Aberrant individual large-scale functional network connectivity and topology in chronic insomnia disorder with and without depression.

Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia.

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Locus coeruleus co-activation patterns at rest show higher state persistence in patients with dissociative seizures: A Pilot Study.

OBJECTIVE: Dissociative seizures are paroxysmal disruptions of awareness and behavioral control in the context of affective arousal. Alterations in stress-related endocrine function have been demonstrated, but the timescale of dissociation suggests that the central locus coeruleus (LC) noradrenergic system is likely pivotal. Here, we investigate whether LC activation at rest is associated with altered brain network dynamics. METHODS: A preliminary co-activation pattern (CAP) analysis of resting-state functional magnetic resonance imaging (fMRI) in 14 patients with dissociative seizures and 14 healthy controls was performed by using the LC as a seeding region. The red nucleus served as a control condition. Entry rates, durations, and state transition probabilities of identified CAPs were calculated. Analyses were corrected for demographic, technical, and clinical confounders including depression and anxiety. RESULTS: Three LC-related CAPs were identified, with the dominant two showing inverse activations and deactivations of the default mode network and the attention networks, respectively. Analysis of transition probabilities between and within the three CAPs revealed higher state persistence in patients compared to healthy controls for both CAP2LC (Cohen's d = -0.55; p = 0.01) and CAP3LC (Cohen's d = -0.57; p = 0.01). The control analysis using the red nucleus as a seed yielded similar CAPs, but no significant between-group differences in transition probabilities. SIGNIFICANCE: Higher state persistence of LC-CAPs in patients with dissociative seizures generates the novel hypothesis that arousal-related impairments of network switching might be a candidate neural mechanism of dissociation. PLAIN LANGUAGE SUMMARY: Dissociative seizures often arise during high affective arousal. The locus coeruleus is a brain structure involved in managing such acute arousal states. We investigated whether the activity of the locus coeruleus correlates with activity in other regions of the brain (which we refer to as "brain states"), and whether those brain states were different between patients with dissociative seizures and healthy controls. We found that patients tended to stay in certain locus coeruleus-dependent brain states instead of switching between them. This might be related to the loss of awareness and disruptions of brain functions ("dissociation") that patients experience during seizures.

Altered resting-state and dynamic functional connectivity of hypothalamic in first-episode depression: A functional magnetic resonance imaging study.

The hypothalamus is an important component of the hypothalamic-pituitary-adrenal axis and an important brain region of the limbic system. Twenty-four first depressive episode(FDE) patients and 25 healthy controls were recruited for this study. The hypothalamus was used as a seed to observe the characteristics of resting state and dynamic functional connectivity (FC) changes in FDE patients, and further observed the correlation between the different brain regions and clinical symptoms. The results found that compared with the HC group, the FDE group showed sFC was increased of the left hypothalamus with right superior parietal gyrus and right middle temporal gyrus, and dFC was increased of the left hypothalamus with left inferior occipital gyrus. And sFC was increased of the right hypothalamus with right orbital part of inferior frontal gyrus, right supplementary motor area, and right middle temporal gyrus, and the dFC was also increased of right hypothalamus with right superior parietal gyrus and left middle temporal gyrus. In addition,there was a negative correlation between dFC values of the right hypothalamus with the right superior parietal gyrus and clinical symptoms in the FDE group. This study provides new insights into understanding the altered neuropathological mechanisms of the hypothalamic circuit in FDE.

Stable individual differences from dynamic patterns of function: brain network flexibility predicts openness/intellect, intelligence, and psychoticism.

A growing understanding of the nature of brain function has led to increased interest in interpreting the properties of large-scale brain networks. Methodological advances in network neuroscience provide means to decompose these networks into smaller functional communities and measure how they reconfigure over time as an index of their dynamic and flexible properties. Recent evidence has identified associations between flexibility and a variety of traits pertaining to complex cognition including creativity and working memory. The present study used measures of dynamic resting-state functional connectivity in data from the Human Connectome Project (n = 994) to test associations with Openness/Intellect, general intelligence, and psychoticism, three traits that involve flexible cognition. Using a machine-learning cross-validation approach, we identified reliable associations of intelligence with cohesive flexibility of parcels in large communities across the cortex, of psychoticism with disjoint flexibility, and of Openness/Intellect with overall flexibility among parcels in smaller communities. These findings are reasonably consistent with previous theories of the neural correlates of these traits and help to expand on previous associations of behavior with dynamic functional connectivity, in the context of broad personality dimensions.

