Thalamic volume reduction in drug-naive patients with new-onset genetic generalized epilepsy

OBJECTIVE: Patients with genetic generalized epilepsy (GGE) have subtle morphologic abnormalities of the brain revealed with magnetic resonance imaging (MRI), particularly in the thalamus. However, it is unclear whether morphologic abnormalities of the brain in GGE are a consequence of repeated seizures over the duration of the disease, or are a consequence of treatment with antiepileptic drugs (AEDs), or are independent of these factors. Therefore, we measured brain morphometry in a cohort of AED-naive patients with GGE at disease onset. We hypothesize that drug-naive patients at disease onset have gray matter changes compared to age-matched healthy controls. METHODS: We performed quantitative measures of gray matter volume in the thalamus, putamen, caudate, pallidum, hippocampus, precuneus, prefrontal cortex, precentral cortex, and cingulate in 29 AED-naive patients with new-onset GGE and compared them to 32 age-matched healthy controls. We subsequently compared the shape of any brain structures found to differ in gray matter volume between the groups. RESULTS: The thalamus was the only structure to show reduced gray matter volume in AED-naive patients with new-onset GGE compared to healthy controls. Shape analysis revealed that the thalamus showed deflation, which was not uniformly distributed, but particularly affected a circumferential strip involving anterior, superior, posterior, and inferior regions with sparing of medial and lateral regions. SIGNIFICANCE: Structural abnormalities in the thalamus are present at the initial onset of GGE in AED-naive patients, suggesting that thalamic structural abnormality is an intrinsic feature of GGE and not a consequence of AEDs or disease duration

Optimising EEG-fMRI for Localisation of Focal Epilepsy in Children

BACKGROUND: Early surgical intervention in children with drug resistant epilepsy has benefits but requires using tolerable and minimally invasive tests. EEG-fMRI studies have demonstrated good sensitivity for the localization of epileptic focus but a poor yield although the reasons for this have not been systematically addressed. While adults EEG-fMRI studies are performed in the "resting state"; children are commonly sedated however, this has associated risks and potential confounds. In this study, we assessed the impact of the following factors on the tolerability and results of EEG-fMRI in children: viewing a movie inside the scanner; movement; occurrence of interictal epileptiform discharges (IED); scan duration and design efficiency. This work's motivation is to optimize EEG-fMRI parameters to make this test widely available to paediatric population. METHODS: Forty-six children with focal epilepsy and 20 controls (6-18) underwent EEG-fMRI. For two 10 minutes sessions subjects were told to lie still with eyes closed, as it is classically performed in adult studies ("rest sessions"), for another two sessions, subjects watched a child friendly stimulation i.e. movie ("movie sessions"). IED were mapped with EEG-fMRI for each session and across sessions. The resulting maps were classified as concordant/discordant with the presumed epileptogenic focus for each subject. FINDINGS: Movement increased with scan duration, but the movie reduced movement by ~40% when played within the first 20 minutes. There was no effect of movie on the occurrence of IED, nor in the concordance of the test. Ability of EEG-fMRI to map the epileptogenic region was similar for the 20 and 40 minute scan durations. Design efficiency was predictive of concordance. CONCLUSIONS: A child friendly natural stimulus improves the tolerability of EEG-fMRI and reduces in-scanner movement without having an effect on IED occurrence and quality of EEG-fMRI maps. This allowed us to scan children as young as 6 and obtain localising information without sedation. Our data suggest that ~20 minutes is the optimal length of scanning for EEG-fMRI studies in children with frequent IED. The efficiency of the fMRI design derived from spontaneous IED generation is an important factor for producing concordant results

Revealing epilepsy type using a computational analysis of interictal EEG

This is the final version. Available from Nature Research via the DOI in this record.All materials (functional networks and code) are available upon request from the corresponding author.Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.Medical Research Council (MRC)Wellcome TrustEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome Trus

Revealing epilepsy type using a computational analysis of interictal EEG

This is the final version. Available from Nature Research via the DOI in this record.All materials (functional networks and code) are available upon request from the corresponding author.Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.Medical Research Council (MRC)Wellcome TrustEpilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome Trus

Decoding the neural representation of fine-grained conceptual categories

Neuroscientific research on conceptual knowledge based on the grounded cognition framework has shed light on the organization of concrete concepts into semantic categories that rely on different types of experiential information. Abstract concepts have traditionally been investigated as an undifferentiated whole, and have only recently been addressed in a grounded cognition perspective. The present fMRI study investigated the involvement of brain systems coding for experiential information in the conceptual processing of fine-grained semantic categories along the abstract–concrete continuum. These categories consisted of mental state-, emotion-, mathematics-, mouth action-, hand action-, and leg action-related meanings. Thirty-five sentences for each category were used as stimuli in a 1-back task performed by 36 healthy participants. A univariate analysis failed to reveal category-specific activations. Multivariate pattern analyses, in turn, revealed that fMRI data contained sufficient information to disentangle all six fine-grained semantic categories across participants. However, the category-specific activity patterns showed no overlap with the regions coding for experiential information. These findings demonstrate the possibility of detecting specific patterns of neural representation associated with the processing of fine-grained conceptual categories, crucially including abstract ones, though bearing no anatomical correspondence with regions coding for experiential information as predicted by the grounded cognition hypothesis

Sensorimotor semantics on the spot: brain activity dissociates between conceptual categories within 150 ms

Although semantic processing has traditionally been associated with brain responses maximal at 350–400 ms, recent studies reported that words of different semantic types elicit topographically distinct brain responses substantially earlier, at 100–200 ms. These earlier responses have, however, been achieved using insufficiently precise source localisation techniques, therefore casting doubt on reported differences in brain generators. Here, we used high-density MEG-EEG recordings in combination with individual MRI images and state-of-the-art source reconstruction techniques to compare localised early activations elicited by words from different semantic categories in different cortical areas. Reliable neurophysiological word-category dissociations emerged bilaterally at ~ 150 ms, at which point action-related words most strongly activated frontocentral motor areas and visual object-words occipitotemporal cortex. These data now show that different cortical areas are activated rapidly by words with different meanings and that aspects of their category-specific semantics is reflected by dissociating neurophysiological sources in motor and visual brain systems