Dynamic Functional Network Research Articles

Alterations of Excitation-Inhibition Balance and Brain Network Dynamics Support Sensory Deprivation Theory in Presbycusis.

Sensory deprivation theory is an important hypothesis involving potential pathways between hearing loss and cognitive impairment in patients with presbycusis. The theory suggests that prolonged auditory deprivation in presbycusis, including neural deafferentation, cortical reallocation, and atrophy, causes long-lasting changes and reorganization in brain structure and function. However, neurophysiological changes underlying the cognition-ear link have not been explored. In this study, we recruited 98 presbycusis patients and 60 healthy controls and examined the differences between the two groups in gamma-aminobutyric acid (GABA) and glutamate (Glu) levels in bilateral auditory cortex, excitation-inhibition (E/I) balance (Glu/GABA ratio), dynamic functional network connectivity (dFNC), hearing ability and cognitive performance. Then, correlations with each other were investigated and variables with statistical significance were further analyzed using the PROCESS Macro in SPSS. GABA levels in right auditory cortex and Glu levels in bilateral auditory cortex were lower but E/I balance in right auditory cortex were higher in presbycusis patients compared to healthy controls. Hearing assessments and cognitive performance were worse in presbycusis patients. Three recurring connectivity states were identified after dFNC analysis: State 1 (least frequent, middle-high dFNC strength with negative functional connectivity), State 2 (high dFNC strength), and State 3 (most frequent, low dFNC strength). The occurrence and dwell time of State 3 were higher, on the other hand, the dwell time of State 2 decreased in patients with presbycusis compared to healthy controls. In patients with presbycusis, worse hearing assessments and cognition were correlated with decreased GABA levels, increased E/I balance, and aberrant dFNC, decreased GABA levels and increased E/I balance were correlated with decreased occurrence and dwell time in State 3. In the mediation model, the fractional windows, as well as dwell time in State 3, mediated the relationship between the E/I balance in right auditory cortex and episodic memory (Auditory Verbal Learning Test, AVLT) in presbycusis. Moreover, in patients with presbycusis, we found that worse hearing loss contribute to lower GABA levels, higher E/I balance, and further impact aberrant dFNC, which caused lower AVLT scores. Overall, the results suggest that a shift in E/I balance in right auditory cortex plays an important role in cognition-ear link reorganization and provide evidence for sensory deprivation theory, enhancing our understanding the connection between neurophysiological changes and cognitive impairment in presbycusis. In presbycusis patients, E/I balance may serve as a potential neuroimaging marker for exploring and predicting cognitive impairment.

Unraveling the brain dynamics of Depersonalization-Derealization Disorder: a dynamic functional network connectivity analysis

BackgroundDepersonalization-Derealization Disorder (DPD), a prevalent psychiatric disorder, fundamentally disrupts self-consciousness and could significantly impact the quality of life of those affected. While existing research has provided foundational insights for this disorder, the limited exploration of brain dynamics in DPD hinders a deeper understanding of its mechanisms. It restricts the advancement of diagnosis and treatment strategies. To address this, our study aimed to explore the brain dynamics of DPD.MethodsIn our study, we recruited 84 right-handed DPD patients and 67 healthy controls (HCs), assessing them using the Cambridge Depersonalization Scale and a subliminal self-face recognition task. We also conducted a Transcranial Direct Current Stimulation (tDCS) intervention to understand its effect on brain dynamics, evidenced by Functional Magnetic Resonance Imaging (fMRI) scans. Our data preprocessing and analysis employed techniques such as Independent Component Analysis (ICA) and Dynamic Functional Network Connectivity (dFNC) to establish a comprehensive disease atlas for DPD. We compared the brain's dynamic states between DPDs and HCs using ANACOVA tests, assessed correlations with patient experiences and symptomatology through Spearman correlation analysis, and examined the tDCS effect via paired t-tests.ResultsWe identified distinct brain networks corresponding to the Frontoparietal Network (FPN), the Sensorimotor Network (SMN), and the Default Mode Network (DMN) in DPD using group Independent Component Analysis (ICA). Additionally, we discovered four distinct dFNC states, with State-1 displaying significant differences between DPD and HC groups (F = 4.10, P = 0.045). Correlation analysis revealed negative associations between the dwell time of State-2 and various clinical assessment factors. Post-tDCS analysis showed a significant change in the mean dwell time for State-2 in responders (t-statistic = 4.506, P = 0.046), consistent with previous clinical assessments.ConclusionsOur study suggests the brain dynamics of DPD could be a potential biomarker for diagnosis and symptom analysis, which potentially leads to more personalized and effective treatment strategies for DPD patients.Trial registrationsThe trial was registered at the Chinese Clinical Trial Registry on 03/01/2021 (Registration number: ChiCTR2100041741, https://www.chictr.org.cn/showproj.html?proj=66731) before the trial.