Abnormal thalamocortical network dynamics in patients with migraine and its relationship with electroacupuncture treatment response.

Acupuncture is an effective and safe alternative treatment to prevent and treat migraine, but its central analgesic mechanism remains poorly understood. It is believed that the dysfunction of the thalamocortical connectivity network is an important contributor to migraine pathophysiology. This study aimed to investigate the abnormal thalamocortical network dynamics in patients with migraine without aura (MWoA) before and after an 8-week electroacupuncture treatment. A total of 143 patients with MWoA and 100 healthy controls (HC) were included, and resting-state functional magnetic resonance imaging (fMRI) data were acquired. Dynamic functional network connectivity (dFNC) was calculated for each subject. The modulation effect of electroacupuncture on clinical outcomes of migraine, dFNC, and their association were investigated. In our results, dFNC matrices were classified into two clusters (brain states). As compared with the HC, patients with MWoA had a higher proportion of brain states with a strong thalamocortical between-network connection, implying an abnormal balance of the network organization across dFNC brain states. Correlation analysis showed that this abnormality was associated with summarized clinical measurements of migraine. A total of 60 patients were willing to receive an 8-week electroacupuncture treatment, and 24 responders had 50% changes in headache frequency. In electroacupuncture responders, electroacupuncture could change the abnormal thalamocortical connectivities towards a pattern more similar to that of HC. Our findings suggested that electroacupuncture could relieve the symptoms of migraine and has the potential capacity to regulate the abnormal function of the thalamocortical circuits.

A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI.

BACKGROUND: There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods. NEW METHODS: We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation. RESULTS: The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data. COMPARISON WITH EXISTING METHODS: Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC. CONCLUSION: We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.

Sex heterogeneity of dynamic brain activity and functional connectivity in autism spectrum disorder.

Sex heterogeneity has been frequently reported in autism spectrum disorders (ASD) and has been linked to static differences in brain function. However, given the complexity of ASD and diagnosis-by-sex interactions, dynamic characteristics of brain activity and functional connectivity may provide important information for distinguishing ASD phenotypes between females and males. The aim of this study was to explore sex heterogeneity of functional networks in the ASD brain from a dynamic perspective. Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were analyzed in 128 ASD subjects (64 males/64 females) and 128 typically developing control (TC) subjects (64 males/64 females). A sliding-window approach was adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic functional connectivity (dFC) to characterize time-varying brain activity and functional connectivity respectively. We then examined the sex-related changes in ASD using two-way analysis of variance. Significant diagnosis-by-sex interaction effects were identified in the left anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) and left precuneus in the dALFF analysis. Furthermore, there were significant diagnosis-by-sex interaction effects of dFC variance between the left ACC/mPFC and right ACC, left postcentral gyrus, left precuneus, right middle temporal gyrus and left inferior frontal gyrus, triangular part. These findings reveal the sex heterogeneity in brain activity and functional connectivity in ASD from a dynamic perspective, and provide new evidence for further exploring sex heterogeneity in ASD.

Static and dynamic changes in amplitude of Low-Frequency fluctuations in patients with Self-Limited epilepsy with centrotemporal Spikes (SeLECTS): A Resting-State fMRI study.

