![]() To measure and to analyze brain activities, MRI 1, EEG 2, 3, 4, 5, 6, 7, 8, and functional MRI (fMRI) 9 are widely used for the diagnosis and treatment of diseases such as epilepsy 10, for the prediction of brain surgery outcomes 12, human muscle activity 13, psychological analyses 6, 8, 14, 17, 34, to mention only a few. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others. The models are interpretable and facilitate a better understanding of related brain processes. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. other EEG channels, from where data has not been collected. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. All rights reserved.This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. Studies with MRI at fixed intervals and after SE cessation are necessary.ĭiagnosis EEG Lateralized periodic discharges MRI Nonconvulsive status epilepticus.Ĭopyright © 2021 Elsevier Masson SAS. ![]() However, it is more complicated to move patients to MRI than to perform an EEG in the intensive care unit, and at this time, we do not know how long the signal changes persist after the end of the SE. Additional MRI criteria beyond the classical clinical/EEG criteria of NCSE (rhythmic versus periodic, spatiotemporal evolution of the pattern…) should now be systematically added. With the development of artificial intelligence, MRI has the potential to transform the diagnosis of SE. For the future, it is more interesting to develop a strategy with MRI than SPECT or PET for the diagnosis of NCSE. MRI is more accessible than single photon emission computed tomography (SPECT) or positron emission tomography (PET). It is now easy to transfer MRI to a platform with expertise. The learning curve for MRI is better than for EEG. Continuous video-EEGs require a specialized neurophysiology unit. The interpretation of EEG tracings is not easy, with numerous pitfalls and artifacts. MRI may help identify the ictal nature of LPDs. MRI with hyperintense lesions on FLAIR and DWI provides information related to brain activity over a longer period of time than a standard EEG where only controversial patterns like lateralized periodic discharges (LPDs) may be recorded. MRI has a higher spatial resolution than electroencephalography (EEG). With the development of stroke centers, MRI is available 24h/24 in most hospitals. with metabolic/toxic encephalopathies or Creutzfeldt-Jakob disease. MRI can also aid in the differential diagnosis with generalized NCSE when there is a clinical or EEG doubt, e.g. Approximately half of patients with status epilepticus (SE) have signal changes. Magnetic resonance imaging (MRI) can now be used to diagnose or to provide confirmation of focal nonconvulsive status epilepticus (NCSE).
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