Dynamic Functional Brain Networks Research Articles

Altered static and dynamic functional brain network in knee osteoarthritis: A resting-state functional magnetic resonance imaging study: Static and dynamic FNC in KOA

This study aimed to investigate altered static and dynamic functional network connectivity (FNC) and its correlation with clinical symptoms in patients with knee osteoarthritis (KOA). One hundred and fifty-nine patients with KOA and 73 age- and gender-matched healthy subjects (HS) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluations. Group independent component analysis (GICA) was applied, and seven resting-state networks were identified. Patients with KOA had decreased static FNC within the default mode network (DM), visual network (VS), and cerebellar network (CB) and increased static FNC between the subcortical network (SC) and VS (p < 0.05, FDR corrected). Four reoccurring FNC states were identified using k-means clustering analysis. Although abnormalities in dynamic FNCs of KOA patients have been found using the common window size (22 TR, 44 s), but the results of the clustering analysis were inconsistent when using different window sizes, suggesting dynamic FNCs might be an unstable method to compare brain function between KOA patients and HS. These recent findings illustrate that patients with KOA have a wide range of abnormalities in the static and dynamic FNCs, which provided a reference for the identification of potential central nervous therapeutic targets for KOA treatment and might shed light on the other musculoskeletal pain neuroimaging studies.

Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment

Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Exploring static and dynamic functional brain networks in adolescent depression using a co-produced novel irritability paradigm

Background Irritability is a core symptom of depression in adolescence and a risk factor for emotion regulation problems. However, its neural correlates are not well understood. Existing functional magnetic resonance imaging (fMRI) research on irritability typically overlooks its social context. Methods Here, we pilot a novel naturalistic fMRI paradigm targeting the social nature of irritability that was co-produced with young people (N = 88) and apply it in an independent sample of youth (N = 29, mean age 18.9 years, 77% female) with self-reported low mood that were aged 16 to 20 years. Participants were also fluent English speakers, free from MRI contraindications, and did not report a diagnosis of a neurological or neurodevelopmental condition. Our aim was to investigate whether graph theoretic and dynamical properties of functional brain networks differed between a resting state scan and our irritability paradigm. We also examined whether these brain features were associated with depressive symptoms and trait irritability. Results Using Leading Eigenvector Dynamics Analysis (LEiDA), we found that the dynamic properties of brain networks comprising default-mode and fronto-parietal regions differed significantly during the irritability paradigm compared to the rest condition. While no gross static topological differences were found between these two conditions, we found that some dynamic and topological features of emotion-related brain networks were related to trait irritability and depressive symptoms in our sample. Conclusions Although the current findings are preliminary due to the pilot nature of this study, this work showcases the feasibility of co-produced research in neuroimaging and lays a strong foundation for further study.

Abnormal multi-layered dynamic cortico-subcortical functional connectivity in major depressive disorder and generalized anxiety disorder

Comorbidity has been frequently observed between generalized anxiety disorder (GAD) and major depressive disorder (MDD), however, common and distinguishable alterations in the topological organization of functional brain networks remain poorly understood. We sought to determine a robust and sensitive functional connectivity marker for diagnostic classification and symptom severity prediction. Multi-layered dynamic functional connectivity including whole brain, network-node and node-node layers via graph theory and gradient analyses were applied to functional MRI resting-state data obtained from 31 unmedicated GAD and 34 unmedicated MDD patients as well as 33 age and education matched healthy controls (HC). GAD and MDD symptoms were assessed using Penn State Worry Questionnaire and Beck Depression Inventory II, respectively. Three network measures including global properties (i.e., global efficiency, characteristic path length), regional nodal property (i.e., degree) and connectivity gradients were computed. Results showed that both patient groups exhibited abnormal dynamic cortico-subcortical topological organization compared to healthy controls, with MDD > GAD > HC in degree of randomization. Furthermore, our multi-layered dynamic functional connectivity network model reached 77% diagnostic accuracy between GAD and MDD and was highly predictive of symptom severity, respectively. Gradients of functional connectivity for superior frontal cortex-subcortical regions, middle temporal gyrus-subcortical regions and amygdala-cortical regions contributed more in this model compared to other gradients. We found shared and distinct cortico-subcortical connectivity features in dynamic functional brain networks between GAD and MDD, which together can promote the understanding of common and disorder-specific topological organization dysregulations and facilitate early neuroimaging-based diagnosis.

Abnormal dynamic functional network connectivity in patients with early-onset bipolar disorder.

