Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white‐matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white‐matter structural architecture, it cannot detect neural activity or white‐matter functions. Recent studies demonstrated the functional organization of white‐matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white‐matter dysfunctions in BECT. Resting‐state fMRI data were collected from 24 new‐onset drug‐naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white‐matter functional networks were obtained using a clustering analysis on voxel‐by‐voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub‐bands (Slow‐5:0.01–0.027, Slow‐4:0.027–0.073, Slow‐3:0.073–0.198, and Slow‐2:0.198–0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white‐matter networks based on their correlations with multiple gray‐matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency‐specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow‐3 and Slow‐4. This study provided further support on the existence of white‐matter functional networks which exhibited frequency‐specific properties, and extended abnormalities of rolandic area from the perspective of white‐matter dysfunction in BECT.     

Correlation between scalp high-frequency oscillations and prognosis in patients with benign epilepsy of childhood with centrotemporal spikes

Aims

The study aimed to explore whether high-frequency oscillations (HFOs) can predict seizure risk and atypical manifestations of benign epilepsy of childhood with centrotemporal spikes (BECTS).

Methods

We recruited 60 patients and divided them into three groups: (1) seizure-free BECTS, (2) active typical BECTS, and (3) active atypical forms of BECTS. Electroencephalogram was used to record the number, location, average amplitude, and duration of spikes, and spike ripples were analyzed using time-frequency technology. Multivariable logistic regression analysis was used to investigate independent predictive factors for prognosis.

Results

The number of sleep spike ripples, rather than spikes, was an independent risk factor for the active period of the disease (odds ratio [OR] = 4.714, p = 0.003) and atypical forms of BECTS (OR = 1.455, p = 0.049); the optimal thresholds for the spike ripple rate were >0 (area under the curve [AUC] = 0.885, sensitivity = 96.15%, specificity = 73.33%) and >0.6/min (AUC = 0.936, sensitivity = 84.21%, specificity = 96.15%), respectively. Furthermore, in typical BECTS, the spike ripple rate showed significant negative correlations with time since the last seizure (ρ = −0.409, p = 0.009) and age (ρ = −0.379, p = 0.016), while the spike rate did not.

Conclusion

Spike ripple was a marker for distinguishing typical and atypical forms of BECTS and reflected the risk of seizure recurrence better than the spike alone. The present findings might assist clinicians in BECTS treatment.     

Resting‐state functional connectivity predicts the strength of hemispheric lateralization for language processing in temporal lobe epilepsy and normals

In temporal lobe epilepsy (TLE), determining the hemispheric specialization for language before surgery is critical to preserving a patient's cognitive abilities post‐surgery. To date, the major techniques utilized are limited by the capacity of patients to efficiently realize the task. We determined whether resting‐state functional connectivity (rsFC) is a reliable predictor of language hemispheric dominance in right and left TLE patients, relative to controls. We chose three subregions of the inferior frontal cortex (pars orbitalis, pars triangularis, and pars opercularis) as the seed regions. All participants performed both a verb generation task and a resting‐state fMRI procedure. Based on the language task, we computed a laterality index (LI) for the resulting network. This revealed that 96% of the participants were left‐hemisphere dominant, although there remained a large degree of variability in the strength of left lateralization. We tested whether LI correlated with rsFC values emerging from each seed. We revealed a set of regions that was specific to each group. Unique correlations involving the epileptic mesial temporal lobe were revealed for the right and left TLE patients, but not for the controls. Importantly, for both TLE groups, the rsFC emerging from a contralateral seed was the most predictive of LI. Overall, our data depict the broad patterns of rsFC that support strong versus weak left hemisphere language laterality. This project provides the first evidence that rsFC data may potentially be used on its own to verify the strength of hemispheric dominance for language in impaired or pathologic populations. Hum Brain Mapp, 36:288–303, 2015. © 2014 Wiley Periodicals, Inc.     

