The Temporal Instability of Resting State Network Connectivity in Intractable Epilepsy

Intrinsic brain network stability during kainic acid-induced epileptogenesis

OBJECTIVE

Altered intrinsic brain networks have been revealed in patients with epilepsy and are strongly associated with network reorganization in the latent period. However, the development and reliability of intrinsic brain networks in the early period of epileptogenesis are not well understood. The current study aims to fill this gap by investigating the test-retest reliability of intrinsic brain networks in the early stage of epileptogenesis.

METHODS

We used the rat intrahippocampal kainic acid model of mesial temporal lobe epilepsy. Three sessions of resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired over a 2-week period from 9 sham control rats and 12 rats that later developed spontaneous epilepsy (KA). A group independent component analysis (GICA) approach was used to identify the intrinsic brain networks. Both within and between networks were identified, and test-retest reliability was assessed using the intraclass correlation coefficient (ICC).

RESULTS

Our results showed good-to-excellent within-network stability of resting-state functional brain connectivity in most intrinsic brain networks in sham control rats and in the KA group, except for frontal cortex (FCN) and hippocampal networks (HPN). Further analysis of the between networks showed an increase in variation in the KA brain compared to the sham controls.

SIGNIFICANCE

Overall, our study demonstrated a "moderately stable" phase of the intrinsic brain network in a 2-week latent period window, with an altered between- and within-network connectome feature.

PLAIN LANGUAGE SUMMARY

This fMRI study explored how brain connectivity changes in healthy animals compared to animals in the latent period of epilepsy. We found that functional connectivity increased during the latent period compared to the control group, and this increase persisted across all tested sessions. Additionally, brain networks became less stable in the epilepsy group, particularly in the frontal cortex and hippocampus. These observations provide further insight into how brain networks change and persist during the early stages of epileptogenesis.

Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study

Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study. Group independent component analysis (GICA) was used to determine target resting-state networks, namely, executive control network (ECN), default mode network (DMN), sensorimotor network (SMN) and dorsal attentional network (DAN). Sliding window and k-means clustering analyses were used to identify dFNC patterns and temporal properties in each subject. We compared sFNC and dFNC differences between the StD and HCs groups. Relationships between changes in FNC strength, temporal properties, and neurophysiological score were evaluated by Spearman's correlation analysis. The sFNC analysis revealed decreased FNC strength in StD individuals, including the DMN-CEN, DMN-SMN, SMN-CEN, and SMN-DAN. In the dFNC analysis, 4 reoccurring FNC patterns were identified. Compared to HCs, individuals with StD had increased mean dwell time and fraction time in a weakly connected state (state 4), which is associated with self-focused thinking status. In addition, the StD group demonstrated decreased dFNC strength between the DMN-DAN in state 2. sFNC strength (DMN-ECN) and temporal properties were correlated with HAMD-17 score in StD individuals (all p < 0.01). Our study provides new evidence on aberrant time-varying brain activity and large-scale network interaction disruptions in StD individuals, which may provide novel insight to better understand the underlying neuropathological mechanisms.

Atypical dynamic network reconfiguration and genetic mechanisms in patients with major depressive disorder

BACKGROUND

Brain dynamics underlie complex forms of flexible cognition or the ability to shift between different mental modes. However, the precise dynamic reconfiguration based on multi-layer network analysis and the genetic mechanisms of major depressive disorder (MDD) remains unclear.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data were acquired from the REST-meta-MDD consortium, including 555 patients with MDD and 536 healthy controls (HC). A time-varying multi-layer network was constructed, and dynamic modular characteristics were used to investigate the network reconfiguration. Additionally, partial least squares regression analysis was performed using transcriptional data provided by the Allen Human Brain Atlas (AHBA) to identify genes associated with atypical dynamic network reconfiguration in MDD.

RESULTS

In comparison to HC, patients with MDD exhibited lower global and local recruitment coefficients. The local reduction was particularly evident in the salience and subcortical networks. Spatial transcriptome correlation analysis revealed an association between gene expression profiles and atypical dynamic network reconfiguration observed in MDD. Further functional enrichment analyses indicated that positively weighted reconfiguration-related genes were primarily associated with metabolic and biosynthetic pathways. Additionally, negatively enriched genes were predominantly related to programmed cell death-related terms.

