighted images alongside behavioral and cognitive assessments across multiple domains, including language, motor, memory, and attention, collected at two weeks, three months, and one year post-stroke. The SSMN was constructed using the AAL atlas by evaluating the similarities of regional probability density derived from gray matter volume. Network disconnection and the disconnectome were evaluated by examining changes in network edges and global topological properties. The functional relevance of the SSMN was explored through its associations with post-stroke behavioral and cognitive deficits and recovery, as well as by developing machine-learning-based prediction models. RESULTS: The findings revealed that the SSMN was sensitive to post-stroke connectional and connectomal disruptions. Domain-specific disruptions in the SSMN were predictable of post-stroke deficits, with correlation pattern aligning with the neurobiological substrates of each domain. Furthermore, the predictive performance of SSMN-based models was comparable to that of other imaging modalities. Notably, normalization of the SSMN within one year post-stroke was significantly associated with functional recovery. CONCLUSIONS: These results highlight the potential of the SSMN as a novel structural imaging modality for evaluating post-stroke deficits and recovery, offering valuable insights into the neurobiological mechanisms of rehabilitation. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The study is supported by the National Social Science Foundation of China (No. 20&ZD296), Key-Area Research and Development Program of Guangdong Province (No. 2019B030335001), National Natural Science Foundation of China (No.32100889). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The datasets analysed during the current study are available in the https://cnda.wustl.edu/data/projects/CCIR_00299. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets analysed during the current study are available in the https://cnda.wustl.edu/data/projects/CCIR_00299.

Background: Language impairments, which affect both structural aspects of language and pragmatic use, are frequently observed in autism spectrum disorder (ASD). These impairments are often associated with atypical brain development and unusual network interaction patterns. However, a neurological framework remains elusive to explain them. Methods: In this study, we utilized the dynamic "meta-networking" framework of language-a theoretical model that describes the domain-segregation dynamics during resting states-to investigate cortical language network abnormalities in ASD aged 5-40 years. Results: Our findings revealed distinct developmental trajectories for three domain-specific language subnetworks in ASD, characterized by unique patterns of hypo- and hyper-connectivity that vary with age. Notably, these language network abnormalities proved to be strong predictors of verbal Intelligence Quotient and communication deficits, though they did not predict social abilities or stereotypical behaviors. Limitations: Due to the limited availability of linguistic data, our study was unable to assess the language deficit profiles of individuals with ASD. Conclusions: Collectively, these findings refined our understanding of the network mechanisms for language and communication deficits in ASD. ### Competing Interest Statement The authors have declared no competing interest.

Numerous dynamic functional connectivity (dFC) methods have been proposed to study time-resolved network reorganization in rest and task fMRI. However, a comprehensive comparison of their performance is lacking. In this study, we compared the efficacy of seven dFC methods (and their enhanced versions) to track transient network reconfiguration using simulation data. The seven methods include flexible least squares (FLS), dynamic conditional correlation (DCC), general linear Kalman filter (GLKF), multiplication of temporal derivatives (MTD), sliding-window functional connectivity with L1-regularization (SWFC), hidden Markov models (HMM), and hidden semi-Markov models (HSMM). Multiple datasets of non-fMRI-BOLD and fMRI-BOLD signals with predefined covariance structures, signal-to-noise ratio levels, and sojourn time distributions were simulated. We adopted inter-subject analysis to eliminate the effects of signals of non-interest, resulting in enhanced methods: ISSWFC, ISMTD, ISDCC, ISFLS, ISKF, ISHMM, and ISHSMM. Efficacy was defined as the spatiotemporal association between simulated and estimated data. We found that all enhanced dFC methods outperformed their original versions. Efficacies depend on several factors, such as considering the neurovascular effect in simulated data, the covariance structure between two time series, state sojourn distribution, and signal-to-noise ratio levels. These results highlight the importance of selecting appropriate dFC methods in fMRI study. ### Competing Interest Statement The authors have declared no competing interest.

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multilayered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC).Method: ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative functional Magnetic Resonance Imaging (fMRI) datasets.Results: (1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; (2) ISDCC identified synchronized network reconfiguration patterns across listeners; (3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; and (4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension.Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli. Impact statement A novel model-based method, intersubject dynamic conditional correlation (ISDCC), was introduced to track the framewise network implication during naturalistic stimuli. First, ISDCC accurately unveiled underlying network reconfiguration patterns in simulated task-fMRI data. Second, ISDCC effectively differentiated between stimulus types with varying temporal coherence. Third, ISDCC unveiled correlations between the activity of the default mode network (DMN) and listener engagement during narrative comprehension, shedding light on how the DMN accumulates and integrates information during narrative comprehension. These findings provide valuable insights for researchers investigating the network processes involved in responding to naturalistic stimuli.

Brain network dynamics not only endow the brain with flexible coordination for various cognitive processes but also with a huge potential of neuroplasticity for development, skill learning, and after cerebral injury. Diffusive and progressive glioma infiltration triggers the neuroplasticity for functional compensation, which is an outstanding pathophysiological model for the investigation of network reorganization underlying neuroplasticity. In this study, we employed dynamic conditional correlation to construct framewise language networks and investigated dynamic reorganizations in 83 patients with left hemispheric gliomas involving language networks (40 patients without aphasia and 43 patients with aphasia). We found that, in healthy controls (HCs) and patients, the language network dynamics in resting state clustered into 4 temporal-reoccurring states. Language deficits-severity-dependent topological abnormalities of dFCs were observed. Compared with HCs, suboptimal language network dynamics were observed for those patients without aphasia, while more severe network disruptions were observed for those patients with aphasia. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the 4 states significantly predicted individual patients' language scores. These findings shed light on our understanding of metaplasticity in glioma. Glioma-induced language network reorganizations were investigated under a dynamic "meta-networking" (network of networks) framework. In healthy controls and patients with glioma, the framewise language network dynamics in resting-state robustly clustered into 4 temporal-reoccurring states. The spatial but not temporal language deficits-severity-dependent abnormalities of dFCs were observed in patients with left hemispheric gliomas involving language network. Language network dynamics significantly predicted individual patients' language scores.