Functional Connectivity Networks Research Articles

Reconfigurations of dynamic functional network connectivity after 1HZ repetitive transcranial magnetic stimulation in insomnia disorder.

The objective of the current study was to investigate the dynamic functional connectivity among large-scale brain networks in patients with insomnia, and to assess the efficacy of repetitive transcranial magnetic stimulation (rTMS) treatment in these individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) data from 62 insomnia patients and 69 healthy controls were used to compare differences in dynamic functional connectivity between the two groups. A total of 26 insomnia patients underwent rTMS for four weeks. Changes in dynamic functional network connectivity was observed in insomnia patients following treatment. Additionally, the relationship between clinical symptoms and insomnia was analyzed using topological and correlation analyses. Our findings demonstrated that insomnia patients exhibited a significantly lower fraction rate of negative connectivity between the dorsal default mode network (dDMN) and the visual network (VN) compared to healthy controls, while showing strong positive connectivity within the VN and the auditory network (AUN). It may be attributed to the restoration of normal dynamic functional connectivity between the dDMN and VN in insomnia patients following rTMS. Furthermore, the dynamic functional connectivity between the dDMN and VN was found to predict sleep quality and treatment outcome in insomnia patients. Abnormal dynamic functional network connectivity between the dDMN and VN is a hallmark of insomnia, and may serve as a biomarker to assess the effects of rTMS treatment in insomnia patients.

Altered brain functional network connectivity and topology in type 2 diabetes mellitus

Altered brain functional network connectivity and topology in type 2 diabetes mellitus

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting-state brain networks

Abstract Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting-state activity is best characterized as reflecting noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) framework. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generate several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

Unveiling the functional connectivity of astrocytic networks with AstroNet, a graph reconstruction algorithm coupled to image processing

Astrocytes form extensive networks with diverse calcium activity, yet the organization and connectivity of these networks across brain regions remain largely unknown. To address this, we developed AstroNet, a data-driven algorithm that uses two-photon calcium imaging to map temporal correlations in astrocyte activation. By organizing individual astrocyte activation events chronologically, our method reconstructs functional networks and extracts local astrocyte correlations. We create a graph of the astrocyte network by tallying direct co-activations between pairs of cells along these activation pathways. Applied to the CA1 hippocampus and motor cortex, AstroNet reveals notable differences: astrocytes in the hippocampus display stronger connectivity, while cortical astrocytes form sparser networks. In both regions, smaller, tightly connected sub-networks are embedded within a larger, loosely connected structure. This method not only identifies astrocyte activation paths and connectivity but also reveals distinct, region-specific network patterns, providing new insights into the functional organization of astrocytic networks in the brain.

Abnormal resting-state brain network dynamics in toddlers with autism spectrum disorder.

Emerging evidence suggests aberrant functional connectivity (FC) of brain networks in children, adolescents, and adults with autism spectrum disorder (ASD). However, little is known about alterations of dynamic FC in toddlers with ASD. The aim of this study was to investigate the characteristics of brain network dynamics in ASD toddlers. We analyzed resting-state functional magnetic resonance imaging data of 41 ASD and 23 typically-developing (TD) toddlers aged 15-45 months using leading eigenvector dynamics analysis. The between-group differences in the temporal properties of state were evaluated, followed by correlation analysis of these differences with autistic factors in ASD. Compared with TD subjects, ASD toddlers had a lower occurrence rate and mean dwell time in the global state. The mean dwell time of this state was negatively correlated with the body and object use of the autism behavior checklist (ABC) score. The ASD group presented a higher occurrence probability in the state related to the visual network, and the dwell time of this state was positively correlated with the relationship building of the ABC score. Moreover, the occurrence probability of the state related to the dorsal attention network and default mode network was positively correlated with the body and object use of the ABC score. Our findings indicate that there are differences in the developmental patterns of large-scale brain functional networks in ASD toddlers, which may contribute to the early identification of ASD and provide meaningful insights into its potential neural mechanisms.

Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury

IntroductionRehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG–EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.MethodsEight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG–EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.ResultsAmong the three functional connectivity methods investigated—coherence, Pearson correlation coefficient and mutual information (MI)—the MI-based EEG–EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.ConclusionOur results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.

