Brain Network Dynamics Research Articles

Decreased Dynamic Segregation but Increased Dynamic Integration of the Resting-state Functional Networks During Normal Aging

A hallmark of the aging process is increased connectivity between networks and decreased connectivity within networks, which to some extent reflects the reorganization of the brain networks during normal aging. Considering the brain as a complex dynamic system, emerging evidence suggests the time-varying connectivity patterns to be more informative of brain functions. However, the age effect on the dynamic reconfiguration of intrinsic resting state networks is still elusive. By tracking the ongoing formation and dissipation of putative functional modules across time and space, we explored the age-related changes of segregation and integration and further elucidated the underlying brain network dynamics mechanism during normal aging. Results showed that aging strongly weakened dynamic global segregation while enhanced dynamic global integration across the whole brain. Aging was associated with decreasing dynamic segregation of most networks (except the cerebellum) while increasing dynamic integration of only a few networks at the large-scale network level. Notably, the fronto-parietal network, the default mode network, the visual network, and a small group of nodes from these networks, whose dynamic segregation and integration, were both modulated by age. These findings provide direct evidence that there are remarkable changes of dynamic network architecture across the human adult lifespan and suggest the age-related modulations of dynamic segregation and integration intuitively reflect the adaptive changes of the functional dedifferentiation and compensation in older adults.

Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources

Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.

Brain network dynamics during spontaneous strategy shifts and incremental task optimization

With practice, humans improve their performance in a task by either optimizing a known strategy or discovering a novel, potentially more fruitful strategy. We investigated the neural processes underlying these two fundamental abilities by applying fMRI in a task with two possible alternative strategies. For analysis we combined time-resolved network analysis with Coherence Density Peak Clustering (Allegra et al., 2017), univariate GLM, and multivariate pattern classification. Converging evidence showed that the posterior portion of the default network, i.e. the precuneus and the angular gyrus bilaterally, has a central role in the optimization of the current strategy. These regions encoded the relevant spatial information, increased the strength of local connectivity as well as the long-distance connectivity with other relevant regions in the brain (e.g., visual cortex, dorsal attention network). The connectivity increase was proportional to performance optimization. By contrast, the anterior portion of the default network (i.e. medial prefrontal cortex) and the rostral portion of the fronto-parietal network were associated with new strategy discovery: an early increase of local and long-range connectivity centered on these regions was only observed in the subjects who would later shift to a new strategy. Overall, our findings shed light on the dynamic interactions between regions related to attention and with cognitive control, underlying the balance between strategy exploration and exploitation. Results suggest that the default network, far from being “shut-down” during task performance, has a pivotal role in the background exploration and monitoring of potential alternative courses of action.

Cortical Excitation:Inhibition Imbalance Causes Abnormal Brain Network Dynamics as Observed in Neurodevelopmental Disorders.

Abnormal brain development manifests itself at different spatial scales. However, whether abnormalities at the cellular level can be diagnosed from network activity measured with functional magnetic resonance imaging (fMRI) is largely unknown, yet of high clinical relevance. Here a putative mechanism reported in neurodevelopmental disorders, that is, excitation-to-inhibition ratio (E:I), was chemogenetically increased within cortical microcircuits of the mouse brain and measured via fMRI. Increased E:I caused a significant “reduction” of long-range connectivity, irrespective of whether excitatory neurons were facilitated or inhibitory Parvalbumin (PV) interneurons were suppressed. Training a classifier on fMRI signals, we were able to accurately classify cortical areas exhibiting increased E:I. This classifier was validated in an independent cohort of Fmr1y/− knockout mice, a model for autism with well-documented loss of parvalbumin neurons and chronic alterations of E:I. Our findings demonstrate a promising novel approach towards inferring microcircuit abnormalities from macroscopic fMRI measurements.

Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls

Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with schizophrenia and healthy controls. We applied multivariate pattern analysis of microstate features to create a specified feature set to represent microstate characteristics. Machine learning approaches using these features for classification of patients with schizophrenia were compared with prior EEG based machine learning studies. Our microstate segmentation in both patients with schizophrenia and healthy controls yielded topographies that were similar to the normative database established earlier by Koenig et al. Our machine learning model was based on large sample size, low number of features and state-of-art K-fold cross-validation technique. The multivariate analysis revealed three patterns of correlated features, which yielded an AUC of 0.84 for the group separation (accuracy: 82.7%, sensitivity/specificity: 83.5%/85.3%). Microstate segmentation of resting state EEG results in informative features to discriminate patients with schizophrenia from healthy individuals. Moreover, alteration in microstate measures may represent disturbed activity of networks in patients with schizophrenia.

