Segmenting electroencephalography wires reduces radiofrequency shielding artifacts in simultaneous electroencephalography and functional magnetic resonance imaging at 7 T

Purpose: Simultaneous scalp electroencephalography and functional magnetic resonance imaging (EEG-fMRI) enable noninvasive assessment of brain function with high spatial and temporal resolution. However, at ultra-high field, the data quality of both modalities is degraded by mutual interactions. Here, we thoroughly investigated the radiofrequency (RF) shielding artifact of a state-of-the-art EEG-fMRI setup, at 7 T, and design a practical solution to limit this issue. Methods: Electromagnetic field simulations and MR measurements assessed the shielding effect of the EEG setup, more specifically the EEG wiring. The effectiveness of segmenting the wiring with resistors to reduce the transmit field disruption was evaluated on a wire-only EEG model and a simulation model of the EEG cap. Results: The EEG wiring was found to exert a dominant effect on the disruption of the transmit field, whose intensity varied periodically as a function of the wire length. Breaking the electrical continuity of the EEG wires into segments shorter than one quarter RF wavelength in air (25 cm at 7 T) reduced significantly the RF shielding artifacts. Simulations of the EEG cap with segmented wires indicated similar improvements for a moderate increase of the power deposition. Conclusion: We demonstrated that segmenting the EEG wiring into shorter lengths using commercially available nonmagnetic resistors is effective at reducing RF shielding artifacts in simultaneous EEG-fMRI. This prevents the formation of RF-induced standing waves, without substantial specific absorption rate (SAR) penalties, and thereby enables benefiting from the functional sensitivity boosts achievable at ultra-high field.

Spatio-temporal coupling of the electric and hemodynamic brain responses in humans

Roberto Martuzzi

Our comprehension of human brain functions and their dynamics has been dramatically improved by recent developments in non-invasive imaging techniques. These methods can be divided into two different categories, according to the nature of the measured signal: hemodynamic techniques, such as functional magnetic resonance imaging (fMRI) and positiron emission tomography (PET), and electromagnetic techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). These two categories are have complementary characteristics: hemodynamic techniques have a good spatial resolution (on a millimeter spatial scale) but have a poor temporal resolution, which is inherently limited by the rate changes in blood flow and oxygenation. Electromagnetic techniques have sub-millisecond temporal resolution but have a poor spatial resolution, since the analysis of intracranial generators requires the solution of an underdetermined inverse problem (i.e. there are infinite solutions that can explain equally well the same scalp-recorded distribution). The complementarity of the characteristics of these two families of methods allowed researchers to suppose that the understanding of spatio-temporal brain dynamics can be drastically improved by their combination (so-called multimodal imaging). Unfortunately some caveats hinder such combination. First, the nature of neurovascular coupling is still poorly understood. Second, analytical methods for multimodal imaging are largely in their infancy. The first part of this thesis focuses on the analysis of the temporal characteristics of the blood oxygenation level dependent (BOLD) signal and on how they are modulated by stimulus conditions. To analyze the BOLD dynamics, a novel method for synchronizing stimulus delivery and volume acquisition was developed. This method allows for estimating the BOLD signal with a high temporal resolution (in this thesis up to 125 ms) and for studying how the temporal characteristics (in this thesis mainly the BOLD peak latency and slope) are modulated by stimulus conditions (with an approach similar to that used in the analysis of the EEG evoked potentials). We applied this novel technique to a simple reaction time task to lateralized visual stimuli (the so-called Poffenberger paradigm) as well as to a multisensory auditory-visual reaction time task. In the first study (the Poffenberger paradigm) the analysis of BOLD dynamics supported the theory of a bilateral visuo-motor pathway even in the case of a visual stimulus ipsilateral to the responding hand. In the second study, (the auditory-visual multisensory reaction-time task), the analysis showed auditory-visual interactions within both primary auditory and visual cortices that could not be otherwise revealed by traditional fMRI analysis methods since it does not involve changes in signal amplitude. The second part of this thesis focuses on the comparison of the statistical results obtained by the analyses of fMRI and of the intracranial local field potentials (LFPs), estimated by the ELECTRA inverse solution. We first developed a new method for the analysis of EEG data. This method is based on the statistical comparison of the spectral characteristics of the estimated intracranial LFPs of the pre- and post- stimulus onset periods. Each single trial is analyzed independently, without including an averaging step, so that the information carried by high frequencies is preserved. We also propose a new metric, called resemblance, to investigate the relationship between fMRI and the estimated intracranial LFPs. Single-trial analysis and the resemblance metric were applied in an experiment involving separate EEG and fMRI acquisitions during the same passive visual stimulation protocol. This experiment revealed that only a limited set of LFP frequencies shows a spatial correlation with fMRI. This set of frequencies changes across brain areas, such that progression from lower to higher cortical levels of visual processing incorporates at each step new frequencies. In conclusion, in this thesis we show that the estimation and the analysis of the BOLD time course can give an important contribution to better understanding brain functions and brain organization. To fully understand the meaning of changes in BOLD dynamics, we need a better knowledge of the neuro-vascular coupling. To do that, we introduced a new method for evaluating the relationship between EEG and fMRI across frequencies and anatomical regions.