Intrinsic brain activity as a diagnostic biomarker in children with benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECTS) is often associated with neural circuit dysfunction, particularly during the transient active state characterized by interictal epileptiform discharges (IEDs). Little is known, however, about the functional neural circuit abnormalities in BECTS without IEDs, or if such abnormalities could be used to differentiate BECTS patients without IEDs from healthy controls (HCs) for early diagnosis. To this end, we conducted resting‐state functional magnetic resonance imaging (RS‐fMRI) and simultaneous Electroencephalogram (EEG) in children with BECTS (n = 43) and age‐matched HC (n = 28). The simultaneous EEG recordings distinguished BECTS with IEDs (n = 20) from without IEDs (n = 23). Intrinsic brain activity was measured in all three groups using the amplitude of low frequency fluctuation at rest. Compared to HC, BECTS patients with IEDs exhibited an intrinsic activity abnormality in the thalamus, suggesting that thalamic dysfunction could contribute to IED emergence while patients without IEDs exhibited intrinsic activity abnormalities in middle frontal gyrus and superior parietal gyrus. Using multivariate pattern classification analysis, we were able to differentiate BECTS without IEDs from HCs with 88.23% accuracy. BECTS without epileptic transients can be distinguished from HC and BECTS with IEDs by unique regional abnormalities in resting brain activity. Both transient abnormalities as reflected by IEDs and chronic abnormalities as reflected by RS‐fMRI may contribute to BECTS development and expression. Intrinsic brain activity and multivariate pattern classification techniques are promising tools to diagnose and differentiate BECTS syndromes. Hum Brain Mapp 36:3878–3889, 2015. © 2015 Wiley Periodicals, Inc.     

Functional connectivity‐based parcellation of the human sensorimotor cortex

Task‐based functional magnetic resonance imaging (fMRI) has been successfully employed to obtain somatotopic maps of the human sensorimotor cortex. Here, we showed through direct comparison that a similar functional map can be obtained, independently of a task, by performing a connectivity‐based parcellation of the sensorimotor cortex based on resting‐state fMRI. Cortex corresponding to two adjacent Brodmann areas (BA 3 and BA 4) was selected as the sensorimotor area. Parcellation was obtained along a medial–lateral axis, which was confirmed to be somatotopic (corresponding roughly to an upper, middle and lower limb, respectively) by comparing it with maps obtained using motoric task‐based fMRI in the same participants. Interestingly, the resting‐state parcellation map demonstrated higher correspondence to the task‐based divisions after individuals performed the motor task. Using the resting‐state fMRI data, we also observed higher functional correlations between the centrally located hand region and the other two regions, than between the foot and tongue. The functional relevance of these somatosensory parcellation results indicates the feasibility of a wide range of potential applications to brain mapping.     

Mutation screening of the PRRT2 gene for benign epilepsy with centrotemporal spikes in Chinese mainland population

Proline-rich transmembrane protein 2 gene (PRRT2) mutations are reported to cause common paroxysmal neurological disorders and show a remarkable pleiotropy. Benign epilepsy with centrotemporal spikes (BECTS) is considered to be the most common epilepsy syndrome in childhood. It is placed among the idiopathic localization related epilepsies. Recently, it was reported that a girl with a PRRT2 mutation c.649_650insC developed infantile focal epilepsy with bilateral spikes which resembled the rolandic spikes. Hereby we performed a comprehensive genetic mutation screening of PRRT2 gene in a cohort of 53 sporadic BECTS patients. None of the 53 sporadic BECTS patients and other 250 controls carried mutations including c.649_650insC in PRRT2. Our data indicated that the PRRT2 mutations might most likely not be associated with BECTS in Chinese mainland population.     

Resting‐state functional connectivity and motor imagery brain activation

Motor imagery (MI) relies on the mental simulation of an action without any overt motor execution (ME), and can facilitate motor learning and enhance the effect of rehabilitation in patients with neurological conditions. While functional magnetic resonance imaging (fMRI) during MI and ME reveals shared cortical representations, the role and functional relevance of the resting‐state functional connectivity (RSFC) of brain regions involved in MI is yet unknown. Here, we performed resting‐state fMRI followed by fMRI during ME and MI with the dominant hand. We used a behavioral chronometry test to measure ME and MI movement duration and compute an index of performance (IP). Then, we analyzed the voxel‐matched correlation between the individual MI parameter estimates and seed‐based RSFC maps in the MI network to measure the correspondence between RSFC and MI fMRI activation. We found that inter‐individual differences in intrinsic connectivity in the MI network predicted several clusters of activation. Taken together, present findings provide first evidence that RSFC within the MI network is predictive of the activation of MI brain regions, including those associated with behavioral performance, thus suggesting a role for RSFC in obtaining a deeper understanding of neural substrates of MI and of MI ability. Hum Brain Mapp 37:3847–3857, 2016. © 2016 Wiley Periodicals, Inc.     

