Local blur estimation based on toggle mapping

International audienceA local blur estimation method is proposed, based on the difference between the gradient and the residue of the toggle mapping. This method is able to compare the quality of images with different content and does not require a contour detection step. Qualitative results are shown in the context of the LINX project. Then, quantitative results are given on DIQA database, outperforming the combination of classical blur detection methods reported in the literature

: Seizure onset zone imaging

International audienceStereo-electroencephalography is used to localize the seizure onset zone and connected neuronal networks in surgical candidates suffering from intractable focal epilepsy. The concept of an epileptogenicity index has been proposed recently to represent the likelihood of various regions being part of the seizure onset zone. It quantifies low-voltage fast activity, the electrophysiological signature of seizure onset usually assessed visually by neurologists. Here, we revisit epileptogenicity in light of neuroimaging tools such as those provided in statistical parametric mapping software. Our goal is to propose a robust approach, allowing easy exploration of patients' brains in time and space. The procedure is based upon statistical parametric mapping, which is an established framework for comparing multi-dimensional image data that allows one to correct for inherent multiple comparisons. Statistics can also be performed at the group level, between seizures in the same patient or between patients suffering from the same type of epilepsy using normalization of brains to a common anatomic atlas. Results are obtained from three case studies (insular reflex epilepsy, cryptogenic frontal epilepsy and lesional occipital epilepsy) where tailored resection was performed, and from a group of 10 patients suffering from mesial temporal lobe epilepsy. They illustrate the basics of the technique and demonstrate its very good reproducibility and specificity. Most importantly, the proposed approach to the quantification of the seizure onset zone allows one to summarize complex signals in terms of a time-series of statistical parametric maps that can support clinical decisions. Quantitative neuroimaging of stereo-electroencephalographic features of seizures might thus help to provide better pre-surgical assessment of patients undergoing resective surgery

Studying Network Mechanisms Using Intracranial Stimulation in Epileptic Patients

Patients suffering from focal drug-resistant epilepsy who are explored using intracranial electrodes allow to obtain data of exceptional value for studying brain dynamics in correlation with pathophysiological and cognitive processes. Direct electrical stimulation (DES) of cortical regions and axonal tracts in those patients elicits a number of very specific perceptual or behavioral responses, but also abnormal responses due to specific configurations of epileptic networks. Here, we review how anatomo-functional brain connectivity and epilepsy network mechanisms can be assessed from DES responses measured in patients. After a brief summary of mechanisms of action of brain electrical stimulation, we recall the conceptual framework for interpreting DES results in the context of brain connectivity and review how DES can be used for the characterization of functional networks, the identification of the seizure onset zone, the study of brain plasticity mechanisms, and the anticipation of epileptic seizures. This pool of exceptional data may be underexploited by fundamental research on brain connectivity and leaves much to be learned

Overt speech decoding from cortical activity: a comparison of different linear methods

IntroductionSpeech BCIs aim at reconstructing speech in real time from ongoing cortical activity. Ideal BCIs would need to reconstruct speech audio signal frame by frame on a millisecond-timescale. Such approaches require fast computation. In this respect, linear decoder are good candidates and have been widely used in motor BCIs. Yet, they have been very seldomly studied for speech reconstruction, and never for reconstruction of articulatory movements from intracranial activity. Here, we compared vanilla linear regression, ridge-regularized linear regressions, and partial least squares regressions for offline decoding of overt speech from cortical activity.MethodsTwo decoding paradigms were investigated: (1) direct decoding of acoustic vocoder features of speech, and (2) indirect decoding of vocoder features through an intermediate articulatory representation chained with a real-time-compatible DNN-based articulatory-to-acoustic synthesizer. Participant's articulatory trajectories were estimated from an electromagnetic-articulography dataset using dynamic time warping. The accuracy of the decoders was evaluated by computing correlations between original and reconstructed features.ResultsWe found that similar performance was achieved by all linear methods well above chance levels, albeit without reaching intelligibility. Direct and indirect methods achieved comparable performance, with an advantage for direct decoding.DiscussionFuture work will address the development of an improved neural speech decoder compatible with fast frame-by-frame speech reconstruction from ongoing activity at a millisecond timescale