OBJECTIVE: This study aims to explore differences in the static and dynamic amplitude of low-frequency fluctuations (sALFF and dALFF) in resting-state functional MRI (rs-fMRI) data between patients with Benign childhood epilepsy with centrotemporal spikes (SeLECTS) and healthy controls (HCs). MATERIALS AND METHODS: We recruited 45 patient with SeLECTS and 55 HCs, employing rs-fMRI to assess brain activity. The analysis utilized a two-sample t-test for primary comparisons, supplemented by stratification and matching based on clinical and demographic characteristics to ensure comparability between groups. Post hoc analyses assessed the relationships between sALFF/dALFF alterations and clinical demographics, incorporating statistical adjustments for potential confounders and performing sensitivity analysis to test the robustness of our findings. RESULTS: Our analysis identified significant differences in sALFF and dALFF between patient with SeLECTS and HCs. Notably, increases in sALFF and dALFF were observed in the right middle temporal gyrus and left superior temporal gyrus among patient with SeLECTS, while a decrease in dALFF was seen in the right cerebellum crus 1. Additionally, a positive correlation was found between abnormal dALFF variability in specific brain regions and various clinical and demographic factors of patient with SeLECTS, with age being one such influential factor. CONCLUSION: This investigation provides insights into the assessment of local brain activity in SeLECTS through both static and dynamic approaches. It highlights the significance of non-invasive neuroimaging techniques in understanding the complexities of epilepsy syndromes like SeLECTS and emphasizes the need to consider a range of clinical and demographic factors in neuroimaging studies of neurological disorders.

Characterization of the temporal stability of ToM and pain functional brain networks carry distinct developmental signatures during naturalistic viewing.

A temporally stable functional brain network pattern among coordinated brain regions is fundamental to stimulus selectivity and functional specificity during the critical period of brain development. Brain networks that are recruited in time to process internal states of others' bodies (like hunger and pain) versus internal mental states (like beliefs, desires, and emotions) of others' minds allow us to ask whether a quantitative characterization of the stability of these networks carries meaning during early development and constrain cognition in a specific way. Previous research provides critical insight into the early development of the theory-of-mind (ToM) network and its segregation from the Pain network throughout normal development using functional connectivity. However, a quantitative characterization of the temporal stability of ToM networks from early childhood to adulthood remains unexplored. In this work, reusing a large sample of children (n = 122, 3-12 years) and adults (n = 33) dataset that is available on the OpenfMRI database under the accession number ds000228, we addressed this question based on their fMRI data during a short and engaging naturalistic movie-watching task. The movie highlights the characters' bodily sensations (often pain) and mental states (beliefs, desires, emotions), and is a feasible experiment for young children. Our results tracked the change in temporal stability using an unsupervised characterization of ToM and Pain networks DFC patterns using Angular and Mahalanobis distances between dominant dynamic functional connectivity subspaces. Our findings reveal that both ToM and Pain networks exhibit lower temporal stability as early as 3-years and gradually stabilize by 5-years, which continues till adolescence and late adulthood (often sharing similarity with adult DFC stability patterns). Furthermore, we find that the temporal stability of ToM brain networks is associated with the performance of participants in the false belief task to access mentalization at an early age. Interestingly, higher temporal stability is associated with the pass group, and similarly, moderate and low temporal stability are associated with the inconsistent group and the fail group. Our findings open an avenue for applying the temporal stability of large-scale functional brain networks during cortical development to act as a biomarker for multiple developmental disorders concerning impairment and discontinuity in the neural basis of social cognition.

Association between clinical features and decreased degree centrality and variability in dynamic functional connectivity in the obsessive-compulsive disorder.

Neuroimaging studies have indicated widespread brain structural and functional disruptions in patients with obsessive-compulsive disorder (OCD). However, the underlying mechanism of these changes remains unclear. A total of 45 patients with OCD and 42 healthy controls (HC) were enrolled. The study investigated local degree centrality (DC) abnormalities and employed abnormal regions of DC as seeds to investigate variability in dynamic functional connectivity (dFC) in the whole brain using a sliding window approach to analyze resting-state functional magnetic resonance imaging. The relationship between abnormal DC and dFC as well as the clinical features of OCD were examined using correlation analysis. Our findings suggested decreased DC in the bilateral thalamus, bilateral precuneus, and bilateral cuneus in OCD patients and a nominally negative correlation between the DC value in the thalamus and illness severity measured using the Yale-Brown Obsessive Compulsive Scale (Y-BOCS). In addition, seed-based dFC analysis showed that compared to measurements in the HC, the patients had decreased dFC variability between the left thalamus and the left cuneus and right lingual gyrus, and between the bilateral cuneus and bilateral postcentral gyrus, and a nominally positive correlation between the duration of illness and dFC variability between the left cuneus and left postcentral gyrus. These results indicated that OCD patients had decreased hub importance in the bilateral thalamus and cuneus throughout the entire brain. This reduction was associated with impaired coupling with dynamic function in the visual cortex and sensorimotor network and provided novel insights into the neurophysiological mechanisms underlying OCD.

Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders whose pathobiology remains elusive. This study aimed to investigate the possible neural mechanisms underlying ASD using a dynamic brain network model and a relatively large-sample, multi-site dataset. Resting-state functional magnetic resonance imaging data were acquired from 208 ASD patients and 227 typical development (TD) controls, who were drawn from the multi-site Autism Brain Imaging Data Exchange (ABIDE) database. Brain network flexibilities were estimated and compared between the ASD and TD groups at both global and local levels, after adjusting for sex, age, head motion, and site effects. The results revealed significantly increased brain network flexibilities (indicating a decreased stability) at the global level, as well as at the local level within the default mode and sensorimotor areas in ASD patients than TD participants. Additionally, significant ASD-related decreases in flexibilities were also observed in several occipital regions at the nodal level. Most of these changes were significantly correlated with the Autism Diagnostic Observation Schedule (ADOS) total score in the entire sample. These results suggested that ASD is characterized by significant changes in temporal stabilities of the functional brain network, which can further strengthen our understanding of the pathobiology of ASD.

Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states.

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development.

Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model's explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.

Spatio-temporal learning and explaining for dynamic functional connectivity analysis: Application to depression.

BACKGROUND: Functional connectivity has been shown to fluctuate over time. The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data, which would be helpful to produce tools of early depression diagnosis and enhance our understanding of depressive etiology. METHODS: The resting-state fMRI data of 178 subjects were collected, including 89 MDD and 89 healthy controls. We propose a spatio-temporal learning and explaining framework for dFC analysis. A yet effective spatio-temporal model is developed to classifying MDD from healthy controls with dFCs. The model is a stacking neural network model, which learns network structure information by a multi-layer perceptron based spatial encoder, and learns time-varying patterns by a Transformer based temporal encoder. We propose to explain the spatio-temporal model with a two-stage explanation method of importance feature extracting and disorder-relevant pattern exploring. The layer-wise relevance propagation (LRP) method is introduced to extract the most relevant input features in the model, and the attention mechanism with LRP is applied to extract the important time steps of dFCs. The disorder-relevant functional connections, brain regions, and brain states in the model are further explored and identified. RESULTS: We achieved the best classification performance in identifying MDD from healthy controls with dFC data. The top important functional connectivity, brain regions, and dynamic states closely related to MDD have been identified. LIMITATIONS: The data preprocessing may affect the classification performance of the model, and this study needs further validation in a larger patient population. CONCLUSIONS: The experimental results demonstrate that the proposed spatio-temporal model could effectively classify MDD, and uncover structural and temporal patterns of dFCs in depression.

The critical mediating roles of the middle temporal gyrus and ventrolateral prefrontal cortex in the dynamic processing of interpersonal emotion regulation.