CONCLUSIONS

Our findings offer robust evidence of the atypical dynamic reconfiguration in patients with MDD from a novel perspective. These results offer valuable insights for further exploration into the mechanisms underlying MDD.

Current State of Functional MRI in the Presurgical Planning of Brain Tumors

Surgical resection of brain tumors is challenging because of the delicate balance between maximizing tumor removal and preserving vital brain functions. Functional MRI (fMRI) offers noninvasive preoperative mapping of widely distributed brain areas and is increasingly used in presurgical functional mapping. However, its impact on survival and functional outcomes is still not well-supported by evidence. Task-based fMRI (tb-fMRI) maps blood oxygen level-dependent (BOLD) signal changes during specific tasks, while resting-state fMRI (rs-fMRI) examines spontaneous brain activity. rs-fMRI may be useful for patients who cannot perform tasks, but its reliability is affected by tumor-induced changes, challenges in data processing, and noise. Validation studies comparing fMRI with direct cortical stimulation (DCS) show variable concordance, particularly for cognitive functions such as language; however, concordance for tb-fMRI is generally greater than that for rs-fMRI. Preoperative fMRI, in combination with MRI tractography and intraoperative DCS, may result in improved survival and extent of resection and reduced functional deficits. fMRI has the potential to guide surgical planning and help identify targets for intraoperative mapping, but there is currently limited prospective evidence of its impact on patient outcomes. This review describes the current state of fMRI for preoperative assessment in patients undergoing brain tumor resection. Keywords: MR-Functional Imaging, CNS, Brain/Brain Stem, Anatomy, Oncology, Functional MRI, Functional Anatomy, Task-based, Resting State, Surgical Planning, Brain Tumor © RSNA, 2023.

Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy

OBJECTIVE

We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state.

METHODS

We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC).

RESULTS

Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs.

CONCLUSIONS

Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs.

SIGNIFICANCE

Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.

Abnormal Static and Dynamic Functional Connectivity in Left and Right Temporal Lobe Epilepsy

Objective

Temporal lobe epilepsy (TLE) can be conceptualized as a network disease. However, the network characteristics in lateralization remain controversial.

Methods

In this study, resting-state functional MRI scans were acquired from 53 TLE patients [22 with left-side TLE (LTLE) and 31 with right-side TLE (RTLE)] and 37 matched healthy controls. We focused on the characteristics of static and dynamic functional connectivity, including static connectivity patterns and topological properties, as well as temporal properties of the dynamic connectivity state and the variability of the dynamic connectivity and network topological organization. Correlation analyses were conducted between abnormal static and dynamic properties and cognitive performances.

Results

The static functional connectivity analysis presented a significantly decreased cortical-cortical connectivity pattern and increased subcortical-cortical connectivity pattern in RTLE. The global-level network in RTLE showed a significant decrease in global efficiency. The dynamic functional connectivity analysis revealed that RTLE patients exhibited aberrant connectivity states, as well as increased variability in the subcortical-cortical connectivity. The global-level network in RTLE revealed increased variance in global efficiency and local efficiency. The static or dynamic functional connectivity in LTLE did not show any significant abnormalities. The altered dynamic properties were associated with worsening cognitive performance in language and conceptual thinking by the TLE patients.

Conclusion

Our findings demonstrated the presence of abnormalities in the static and dynamic functional connectivity of TLE patients. RTLE patients exhibited more pronounced aberrant connectivity patterns and topological properties, which might represent a mechanism for reconfiguration of brain networks in RTLE patients. These observations extended our understanding of the pathophysiological network mechanisms of TLE.

