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

Brain network alterations of mesial temporal lobe epilepsy with cognitive dysfunction following anterior temporal lobectomy

The aims of this study were to investigate the brain network connectivity alterations of intractable unilateral mesial temporal lobe epilepsy (MTLE) with cognitive dysfunction before and after anterior temporal lobectomy (ATL) using resting-state functional magnetic resonance imaging (rs-fMRI) study and to further observe the correlation between the brain network connectivity with cognitive performance. Fourteen patients with unilateral left MTLE before and after ATL were compared with thirty healthy controls (HCs) on functional connectivity (FC) between resting-state networks (RSNs). The correlation between the neuropsychological tests of patients and abnormal FC was further investigated. When compared with the HCs, patients before surgery showed significantly changed FC between special RSNs. No difference of FC was found between each RSN when patients were compared with the HCs after surgery. Compared with patients before surgery, patients after surgery showed significantly decreased FC between RSNs. Abnormal FC between RSNs significantly correlated with Montreal Cognitive Assessment (MoCA) scores. Our study suggested that dynamic alterations of RSN after ATL in unilateral MTLE may be closely related with seizure generating. However, unchanged FC between RSN before and after ATL may be closely related with cognitive performance. The present findings may help us understand the feature of brain network alterations in patients with left MTLE who became seizure-free following ATL.

Cognitive decline and white matter changes in mesial temporal lobe epilepsy

Noninvasive imaging plays a pivotal role in assessing the brain structural and functional changes in presurgical mesial temporal lobe epilepsy (MTLE) patients. Our goal was to study the relationship between the changes of cerebral white matter (WM) and cognitive functions in MTLE patients.Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) MRI were performed on 24 right-handed MTLE patients (12 with left MTLE and 12 with right MTLE) and 12 matching healthy controls. Gray matter (GM), WM, and whole brain (WB) volumes were measured with VBM while fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured with TBSS. All patients and controls also underwent Montreal Cognitive Assessment (MoCA) before MRI.WM volume and the ratio of WM volume versus WB volume were significantly lower in MTLE patients compared with controls. WM volume in MTLE patients had a positive correlation with MoCA score (r = 0.71, P < .001) and a negative correlation with the duration of epilepsy (r = -0.693, P < .001). Volumetric differences were mainly located in the corpus callosum, uncinate fasciculus, inferior longitudinal fasciculus, and superior longitudinal fasciculus. FA of both left MTLE and right MTLE groups was significantly decreased, while MD, AD, and RD were significantly increased. Most left MTLE patients showed bilateral WM fiber tract changes versus ipsilateral changes for right MTLE patients.Changes in DTI parameters and WM volume were found in MTLE patients and more ipsilateral changes were seen with right-sided MTLE. Cognitive changes of MTLE patients were found to be correlated with the changes in WM structure. These findings not only provide useful information for lateralization of the seizure focus but can also be used to explain functional connectivity disorders which may be an important physiological basis for cognitive changes in patients with MTLE.

Transdiagnostic and diagnosis-specific dynamic functional connectivity anchored in the right anterior insula in major depressive disorder and bipolar depression

Dysfunctional and abnormal functional connectivity in the right anterior insula (rAI) may underlie the pathophysiology of depression episode in bipolar disorder (BD) and of major depressive disorder (MDD). In this study, we examined the dynamic functional connectivity (dFC) of the rAI of 30 patients with BD, 30 patients with MDD, and 30 healthy controls. In the functional separation of rAI, the right dorsal AI (rdAI) and ventral AI (rvAI) were defined as seed regions. Sliding-window correlation of rAI subregions was implemented to measure the variance of dFC. BD and MDD shared abnormality in dFC, such as the decreased dFC between the rvAI and right ventrolateral prefrontal cortex. Others were disorder-specific and included MDD-related increases in dFC between the rvAI and right precuneus, temporal pole, and left dorsolateral prefrontal cortex. This observation is in stark contrast to BD-related increases in the dFC between the rdAI and left inferior parietal lobule and right middle occipital gyrus. The abnormal dFC of rAI shared by BD and MDD supports the importance of rAI in the common pathophysiology of these disorders. Meanwhile, disorder-specific abnormalities that attribute to the dorsal and ventral divisions of rAI can be used as biomarkers to differentiate BD from MDD.

