Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure

The Reorganization of Insular Subregions in Individuals with Below-Level Neuropathic Pain following Incomplete Spinal Cord Injury

Objective

To investigate the reorganization of insular subregions in individuals suffering from neuropathic pain (NP) after incomplete spinal cord injury (ISCI) and further to disclose the underlying mechanism of NP.

Method

The 3D high-resolution T1-weighted structural images and resting-state functional magnetic resonance imaging (rs-fMRI) of all individuals were obtained using a 3.0 Tesla MRI system. A comparative analysis of structure and function connectivity (FC) with insular subareas as seeds in 10 ISCI individuals with below-level NP (ISCI-P), 11 ISCI individuals without NP (ISCI-N), and 25 healthy controls (HCs) was conducted. Associations between the structural and functional alteration of insula subregions and visual analog scale (VAS) scores were analyzed using the Pearson correlation in SPSS 20.

Results

Compared with ISCI-N patients, when the left posterior insula as the seed, ISCI-P showed increased FC in right cerebellum VIIb and cerebellum VIII, Brodmann 37 (BA 37). When the left ventral anterior insula as the seed, ISCI-P indicated enhanced FC in right BA18 compared with ISCI-N patients. These increased FCs positively correlated with VAS scores. Relative to HCs, ISCI-P presented increased FC in the left hippocampus when the left dorsal anterior insula was determined as the seed. There was no statistical difference in the volume of insula subregions among the three groups.

Conclusion

Our study indicated that distinctive patterns of FC in each subregion of insula suggest that the insular subareas participate in the NP processing through different FC following ISCI. Further, insula subregions could serve as a therapeutic target for NP following ISCI.

Differential Reorganization of SMA Subregions After Stroke: A Subregional Level Resting-State Functional Connectivity Study

Background and Purpose: The human supplementary motor area (SMA) contains two functional subregions of the SMA proper and preSMA; however, the reorganization patterns of the two SMA subregions after stroke remain uncertain. Meanwhile, a focal subcortical lesion may affect the overall functional reorganization of brain networks. We sought to identify the differential reorganization of the SMA subregions after subcortical stroke using the resting-state functional connectivity (rsFC) analysis. Methods: Resting-state functional MRI was conducted in 25 patients with chronic capsular stroke exhibiting well-recovered global motor function (Fugl-Meyer score >90). The SMA proper and preSMA were identified by the rsFC-based parcellation, and the rsFCs of each SMA subregion were compared between stroke patients and healthy controls. Results: Despite common rsFC with the fronto-insular cortex (FIC), the SMA proper and preSMA were mainly correlated with the sensorimotor areas and cognitive-related regions, respectively. In stroke patients, the SMA proper and preSMA exhibited completely different functional reorganization patterns: the former showed increased rsFCs with the primary sensorimotor area and caudal cingulate motor area (CMA) of the motor execution network, whereas the latter showed increased rsFC with the rostral CMA of the motor control network. Both of the two SMA subregions showed decreased rsFC with the FIC in stroke patients; the preSMA additionally showed decreased rsFC with the prefrontal cortex (PFC). Conclusion: Although both SMA subregions exhibit functional disconnection with the cognitive-related areas, the SMA proper is implicated in the functional reorganization within the motor execution network, whereas the preSMA is involved in the functional reorganization within the motor control network in stroke patients.

Dynamic functional connectivity and graph theory metrics in a rat model of temporal lobe epilepsy reveal a preference for brain states with a lower functional connectivity, segregation and integration

Epilepsy is a neurological disorder characterized by recurrent epileptic seizures. The involvement of abnormal functional brain networks in the development of epilepsy and its comorbidities has been demonstrated by electrophysiological and neuroimaging studies in patients with epilepsy. This longitudinal study investigated changes in dynamic functional connectivity (dFC) and network topology during the development of epilepsy using the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy (TLE). Resting state functional magnetic resonance images (rsfMRI) of 20 IPKA animals and 7 healthy control animals were acquired before and 1, 3, 6, 10 and 16 weeks after status epilepticus (SE) under medetomidine anaesthesia using a 7 T MRI system. Starting from 17 weeks post-SE, hippocampal EEG was recorded to determine the mean daily seizure frequency of each animal. Dynamic FC was assessed by calculating the correlation matrices between fMRI time series of predefined regions of interest within a sliding window of 50 s using a step length of 2 s. The matrices were classified into 6 FC states, each characterized by a correlation matrix, using k-means clustering. In addition, several time-variable graph theoretical network metrics were calculated from the time-varying correlation matrices and classified into 6 states of functional network topology, each characterized by a combination of network metrics. Our results showed that FC states with a lower mean functional connectivity, lower segregation and integration occurred more often in IPKA animals compared to control animals. Functional connectivity also became less variable during epileptogenesis. In addition, average daily seizure frequency was positively correlated with percentage dwell time (i.e. how often a state occurs) in states with high mean functional connectivity, high segregation and integration, and with the number of transitions between states, while negatively correlated with percentage dwell time in states with a low mean functional connectivity, low segregation and low integration. This indicates that animals that dwell in states of higher functional connectivity, higher segregation and higher integration, and that switch more often between states, have more seizures.

Altered intra- and inter-network functional connectivity in patients with persistent postural-perceptual dizziness

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Intra-network functional connectivity (FC) in the right precuneus within the posterior default mode network was decreased in PPPD patients.

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Intra-network FC between the right precuneus and the bilateral precuneus and left premotor cortex was decreased.

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Intra-network FC between the right precuneus and bilateral corpus callosum was enhanced.

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Inter-network FC was increased between the visual network and auditory, sensorimotor networks.

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FC changes were negatively correlated with dizziness handicap inventory functional scores.

Background

Persistent postural-perceptual dizziness (PPPD) is a functional vestibular disorder characterized by persistent dizziness, unsteadiness, and non-spinning vertigo. It is the most common cause of chronic vestibular syndrome, but its pathogenesis is currently unclear. Recent studies have indicated that sensory integration may be altered in PPPD patients.

Objective

Using independent component analysis (ICA) combined with seed-based functional connectivity analysis, we aimed to analyze changes in brain network functional connectivity (FC) in PPPD patients during the resting state and to explore the underlying pathogenesis of PPPD, particularly the abnormal integration of multiple sensations.

Methods

Study subjects included 12 PPPD patients and 12 healthy controls and were recruited from January to August 2018. Detailed medical data were collected from all participants. Vestibular function, neurological and medical examinations were conducted to exclude other diseases associated with chronic dizziness. Functional MRI was performed on all subjects. ICA and seed-based functional connectivity analysis were performed to examine changes in intra- and inter-network FC in PPPD patients.

Results

In total, 13 independent components were identified using ICA. Compared with healthy controls, PPPD patients showed decreased intra-network FC in the right precuneus within the posterior default mode network. Moreover, seed-based functional connectivity analysis showed decreased intra-network FC between the right precuneus and the bilateral precuneus and left premotor cortex, and enhanced FC between the right precuneus and bilateral corpus callosum. With respect to the inter-network, FC in PPPD patients was increased between the occipital pole visual network and auditory, sensorimotor networks, as well as the lateral visual and auditory networks. Additional analyses showed that FC changes were negatively correlated with dizziness handicap inventory functional scores.

