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Telesford QK, Gonzalez-Moreira E, Xu T, Tian Y, Colcombe SJ, Cloud J, Russ BE, Falchier A, Nentwich M, Madsen J, Parra LC, Schroeder CE, Milham MP, Franco AR. An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI. Sci Data 2023; 10:554. [PMID: 37612297 PMCID: PMC10447527 DOI: 10.1038/s41597-023-02458-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 08/09/2023] [Indexed: 08/25/2023] Open
Abstract
In this work, we present a dataset that combines functional magnetic imaging (fMRI) and electroencephalography (EEG) to use as a resource for understanding human brain function in these two imaging modalities. The dataset can also be used for optimizing preprocessing methods for simultaneously collected imaging data. The dataset includes simultaneously collected recordings from 22 individuals (ages: 23-51) across various visual and naturalistic stimuli. In addition, physiological, eye tracking, electrocardiography, and cognitive and behavioral data were collected along with this neuroimaging data. Visual tasks include a flickering checkerboard collected outside and inside the MRI scanner (EEG-only) and simultaneous EEG-fMRI recordings. Simultaneous recordings include rest, the visual paradigm Inscapes, and several short video movies representing naturalistic stimuli. Raw and preprocessed data are openly available to download. We present this dataset as part of an effort to provide open-access data to increase the opportunity for discoveries and understanding of the human brain and evaluate the correlation between electrical brain activity and blood oxygen level-dependent (BOLD) signals.
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Affiliation(s)
- Qawi K Telesford
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Eduardo Gonzalez-Moreira
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Yiwen Tian
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Stanley J Colcombe
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Jessica Cloud
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Brian E Russ
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Arnaud Falchier
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Maximilian Nentwich
- Department of Biomedical Engineering, The City College of the City University of New York, New York, NY, USA
| | - Jens Madsen
- Department of Biomedical Engineering, The City College of the City University of New York, New York, NY, USA
| | - Lucas C Parra
- Department of Biomedical Engineering, The City College of the City University of New York, New York, NY, USA
| | - Charles E Schroeder
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Departments of Psychiatry and Neurology, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Michael P Milham
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Alexandre R Franco
- Center for Brain Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA.
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2
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Fang Z, Lynn E, Huc M, Fogel S, Knott VJ, Jaworska N. Simultaneous EEG+fMRI study of brain activity during an emotional Stroop task in individuals in remission from depression. Cortex 2022; 155:237-250. [DOI: 10.1016/j.cortex.2022.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/25/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022]
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3
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Song C, Yu B, Wang J, Zhu Y, Zhang X. Effects of Moderate to High Static Magnetic Fields on Reproduction. Bioelectromagnetics 2022; 43:278-291. [PMID: 35485707 DOI: 10.1002/bem.22404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 03/09/2022] [Accepted: 04/09/2022] [Indexed: 11/08/2022]
Abstract
With the wide application of magnetic resonance imaging in hospitals and permanent magnets in household items, people have increased exposure to various types of static magnetic fields (SMFs) with moderate and high intensities, which has caused a considerable amount of public concern. Studies have shown that some aspects of gametogenesis and early embryonic development can be significantly affected by SMFs, while others have shown no effects. This review summarizes the experimental results of moderate to high-intensity SMFs (1 mT-16.7 T) on the reproductive development of different model animals, and we find that the effects of SMFs are variable depending on experimental conditions. In general, the effects of inhomogeneous SMFs seem to be more significant compared to that of homogeneous SMFs, which is likely due to magnetic forces generated by the magnetic field gradient. Moreover, some electromagnetic fields may have induced bioeffects because of nonnegligible gradient and heat effect, which are much reduced in superconducting magnets. We hope this review can provide a starting point for more in-depth analysis of various SMFs on reproduction, which is indispensable for evaluating the safety and potential applications of SMFs on living organisms in the future. © 2022 Bioelectromagnetics Society.