Altered Spontaneous Regional Brain Activity in the Insula and Visual Areas of Professional Traditional Chinese Pingju Opera Actors

Recent resting-state fMRI studies have revealed neuroplastic alterations after long-term training. However, the neuroplastic changes that occur in professional traditional Chinese Pingju opera actors remain unclear. Twenty professional traditional Chinese Pingju opera actors and 20 age-, sex-, and handedness-matched laymen were recruited. Resting-state fMRI was obtained by using an echo-planar imaging sequence, and two metrics, amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), were utilized to assess spontaneous neural activity during resting state. Our results demonstrated that compared with laymen, professional traditional Chinese Pingju actors exhibited significantly decreased ALFF in the bilateral calcarine gyrus and cuneus; decreased ReHo in the bilateral superior occipital and calcarine gyri, cuneus, and right middle occipital gyrus; and increased ReHo in the left anterior insula. In addition, no significant association was found between spontaneous neural activity and Pingju opera training duration. Overall, the changes observed in spontaneous brain activity in professional traditional Chinese Pingju opera actors may indicate their superior performance of multidimensional professional skills, such as music and face perception, dancing, and emotional representation.

Static and dynamic connectomics differentiate between depressed patients with and without suicidal ideation

Neural circuit dysfunction underlies the biological mechanisms of suicidal ideation (SI). However, little is known about how the brain's "dynome" differentiate between depressed patients with and without SI. This study included depressed patients (n = 48) with SI, without SI (NSI), and healthy controls (HC, n = 30). All participants underwent resting-state functional magnetic resonance imaging. We constructed dynamic and static connectomics on 200 nodes using a sliding window and full-length time-series correlations, respectively. Specifically, the temporal variability of dynamic connectomic was quantified using the variance of topological properties across sliding window. The overall topological properties of both static and dynamic connectomics further differentiated between SI and NSI, and also predicted the severity of SI. The SI showed decreased overall topological properties of static connectomic relative to the HC. The SI exhibited increases in overall topological properties with regard to the dynamic connectomic when compared with the HC and the NSI. Importantly, combining the overall topological properties of dynamic and static connectomics yielded mean 75% accuracy (all p < .001) with mean 71% sensitivity and mean 75% specificity in differentiating between SI and NSI. Moreover, these features may predict the severity of SI (mean r = .55, all p < .05). The findings revealed that combining static and dynamic connectomics could differentiate between SI and NSI, offering new insight into the physiopathological mechanisms underlying SI. Furthermore, combining the brain's connectome and dynome may be considered a neuromarker for diagnostic and predictive models in the study of SI.

Changes in Dynamics Within and Between Resting-State Subnetworks in Juvenile Myoclonic Epilepsy Occur at Multiple Frequency Bands

Time-varying connectivity analyses have indicated idiopathic generalized epilepsy (IGE) could cause significant abnormalities in dynamic connective pattern within and between resting-state sub-networks (RSNs). However, previous studies mainly focused on the IGE-induced dynamic changes of functional connectivity (FC) in specific frequency band (0.01–0.08 Hz or 0.01–0.15 Hz), ignoring the changes across different frequency bands. Here, 24 patients with IGE characterized by juvenile myoclonic epilepsy (JME) and 24 matched healthy controls were studied using a data-driven frequency decomposition approach and a sliding window approach. The RSN dynamics, including intra-RSN dynamics and inter-RSN dynamics, was further calculated to investigate dynamic FC changes within and between RSNs in JME patients in each decomposed frequency band. Compared to healthy controls, JME patients not only showed frequency-dependent decrease in intra-RSN dynamics within multiple RSNs but also exhibited fluctuant alterations in inter-RSN dynamics among several RSNs over different frequency bands especially in the ventral/dorsal attention network and the subcortical network. Additionally, the disease severity had significantly negative correlations with both intra-RSN dynamics within the subcortical network and inter-RSN dynamics between the subcortical network and the default network at the lower frequency band (0.0095–0.0195 Hz). These results suggested that abnormal dynamic FC within and between RSNs in JME occurs at multiple frequency bands and the lower frequency band (0.0095–0.0195 Hz) was probably more sensitive to JME-caused dynamic FC abnormalities. The frequency subdivision and selection are potentially helpful for detecting particular changes of dynamic FC in JME.

