Dynamic Functional Network Research Articles

Discriminating mild traumatic brain injury using sparse dictionary learning of functional network dynamics.

Mild traumatic brain injury (mTBI) is usually caused by a bump, blow, or jolt to the head or penetrating head injury, and carries the risk of inducing cognitive disorders. However, identifying the biomarkers for the diagnosis of mTBI is challenging as evident abnormalities in brain anatomy are rarely found in patients with mTBI. In this study, we tested whether the alteration of functional network dynamics could be used as potential biomarkers to better diagnose mTBI. We propose a sparse dictionary learning framework to delineate spontaneous fluctuation of functional connectivity into the subject‐specific time‐varying evolution of a set of overlapping group‐level sparse connectivity components (SCCs) based on the resting‐state functional magnetic resonance imaging (fMRI) data from 31 mTBI patients in the early acute phase (<3 days postinjury) and 31 healthy controls (HCs). The identified SCCs were consistently distributed in the cohort of subjects without significant inter‐group differences in connectivity patterns. Nevertheless, subject‐specific temporal expression of these SCCs could be used to discriminate patients with mTBI from HCs with a classification accuracy of 74.2% (specificity 64.5% and sensitivity 83.9%) using leave‐one‐out cross‐validation. Taken together, our findings indicate neuroimaging biomarkers for mTBI individual diagnosis based on the temporal expression of SCCs underlying time‐resolved functional connectivity.

Abnormal Brain Functional Network Dynamics in Acute CO Poisoning.

Aims: Carbon monoxide poisoning is a common condition that can cause severe neurological sequelae. Previous studies have revealed that functional connectivity in carbon monoxide poisoning is abnormal under the assumption that it is resting during scanning and have focused on studying delayed encephalopathy in carbon monoxide poisoning. However, studies of functional connectivity dynamics in the acute phase of carbon monoxide poisoning may provide a more insightful perspective for understanding the neural mechanisms underlying carbon monoxide poisoning. To our knowledge, this is the first study that explores abnormal brain network dynamics in the acute phase of carbon monoxide poisoning.Methods: Combining the sliding window method and k-means algorithm, we identified four recurrent dynamic functional cognitive impairment states from resting-state functional magnetic resonance imaging data from 29 patients in the acute phase of carbon monoxide poisoning and 29 healthy controls. We calculated between-group differences in the temporal properties and intensity of dFC states, and we also performed subgroup analyses to separately explore the brain network dynamics characteristics of adult vs. child carbon monoxide poisoning groups. Finally, these differences were correlated with patients’ cognitive performance in the acute phase of carbon monoxide poisoning and coma duration.Results: We identified four morphological patterns of brain functional network connectivity. During the acute phase of carbon monoxide poisoning, patients spent more time in State 2, which is characterized by positive correlation between SMN and CEN, and negative correlation between DMN and SMN. In addition, the fractional window and mean dwell time of State 2 were positively correlated with coma duration. The subgroup analysis results demonstrated that the acute phase of childhood carbon monoxide poisoning had greater dFNC time variability than adult carbon monoxide poisoning.Conclusion: Our findings reveal that patients in the acute phase of carbon monoxide poisoning exhibit dynamic functional abnormalities. Furthermore, children have greater dFNC instability following carbon monoxide poisoning than adults. This advances our understanding of the pathophysiological mechanisms underlying acute carbon monoxide poisoning.

Evidence for Transcranial Magnetic Stimulation Induced Functional Connectivity Oscillations in the Brain.

Transcranial magnetic stimulation (TMS) is an effective research tool to elucidate mechanisms of function in the brain. Despite its widespread use, very few studies have looked at dynamic functional connectivity responses to TMS. This work performs an exploratory analysis of dynamic functional network connectivity (dynFNC) to evaluate evidence of brain response to TMS. Results show clear functional dynamic patterns categorized by frequency. Some patterns appear to be more directly linked to TMS, but there is one pattern that might be a TMS-independent response to the excitation. This first look presents an analysis methodology and important results to consider in future research.

Multiframe Evolving Dynamic Functional Network Connectivity Motifs (Evodfncs) from Continuity-Preserving Planar Embedding.

The study of brain network connectivity as a time-varying property began relatively recently and to date has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group- level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a planar embedding of the high-dimensional whole-brain connectivity dynamics that preserves important features, such as trajectory continuity, characterizing dynamics in the native high dimensional state space. The method is validated in application to a large rs- fMRI study of schizophrenia where it extracts naturalistic fluidly-varying connectivity motifs that differ between schizophrenia patients (SZs) and healthy controls (HC).Functional Magnetic Resonance Imaging, Functional Network Connectivity, Dynamic Functional Network Connectivity, Schizophrenia.