EEG spatial inter-channel connectivity analysis: A GCN-based dual stream approach to distinguish mental fatigue status

Mental fatigue is defined as a decline in the ability and efficiency of mental activities. A lot of research suggests that the transition from alertness to fatigue is accompanied by alterations in correlation patterns among various brain regions. However, conventional methods for detecting mental fatigue seldom emphases inter-channel connectivity in the spatial domain. To fill this gap, this paper explores the spatial inter-channel connectivity in alertness and fatigue, employing spectral graph convolutional networks (GCN) for mental fatigue detection. We utilized Pearson correlation coefficients (PCC) to establish temporal connections and magnitude-squared coherence (MSC) for spectral connections. Topological features of the brain network were then analysed. To enhance the learning of spatial inter-channel connectivity, a dual-graph strategy transforms edge features into node features, serving as inputs to the spectral GCN. By simultaneously learning PCC and MSC features, the model results indicate significant differences in some brain network characteristics between alert and fatigue states. It confirms that the synchronicity of brain operations differs in the alert state compared to mental fatigue, and indicates that fatigue states can influence correlation patterns among different brain regions. Our approach is evaluated on a self-designed experimental dataset containing 7 subjects, demonstrating a classification accuracy of 89.59 % in group-level experiments and 95.24 % at the subject level. Additionally, on the public dataset SEED-VIG containing 23 subjects, our method achieves an accuracy of 86.58 %. In summary, this paper proposes a neural network approach based on a dynamic functional connectivity network. The network integrates both temporal and spectral connections with the goal of simultaneously learning spatial inter-channel connectivity in time and frequency domains. This effectively accomplishes fatigue state detection, highlighting that fatigue significantly influences correlations among different brain regions.

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework.

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

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.

Sex-differential markers of psychiatric risk and treatment response based on premature aging of functional brain network dynamics and peripheral physiology

BackgroundAging is a multilevel process of gradual decline that predicts morbidity and mortality. Independent investigations have implicated senescence of brain and peripheral physiology in psychiatric risk, but it is unclear whether these effects stem from unique or shared mechanisms. MethodsTo address this question, we analyzed clinical, blood chemistry and resting state functional neuroimaging data in a healthy aging cohort (N= 427; age 36-100 years) and two disorder-specific samples encompassing patients with early psychosis (100 patients, 16-35 years) and major depressive disorder (MDD) (104 patients, 20-76 years). ResultsWe identified sex-dependent coupling between blood chemistry markers of metabolic senescence (i.e., homeostatic dysregulation), functional brain network aging, and psychiatric risk. In females, premature aging of frontoparietal and somatomotor networks was linked to greater homeostatic dysregulation. It also predicted the severity and treatment resistance of mood symptoms (depression/anxiety [all three samples], anhedonia [MDD]) and social withdrawal/behavioral inhibition (avoidant personality disorder [healthy aging]; negative symptoms [early psychosis]). In males, premature aging of the default mode, cingulo-opercular, and visual networks was linked to reduced homeostatic dysregulation and predicted severity and treatment resistance of symptoms relevant to hostility/aggression (antisocial personality disorder [healthy aging]; mania/positive symptoms [early psychosis]), impaired thought processes (early psychosis, MDD) and somatic problems (healthy aging, MDD). ConclusionsOur findings identify sexually dimorphic relationships between brain dynamics, peripheral physiology, and risk for psychiatric illness, suggesting that the specificity of putative risk biomarkers and precision therapeutics may be improved by considering sex and other relevant personal characteristics.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

Levodopa therapy affects brain functional network dynamics in Parkinson's disease

Levodopa (L-dopa) therapy is the most effective pharmacological treatment for motor symptoms of Parkinson's disease (PD). However, its effect on brain functional network dynamics is still unclear. Here, we recruited 26 PD patients and 24 healthy controls, and acquired their resting-state functional MRI data before (PD-OFF) and after (PD-ON) receiving 400 mg L-dopa. Using the independent component analysis and the sliding-window approach, we estimated the dynamic functional connectivity (dFC) and examined the effect of L-dopa on the temporal properties of dFC states, the variability of dFC and functional network topological organization. We found that PD-ON showed decreased mean dwell time in sparsely connected State 2 than PD-OFF, the transformation of the dFC state is more frequent and the variability of dFC was decreased within the auditory network and sensorimotor network in PD-ON. Our findings provide new insights to understand the dynamic neural activity induced by L-dopa therapy in PD patients.

