Altered static and dynamic cerebellar-cerebral functional connectivity in acute pontine infarction.

This study investigates abnormalities in cerebellar-cerebral static and dynamic functional connectivity among patients with acute pontine infarction, examining the relationship between these connectivity changes and behavioral dysfunction. Resting-state functional magnetic resonance imaging was utilized to collect data from 45 patients within seven days post-pontine infarction and 34 normal controls. Seed-based static and dynamic functional connectivity analyses identified divergences in cerebellar-cerebral connectivity features between pontine infarction patients and normal controls. Correlations between abnormal functional connectivity features and behavioral scores were explored. Compared to normal controls, left pontine infarction patients exhibited significantly increased static functional connectivity within the executive, affective-limbic, and motor networks. Conversely, right pontine infarction patients demonstrated decreased static functional connectivity in the executive, affective-limbic, and default mode networks, alongside an increase in the executive and motor networks. Decreased temporal variability of dynamic functional connectivity was observed in the executive and default mode networks among left pontine infarction patients. Furthermore, abnormalities in static and dynamic functional connectivity within the executive network correlated with motor and working memory performance in patients. These findings suggest that alterations in cerebellar-cerebral static and dynamic functional connectivity could underpin the behavioral dysfunctions observed in acute pontine infarction patients.

Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.

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.

Evaluation of the Modulation Effects Evoked by Different Transcutaneous Auricular Vagus Nerve Stimulation Frequencies Along the Central Vagus Nerve Pathway in Migraine: A Functional Magnetic Resonance Imaging Study.

OBJECTIVES: Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising treatment option for migraines. This study aims to investigate the modulation effects of different taVNS frequencies along the central vagus nerve pathway in migraineurs. MATERIALS AND METHODS: Twenty-four migraineurs were recruited for a single-blind, crossover magnetic resonance imaging (MRI) study. The study consisted of two taVNS MRI scan sessions, in which either 1-Hz or 20-Hz taVNS was applied in a random order. Seed-based static and dynamic functional connectivity (FC) analyses were performed using two key nodes of the vagus nerve pathway, the nucleus tractus solitarius (NTS) and the locus coeruleus (LC). RESULTS: Static FC (sFC) analysis showed that 1) continuous 1-Hz taVNS resulted in an increase of NTS/LC-occipital cortex sFC and a decrease of NTS-thalamus sFC compared with the pre-1-Hz taVNS resting state, 2) continuous 20-Hz taVNS resulted in an increase of the LC-anterior cingulate cortex (ACC) sFC compared with the pre-20-Hz taVNS resting state, 3) 1-Hz taVNS produced a greater LC-precuneus and LC-inferior parietal cortex sFC than 20 Hz, and 4) 20-Hz taVNS increased LC-ACC and LC-super temporal gyrus/insula sFC in comparison with 1 Hz. Dynamic FC (dFC) analysis showed that compared with the pre-taVNS resting state, 1-Hz taVNS decreased NTS-postcentral gyrus dFC (less variability), 20-Hz taVNS decreased dFC of the LC-superior temporal gyrus and the LC-occipital cortex. Finally, a positive correlation was found between the subjects' number of migraine attacks in the past four weeks and the NTS-thalamus sFC during pre-taVNS resting state. CONCLUSIONS: 1-Hz and 20-Hz taVNS may modulate the sFC and dFC of key nodes in the central vagus nerve pathway differently. Our findings highlight the importance of stimulation parameters (frequencies) in taVNS treatment.

Altered brain structural and functional connectivity in schizotypy.

BACKGROUND: 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'. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Abnormal static and dynamic functional connectivity of networks related to cognition in patients with subcortical ischemic vascular disease.

