Silvia Francesca Storti

EEG and fMRI Coregistration to Investigate the Cortical Oscillatory Activities During Finger Movement

Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) may be used to identify blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG event. In this study we used EEG-fMRI to determine the possible correlation between topographical movement-related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI-BOLD cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 9 subjects during eyes-open condition inside a 1.5 T magnetic resonance (MR) scanner using a MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during␣fMRI acquisition. For EEG data analysis we used the event-related-synchronization/desynchronization (ERS/ERD) approach to investigate where movement-related decreases in alpha and beta power are located. For image statistical analysis we used a general linear model (GLM) approach. There was a significant correlation between the positive-negative ratio of BOLD signal peaks and ERD values in the electrodes over the region of activation. We conclude that combined EEG-fMRI may be used to investigate movement-related oscillations of the human brain inside an MRI scanner and the movement-related changes in the EMG or EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.

Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements

BackgroundRobot-assisted therapy in patients with neurological disease is an attempt to improve function in a moderate to severe hemiparetic arm. A better understanding of cortical modifications after robot-assisted training could aid in refining rehabilitation therapy protocols for stroke patients. Modifications of cortical activity in healthy subjects were evaluated during voluntary active movement, passive robot-assisted motor movement, and motor imagery tasks performed under unimanual and bimanual protocols.MethodsTwenty-one channel electroencephalography (EEG) was recorded with a video EEG system in 8 subjects. The subjects performed robot-assisted tasks using the Bi-Manu Track robot-assisted arm trainer. The motor paradigm was executed during one-day experimental sessions under eleven unimanual and bimanual protocols of active, passive and imaged movements. The event-related-synchronization/desynchronization (ERS/ERD) approach to the EEG data was applied to investigate where movement-related decreases in alpha and beta power were localized.ResultsVoluntary active unilateral hand movement was observed to significantly activate the contralateral side; however, bilateral activation was noted in all subjects on both the unilateral and bilateral active tasks, as well as desynchronization of alpha and beta brain oscillations during the passive robot-assisted motor tasks. During active-passive movement when the right hand drove the left one, there was predominant activation in the contralateral side. Conversely, when the left hand drove the right one, activation was bilateral, especially in the alpha range. Finally, significant contralateral EEG desynchronization was observed during the unilateral task and bilateral ERD during the bimanual task.ConclusionsThis study suggests new perspectives for the assessment of patients with neurological disease. The findings may be relevant for defining a baseline for future studies investigating the neural correlates of behavioral changes after robot-assisted training in stroke patients.

Brain oscillatory activity during motor imagery in EEG-fMRI coregistration

The purpose of the present work was to investigate the correlation between topographical changes in brain oscillatory activity and the blood oxygenation level-dependent (BOLD) signal during a motor imagery (MI) task using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) coregistration. EEG was recorded in 7 healthy subjects inside a 1.5 T MR scanner during the imagination of the kinesthetic experience of movement. A Fast Fourier Transform was applied to EEG signal in the rest and active conditions. We used the event-related-synchronization (ERS)/desynchronization (ERD) approach to characterize where the imagination of movement produces a decrease in alpha and beta power. The mean alpha map showed ERD decrease localized over the contralateral sensory motor area (SM1c) and a light desynchronization in the ipsilateral sensory motor area (SM1i); whereas the mean beta map showed ERD decrease over the supplementary motor area (SMA). fMRI showed significant activation in SMA, SM1c, SM1i. The correlation is negative in the contralateral side and positive in the ipsilateral side. Using combined EEG-fMRI signals we obtained useful new information on the description of the changes in oscillatory activity in alpha and beta bands during MI and on the investigation of the sites of BOLD activity as possible sources in generating these rhythms. By correlating BOLD and ERD/ERS we may identify more accurately which regions contribute to changes of the electrical response.

Behavioral and Neurophysiological Effects of Repetitive Transcranial Magnetic Stimulation on the Minimally Conscious State: A Case Study

Background. In 2007, Schiff et al reported a patient in a minimally conscious state (MCS) who responded to deep brain stimulation (DBS), but clinicians cannot predict which patients might respond prior to the implantation of electrodes. Methods. A patient in a MCS for 5 years participated in an ABA design alternating between repetitive transcranial magnetic stimulation (rTMS) and peripheral nerve stimulation. rTMS (condition A) involved the delivery of 10 trains of 100 stimuli at 20 Hz using a stimulator with a 70-mm figure-of-eight coil to elicit a contraction of the abductor pollicis brevis. Condition B used median nerve electrical stimulation. Results. After peripheral stimulation, the patient did not exhibit clinical, behavioral, or electroencephalographic (EEG) changes. The frequency of specific and meaningful behaviors increased after rTMS, along with the absolute and relative power of the EEG δ, β, and α bands. Conclusion. These results suggest that rTMS may improve awareness and arousal in MCS. If these results are reproducible, rTMS may identify subgroups of MCS patients who might benefit from DBS.

Integrating EEG and fMRI in epilepsy.

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes.

Continuous EEG–fMRI in patients with partial epilepsy and focal interictal slow-wave discharges on EEG.