Central Neural Correlates During Inhibitory Control in Lifelong Premature Ejaculation Patients

Lifelong premature ejaculation (LPE) is a common male sexual dysfunction. Lack of active control for rapid ejaculation brought great distress to sexual harmony and even fertility. Previous neurophysiology studies revealed an ejaculation-related control mechanism in the brain. However, it remains unclear whether this inhibitory network is altered in LPE patients. The present study investigated the central inhibitory network function of LPE patients by using stop signal task (SST)-related functional magnetic resonance imaging (fMRI) and resting-state functional connectivity (FC) analysis. The results showed no difference in task-related behavioral performance or neural activation during response inhibition between LPE patients and controls. However, LPE patients showed a significantly different correlation pattern between the stop signal reaction time (SSRT) and left inferior frontal gyrus (IFG) activation during successful inhibition, in which a typical negative correlation between SSRT and the activation was completely disappeared in patients. In addition, using the left IFG as a seed, patients showed weaker FC between the seed and two areas (left dentate nucleus (DN) and right frontal pole) compared with controls. These data suggest that LPE patients have an abnormal brain control network, which may contribute to the reduced central control of rapid ejaculation. This study provides new insights into the neural mechanism of LPE involving the central inhibitory network, which may offer an underlying intervention target for future treatment.

Information processing speed in multiple sclerosis: Relevance of default mode network dynamics

Objective

To explore the added value of dynamic functional connectivity (dFC) of the default mode network (DMN) during resting-state (RS), during an information processing speed (IPS) task, and the within-subject difference between these conditions, on top of conventional brain measures in explaining IPS in people with multiple sclerosis (pwMS).

Methods

In 29 pwMS and 18 healthy controls, IPS was assessed with the Letter Digit Substitution Test and Stroop Card I and combined into an IPS-composite score. White matter (WM), grey matter (GM) and lesion volume were measured using 3 T MRI. WM integrity was assessed with diffusion tensor imaging. During RS and task-state fMRI (i.e. symbol digit modalities task, IPS), stationary functional connectivity (sFC; average connectivity over the entire time series) and dFC (variation in connectivity using a sliding window approach) of the DMN was calculated, as well as the difference between both conditions (i.e. task-state minus RS; ΔsFC-DMN and ΔdFC-DMN). Regression analysis was performed to determine the most important predictors for IPS.

Results

Compared to controls, pwMS performed worse on IPS-composite (p = 0.022), had lower GM volume (p < 0.05) and WM integrity (p < 0.001), but no alterations in sFC and dFC at the group level. In pwMS, 52% of variance in IPS-composite could be predicted by cortical volume (β = 0.49, p = 0.01) and ΔdFC-DMN (β = 0.52, p < 0.01). After adding dFC of the DMN to the model, the explained variance in IPS increased with 26% (p < 0.01).

Conclusion

On top of conventional brain measures, dFC from RS to task-state explains additional variance in IPS. This highlights the potential importance of the DMN to adapt upon cognitive demands to maintain intact IPS in pwMS.