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.

Dynamic functional network connectivity and its association with lipid metabolism in Alzheimer's disease.

The study aims to examine the changing trajectory characteristics of dynamic functional network connectivity (dFNC) and its correlation with lipid metabolism-related factors across the Alzheimer's disease (AD) spectrum populations. Data from 242 AD spectrum subjects, including biological, neuroimaging, and general cognition, were obtained from the Alzheimer's Disease Neuroimaging Initiative for this cross-sectional study. The study utilized a sliding-window approach to assess whole-brain dFNC, investigating group differences and associations with biological and cognitive factors. Abnormal dFNC was used in the classification of AD spectrum populations by support vector machine. Mediation analysis was performed to explore the relationships between lipid-related indicators, dFNC, cerebrospinal fluid (CSF) biomarkers, and cognitive performance. Significant group difference concerning were observed in relation to APOE-ε4 status, CSF biomarkers, and cognitive scores. Two reoccurring connectivity states were identified: state-1 characterized by frequent but weak connections, and state-II characterized by less frequent but strong connections. Pre-AD subjects exhibited a preference for spending more time in state-I, whereas AD patients tended remain in state-II for longer periods. Group difference in dFNC was primarily found between AD and non-AD participants within each state. The dFNC of state-I yielded strong power to distinguish AD from other groups compared with state-II. APOE-ε4+, high polygenic score, and high serum lipid group were strongly associated with network disruption between association cortex system and sensory cortex system that characterized elevation of cognitive function, which may suggest a compensatory mechanism of dFNC in state-I, whereas differential connections of state-II mediated the relationships between APOE-ε4 genotype and CSF biomarkers, and cognitive indicators. The dysfunction of dFNC temporal-spatial patterns and increased cognition in individuals with APOE-ε4, high polygenic score, and higher serum lipid levels shed light on the lipid-related mechanisms of dynamic network reorganization in AD.

Dynamic Functional Network Connectivity in Acute Incomplete Cervical Cord Injury Patients and Its Associations With Sensorimotor Dysfunction Measures

BackgroundDynamic functional network connectivity (dFNC) captures temporal variations in functional connectivity during MRI acquisition. However, the neural mechanisms driving dFNC alterations in the brain networks of patients with Acute incomplete cervical cord injury (AICCI) remain unclear. MethodsThis study included 16 AICCI patients and 16 healthy controls (HC). Initially, Independent Component Analysis (ICA) was employed to extract whole-brain independent components (ICs) from resting-state functional MRI (rs-fMRI) data. Subsequently, a sliding time window approach, combined with k-means clustering, was used to estimate dFNC states for each participant. Finally, a correlation analysis was conducted to examine the association between sensorimotor dysfunction scores in AICCI patients and the temporal characteristics of dFNC. ResultICA was employed to extract 26 whole-brain ICs. Subsequent dynamic analysis identified four distinct connectivity states across the entire cohort. Notably, AICCI patients demonstrated a significant preference for State 3 compared to HC, as evidenced by a higher frequency and longer duration spent in this state. Conversely, State 4 exhibited a reduced frequency and shorter dwell time in AICCI patients. Moreover, correlation analysis revealed a positive association between sensorimotor dysfunction and both the mean dwell time and the fractional of time spent in State 3. ConclusionsPatients with AICCI demonstrate abnormal connectivity within dFNC states, and the temporal characteristics of dFNC are associated with sensorimotor dysfunction scores. These findings highlight the potential of dFNC as a sensitive biomarker for detecting network functional changes in AICCI patients, providing valuable insights into the dynamic alterations in brain connectivity related to sensorimotor dysfunction in this population.

Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss.

Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3years. Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.

Static and Dynamic Dysconnectivity in Early Psychosis: Relationship With Symptom Dimensions.

Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors. We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations. Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049). Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.