Decreased Intrinsic Neural Timescales in Mesial Temporal Lobe Epilepsy

It is well established that epilepsy is characterized by the destruction of the information capacity of brain network and the interference with information processing in regions outside the epileptogenic focus. However, the potential mechanism remains poorly understood. In the current study, we applied a recently proposed approach on the basis of resting-state fMRI data to measure altered local neural dynamics in mesial temporal lobe epilepsy (mTLE), which represents how long neural information is stored in a local brain area and reflect an ability of information integration. Using resting-state-fMRI data recorded from 36 subjects with mTLE and 36 healthy controls, we calculated the intrinsic neural timescales (INT) of neural signals by summing the positive magnitude of the autocorrelation of the resting-state brain activity. Compared to healthy controls, the INT values were significantly lower in patients in the right orbitofrontal cortices, right insula, and right posterior lobe of cerebellum. Whereas, we observed no statistically significant changes between patients with long- and short-term epilepsy duration or between left-mTLE and right-mTLE. Our study provides distinct insight into the brain abnormalities of mTLE from the perspective of the dynamics of the brain activity, highlighting the significant role of intrinsic timescale in understanding neurophysiological mechanisms. And we postulate that altered intrinsic timescales of neural signals in specific cortical brain areas may be the neurodynamic basis of cognitive impairment and emotional comorbidities in mTLE patients.

Dynamic connectivity predicts acute motor impairment and recovery post-stroke

Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivity was estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later. A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patients with and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.67 ± 0.01). In contrast, patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on the variability of dynamic connectivity (0.83 ± 0.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortex and putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivity configurations (0.89 ± 0.01) in combination with the initial impairment. Here, better recovery was linked to a shorter time spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acute impairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery.

Regional and remote connectivity patterns in focal extratemporal lobe epilepsy

Background

Focal epilepsy accounts for most epilepsy cases, and frontal lobe epilepsy (FLE) accounts for the largest proportion of cases of extratemporal epilepsy syndrome. The epileptogenic zone is usually not easy to locate, contributing to a lack of imaging studies. The objective of this study was to evaluate functional connectivity patterns to explore the underlying pathological mechanisms of this disorder.

Methods

Forty-three patients with focal extratemporal epilepsy [mean age ± standard deviation (SD): 29.51±8.04 years, 19 males] and the same number of healthy controls (mean age ± SD: 29.56±8.02 years, 19 males) were recruited to undergo functional magnetic resonance imaging. Mean regional homogeneity (ReHo) was measured, and regions showing significant alterations in ReHo in patients were identified to examine functional connectivity (FC). In particular, FC within the default mode network (DMN) in patients was analyzed.

Results

Patients with extratemporal lobe epilepsy showed significantly higher ReHo in the bilateral precentral gyrus, and lower ReHo in frontal-cerebellum regions than healthy controls [P<0.05, Gaussian random field (GRF)-corrected]. FC analysis based on regions of interest showed significantly higher connectivity in the frontoparietal-insula region and lowered FC in the frontal-cerebellum regions (P<0.05, GRF-corrected). Altered FC within DMN was also demonstrated (P<0.05, GRF-corrected).

Conclusions

Analyses of ReHo and FC based on regions of interest suggest epilepsy-related neural networks are located mainly in frontal regions in extratemporal lobe epilepsy. These findings reveal disruptions of interactions and connectivity of large-scale neural networks and frontotemporal-cerebellar regions, suggesting connectivity-based pathophysiology.

Drug-resistant epilepsy: From multiple hypotheses to an integral explanation using preclinical resources

Drug-resistant epilepsy affects approximately one-third of the patients with epilepsy. The pharmacoresistant condition in epilepsy is mainly explained by six hypotheses. In addition, several experimental models have been used to understand the mechanisms involved in pharmacoresistant epilepsy and to identify novel therapies to control this condition. However, the global prevalence of this disease persists without changes. Several factors can explain this situation. First of all, the pharmacoresistant epilepsy is explained by different and independent hypotheses. Each hypothesis indicates specific mechanisms to explain the drug-resistant condition in epilepsy. However, there are different findings suggesting common mechanisms between the different hypotheses. Other important situation is that the experimental models designed for the screening of drugs with potential anticonvulsant effect do not consider factors such as age, gender, type of epilepsy, and comorbid disorders. The present review focuses on indicating the limitations for each hypothesis and the relationships among them. The relevance to consider central and peripheral phenomena associated with the drug-resistant condition in different types of epilepsy is also indicated. The necessity to establish a global hypothesis that integrates all the phenomena associated with the pharmacoresistant epilepsy is proposed. This article is part of the Special Issue "NEWroscience 2018".