To explore the changes in dynamic functional brain network connectivity (dFNC) in patients with early-onset bipolar disorder (BD). Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 39 patients with early-onset BD and 22 healthy controls (HCs). Four repeated and stable dFNC states were characterised by independent component analysis (ICA), sliding time windows and k-means clustering, and three dFNC temporal metrics (fraction of time, mean dwell time and number of transitions) were obtained. The dFNC temporal metrics and the differences in dFNC between the two groups in different states were evaluated, and the correlations between the differential dFNC metrics and neuropsychological scores were analysed. The dFNC analysis showed four connected patterns in all subjects. Compared with the HCs, the dFNC patterns of early-onset BD were significantly altered in all four states, mainly involving impaired cognitive and perceptual networks. In addition, early-onset BD patients had a decreased fraction of time and mean dwell time in state 2 and an increased mean dwell time in state 3 (p < 0.05). The mean dwell time in state 3 of BD showed a positive correlation trend with the HAMA score (r = 0.4049, p = 0.0237 × 3 > 0.05 after Bonferroni correction). Patients with early-onset BD had abnormal dynamic properties of brain functional network connectivity, suggesting that their dFNC was unstable, mainly manifesting as impaired coordination between cognitive and perceptual networks. This study provided a new imaging basis for the neuropathological study of emotional and cognitive deficits in early-onset BD.

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli.

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

Dynamic functional brain networks in Alzheimer’s disease and healthy ageing

AbstractBackgroundBrain network analysis from resting‐state functional MRI (fMRI) has helped us to understand cortical activity in dementia. Likewise, dynamic brain networks' analysis, which takes into account temporal fluctuations in the resting‐state fMRI signal, has helped to reveal patterns of activity, usually averaged out by the conventional functional network analysis. These patterns of activity, or dynamic networks, reveal transient (meta‐stable) dynamics. Dynamic functional networks analysis from fMRI data has shown a potential to unveil clinically relevant information.MethodResting‐state fMRI from 187 participants belonging to Alzheimer’s Disease (AD) and cognitively‐normal Healthy Elderly (HE) from the ADNI‐3 database were analysed. Functional network was constructed from fMRI signal of the 120 cortical and sub‐cortical regions of the Jülich Atlas for each participants, and averaged across the two study groups. Dynamic functional connectivity is calculated using sliding window approach and compared to conventional (‘static’) functional connectivity within each study group and between them.ResultSignificant differences when comparing AD with HE individuals were found in the dynamic, but not static functional networks. These differences were restricted to the white matter regions, and to the inferior and superior parietal, and somatosensory cortices.ConclusionOur results demonstrate the existence of common and distinct patterns of static and dynamic brain connectivity in HE and AD, which highlight the importance of including non‐stationary information into the brain network analysis in Alzheimer’s disease.

A progressive learning classifier based on dynamic differential weighted network for feature identification of brain network series

A great effort has been dedicated to investigating resting-state functional connectivity (rs-FC). Recent evidence insinuates that dynamic functional connectivity (dFC) may be a more sensitive marker than rs-FC. Although electroencephalography (EEG) with millisecond temporal resolution has the potential to capture the dynamic evolutionary patterns of the human brain, EEG-based dynamic functional connectivity has not been sufficiently studied. Thus, we propose a progressive learning classifier based on dynamic differential weighted networks (PLC-DWN) to identify EEG-based dynamic functional brain networks efficiently. In PLC-DWN, dynamic differential weighted brain networks are first constructed to capture the temporal evolutionary properties of brain network series more efficiently. Furthermore, a Laplacian network embedding and mapping method is proposed in PLC-DWN to automatically extract the gradient features of eigen sub-networks in a brain network via local attribute embedding and global gradient mapping. Compared with the mainstream graph theoretical analysis, the gradient features extracted automatically can more comprehensively depict the dynamic connection information. To identify time-variant gradient features accurately, a progressive learning classifier developed in PLC-DWN progressively intensifies its performance via concise extreme learning machines (ELMs) and a self-adapting learning structure. The PLC-DWN has simultaneously been applied to epilepsy prediction and emotion recognition tasks involving nearly 943 h of EEG signals from 66 subjects. Exhaustive experiments demonstrate that PLC-DWN outperforms state-of-the-art methods in terms of accuracy, sensitivity, and specificity. Furthermore, multiple comparative models were constructed to verify the effectiveness of each innovative module within PLC-DWN.

Aberrant Static and Dynamic Functional Brain Network in Depression Based on EEG Source Localization.