The Influence of Spatial Smoothing Kernel Size on the Whole-brain Dynamic Functional Network Connectivity and Meta-state Parameters.

In functional magnetic resonance imaging (fMRI), spatial smoothing procedure is generally a stable step in the preprocessing stream. Previous research (including ours) suggested dependency of the static functional connectivity on the size of the spatial smoothing kernel size. But its impact on the time-varying patterns of functional connectivity has not been investigated. Here, we sought to identify the effects of spatial smoothing on brain dynamics by performing dynamic functional network connectivity (dFNC) and meta-state analysis, a unique approach capable of examining a higher-dimensional temporal dynamism of whole-brain functional connectivity. Gaussian smoothing kernel with different widths at half of the maximum of the height of the Gaussian (4, 8, and 12 mm FWHM) were used during preprocessing prior to the group independent component analysis (ICA) with a relatively high model order of 75. dFNC was conducted using the sliding-time window approach and k-means clustering algorithm. Meta-state dynamics method was performed by reducing the number of windowed FNC correlations using principal components analysis (PCA), temporal and spatial ICA and k-means. Results revealed robust effects of spatial smoothing on the connectivity dynamics of several network pairs including a variety of cognitive/attention networks in a connectivity state with the highest occurrence (FDR corrected-p < 0.01). Meta-state analyses indicated significant changes in meta-state metrics including the number of meta-states, meta-state changes, meta-state span, and the total distance. These changes were particularly pronounced when we compared resting state data smoothed with 8 vs. 12 mm FWHM. Our preliminary findings give insights into the effects of spatial smoothing kernel size on the dynamics of functional connectivity and its consequences on meta-state parameters. It also provides further indication of the importance of evaluating variance associated with preprocessing steps on analysis outcomes.

Dynamic functional network connectivity reveals the brain functional alterations in lung cancer patients after chemotherapy.

This study aimed to investigate alterations of brain functional network connectivity (FNC) in lung cancer patients after chemotherapy and explore links between these FNC differences and cognitive impairment.Twenty-two lung cancer patients receiving chemotherapy and 26 healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and neuropsychological testing. Group independent component analysis (GICA) was applied to rs-fMRI data to extract whole-brain resting state networks (RSNs). Static and dynamic FNC (dFNC) were constructed to reveal RSNs connectivity alterations between lung cancer patients and HCs group, and the correlations between the group differences in RSNs and cognitive performance were analyzed.Our findings revealed that chemotherapeutics can produce widespread connectivity abnormalities in RSNs, mainly focused on default mode network (DMN) and executive control network. Furthermore, the dFNC analysis help identify network configurations of each state and capture more chemotherapy-induced disorders of interactions between and within RSNs, which mainly includes sensorimotor network, attentional network and auditory network. In addition, after chemotherapy, the lung cancer patients spend shorter mean dwell time (MDT) in state 2. The decreased dFNC between DMN [independent component 5 (IC5)] and DMN (IC6) in the lung cancer patients after chemotherapy in state 4 was negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r=-0.447, p=0.042).The dFNC analysis enrich our understanding of the neural mechanisms underlying the chemobrain, and suggested that the temporal dynamics of FNC could be a potential effective method to detect cognitive changes in lung cancer patients receiving chemotherapy.

Functional network dynamics and decreased conscientiousness in multiple sclerosis.

Conscientiousness is a personality trait that declines in people with multiple sclerosis (PwMS) and its decline predicts worse clinical outcomes. This study aims to investigate the neural underpinnings of lower Conscientiousness in PwMS by examining MRI anomalies in functional network dynamics. 70 PwMS and 50 healthy controls underwent personality assessment and resting-state MRI. Associations with dynamic functional network properties (i.e., eigenvector centrality) were evaluated, using a dynamic sliding-window approach. In PwMS, lower Conscientiousness was associated with increased variability of centrality in the left insula (tmax = 4.21) and right inferior parietal lobule (tmax = 3.79); a relationship also observed in regressions accounting for handedness, disease duration, disability, and tract disruption in relevant structural networks (ΔR2 = 0.071, p = 0.003; ΔR2 = 0.094, p = 0.004). Centrality dynamics of the observed regions were not associated with Neuroticism (R2 < 0.001, p = 0.956; R2 < 0.001, p = 0.945). As well, higher Conscientiousness was associated with greater variability in connectivity for the left insula with the default-mode network (F = 3.92, p = 0.023) and limbic network (F = 5.66, p = 0.005). Lower Conscientiousness in PwMS was associated with increased variability in network centrality, most prominently for the left insula and right inferior parietal cortex. This effect, specific to Conscientiousness and significant after accounting for disability and structural network damage, could indicate that overall stable network centrality is lost in patients with low Conscientiousness, especially for the insula and right parietal cortex. The positive relationship between Conscientiousness and variability of connectivity between left insula and default-mode network potentially affirms that dynamics between the salience and default-mode networks is related to the regulation of behavior.

