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Objectives Unruptured brain arteriovenous malformations (AVMs) typically do not cause aphasia, even when the traditional language areas are affected by the nidus. We attempted to elucidate its language reorganization mechanism by analyzing the alterations in functional connectivity using functional connectivity (FC) and track‐weighted static functional connectivity (TW‐sFC) approaches. Methods This cross‐sectional study prospectively enrolled patients with AVMs involving left‐hemisphere language areas and healthy controls. All participants underwent resting‐state functional magnetic resonance imaging (rs‐fMRI) and diffusion tensor imaging scans. Conventional FC analysis was used to investigate the spatially segregated functional connectivity in the gray matter, and the TW‐sFC method was applied to explore the functional connectivity constrained by the white matter. Results 34 AVM patients with lesions involving the left cerebral hemisphere and 27 healthy subjects were included. FC analysis findings revealed decreased FC intensity between the left‐hemisphere language‐associated regions and their right‐hemisphere homologs in AVM patients. Additionally, increased FC intensity was observed between the anterior cingulate cortex (ACC) and the language‐related areas in bilateral cerebellar hemispheres (lobule VIII, VIIb, Crus I, and Crus II). The TW‐sFC results demonstrated increased intensity in multiple right‐hemisphere fiber bundles, the left anterior thalamic radiation (ATR) and the callosum. Conclusions Three factors may contribute to maintaining intact language function in AVM patients, including the weakened inhibitory effect from the left dominant cerebral hemisphere over the right cerebral hemisphere leading to activation of the potential language functions of the right cerebral hemisphere (inter‐cerebral connection reorganization), the functional upregulation of the cerebral language areas by cerebellar language‐related brain regions via ACC (cerebrocerebellar connection reorganization), as well as the enhanced functions of the brain areas surrounding the lesion in the left cerebral hemisphere (intracerebral connection reorganization). Trial Registration This study is registered in the Chinese Trial Registry (clinical trial number: ChiCTR1900020993) Arteriovenous malformations involving the language area usually do not lead to language dysfunction, so it provides a unique model for studying language reorganization. Three factors may contribute to maintaining intact language function in AVM patients: inter‐cerebral connection reorganization; cerebrocerebellar connection reorganization; intracerebral connection reorganization.

Schizotypy refers to schizophrenia-like traits below the clinical threshold in the general population. The pathological development of schizophrenia has been postulated to evolve from the initial coexistence of 'brain disconnection' and 'brain connectivity compensation' to 'brain connectivity decompensation'. In this study, we examined the brain connectivity changes associated with schizotypy by combining brain white matter structural connectivity, static and dynamic functional connectivity analysis of diffusion tensor imaging data and resting-state functional magnetic resonance imaging data. A total of 87 participants with a high level of schizotypal traits and 122 control participants completed the experiment. Group differences in whole-brain white matter structural connectivity probability, static mean functional connectivity strength, dynamic functional connectivity variability and stability among 264 brain sub-regions of interests were investigated. We found that individuals with high schizotypy exhibited increased structural connectivity probability within the task control network and within the default mode network; increased variability and decreased stability of functional connectivity within the default mode network and between the auditory network and the subcortical network; and decreased static mean functional connectivity strength mainly associated with the sensorimotor network, the default mode network and the task control network. These findings highlight the specific changes in brain connectivity associated with schizotypy and indicate that both decompensatory and compensatory changes in structural connectivity within the default mode network and the task control network in the context of whole-brain functional disconnection may be an important neurobiological correlate in individuals with high schizotypy.

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of \"synchronicity\" of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.

This study was conducted to explore differences in static functional connectivity (sFC) and dynamic functional connectivity (dFC) alteration patterns in the primary visual area (V1) among high myopia (HM) patients and healthy controls (HCs) seed-based functional connectivity (FC) analysis. Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 82 HM patients and 59 HCs who were closely matched for age, sex, and weight. Seed-based FC analysis was performed to identify alterations in the sFC and dFC patterns of the V1 in HM patients and HCs. Associations between mean sFC and dFC signal values and clinical symptoms in distinct brain areas among HM patients were identified correlation analysis. Static and dynamic changes in brain activity in HM patients were investigated by assessments of sFC and dFC calculation of the total time series mean and sliding-window analysis. In the left anterior cingulate gyrus (L-ACG)/left superior parietal gyrus (L-SPG) and left V1, sFC values were significantly greater in HM patients than in HCs. In the L-ACG and right V1, sFC values were also significantly greater in HM patients than in HCs [two-tailed, voxel-level < 0.01, Gaussian random field (GRF) correction, cluster-level < 0.05]. In the left calcarine cortex (L-CAL) and left V1, dFC values were significantly lower in HM patients than in HCs. In the right lingual gyrus (R-LING) and right V1, dFC values were also significantly lower in HM patients than in HCs (two-tailed, voxel-level < 0.01, GRF correction, cluster-level < 0.05). Patients with HM exhibited significantly disturbed FC between the V1 and various brain regions, including L-ACG, L-SPG, L-CAL, and R-LING. This disturbance suggests that patients with HM could exhibit impaired cognitive and emotional processing functions, top-down control of visual attention, and visual information processing functions. HM patients and HCs could be distinguished from each other with high accuracy using sFC and dFC variabilities. These findings may help to identify the neural mechanism of decreased visual performance in HM patients.

Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter‐network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time‐varying connectivity strength across functional regions from each source network, compared to HC. C‐means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low‐scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large‐scale functional entropy correlation. K‐means clustering analysis on time‐indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions. Patients with schizophrenia exhibit elevated inter‐network connectivity entropy spanning a wide range of functional brain networks, indicating higher randomness in connectivity strength distribution across various functional brain regions.

Listening to music is a ubiquitous human activity, but little is known about its functional cerebral correlates. We investigated the dynamics of fMRI‐based brain activation patterns associated with two musical compositions and examined whether these patterns are modulated by the degree of musical expertise. Specifically, 24 aspiring professionals and 17 amateur musicians listened to a baroque composition by J. S. Bach and an early modern piece by A. Webern. Using measures of dynamic and static functional connectivity and graph theory, we identified two distinct brain states: one characterized by higher modularity (greater segregation), and the other by higher global efficiency (greater integration). Participants spent more time in the segregated state while listening to Bach, and more frequently shifted to the integrated state during Webern's piece. An anticorrelation was observed between segregation and music complexity as measured by permutation entropy, indicating that music with higher complexity elicited more integrated brain states. Individuals with greater musical expertise demonstrated higher global efficiency during the Webern piece and engaged more frontal, temporal, and parietal regions as functional hubs. These findings suggest that musical form and expertise jointly shape the brain's functional organization during naturalistic music listening. Key Points Dynamics of brain states are modulated by the processing requirements of music pieces. Greater musical expertise is associated with enhanced network integration in the listening condition with higher musical complexity. Greater musical expertise is associated with increased flexibility in the brain's functional organization, potentially enabling more effective adaptation to the challenges of music listening. Brain state dynamics adjust to the processing demands of different musical pieces, with higher expertise linked to higher network integration and flexibility in functional organization, enabling more effective adaptation to the challenges of music listening.

The abnormal insular-related static and dynamic functional connectivity (sFC and dFC) in patients with trigeminal neuralgia (TN) is not well understood. Therefore, we aimed to explore alterations in the sFC and dFC of the insular cortex(IC) and the relationships between functional connectivity (FC) alterations and clinical measures in TN. The study included 40 patients with TN and 30 healthy controls (HCs) who underwent resting-state functional magnetic resonance imaging and completed the visual analog scale (VAS) and mood questionnaires. Voxel-based sFC and dFC in the bilateral IC were computed. For the voxel-wise FC differences between TN and HC, a two-sample t-test was performed on the individual maps in a voxel-by-voxel manner. To examine linear relationships with clinical measures, Pearson correlations were calculated between FC alterations and VAS and mood measures. Compared with HCs, patients with TN exhibited significantly decreased sFC between the bilateral IC and right superior temporal gyrus, left Rolandic operculum, left thalamus, left supramarginal gyrus, and left superior parietal gyrus, and decreased dFC between the IC and cerebellar vermis. Correlation analysis revealed negative correlations between sFC of IC-thalamus and VAS and dFC of IC-vermis and duration of pain. Our findings reveal brain regions related to pain sensation and regulation showing abnormal sFC and dFC alterations in TN, suggesting their pivotal roles in the neural mechanisms underlying TN.

Generalized tonic-clonic seizures (GTCS) are a subtype of generalized seizures exhibiting bursts of bilaterally synchronous generalized spike-wave discharges. Numerous neuroimaging studies have reported aberrant functional activity and topological organization of brain network in epilepsy patients with GTCS, but most studies have focused on adults. However, the effect of GTCS on the spatial and temporal properties of brain function in children remains unclear. The present study aimed to explore whole-brain static (sFC) and dynamic functional connectivity (dFC) in children with GTCS. Twenty-three children with GTCS and 32 matched healthy controls (HCs) were recruited for the present study. Resting-state functional magnetic resonance imaging (MRI) data were collected for each subject. The group independent component analysis method was used to obtain independent components (ICs). Then, sFC and dFC methods were applied and the differences in functional connectivity (FC) were compared between the children with GTCS and the HCs. Additionally, we investigated the correlations between the dFC indicators and epilepsy duration. Compared to HCs, GTCS patients exhibited a significant decrease in sFC strengths among most networks. The K-means clustering method was implemented for dFC analysis, and the optimal number of clusters was estimated: two discrete connectivity configurations, State 1 (strong connection) and State 2 (weak connection). The decreased dFC mainly occurred in State 1, especially the dFC between the visual network (VIS) and somatomotor network (SMN); but the increased dFC mainly occurred in State 2 among most networks in GTCS children. In addition, GTCS children showed significantly shorter mean dwell time and lower fractional windows in stronger connected State 1, while GTCS children showed significantly longer mean dwell time in weaker connected State 2. In addition, the dFC properties, including mean dwell time and fractional windows, were significantly correlated with epilepsy duration. Conclusion: Our results indicated that GTCS epilepsy not only alters the connectivity strength but also changes the temporal properties of connectivity in networks in the whole brain. These findings also emphasized the differences in sFC and dFC in children with GTCS. Combining sFC and dFC methods may provide more comprehensive understanding of the abnormal changes in brain architecture in children with GTCS.

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease.

This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non‐linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual‐sensorimotor connectivity for schizophrenia patients for the FA‐sFNC and sMRI‐sFNC modality pairs, respectively. Additionally, our results generally indicate decreased fractional corpus callosum anisotropy, and decreased spatial ICA map and voxel‐based morphometry strength in the superior frontal lobe as found in the FA‐sFNC, sMRI‐FA, and sMRI‐ICA modality pair clusters. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data which we hope challenges the reader to think differently about how modalities interact.