Conclusion

In PPPD patients, dysfunction in the precuneus may cause abnormalities in external environment monitoring and in posture and movement regulation. Compensatory strategies may then be adopted to maintain balance. At the local level, information exchange between the two cerebral hemispheres is enhanced via the corpus callosum. At the whole brain level, through enhancement of functional activities of the visual network, the integration of multiple sensations and the regulation of posture and movement are primarily driven by visual information.

Altered Dynamic Neural Activity in the Default Mode Network in Lung Cancer Patients After Chemotherapy

Background

Few studies have examined functional brain changes specifically associated with chemotherapy (CTx) in patients with lung cancer. This prospective longitudinal research aimed to explore the change in intrinsic brain activity by investigating patients with lung cancer after CTx.

Material/Methods

Sixteen patients and 20 healthy individuals were enrolled in this study. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dynamic amplitude of low-frequency fluctuation (dALFF), and dynamic regional homogeneity (dReHo) were computed. The group differences in resting state functional magnetic resonance imaging (rs-fMRI) parameters were compared. Alterations in the rs-fMRI parameters from before CTx to after CTx were assessed using the paired t-test. We performed correlation analyses between rs-fMRI parameters and Montreal Cognitive Assessment (MoCA) scores.

Results

We found statistically significant differences in MoCA scores before CTx and after CTx. Compared to the healthy group, rs-fMRI values decreased in the frontal regions as well as parietal regions compared to values before CTx. In addition, we found significantly decreased rs-fMRI values in the default-mode network (DMN) region of the brain before CTx compared to after CTx. We found no significant correlations between altered intrinsic activity values and MoCA scores.

Conclusions

The current study indicated that patients with lung cancer after CTx had decreased dynamic brain activity in the DMN region, and the DMN is vulnerable when patients undergoing CTx.

Enhanced positive functional connectivity strength in left-sided chronic subcortical stroke

Patients with stroke often exhibit evidence of abnormal functional connectivity (FC). However, whether and how anatomical distance affects FC at rest remains unclear in patients with chronic subcortical stroke. Eighty-six patients with chronic (more than six months post-onset) subcortical stroke (44 left-sided patients and 42 right-sided patients) with different degrees of functional recovery, and 75 matched healthy controls underwent resting-state functional magnetic resonance imaging scanning. Positive functional connectivity strength (FCS) was computed for each voxel in the brain using a data-driven whole-brain resting state FCS method, which was further divided into short- and long-range FCS. Compared with healthy controls, patients with left-sided infarctions exhibited stronger global- and long-range FCS in the left sensorimotor cortex (SMC), and no significant intergroup difference was found for short-range FCS. No significant differences were found between the patients with right-sided infarctions and healthy controls for global, long- and short-range FCS. These findings suggested that the positive FCS alteration was connection-distance dependent within patients with left-sided chronic subcortical stroke. Also, a positive correlation was found between the FCS in the left SMC and the accuracy of the Flanker test, reflecting a compensatory FCS alteration for altered attention and executive function abilities exhibited by those with left-sided stroke.

Dynamic Functional Connectivity Strength Within Different Frequency-Band in Schizophrenia

As a complex psychiatric disorder, schizophrenia is interpreted as a "dysconnection" syndrome, which is linked to abnormal integrations in between distal brain regions. Recently, neuroimaging has been widely adopted to investigate how schizophrenia affects brain networks. Furthermore, some studies reported frequency dependence of the abnormalities of functional network in schizophrenia, however, dynamic functional connectivity with frequency dependence is rarely used to explore changes in the whole brain of patients with schizophrenia (SZ). Therefore, in the current study, dynamic functional connectivity strength (dFCS) was performed on resting-state functional magnetic resonance data from 96 SZ patients and 121 healthy controls (HCs) at slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz) frequency bands and further assessed whether the altered dFCS was correlated to clinical symptoms in SZ patients. Results revealed that decreased dFCS of schizophrenia were found in salience, auditory, sensorimotor, visual networks, while increased dFCS in cerebellum, basal ganglia, and prefrontal networks were observed across different frequency bands. Specifically, the thalamus subregion of schizophrenic patients exhibited enhanced dynamic FCS in slow-5 and slow-4, while reduced in slow-3. Moreover, in slow-5 and slow-4, significant interaction effects between frequency and group were observed in the left calcarine cortex, the bilateral inferior orbitofrontal gyrus, and anterior cingulum cortex (ACC). Furthermore, the altered dFCS of insula, thalamus (THA), calcarine cortex, orbitofrontal gyrus, and paracentral lobule were partial correlated with clinical symptoms of SZ patients in slow-5 and slow-4 bands. These results demonstrate the abnormalities of dFCS in schizophrenia patients is rely on different frequency bands and may provide potential implications for exploring the neuropathological mechanism of schizophrenia.

Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia

Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, each of which is associated with its own limitations. Multimodal combinations can more effectively utilize various information, but previous multimodal research mostly focuses on extracting local features, rather than carrying out research based on network perspective. This study included 135 patients with schizophrenia and 148 sex- and age-matched healthy controls. Functional magnetic resonance imaging, diffusion tensor imaging, and structural magnetic resonance imaging data were used to construct the functional, anatomical, and morphological networks of each participant, respectively. These networks were used in combination with machine learning to identify more consistent biomarkers of brain connectivity and explore the relationships between different modalities. We found that although each modality had divergent connectivity biomarkers, the convergent pattern was that all were mostly located within the basal ganglia-thalamus-cortex circuit. Furthermore, using the biomarkers of these 3 modalities as a feature yielded the highest classification accuracy (91.75%, relative to a single modality), suggesting that the combination of multiple modalities could be effectively utilized to obtain complementary information regarding different mode networks; furthermore, this information could help distinguish patients. These findings provide direct evidence for the disconnection hypothesis of schizophrenia, suggesting that abnormalities in the basal ganglia-thalamus-cortex circuit can be used as a biomarker of schizophrenia.

Effect of emotional enhancement of memory on recollection process in young adults: the influence factors and neural mechanisms

Emotional enhancement of memory (EEM) is thought to modulate memory recollection rather than familiarity. However, the contributing factors and neural mechanisms are not well understood. To address these issues, we investigated how valence, arousal, and the amount of devoted attention influence the EEM effect on recollection. We also compared the topological properties among hippocampus- and perirhinal and entorhinal cortex-mediated emotional memory processing networks. Finally, we evaluated the correlations between emotional memory/EEM and inherent properties (i.e., amplitude of low-frequency fluctuation and node degree, efficiency, and betweenness) of the hippocampus and perirhinal and entorhinal cortices in 59 healthy young adults by resting-state functional magnetic resonance imaging. EEM was elicited by incidental encoding, negative images, and positive high-arousal images. The hippocampus was correlated with recollection sensitivity and EEMnegative-high-arousal. The emotional memory processing network mediated by the hippocampus had higher clustering coefficient, local efficiency, and normalized characteristic path length but lower normalized global efficiency than those mediated by the perirhinal and entorhinal cortices. The entorhinal cortex was associated with both recollection and familiarity sensitivity, but showed different correlation patterns. The perirhinal cortex was highly correlated with familiarity sensitivity of negative low-arousal stimuli. These results demonstrate that the EEM effect on memory recollection is influenced by valence, stimulus arousal, and amount of attention involved during encoding. Moreover, the hippocampus and perirhinal and entorhinal cortices play distinct roles in the recollection and familiarity of emotional memory and the EEM effect.

Abnormal dynamics of functional connectivity density in children with benign epilepsy with centrotemporal spikes

Converging evidence has shown the link between benign epilepsy with centrotemporal spikes (BECTS) and abnormal functional connectivity among distant brain regions. However, prior research in BECTS has not examined the dynamic changes in functional connectivity as networks form. We combined functional connectivity density (FCD) mapping and sliding windows correlation analyses, to fully capture the functional dynamics in patients with respect to the presence of interictal epileptic discharges (IEDs). Resting-state fMRI was performed in 43 BECTS patients and 28 healthy controls (HC). Patients were further classified into two subgroups, namely, IED (n = 20) and non-IED (n = 23) depending on the simultaneous EEG-fMRI recordings. The global dynamic FCD (dFCD) was measured using sliding window correlation. Then we quantified dFCD variability using their standard deviation. Compared with HC, patients with and without IEDs both showed invariable dFCD (decreased) among the orbital fontal cortex, anterior cingulate cortex and striatum, as well as variable dFCD (increased) in the posterior default mode network (P < 0.05, AlphaSim corrected). Correlation analysis indicated that the variable dFCD in precuneus was related to seizure onset age (P < 0.05, uncorrected). BECTS with IEDs showed variable dFCD in regions related to the typical seizure semiology. The abnormal patterns of fluctuating FCD in BECTS suggest that both active and chronic epileptic state may contribute to altered dynamics of functional connectivity associated with cognitive disturbances and developmental alterations. These findings highlight the importance of considering fluctuating dynamic neural communication among brain systems to deepen our understanding of epilepsy diseases.

Beyond the Epileptic Focus: Functional Epileptic Networks in Focal Epilepsy

Focal epilepsy can be conceptualized as a network disorder, and the functional epileptic network can be described as a complex system of multiple brain areas that interact dynamically to generate epileptic activity. However, we still do not fully understand the functional architecture of epileptic networks. We studied a cohort of 21 patients with extratemporal focal epilepsy. We used independent component analysis of functional magnetic resonance imaging (fMRI) data. In order to identify the epilepsy-related components, we examined the general linear model-derived electroencephalography-fMRI (EEG–fMRI) time courses associated with interictal epileptic activity as intrinsic hemodynamic epileptic biomarkers. Independent component analysis revealed components related to the epileptic time courses in all 21 patients. Each epilepsy-related component described a network of spatially distributed brain areas that corresponded to the specific epileptic network in each patient. We also provided evidence for the interaction between the epileptic activity generated at the epileptic network and the physiological resting state networks. Our findings suggest that independent component analysis, guided by EEG–fMRI epileptic time courses, have the potential to define the functional architecture of the epileptic network in a noninvasive way. These data could be useful in planning invasive EEG electrode placement, guiding surgical resections, and more effective therapeutic interventions.

Depression Affects Intrinsic Brain Activity in Patients With Mild Cognitive Impairment

Numerous observational studies have shown that depressive symptoms are common in individuals with mild cognitive impairment (MCI) who have a higher rate of progress to dementia. However, it is still uncertain whether there are any differences between MCI patients with and without depression symptom in their brain function activities. Here we have identified the brain function activity differences in two groups of MCI patients (with depression or without depression) using the resting state MRI (rsfMRI) measurements. 76 right-handed MCI subjects have been recruited in this study, including 27 MCI patients with depression symptom (MCID), 49 MCI patients without depression symptom (MCIND). Analyses based on 7 rsfMRI measurements, including four static measurements (ALFF, fALFF, PerAF, and ReHo) and three dynamic measurements (dALFF, dfALFF, and dReHo) have been used to explore the temporal variability of intrinsic brain activity. No significant differences in ALFF and dALFF between the two group were found. In the MCID group, fALFF decreased in temporal gyrus, frontal gyrus, inferior occipital gyrus, middle frontal gyrus and cerebellum, but increased in cuneus, calcarine, lingual; while PerAF increased in left parahippocampus. The differences of ReHo in the two groups was only found in cerebellum. Compared to MCIND group, dfALFF in MCID decreased in cuneus, occipital gyrus and calcarine, while dReHo in MCID increased in bilateral temporal gyrus, frontal gyrus, superior parietal gyrus, inferior parietal gyrus and precuneus. Our results may provide a better understanding in the relationship between the depressive symptoms and memory deficits.

Dysfunctional Dynamics of Intra- and Inter-network Connectivity in Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is characterized by the transient fluctuating cognition and recurrent visual hallucinations, which may be caused by disorders of the intrinsic brain network dynamics. However, little is known regarding the dynamic features of the brain network behind these symptoms in DLB. In the present study, the intra- and inter-brain network dynamics were explored on a time scale in 17 DLB and 20 healthy controls (HC) applying a sliding-window method followed by k-means clustering analysis. To further evaluate the impact of network dynamics on brain performance, the local and global efficiency of the brain network was calculated. Compared with HC, the dynamic functional connectivity variation matrix in DLB patients was represented by a mixed change of intra-network increase and inter-network decrease. DLB patients devoted more time to a negative connectivity pattern, which represents a state of functional separation. Furthermore, the local efficiency of DLB patients was significantly lower compared with HC. These observations indicate an altered dynamic variability and disorders to the time allocation of state sequences in DLB, which might result in a disturbance of the intricate brain network dynamic properties, thereby leading to a lack of integration and flexibility and an ineffective brain function. In conclusion, dynamic functional connectivity analysis could identify differences between DLB and HC, providing evidences for DLB diagnosis and contributing to the understanding of the widespread clinical features and complex treatment strategies in DLB patients.

Overrepresentation of epilepsy in children with type 1 diabetes is declining in a longitudinal population study in Finland

AIM

The aim was to determine temporal changes in increased risk of epilepsy among children with type 1 diabetes.

METHODS

The incidence of epilepsy up to age 15 in children with prior type 1 diabetes was analysed regarding the general Finnish child population using data from the Finnish nationwide hospital register. Type 1 diabetes and epilepsy were identified by the International Classification of Diseases 9th and 10th revision codes. Epilepsy was defined according to ILAE guidelines. The analyses were done using negative binomial regression models.

RESULTS

Preceding type 1 diabetes was diagnosed in 6162 (0.91%) of the 679 375 general children population. Incidence rate of new-onset epilepsy among children with type 1 diabetes was higher than in controls (140 vs 82 per 100 000 person-years at risk, respectively). The excess incidence diminished with time (P = 0.033 for diabetes to birth cohort interaction), from over twofold in birth cohort 1990-1993 [incidence rate ratio 2.2 (95% CI 1.7-2.9)] to 40% in birth cohort 1998-2000 [1.4 (95% CI 1.001-1.9)].

CONCLUSION

In a population study setting, children with type 1 diabetes had an increased, but slowly declining risk of developing epilepsy. Future research may elucidate the underlying mechanisms.

Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional Networks of the Brain

Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain.

Dynamic Alterations of Spontaneous Neural Activity in Parkinson's Disease: A Resting-State fMRI Study

Objective: To investigate the dynamic amplitude of low-frequency fluctuations (dALFFs) in patients with Parkinson's disease (PD) and healthy controls (HCs) and further explore whether dALFF can be used to test the feasibility of differentiating PD from HCs. Methods: Twenty-eight patients with PD and 28 demographically matched HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans and neuropsychological tests. A dynamic method was used to calculate the dALFFs of rs-fMRI data obtained from all subjects. The dALFF alterations were compared between the PD and HC groups, and the correlations between dALFF variability and disease duration/neuropsychological tests were further calculated. Then, the statistical differences in dALFF between both groups were selected as classification features to help distinguish patients with PD from HCs through a linear support vector machine (SVM) classifier. The classifier performance was assessed using a permutation test (repeated 5,000 times). Results: Significantly increased dALFF was detected in the left precuneus in patients with PD compared to HCs, and dALFF variability in this region was positively correlated with disease duration. Our results show that 80.36% (p < 0.001) subjects were correctly classified based on the SVM classifier by using the leave-one-out cross-validation method. Conclusion: Patients with PD exhibited abnormal dynamic brain activity in the left precuneus, and the dALFF variability could distinguish PD from HCs with high accuracy. Our results showed novel insights into the pathophysiological mechanisms of PD.

Reconfiguration of dynamic large-scale brain network functional connectivity in generalized tonic-clonic seizures

An increasing number of studies in patients with generalized tonic–clonic seizures (GTCS) have reported the alteration of functional connectivity (FC) in many brain networks. However, little is known about the underlying temporal variability of FC in large‐scale brain functional networks in patients. Recently, dynamic FC could provide novel insight into the physiological mechanisms in the brain. Here, we recruited 63 GTCS and 65 age‐ and sex‐matched healthy controls. Dynamic FC approaches were used to evaluate alterations in the temporal variability of FC in patients at the region‐ and network‐levels. In addition, two kinds of brain templates (>10² and > 10³ regions) and two kinds of temporal variability FC approaches were adopted to verify the stability of the results. Patients showed increased FC variability in regions of the default mode network (DMN), ventral attention network (VAN) and motor‐related areas. The DAN, VAN, and DMN illustrated enhanced FC variability at the within‐network level. In addition, increased FC variabilities between networks were found between the DMN and cognition‐related networks, including the VAN, dorsal attention network and frontal–parietal network in GTCS. Meanwhile, the alterations in FC variability were relatively consistent across different methods and templates. Therefore, the consistent alteration of FC variability would reflect a dynamic restructuring of the large‐scale brain networks in patients with GTCS. Overly frequent information communication among cognition‐related networks, especially in the DMN, might play a role in the epileptic activity and/or cognitive dysfunction in patients.

Altered dynamic functional connectivity in weakly-connected state in major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is accompanied by abnormal changes in dynamic functional connectivity (FC) among brain regions. The aim of this study is to investigate whether the abnormalities of dynamic FC in MDD are state-dependent (related to a specific connectivity state).

METHODS

We performed time-varying connectivity analysis on resting-state functional magnetic resonance imaging (rs-fMRI) of 49 MDD patients and 54 matched healthy controls (HCs). FC differences between groups in each connectivity state were analyzed and associations between disease severity and dynamics of aberrant FC were explored.

RESULTS

Two distinct connectivity states (i.e., weakly-connected and strongly-connected state) were identified. Compared to HCs, MDD patients were associated with increased mean dwell time and decreased FC between and within subnetworks in the weakly-connected state. Dynamics of reduced FC between cognitive control network and default mode network as well as within cognitive control network predicted individual differences in depression symptom severity.

CONCLUSIONS

Our findings suggested that the MDD-caused FC alterations mostly appeared in the weakly-connected state, which might contribute to clinical diagnosis of MDD.

SIGNIFICANCE

These findings provide new perspectives for understanding the state-dependent neurophysiological mechanisms in MDD.

The preference for future outcomes correlates with the temporal variability of functional connectivity among brain regions

People inevitably make decisions between short-term and long-term consequences across domains like education, health and economics. In this kind of decision, the tendency to discount the value of later-larger rewards with increasing delays is defined as delay discounting (DD). A recent review has suggested that three neural systems which respectively responsible for valuation, prospection and cognitive control (e.g., ventromedial prefrontal cortex [vmPFC], hippocampus, precuneus and dorsolateral prefrontal cortex [dlPFC]) could interact with each other flexibly to have impacts on DD. However, to date, there is little attention paid on the connection between the DD and the dynamic interaction of brain regions.To tackle this issue, we investigate the relationship between the DD and the time-varying connectivity among brain regions in two samples of young adults. Results in sample 1 found that the DD was negatively correlated with the temporal variability of functional connectivity [FC] between the vmPFC and precuneus, and between the vmPFC and the left superior frontal gyrus. And the temporal variabilities of FC between the ventral striatum and right dlPFC, and between the ventral striatum and dorsomedial prefrontal cortex were also negatively related to DD. Furthermore, the main results were well replicated and validated in another sample using different analysis parameters. Overall, our findings reveal that temporal fluctuation of FC within default mode and fronto-striatal circuits can favor for prospecting future, cognitive control and valuation of delayed incentives, and this flexible connectivity patterns generally have association with preference for future outcomes.

Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.

Altered dynamic global signal topography in antipsychotic-naive adolescents with early-onset schizophrenia

Schizophrenia (SCZ) is a severe neuropsychiatric disease associated with dysfunction of brain regions and networks. Recent, functional magnetic resonance imaging (fMRI) studies have determined that the global signal (GS) is an important source of the local neuronal activity. However, the dynamics of this effect in SCZ remains unknown. To address this issue, 39 drug-naive patients with early-onset schizophrenia (EOS) and 31 age-, gender- and education-matched healthy controls underwent resting-state fMRI scans. Dynamic functional connectivity (DFC) was employed to assess the dynamic patterns of the GS in EOS. Dynamic analysis demonstrated that the topography of the GS in EOS can be divided into five different states. In the state1, the GS mainly affected the sensory regions. In the state2, the GS mainly affected the default mode network (DMN). In the state3, the GS mainly affected the frontoparietal network and the cingulate-opercular network. In the state4, the GS mainly affected the sensory and subcortical regions. In the state5, the GS mainly affected the sensory regions and DMN. In particular, the changes in the cerebellum, putamen and supramarginal gyrus was inversely proportional to the clinical symptoms. Our findings demonstrate that the influence of the GS on brain networks is dynamic and changes of this relationship may associate with clinical behavior in SCZ.

Abnormal Brain Functional Connectivity Strength in the Overactive Bladder Syndrome: A Resting-State fMRI Study

OBJECTIVE

To investigate the whole-brain functional connectivity strength (FCS) of patients with the overactive bladder syndrome (OAB).

METHODS

This study investigates the changes of intrinsic whole brain functional connectivity pattern in OAB using FCS. We acquired resting-state fMRI data from 26 OAB patients and 28 healthy controls. FCS was used to compute the long-range and short-range FCS values for each voxel in the brain of each subject. The long or short-range FCS maps were compared between OAB patients and healthy controls. Pearson's correlation coefficients was also performed between abnormal FCS regions and clinical/psychometric scores in patients.

RESULTS

Compared with healthy control subjects, the OAB patients exhibited significantly decreased short-range FCS in the right medial superior frontal gyrus and bilateral anterior cingulate gyrus, and increased short-range FCS in the middle frontal gyrus, the precentral gyrus, and bilateral caudate nucleus. In addition, significantly decreased long-range FCS was found in bilateral middle cingulate gyrus and posterior cingulate gyrus. Furthermore, the abnormal FCS values in the right caudate nucleus showed significantly negative correlation with Self-Rating Depression Scale of OAB patients.

CONCLUSION

Patients with OAB have abnormal short-range and long-range FCS in brain regions associated with brain-bladder network. Our study provides new insights into the underlying brain network topology of OAB.

Intermittent Theta-Burst Stimulation Reverses the After-Effects of Contralateral Virtual Lesion on the Suprahyoid Muscle Cortex: Evidence From Dynamic Functional Connectivity Analysis

Contralateral intermittent theta burst stimulation (iTBS) can potentially improve swallowing disorders with unilateral lesion of the swallowing cortex. However, the after-effects of iTBS on brain excitability remain largely unknown. Here, we investigated the alterations of temporal dynamics of inter-regional connectivity induced by iTBS following continuous TBS (cTBS) in the contralateral suprahyoid muscle cortex. A total of 20 right-handed healthy subjects underwent cTBS over the left suprahyoid muscle motor cortex and then immediately afterward, iTBS was applied to the contralateral homologous area. All of the subjects underwent resting-state functional magnetic resonance imaging (Rs-fMRI) pre- and post-TBS implemented on a different day. We compared the static and dynamic functional connectivity (FC) between the post-TBS and the baseline. The whole-cortical time series and a sliding-window correlation approach were used to quantify the dynamic characteristics of FC. Compared with the baseline, for static FC measurement, increased FC was found in the precuneus (BA 19), left fusiform gyrus (BA 37), and right pre/post-central gyrus (BA 4/3), and decreased FC was observed in the posterior cingulate gyrus (PCC) (BA 29) and left inferior parietal lobule (BA 39). However, in the dynamic FC analysis, post-TBS showed reduced FC in the left angular and PCC in the early windows, and in the following windows, increased FC in multiple cortical areas including bilateral pre- and postcentral gyri and paracentral lobule and non-sensorimotor areas including the prefrontal, temporal and occipital gyrus, and brain stem. Our results indicate that iTBS reverses the aftereffects induced by cTBS on the contralateral suprahyoid muscle cortex. Dynamic FC analysis displayed a different pattern of alteration compared with the static FC approach in brain excitability induced by TBS. Our results provide novel evidence for us in understanding the topographical and temporal aftereffects linked to brain excitability induced by different TBS protocols and might be valuable information for their application in the rehabilitation of deglutition.

Altered Local and Large-Scale Dynamic Functional Connectivity Variability in Posttraumatic Stress Disorder: A Resting-State fMRI Study

Posttraumatic stress disorder (PTSD) is a psychiatric condition that can emerge after exposure to an exceedingly traumatic event. Previous neuroimaging studies have indicated that PTSD is characterized by aberrant resting-state functional connectivity (FC). However, few existing studies on PTSD have examined dynamic changes in resting-state FC related to network formation, interaction, and dissolution over time. In this study, we compared the dynamic resting-state local and large-scale FC between PTSD patients (n = 22) and healthy controls (HC; n = 22; conducted as standard deviation in resting-state local and large-scale FC over a series of sliding windows). Local dynamic FC was examined by calculating the dynamic regional homogeneity (dReHo), and large-scale dynamic FC (dFC) was investigated between regions with significant dReHo group differences. For the PTSD patients, we also investigated the relationship between symptom severity and dFC/dReHo. Our results showed that PTSD patients were characterized by I) increased dynamic (more variable) dReHo in left precuneus (PCu); II) increased dynamic (more variable) dFC between the left PCu and left insula; and III) decreased dFC between left PCu and left inferior parietal lobe (IPL), and decreased dFC between left PCu and right PCu. However, there is no significant correlation between the clinical indicators and dReHo/dFC after the family-wise-error (FWE) correction. These findings provided the initial evidence that PTSD is characterized by aberrant patterns of fluctuating communication within brain system such as the default mode network (DMN) and among different brain systems such as the salience network and the DMN.

Dysregulated Maturation of the Functional Connectome in Antipsychotic-Naïve, First-Episode Patients With Adolescent-Onset Schizophrenia

BACKGROUND

Schizophrenia has been conceptualized as a brain network disorder rooted in dysregulated neurodevelopmental processes. Recent neuroimaging studies revealed disrupted brain connectomic organization in adult schizophrenia patients. However, altered developmental trajectories of the functional connectome during the adolescent maturational stage have not been examined.

METHODS

The present study combined functional MRI with a graph theoretical approach to examine functional network topology and its age-related development in 39 medication naïve, first-episode patients with adolescent-onset schizophrenia and 31 matched controls (age range: 12-18 years).

RESULTS

Patients demonstrated impaired large-scale integration as reflected by reduced global efficiency as well as decreased regional nodal efficiency in highly integrative network hubs, most consistently the hippocampal formation and the precuneus. Furthermore, the left hippocampus showed opposite age-efficiency associations in healthy controls and patients, indicating dysregulated maturational trajectories in adolescent schizophrenia and a particular vulnerability of this region during early pathological attack.

CONCLUSIONS

The findings allow an integrative perspective on network and neurodevelopmental perspectives on schizophrenia, suggesting that dysregulated maturation of the functional connectome during adolescence might reflect an early marker for the disorder.

Brain networks of happiness: dynamic functional connectivity among the default, cognitive and salience networks relates to subjective well-being

Subjective well-being (SWB) reflects the cognitive and emotional evaluations of an individual's life and plays an important role in individual's success in health, work and social relationships. Although previous studies have revealed the spontaneous brain activity underlying SWB, little is known about the relationship between brain network interactions and SWB. The present study investigated the static and dynamic functional connectivity among large-scale brain networks during resting state functional magnetic resonance imaging (fMRI) in relation to SWB in two large independent datasets. The results showed that SWB is negatively correlated with static functional connectivity between the salience network (SN) and the anterior default mode network (DMN). Dynamic functional network connectivity (dFNC) analysis found that SWB is negatively correlated with the fraction of time that participants spent in a brain state characterized by weak cross-network connectivity (between the DMN, SN and frontal-parietal network [FPN]) and strong within-network connectivity (within the DMN and within the FPN). This connectivity profile may account for the good mental adaptability and flexible information communication of people with high levels of SWB. The dFNC results were well replicated with different analysis parameters and further validated in an independent sample. Taken together, these findings reveal that the dynamic interaction between networks involved in self-reflection, emotional regulation and cognitive control underlies SWB.

Anxiety and depression symptoms disrupt resting state connectivity in patients with genetic generalized epilepsies

OBJECTIVE

To analyze the lifetime trajectories in genetic generalized epilepsies (GGEs) and investigate the impact of symptoms of anxiety and depression on resting state functional connectivity (FC).

METHODS

Seventy-four GGE patients were classified according to the pharmacological response as seizure-free (12 patients), pharmacoresistant (PhR; 14 patients), and fluctuating (FL; 48 patients). Fifty-four subjects completed both the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), and 38 also underwent 3-T resting state functional magnetic resonance imaging. These 38 patients were subdivided into a positive group (13 patients with concurrent symptoms of depression and anxiety) and a negative group (21 asymptomatic patients and four with mild anxiety or depression symptoms). For FC analysis of resting state networks, we matched 38 healthy asymptomatic volunteers and used the UF2C toolbox running on MATLAB2017/SPM12.

RESULTS

The PhR group presented shorter duration of epilepsy (P = 0.016) and follow-up (P < 0.001) compared to the FL group. The PhR group showed higher levels (median = 20) on the BAI and BDI. Myoclonic seizures were the most difficult to control, as 50% of subjects persisted with them at last appointment, compared to generalized tonic-clonic seizures and absence seizures (<40%). Patients with concurrent anxiety and depression symptoms were 7.7 times more likely to exhibit pharmacoresistant seizures, although an increase of 1 year of epilepsy duration was associated with a decrease in the odds of presenting pharmacoresistance by a factor of 0.9. Overall, FC was altered between default mode network (DMN) and visuospatial/dorsal attention. However, only the positive group displayed abnormal FC between DMN and left executive control network, and between salience and visuospatial/dorsal attention.

SIGNIFICANCE

Our findings may help clinicians to have a better understanding of GGE clinical course and increase attention to the potential relationship of psychopathologies and brain connectivity.

Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder

BACKGROUND

Clinically distinguishing bipolar disorder (BD) from major depressive disorder (MDD) during depressive states is difficult. Neuroimaging findings suggested that patients with BD and those with MDD differed with respect to the gray matter volumes of their subcortical structures, especially in their striatum. However, whether these disorders have different effects on functionally striatal neuronal activity and connectivity is unclear.

METHODS

Arterial spin labeling and resting-state functional MRI was performed on 25 currently depressive patients with BD, 25 depressive patients with MDD, and 34 healthy controls (HCs). The functional properties of striatal neuronal activity (cerebral blood flow, CBF) and its functional connectivity (FC) were analyzed, and the results from the three groups were compared. The result of the multiple comparisons was corrected on the basis of the Gaussian Random Field theory.

RESULTS

The patients with BD and those with MDD both had higher CBF values than the HCs in the right caudate and right putamen. The hyper-metabolism of right striatum in BD patients was associated with increased average duration per depressive episode. The two disorders showed commonly increased FC between the striatum and dorsolateral prefrontal cortex, whereas the altered FC of the striatum with precuneus/cuneus was observed only in patients with BD.

CONCLUSIONS

Patients with BD and those with MDD had a common deficit in their prefrontal-limbic-striatal circuits. The altered striato-precuneus FC can be considered as a marker for the differentiation of patients with BD from those with MDD.

Spatial complexity of brain signal is altered in patients with generalized anxiety disorder

BACKGROUND

Is it healthy to be chaotic? Recent studies have argued that mental disorders are associated with more orderly neural activities, corresponding to a less flexible functional system. These conclusions were derived from altered temporal complexity. However, the relationship between spatial complexity and health is unknown, although spatial configuration is of importance for normal brain function.

METHODS

Based on resting-state functional magnetic resonance imaging data, we used Sample entropy (SampEn) to evaluate the altered spatial complexity in patients with generalized anxiety disorder (GAD; n = 47) compared to healthy controls (HCs; n = 38) and the relationship between spatial complexity and anxiety level.

RESULTS

Converging results showed increased spatial complexity in patients with GAD, indicating more chaotic spatial configuration. Interestingly, inverted-U relationship was revealed between spatial complexity and anxiety level, suggesting complex relationship between health and the chaos of human brain.

LIMITATIONS

Anxiety-related alteration of spatial complexity should be verified at voxel level in a larger sample and compared with results of other indices to clarify the mechanism of spatial chaotic of anxiety.

CONCLUSIONS

Altered spatial complexity in the brain of GAD patients mirrors inverted-U relationship between anxiety and behavioral performance, which may reflect an important characteristic of anxiety. These results indicate that SampEn is a good reflection of human health or trait mental characteristic.

Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan

The transition from early adulthood to the elder is marked by functional and structural brain transformations. Many previous studies examined the correlation between the functional connectivity (FC) and aging using resting-state fMRI. Results showed that the changes in FC are linked to aging as well as the cognitive ability changes. However, some researchers proposed that the FC is not static but dynamic changes during the resting-state fMRI scan. In this study, we examined the correlation between the resting-state dynamic functional network connectivity and age using resting scan data of 434 subjects. The results suggested: (a) age is negatively associated with variability of dynamic functional network connectivity state; (b) the dwell time of each age range spends in each state is different; (c) the dynamic graph metrics curve of each age ranges is different and 19-30 age range has the largest average global efficiency and average local efficiency; (d) some dynamic functional network connectivity measures were correlated to the certain cognitive ability. Overall, the results suggested the changes in dynamic functional network connectivity measures may be a characteristic of the aging process and in further investigations may provide a deep understanding of the aging process.

Modular Functional-Metabolic Coupling Alterations of Frontoparietal Network in Schizophrenia Patients

Background: Brain functional dysconnectivity, as well as altered network organization, have been demonstrated to occur in schizophrenia. Brain networks are increasingly understood to exhibit modular community structures, which provides advantages in robustness and functional adaptivity. The frontoparietal network (FPN) serves as an important functional module, and metabolic and functional alterations in the FPN are associated with the pathophysiology of schizophrenia. However, how intra-modular biochemical disruptions lead to inter-modular dysfunction of the FPN, remains unclear. In this study, we aim to investigate alterations in the modular functional-metabolic coupling of the FPN, in patients with schizophrenia. Methods: We combined resting-state functional magnetic resonance imaging (rs-fMRI) and magnetic resonance spectroscopy (MRS) technology and acquired multimodal neuroimaging data in 20 patients with schizophrenia and 26 healthy controls. For the MRS, the dorsolateral prefrontal cortex (DLPFC) region within the FPN was explored. Metabolites including gamma aminobutyric acid (GABA), N-aspart-acetyl (NAA) and glutamate + glutamine (Glx) were quantified, using LCModel software. A graph theoretical approach was applied for functional modular parcellation. The relationship between inter/intra-modular connectivity and metabolic concentration was examined using the Pearson correlation analysis. Moreover, correlations with schizophrenia symptomatology were investigated by the Spearman correlation analysis. Results: The functional topological network consisted of six modules in both subject groups, namely, the default mode, frontoparietal, central, hippocampus, occipital, and subcortical modules. Inter-modular connectivity between the frontoparietal and central modules, and the frontoparietal and the hippocampus modules was decreased in the patient group compared to the healthy controls, while the connectivity within the frontoparietal modular increased in the patient group. Moreover, a positive correlation between the frontoparietal and central module functional connectivity and the NAA in the DLPFC was found in the healthy control group (r = 0.614, p = 0.001), but not in the patient group. Significant functional dysconnectivity between the frontoparietal and limbic modules was correlated with the clinical symptoms of patients. Conclusions: This study examined the links between functional connectivity and the neuronal metabolic level in the DLPFC of SCZ. Impaired functional connectivity of the frontoparietal areas in SCZ, may be partially explained by a neurochemical-functional connectivity decoupling effect. This disconnection pattern can further provide useful insights in the cognitive and perceptual impairments of schizophrenia in future studies.

Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach

Most fMRI studies of the brain's intrinsic functional connectivity (FC) have assumed that this is static; however, it is now clear that it changes over time. This is particularly relevant in epilepsy, which is characterized by a continuous interchange between epileptic and normal brain states associated with the occurrence of epileptic activity. Interestingly, recurrent states of dynamic FC (dFC) have been found in fMRI data using unsupervised learning techniques, assuming either their sparse or non-sparse combination. Here, we propose an l₁-norm regularized dictionary learning (l₁-DL) approach for dFC state estimation, which allows an intermediate and flexible degree of sparsity in time, and demonstrate its application in the identification of epilepsy-related dFC states using simultaneous EEG-fMRI data. With this l₁-DL approach, we aim to accommodate a potentially varying degree of sparsity upon the interchange between epileptic and non-epileptic dFC states. The simultaneous recording of the EEG is used to extract time courses representative of epileptic activity, which are incorporated into the fMRI dFC state analysis to inform the selection of epilepsy-related dFC states. We found that the proposed l₁-DL method performed best at identifying epilepsy-related dFC states, when compared with two alternative methods of extreme sparsity (k-means clustering, maximum; and principal component analysis, minimum), as well as an l₀-norm regularization framework (l₀-DL), with a fixed amount of temporal sparsity. We further showed that epilepsy-related dFC states provide novel insights into the dynamics of epileptic networks, which go beyond the information provided by more conventional EEG-correlated fMRI analysis, and which were concordant with the clinical profile of each patient. In addition to its application in epilepsy, our study provides a new dFC state identification method of potential relevance for studying brain functional connectivity dynamics in general.

Dynamic Alterations in Spontaneous Neural Activity in Multiple Brain Networks in Subacute Stroke Patients: A Resting-State fMRI Study

Objective: To examine whether subacute stroke patients would exhibit abnormal dynamic characteristics of brain activity relative to healthy controls (HC) and to investigate whether the altered dynamic regional indexes were associated with clinical behavior in stroke patients. Methods: The dynamic amplitude of low-frequency fluctuations (dALFF) and dynamic regional homogeneity (dReHo) in 42 subacute stroke patients and 55 healthy controls were compared. Correlation analyses between dALFF and dReHo in regions showing significant intergroup differences and clinical scores (i.e., the National Institutes of Health Stroke Scale, Fugl-Meyer assessment and lesion volume size) were conducted in stroke patients. Receiver operating characteristic (ROC) curve analysis was used to determine the potential value of altered dynamic regional indexes to identify stroke patients. Results: Significantly dALFF in the bilateral cerebellum posterior lobe (CPL), ipsilesional superior parietal lobe, ipsilesional inferior temporal gyrus (ITG), the midline supplementary motor area (SMA), ipsilesional putamen and lentiform nucleus were detected in stroke patients compared to HC. Relative to the HC group, the stroke patients showed significant differences in dReHo in the contralesional rectal gyrus, contralesional ITG, contralesional pons, ipsilesional middle frontal gyrus (MFG). Significant correlations between dALFF variability in midline SMA and Fugl-Meyer assessment (FMA) scores or between dReHo variability in the ipsilesional MFG and FMA scores were detected in stroke patients. Furthermore, the ROC curve revealed that dynamic ALFF at SMA and ReHo at ipsilesional MFG might have the potential to distinguish stroke patients. Conclusion: The pattern of intrinsic brain activity variability is altered in stroke patients compared with HC, and dynamic ALFF/ReHo might be potential tools to assess stroke patients' motor function.

Increased Temporal Dynamics of Intrinsic Brain Activity in Sensory and Perceptual Network of Schizophrenia

Schizophrenic subject is thought as a self-disorder patient related with abnormal brain functional network. It has been hypothesized that self-disorder is associated with the deficient functional integration of multisensory body signals in schizophrenic subjects. To further verify this assumption, 53 chronic schizophrenic subjects and 67 healthy subjects were included in this study and underwent resting-state functional magnetic resonance imaging. The data-driven methods, whole-brain temporal variability of fractional amplitude of low-frequency fluctuations and regional homogeneity (ReHo), were used to investigate dynamic local functional connectivity and dynamic local functional activity changes in schizophrenic subjects. Patients with schizophrenia exhibited increased temporal variability ReHo and fractional amplitude of low-frequency fluctuations across time windows within sensory and perception network (such as occipital gyrus, precentral and postcentral gyri, superior temporal gyrus, and thalamus). Critically, the increased dynamic ReHo of thalamus is significantly correlated with positive and total symptom of schizophrenic subjects. Our findings revealed that deficit in sensory and perception functional networks might contribute to neural physiopathology of self-disorder in schizophrenic subjects.

Altered Brain Signal Variability in Patients With Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is characterized by a chronic, continuous symptom of worry and exaggerated startle response. Although functional abnormality in GAD has been widely studied using functional magnetic resonance imaging (fMRI), the dynamic signatures of GAD are not fully understood. As a vital index of brain function, brain signal variability (BSV) reflects the capacity of state transition of neural activities. In this study, we recruited 47 patients with GAD and 38 healthy controls (HCs) to investigate whether or not BSV is altered in patients with GAD by measuring the standard deviation of fMRI signal of each voxel. We found that patients with GAD exhibited decreased BSV in widespread regions including the visual network, sensorimotor network, frontoparietal network, limbic system, and thalamus, indicating an inflexible brain state transfer pattern in these systems. Furthermore, the correlation between BSV and trait anxiety score was prone to be positive in patients with GAD but negative in HCs. The opposite relationships between BSV and anxiety level in the two groups indicate that the brain with moderate anxiety level may stay in the most stable rather than in the flexible state. As the first study of BSV in GAD, we revealed extensively decreased BSV in patients with GAD similar to that in other mental disorders but with a non-linear relationship between BSV and anxiety level indicating a novel neurodynamic mechanism of the anxious brain.

Disrupted Brain Network Efficiency and Decreased Functional Connectivity in Multi-sensory Modality Regions in Male Patients With Alcohol Use Disorder

Background: Recent studies have reported altered efficiency in selective brain regions and functional networks in patients with alcohol use disorder (AUD). Inefficient processing can reflect or arise from the disorganization of information being conveyed from place to place. However, it remains unknown whether the efficiency and functional connectivity are altered in large-scale topological organization of patients with AUD.

Methods: Resting-state functional magnetic resonance imaging (rsfMRI) data were experimentally collected from 21 right-handed males with AUD and 21 right-handed, age-, gender- and education-matched healthy controls (HCs). Graph theory was used to investigate inter-group differences in the topological parameters (global and nodal) of networks and inter-regional functional connectivity. Correlations between group differences in network properties and clinical variables were also investigated in the AUD group.