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Affiliation(s)
- Chao Song
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,University of Science and Technology of China, Hefei, China
| | - Biao Yu
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,University of Science and Technology of China, Hefei, China
| | - Junjun Wang
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Yiming Zhu
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Xin Zhang
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,University of Science and Technology of China, Hefei, China.,Institutes of Physical Science and Information Technology, Anhui University, Hefei, China.,International Magnetobiology Frontier Research Center (iMFRC), Science Island, Hefei, China
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4
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The Effect of Magnetic Field Gradient and Gadolinium-Based MRI Contrast Agent Dotarem on Mouse Macrophages. Cells 2022; 11:cells11050757. [PMID: 35269379 PMCID: PMC8909262 DOI: 10.3390/cells11050757] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 02/04/2023] Open
Abstract
Magnetic resonance imaging (MRI) is widely used in diagnostic medicine. MRI uses the static magnetic field to polarize nuclei spins, fast-switching magnetic field gradients to generate temporal and spatial resolution, and radiofrequency (RF) electromagnetic waves to control the spin orientation. All these forms of magnetic static and electromagnetic RF fields interact with human tissue and cells. However, reports on the MRI technique's effects on the cells and human body are often inconsistent or contradictory. In both research and clinical MRI, recent progress in improving sensitivity and resolution is associated with the increased magnetic field strength of MRI magnets. Additionally, to improve the contrast of the images, the MRI technique often employs contrast agents, such as gadolinium-based Dotarem, with effects on cells and organs that are still disputable and not fully understood. Application of higher magnetic fields requires revisiting previously observed or potentially possible bio-effects. This article focuses on the influence of a static magnetic field gradient with and without a gadolinium-based MRI contrast agent (Dotarem) and the cellular and molecular effects of Dotarem on macrophages.
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5
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Dowdle LT, Ghose G, Chen CCC, Ugurbil K, Yacoub E, Vizioli L. Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI. Prog Neurobiol 2021; 207:102171. [PMID: 34492308 DOI: 10.1016/j.pneurobio.2021.102171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/09/2021] [Accepted: 09/02/2021] [Indexed: 01/25/2023]
Abstract
Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States.
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neuroscience, University of Minnesota, 321 Church St SE, Minneapolis, MN, 55455, United States
| | - Clark C C Chen
- Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th St SE, Minneapolis, MN, 55455, United States; Department of Neurosurgery, University of Minnesota, 500 SE Harvard St, Minneapolis, MN, 55455, United States.
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6
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Scrivener CL. When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI. Front Neurosci 2021; 15:636424. [PMID: 34267620 PMCID: PMC8276697 DOI: 10.3389/fnins.2021.636424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/01/2021] [Indexed: 11/19/2022] Open
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.
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Affiliation(s)
- Catriona L. Scrivener
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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7
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Tian X, Wang D, Feng S, Zhang L, Ji X, Wang Z, Lu Q, Xi C, Pi L, Zhang X. Effects of 3.5-23.0 T static magnetic fields on mice: A safety study. Neuroimage 2019; 199:273-280. [PMID: 31158482 DOI: 10.1016/j.neuroimage.2019.05.070] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 05/03/2019] [Accepted: 05/27/2019] [Indexed: 12/19/2022] Open
Abstract
People are exposed to various magnetic fields, including the high static/steady magnetic field (SMF) of MRI, which has been increased to 9.4 T in preclinical investigations. However, relevant safety studies about high SMF are deficient. Here we examined whether 3.5-23.0 T SMF exposure for 2 h has severe long-term effects on mice using 112 C57BL/6J mice. The food/water consumption, blood glucose levels, blood routine, blood biochemistry, as well as organ weight and HE stains were all examined. The food consumption and body weight were slightly decreased for 23.0 T-exposed mice (14.6%, P < 0.01, and 1.75-5.57%, P < 0.05, respectively), but not the other groups. While total bilirubin (TBIL), white blood cells, platelet and lymphocyte numbers were affected by some magnetic conditions, most of them were still within normal reference range. Although 13.5 T magnetic fields with the highest gradient (117.2 T/m) caused spleen weight increase, the blood count and biochemistry results were still within the control reference range. Moreover, the highest field 23.0 T with no gradient did not cause organ weight or blood biochemistry abnormality, which indicates that field gradient is a key parameter. Collectively, these data suggest 3.5-23.0 T static magnetic field exposure for 2 h do not have severe long-term effects on mice.