Resting-state functional connectivity between the frontoparietal network and the default mode network is aberrantly increased in ankylosing spondylitis

Lower back pain comprises the majority of the disease burden of patients with ankylosing spondylitis (AS), while the alterations of the large-scale brain networks could be implicated in the neuropathophysiology of pain. The frontoparietal network (FPN) is known as a pain modulation hub, with key nodes dorsolateral prefrontal cortex (dlPFC) and ventrolateral prefrontal cortex (vlPFC) participating in the pain modulation and reappraisal process. In this study, we adopted the analytical approaches of independent component analysis (ICA) and seed-based correlation analysis (SCA) to examine the resting-state functional connectivity (rsFC) of the large-scale brain networks, notably FPN, between 82 AS patients and 61 healthy controls (HCs). We also investigated the correlation between the rsFC and the clinical measures of AS patients. Both ICA and SCA consistently showed that the rsFC between FPN and mPFC, a key node of the default mode network (DMN), was significantly increased in AS. In addition, SCA also identified a cluster at the right posterior lobe of cerebellum which exhibited increased rsFC with the posterior cingulate cortex, and the right lateral prefrontal cortex also showed increased rsFC with the right dlPFC. Correlation analysis showed that the rsFC between mPFC and the left anterior prefrontal cortex was significantly correlated with C-reactive protein in AS. The increased FPN-DMN connectivity could contribute to the neuropathophysiology of lower back pain in AS, with potential association with faulty pain modulation and reappraisal mechanisms facilitated by the FPN.

Large-scale functional network connectivity alterations in adolescents with major depression and non-suicidal self-injury.

Non-suicidal self-injury (NSSI) is prevalent among adolescent populations worldwide, yet its neuropathological mechanisms remain unclear. This study aimed to investigate brain functional differences in NSSI patients by utilizing large-scale functional networks and examining their correlation with clinical outcomes. Cross-sectional clinical and functional magnetic resonance imaging (fMRI) data were collected from 42 patients and 47 healthy controls. Independent component analysis (ICA) was utilized to investigate changes in both intra-network and inter-network functional connectivity. We then investigated the potential association between functional network connectivity and clinical self-injurious behavior. The results revealed significant abnormalities in intra-network functional connectivity within the left middle cingulum gyrus, right angular gyrus, and middle frontal gyrus in patients with NSSI. Additionally, we found altered inter-network connectivity patterns, particularly between higher-order cognitive networks and primary sensory networks, suggesting potential disruptions in multisensory integration and emotional regulation in these patients. This study revealed significant alterations in large-scale functional network connectivity in adolescents with depression and NSSI, particularly in networks related to emotion regulation and cognitive control. These findings provide novel perspectives on the neurobiological mechanisms of NSSI and suggest possible avenues for early intervention and treatment.

P0779 Resting-state MRI data fusion reveals distinct disease-state dependent patterns for fatigue, depression and anxiety in Crohn’s Disease

Abstract Background Many persons with Crohn’s Disease (CD) suffer from symptoms not directly linked to the gastrointestinal tract. Among these extraintestinal symptoms (EIS), fatigue, depression and anxiety are the most prevalent, especially during active disease. Growing evidence links disturbed brain-gut-interactions to EIS. We examined intrinsic brain activity in patients with active and remitted CD using a multivariate MRI data fusion approach, evaluated the relationship of functional joint components with EIS and inflammatory markers, and explored the components’ associated neurotransmitter systems. Methods Patients (n=71) with active (aCD, n=47) or remitted disease (rCD, n=24) and healthy controls (HC, n=35) underwent resting-state functional magnetic resonance imaging and completed questionnaires for symptoms of fatigue, depression and anxiety. Patients provided stool samples for faecal calprotectin (fC) analysis. A joint independent component analysis (jICA) of intrinsic brain function (ALFF and ReHo) was conducted to detect convergent neural activity patterns in functionally distinct neural networks (components) that differ between aCD, rCD, and HC. Functional connectivity strength (FCS) in networks with disease state-dependent changes were correlated with EIS and fC, and we performed cross-modal correlations between MRI components and specific PET/SPECT receptor probability maps. Results JICA identified two components that differed between aCD and rCD. One showed a pattern of predominantly orbitofrontal-cingulate cortex connectivity, with lower FCS in aCD vs rCD and HC (Fig. 1A top). This component related to serotonergic and dopaminergic transmission with significant associations between FCS and fatigue as well as fC (Fig. 1B). The other component revealed lower FCS in a sensorimotor network (Fig. 1A middle) with predominantly dopaminergic neurotransmission in rCD vs aCD and HC that was associated with anxiety and depression (Fig. 1B). A third component, predominantly involving cortical midline regions attributed to the so-called “default mode network” was found to differ between HC and CD irrespective of disease status (Fig. 1A bottom). Here, FCS was not associated with EIS (Fig 1B). Conclusion Functional connectivity of distinct resting-state networks differs between disease states and differentially relates to EIS. A sensorimotor network is associated with symptoms of depression and anxiety and dopaminergic neurotransmission, while an affective network correlates with fatigue and serotonergic/dopaminergic neurotransmission. The latter shows the relationship with fatigue only in rCD, suggesting a contribution of disturbed brain-gut-interactions especially in fatigue during remission.