Brain functional connectivity patterns in focal cortical dysplasia related epilepsy

OBJECTIVE

Focal cortical dysplasia (FCD) appears to be strongly associated with intractable epilepsy. Although patients with FCD are candidates for epilepsy surgery, gray matter structural abnormalities can extend beyond the primary lesion, which makes surgery less effective. The objective of this study was to evaluate functional connectivity patterns in epilepsy associated with FCD to explore the underlying pathological mechanism of this disorder.

METHODS

A total of 34 patients (14 men) with FCD and epilepsy [mean age ± standard deviation (SD), 24.5 ± 9.8 years; range, 8-47 years] and 34 age-matched healthy controls (14 men, 24.6 ± 9.7 years) underwent functional magnetic resonance imaging. Independent component analysis (ICA), seed-based functional connectivity, and graph theory were applied to analyze functional connectivity patterns in the brain.

RESULTS

Patients showed more connections among dorsal attention network, anterior default mode network, and sensorimotor brain networks than healthy controls based on ICA. Analysis of connectivity between regions of interest (ROIs) showed greater functional connectivity in patients between frontal and temporal regions, but lower connectivity between the cerebellum and frontal regions. The normalized characteristic path length was significantly higher in group of patients, but the two groups showed no significant differences in global or regional efficiency, clustering coefficient or characteristic path length.

CONCLUSIONS

Analysis of ICA-derived and ROI-based functional connectivity suggests that disrupted interactions and dysconnectivity in large-scale neural networks and frontotemporal-cerebellar regions may contribute to underlying pathological mechanisms in FCD-related epilepsy.

Respiratory-related brain pulsations are increased in epilepsy-a two-centre functional MRI study

Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11-0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01-0.1 Hz) and cardiovascular (0.8-1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.

Alterations in intra- and internetwork functional connectivity associated with levetiracetam treatment in temporal lobe epilepsy

OBJECTIVE

Levetiracetam (LEV) is an antiepileptic drug with a novel pharmacological mechanism. Advances in functional magnetic resonance imaging (fMRI) enable researchers to explore the cognitive effects of antiepileptic drugs on the living brain. This study aimed to explore how the functional connectivity patterns of the cognitive networks changed in association with LEV treatment.

METHODS

Patients with temporal lobe epilepsy (TLE), including both users and nonusers of LEV, were included in this study along with healthy controls. Core cognitive networks were extracted using an independent component analysis approach. Functional connectivity patterns within and between networks were investigated. The relationships between functional connectivity patterns and clinical characteristics were also examined.

RESULTS

The patterns of intranetwork connectivity in the default mode network (DMN), left executive control network (lECN), and dorsal attention network (DAN) differed among the three groups. The internetwork interactions did not show intergroup differences once corrected for multiple comparisons. No correlation between functional connectivity and clinical characteristics was found in patients with TLE.

CONCLUSIONS

Changes in intranetwork connectivity are a key effect of LEV administration.

SIGNIFICANCE

Alterations in intranetwork connectivity patterns may underlie the cognitive effects of LEV administration; this finding improves our understanding of the neural mechanisms of LEV therapy.

Frequency-specific changes in the default mode network in patients with cingulate gyrus epilepsy

To identify abnormal functional connectivity of the default mode network in cingulate gyrus epilepsy, which may yield new information about the default mode network and suggest a new cingulate gyrus epilepsy biomarker. Fifteen patients with cingulate gyrus epilepsy (mean age = 21 years) and 15 healthy controls (mean age = 24 years) were studied in the resting state using magnetoencephalography. Twelve brain areas of interest in the default mode network were extracted and investigated with multifrequency signals that included alpha (α, 8–13 Hz), beta (β, 14–30 Hz), and gamma (γ, 31–80 Hz) band oscillations. Patients with cingulate gyrus epilepsy had significantly greater connectivity in all three frequency bands (α, β, γ). A frequency‐specific elevation of functional connectivity was found in patients compared to controls. The greater functional connectivity in the γ band was significantly more prominent than that of the α and β bands. Patients with cingulate gyrus epilepsy and controls differed significantly in functional connectivity between the left angular gyrus and left posterior cingulate cortex in the α, β, and γ bands. The results of the node degree analysis were similar to those of the functional connectivity analysis. Our findings reveal for the first time that brain activity in the γ band may play a key role in the default mode network in cingulate gyrus epilepsy. Altered functional connectivity of the left angular gyrus and left posterior cingulate cortex may be a new biomarker for cingulate gyrus epilepsy.