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Affiliation(s)
- Xiaofei Tian
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China; Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, 230601, PR China
| | - Dongmei Wang
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China; Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, Anhui, 230026, PR China
| | - Shuang Feng
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China; Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, Anhui, 230026, PR China
| | - Lei Zhang
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China
| | - Xinmiao Ji
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China
| | - Ze Wang
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China; Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, 230026, PR China
| | - Qingyou Lu
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China; Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, 230026, PR China; Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions, Hefei, Anhui, 230031, PR China
| | - Chuanying Xi
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China
| | - Li Pi
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China
| | - Xin Zhang
- High Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, PR China; Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, 230601, PR China; Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, Anhui, 230026, PR China.
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8
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Tian L, Zimmerman B, Akhtar A, Yu KJ, Moore M, Wu J, Larsen RJ, Lee JW, Li J, Liu Y, Metzger B, Qu S, Guo X, Mathewson KE, Fan JA, Cornman J, Fatina M, Xie Z, Ma Y, Zhang J, Zhang Y, Dolcos F, Fabiani M, Gratton G, Bretl T, Hargrove LJ, Braun PV, Huang Y, Rogers JA. Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring. Nat Biomed Eng 2019; 3:194-205. [PMID: 30948811 DOI: 10.1038/s41551-019-0347-x] [Citation(s) in RCA: 145] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 01/02/2019] [Indexed: 12/11/2022]
Abstract
Skin-interfaced medical devices are critically important for diagnosing disease, monitoring physiological health and establishing control interfaces with prosthetics, computer systems and wearable robotic devices. Skin-like epidermal electronic technologies can support these use cases in soft and ultrathin materials that conformally interface with the skin in a manner that is mechanically and thermally imperceptible. Nevertheless, schemes so far have limited the overall sizes of these devices to less than a few square centimetres. Here, we present materials, device structures, handling and mounting methods, and manufacturing approaches that enable epidermal electronic interfaces that are orders of magnitude larger than previously realized. As a proof-of-concept, we demonstrate devices for electrophysiological recordings that enable coverage of the full scalp and the full circumference of the forearm. Filamentary conductive architectures in open-network designs minimize radio frequency-induced eddy currents, forming the basis for structural and functional compatibility with magnetic resonance imaging. We demonstrate the use of the large-area interfaces for the multifunctional control of a transhumeral prosthesis by patients who have undergone targeted muscle-reinnervation surgery, in long-term electroencephalography, and in simultaneous electroencephalography and structural and functional magnetic resonance imaging.
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Affiliation(s)
- Limei Tian
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Benjamin Zimmerman
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Aadeel Akhtar
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ki Jun Yu
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Matthew Moore
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jian Wu
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Center for Mechanics and Materials and Center for Flexible Electronics Technology, Tsinghua University, Beijing, China
| | - Ryan J Larsen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jung Woo Lee
- Department of Materials Science and Engineering, Pusan National University, Busan, Republic of Korea
| | - Jinghua Li
- Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yuhao Liu
- Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Brian Metzger
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Subing Qu
- Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Xiaogang Guo
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Center for Mechanics and Materials and Center for Flexible Electronics Technology, Tsinghua University, Beijing, China
| | - Kyle E Mathewson
- Department of Psychology, University of Alberta, Edmonton, Alberta, Canada
| | - Jonathan A Fan
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Jesse Cornman
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Michael Fatina
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Zhaoqian Xie
- Departments of Civil and Environmental Engineering, Mechanical Engineering, and Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Yinji Ma
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Center for Mechanics and Materials and Center for Flexible Electronics Technology, Tsinghua University, Beijing, China
| | - Jue Zhang
- Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yihui Zhang
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Center for Mechanics and Materials and Center for Flexible Electronics Technology, Tsinghua University, Beijing, China
| | - Florin Dolcos
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Timothy Bretl
- Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Levi J Hargrove
- Feinberg School of Medicine, Northwestern University, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Paul V Braun
- Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yonggang Huang
- Departments of Civil and Environmental Engineering, Mechanical Engineering, and Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - John A Rogers
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA. .,Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA. .,Departments of Materials Science and Engineering, Biomedical Engineering, Neurological Surgery, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Simpson Querrey Institute and Feinberg Medical School Center for Bio-Integrated Electronics, Northwestern University, Evanston, IL, USA.