Dysfunctional large-scale brain networks in drug-naïve depersonalization-derealization disorder patients

BackgroundDepersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.MethodsA total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within‐and between‐network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large‐scale brain networks and scores of clinical assessments in DPRD patients.ResultsCompared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.ConclusionOverall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. Our findings may provide potential targets for therapeutic interventions.

Neural Correlates of Social Touch Processing: An fMRI Study on Brain Functional Connectivity.

The significance of tactile stimulation in human social development and personal interaction is well documented; however, the underlying cerebral processes remain under-researched. This study employed functional magnetic resonance imaging (fMRI) to investigate the neural correlates of social touch processing, with a particular focus on the functional connectivity associated with the aftereffects of touch. A total of 27 experimental subjects were recruited for the study, all of whom underwent a 5-minute calf and foot massage prior to undergoing resting-state fMRI. Additionally, 11 healthy controls participated solely in the resting-state fMRI recording. A functional connectivity network analysis was conducted to examine the alterations in connections between different brain regions following massage. The findings indicated the involvement of discrete neural networks in the processing of social touch, with notable discrepancies in functional connectivity observed between the experimental and control groups. The study revealed that the control group exhibited a higher degree of connectivity within a subnetwork comprising 25 connections and 23 nodes than the experimental group following the massage intervention. The experimental group showed hypoactivation in this subnetwork following the massage. The left anterior pulvinar thalamus and the right pregenual anterior cingulate cortex, which serve as the key hubs within this subnetwork, exhibited higher clustering and increased node strength in the control group. Relatively small and unequal sample sizes are the limitations of the study that may affect the generalizability of the results. These findings elucidate the neural underpinnings of tactile experiences and their potential impact on behavior and emotional state. Gaining insight into these mechanisms could inform therapeutic approaches that utilize touch to mitigate stress and enhance mental health. From a practical standpoint, our results have significant implications for the development of sensory stimulation strategies for patients with prolonged disorders of consciousness, sensory loss, autism spectrum disorders, or limited access to tactile interaction in their upper extremities.

Abnormal alterations in structure-function coupling at the modular level in patients with postherpetic neuralgia

To investigate the presence of modular loss of coupling and abnormal alterations in functional and structural networks in the brain networks of patients with postherpetic neuralgia (PHN). We collected resting-state functional magnetic resonance imaging data and diffusion tensor imaging data from 82 healthy controls (HCs) and 71 PHN patients, generated structural connectivity (SC) and functional connectivity (FC) networks, and assessed the corresponding clinical information assessment. Based on AAL(90) mapping, the brain network was divided into 9 modules, and the structural–functional connectivity (SC–FC) coupling was compared at the whole-brain level and within the modules, as well as alterations in the topological properties of the brain network in the patient group. Finally, correlation analyses were performed using the following clinical scales: Visual Analogue Scale (VAS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD). Compared with HCs, patients with PHN had reduced global efficiency (Eg) and local efficiency (Eloc) of structural and functional networks. The FC in the PHN group presented abnormal node clustering coefficients (NCp), local node efficiencies (NLe), and node efficiencies (Ne), and the SC presented abnormal node degrees (Dc), NCp, NLe, characteristic path lengths (NLp), and Ne. In addition, SC–FC coupling was reduced in the patient default network (DMN), salient network (SN), and visual network (VIS). Moreover, the degree of impairment of graph theory indicators was significantly positively correlated with scales such as VAS scores, and the coupling of modules was significantly negatively correlated with the early course of the patient’s disease. Large-scale impaired topological properties of the FC and SC networks were observed in patients with PHN, and SC–FC decoupling was detected in these modules of the DMN, SN, and VIS. These aberrant alterations may have led to over-transmission of pain information or central sensitization of pain.

