Simon Tousseyn

Progressive myoclonic epilepsy as an adult-onset manifestation of Leigh syndrome due to m.14487T > C

Background: m.14487T>C, a missense mutation (p.M63V) affecting the ND6 subunit of complex I of the mitochondrial respiratory chain, has been reported in isolated childhood cases with Leigh syndrome (LS) and progressive dystonia. Adult-onset phenotypes have not been reported. Objectives: To determine the clinical–neurological spectrum and associated mutation loads in an extended m.14487T>C family. Methods: A genotype–phenotype correlation study of a Belgian five-generation family with 12 affected family members segregating m.14487T>C was carried out. Clinical and mutation load data were available for nine family members. Biochemical analysis of the respiratory chain was performed in three muscle biopsies. Results: Heteroplasmic m.14487T>C levels (36–52% in leucocytes, 97–99% in muscle) were found in patients with progressive myoclonic epilepsy (PME) and dystonia or progressive hypokinetic-rigid syndrome. Patients with infantile LS were homoplasmic (99–100% in leucocytes, 100% in muscle). We found lower mutation loads (between 8 and 35% in blood) in adult patients with clinical features including migraine with aura, Leber hereditary optic neuropathy, sensorineural hearing loss and diabetes mellitus type 2. Despite homoplasmic mutation loads, complex I catalytic activity was only moderately decreased in muscle tissue. Interpretation: m.14487T>C resulted in a broad spectrum of phenotypes in our family. Depending on the mutation load, it caused severe encephalopathies ranging from infantile LS to adult-onset PME with dystonia. This is the first report of PME as an important neurological manifestation of an isolated mitochondrial complex I defect.

ICA extracts epileptic sources from fMRI in EEG-negative patients: a retrospective validation study.

Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.

Correspondence between large-scale ictal and interictal epileptic networks revealed by single photon emission computed tomography (SPECT) and electroencephalography (EEG)–functional magnetic resonance imaging (fMRI)

Epilepsy is increasingly recognized as a network disorder, but the spatial relationship between ictal and interictal networks is still largely unexplored. In this work, we compared hemodynamic changes related to seizures and interictal spikes on a whole brain scale.

A prospective fMRI-based technique for localising the epileptogenic zone in presurgical evaluation of epilepsy.

There is growing evidence for the benefits of simultaneous EEG-fMRI as a non-invasive localising tool in the presurgical evaluation of epilepsy. However, many EEG-fMRI studies fail due to the absence of interictal epileptic discharges (IEDs) on EEG. Here we present an algorithm which makes use of fMRI as sole modality to localise the epileptogenic zone (EZ). Recent studies using various model-based or data-driven fMRI analysis techniques showed that it is feasible to find activation maps which are helpful in the detection of the EZ. However, there is lack of evidence that these techniques can be used prospectively, due to (a) their low specificity, (b) selecting multiple activation maps, or (c) a widespread epileptic network indicated by the selected maps. In the current study we present a method based on independent component analysis and a cascade of classifiers that exclusively detects a single map related to interictal epileptic brain activity. In order to establish the sensitivity and specificity of the proposed method, it was evaluated on a group of 18 EEG-negative patients with a single well-defined EZ and 13 healthy controls. The results show that our method provides maps which correctly indicate the EZ in several (N=4) EEG-negative cases but at the same time maintaining a high specificity (92%). We conclude that our fMRI-based approach can be used in a prospective manner, and can extend the applicability of fMRI to EEG-negative cases.

Sensitivity and Specificity of Interictal EEG-fMRI for Detecting the Ictal Onset Zone at Different Statistical Thresholds

There is currently a lack of knowledge about electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) specificity. Our aim was to define sensitivity and specificity of blood oxygen level dependent (BOLD) responses to interictal epileptic spikes during EEG-fMRI for detecting the ictal onset zone (IOZ). We studied 21 refractory focal epilepsy patients who had a well-defined IOZ after a full presurgical evaluation and interictal spikes during EEG-fMRI. Areas of spike-related BOLD changes overlapping the IOZ in patients were considered as true positives; if no overlap was found, they were treated as false-negatives. Matched healthy case-controls had undergone similar EEG-fMRI in order to determine true-negative and false-positive fractions. The spike-related regressor of the patient was used in the design matrix of the healthy case-control. Suprathreshold BOLD changes in the brain of controls were considered as false positives, absence of these changes as true negatives. Sensitivity and specificity were calculated for different statistical thresholds at the voxel level combined with different cluster size thresholds and represented in receiver operating characteristic (ROC)-curves. Additionally, we calculated the ROC-curves based on the cluster containing the maximal significant activation. We achieved a combination of 100% specificity and 62% sensitivity, using a Z-threshold in the interval 3.4–3.5 and cluster size threshold of 350 voxels. We could obtain higher sensitivity at the expense of specificity. Similar performance was found when using the cluster containing the maximal significant activation. Our data provide a guideline for different EEG-fMRI settings with their respective sensitivity and specificity for detecting the IOZ. The unique cluster containing the maximal significant BOLD activation was a sensitive and specific marker of the IOZ.

A reliable and time-saving semiautomatic spike-template–based analysis of interictal EEG–fMRI

A prerequisite for the implementation of interictal electroencephalography–correlated functional magnetic resonance imaging (EEG‐fMRI) in the presurgical work‐up for epilepsy surgery is straightforward processing. We propose a new semi‐automatic method as alternative for the challenging and time‐consuming visual spike identification.

Automatic selection of epileptic independent fMRI components.

EEG-correlated fMRI analysis has proven to be useful in localizing regions of BOLD activation related to epileptic activity. However, as EEG does not always provide reliable information, purely fMRI-based data-driven techniques are invaluable. Recently, we have shown that independent component analysis (ICA) can extract sources related to the epileptic network even in such EEG-negative cases [1]. Moreover, these sources were shown to be informative with respect to the seizure onset zone (SOZ). In order to utilize this concept in clinical practice in a prospective manner, this work aims at developing an automatic technique for selecting the epileptic sources. The proposed approach applies a cascade of two classifiers. In the first step artifact related sources are discarded. In the second step the sources are characterized by four discriminative features and epileptic sources are selected from among other BOLD-related components. Our technique reaches a promising 77% specificity and provides concordant sources with the EEG-correlated fMRI activation maps or with the SOZ in 71% of the cases.

ICA Component Selection Based on Sparse Activelet Reconstruction for fMRI Analysis in Refractory Focal Epilepsy

EEG-fMRI is a recently emerging tool that can be used in the presurgical evaluation of focal epilepsy patients. Standard analysis techniques rely on the principle that fMRI can provide accurate localization of hemodynamic changes corresponding to events observed on EEG. However, its applicability is limited as EEG does not always provide sufficient and reliable information on the timing of the epileptic activity. Therefore, there is an increasing demand for techniques capable of localizing the epileptic activity based solely on the fMRI time series. Independent component analysis (ICA) has been shown to separate epileptic activity in the fMRI from other neural sources, artifacts and noise. We propose here to automatically detect the epileptic component based on sparse reconstruction in the activelet basis. The algorithm was evaluated on a dataset of 10 patients. It is shown that the largest activation cluster of the identified component overlapped with the ictal onset zone (IOZ) in all 3 patients with sparse interictal spike timing. In the 7 other patients, the selected component either overlapped with the IOZ and/or the ictal hyperperfusion, or correlated with the EEG-derived time course of the interictal activity. We conclude that the proposed technique might be able to identify epileptic components without using EEG.