PURPOSE: To investigate the specific features of static functional connectivity (SFC) and dynamic functional connectivity (DFC) of networks related to cognition in patients with subcortical ischemic vascular disease (SIVD). METHODS: In this retrospective study, resting-state functional MRI data and a series of cognitive scores were obtained from 38 patients with SIVD and 23 normal controls. Independent component analysis, sliding window method, k-means clustering analysis and graph theory method were used to examine FC between the default mode network (DMN), dorsal attention network (DAN), frontoparietal network (FPN), salience network (SN) and executive control network (ECN) in patients with SIVD. Then, correlations between abnormal FC features and cognition were assessed. RESULTS: Compared with normal controls, SFC within the DMN significantly increased and SFC between the DMN and DAN significantly decreased in patients with SIVD. The decreased DFC mainly occurred in weakly connected states, especially the DFC of the SN; but the increased DFC, global network efficiency and local network efficiency and the decreased mean dwell time (MDT) and frequency mainly occurred in strongly connected states in SIVD patients. Moreover, aberrant SFC, DFC and MDT were significantly correlated with patients' cognitive scores. CONCLUSION: The overall results are suggestive of abnormal functional segregation and integration of SFC and DFC among networks related to cognition, especially in the SN. This may advance our comprehensive understanding of the abnormal changes in brain network connectivity in patients with SIVD. Our findings also highlight DFC may be an effective neuroimaging marker for the clinical diagnosis of SIVD.

Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks.

The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5-min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network-switching pattern (i.e., transition probability and mean interval time) and the time-allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5-48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.

The role of the thalamic subregions in major depressive disorder with childhood maltreatment: Evidences from dynamic and static functional connectivity.

BACKGROUND: Major depressive disorder (MDD) with a history of childhood maltreatment represents a highly prevalent clinical phenotype. Previous studies have demonstrated functional alterations of the thalamus among MDD. However, no study has investigated the static and dynamic changes in functional connectivity (FC) within thalamic subregions among MDD with childhood maltreatment. METHODS: This study included four groups: MDD with childhood maltreatment (n = 48), MDD without childhood maltreatment (n = 30), healthy controls with childhood maltreatment (n = 57), and healthy controls without childhood maltreatment (n = 46). Sixteen thalamic subregions were selected as seed to investigate group-differences in dynamic FC (dFC) and static FC (sFC). Correlation analyses were performed to assess the associations between abnormal FC and maltreatment severity. Eventually, moderation analyses were employed to explore the moderating role of abnormal FC in the relationship between maltreatment and depressive severity. RESULTS: MDD with childhood maltreatment exhibit abnormal thalamic subregions FC compared to MDD without childhood maltreatment, characterized by abnormalities with the sFC of the rostral anterior cingulate cortex, with the dFC of the calcarine, middle cingulate cortex, precuneus cortex and superior temporal gyrus. Furthermore, sFC with the rostral anterior cingulate cortex and dFC with the middle cingulate cortex were correlated with the severity of maltreatment. Additionally, dFC with the superior temporal gyrus moderates the relationship between maltreatment and depression severity. LIMITATIONS: The cross-sectional designs fail to infer causality. CONCLUSIONS: Our findings support thalamic dysfunction as neurobiological features of childhood maltreatment as well as vulnerability to MDD.

Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data.

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors, and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence.

Effects of childhood maltreatment and major depressive disorder on functional connectivity in hippocampal subregions.

Major Depressive Disorder (MDD) with childhood maltreatment is a prevalent clinical phenotype. Prior studies have observed abnormal hippocampal activity in MDD patients, considering the hippocampus as a single nucleus. However, there is limited research investigating the static and dynamic changes in hippocampal subregion functional connectivity (FC) in MDD patients with childhood maltreatment. Therefore, we employed static and dynamic FC analyses using hippocampal subregions, including the anterior hippocampus and posterior hippocampus, as seed regions to investigate the neurobiological alterations associated with MDD resulting from childhood maltreatment. This study involved four groups: MDD with (n = 48) and without childhood maltreatment (n = 30), as well as healthy controls with (n = 57) and without (n = 46) childhood maltreatment. Compared to MDD patients without childhood maltreatment, those with childhood maltreatment exhibit altered FC between the hippocampal subregion and multiple brain regions, including the anterior cingulate gyrus, superior frontal gyrus, putamen, calcarine gyrus, superior temporal gyrus, angular gyrus, and supplementary motor area. Additionally, dynamic FC between the right medial-2 hippocampal head and the right calcarine gyrus shows a positive correlation with childhood maltreatment across all its subtypes. Moreover, dFC between the right hippocampal tail and the left angular gyrus moderates the relationship between childhood maltreatment and the depression severity. Our findings of distinct FC patterns within hippocampal subregions provide new clues for understanding the neurobiological basis of MDD with childhood maltreatment.

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.

Dynamic functional connectivity is modulated by the amount of p-Tau231 in blood in cognitively intact participants.