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9
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Relationship Between Alpha Rhythm and the Default Mode Network: An EEG-fMRI Study. J Clin Neurophysiol 2018; 34:527-533. [PMID: 28914659 DOI: 10.1097/wnp.0000000000000411] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Reports of the relationship between the default mode network (DMN) and alpha power are conflicting. Our goal was to assess this relationship by analyzing concurrently obtained EEG/functional MRI data using hypothesis-independent methods. METHODS We collected functional MRI and EEG data during eyes-closed rest in 20 participants aged 19 to 37 (10 females) and performed independent component analysis on the functional MRI data and a Hamming-windowed fast Fourier transform on the EEG data. We correlated functional MRI fluctuations in the DMN with alpha power. RESULTS Of the six independent components found to have significant relationships with alpha, four contained DMN-associated regions: One independent component was positively correlated with alpha power, whereas all others were negatively correlated. Furthermore, two independent components with opposite relationships with alpha had overlapping voxels in the medial prefrontal cortex and posterior cingulate cortex, suggesting that subpopulations of neurons within these classic nodes within the DMN may have different relationships to alpha power. CONCLUSIONS Different parts of the DMN exhibit divergent relationships to alpha power. Our results highlight the relationship between DMN activity and alpha power, indicating that networks, such as the DMN, may have subcomponents that exhibit different behaviors.
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10
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Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. Sci Rep 2018; 8:8902. [PMID: 29891929 PMCID: PMC5995808 DOI: 10.1038/s41598-018-27187-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 05/30/2018] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.
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11
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Abstract
Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Emery Brown
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, and Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana 47906, USA
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12
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Voss HU. Hypersampling of pseudo-periodic signals by analytic phase projection. Comput Biol Med 2018; 98:159-167. [PMID: 29800881 DOI: 10.1016/j.compbiomed.2018.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 04/24/2018] [Accepted: 05/03/2018] [Indexed: 01/07/2023]
Abstract
A method to upsample insufficiently sampled experimental time series of pseudo-periodic signals is proposed. The result is an estimate of the pseudo-periodic cycle underlying the signal. This "hypersampling" requires a sufficiently sampled reference signal that defines the pseudo-periodic dynamics. The time series and reference signal are combined by projecting the time series values to the analytic phase of the reference signal. The resulting estimate of the pseudo-periodic cycle has a considerably higher effective sampling rate than the time series. The procedure is applied to time series of MRI images of the human brain. As a result, the effective sampling rate could be increased by three orders of magnitude. This allows for capturing the waveforms of the very fast cerebral pulse waves traversing the brain. Hypersampling is numerically compared to the more commonly used retrospective gating. An outlook regarding EEG and optical recordings of brain activity as the reference signal is provided.
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Affiliation(s)
- Henning U Voss
- Department of Radiology, Weill Cornell Medicine, Citigroup Biomedical Imaging Center, 516 E 72nd Street, New York, NY, 10021, United States.
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13
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Mash LE, Reiter MA, Linke AC, Townsend J, Müller RA. Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective. Dev Neurobiol 2018; 78:456-473. [PMID: 29266810 PMCID: PMC5897150 DOI: 10.1002/dneu.22570] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/18/2017] [Accepted: 12/18/2017] [Indexed: 12/22/2022]
Abstract
Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.
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Affiliation(s)
- Lisa E. Mash
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Maya A. Reiter
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Annika C. Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Jeanne Townsend
- University of California, San Diego, Department of Neurosciences
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
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14
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Marino M, