A comparison of independent component analysis (ICA) of fMRI and electrical source imaging (ESI) in focal epilepsy reveals misclassification using a classifier
| Autor: | Tonicarlo Rodrigues Velasco, Danilo Maziero, Carlo Rondinoni, Carlos Ernesto Garrido Salmon, Marcio Sturzbecher, Agustin Lage Castellanos, David W. Carmichael |
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| Rok vydání: | 2015 |
| Předmět: |
Spatial correlation
Speech recognition Electroencephalography EEG-fMRI Epilepsy medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Ictal General linear model Brain Mapping Principal Component Analysis Radiological and Ultrasound Technology medicine.diagnostic_test EPILEPSIA Brain Signal Processing Computer-Assisted medicine.disease Independent component analysis Magnetic Resonance Imaging Oxygen Neurology Neurology (clinical) Epilepsies Partial Anatomy Psychology Classifier (UML) |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | Interictal epileptiform discharges (IEDs) can produce haemodynamic responses that can be detected by electroencephalography-functional magnetic resonance imaging (EEG-fMRI) using different analysis methods such as the general linear model (GLM) of IEDs or independent component analysis (ICA). The IEDs can also be mapped by electrical source imaging (ESI) which has been demonstrated to be useful in presurgical evaluation in a high proportion of cases with focal IEDs. ICA advantageously does not require IEDs or a model of haemodynamic responses but its use in EEG-fMRI of epilepsy has been limited by its ability to separate and select epileptic components. Here, we evaluated the performance of a classifier that aims to filter all non-BOLD responses and we compared the spatial and temporal features of the selected independent components (ICs). The components selected by the classifier were compared to those components selected by a strong spatial correlation with ESI maps of IED sources. Both sets of ICs were subsequently compared to a temporal model derived from the convolution of the IEDs (derived from the simultaneously acquired EEG) with a standard haemodynamic response. Selected ICs were compared to the patients’ clinical information in 13 patients with focal epilepsy. We found that the misclassified ICs clearly related to IED in 16/25 cases. We also found that the classifier failed predominantly due to the increased spectral range of fMRIs temporal responses to IEDs. In conclusion, we show that ICA can be an efficient approach to separate responses related to epilepsy but that contemporary classifiers need to be retrained for epilepsy data. Our findings indicate that, for ICA to contribute to the analysis of data without IEDs to improve its sensitivity, classification strategies based on data features other than IC time course frequency is required. |
| Databáze: | OpenAIRE |
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