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.

Time-resolved connectome of the five-factor model of personality

The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant 'connectome' is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the 'communication' across large-scale networks offers a mechanistic description of the capacity of conscientious people to 'protect' non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

A sinister subject: Quantifying handedness-based recruitment biases in current neuroimaging research

Approximately ten per cent of humans are left-handed or ambidextrous (adextral). It has been suggested that, despite their sizable representation at the whole-population level, this demographic is largely avoided by researchers within the neuroimaging community. To date, however, no formal effort has been made to quantify the extent to which adextrals are excluded from neuroimaging-based research. Here, we aimed to address this question in a review of over 1,000 recent articles published in high-impact, peer-reviewed, neuroimaging-focused journals. Specifically, we sought to ascertain whether, and the extent to which adextrals are underrepresented in neuroimaging study samples, and to delineate potential trends in this bias. Handedness data were available for over 30,000 research subjects; only around 3%-4% of these individuals were adextral-considerably less than the 10% benchmark one would expect if neuroimaging samples were truly representative of the general population. This observation was generally consistent across different areas of research, but was modulated by the demographic characteristics of neuroimaging participants. The epistemological and ethical implications of these findings are discussed.

Altered Functional Connectivity in Children With Low-Function Autism Spectrum Disorders

Neuroimaging studies have shown that autism spectrum disorders (ASDs) may be associated with abnormalities in brain structures and functions at rest as well as during cognitive tasks. However, it remains unclear if functional connectivity (FC) of all brain neural networks is also changed in these subjects. In this study, we acquired functional magnetic resonance imaging scans from 93 children with ASD and 79 matched healthy subjects. Group independent component analysis was executed for all of the participants to estimate FC. One-sample t-tests were then performed to obtain the networks for each group. Group differences in the different brain networks were tested using two-sample t-tests. Finally, relationships between abnormal FC and clinical variables were investigated with Pearson’s correlation analysis. The results from one-sample t-tests revealed nine networks with similar spatial patterns in these two groups. When compared with the controls, children with ASD showed increased connectivity in the right dorsolateral superior frontal gyrus and left middle frontal gyrus (MFG) within the occipital pole network. Children with ASD also showed decreased connectivity in the left gyrus rectus, left middle occipital gyrus, right angular gyrus, right MFG and right inferior frontal gyrus (IFG), orbital part within the lateral visual network (LVN), the left IFG, right precuneus, and right angular gyrus within the left frontoparietal (cognition) network. Furthermore, the mean FC values within the LVN showed significant positive correlations with total score of the Childhood Autism Rating Scale. Our findings indicate that abnormal FC extensively exists within some networks in children with ASD. This abnormal FC may constitute a biomarker of ASD. Our results are an important contribution to the study of neuropathophysiological mechanisms in children with ASD.

Altered intrinsic brain activity associated with outcome in frontal lobe epilepsy

Frontal lobe epilepsy (FLE) is the second most common type of the focal epilepsies. Our understanding of this disease has been revolutionized over the past decade, but variable treatment outcomes persist and the underlying functional mechanisms responsible for this have yet to be deciphered. This study was designed to determine how intrinsic brain connectivity related to treatment response in patients with FLE. 50 patients with FLE and 28 healthy controls were enrolled in this study and underwent functional MRI at baseline. At the end of 12-month follow up period, all patients with FLE were classified, based on their responses to AEDs treatment, into drug-responsive and drug-refractory groups. The amplitude of low-frequency fluctuation (ALFF) was calculated amongst the three groups in order to detect regional neural function integration. The responsive group showed decreased ALFF only in the left ventromedial prefrontal cortex (vmPFC), while the refractory group showed decreased ALFF in the left vmPFC, right superior frontal gyrus (SFG), and supramarginal gyrus (SMG) relative to healthy controls. In addition, both the responsive and refractory groups showed increased ALFF in the precuneus and postcentral gyrus when compared to the healthy controls. Furthermore, the refractory group exhibited significantly decreased ALFF in the left vmPFC, right SFG and SMG, relative to the responsive group. Focal spontaneous activity, as assessed by ALFF, was associated with response to antiepileptic treatment in patients with FLE. Patients with refractory frontal lobe epilepsy exhibited decreased intrinsic brain activity. Our findings provide novel neuroimaging evidence into the mechanisms of medically-intractable FLE at the brain level.

