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Bridging Multimodal Neurodynamic Sensor Data

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 15510

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Special Issue Editors

Dr. Giorgio Bonmassar

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Guest Editor

Harvard Medical School – Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02114, USA
Interests: Brain; EEG; electroencephalography; epilepsy; fMRI

Dr. Jyrki Ahveninen

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Guest Editor

Special Issue Information

Dear Colleagues,

Independently, electro-/magnetophysiological (EEG, MEG) and hemodynamic (fMRI) measures of brain function offer compromises between spatial and temporal resolution, which limit their applicability in studies of how the human brain works in health and disease. During the past two decades, researchers have pursued ways to mitigate these limitations by using analysis and/or data acquisition approaches that combine the high temporal (MEG, EEG) and spatial (fMRI) resolution of different techniques. However, despite the significant advantages offered by this multimodal imaging (MM) approach, cross-modal artifacts and other caveats have limited their widespread use. The purpose of this special issue is a broad appeal for a deeper investigation and development of new MM tools and applications. Contributions are invited from researchers engaged in at least two modalities (EEG, MEG, ECoG, sEEG, and fMRI) and applying novel techniques that combine the MM data of neural activity to reveal brain dynamics. The Special Issue will also welcome application manuscripts in the fields of MM resting-state connectivity, MM neural decoding, as well as in MM cognition and MM perception studies. Finally, the Special Issue will also focus on the latest technological MM advancements and tools, and the safety of MM imaging.

Prof. Giorgio Bonmassar
Prof. Jyrki Ahveninen
Guest Editors

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Keywords

EEG
fMRI
MEG
ECoG
sEEG

Published Papers (4 papers)

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Research

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25 pages, 12022 KiB

Open AccessArticle

Aluminum Thin Film Nanostructure Traces in Pediatric EEG Net for MRI and CT Artifact Reduction

by Hongbae Jeong, Georgios Ntolkeras, Tracy Warbrick, Manfred Jaschke, Rajiv Gupta, Michael H. Lev, Jurriaan M. Peters, Patricia Ellen Grant and Giorgio Bonmassar

Sensors 2023, 23(7), 3633; https://doi.org/10.3390/s23073633 - 31 Mar 2023

Viewed by 2286

Abstract

Magnetic resonance imaging (MRI) and continuous electroencephalogram (EEG) monitoring are essential in the clinical management of neonatal seizures. EEG electrodes, however, can significantly degrade the image quality of both MRI and CT due to substantial metallic artifacts and distortions. Thus, we developed a novel thin film trace EEG net (“NeoNet”) for improved MRI and CT image quality without compromising the EEG signal quality. The aluminum thin film traces were fabricated with an ultra-high-aspect ratio (up to 17,000:1, with dimensions 30 nm × 50.8 cm × 100 µm), resulting in a low density for reducing CT artifacts and a low conductivity for reducing MRI artifacts. We also used numerical simulation to investigate the effects of EEG nets on the B₁ transmit field distortion in 3 T MRI. Specifically, the simulations predicted a 65% and 138% B₁ transmit field distortion higher for the commercially available copper-based EEG net (“CuNet”, with and without current limiting resistors, respectively) than with NeoNet. Additionally, two board-certified neuroradiologists, blinded to the presence or absence of NeoNet, compared the image quality of MRI images obtained in an adult and two children with and without the NeoNet device and found no significant difference in the degree of artifact or image distortion. Additionally, the use of NeoNet did not cause either: (i) CT scan artifacts or (ii) impact the quality of EEG recording. Finally, MRI safety testing confirmed a maximum temperature rise associated with the NeoNet device in a child head-phantom to be 0.84 °C after 30 min of high-power scanning, which is within the acceptance criteria for the temperature for 1 h of normal operating mode scanning as per the FDA guidelines. Therefore, the proposed NeoNet device has the potential to allow for concurrent EEG acquisition and MRI or CT scanning without significant image artifacts, facilitating clinical care and EEG/fMRI pediatric research. Full article

(This article belongs to the Special Issue Bridging Multimodal Neurodynamic Sensor Data)

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20 pages, 7471 KiB

Open AccessArticle

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors

by Joshua Levitt, André van der Kouwe, Hongbae Jeong, Laura D. Lewis and Giorgio Bonmassar

Sensors 2023, 23(7), 3539; https://doi.org/10.3390/s23073539 - 28 Mar 2023

Cited by 1 | Viewed by 1568

Abstract

We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. Methods: The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. Results: MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. Conclusions: The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise. Full article

(This article belongs to the Special Issue Bridging Multimodal Neurodynamic Sensor Data)

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13 pages, 2664 KiB

Open AccessArticle

A New Phased-Array Magnetic Resonance Imaging Receive-Only Coil for HBO2 Studies

by Azma Mareyam, Erik Shank, Lawrence L. Wald, Michael K. Qin and Giorgio Bonmassar

Sensors 2022, 22(16), 6076; https://doi.org/10.3390/s22166076 - 14 Aug 2022

Viewed by 1878

Abstract

The paper describes a new magnetic resonance imaging (MRI) phased-array receive-only (Rx) coil for studying decompression sickness and disorders of hyperbaricity, including nitrogen narcosis. Functional magnetic resonance imaging (fMRI) is noninvasive, is considered safe, and may allow studying the brain under hyperbaric conditions. All of the risks associated with simultaneous MRI and HBO2 therapy are described in detail, along with all of the mitigation strategies and regulatory testing. One of the most significant risks for this type of study is a fire in the hyperbaric chamber caused by the sparking of the MRI coils as a result of high-voltage RF arcs. RF pulses at 128 MHz elicit signals from human tissues, and RF sparking occurs commonly and is considered safe in normobaric conditions. We describe how we built a coil for HBO2-MRI studies by modifying an eight-channel phased-array MRI coil with all of the mitigation strategies discussed. The coil was fabricated and tested with a unique testing platform that simulated the worst-case RF field of a three-Tesla MRI in a Hyperlite hyperbaric chamber at 3 atm pressure. The coil was also tested in normobaric conditions for image quality in a 3 T scanner in volunteers and SNR measurement in phantoms. Further studies are necessary to characterize the coil safety in HBO2/MRI. Full article

(This article belongs to the Special Issue Bridging Multimodal Neurodynamic Sensor Data)

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Review

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31 pages, 4235 KiB

Open AccessReview

Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

by Tracy Warbrick

Sensors 2022, 22(6), 2262; https://doi.org/10.3390/s22062262 - 15 Mar 2022

Cited by 27 | Viewed by 8892

Abstract

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented. Full article

(This article belongs to the Special Issue Bridging Multimodal Neurodynamic Sensor Data)

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