Results: The brain networks of the AUD group showed decreased global efficiency when compared with the HC group. Besides, increased nodal efficiency was found in the left orbitofrontal cortex (OFC), while reduced nodal efficiency was observed in the right OFC, right fusiform gyrus (FFG), right superior temporal gyrus, right inferior occipital gyrus (IOG), and left insula. Moreover, hypo-connectivity was detected between the right dorsolateral prefrontal cortex (DLPFC) and right superior occipital gyrus (SOG) in the AUD group when compared with the HC group. The nodal efficiency of the left OFC was associated with cognitive performance in the AUD group.

Conclusions: AUD patients exhibited alterations in brain network efficiency and functional connectivity, particularly in regions linked to multi-sensory modalities. These disrupted topological properties may help to obtain a more comprehensive understanding of large-scale brain network activity. Furthermore, these data provide a potential neural mechanism of impaired cognition in individuals with AUD.

Altered Brain Structure and Functional Connectivity of Primary Visual Cortex in Optic Neuritis

Previous studies have revealed brain adaptations to injury that occurs in optic neuritis (ON); however, the mechanisms underlying the functional connectivity (FC) and gray matter volume (GMV) changes in ON have not been clarified. Here, 51 single attack ON patients and 45 recurrent attacks ON patients were examined using structural MRI and resting-state functional MRI (RS-fMRI), and compared to 49 age- and gender-matched healthy controls (HC). FC analysis with a seed in primary visual cortex (V1 area) was used to assess the differences among three groups. Whole brain GMV was assessed using voxel-based morphometry (VBM). Correlation analyses were performed between FC results, structural MRI and clinical variables. We found positive correlations between the Paced Auditory Serial Addition Test (PASAT) score and FC in V1 area with bilateral middle frontal gyrus. Disease duration is significantly negatively related to FC in V1 area with the left inferior parietal lobule. Compared to the HC, single attack ON patients were found to have decreased FC values in the frontal, temporal lobes, right inferior occipital gyrus, right insula, right inferior parietal lobule, and significant increased FC values in the left thalamus. Recurrent attacks ON patients had the same pattern with single attack ON. No significant differences were found in brain GMV among three groups. This study provides the imaging evidence that impairment and compensation of V1 area connectivity coexist in ON patients, and provides important insights into the underlying neural mechanisms of ON.

Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification

High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance.

Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures

Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.

Resting state connectivity in neocortical epilepsy: The epilepsy network as a patient-specific biomarker

OBJECTIVE

Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients.

METHODS

The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort.

RESULTS

All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level.

CONCLUSIONS

The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations.

SIGNIFICANCE

We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.

Juvenile myoclonic epilepsy has hyper dynamic functional connectivity in the dorsolateral frontal cortex

PURPOSE

Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach.

METHODS

Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density.

RESULTS

The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results.

CONCLUSION

Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.

Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder

Time-invariant resting-state functional connectivity studies have illuminated the crucial role of the right anterior insula (rAI) in prominent social impairments of autism spectrum disorder (ASD). However, a recent dynamic connectivity study demonstrated that rather than being stationary, functional connectivity patterns of the rAI vary significantly across time. The present study aimed to explore the differences in functional connectivity in dynamic states of the rAI between individuals with ASD and typically developing controls (TD). Resting-state functional magnetic resonance imaging data obtained from a publicly available database were analyzed in 209 individuals with ASD and 298 demographically matched controls. A k-means clustering algorithm was utilized to obtain five dynamic states of functional connectivity of the rAI. The temporal properties, frequency properties, and meta-analytic decoding were first identified in TD group to obtain the characteristics of each rAI dynamic state. Multivariate analysis of variance was then performed to compare the functional connectivity patterns of the rAI between ASD and TD groups in obtained states. Significantly impaired connectivity was observed in ASD in the ventral medial prefrontal cortex and posterior cingulate cortex, which are two critical hubs of the default mode network (DMN). States in which ASD showed decreased connectivity between the rAI and these regions were those more relevant to socio-cognitive processing. From a dynamic perspective, these findings demonstrate partially impaired resting-state functional connectivity patterns between the rAI and DMN across states in ASD, and provide novel insights into the neural mechanisms underlying social impairments in individuals with ASD.

Functional Connectivity Within the Executive Control Network Mediates the Effects of Long-Term Tai Chi Exercise on Elders' Emotion Regulation

Previous research has identified the effects of tai chi exercise on elders' executive control or on their emotion regulation. However, few works have attempted to reveal the relationships between tai chi, executive control, and emotion regulation in the same study. The current resting-state study investigated whether the impact of tai chi on elders' emotion regulation was mediated by the resting-state functional connectivity within the executive control network. A total of 26 elders with long-term tai chi experience and 26 demographically matched healthy elders were recruited. After the resting-state scan, both groups were required to complete a series of questionnaires, including the Five Facets Mindfulness Questionnaire (FFMQ), and a sequential decision task, which offered an index of the subjects' emotion-regulation ability by calculating how their emotional response could be affected by the objective outcomes of their decisions. Compared to the control group, the tai chi group showed higher levels of non-judgment of inner experiences (a component of the FFMQ), stronger emotion-regulation ability, and a weaker resting-state functional connectivity between the dorsolateral prefrontal cortex (DLPFC) and the middle frontal gyrus (MFG). Moreover, the functional connectivity between the DLPFC and the MFG in the tai chi group fully mediated the impact of non-judgment of inner experience on their emotion-regulation ability. These findings highlighted that the modulation of non-judgment of inner experience on long-term tai chi practitioners' emotion regulation was achieved through decreased functional connectivity within the executive control network.

Disrupted Coupling Between the Spontaneous Fluctuation and Functional Connectivity in Idiopathic Generalized Epilepsy

Purpose: The purpose of this study was to comprehensively evaluate alterations of resting-state spontaneous brain activity in patients with idiopathic generalized epilepsy (IGE) and its subgroups [juvenile myoclonic epilepsy (JME) and generalized tonic-clonic seizures (GTCS)]. Methods: Resting state functional magnetic resonance imaging (fMRI) data were acquired from 60 patients with IGE and 60 healthy controls (HCs). Amplitude of low frequency fluctuation (ALFF), global functional connectivity density (gFCD), local FCD (lFCD), and long range FCD (lrFCD) were used to evaluate spontaneous brain activity in the whole brain. Moreover, the coupling between ALFF and FCDs (gFCD, lFCD, and lrFCD) was analyzed on both voxel-wise and subject-wise levels. Two-sample t-tests were used to analyze the difference in ALFF, FCDs and coupling on a subject-wise level between the two groups. Nonparametric permutation tests were used to evaluate differences in coupling on a voxel-wise level. Key findings: Patients with IGE and its subgroups showed reduced ALFF, gFCD and lrFCD in posterior regions of the default mode network (DMN). In addition, decreased ALFF and increased coupling with FCD were found in the cerebellum, while decreased coupling was observed in the bilateral pre- and postcentral gyrus in IGE compared with the coupling in HCs. Similar findings were found in the analysis between each of the two subgroups of IGE (JME and GTCS) and HCs, and JME patients had increased coupling in the cerebellum and bilateral middle occipital gyrus compared with coupling in the GTCS patients. Significance: This study demonstrated a multifactor abnormality of the DMN in IGE and emphasized that the abnormality in the cerebellum was associated with dysfunctional motor symptoms during seizures and might participate in the regulation of GSWDs in IGE.