EPFL2006

Simultaneous EEG-fMRI at ultra-high field for the study of human brain function

João Pedro Forjaco Jorge

Scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have highly complementary domains, and their combination has been actively sought within neuroscience research. The important gains in fMRI sensitivity achieved with higher field strengths open exciting perspectives for combined EEG-fMRI; however, simultaneous acquisitions are subject to highly undesirable interactions between the two modalities, which can strongly compromise data quality and subject safety, and most of these interactions are increased at higher fields. The work described in this thesis was centered on the development of simultaneous EEG-fMRI in humans at 7T, covering aspects of subject safety, signal quality assessment, and quality improvement. Additionally, given the potential value of high-field EEG-fMRI to study the neuronal correlates of so-called negative BOLD responses, an initial fMRI study was dedicated to these phenomena. The initial fMRI study aimed to characterize positive (PBR) and negative BOLD responses (NBR) to visual checkerboard stimulation of varying contrast and duration, focusing on NBRs occurring in visual and in auditory cortical regions. Results showed that visual PBRs and both visual and auditory NBRs significantly depend on stimulus contrast and duration, suggesting a dynamic system of visual-auditory interactions, sensitive to stimulus contrast and duration. The neuronal correlates of these interactions could not be addressed in higher detail with fMRI alone, yet could potentially be clarified in future work with combined EEG-fMRI. Moving on to simultaneous EEG-fMRI implementation, the first stage comprised an assessment of potential safety concerns at 7T. The safety tests comprised numerical simulations of RF power distribution and real temperature measurements on a phantom during acquisition. Overall, no significant safety concerns were found for the setup tested. A characterization of artifacts induced on MRI data due to the presence of EEG components was then performed. With the introduction of the EEG system, functional and anatomical images exhibited general losses in spatial SNR, with a smaller loss in temporal SNR in fMRI data. B0 and B1 field mapping pointed towards RF pulse disruption as the major degradation mechanism affecting MRI data. The main part of this work focused on EEG artifacts induced by MRI. The first step focused on optimizing signal transmission between the EEG cap and amplifiers, to minimize artifact contamination at this important stage. Along this line, adequate cable shortening and bundling effectively reduced environment noise in EEG recordings. Simultaneous acquisitions were then performed on humans using the optimized setup. On average, EEG data exhibited clear alpha modulation and average visual evoked potentials (VEP), with concomitant BOLD signal changes. In the second step, a novel approach for head motion artifact detection was developed, based on a simple modification of the EEG cap, and simultaneous acquisitions were performed in volunteers undergoing visual checkerboard stimulation. After gradient artifact correction, EEG signal variance was found to be largely dominated by pulse artifacts, but contributions from spontaneous motion were still comparable to those of neuronal activity. Using a combination of pulse artifact correction, motion artifact correction and ICA denoising, strong improvements in data quality could be obtained, especially at a single-trial level.

EPFL2016

Spatiotemporal stimulation strategies in neuroprostheses to improve locomotion after spinal cord injury

Jérôme Gandar

Spinal cord injury (SCI) disrupts communication within central nervous system and lead to range of neurological disorders including paralysis. Current rehabilitation strategies to restore locomotion are poorly effective in people with severe SCI. Epidural electrical stimulation (EES) showed promising results in animal models and humans to improve recovery. However, EES protocols have remained empirical, which has limited the efficacy of this paradigm. In this thesis, I report my contribution to the development of EES related software and hardware that were driven by the under- standing of the mechanisms underlying EES. These neurotechnologies showed a remarkable efficacy to reestablish locomotion in animal models of leg paralysis. EES protocols have primarily been delivered with continuous stimulations applied over the middle of the spinal cord, independently of current limb positions. However the natural activity of the spinal cord does not correspond to this limited monotone modulation. In the first part of this thesis, we developed spatiotemporal stimulation protocols that seek to reproduce the natural activity of the spinal cord during walking. Using computational models, we identified optimal electrode locations that target individual posterior roots. These simulations steered the design of spatially selective spinal implants. Control software triggered stimulation trains in real-time to engage extensor and flexor synergies according to limb positions. This spatiotemporal neuro- modulation therapy enhanced locomotor performances in rats with severe SCI. However, these spatiotemporal neuromodulation protocols were controlled via an external computer. The animal had no control over the occurrence of the stimulation. To remedy this limitation, we designed a brain-spine interface. We linked motor states decoded from leg motor cortex activity to spinal cord stimulation protocols to elicited the intended movements. We implemented this brain-spine interface in a nonhuman primate models of transient unilateral leg paralysis. All the animals immediately regained movements of the paralyzed leg. There is often a mechanical mismatch between current stiff implants and the soft neural tissues of the host. The third part of this thesis is dedicated to the design and fabrication of soft neural implants that present the same elasticity as the dura mater. These implants maintain the ability to deliver electrochemical stimulation while being stretched. Such interfaces can be applied to multiple neuroprosthetic applications, including the restoration of locomotion in paralyzed rats. In the last part of this thesis, I addressed the current limitations of spinal implant specificity. We found that EES activates the large afferent fibers within the dorsal rots and developed novel implants that take advantage of the known trajectories of the dorsal roots to increase the specificity of EES. First, we built a precise anatomical computational model of the lumbosacral roots. We then ran simulations that identi- fied multipolar electrode configurations capable of steering the current specifcally. These results drove the fabrication of soft neural implants that we inserted chronically in rat models of severe SCI. Experiments showed that multipolar EES increases the specificity of the stimulation compared to conventional protocols. All the concepts developed and validated in my thesis are already applied or are steering applications in humans with SCI.

EPFL2019