Interpersonal emotion regulation (IER) is a crucial ability for effectively recovering from negative emotions through social interaction. It has been emphasized that the empathy network, cognitive control network, and affective generation network sustain the deployment of IER. However, the temporal dynamics of functional connectivity among these networks of IER remains unclear. This study utilized IER task-fMRI and sliding window approach to examine both the stationary and dynamic functional connectivity (dFC) of IER. Fifty-five healthy participants were recruited for the present study. Through clustering analysis, four distinct brain states were identified in dFC. State 1 demonstrated situation modification stage of IER, with strong connectivity between affective generation and visual networks. State 2 exhibited pronounced connectivity between empathy network and both cognitive control and affective generation networks, reflecting the empathy stage of IER. Next, a 'top-down' pattern is observed between the connectivity of cognitive control and affective generation networks during the cognitive control stage of state 3. The affective response modulation stage of state 4 mainly involved connections between empathy and affective generation networks. Specifically, the degree centrality of the left middle temporal gyrus (MTG) mediated the association between one's IER tendency and the regulatory effects in state 2. The betweenness centrality of the left ventrolateral prefrontal cortex (VLPFC) mediated the association between one's IER efficiency and the regulatory effects in state 3. Altogether, these findings revealed that dynamic connectivity transitions among empathy, cognitive control, and affective generation networks, with the left VLPFC and MTG playing dominant roles, evident across the IER processing.

Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models.

BACKGROUD: Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database. RESULTS: Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function. CONCLUSIONS: This study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE.

Abnormal dynamics of functional connectivity density and effective connectivity in overactive bladder.

PURPOSE: The study aims to analyze alterations in dynamic functional connectivity density (dFCD) and effective connectivity (dEC) patterns using functional magnetic resonance imaging (fMRI), hypothesizing that overactive bladder (OAB) patients will exhibit distinct dFCD and dEC patterns, reflecting altered neural communication underlying the OAB. METHODS: Forty-three female OAB patients and 40 female healthy controls (HC) underwent resting-state fMRI. Sliding window correlation was used to calculate the variability of the dFCD. The changes in dFCD-based dEC between the two groups were examined using Granger causal analysis. To describe the time-varying Granger causality, a sliding-window approach was utilized to divide time courses into a group of windows. We adopted a k-means clustering method to cluster all matrices into discrete connectivity states. RESULTS: Compared with HC, OAB females consistently had a dFCD (decreased) in the left anterior cingulate cortex (ACC) and left medial prefrontal cortex (mPFC) (p < 0.05, GRF corrected). In state 1, OAB patients had excitatory effective connections from bilateral ACC to left mPFC in comparison to HC. In state 2, there was an increase in dEC from the SMA to the mPFC. Participants with OAB showed significantly more inhibitory dorsolateral prefrontal cortex (dlPFC) connections between the left ACC and the right ACC in state 4, as well as an excitatory dEC connection between the right dlPFC and the left ACC in state 2 (p < 0.05, GRF corrected). CONCLUSION: OAB patients demonstrate significant alterations in dFCD and dEC patterns, which may be indicative of the neural mechanisms involved in OAB pathophysiology.

Altered cerebellar-cerebral dynamic functional connectivity in patients with pontine stroke: a resting-state fMRI study.

Potential changes in patterns of dynamic functional network connections at the cerebellar-cerebral level in pontine infarction (PI) patients remain unclear. The study aimed to investigate the abnormal patterns of dynamic functional connectivity (dFC) between the cerebellar subregions within networks and regions of the cerebral cortex in patients with PI. Forty-six chronic left pontine infarction (LPI), 32 chronic right pontine infarction (RPI), and 50 healthy controls (HCs) were recruited to undergo resting-state fMRI scans. Cerebellar-cerebral dFC was characterized using the sliding window method and seed-based connectivity analyses. Correlations between altered dFC values and clinical variables (The Rey Auditory Verbal Learning Test and Flanker task) in PI patients and healthy controls were investigated. Compared with HCs, the PI groups showed significantly aberrant cerebellar-cerebral dFC between cerebellar subregions within networks and supratentorial cerebral cortex, including executive, default-mode, and motor networks. Furthermore, Correlation analysis showed a decoupling between abnormal dFC and cognitive functions in PI patients. These findings indicate that PI patients are accompanied by damage to cerebellar subregions within networks and cerebellar-cerebral pathways, which may provide a potential target for treatment or an indication of therapeutic efficacy.

Abnormal large-scale brain functional network dynamics in social anxiety disorder.

AIMS: Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD. METHODS: We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored. RESULTS: Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration. CONCLUSION: These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.