Depression is accompanied by abnormalities in large-scale functional brain networks. This paper combined static and dynamic methods to analyze the abnormal topology and changes of functional connectivity network (FCN) of depression. We collected resting-state EEG recordings from 27 depressed subjects and 28 normal subjects, then obtained 68 regions of interests (ROIs) by source localization. We took ROIs as the nodes and correlations as the edges to build FCNs and analyzed static network based on graph theory. We used a sliding window method followed by k-means clustering, states analyses and trend analysis of network metrics over time to study dynamic connectivity. The clustering coefficient (CC) and local efficiency in depression were increased, the characteristic path length and global efficiency were decreased, and local metrics had different manifestations in different resting state networks (RSNs); Depression had reduced connectivity in most RSNs, but increased connectivity in the default mode network, and there was a decoupling phenomenon between different RSNs; Depressed patients spent more time in sparsely connected states, their FCN's flexibility was less than normal subjects; The trend of CC over time was opposite between two groups. Most metrics in normal showed a relatively stronger correlation with time. Our research may provide a deeper understanding of neurophysiological mechanisms of depression and new biomarkers for clinical diagnosis of depression.

Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives

Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network's quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network's temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.

Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks.

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

Effects of Microstate Dynamic Brain Network Disruption in Different Stages of Schizophrenia.

Schizophrenia is a heterogeneous mental disorder with unknown etiology or pathological characteristics. Microstate analysis of the electroencephalogram (EEG) signal has shown significant potential value for clinical research. Importantly, significant changes in microstate-specific parameters have been extensively reported; however, these studies have ignored the information interactions within the microstate network in different stages of schizophrenia. Based on recent findings, since rich information about the functional organization of the brain can be revealed by functional connectivity dynamics, we use the first-order autoregressive model to construct the functional connectivity of intra- and intermicrostate networks to identify information interactions among microstate networks. We demonstrate that, beyond abnormal parameters, disrupted organization of the microstate networks plays a crucial role in different stages of the disease by 128-channel EEG data collected from individuals with first-episode schizophrenia, ultrahigh-risk, familial high-risk, and healthy controls. According to the characteristics of the microstates of patients at different stages, the parameters of microstate class A are reduced, those of class C are increased, and the transitions from intra- to intermicrostate functional connectivity are gradually disrupted. Furthermore, decreased integration of intermicrostate information might lead to cognitive deficits in individuals with schizophrenia and those in high-risk states. Taken together, these findings illustrate that the dynamic functional connectivity of intra- and intermicrostate networks captures more components of disease pathophysiology. Our work sheds new light on the characterization of dynamic functional brain networks based on EEG signals and provides a new interpretation of aberrant brain function in different stages of schizophrenia from the perspective of microstates.

Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson's Disease following Dopaminergic Treatment.

Dopaminergic treatment (DT), the standard therapy for Parkinson's disease (PD), alters the dynamics of functional brain networks at specific time scales. Here, we explore the scale-free functional connectivity (FC) in the PD population and how it is affected by DT. We analyzed the electroencephalogram of: (i) 15 PD patients during DT (ON) and after DT washout (OFF) and (ii) 16 healthy control individuals (HC). We estimated FC using bivariate focus-based multifractal analysis, which evaluated the long-term memory and multifractal strength of the connections. Subsequent analysis yielded network metrics (node degree, clustering coefficient and path length) based on FC estimated by or . Cognitive performance was assessed by the Mini Mental State Examination (MMSE) and the North American Adult Reading Test (NAART). The node degrees of the networks were significantly higher in ON, compared to OFF and HC, while clustering coefficient and path length significantly decreased. No alterations were observed in the networks. Significant positive correlations were also found between the metrics of networks and NAART scores in the HC group. These results demonstrate that DT alters the multifractal coupled dynamics in the brain, warranting the investigation of scale-free FC in clinical and pharmacological studies.

Repeated High-Definition Transcranial Direct Current Stimulation Modulated Temporal Variability of Brain Regions in Core Neurocognitive Networks Over the Left Dorsolateral Prefrontal Cortex in Mild Cognitive Impairment Patients.

Early intervention of amnestic mild cognitive impairment (aMCI) may be the most promising way for delaying or even preventing the progression to Alzheimer's disease. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that has been recognized as a promising approach for the treatment of aMCI. In this paper, we aimed to investigate the modulating mechanism of tDCS on the core neurocognitive networks of brain. We used repeated anodal high-definition transcranial direct current stimulation (HD-tDCS) over the left dorsolateral prefrontal cortex and assessed the effect on cognition and dynamic functional brain network in aMCI patients. We used a novel method called temporal variability to depict the characteristics of the dynamic brain functional networks. We found that true anodal stimulation significantly improved cognitive performance as measured by the Montreal Cognitive Assessment after simulation. Meanwhile, the Mini-Mental State Examination scores showed a clear upward trend. More importantly, we found significantly altered temporal variability of dynamic functional connectivity of regions belonging to the default mode network, central executive network, and the salience network after true anodal stimulation, indicating anodal HD-tDCS may enhance brain function by modulating the temporal variability of the brain regions. These results imply that ten days of anodal repeated HD-tDCS over the LDLPFC exerts beneficial effects on the temporal variability of the functional architecture of the brain, which may be a potential neural mechanism by which HD-tDCS enhances brain functions. Repeated HD-tDCS may have clinical uses for the intervention of brain function decline in aMCI patients.

Problematic smartphone use is associated with differences in static and dynamic brain functional connectivity in young adults.

This study aimed to investigate the possible associations between problematic smartphone use and brain functions in terms of both static and dynamic functional connectivity patterns. Resting-state functional magnetic resonance imaging data were scanned from 53 young healthy adults, all of whom completed the Short Version of the Smartphone Addiction Scale (SAS-SV) to assess their problematic smartphone use severity. Both static and dynamic functional brain network measures were evaluated for each participant. The brain network measures were correlated the SAS-SV scores, and compared between participants with and without a problematic smartphone use after adjusting for sex, age, education, and head motion. Two participants were excluded because of excessive head motion, and 56.9% (29/51) of the final analyzed participants were found to have a problematic smartphone use (SAS-SV scores ≥ 31 for males and ≥ 33 for females, as proposed in prior research). At the global network level, the SAS-SV score was found to be significantly positively correlated with the global efficiency and local efficiency of static brain networks, and negatively correlated with the temporal variability using the dynamic brain network model. Large-scale subnetwork analyses indicated that a higher SAS-SV score was significantly associated with higher strengths of static functional connectivity within the frontoparietal and cinguloopercular subnetworks, as well as a lower temporal variability of dynamic functional connectivity patterns within the attention subnetwork. However, no significant differences were found when directly comparing between the groups of participants with and without a problematic smartphone use. Our results suggested that problematic smartphone use is associated with differences in both the static and dynamic brain network organizations in young adults. These findings may help to identify at-risk population for smartphone addiction and guide targeted interventions for further research. Nevertheless, it might be necessary to confirm our findings in a larger sample, and to investigate if a more applicable SAS-SV cutoff point is required for defining problematic smartphone use in young Chinese adults nowadays.

Mixtures of large-scale dynamic functional brain network modes

Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical “modes”. The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100–150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM’s while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM’s assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.

Structural connections between the noradrenergic and cholinergic system shape the dynamics of functional brain networks

Complex cognitive abilities are thought to arise from the ability of the brain to adaptively reconfigure its internal network structure as a function of task demands. Recent work has suggested that this inherent flexibility may in part be conferred by the widespread projections of the ascending arousal systems. While the different components of the ascending arousal system are often studied in isolation, there are anatomical connections between neuromodulatory hubs that we hypothesise are crucial for mediating key features of adaptive network dynamics, such as the balance between integration and segregation. To test this hypothesis, we estimated the strength of structural connectivity between key hubs of the noradrenergic and cholinergic arousal systems (the locus coeruleus [LC] and nucleus basalis of Meynert [nbM], respectively). We then asked whether the strength of structural LC and nbM inter-connectivity was related to individual differences in the emergent, dynamical signatures of functional integration measured from resting state fMRI data, such as network and attractor topography. We observed a significant positive relationship between the strength of white-matter connections between the LC and nbM and the extent of network-level integration following BOLD signal peaks in LC relative to nbM activity. In addition, individuals with denser white-matter streamlines interconnecting neuromodulatory hubs also demonstrated a heightened ability to shift to novel brain states. These results suggest that individuals with stronger structural connectivity between the noradrenergic and cholinergic systems have a greater capacity to mediate the flexible network dynamics required to support complex, adaptive behaviour. Furthermore, our results highlight the underlying static features of the neuromodulatory hubs can impose some constraints on the dynamic features of the brain.

Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

NDCN-Brain: An Extensible Dynamic Functional Brain Network Model.

As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.