INTRODUCTION: Electrophysiology and plasma biomarkers are early and non-invasive candidates for Alzheimer's disease detection. The purpose of this paper is to evaluate changes in dynamic functional connectivity measured with magnetoencephalography, associated with the plasma pathology marker p-tau231 in unimpaired adults. METHODS: 73 individuals were included. Static and dynamic functional connectivity were calculated using leakage corrected amplitude envelope correlation. Each source's strength entropy across trials was calculated. A data-driven statistical analysis was performed to find the association between functional connectivity and plasma p-tau231 levels. Regression models were used to assess the influence of other variables over the clusters' connectivity. RESULTS: Frontotemporal dynamic connectivity positively associated with p-tau231 levels. Linear regression models identified pathological, functional and structural factors that influence dynamic functional connectivity. DISCUSSION: These results expand previous literature on dynamic functional connectivity in healthy individuals at risk of AD, highlighting its usefulness as an early, non-invasive and more sensitive biomarker.

Structural and functional neural patterns among sub-threshold bulimia nervosa: Abnormalities in dorsolateral prefrontal cortex and orbitofrontal cortex.

BACKGROUND: Disordered eating behaviors are prevalent among youngsters and highly associated with dysfunction in neurocognitive systems. We aimed to identify the potential changes in individuals with bulimia symptoms (sub-BN) to generate insights to understand developmental pathophysiology of bulimia nervosa. METHODS: We investigated group differences in terms of degree centrality (DC) and gray matter volume (GMV) among 145 undergraduates with bulimia symptoms and 140 matched control undergraduates, with the secondary analysis of the whole brain connectivity in these regions of interest showing differences in static functional connectivity (FC). RESULTS: The sub-BN group exhibited abnormalities of the right dorsolateral prefrontal cortex and right orbitofrontal cortex in both GMV and DC, and displayed decreased FC between these regions and the precuneus. We also observed that sub-BN presented with reduced FC between the calcarine and superior temporal gyrus, middle temporal gyrus and inferior parietal gyrus. Additionally, brain-behavioral associations suggest a distinct relationship between these FCs and psychopathological symptoms in sub-BN group. CONCLUSIONS: Our study demonstrated that individuals with bulimia symptoms present with aberrant neural patterns that mainly involved in cognitive control and reward processing, as well as attentional and self-referential processing, which could provide important insights into the pathology of BN.

Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia 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 schizophrenia patients and demographically matched 160 healthy controls. Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available brain networks between SZ patients and healthy controls (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 healthy control group. 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 it appears healthy for a brain to primarily circulate through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. However, individuals with SZ seem to struggle with transiently attaining these more focused and structured connectivity patterns. Proposed ICE measure presents a novel framework for gaining deeper insights into understanding mechanisms of healthy and disease brain states and a substantial step forward in the developing advanced methods of diagnostics of mental health conditions.

Altered static and dynamic functional connectivity of habenula in first-episode, drug-naïve schizophrenia patients, and their association with symptoms including hallucination and anxiety.

Background and objective: The pathogenesis of schizophrenia (SCH) is related to the dysfunction of monoamine neurotransmitters, and the habenula participates in regulating the synthesis and release of dopamine. We examined the static functional connectivity (sFC) and dynamic functional connectivity (dFC) of habenula in first-episode schizophrenia patients using resting state functional magnetic resonance imaging (rs-fMRI) in this study. Methods: A total of 198 first-Episode, drug-Naïve schizophrenia patients and 199 healthy controls (HC) underwent rs-fMRI examinations. The sFC and dFC analysis with habenula as seed was performed to produce a whole-brain diagram initially, which subsequently were compared between SCH and HC groups. Finally, the correlation analysis of sFC and dFC values with the Positive and Negative Symptom Scale (PANSS) were performed. Results: Compared with the HC groups, the left habenula showed increased sFC with the bilateral middle temporal gyrus, bilateral superior temporal gyrus, and right temporal pole in the SCH group, and the right habenula exhibited increased sFC with the left middle temporal gyrus, left superior temporal gyrus, and left angular gyrus. Additionally, compared with the HC group, the left habenula showed decreased dFC with the bilateral cuneus gyrus and bilateral calcarine gyrus in the SCH group. The PANSS negative sub-scores were positively correlated with the sFC values of the bilateral habenula with the bilateral middle temporal gyrus, superior temporal gyrus and angular gyrus. The PANSS general sub-scores were positively correlated with the sFC values of the right habenula with the left middle temporal gyrus and left superior temporal gyrus. The hallucination scores of PANSS were negatively correlated with the sFC values of the left habenula with the bilateral cuneus gyrus and bilateral calcarine gyrus; The anxiety scores of PANSS were positively correlated with the dFC values of the left habenula with the right temporal pole. Conclusion: This study provides evidence that the habenula of the first-episode schizophrenia patients presented abnormal static functional connectivity with temporal lobe and angular gyrus, and additionally showed weakened stability of functional connectivity in occipital lobe. This abnormality is closely related to the symptoms of hallucination and anxiety in schizophrenia, which may indicate that the habenula involved in the pathophysiology of schizophrenia by affecting the dopamine pathway.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states.

