Generalised spike-wave discharges (GSW) in idiopathic generalised epilepsy (IGE) appear to have abrupt onset on EEG. However, in rodent models, GSW emerge during evolving brain network states. Using EEG-fMRI, Tangwiriyasakul et al. reveal that GSW onset in human IGE, as in rodent models, emerges during evolving brain network states.

Objective: Drug-resistant temporal lobe epilepsy (TLE) often requires thorough investigation to define the epileptogenic zone for surgical treatment. We used simultaneous interictal scalp EEG-fMRI to evaluate its value for predicting longterm post-surgical outcome.Methods: 30 patients undergoing pre-surgical evaluation and proceeding to temporal lobe (TL) resection were studied.Interictal epileptiform discharges (IEDs) were identified on intra-MRI EEG and used to build a model of hemodynamic changes. In addition, topographic electroencephalographic correlation maps were calculated between the average IED during video-EEG and intra-MRI EEG and used as a condition. This allowed the analysis of all data irrespective of the presence of IED on intra-MRI EEG. Mean follow-up after surgery was 46 months. ILAE outcomes 1 and 2 were considered good and 3 to 6 poor surgical outcome. Hemodynamic maps were classified according to the presence (Concordant) or absence (Discordant) of BOLD change in the TL overlapping with the surgical resection. Results:The proportion of patients with good surgical outcome was significantly higher (13/16; 81%) in Concordant than in Discordant group (3/14; 21%) (Chi-squared test, Yates correction, p=0.003) and multivariate analysis showed that Concordant BOLD maps were independently related to good surgical outcome (p=0.007). Sensitivity and specificity of EEG-fMRI results to identify patients with good surgical outcome were 81% and 79%, respectively and positive and negative predictive values were 81% and 79%, respectively.

Different noise sources in fMRI acquisition can lead to spurious false positives and reduced sensitivity. We have developed a biophysically-based model (named FIACH: Functional Image Artefact Correction Heuristic) which extends current retrospective noise control methods in fMRI. FIACH can be applied to both General Linear Model (GLM) and resting state functional connectivity MRI (rs-fcMRI) studies. FIACH is a two-step procedure involving the identification and correction of non-physiological large amplitude temporal signal changes and spatial regions of high temporal instability. We have demonstrated its efficacy in a sample of 42 healthy children while performing language tasks that include overt speech with known activations. We demonstrate large improvements in sensitivity when FIACH is compared with current methods of retrospective correction. FIACH reduces the confounding effects of noise and increases the study's power by explaining significant variance that is not contained within the commonly used motion parameters. The method is particularly useful in detecting activations in inferior temporal regions which have proven problematic for fMRI. We have shown greater reproducibility and robustness of fMRI responses using FIACH in the context of task induced motion. In a clinical setting this will translate to increasing the reliability and sensitivity of fMRI used for the identification of language lateralisation and eloquent cortex. FIACH can benefit studies of cognitive development in young children, patient populations and older adults.