Spike definition mri12/13/2023 ![]() ![]() Diagnosis of any abnormal changes in the brain may indicate a disorder 14, therefore awareness of the neuronal behavior along with the biomechanical structure can be remarkably effective 2. The study of biological signals, which contain information about the activity and structure of the brain, has received much attention from researchers 3, 10 but still few studies combine data such as EEG and MRI to enhance reliability, accuracy and interpretability of the models 14.Īnalysis and prediction of bio-signals, due to their unique features and complexities, has always been a challenging task 15, 16. 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. In conclusion, the degree of agreement between the BOLD and EEG source localization indicates that the combination of these two noninvasive techniques offers the possibility of advancing the study of the generators of epileptiform electrical activity.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. The overall mean distance between the main moving dipole and the center of the nearest BOLD activation was 3.5 and 2.2 cm for the negative and positive peaks, respectively, including one case of a deep BOLD activation, in which the distance was 5 cm. In the five cases with structural abnormality visible on T1 scans, the BOLD activation overlapped or was in close proximity to the abnormality. In all cases dipole models could be found that explained a sufficient amount of the data and that were anatomically concordant with the BOLD localization. The BOLD and structural images were coregistered, allowing the measurement of distances between the generator models and BOLD activation(s) and structural lesion when present. EEG source analysis solutions based on 64-channel EEG recorded in a separate session outside the scanner were obtained using dipole models and compared to the BOLD localization. We studied six patients with localization-related epilepsy, frequent interictal epileptiform discharges, and positive spike-triggered blood oxygen level-dependent functional MRI (BOLD-fMRI) findings. ![]()
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