A Comparison of Independent Component Analysis (ICA) of fMRI and Electrical Source Imaging (ESI) in Focal Epilepsy Reveals Misclassification Using a Classifier.

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.

Brain complex network analysis by means of resting state fMRI and graph analysis: will it be helpful in clinical epilepsy?

Functional magnetic resonance imaging (fMRI) has just completed 20 years of existence. It currently serves as a research tool in a broad range of human brain studies in normal and pathological conditions, as is the case of epilepsy. To date, most fMRI studies aimed at characterizing brain activity in response to various active paradigms. More recently, a number of strategies have been used to characterize the low-frequency oscillations of the ongoing fMRI signals when individuals are at rest. These datasets have been largely analyzed in the context of functional connectivity, which inspects the covariance of fMRI signals from different areas of the brain. In addition, resting state fMRI is progressively being used to evaluate complex network features of the brain. These strategies have been applied to a number of different problems in neuroscience, which include diseases such as Alzheimer's, schizophrenia, and epilepsy. Hence, we herein aimed at introducing the subject of complex network and how to use it for the analysis of fMRI data. This appears to be a promising strategy to be used in clinical epilepsy. Therefore, we also review the recent literature that has applied these ideas to the analysis of fMRI data in patients with epilepsy.

Application of partial directed coherence to the analysis of resting-state EEG-fMRI data.

The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data potentially allows measurement of brain signals with both high spatial and temporal resolution. Partial directed coherence (PDC) is a Granger causality measure in the frequency domain, which is often used to infer the intensity of information flow over the brain from EEG data. In the current study, we propose a new approach to investigate functional connectivity in resting-state (RS) EEG-fMRI data by combining time-varying PDC with the analysis of blood oxygenation level-dependent (BOLD) signal fluctuations. Basically, we aim to identify brain circuits that are more active when the information flow is increased between distinct remote neuronal modules. The usefulness of the proposed method is illustrated by application to simultaneously recorded EEG-fMRI data from healthy subjects at rest. Using this approach, we decomposed the nodes of RS networks in fMRI data according to the frequency band and directed flow of information provided from EEG. This approach therefore has the potential to inform our understanding of the regional characteristics of oscillatory processes in the human brain.

Quantification of BOLD fMRI parameters to infer cerebrovascular reactivity of the middle cerebral artery.

PURPOSE: To quantify the amplitude and temporal aspects of the blood oxygenation level-dependent (BOLD) response to an auditory stimulus during normocapnia and hypercapnia in healthy subjects in order to establish which BOLD parameters are best suited to infer the cerebrovascular reactivity (CVR) in the middle cerebral artery (MCA) territory. MATERIALS AND METHODS: Twenty healthy volunteers (mean age: 23.6 ± 3.7 years, 11 women) were subjected to a functional paradigm composed of five epochs of auditory stimulus (3 sec) intercalated by six intervals of rest (21 sec). Two levels of hypercapnia were achieved by a combination of air and CO2 while the end-tidal CO2 (ETCO2 ) was continually measured. An autoregressive method was applied to analyze four parameters of the BOLD signal: onset-time, time-to-peak, full-width-at-half-maximum (FWHM), and amplitude. RESULTS: BOLD onset time (P < 0.001) and full-width at half-maximum (FWHM) (P < 0.05) increased linearly, while BOLD amplitude decreased (P < 0.001) linearly with increasing levels of hypercapnia. Test-retest for reproducibility in five subjects revealed excellent concordance for onset time and amplitude. CONCLUSION: The robust linear dependence of BOLD onset time, FWHM, and amplitude to hypercapnia suggest future application of this protocol in clinical studies aimed at evaluating CVR of the MCA territory.

Imagem por Fontes Eletroencefalográficas - Uma Revisão Sistemática Sobre Sua Acurácia na Determinação da Zona Epileptogênica em Pacientes com Epilepsia Focal Refratária / EEG source imaging - a systematic review about its accuracy in defining the epileptogenic zone in patients with refractory focal epilepsy

O objetivo da avaliação pré-cirúrgica em pacientes com epilepsia refratária é delimitar a zona epileptogênica (ZE), área do encéfalo capaz de gerar crises e cuja ressecção tem o potencial para abolir ou reduzir as crises do paciente. Neste sentido, há um grande esforço no desenvolvimento e aprimoramento de técnicas diagnósticas não invasivas que possam localizar a ZE com precisão, buscando evitar ou diminuir a utilização de métodos invasivos, de custo e risco elevados. Uma técnica diagnóstica que tem recebido renovada atenção é a Imagem de Fontes Eletroencefalográficas (IFE). O uso dessa técnica se baseia no fato de que a localização da área do encéfalo geradora das descargas interictais (zona irritativa) guarda próxima relação com a ZE. Estudos recentes têm sugerido que a IFE tem um potencial para determinar a localização da ZE similar à magnetoencefalografia. Nesta revisão, analisamos estudos recentes utilizando a técnica na localização da ZE de pacientes com epilepsia refratária. Encontramos evidências de que a acurácia média do teste foi de 79%, bastante similar à acurácia da Imagem por Fontes Magnéticas reportada na literatura, que é de aproximadamente 77%.

The main goal of presurgical evaluation in patients with refractory epilepsy is to define the localization and extension of epileptogenic zone (EZ), the brain area responsible for generating seizures and whose resection has the potential to reduce or abolish epileptic seizures. Therefore, there has been an effort to develop diagnostic tests that can accurately localize the EZ non-invasively, avoiding invasive investigations that are risky and expensive. A diagnostic technique that has received renewed interest is electroencephalographic source imaging (ESI). This technique is based on the assumption that the irritative zone, the brain area that generates interictal EEG spikes, is spatially related with the EZ. Recent studies have shown that EEG has the potential to determine the localization of EZ similar to magnetoencephalography. In this review, we searched for studies reporting the accuracy of ESI on presurgical evaluation of patients with refractory epilepsy. We found that the accuracy of the test was 79% overall, similar to the accuracy of magnetic source imaging reported in the literature (77%).

From EEG to BOLD: brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions.

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic's homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements.

Detection of auditory cortex activity by fMRI using a dependent component analysis.

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.