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Problems with information processing speed occur often in multiple sclerosis (MS)

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Dynamics in brain communication can reflect information transfer within the brain

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With fMRI, dynamic communication can be measured, which increases upon task demands

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This increase in dynamics explains information processing speed in MS

Aberrant brain functional connectome in patients with obstructive sleep apnea

OBJECTIVE

Obstructive sleep apnea (OSA) is accompanied by widespread abnormal spontaneous regional activity related to cognitive deficits. However, little is known about the topological properties of the functional brain connectome of patients with OSA. This study aimed to use the graph theory approaches to investigate the topological properties and functional connectivity (FC) of the functional connectome in patients with OSA, based on resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Forty-five male patients with newly diagnosed untreated severe OSA and 45 male good sleepers (GSs) underwent a polysomnography (PSG), clinical evaluations, and rs-fMRI scans. The automated anatomical labeling (AAL) atlas was used to construct the functional brain connectome. The topological organization and FC of brain functional networks in patients with OSA were characterized using graph theory methods and investigated the relationship between functional network topology and clinical variables.

RESULTS

Both the patients with OSA and the GSs exhibited high-efficiency "small-world" network attributes. However, the patients with OSA exhibited decreased σ, γ, Eglob; increased Lp, λ; and abnormal nodal centralities in several default-mode network (DMN), salience network (SN), and central executive network (CEN) regions. However, the patients with OSA exhibited abnormal functional connections between the DMN, SN, and CEN. The disrupted FC was significantly positive correlations with the global network metrics γ and σ. The global network metrics were significantly correlated with the Epworth Sleepiness Scale (ESS) score, Montreal Cognitive Assessment (MoCA) score, and oxygen desaturation index.

CONCLUSION

The findings suggest that the functional connectome of patients with OSA exhibited disrupted functional integration and segregation, and functional disconnections of the DMN, SN, and CEN. The aberrant topological attributes may be associated with disrupted FC and cognitive functions. These topological abnormalities and disconnections might be potential biomarkers of cognitive impairments in patients with OSA.

Dynamic Network Analysis Reveals Altered Temporal Variability in Brain Regions after Stroke: A Longitudinal Resting-State fMRI Study

Recent fMRI studies have demonstrated that resting-state functional connectivity (FC) is of nonstationarity. Temporal variability of FC reflects the dynamic nature of brain activity. Exploring temporal variability of FC offers a new approach to investigate reorganization and integration of brain networks after stroke. Here, we examined longitudinal alterations of FC temporal variability in brain networks after stroke. Nineteen stroke patients underwent resting fMRI scans across the acute stage (within-one-week after stroke), subacute stage (within-two-weeks after stroke), and early chronic stage (3-4 months after stroke). Nineteen age- and sex-matched healthy individuals were enrolled. Compared with the controls, stroke patients exhibited reduced regional temporal variability during the acute stages, which was recovered at the following two stages. Compared with the acute stage, the subacute stage exhibited increased temporal variability in the primary motor, auditory, and visual cortices. Across the three stages, the temporal variability in the ipsilesional precentral gyrus (PreCG) was increased first and then reduced. Increased temporal variability in the ipsilesional PreCG from the acute stage to the subacute stage was correlated with motor recovery from the acute stage to the early chronic stage. Our results demonstrated that temporal variability of brain network might be a potential tool for evaluating and predicting motor recovery after stroke.

Brain network alteration in patients with temporal lobe epilepsy with cognitive impairment

The aims of this study were to investigate the brain network alternation in patients with temporal lobe epilepsy (TLE) with and without cognitive impairment (CI) using functional magnetic resonance imaging (fMRI) and to further explore the potential mechanisms of epilepsy-induced CI. Forty patients with TLE and nineteen healthy controls (HCs) were recruited for this study. All participants received the Montreal Cognitive Assessment (MoCA) test, and the patients were divided into CI (n=21) and cognitive nonimpairment (CNI) groups (n=19) according to MoCA performance. Functional connectivity (FC) differences of resting state networks (RSNs) were compared among the CI, CNI, and HC groups. Correlation between FC and MoCA scores was also observed. When compared with the HC group, significantly decreased FC between medial visual network (mVN) and left frontoparietal network (lFPN) as well as between visuospatial network (VSN) and the anterior default mode network (aDMN) were revealed in both CI and CNI groups. In addition, significantly decreased FC between lFPN and executive control network (ECN) and increased FC between ECN and sensorimotor-related network (SMN) were found in CNI and CI groups, respectively. When compared with the CNI group, the CI group exhibited significant increased FC between ECN and lFPN as well as between ECN and SMN. Moreover, in the CI group, FC between ECN and lFPN showed negative correlation with attention scores. Our findings suggested that cognitive networks are different from epileptic networks, and the increased FC between RSNs closely related to cognitive function changes may help us to further understand the mechanism of CI in TLE.

A method to assess randomness of functional connectivity matrices

BACKGROUND

Functional magnetic resonance imaging (fMRI) allows for the measurement of functional connectivity of the brain. In this context, graph theory has revealed distinctive non-random connectivity patterns. However, the application of graph theory to fMRI often utilizes non-linear transformations (absolute value) to extract edge representations.

NEW METHOD

In contrast, this work proposes a mathematical framework for the analysis of randomness directly from functional connectivity assessments. The framework applies random matrix theory to the analysis of functional connectivity matrices (FCMs). The developed randomness measure includes its probability density function and statistical testing method.

RESULTS

The utilized data comes from a previous study including 603 healthy individuals. Results demonstrate the application of the proposed method, confirming that whole brain FCMs are not random matrices. On the other hand, several FCM submatrices did not significantly test out of randomness.

COMPARISON WITH EXISTING METHODS

The proposed method does not replace graph theory measures; instead, it assesses a different aspect of functional connectivity. Features not included in graph theory are small numbers of nodes, testing submatrices of an FCM and handling negative as well as positive edge values.

CONCLUSION

The random test not only determines randomness, but also serves as an indicator of smaller non-random patterns within a non-random FCM. Outcomes suggest that a lower order model may be sufficient as a broad description of the data, but it also indicates a loss of information. The developed randomness measure assesses a different aspect of randomness from that of graph theory.

Gender differences in functional connectivities between insular subdivisions and selective pain-related brain structures

Background

The incidence of pain disorders in women is higher than in men, making gender differences in pain a research focus. The human insular cortex is an important brain hub structure for pain processing and is divided into several subdivisions, serving different functions in pain perception. Here we aimed to examine the gender differences of the functional connectivities (FCs) between the twelve insular subdivisions and selected pain-related brain structures in healthy adults.

Methods

Twenty-six healthy males and 11 age-matched healthy females were recruited in this cross-sectional study. FCs between the 12 insular subdivisions (as 12 regions of interest (ROIs)) and the whole brain (ROI-whole brain level) or 64 selected pain-related brain regions (64 ROIs, ROI-ROI level) were measured between the males and females.

Results

Significant gender differences in the FCs of the insular subdivisions were revealed: (1) The FCs between the dorsal dysgranular insula (dId) and other brain regions were significantly increased in males using two different techniques (ROI-whole brain and ROI-ROI analyses); (2) Based on the ROI-whole brain analysis, the FC increases in 4 FC-pairs were observed in males, including the left dId - the right median cingulate and paracingulate/ right posterior cingulate gyrus/ right precuneus, the left dId - the right median cingulate and paracingulate, the left dId - the left angular as well as the left dId - the left middle frontal gyrus; (3) According to the ROI-ROI analysis, increased FC between the left dId and the right rostral anterior cingulate cortex was investigated in males.

Conclusion

In summary, the gender differences in the FCs of the insular subdivisions with pain-related brain regions were revealed in the current study, offering neuroimaging evidence for gender differences in pain processing.

Trial registration

ClinicalTrials.gov, NCT02820974. Registered 28 June 2016.

Alterations of Resting-State Static and Dynamic Functional Connectivity of the Dorsolateral Prefrontal Cortex in Subjects with Internet Gaming Disorder

