Epilepsy-related brain networks in ring chromosome 20 syndrome: an EEG-fMRI study

Temporal Lobe Spikes Affect Distant Intrinsic Connectivity Networks

Objective: To evaluate local and distant blood oxygen level dependent (BOLD) signal changes related to interictal epileptiform discharges (IED) in drug-resistant temporal lobe epilepsy (TLE).

Methods: Thirty-three TLE patients undergoing EEG–functional Magnetic Resonance Imaging (fMRI) as part of the presurgical workup were consecutively enrolled. First, a single-subject spike-related analysis was performed: (a) to verify the BOLD concordance with the presumed Epileptogenic Zone (EZ); and (b) to investigate the Intrinsic Connectivity Networks (ICN) involvement. Then, a group analysis was performed to search for common BOLD changes in TLE.

Results: Interictal epileptiform discharges were recorded in 25 patients and in 19 (58%), a BOLD response was obtained at the single-subject level. In 42% of the cases, BOLD changes were observed in the temporal lobe, although only one patient had a pure concordant finding, with a single fMRI cluster overlapping (and limited to) the EZ identified by anatomo-electro-clinical correlations. In the remaining 58% of the cases, BOLD responses were localized outside the temporal lobe and the presumed EZ. In every patient, with a spike-related fMRI map, at least one ICN appeared to be involved. Four main ICNs were preferentially involved, namely, motor, visual, auditory/motor speech, and the default mode network. At the single-subject level, EEG–fMRI proved to have high specificity (above 65%) in detecting engagement of an ICN and the corresponding ictal/postictal symptom, and good positive predictive value (above 67%) in all networks except the visual one. Finally, in the group analysis of BOLD changes related to IED revealed common activations at the right precentral gyrus, supplementary motor area, and middle cingulate gyrus.

Significance: Interictal temporal spikes affect several distant extra-temporal areas, and specifically the motor/premotor cortex. EEG–fMRI in patients with TLE eligible for surgery is recommended not for strictly localizing purposes rather it might be useful to investigate ICNs alterations at the single-subject level.

Ring Chromosome 20 Syndrome: Genetics, Clinical Characteristics, and Overlapping Phenotypes

Ring chromosome 20 [r(20)] syndrome is a rare condition characterized by a non-supernumerary ring chromosome 20 replacing a normal chromosome 20. It is commonly seen in a mosaic state and is diagnosed by means of karyotyping. r(20) syndrome is characterized by a recognizable epileptic phenotype with typical EEG pattern, intellectual disability manifesting after seizure onset in otherwise normally developing children, and behavioral changes. Despite the distinctive phenotype, many patients still lack a diagnosis-especially in the genomic era-and the pathomechanisms of ring formation are poorly understood. In this review we address the genetic and clinical aspects of r(20) syndrome, and discuss differential diagnoses and overlapping phenotypes, providing the reader with useful tools for clinical and laboratory practice. We also discuss the current issues in understanding the mechanisms through which ring 20 chromosome causes the typical manifestations, and present unpublished data about methylation studies. Ultimately, we explore future perspectives of r(20) research. Our intended audience is clinical and laboratory geneticists, child and adult neurologists, and genetic counselors.

Specificity of electroclinical features in the diagnosis of ring chromosome 20

BACKGROUND

Ring chromosome 20 (R20) syndrome is a chromosomal disorder characterized mainly by drug-resistant frontal lobe seizures, recurrent nonconvulsive status epilepticus (NCSE), and typical EEG features. The aim of this study was to investigate if this triad is common and specific to all patients with R20.

METHODS

In this cross-sectional study (from 2000 to 2011), we selected patients who fulfilled at least two out of three criteria: drug-resistant frontal lobe seizures, recurrent NCSE, and characteristic electroencephalography (EEG) features. In all patients, diagnosis was based on karyotype analysis of at least 100 metaphases.