Repetitive transcranial magnetic stimulation reshaped the dynamic reconfiguration of the executive and reward networks in individuals with tobacco use disorder

BackgroundStudies have demonstrated the potential of repetitive transcranial magnetic stimulation (rTMS) to decrease smoking cravings in individuals with tobacco use disorder (TUD). However, the neural features underlying the effects of rTMS treatment, especially the dynamic attributes of brain networks associated with the treatment, remain unclear. MethodsUsing dynamic functional connectivity analysis, this study first explored the differences in dynamic functional network features between 60 subjects with TUD and 64 nonsmoking healthy controls (HCs). Then, the left dorsolateral prefrontal cortex (DLPFC) was targeted for a five-day course of rTMS treatment in the 60 subjects with TUD (active rTMS in 42 subjects and sham treatment in 18 subjects). We explored the effect of rTMS on the dynamic network features associated with rTMS by comparing the actively treated group and the sham group. ResultsCompared to nonsmokers, TUD subjects exhibited an increased integration coefficient between the frontoparietal network (FPN) and the basal ganglia network (BGN) and a reduced integration coefficient between the medial frontal network (MFN) and the FPN. Analysis of variance revealed that rTMS treatment reduced the integration coefficient between the FPN and BGN and improved the recruitment coefficient of the FPN. LimitationsThis study involved a limited sample of young male smokers, and the findings may not generalize to older smokers or female smokers with an extensive history of smoking. ConclusionrTMS treatment of the left DLPFC exhibited significant effectiveness in restructuring the neural circuits associated with TUD while significantly mitigating smoking cravings.

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.

Directed brain interactions over time: A resting-state EEG comparison between schizophrenia and healthy individuals

Understanding the neurophysiological mechanisms of schizophrenia (SZ) is one of the challenges of neuroscience. Many anatomical and functional studies have pointed to problems in brain connectivity in SZ individuals. However, little is known about the relationships between specific brain regions and impairments in brain connectivity in SZ individuals. Herein we propose a new approach using time-varying graphs and the motif synchronization method to build dynamic brain functional networks (BFNs). Dynamic BFNs were constructed from resting-state electroencephalography (rs-EEG) of 14 schizophrenia (SZ) individuals and 14 healthy controls (HCs). BFNs were evaluated based on the percentage of synchronization importance between a pair of regions (considering external and internal interactions) over time. We found differences in the directed interaction between brain regions in SZ individuals compared to the control group. Our method revealed low bilaterally directed interactions between the temporal lobes in SZ individuals compared to HCs, indicating a potential link between altered brain connectivity and the characteristic symptoms of schizophrenia. From a clinical perspective, these results shed light on developing new therapeutic approaches targeting these specific neural interactions that are altered in individuals with SZ. This knowledge allows the application of better interventions focused on restoring or compensating for interrupted connectivity patterns.

Disrupted Dynamic Network Attribution Associated with Gait Disorder in Cerebral Small Vessel Disease.

Background and Aims: Previous research has focused on static functional connectivity in gait disorders caused by cerebral small vessel disease (CSVD), neglecting dynamic functional connections and network attribution. This study aims to investigate alterations in dynamic functional network connectivity (dFNC) and topological organization variance in CSVD-related gait disorders. Methods: A total of 85 patients with CSVD, including 41 patients with CSVD and gait disorders (CSVD-GD), 44 patients with CSVD and non-gait disorders (CSVD-NGD), and 32 healthy controls (HC), were enrolled in this study. Five networks composed of 10 independent components were selected using independent component analysis. Sliding time window and k-means clustering methods were used for dFNC analysis. The relationship between alterations in the dFNC properties and gait metrics was further assessed. Results: Three reproducible dFNC states were determined (State 1: sparsely connected, State 2: intermediate pattern, and State 3: strongly connected). CSVD-GD showed significantly higher fractional windows (FW) and mean dwell time (MDT) in State 1 compared with CSVD-NGD. Higher local efficiency variance was observed in the CSVD-GD group compared with HC, but no differences were found in the global efficiency comparison. Both the FW and MDT in State 1 were negatively correlated with gait speed and step length, and the relationship between MDT of State 1 and gait speed was mediated by overall cognition, information processing speed, and executive function. Conclusions: Our study uncovered abnormal dFNC indicators and variations in topological organization in CSVD-GD, offering potential early prediction indicators and freshening insights into the underlying pathogenesis of gait disturbances in CSVD.

Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

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

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. 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. 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. 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.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.