Brain Network Dynamics Correlate with Personality Traits.

Identifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system that can be studied by using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks. In this study, we aimed at evaluating the feasibility of using dynamic network measures to predict personality traits. Using the electro-encephalography (EEG)/magneto-encephalography (MEG) source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: (1) resting-state EEG data acquired from 56 subjects; (2) resting-state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated. Similar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting-state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks. These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

Temporal variability of brain networks predicts individual differences in bistable perception

When ambiguous visual stimuli are presented to the eyes, conscious perception can spontaneously alternate across the competing interpretations – which was known as bistable perception. The spontaneous alternation of perception might indicate a connection between bistable perception and the dynamic interaction of brain networks. Here, we hypothesized that individual differences in perceptual dynamics may be reflected in dynamics of spontaneous neural activities. To test this idea, we investigated the relationship between the percept duration and the reconfiguration patterns of dynamic brain networks as measured by the functional connectivity (FC) during the resting state. Firstly, we found that individual difference of percept duration is associated with the temporal variability of the brain regions which were previously reported in studies of bistable perception, including anterior cingulate cortex (ACC), dorsal medial prefrontal cortex (DMPFC), dorsal lateral prefrontal cortex (DLPFC), superior parietal lobule (SPL), inferior parietal lobule (IPL), precuneus, insula, and V5. Secondly, there is a positive relationship between the temporal variability within the frontal-parietal network (FPN) and the percept duration. Thirdly, our results indicated that individual difference of bistable perception was related to the dynamic interaction between large-scale functional networks including default mode network (DMN), FPN, cingulo-opercular network (CON), dorsal attention network (DAN), salience network (SN), memory retrieval network (MRN). Altogether, our results demonstrated that inter-individual variability in bistable perception was associated with dynamic coupling of brain regions and networks involved in primary visual processing, spatial attention, and cognitive control.

Research on electroencephalogram specifics in patients with schizophrenia under cognitive load

Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.

Local Perturbations of Cortical Excitability Propagate Differentially Through Large-Scale Functional Networks.

Electrophysiological recordings have established that GABAergic interneurons regulate excitability, plasticity, and computational function within local neural circuits. Importantly, GABAergic inhibition is focally disrupted around sites of brain injury. However, it remains unclear whether focal imbalances in inhibition/excitation lead to widespread changes in brain activity. Here, we test the hypothesis that focal perturbations in excitability disrupt large-scale brain network dynamics. We used viral chemogenetics in mice to reversibly manipulate parvalbumin interneuron (PV-IN) activity levels in whisker barrel somatosensory cortex. We then assessed how this imbalance affects cortical network activity in awake mice using wide-field optical neuroimaging of pyramidal neuron GCaMP dynamics as well as local field potential recordings. We report 1) that local changes in excitability can cause remote, network-wide effects, 2) that these effects propagate differentially through intra- and interhemispheric connections, and 3) that chemogenetic constructs can induce plasticity in cortical excitability and functional connectivity. These findings may help to explain how focal activity changes following injury lead to widespread network dysfunction.

Wheel graph strategy for PEV localization of networks

Investigation of eigenvector localization properties of complex networks is not only important for gaining insight into fundamental network problems such as network centrality measure, spectral partitioning, development of approximation algorithms, but is also crucial for understanding many real-world phenomena such as disease spreading, criticality in brain network dynamics. For a network, an eigenvector is said to be localized when most of its components take value near to zero, with a few components taking very high values. In this article, we devise a methodology to construct a principal eigenvector (PEV) localized network from a given input network. The methodology relies on adding a small component having a wheel graph to the given input network. By extensive numerical simulation and an analytical formulation based on the largest eigenvalue of the input network, we compute the size of the wheel graph required to localize the PEV of the combined network. Using the susceptible-infected-susceptible model, we demonstrate the success of this method for various models and real-world networks considered as input networks. We show that on such PEV localized networks, the disease gets localized within a small region of the network structure before the outbreaks. The study is relevant in controlling spreading processes on complex systems represented by networks.