Deriving frequency‐dependent spatial patterns in MEG‐derived resting state sensorimotor network: A novel multiband ICA technique

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band‐limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass‐filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well‐characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single‐band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779–791, 2017. © 2016 Wiley Periodicals, Inc.     

Role of the sensorimotor cortex in tourette syndrome using multimodal imaging

Tourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and vocal tics. Most patients describe uncomfortable premonitory sensations preceding the tics and a subjective experience of increased sensitivity to tactile stimuli. These reports indicate that a sensory processing disturbance is an important component of TS together with motor phenomena. Thus, we focused our investigation on the role of the sensorimotor cortex (SMC) in TS using multimodal neuroimaging techniques. We measured the gamma‐aminobutyric acid (GABA)+/Creatine (Cre) ratio in the SMC using GABA ¹H magnetic resonance spectroscopy. We recorded the baseline beta activity in the SMC using magnetoencephalography and correlated GABA+/Cre ratio with baseline beta band power. Finally, we examined the resting state functional connectivity (FC) pattern of the SMC using functional magnetic resonance imaging (fMRI). GABA+/Cre ratio in the SMC did not differ between patients and controls. Correlation between the baseline beta band power and GABA+/Cre ratio was abnormal in patients. The anterior insula showed increased FC with the SMC in patients. These findings suggest that altered limbic input to the SMC and abnormal GABA‐mediated beta oscillations in the SMC may underpin some of the sensorimotor processing disturbances in TS and contribute to tic generation. Hum Brain Mapp 35:5834–5846, 2014. Published 2014. This article is a U.S. Government work and is in the public domain in the USA .     

动态脑电图对典型与非典型伴有中央-颞区棘波的儿童良性癫痫的诊断

目的探讨动态脑电图(AEEG)对典型与非典型伴中央-颞区棘波的儿童良性癫痫(BECCT)的诊断价值,并进行随访分析。方法收集伴中央-颞区棘波的儿童良性癫痫的患儿资料50例,分典型组31例,非典型组19例。所有患儿均进行AEEG检查,对检查结果进行对比分析及治疗后随访。结果清醒期EEG描记时,典型组异常放电8例(25.8%),非典型组异常放电7例(36.8%),在睡眠期EEG描记时均有异常放电。结论 AEEG对典型与非典型伴中央-颞区棘波的儿童良性癫痫均有重要的诊断价值。     

Intellectual functioning in children with epilepsy: Frontal lobe epilepsy,childhood absence epilepsy and benign epilepsy with centro-temporal spikes

PurposeThe purpose of our study is to describe intellectual functioning in three common childhood epilepsy syndromes – frontal lobe epilepsy (FLE), childhood absence epilepsy (CAE) and benign epilepsy with centro-temporal spikes (BECTS). And also to determine the influence of epilepsy related variables, type of epilepsy, age at epilepsy onset, duration and frequency of epilepsy, and treatment on the scores.MethodsIntellectual functioning was examined in a group of 90 children with epilepsy (30 FLE, 30 CAE, 30 BECTS), aged 6–15 years, and compared with a control group (30). All subjects obtained a Full Scale IQ ≥ 70 and they were receiving no more than two antiepileptic medications. Participants completed the Wechsler Intelligence Scale for Children – Third Edition. The impact of epilepsy related variables (type of epilepsy, age at epilepsy onset, duration of epilepsy, seizure frequency and anti-epileptic drugs) on intellectual functioning was examined.ResultsChildren with FLE scored significantly worse than controls on WISC-III Verbal IQ, Full Scale IQ and Processing Speed Index. There was a trend for children with FLE to have lower intelligence scores than CAE and BECTS groups. Linear regression analysis showed no effect for age at onset, frequency of seizures and treatment. Type of epilepsy and duration of epilepsy were the best indicators of intellectual functioning.ConclusionIt is crucial that children with FLE and those with a longer active duration of epilepsy are closely monitored to allow the early identification and evaluation of cognitive problems, in order to establish adequate and timely school intervention plans.     

Spatio‐temporal dynamics of resting‐state brain networks improve single‐subject prediction of schizophrenia diagnosis

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding‐window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting‐state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting‐state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting‐state dynamics are not trivially attributable to sampling variability and head motion.     