Dynamic Functional Network Connectivity Associated with Musical Emotions Evoked by Different Tempi.

Introduction: Music tempo has strong clinical maneuverability and positive emotional effect in music therapy, which can directly evoke multiple emotions and dynamic neural changes in the whole brain. However, the precise relationship between music tempo and its emotional effects remains unclear. The present study aimed to investigate the dynamic network connectivity (dFNC) associated with emotions elicited by music at different tempi. Materials and Methods: We obtained emotion ratings of fast-tempo (155-170 beats per minute [bpm]), middle-tempo (90 bpm), and slow-tempo (50-60 bpm) piano music from 40 participants both during and after functional magnetic resonance imaging (fMRI). Group independent component analysis (ICA), sliding time window correlations, and k-means clustering were used to assess the dFNC of fMRI data. Paired t-tests were conducted to compare the difference of neural networks. Results: (1) Fast music was associated with higher ratings of emotional valence and arousal, which were accompanied with increasing dFNC between somatomotor (SM) and cingulo-opercular (CO) networks and decreasing dFNC between frontoparietal and SM networks. (2) Even with stronger activation in auditory (AUD) networks, slow music was associated with weaker emotion than fast music, with decreasing functional network connectivity across the brain and the participation of default mode (DM). (3) Middle-tempo music elicited moderate emotional activation with the most stable dFNC in the whole brain. Conclusion: Faster music increases neural activity in the SM and CO regions, increasing the intensity of the emotional experience. In contrast, slower music was associated with decreasing engagement of AUD and stable engagement of DM, resulting in a weak emotional experience. These findings suggested that the time-varying aspects of functional connectivity can help to uncover the dynamic neural substrates of tempo-evoked emotion while listening to music.

Sex-specific static and dynamic functional networks of sub-divisions of striatum linking to the greed personality trait

The study of greed has been broadly investigated and discussed in the field of social sciences, including economics, political science, and psychology. However, the neural mechanisms underlying greed personality trait (GPT) have received little attention from the cognitive neuroscience field and still remain unclear. In this study, we explored the associations between GPT and static/dynamic reward circuit—specifically its sub-regions’ functional networks including caudate, nucleus accumbens (NAcc), and putamen. Behavioral analyses revealed significant associations of GPT with Past-Negative and Present-Fatalistic time attitude as well as attention impulsivity. Imaging analyses revealed a significant interaction effect between sex and GPT on the static reward functional networks. In particular, GPT was positively correlated with static caudate-NAcc, caudate-cerebellum, and NAcc-parahippocampus/medial orbitofrontal cortex (PHG/mOFC) for males but negatively correlated for females. GPT was also marginally and negatively correlated with static putamen-occipital pole functional connectivities among males. Interestingly, sex difference interaction patterns were further observed in the dynamic reward functional networks. Further, dynamic reward functional networks also exhibited some specific characteristics, manifesting in more brain regions involved for greedy behaviors. These findings suggest sex-specific static and dynamic functional networks underlying human dispositional greed, and also implicate the critical contributions of reward circuit, especially for sub-circuits of reward, on greed.

Differences in Disrupted Dynamic Functional Network Connectivity Among Children, Adolescents, and Adults With Attention Deficit/Hyperactivity Disorder: A Resting-State fMRI Study.

Attention deficit hyperactivity disorder (ADHD) is one of the most widespread mental disorders and often persists from childhood to adulthood, and its symptoms vary with age. In this study, we aim to determine the disrupted dynamic functional network connectivity differences in adult, adolescent, and child ADHD using resting-state functional magnetic resonance imaging (rs-fMRI) data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years). We hypothesized that functional connectivity is time-varying and that there are within- and between-network connectivity differences among the three age groups. Nine functional networks were identified using group ICA, and three FC-states were recognized based on their dynamic functional network connectivity (dFNC) pattern. Fraction of time, mean dwell time, transition probability, degree-in, and degree-out were calculated to measure the state dynamics. Higher-order networks including the DMN, SN, and FPN, and lower-order networks comprising the SMN, VN, SC, and AUD were frequently distributed across all states and were found to show connectivity differences among the three age groups. Our findings imply abnormal dynamic interactions and dysconnectivity associated with different ADHD, and these abnormalities differ between the three ADHD age groups. Given the dFNC differences between the three groups in the current study, our work further provides new insights into the mechanism subserved by age difference in the pathophysiology of ADHD and may set the grounds for future case-control studies in the individual age groups, as well as serving as a guide in the development of treatment strategies to target these specific networks in each age group.

Dissemination in time and space in presymptomatic granulin mutation carriers: A GENFI dynamic functional network connectivity study

The presymptomatic brain changes of granulin (GRN) disease, preceding by years frontotemporal dementia, need to be still fully characterized. Dynamic functional network connectivity (dFNC) allows to capture both spatial networks configurations and their dynamic changes over time. Aim of the study is to investigate the dFNC in 141 presymptomatic GRN mutation carriers and 282 non-carriers from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort.

Aberrant functional brain network dynamics in patients with functional constipation.

The aberrant static functional connectivity of brain network has been widely investigated in patients with functional constipation (FCon). However, the dynamics of brain functional connectivity in FCon patients remained unknown. This study aimed to detect the brain dynamics of functional connectivity states and network topological organizations of FCon patients and investigate the correlations of the aberrant brain dynamics with symptom severity. Eighty‐three FCon patients and 80 healthy subjects (HS) were included in data analysis. The spatial group independent component analysis, sliding‐window approach, k‐means clustering, and graph‐theoretic analysis were applied to investigate the dynamic temporal properties and coupling patterns of functional connectivity states, as well as the time‐variation of network topological organizations in FCon patients. Four reoccurring functional connectivity states were identified in k‐means clustering analysis. Compared to HS, FCon patients manifested the lower occurrence rate and mean dwell time in the state with a complex connection between default mode network and cognitive control network, as well as the aberrant anterior insula–cortical coupling patterns in this state, which were significantly correlated with the symptom severity. The graph‐theoretic analysis demonstrated that FCon patients had higher sample entropy at the nodal efficiency of anterior insula than HS. The current findings provided dynamic perspectives for understanding the brain connectome of FCon and laid the foundation for the potential treatment of FCon based on brain connectomics.

Analysis of complexity and dynamic functional connectivity based on resting-state EEG in early Parkinson's disease patients with mild cognitive impairment.

To explore the abnormal brain activity of early Parkinson's disease with mild cognitive impairment (ePD-MCI) patients, the study analyzed the dynamic fluctuation of electroencephalogram (EEG) signals and the dynamic change of information communication between EEG signals of ePD-MCI patients. In this study, we recorded resting-state EEG signals of 30 ePD-MCI patients and 37 early Parkinson's disease without mild cognitive impairment (ePD-nMCI) patients. First, we analyzed the difference of the complexity of EEG signals between the two groups. And we found that the complexity in the ePD-MCI group was significantly higher than that in the ePD-nMCI group. Then, by analyzing the dynamic functional network (DFN) topology based on the optimal sliding-window, we found that the temporal correlation coefficients of ePD-MCI patients were lower in the delta and theta bands than those in the ePD-nMCI patients. The temporal characteristic path length of ePD-MCI patients in the alpha band was higher than that of ePD-nMCI patients. In the theta and alpha bands, the temporal small world degrees of ePD-MCI patients were lower than that of patients with ePD-nMCI. In addition, the functional connectivity strength of ePD-MCI patients affected by cognitive impairment was weaker than that of ePD-nMCI patients, and the stability of dynamic functional connectivity network was decreased. This finding may serve as a biomarker to identify ePD-MCI and contribute to the early intervention treatment of ePD-MCI.

The Temporal Dynamics of Functional Networks are Modulated by Acupuncture: an EEG Microstates Study

Information transition delay is an inherent factor in neuronal systems and has great influences on their dynamical behaviors. For theoretical studies about time delay in neuronal systems, researchers usually set all connections between neurons to be delayed. However, in real neural systems, communication between some connected neurons maybe instantaneous which make the connection be delay-free. Thus, we will discuss effects of partial time delay on detecting subthreshold signal of neuronal systems in this paper from the viewpoint of stochastic resonance. Partial time delay is introduced by assuming that time delay between two connected neurons to be τ (which is not zero) with a probability pdelay. With the obtained numerical results, we show that the ability of detecting subthreshold signal could be reduced or enhanced strongly depending on the values of pdelay and τ. Especially, the detecting ability could be enhanced by a wide range of time delay τ when pdelay is small. It means that partial time delay could have quite different influences on the ability of detecting subthreshold signal as comparing with the full time delay case (all connections are delayed). Meanwhile, we also exhibit that network topology, noise intensity and coupling strength could have great influences on the effects of partial time delay on detecting subthreshold signal of the neuronal systems.

Altered Spatial Organization of Dynamic Functional Network in Schizophrenia

Altered Spatial Organization of Dynamic Functional Network in Schizophrenia

Dynamic Functional Network Switching Rate in Autism Disorder

Dynamic Functional Network Switching Rate in Autism Disorder

Dynamic Functional Network Connectivity Associated With Emotions Evoked by Fast-Tempo Music

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Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease.

Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.

Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective

Background: Parkinson's disease (PD) is a highly heterogeneous disease, especially in the clinical characteristics and prognosis. The PD is divided into two subgroups: tremor-dominant phenotype and non-tremor-dominant phenotype. Previous studies reported abnormal changes between the two PD phenotypes by using the static functional connectivity analysis. However, the dynamic properties of brain networks between the two PD phenotypes are not yet clear. Therefore, we aimed to uncover the dynamic functional network connectivity (dFNC) between the two PD phenotypes at the subnetwork level, focusing on the temporal properties of dFNC and the variability of network efficiency.Methods: We investigated the resting-state functional MRI (fMRI) data from 29 tremor-dominant PD patients (PDTD), 25 non-tremor-dominant PD patients (PDNTD), and 20 healthy controls (HCs). Sliding window approach, k-means clustering, independent component analysis (ICA), and graph theory analysis were applied to analyze the dFNC. Furthermore, the relationship between alterations in the dynamic properties and clinical features was assessed.Results: The dFNC analyses identified four reoccurring states, one of them showing sparse connections (state I). PDTD patients stayed longer time in state I and showed increased FNC between BG and vSMN in state IV. Both PD phenotypes exhibited higher FNC between dSMN and FPN in state II and state III compared with the controls. PDNTD patients showed decreased FNC between BG and FPN but increased FNC in the bilateral FPN compared with both PDTD patients and controls. In addition, PDNTD patients exhibited greater variability in global network efficiency. Tremor scores were positively correlated with dwell time in state I along with increased FNC between BG and vSMN in state IV.Conclusions: This study explores the dFNC between the PDTD and PDNTD patients, which offers new evidence on the abnormal time-varying brain functional connectivity and their network destruction of the two PD phenotypes, and may help better understand the neural substrates underlying different types of PD.

Aberrant Static and Dynamic Functional Network Connectivity in Acute Mild Traumatic Brain Injury with Cognitive Impairment.

This study aimed to investigate differences in static and dynamic functional network connectivity (FNC) and explore their association with neurocognitive performance in acute mild traumatic brain injury (mTBI). A total of 76 patients with acute mTBI and 70 age-matched and sex-matched healthy controls were enrolled (age 43.79 ± 10.22 years vs. 45.63 ± 9.49 years; male/female: 34/42 vs. 38/32; all p > 0.05) and underwent resting-state functional magnetic resonance imaging (fMRI) scan (repetition time/echo time = 2000/30 ms, 230 volumes). Independent component analysis was conducted to evaluate static and dynamic FNC patterns on the basis of nine resting-state networks, namely, auditory network (AUDN), dorsal attention network (dAN), ventral attention network (vAN), default mode network (DMN), left frontoparietal network (LFPN), right frontoparietal network (RFPN), somatomotor network (SMN), visual network (VN), and salience network (SN). Spearman's correlation among aberrances in FNC values, and Montreal cognitive assessment (MoCA) scores was further measured in mTBI. Compared with controls, patients with mTBI showed wide aberrances of static FNC, such as reduced FNC in DMN-vAN and VN-vAN pairs. The mTBI patients exhibited aberrant dynamic FNC in state2, involving reduced FNC aberrance in the vAN with AUDN, VN with DMN and dAN, and SN with SMN and vAN. Reduced dFNC in the SN-vAN pair was negatively correlated with the MoCA score. Our findings suggest that aberrant static and dynamic FNC at the acute stage may contribute to cognitive symptoms, which not only may expand knowledge regarding FNC cognition relations from the static perspective but also from the dynamic perspective.