Internet gaming disorder (IGD), a major behavior disorder, has gained increasing attention. Recent studies indicate altered resting-state static functional connectivity (FC) of the dorsolateral prefrontal cortex (DLPFC) in subjects with IGD. Whereas static FC often provides information on functional changes in subjects with IGD, investigations of temporal changes in FC between the DLPFC and the other brain regions may shed light on the dynamic characteristics of brain function associated with IGD. Thirty subjects with IGD and 30 healthy controls (HCs) matched for age, gender and education status were recruited. Using the bilateral DLPFC as seeds, static FC and dynamic FC maps were calculated and compared between groups. Correlations between alterations in static FC and dynamic FC and clinical variables were also investigated within the IGD group. The IGD group showed significantly lower static FC between the right DLPFC and the left rolandic operculum while higher static FC between the right DLPFC and the left pars triangularis when compared to HCs. The IGD group also had significantly decreased dynamic FC between the right DLPFC and the left insula, right putamen and left precentral gyrus, and increased dynamic FC in the left precuneus. Moreover, the dynamic FC between the right DLPFC and the left insula was negatively correlated with the severity of IGD. Dynamic FC can be used as a powerful supplement to static FC, helping us obtain a more comprehensive understanding of large-scale brain network activity in IGD and put forward new ideas for behavioral intervention therapy for it.

Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment

Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FC may contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method.

Differential patterns of dynamic functional connectivity variability of striato-cortical circuitry in children with benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECTS) is characterized by abnormal (static) functional interactions among cortical and subcortical regions, regardless of the active or chronic epileptic state. However, human brain connectivity is dynamic and associated with ongoing rhythmic activity. The dynamic functional connectivity (dFC) of the distinct striato-cortical circuitry associated with or without interictal epileptiform discharges (IEDs) are poorly understood in BECTS. Herein, we captured the pattern of dFC using sliding window correlation of putamen subregions in the BECTS (without IEDs, n = 23; with IEDs, n = 20) and sex- and age-matched healthy controls (HCs, n = 28) during rest. Furthermore, we quantified dFC variability using their standard deviation. Compared with HCs and patients without IEDs, patients with IEDs exhibited excessive variability in the dorsal striatal-sensorimotor circuitry related to typical seizure semiology. By contrast, excessive stability (decreased dFC variability) was found in the ventral striatal-cognitive circuitry (p < .05, GRF corrected). In addition, correlation analysis revealed that the excessive variability in the dorsal striatal-sensorimotor circuitry was related to highly frequent IEDs (p < .05, uncorrected). Our finding of excessive variability in the dorsal striatal-sensorimotor circuitry could be an indication of increased sensitivity to regional fluctuations in the epileptogenic zone, while excessive stability in the ventral striatal-cognitive circuitry could represent compensatory mechanisms that prevent or postpone cognitive impairments in BECTS. Overall, the differentiated dynamics of the striato-cortical circuitry extend our understanding of interactions among epileptic activity, striato-cortical functional architecture, and neurocognitive processes in BECTS.

Abnormal intrinsic brain functional network dynamics in Parkinson's disease

See Nieuwhof and Helmich (doi:10.1093/brain/awx267 ) for a scientific commentary on this article .

Parkinson’s disease is a neurodegenerative disorder characterized by nigrostriatal dopamine depletion. Previous studies measuring spontaneous brain activity using resting state functional magnetic resonance imaging have reported abnormal changes in broadly distributed whole-brain networks. Although resting state functional connectivity, estimating temporal correlations between brain regions, is measured with the assumption that intrinsic fluctuations throughout the scan are stable, dynamic changes of functional connectivity have recently been suggested to reflect aspects of functional capacity of neural systems, and thus may serve as biomarkers of disease. The present work is the first study to investigate the dynamic functional connectivity in patients with Parkinson’s disease, with a focus on the temporal properties of functional connectivity states as well as the variability of network topological organization using resting state functional magnetic resonance imaging. Thirty-one Parkinson’s disease patients and 23 healthy controls were studied using group spatial independent component analysis, a sliding windows approach, and graph-theory methods. The dynamic functional connectivity analyses suggested two discrete connectivity configurations: a more frequent, sparsely connected within-network state (State I) and a less frequent, more strongly interconnected between-network state (State II). In patients with Parkinson’s disease, the occurrence of the sparsely connected State I dropped by 12.62%, while the expression of the more strongly interconnected State II increased by the same amount. This was consistent with the altered temporal properties of the dynamic functional connectivity characterized by a shortening of the dwell time of State I and by a proportional increase of the dwell time pattern in State II. These changes are suggestive of a reduction in functional segregation among networks and are correlated with the clinical severity of Parkinson’s disease symptoms. Additionally, there was a higher variability in the network global efficiency, suggesting an abnormal global integration of the brain networks. The altered functional segregation and abnormal global integration in brain networks confirmed the vulnerability of functional connectivity networks in Parkinson’s disease.