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.

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.

Altered physiological brain variation in drug-resistant epilepsy

INTRODUCTION

Functional magnetic resonance imaging (fMRI) combined with simultaneous electroencephalography (EEG-fMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level-dependent signal, suggesting intrinsic alterations in the underlying brain physiology.

METHODS

In this study, we used coefficient of variation (CV) of critically sampled 10 Hz ultra-fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls (n = 10) and patients (n = 10) with drug-resistant epilepsy (DRE).

RESULTS

We showed highly significant voxel-level (p < 0.01, TFCE-corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations (σ) above the control mean (μ) CV_MREG values solely in DRE patients, enabling patient-specific mapping of elevated physiological variance. The most apparent differences in group-level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12-0.4 Hz) and very-low-frequency (VLF = 0.009-0.1 Hz) signal variances were most affected.

CONCLUSIONS

The CV_MREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification.

Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience

It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

Cognition and dementia in older patients with epilepsy

With advances in healthcare and an ageing population, the number of older adults with epilepsy is set to rise substantially across the world. In developed countries the highest incidence of epilepsy is already in people over 65 and, as life expectancy increases, individuals who developed epilepsy at a young age are also living longer. Recent findings show that older persons with epilepsy are more likely to suffer from cognitive dysfunction and that there might be an important bidirectional relationship between epilepsy and dementia. Thus some people with epilepsy may be at a higher risk of developing dementia, while individuals with some forms of dementia, particularly Alzheimer's disease and vascular dementia, are at significantly higher risk of developing epilepsy. Consistent with this emerging view, epidemiological findings reveal that people with epilepsy and individuals with Alzheimer's disease share common risk factors. Recent studies in Alzheimer's disease and late-onset epilepsy also suggest common pathological links mediated by underlying vascular changes and/or tau pathology. Meanwhile electrophysiological and neuroimaging investigations in epilepsy, Alzheimer's disease, and vascular dementia have focused interest on network level dysfunction, which might be important in mediating cognitive dysfunction across all three of these conditions. In this review we consider whether seizures promote dementia, whether dementia causes seizures, or if common underlying pathophysiological mechanisms cause both. We examine the evidence that cognitive impairment is associated with epilepsy in older people (aged over 65) and the prognosis for patients with epilepsy developing dementia, with a specific emphasis on common mechanisms that might underlie the cognitive deficits observed in epilepsy and Alzheimer's disease. Our analyses suggest that there is considerable intersection between epilepsy, Alzheimer's disease and cerebrovascular disease raising the possibility that better understanding of shared mechanisms in these conditions might help to ameliorate not just seizures, but also epileptogenesis and cognitive dysfunction.

Disrupted intrinsic and remote functional connectivity in heterotopia-related epilepsy

OBJECTIVES

Several neuroimaging studies have examined neural interactions in patients with periventricular nodular heterotopia (PNH). However, features of the underlying functional network remain poorly understood. In this study, we examined alterations in the local (regional) and remote (interregional) cerebral networks in this disorder.

METHODS

Twenty-eight subjects all having suffered from PNH with epilepsy, as well as 28 age- and sex- matched healthy controls, were enrolled in this study. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC) were calculated to detect regional neural function and functional network integration, respectively.

RESULTS

Compared with healthy controls, patients with PNH-related epilepsy showed decreased ALFF in the ventromedial prefrontal cortex (vmPFC) and precuneus areas. ALFF values in both areas were negative correlated with epilepsy duration (P < .05, Bonferroni-corrected). Furthermore, patients with PNH-related epilepsy had increased remote interregional FC mainly in bilateral prefrontal and parietal cortices, supramarginal gyrus, dorsal cingulate gyrus, and right insula; lower FC was found in posterior brain regions including bilateral parahippocampal gyrus and inferior temporal gyrus.