Language and Attention Networks Have Distinct Roles in Language Improvement Following an Intensive Comprehensive Aphasia Program.

Attention is known to play an important role in language, and attentional deficits have been associated with language impairments in people with aphasia (PWA). A prior study by our laboratory indicated that behavioral measures for PWA participating in an intensive comprehensive aphasia program (ICAP) clustered into 1 language and 1 attention-related factor, with each factor correlated with independent resting state functional connectivity (rsFC) networks. The present study includes additional attention measures and participants to better assess the relationship between attention, language, and rsFC. PWA participated in 120 hours of ICAP therapy over 4 weeks, between April 2018 and August 2022. Participants were evaluated with the Western Aphasia Battery and Conners' Continuous Performance Test, pre- and post-ICAP. rsFC data were collected during functional magnetic resonance imaging scans pre- and post-ICAP. Principal component analysis identified behavioral score associations pre- and post-ICAP. FC matrices and graph measures were evaluated for both PWA and healthy controls. Twenty-three PWA participated (19 included in the final analysis). Data for 179 age and sex-matched healthy controls were taken from a public data set. The principal component analysis indicated 1 language and 2 attention-related principal components (PCs). The attention PCs were labeled Attention Accuracy and Attention Rate, in accordance with their best-associated behavioral measures. Importantly, each PC was associated with distinct networks, including higher-order networks, so-called because of their involvement in higher-order cognition. Notably, the language PC was significantly correlated with the ventral attention-memory-retrieval network rsFC (r=0.67, P=0.003) and the default mode-frontoparietal network rsFC (r=-0.56, P=0.019). Attention Accuracy was significantly correlated with cingulo-opercular-subcortical network rsFC (r=0.60, P=0.011). Attention Rate was significantly negatively correlated with visual-somatomotor network rsFC (r=-0.74, P=0.0007). This study supports the notion that language improvements for PWAs participating in an ICAP are associated with distinct network changes. Importantly, these networks are not restricted to language networks but also include attention, and task-control networks.

Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning.

Cervical spondylotic myelopathy (CSM) is a debilitating condition that affects the cervical spine, leading to neurological impairments. While the neural mechanisms underlying CSM remain poorly understood, changes in brain network connectivity, particularly within the context of static and dynamic functional network connectivity (sFNC and dFNC), may provide valuable insights into disease pathophysiology. This study investigates brain-wide connectivity alterations in CSM patients using both sFNC and dFNC, combined with machine learning approaches, to explore their potential as biomarkers for disease classification and progression. A total of 191 participants were included in this study, comprising 108 CSM patients and 83 healthy controls (HCs). Resting-state fMRI data were used to derive functional connectivity networks (FCNs), which were further analyzed to obtain sFNC and dFNC features. K-means clustering was applied to identify distinct dFNC states, and machine learning models, including support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), logistic regression (LR), and random forests (RF), were constructed to classify CSM patients and HCs based on FNC features. The sFNC analysis revealed significant alterations in brain network connectivity in CSM patients, including enhanced connectivity between the posterior default mode network (pDMN) and ventral attention network (vAN), and between the right and left frontoparietal networks (rFPN and lFPN), alongside weakened connectivity in multiple other network pairs. K-means clustering of dFNC identified four distinct functional states, with CSM patients exhibiting altered connectivity in State 1 and State 3. Machine learning models based on sFNC demonstrated excellent classification performance, with the SVM model achieving an AUC of 0.92, accuracy of 85.86%, and sensitivity and specificity both exceeding 0.80. Models based on dFNC also performed well, with the State 3-based model yielding an AUC of 0.91 and accuracy of 84.97%. Our findings highlight significant alterations in both sFNC and dFNC in CSM patients, suggesting that these connectivity changes may reflect underlying neural mechanisms of the disease. Machine learning models based on FNC features, particularly SVM, exhibit strong potential for classifying CSM patients and may serve as valuable neuroimaging biomarkers for diagnosis and monitoring disease progression. Future research should explore longitudinal studies and multimodal neuroimaging approaches to further validate these findings.