RESULTS

We identified 36 patients who met at least two of the selected criteria: six patients (16.7%) with R20 and 30 (83.3%) without R20 (non-R20). All patients with R20 met all three criteria. Eleven (36.7%) patients without R20, however, also displayed the full triad. In 19 patients without R20 (63.3%), one of the three clinical features was missing: frontal lobe seizures were not resistant to antiepileptic drugs (AED) in four (13.3%), recurrent NCSE was missing in six (20%), and nine (30%) patients did not have typical EEG features. Based on this data, specificity was 63.3%, positive predictive value was 35.3%, and sensitivity and negative predictive values were 100%. Additionally, a review of all publications describing the R20 phenotype revealed that 81.98% of patients with R20 display the full electroclinical triad.

CONCLUSIONS

In our study, all patients with R20 displayed the three electroclinical characteristics. This is in line with previous reports (presenting high sensitivity and negative predictive value). However, these features can also be observed in other epilepsies and are not specific to R20. Our findings suggest that in the presence of the full triad of symptoms, karyotype analysis focused on chromosome 20 should be conducted.

Epilepsy in ring chromosome 20 syndrome

OBJECTIVE

Ring chromosome 20 syndrome is characterized by severe, drug resistant childhood onset epilepsy, often accompanied by cognitive impairment. We characterized the electro-clinical phenotype and the long-term course of epilepsy in a large series.

METHODS

We reviewed the electro-clinical phenotype of 25 patients (aged 8-59 years), and assessed the relationship between epilepsy severity and clinical and/or genetic variables. We also searched for reports of patients diagnosed with r(20) syndrome in the literature, included those whose clinical information was sufficiently accurate, and compared their clinical features with the ones of our patients.

RESULTS

Epilepsy exhibited an age dependent course. When seizure onset occurred in childhood (21 patients), terrifying hallucinations associated with focal motor seizures, often sleep-related (8 patients), or dyscognitive seizures (13 patients), were prominent features, often evolving into epileptic encephalopathy associated with non-convulsive status epilepticus (11 patients). In the long-term, progressive stabilization of drug resistant epilepsy associated with non-convulsive status epilepticus, focal seizures with motor and autonomic features, and eyelid myoclonia were noticed. Epilepsy onset in adolescence (3 patients) was accompanied by a milder developmental course, dyscognitive seizures and non-convulsive status epilepticus, and no cognitive decline. Only three older patients became seizure free (>5 years) We found statistically significant correlations between age at epilepsy onset and cognitive level. Although in the study cohort the relationship between r(20) ratio, age at epilepsy onset and cognitive level was non-statistically significant, it reached significance evaluating the larger cohort of patients previously published.

SIGNIFICANCE

In ring(20) syndrome, epilepsy has an age dependent course and a worse outcome when age at seizure onset is earlier. The r(20) ratio and severity of cognitive impairment appear to be directly related to each other and inversely correlated with the age at epilepsy onset.

Emerging neuroimaging contribution to the diagnosis and management of the ring chromosome 20 syndrome

Ring chromosome 20 [r(20)] syndrome is an underdiagnosed chromosomal anomaly characterized by severe epilepsy, behavioral problems, and mild-to-moderate cognitive deficits. Since the cognitive and behavioral decline follows seizure onset, this syndrome has been proposed as an epileptic encephalopathy (EE). The recent overwhelming development of advanced neuroimaging techniques has opened a new era in the investigation of the brain networks subserving the EEs. In particular, functional neuroimaging tools are well suited to show alterations related to epileptiform discharges at the network level and to build hypotheses about the mechanisms underlying the cognitive disruption observed in these conditions. This paper reviews the brain circuits and their disruption as revealed by functional neuroimaging studies in patients with [r(20)] syndrome. It discusses the clinical consequences of the neuroimaging findings on the management of patients with [r(20)] syndrome, including their impact to an earlier diagnosis of this disorder. Based on the available lines of evidences, [r(20)] syndrome is characterized by interictal and ictal dysfunctions within basal ganglia-prefrontal lobe networks and by long-lasting effects of the peculiar theta-delta rhythm, which represents an EEG marker of the syndrome on integrated brain networks that subserve cognitive functions.

Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment

The spatial coherence of spontaneous slow fluctuations in the blood-oxygen-level dependent (BOLD) signal at rest is routinely used to characterize the underlying resting-state networks (RSNs). Studies have demonstrated that these patterns are organized in space and highly reproducible from subject to subject. Moreover, RSNs reorganizations have been suggested in pathological conditions. Comparisons of RSNs organization have been performed between groups of subjects but have rarely been applied at the individual level, a step required for clinical application. Defining the notion of modularity as the organization of brain activity in stable networks, we propose Detection of Abnormal Networks in Individuals (DANI) to identify modularity changes at the individual level. The stability of each RSN was estimated using a spatial clustering method: Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2010). Our contributions consisted in (i) providing functional maps of the most stable cores of each networks and (ii) in detecting “abnormal” individual changes in networks organization when compared to a population of healthy controls. DANI was first evaluated using realistic simulated data, showing that focussing on a conservative core size (50% most stable regions) improved the sensitivity to detect modularity changes. DANI was then applied to resting state fMRI data of six patients with focal epilepsy who underwent multimodal assessment using simultaneous EEG/fMRI acquisition followed by surgery. Only patient with a seizure free outcome were selected and the resected area was identified using a post-operative MRI. DANI automatically detected abnormal changes in 5 out of 6 patients, with excellent sensitivity, showing for each of them at least one “abnormal” lateralized network closely related to the epileptic focus. For each patient, we also detected some distant networks as abnormal, suggesting some remote reorganization in the epileptic brain.

Mapping epileptic activity: sources or networks for the clinicians?

Epileptic seizures of focal origin are classically considered to arise from a focal epileptogenic zone and then spread to other brain regions. This is a key concept for semiological electro-clinical correlations, localization of relevant structural lesions, and selection of patients for epilepsy surgery. Recent development in neuro-imaging and electro-physiology and combinations, thereof, have been validated as contributory tools for focus localization. In parallel, these techniques have revealed that widespread networks of brain regions, rather than a single epileptogenic region, are implicated in focal epileptic activity. Sophisticated multimodal imaging and analysis strategies of brain connectivity patterns have been developed to characterize the spatio-temporal relationships within these networks by combining the strength of both techniques to optimize spatial and temporal resolution with whole-brain coverage and directional connectivity. In this paper, we review the potential clinical contribution of these functional mapping techniques as well as invasive electrophysiology in human beings and animal models for characterizing network connectivity.

Mapping (and modeling) physiological movements during EEG-fMRI recordings: the added value of the video acquired simultaneously

BACKGROUND

During resting-state EEG-fMRI studies in epilepsy, patients' spontaneous head-face movements occur frequently. We tested the usefulness of synchronous video recording to identify and model the fMRI changes associated with non-epileptic movements to improve sensitivity and specificity of fMRI maps related to interictal epileptiform discharges (IED).

NEW METHODS

Categorization of different facial/cranial movements during EEG-fMRI was obtained for 38 patients [with benign epilepsy with centro-temporal spikes (BECTS, n=16); with idiopathic generalized epilepsy (IGE, n=17); focal symptomatic/cryptogenic epilepsy (n=5)]. We compared at single subject- and at group-level the IED-related fMRI maps obtained with and without additional regressors related to spontaneous movements. As secondary aim, we considered facial movements as events of interest to test the usefulness of video information to obtain fMRI maps of the following face movements: swallowing, mouth-tongue movements, and blinking.

RESULTS

Video information substantially improved the identification and classification of the artifacts with respect to the EEG observation alone (mean gain of 28 events per exam).

COMPARISON WITH EXISTING METHOD

Inclusion of physiological activities as additional regressors in the GLM model demonstrated an increased Z-score and number of voxels of the global maxima and/or new BOLD clusters in around three quarters of the patients. Video-related fMRI maps for swallowing, mouth-tongue movements, and blinking were comparable to the ones obtained in previous task-based fMRI studies.

CONCLUSIONS

Video acquisition during EEG-fMRI is a useful source of information. Modeling physiological movements in EEG-fMRI studies for epilepsy will lead to more informative IED-related fMRI maps in different epileptic conditions.