CONCLUSIONS

Focal spontaneous hypofunction, as assessed by ALFF, correlates with epilepsy duration in patients with PNH-related epilepsy. Abnormalities existed both within the default-mode network and then across the whole brain, demonstrating that intrinsic brain dysfunction may be related to specific network interactions. Our findings provide novel understanding of the connectivity-based pathophysiological mechanisms of PNH.

Altered Default Mode Network on Resting-State fMRI in Children with Infantile Spasms

Infantile spasms (IS) syndrome is an age-dependent epileptic encephalopathy, which occurs in children characterized by spasms, impaired consciousness, and hypsarrhythmia. Abnormalities in default mode network (DMN) might contribute to the loss of consciousness during seizures and cognitive deficits in children with IS. The purpose of the present study was to investigate the changes in DMN with functional connectivity (FC) and amplitude of low-frequency fluctuation (ALFF), the two methods to discover the potential neuronal underpinnings of IS. The consistency of the two calculate methods of DMN abnormalities in IS patients was also our main focus. To avoid the disturbance of interictal epileptic discharge, our testing was performed within the interictal durations without epileptic discharges. Resting-state fMRI data were collected from 13 patients with IS and 35 sex- and age-matched healthy controls. FC analysis with seed in posterior cingulate cortex (PCC) was used to compare the differences between two groups. We chose PCC as the seed region because PCC is the only node in the DMN that directly interacts with virtually all other nodes according to previous studies. Furthermore, the ALFF values within the DMN were also calculated and compared between the two groups. The FC results showed that IS patients exhibited markedly reduced connectivity between posterior seed region and other areas within DMN. In addition, part of the brain areas within the DMN showing significant difference of FC had significantly lower ALFF signal in the patient group than that in the healthy controls. The observed disruption in DMN through the two methods showed that the coherence of brain signal fluctuation in DMN during rest was broken in IS children. Neuronal functional impairment or altered integration in DMN would be one neuroimaging characteristic, which might help us to understand the underlying neural mechanism of IS. Further studies are needed to determine whether the disturbed FC and ALFF observed in the DMN are related to cognitive performance in IS patients.

The dynamics of functional connectivity in neocortical focal epilepsy

Focal epilepsy is characterised by paroxysmal events, reflecting changes in underlying local brain networks. To capture brain network activity at the maximal temporal resolution of the acquired functional magnetic resonance imaging (fMRI) data, we have previously developed a novel analysis framework called Dynamic Regional Phase Synchrony (DRePS). DRePS measures instantaneous mean phase coherence within neighbourhoods of brain voxels. We use it here to examine how the dynamics of the functional connections of regional brain networks are altered in neocortical focal epilepsy. Using task-free fMRI data from 21 subjects with focal epilepsy and 21 healthy controls, we calculated the power spectral density of DRePS, which is a measure of signal variability in local connectivity estimates. Whole-brain averaged power spectral density of DRePS, or signal variability of local connectivity, was significantly higher in epilepsy subjects compared to healthy controls. Maximal increase in DRePS spectral power was seen in bilateral inferior frontal cortices, ipsilateral mid-cingulate gyrus, superior temporal gyrus, caudate head, and contralateral cerebellum. Our results provide further evidence of common brain abnormalities across people with focal epilepsy. We postulate that dynamic changes in specific cortical brain areas may help maintain brain function in the presence of pathological epileptiform network activity in neocortical focal epilepsy.

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Focal epilepsy is a disease defined by its inherent paroxysmal brain changes.

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However, most fMRI connectivity methods do not capture dynamic brain network activity.

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We use our recently developed method, DRePS, to identify abnormal dynamic brain networks in focal epilepsy.

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Fluctuations of dynamic brain network activity were increased in the frontal-temporal cortex, cingulum and cerebellum, in focal epilepsy.

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These regions may be involved in (re-)gaining cortical homeostasis in an otherwise abnormal brain.