Static and dynamic brain functional connectivity patterns in patients with unilateral moderate-to-severe asymptomatic carotid stenosis

Background and purposeAsymptomatic carotid stenosis (ACS) is an independent risk factor for ischemic stroke and vascular cognitive impairment, affecting cognitive function across multiple domains. This study aimed to explore differences in static and dynamic intrinsic functional connectivity and temporal dynamics between patients with ACS and those without carotid stenosis.MethodsWe recruited 30 patients with unilateral moderate-to-severe (stenosis ≥ 50%) ACS and 30 demographically-matched healthy controls. All participants underwent neuropsychological testing and 3.0T brain MRI scans. Resting-state functional MRI (rs-fMRI) was used to calculate both static and dynamic functional connectivity. Dynamic independent component analysis (dICA) was employed to extract independent circuits/networks and to detect time-frequency modulation at the circuit level. Further imaging-behavior associations identified static and dynamic functional connectivity patterns that reflect cognitive decline.ResultsACS patients showed altered functional connectivity in multiple brain regions and networks compared to controls. Increased connectivity was observed in the inferior parietal lobule, frontal lobe, and temporal lobe. dICA further revealed changes in the temporal frequency of connectivity in the salience network. Significant differences in the temporal variability of connectivity were found in the fronto-parietal network, dorsal attention network, sensory-motor network, language network, and visual network. The temporal parameters of these brain networks were also related to overall cognition and memory.ConclusionsThese results suggest that ACS involves not only changes in the static large-scale brain network connectivity but also dynamic temporal variations, which parallel overall cognition and memory recall.

Functional resting state connectivity is differentially associated with IL-6 and TNF-α in depression and in healthy controls

Inflammatory processes have been implicated in the pathophysiology of depression. In human studies, inflammation has been shown to act as a critical disease modifier, promoting susceptibility to depression and modulating specific endophenotypes of depression. However, there is scant documentation of how inflammatory processes are associated with neural activity in patients with depression. We therefore tested the hypothesis that the peripheral inflammation markers IL-6 and TNF-α correlate with neural resting state network functional connectivity in depression using functional magnetic resonance imaging (fMRI) and compared it with healthy controls. We used fMRI to investigate the functional connectivity (FC) of the resting state Default Mode Network (DMN) and Salience/Ventral Attention Network (SAL) and their association with the peripheral inflammation markers IL-6 and TNF-α in 25 patients with depression and compared it to 24 healthy subjects. Results of this imaging study revealed that both DMN and SAL resting state networks are differentially associated with distinct immunological pathways depending on whether a person has a depressive phenotype or is healthy. While the DMN FC correlated with the concentration of the cytokine IL-6 in healthy subjects, SAL FC’s connectivity correlated with the cytokine TNF-α's concentration. This study highlights the importance of peripheral inflammatory processes in depression and suggests a modulatory effect on neural resting state networks depending on the state of depression.

Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke.

Stroke is a condition characterized by damage to the cerebral vasculature from various causes, resulting in focal or widespread brain tissue damage. Prior neuroimaging research has demonstrated that individuals with stroke present structural and functional brain abnormalities, evident through disruptions in motor, cognitive, and other vital functions. Nevertheless, there is a lack of studies on alterations in static and dynamic functional network connectivity in the brains of stroke patients. Fifty stroke patients and 50 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Initially, the independent component analysis (ICA) method was utilized to extract the resting-state network (RSN). Subsequently, the disparities in static functional network connectivity both within and between networks among the two groups were computed and juxtaposed. Following this, five consistent and robust dynamic functional network connectivity (dFNC) states were derived by integrating the sliding time window method with k-means cluster analysis, and the distinctions in dFNC between the groups across different states, along with the intergroup variations in three dynamic temporal metrics, were assessed. Finally, a support vector machine (SVM) approach was employed to discriminate stroke patients from HCs using FC and FNC as classification features. Comparing the stroke group to the healthy control (HC) group, the stroke group exhibited reduced intra-network functional connectivity (FC) in the right superior temporal gyrus of the ventral attention network (VAN), the left calcarine of the visual network (VN), and the left precuneus of the default mode network (DMN). Regarding static functional network connectivity (FNC), we identified increased connectivity between the executive control network (ECN) and dorsal attention network (DAN), salience network (SN) and DMN, SN-ECN, and VN-ECN, along with decreased connectivity between DAN-DAN, ECN-SN, SN-SN, and DAN-VN between the two groups. Noteworthy differences in dynamic FNC (dFNC) were observed between the groups in states 3 to 5. Moreover, stroke patients demonstrated a significantly higher proportion of time and longer mean dwell time in state 4, alongside a decreased proportion of time in state 5 compared to HC. Finally, utilizing FC and FNC as features, stroke patients could be distinguished from HC with an accuracy exceeding 70% and an area under the curve ranging from 0.8284 to 0.9364. In conclusion, our study reveals static and dynamic changes in large-scale brain networks in stroke patients, potentially linked to abnormalities in visual, cognitive, and motor functions. This investigation offers valuable insights into the neural mechanisms underpinning the functional deficits observed in stroke, thereby aiding in the diagnosis and development of targeted therapeutic interventions for affected individuals.