Electrophysiological dynamics of antagonistic brain networks reflect attentional fluctuations

Neuroimaging evidence suggests that the default mode network (DMN) exhibits antagonistic activity with dorsal attention (DAN) and salience (SN) networks. Here we use human intracranial electroencephalography to investigate the behavioral relevance of fine-grained dynamics within and between these networks. The three networks show dissociable profiles of task-evoked electrophysiological activity, best captured in the high-frequency broadband (HFB; 70–170 Hz) range. On the order of hundreds of milliseconds, HFB responses peak fastest in the DAN, at intermediate speed in the SN, and slowest in the DMN. Lapses of attention (behavioral errors) are marked by distinguishable patterns of both pre- and post-stimulus HFB activity within each network. Moreover, the magnitude of temporally lagged, negative HFB coupling between the DAN and DMN (but not SN and DMN) is associated with greater sustained attention performance and is reduced during wakeful rest. These findings underscore the behavioral relevance of temporally delayed coordination between antagonistic brain networks.

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome.

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

BackgroundMajor depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach.MethodsResting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels.ResultsThe group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10−3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients.ConclusionsOur findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing.

An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activity harmonizes with global signals, we measured electroencephalography (EEG) while single pulses of transcranial magnetic stimulation (TMS) perturbed occipital and parietal cortices. We estimate the rapid network reconfigurations in dynamic network communities within specific frequency bands of the EEG, and characterize two distinct features of network reconfiguration, flexibility and allegiance, among spatially distributed neural sources following TMS. Using distance from the stimulation site to infer local and global effects, we find that alpha activity (8–12 Hz) reflects concurrent local and global effects on network dynamics. Pairwise allegiance of brain regions to communities on average increased near the stimulation site, whereas TMS-induced changes to flexibility were generally invariant to distance and stimulation site. In contrast, communities within the beta (13–20 Hz) band demonstrated a high level of spatial specificity, particularly within a cluster comprising paracentral areas. Together, these results suggest that focal magnetic neurostimulation to distinct cortical sites can help identify both local and global effects on brain network dynamics, and highlight fundamental differences in the manifestation of network reconfigurations within alpha and beta frequency bands.

Graph-Adaptive Semi-Supervised Tracking of Dynamic Processes Over Switching Network Modes

A plethora of network-science related applications call for inference of spatio-temporal graph processes. Such an inference task can be aided by the underlying graph topology that might jump over discrete modes. For example, the connectivity in dynamic brain networks, switches among candidate topologies, each corresponding to a different emotional state, also known as the network mode. Taking advantage of limited nodal observations, the present contribution deals with semi-supervised tracking of dynamic processes over a given candidate set of graphs with unknown switches. Towards this end, a dynamical model is introduced to capture the per-slot spatial correlation using the active topology, as well as the temporal variation across slots through a state-space model. A scalable graph-adaptive Bayesian approach is developed, based on what is termed interacting multi-graph model (IMGM), to track the dynamic nodal processes and the active graph topology on-the-fly. Besides switching topologies, the proposed IMGM algorithm can accommodate various generalizations, including multiple dynamic functions, multiple kernels, and adaptive observation noise covariances. IMGM learns the dynamical model that best fits the data from a pool of available models. Thus, the resultant adaptive algorithm does not require offline model training. Numerical tests with synthetic and real datasets demonstrate the superior tracking performance of the novel approach compared to the mode-clairvoyant existing alternatives.

Evaluating the Mental Workload During Robot-Assisted Surgery Utilizing Network Flexibility of Human Brain

Mental Workload (MWL) is traditionally evaluated by psychophysiological signals using spectral analysis and event-related potentials. Robot-assisted Surgery (RAS) is a complex task that involves human-robot interaction, multitasking, quick and appropriate reactions to various stimuli and unforeseen circumstances, as well as frequent switches between surgical subtasks. There is a lack of standardized methodology for objectively monitoring a surgeon's MWL during RAS. In this study, we propose an innovative framework, using dynamic functional brain network measurements and a deep convolutional neural network, to assess MWL. A model was developed and validated using Electroencephalogram (EEG) data from 22 trainees who performed basic surgical tasks, as well as four surgical fellows and an expert surgeon who carried out cystectomies and prostatectomies. The resulting accuracies of the MWL classification into low, intermediate and high were 93%, 89%, and 91% respectively. The proposed method can be used for continually monitoring mental workload levels in an objective fashion.