Neural and metabolic basis of dynamic resting state fMRI

Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.

Intrinsic functional connectivity variance and state-specific under-connectivity in autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with altered brain connectivity. Previous neuroimaging research demonstrates inconsistent results, particularly in studies of functional connectivity in ASD. Typically, these inconsistent findings are results of studies using static measures of resting-state functional connectivity. Recent work has demonstrated that functional brain connections are dynamic, suggesting that static connectivity metrics fail to capture nuanced time-varying properties of functional connections in the brain. Here we used a dynamic functional connectivity approach to examine the differences in the strength and variance of dynamic functional connections between individuals with ASD and healthy controls (HCs). The variance of dynamic functional connections was defined as the respective standard deviations of the dynamic functional connectivity strength across time. We utilized a large multicenter dataset of 507 male subjects (209 with ASD and 298 HC, from 6 to 36 years old) from the Autism Brain Imaging Data Exchange (ABIDE) to identify six distinct whole-brain dynamic functional connectivity states. Analyses demonstrated greater variance of widespread long-range dynamic functional connections in ASD (P < 0.05, NBS method) and weaker dynamic functional connections in ASD (P < 0.05, NBS method) within specific whole-brain connectivity states. Hypervariant dynamic connections were also characterized by weaker connectivity strength in ASD compared with HC. Increased variance of dynamic functional connections was also related to ASD symptom severity (ADOS total score) (P < 0.05), and was most prominent in connections related to the medial superior frontal gyrus and temporal pole. These results demonstrate that greater intraindividual dynamic variance is a potential biomarker of ASD. Hum Brain Mapp 38:5740-5755, 2017. © 2017 Wiley Periodicals, Inc.

Weaker Functional Connectivity Strength in Patients with Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is related to cognitive impairments and increased risk for dementia. Neuroimaging studies have demonstrated T2DM-related brain structural and functional changes which are partly associated to the cognitive decline. However, few studies focused on the early neuroimaging findingsin T2DM patients. In this study, a data-driven whole-brain resting state functional connectivity strength (rsFCS) methodwas used to evaluate resting functional changes in 53 T2DM patients compared with 55 matched healthy controls (HCs), and to detect the associations between the rsFCSchanges and cognitive functions in T2DM patients. The T2DM patients exhibited weaker long-range rsFCS in the right insula and weaker short-range rsFCS in the right supramarginalgyrus (SG) compared with the HCs. Additionally, seed-based functional connectivity (FC) analysis revealed weaker FC between the right insula and the bilateral superior parietal lobule (SPL), and between the right SG and the bilateral supplementary motor area (SMA)/right SPL in T2DM patientscompared with the HCs. In T2DM patients, negative correlation was found between the long-range rsFCS in the right insula and HbA1c levels; and the FC between the right SG and the bilateral SMA negatively correlated with TMT-A scores. Our results indicated that the rsFCS alteration occurredbefore obvious cognitive deficits in T2DM patients, which might be helpful for understanding the neuromechanism of cognitive declines in T2DM patients.

Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State Analysis

Normal aging is typically characterized by abnormal resting-state functional connectivity (FC), including decreasing connectivity within networks and increasing connectivity between networks, under the assumption that the FC over the scan time was stationary. In fact, the resting-state FC has been shown in recent years to vary over time even within minutes, thus showing the great potential of intrinsic interactions and organization of the brain. In this article, we assumed that the dynamic FC consisted of an intrinsic dynamic balance in the resting brain and was altered with increasing age. Two groups of individuals (N = 36, ages 20–25 for the young group; N = 32, ages 60–85 for the senior group) were recruited from the public data of the Nathan Kline Institute. Phase randomization was first used to examine the reliability of the dynamic FC. Next, the variation in the dynamic FC and the energy ratio of the dynamic FC fluctuations within a higher frequency band were calculated and further checked for differences between groups by non-parametric permutation tests. The results robustly showed modularization of the dynamic FC variation, which declined with aging; moreover, the FC variation of the inter-network connections, which mainly consisted of the frontal-parietal network-associated and occipital-associated connections, decreased. In addition, a higher energy ratio in the higher FC fluctuation frequency band was observed in the senior group, which indicated the frequency interactions in the FC fluctuations. These results highly supported the basis of abnormality and compensation in the aging brain and might provide new insights into both aging and relevant compensatory mechanisms.

Brain structural and functional dissociated patterns in schizophrenia

BACKGROUND

Although previous studies found that aberrations in gray matter volume (GMV) and global functional connectivity density (gFCD) are important characteristics of schizophrenia, to the best of our knowledge no study to date has investigated the associations between the spatial distribution patterns of GMV and gFCD alterations. We investigated pattern changes in gFCD and GMV among patients with schizophrenia and their associated spatial distributions.

METHODS

Ninety-five patients with schizophrenia and 93 matched healthy controls underwent structural and resting-state functional MRI scanning to assess gFCD and GMV.

RESULTS

We found that gFCD increased in the subcortical regions (caudate, pallidum, putamen, and thalami) and limbic system (left hippocampus and parahippocampus), and decreased in the posterior parieto-occipito-temporal cortices (postcentral gyri, occipital cortex, temporo-occipital conjunction, and inferior parietal lobule), in patients with schizophrenia. By contrast, we found decreased GMV in brain regions including the frontal, parietal, temporal, occipital, cingulate cortices, and the insular, striatum, thalamus in these patients. Increased gFCD primarily occurred in subcortical regions including the basal ganglia and some regions of the limbic system. Decreased gFCD appeared primarily in the cortical regions. There were no statistically significant correlations between changes in gFCD and GMV, and their spatial distribution patterns, in different regions.

CONCLUSIONS

Our findings indicate that gFCD and GMV are both perturbed in multiple brain regions in schizophrenia. gFCD and GMV consistently decreased in the cortical regions, with the exception of the Supplementary Motor Area (SMA). However, in the sub-cortical regions, the alterations of gFCD and GMV showed the opposite pattern, with increased gFCD and decreased GMV simultaneously observed in these regions. Overall, our findings suggest that structural and functional alterations appear to contribute independently to the neurobiology of schizophrenia.

Relationship of a common OXTR gene variant to brain structure and default mode network function in healthy humans

A large body of research suggests that oxytocin receptor (OXTR) gene polymorphisms may influence both social behaviors and psychiatric conditions related to social deficits, such as autism spectrum disorders (ASDs), schizophrenia, and mood and anxiety disorders. However, the neural mechanism underlying these associations is still unclear. Relative to controls, patients with these psychiatric conditions show differences in brain structure, and in resting state fMRI (rs-fMRI) signal synchronicity among default mode network (DMN) regions (also known as functional connectivity). We used a stepwise imaging genetics approach in 328 healthy young adults to test the hypothesis that 10 SNPs in OXTR are associated with differences in DMN synchronicity and structure of some of the associated brain regions. As OXTR effects may be sex-dependent, we also tested whether our findings were modulated by sex. OXTR rs2254298 A allele carriers had significantly lower rsFC with PCC in a cluster extending from the right fronto-insular cortex to the putamen and globus pallidus, and in bilateral dorsal anterior cingulate cortex (dACC) compared to individuals with the GG genotype; all observed effects were found only in males. Moreover, compared to the male individuals with GG genotype ofrs2254298, the male A allele carriers demonstrated significantly thinner cortical gray matter in the bilateral dACC. Our findings suggest that there may be sexually dimorphic mechanisms by which a naturally occurring variation of the OXTR gene may influence brain structure and function in DMN-related regions implicated in neuropsychiatric disorders.