PURPOSE To verify whether in patients with partial epilepsy and routine electroenecephalogram (EEG) showing focal interictal slow-wave discharges without spikes combined EEG-functional magnetic resonance imaging (fMRI) would localize the corresponding epileptogenic focus, thus providing reliable information on the epileptic source. METHODS Eight patients with partial epileptic seizures whose routine scalp EEG recordings on presentation showed focal interictal slow-wave activity underwent EEG-fMRI. EEG data were continuously recorded for 24 min (four concatenated sessions) from 18 scalp electrodes, while fMRI scans were simultaneously acquired with a 1.5-Tesla magnetic resonance imaging (MRI) scanner. After recording sessions and MRI artefact removal, EEG data were analyzed offline. We compared blood oxygen level-dependent (BOLD) signal changes on fMRI with EEG recordings obtained at rest and during activation (with and without focal interictal slow-wave discharges). RESULTS In all patients, when the EEG tracing showed the onset of focal slow-wave discharges on a few lateralized electrodes, BOLD-fMRI activation in the corresponding brain area significantly increased. We detected significant concordance between focal EEG interictal slow-wave discharges and focal BOLD activation on fMRI. In patients with lesional epilepsy, the epileptogenic area corresponded to the sites of increased focal BOLD signal. CONCLUSIONS Even in patients with partial epilepsy whose standard EEGs show focal interictal slow-wave discharges without spikes, EEG-fMRI can visualize related focal BOLD activation thus providing useful information for pre-surgical planning.

Combining ESI, ASL and PET for quantitative assessment of drug-resistant focal epilepsy

When localization of the epileptic focus is uncertain, the epileptic activity generator may be more accurately identified with non-invasive imaging techniques which could also serve to guide stereo-electroencephalography (sEEG) electrode implantation. The aim of this study was to assess the diagnostic value of perfusion magnetic resonance imaging with arterial spin labeling (ASL) in the identification of the epileptogenic zone, as compared to the more invasive positron-emission tomography (PET) and other established investigation methods for source imaging of electroencephalography (EEG) data. In 6 patients with drug-resistant focal epilepsy, standard video-EEG was performed to identify clinical seizure semeiology, and high-density EEG, ASL and FDG-PET to non-invasively localize the epileptic focus. A standardized source imaging procedure, low-resolution brain electromagnetic tomography constrained to the individual matter, was applied to the averaged spikes of high-density EEG. Quantification of current density, cerebral blood flow, and standardized uptake value were compared over the same anatomical areas. In most of the patients, source in the interictal phase was associated with an area of hypoperfusion and hypometabolism. Conversely, in the patients presenting with early post-ictal discharges, the brain area identified by electrical source imaging (ESI) as the generating zone appeared to be hyperperfused. In 2 patients in whom the focus remained uncertain, the postoperative follow-up showed the disappearance of epileptic activity. As an innovative and more comprehensive approach to the study of epilepsy, the combined use of ESI, perfusion MRI, and PET may play an increasingly important role in the non-invasive evaluation of patients with refractory focal epilepsy.

Wavelet analysis as a tool for investigating movement-related cortical oscillations in EEG-fMRI coregistration.

Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) identifies blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG events. In this study we used EEG-fMRI to determine the possible correlation between topographical movement-related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 12 subjects during eyes-closed condition inside a three T magnetic resonance (MR) scanner using an MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. For EEG data analysis we used a time–frequency approach to measure time by varying the energy in a signal at a given frequency band by the convolution of the EEG signal with a wavelet family in the alpha and beta bands. The correlation between the BOLD signal associated with the EEG regressor provides that sensory motor region is a source of the EEG. We conclude that combined EEG-fMRI can be used to investigate movement-related oscillations of the human brain inside an MRI scanner and wavelet analysis adds further details on the EEG changes. The movement-related changes in the EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.

Time-frequency analysis of short-lasting modulation of EEG induced by intracortical and transcallosal paired TMS over motor areas.

Dynamic changes in spontaneous electroencephalogram (EEG) rhythms can be seen to occur with a high rate of variability. An innovative method to study brain function is by triggering oscillatory brain activity with transcranial magnetic stimulation (TMS). EEG-TMS coregistration was performed on five healthy subjects during a 1-day experimental session that involved four steps: baseline acquisition, unconditioned single-pulse TMS, intracortical inhibition (ICI, 3 ms) paired-pulse TMS, and transcallosal stimulation over left and right primary motor cortex (M1). A time-frequency analysis based on the wavelet method was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS. Single, paired, and transcallosal TMS applied on the sensorimotor areas induced rapid desynchronization over the frontal and central-parietal electrodes mainly in the alpha and beta bands, followed by a rebound of synchronization, and rapid synchronization of delta and theta activity. Wavelet analysis after a perturbation approach is a novel way to investigate modulation of oscillatory brain activity. The main findings are consistent with the concept that the human motor system may be based on networklike oscillatory cortical activity and might be modulated by single, paired, and transcallosal magnetic pulses applied to M1, suggesting a phenomenon of fast brain activity resetting and triggering of slow activity.

Automatic selection of resting-state networks with functional magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearsons median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥92%) and the occipital network, which includes the medial visual cortical areas (≥94%), and consistent for the attention network (≥80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.