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Abnormal dynamic functional connectivity changes correlated with non-motor symptoms of Parkinson's disease.

Background: Non-motor symptoms are common in Parkinson's disease (PD) patients, decreasing quality of life and having no specific treatments. This research investigates dynamic functional connectivity (FC) changes during PD duration and its correlations with non-motor symptoms. Methods: Twenty PD patients and 19 healthy controls (HC) from PPMI dataset were collected and used in this study. Independent component analysis (ICA) was performed to select significant components from the entire brain. Components were grouped into seven resting-state intrinsic networks. Static and dynamic FC changes during resting-state functional magnetic resonance imaging (fMRI) were calculated based on selected components and resting state networks (RSN). Results: Static FC analysis results showed that there was no difference between PD-baseline (PD-BL) and HC group. Network averaged connection between frontoparietal network and sensorimotor network (SMN) of PD-follow up (PD-FU) was lower than PD-BL. Dynamic FC analysis results suggested four distinct states, and each state's temporal characteristics, such as fractional windows and mean dwell time, were calculated. The state 2 of our study showed positive coupling within and between SMN and visual network, while the state 3 showed hypo-coupling through all RSN. The fractional windows and mean dwell time of PD-FU state 2 (positive coupling state) were statistically lower than PD-BL. Fractional windows and mean dwell time of PD-FU state 3 (hypo-coupling state) were statistically higher than PD-BL. Outcome scales in Parkinson's disease-autonomic dysfunction scores of PD-FU positively correlated with mean dwell time of state 3 of PD-FU. Conclusion: Overall, our finding indicated that PD-FU patients spent more time in hypo-coupling state than PD-BL. The increase of hypo-coupling state and decrease of positive coupling state might correlate with the worsening of non-motor symptoms in PD patients. Dynamic FC analysis of resting-state fMRI can be used as monitoring tool for PD progression.

Abnormal static and dynamic functional network connectivity of the whole-brain in children with generalized tonic-clonic seizures.

Introduction: 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. Methods: 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. Results: 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.

Exploration of static functional connectivity and dynamic functional connectivity alterations in the primary visual cortex among patients with high myopia via seed-based functional connectivity analysis.

Aim: 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) via seed-based functional connectivity (FC) analysis. Methods: 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 via correlation analysis. Static and dynamic changes in brain activity in HM patients were investigated by assessments of sFC and dFC via calculation of the total time series mean and sliding-window analysis. Results: 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 P < 0.01, Gaussian random field (GRF) correction, cluster-level P < 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 P < 0.01, GRF correction, cluster-level P < 0.05). Conclusion: 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.

Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling.

Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a "Dynamic Sensitivity Analysis" framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.

Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations.

Schizophrenia patients with auditory verbal hallucinations (AVHs) are diseased groups of serious psychosis with still unknown etiology. The aim of this research was to identify the neurophysiological correlates of auditory verbal hallucinations. Revealing the neural correlates of auditory hallucination is not merely of great clinical significance, but it is also quite essential to study the pathophysiological correlates of schizophrenia. In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. We found that schizophrenia patients with AVHs were characterized by significant decreased static functional connectivity and enhanced variability of dynamic functional connectivity (non-parametric permutation test, Bonferroni correction, p < 0.05). In addition, the AVHs group also demonstrated increased number of brain states, suggesting brain dynamics enhanced in these patients compared with the non-AVHs group. Our findings suggested that there were abnormalities in the connection of brain language regions in auditory verbal hallucinations. It appears that the interruption of connectivity from the language region might be critical to the pathological basis of AVHs.