Hubs disruption in mesial temporal lobe epilepsy. A resting‐state fMRI study on a language‐and‐memory network

Mesial temporal lobe epilepsy (mTLE) affects the brain networks at several levels and patients suffering from mTLE experience cognitive impairment for language and memory. Considering the importance of language and memory reorganization in this condition, the present study explores changes of the embedded language‐and‐memory network (LMN) in terms of functional connectivity (FC) at rest, as measured with functional MRI. We also evaluate the cognitive efficiency of the reorganization, that is, whether or not the reorganizations support or allow the maintenance of optimal cognitive functioning despite the seizure‐related damage. Data from 37 patients presenting unifocal mTLE were analyzed and compared to 48 healthy volunteers in terms of LMN‐FC using two methods: pairwise correlations (region of interest [ROI]‐to‐ROI) and graph theory. The cognitive efficiency of the LMN‐FC reorganization was measured using correlations between FC parameters and language and memory scores. Our findings revealed a large perturbation of the LMN hubs in patients. We observed a hyperconnectivity of limbic areas near the dysfunctional hippocampus and mainly a hypoconnectivity for several cortical regions remote from the dysfunctional hippocampus. The loss of FC was more important in left mTLE (L‐mTLE) than in right (R‐mTLE) patients. The LMN‐FC reorganization may not be always compensatory and not always useful for patients as it may be associated with lower cognitive performance. We discuss the different connectivity patterns obtained and conclude that interpretation of FC changes in relation to neuropsychological scores is important to determine cognitive efficiency, suggesting the concept of “connectome” would gain to be associated with a “cognitome” concept.     

Striatal hypertrophy and its cognitive effects in new-onset benign epilepsy with centrotemporal spikes

Purpose: Benign epilepsy with centrotemporal spikes (BECTS), the most common childhood epilepsy syndrome, is a neurodevelopmental disorder with a genetic influence. Despite its signature electroencephalographic pattern and distinct focal motor seizure semiology, little is known about the underlying brain anatomic alteration and the corresponding cognitive consequences. Given the motor manifestations of seizures in BECTS, we hypothesize that anatomic networks in BECTS involve a distributed corticostriatal circuit. Methods: We investigated volumetric differences and shape deformities of caudate, putamen, pallidum, and thalamus in a group of children with new‐ and recent‐onset BECTS (N = 13) compared to healthy controls (N = 54). We correlated specific subcortical volumes in BECTS that were significantly different from those in healthy controls with performances in executive function. Key Findings: Children with BECTS demonstrated significantly hypertrophied putamen, which was selective among the subcortical regions examined. Shape analysis showed dorsoventral elongation of the left caudate and bilateral putamen, with subnuclei expansion in ventral and dorsal striatum. Larger putamen volumes were linked to better cognitive performances on two complementary executive function tests. Significance: Children with BECTS showed aberrant volume and shape in subcortical regions that are critical for both motor processing and executive function. It is of importance to note that the hypertrophy appears to be cognitively adaptive, as enlargement was associated with improved cognitive performances. The anatomic abnormalities and their cognitive effects are evident in a group of children with new‐ and recent‐onset epilepsy, suggesting that the structural brain anomalies occurred before the diagnosis of epilepsy.     

Evaluation of levetiracetam and valproic acid as low-dose monotherapies for children with typical benign childhood epilepsy with centrotemporal spikes (BECTS)

PurposeThis study aimed to compare the monotherapeutic efficacies of levetiracetam (LEV) and valproic acid (VPA) in a cohort of newly diagnosed children with typical benign childhood epilepsy with centrotemporal spikes (BECTS).MethodsA total of 56 children with typical BECTS were retrospectively reviewed in the analyses. Thirty-three children received LEV and 23 received VPA as initial monotherapy, and the treatments lasted for at least 18 months.ResultsThe average dosage of LEV was 22.7 ± 4.7 mg/kg/day, and that of VPA was 18.7 ± 5.7 mg/kg/day. The seizure-freedom rates were not significantly different between the two groups at 6 (57.5% vs. 60.9%), 12 (81.8% vs. 73.9%) or 18 months (100% vs. 100%). However, a greater number of the children taking VPA achieved Electroencephalography (EEG) normalization compared to those taking LEV both at 12 (78.3% vs. 45.5%) and 18 months (95.7% vs. 72.7%; p < 0.05). No children discontinued therapy due to adverse effects during the follow-up. Only one child (4.7%) in the VPA group exhibited mild weight gain (BMI increase of 2 at the end of follow-up) but did not withdraw from treatment.ConclusionLow-dosage VPA and LEV monotherapies are equally effective in controlling seizures, but VPA exhibited better efficacy than LEV in improving the electrophysiological abnormalities of children with BECTS. None of the patients discontinued therapy, which was likely due to the administration of low dosages.