Marcio J. Sturzbecher

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 Alzheimers, 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.

Non-extensive entropy and the extraction of BOLD spatial information in event-related functional MRI.

Functional magnetic resonance imaging (fMRI) data analysis has been carried out recently in the framework of information theory, by means of the Shannon entropy. As a natural extension, a method based on the generalized Tsallis entropy was developed to the analysis event-related (ER-fMRI), where a brief stimulus is presented, followed by a long period of rest. The new technique aims for spatial localization neuronal activity due to a specific task. This method does not require a priori hypothesis of the hemodynamic response function (HRF) shape and the linear relation between BOLD responses with the presented task. Numerical simulations were performed so as to determine the optimal values of the Tsallis q parameter and the number of levels, L. In order to avoid undesirable divergences of the Tsallis entropy, only positive q values were studied. Results from simulated data (with L = 3) indicated that, for q = 0.8, the active brain areas are detected with the highest performance. Moreover, the method was tested for an in vivo experiment and demonstrated the ability to discriminate active brain regions that selectively responded to a bilateral motor task.

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 haemodynamics 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.

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.

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.

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.

Anatomical and Functional MRI Changes after One Year of Auditory Rehabilitation with Hearing Aids

Hearing aids (HAs) are an effective strategy for auditory rehabilitation in patients with peripheral hearing deficits. Yet, the neurophysiological mechanisms behind HA use are still unclear. Thus far, most studies have focused on changes in the auditory system, although it is expected that hearing deficits affect a number of cognitive systems, notably speech. In the present study, we used audiometric evaluations in 14 patients with bilateral hearing loss before and after one year of continuous HA use and functional magnetic resonance imaging (fMRI) and cortical thickness analysis in 12 and 10 of them compared with a normal hearing control group. Prior to HA fitting, fMRI activity was found reduced in the auditory and language systems and increased in visual and frontal areas, expanding to multimodal integration cortices, such as the superior temporal gyrus, intraparietal sulcus, and insula. One year after rehabilitation with HA, significant audiometric improvement was observed, especially in free-field Speech Reception Threshold (SRT) test and functional gain, a measure of HA efficiency. HA use increased fMRI activity in the auditory and language cortices and multimodal integration areas. Individual fMRI signal changes from all these areas were positively correlated with individual SRT changes. Before rehabilitation, cortical thickness was increased in parts of the prefrontal cortex, precuneus, fusiform gyrus, and middle temporal gyrus. It was reduced in the insula, supramarginal gyrus, medial temporal gyrus, occipital cortex, posterior cingulate cortex, and claustrum. After HA use, increased cortical thickness was observed in multimodal integration regions, particularly the very caudal end of the superior temporal sulcus, the angular gyrus, and the inferior parietal gyrus/superior temporal gyrus/insula. Our data provide the first evidence that one year of HA use is related to functional and anatomical brain changes, notably in auditory and language systems, extending to multimodal cortices.

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

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.

Simultaneous EEG-fMRI: Integrating Spatial and Temporal Resolution

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are among the most widespread neuroimaging techniques available to noninvasively characterize certain aspects of human brain function. The temporal resolution of EEG is excellent, managing to capture neural events in the order of milliseconds. On the other hand, its spatial resolution lacks precision. Conversely, fMRI offers high spatial resolution, typically on the order of mm3. However, it has limited temporal resolution (∼sec), which is determined by the haemodynamic response. Therefore, simultaneous acquisition of EEG and fMRI is highly desirable. In recent years, the ability to perform combined EEG-fMRI has attracted considerable attention from the neuroscience community. In this chapter, relevant methodological aspects of EEG-fMRI will be first described, focused on the nature of neurophysiologic signals and difficulties to integrate electric and haemodynamic signals derived from both techniques. Second, state of the art strategies related to artifact correction and signal analysis will be described. Finally, a possible use of EEG-fMRI will be presented, focused on its potential application in epilepsy.

134. Language reorganization of patients with auditory deficiencies observed by functional magnetic resonance imaging

Mirror neurons, described in the monkey’s F5 motor area, are active during movement execution but also when the animal watches the same movement. A similar neural execution/observation mechanism, also implicated in the prediction of another person’s movement, exist also in humans. We evaluated if this capacity was affected by an upper-limb amputation. We recorded the readiness potential (RP) while subjects watched a movie where, after 2.0 s, an actor grasped a green object (Mov_obs). In another video, the object was red and the actor’s hand remained stationary (NO-movobs). Four patients with unilateral amputation and 8 control subjects were tested. The RP slope was calculated using linear regression. ANOVA revealed an interaction between experimental conditions and groups. Post-hoc analyses demonstrated that the difference between the Mov_obs and NO-movobs condition occurred both for the control group and for the amputees when they had to predict the impeding action performed with a hand corresponding to their intact hand, but not when amputees had to predict an observed hand movement on the side corresponding to their amputation. The absence of a RP in Mov_obs condition for the amputated side and its preservation in the spared side found in amputees suggests that cortical reorganization that follows amputation of a limb seems to impair the motor anticipation of the corresponding limb.