Transcranial direct current stimulation modulates brain functional connectivity in autism

Autism spectrum disorder (ASD) is characterized by deficits in social interactions, impairments in language and communication, and highly restricted behavioral interests. Transcranial direct current stimulation (tDCS) is a widely used form of noninvasive stimulation and may have therapeutic potential for ASD. So far, despite the widespread use of this technique in the neuroscience field, its effects on network-level neural activity and the underlying mechanisms of any effects are still unclear. In the present study, we used electroencephalography (EEG) to investigate tDCS induced brain network changes in children with ASD before and after active and sham stimulation. We recorded 5 min of resting state EEG before and after a single session of tDCS (of approximately 20 min) over dorsolateral prefrontal cortex (DLPFC). Two network-based methods were applied to investigate tDCS modulation on brain networks: 1) temporal network dynamics were analyzed by comparing “flexibility” changes before vs after stimulation, and 2) frequency specific network changes were identified using non-negative matrix factorization (NMF). We found 1) an increase in network flexibility following tDCS (rapid network configuration of dynamic network communities), 2) specific increase in interhemispheric connectivity within the alpha frequency band following tDCS. Together, these results demonstrate that tDCS could help modify both local and global brain network dynamics, and highlight stimulation-induced differences in the manifestation of network reconfiguration. Meanwhile, frequency–specific subnetworks, as a way to index local and global information processing, highlight the core modulatory effects of tDCS on the modular architecture of the functional connectivity patterns within higher frequency bands.

The chronnectome as a model for Charcot’s ‘dynamic lesion’ in functional movement disorders

This exploratory study set out to investigate dynamic functional connectivity (dFC) in patients with jerky and tremulous functional movement disorders (JT-FMD). The focus in this work is on dynamic brain states, which represent distinct dFC patterns that reoccur in time and across subjects. Resting-state fMRI data were collected from 17 patients with JT-FMD and 17 healthy controls (HC). Symptom severity was measured using the Clinical Global Impression-Severity scale. Depression and anxiety were measured using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), respectively. Independent component analysis was used to extract functional brain components. After computing dFC, dynamic brain states were determined for every subject using k-means clustering. Compared to HC, patients with JT-FMD spent more time in a state that was characterized predominantly by increasing medial prefrontal, and decreasing posterior midline connectivity over time. They also tended to visit this state more frequently. In addition, patients with JT-FMD transitioned significantly more often between different states compared to HC, and incorporated a state with decreasing medial prefrontal, and increasing posterior midline connectivity in their attractor, i.e., the cyclic patterns of state transitions. Altogether, this is the first study that demonstrates altered functional brain network dynamics in JT-FMD that may support concepts of increased self-reflective processes and impaired sense of agency as driving factors in FMD.

Evoked and intrinsic brain network dynamics in children with autism spectrum disorder

ObjectiveBrain dynamics underlie flexible cognition and behavior, yet little is known regarding this relationship in autism spectrum disorder (ASD). We examined time-varying changes in functional co-activation patterns (CAPs) across rest and task-evoked brain states to characterize differences between children with ASD and typically developing (TD) children and identify relationships with severity of social behaviors and restricted and repetitive behaviors. Method17 children with ASD and 27 TD children ages 7–12 completed a resting-state fMRI scan and four runs of a non-cued attention switching task. Metrics indexing brain dynamics were generated from dynamic CAPs computed across three major large-scale brain networks: midcingulo-insular (M-CIN), medial frontoparietal (M-FPN), and lateral frontoparietal (L-FPN). ResultsFive time-varying CAPs representing dynamic co-activations among network nodes were identified across rest and task fMRI datasets. Significant Diagnosis × Condition interactions were observed for the dwell time of CAP 3, representing co-activation between nodes of the M-CIN and L-FPN, and the frequency of CAP 1, representing co-activation between nodes of the L-FPN. A significant brain-behavior association between dwell time of CAP 5, representing co-activation between nodes of the M-FPN, and social abilities was also observed across both groups of children. ConclusionAnalysis of brain co-activation patterns reveals altered dynamics among three core networks in children with ASD, particularly evident during later stages of an attention task. Dimensional analyses demonstrating relationships between M-FPN dwell time and social abilities suggest that metrics of brain dynamics may index individual differences in social cognition and behavior.

Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder.

Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static measures of functional connectivity. Recent work has focused on identifying transient configurations of brain activity, yet several open questions remain regarding the nature of specific brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP) approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 children (ages 6–13 years; n = 75 ASD; n = 138 male). Following brain parcellation by using independent component analysis, dynamic CAP analyses were conducted and k-means clustering was used to determine transient activation patterns of the M-CIN. The frequency of occurrence of different dynamic CAP brain states was then compared between children with ASD and typically developing (TD) children. Dynamic brain configurations characterized by coactivation of the M-CIN with central executive/lateral fronto-parietal and default mode/medial fronto-parietal networks appeared less frequently in children with ASD compared with TD children. This study highlights the utility of time-varying approaches for studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate that altered M-CIN dynamics in ASD may underlie the inflexible behaviors commonly observed in children with the disorder.