Default mode network functional connectivity as a transdiagnostic biomarker of cognitive function.

The default mode network (DMN) is intricately linked with processes such as self-referential thinking, episodic memory recall, goal-directed cognition, self-projection, and theory of mind. Over recent years, there has been a surge in examining its functional connectivity, particularly its relationship with frontoparietal networks (FPN) involved in top-down attention, executive function, and cognitive control. The fluidity in switching between these internal and external modes of processing-highlighted by anti-correlated functional connectivity-has been proposed as an indicator of cognitive health. Due to the ease of estimation of functional connectivity-based measures through resting state fMRI paradigms, there is now a wealth of large-scale datasets, paving the way for standardized connectivity benchmarks. This review delves into the promising role of DMN connectivity metrics as potential biomarkers of cognitive state across attention, internal mentation, mind wandering and meditation states, and investigating deviations in trait-level measures across aging and in clinical conditions such as Alzheimer's disease, Parkinson's, depression, ADHD, and others. Additionally, we tackle the issue of reliability of network estimation and functional connectivity and share recommendations for using functional connectivity measures as a biomarker of cognitive health.

The pharmacodynamic modulation effect of oxytocin on resting state functional connectivity network topology.

Neuroimaging studies have demonstrated that intranasal oxytocin has extensive effects on the resting state functional connectivity of social and emotional processing networks and may have therapeutic potential. However, the extent to which intranasal oxytocin modulates functional connectivity network topology remains less explored, with inconsistent findings in the existing literature. To address this gap, we conducted an exploratory data-driven study. We recruited 142 healthy males and administered 24 IU of intranasal oxytocin or placebo in a randomized controlled double-blind design. Resting-state functional MRI data were acquired for each subject. Network-based statistical analysis and graph theoretical approaches were employed to evaluate oxytocin's effects on whole-brain functional connectivity and graph topological measures. Our results revealed that oxytocin altered connectivity patterns within brain networks involved in sensory and motor processing, attention, memory, emotion and reward functions as well as social cognition, including the default mode, limbic, frontoparietal, cerebellar, and visual networks. Furthermore, oxytocin increased local efficiency, clustering coefficients, and small-world propensity in specific brain regions including the cerebellum, left thalamus, posterior cingulate cortex, right orbitofrontal cortex, right superior frontal gyrus, left inferior frontal gyrus, and right middle orbitofrontal cortex, while decreasing nodal path topological measures in the left and right caudate. These findings suggest that intranasal oxytocin may produce its functional effects through influencing the integration and segregation of information flow within small-world brain networks, particularly in regions closely associated with social cognition and motivation.

Static and dynamic connectivity structure of white-matter functional networks across the adult lifespan.

Aging of the human brain involves intricate biological processes, resulting in complex changes in structure and function. While the effects of aging on gray matter (GM) connectivity are extensively studied, white matter (WM) functional changes have received comparatively less attention. This study examines age-related WM functional dynamics using resting-state fMRI across the adult lifespan. We identified GM and WM functional networks (FNs) using k-means clustering. Static and dynamic analyses of WM functional network connectivity (FNC) were performed to explore age effects on WM-FNs and recurrent patterns of dynamic FNC. We identified 9 WM and 12 GM FNs. Age-related effects on WM FNC strength and WM-GM FNC dynamics included linear positive and U-shaped age trajectories in static FNC strength, and linear negative and inverted U-shaped trajectories in FNC temporal variability. Three distinct brain states with significant age-related differences were identified and validated. These findings were largely replicated in the validation analysis. High integration and low temporal variability in WM-GM FNC may indicate reduced adaptability of the network system in older adults, offering insights into brain aging processes.