3 research outputs found

    Decoding memory processing from electro-corticography in human posteromedial cortex

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    peer reviewedRecently machine learning models have been applied to neuroimaging data, which allow predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. These pattern recognition based methods present clear benefits over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each feature in the model. Machine learning methods have been applied to a range of data, from MRI to EEG. However, these multivariate techniques have scarcely been applied to electrocorticography (ECoG) data to investigate cognitive neuroscience questions. In this work, we used previously published ECoG data from 8 subjects to show that machine learning techniques can complement univariate techniques and be more sensitive to certain effects

    EEG-fMRI signatures of spontaneous brain activity in healthy volunteers and epilepsy patients

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    Background: Functional magnetic resonance imaging (fMRI) provides maps of haemodynamic activity with uniform resolution across the brain. Simultaneous recording of electroencephalography (EEG) during fMRI (EEG-fMRI) was developed to localize spontaneously occurring epileptiform discharges. In focal epilepsy, it can identify candidate brain regions for surgical removal as a treatment option in medically refractory epilepsy; and in generalized epilepsy syndromes reveals those involved during the EEG changes. In healthy subjects, EEG-fMRI has linked spontaneous ongoing EEG activity with fMRI resting state networks. Methods: After method refinements, patients with medically refractory focal epilepsy and those with generalized epilepsy were studied with EEG-fMRI and group analyses performed to identify typical sets of brain regions involved in the epileptic process. Findings: In individual patients with refractory focal epilepsy, EEG-fMRI can produce activity maps including the seizure onset zone and propagated epileptic activity. Clinically, these can be confirmatory of results from alternative diagnostic techniques, or alternatively serve to generate a hypothesis on the potential epileptic focus, but under certain conditions may also be of negative predictive value with respect to surgical treatment success. At the group level in patients with temporal lobe epilepsy and complex partial seizures as well as in patients with generalized epilepsy and absence seizures, altered resting state network activity during EEG changes were found in default mode brain regions fitting well the ictal semiology, because these are known to reduce their activity during states of reduced consciousness. In (1) lateralized temporal lobe epilepsies, (2) an unselected mix of focal epilepsies, and (3) generalized epilepsies, activity increases occurred in typical brain regions suggesting an associated “hub function”, namely ipsilateral to the presumed cortical focus in the hippocampus; in an area near the frontal piriform cortex; and bilaterally in the thalamus, respectively. These findings argue for a network rather than a zone concept of epilepsy
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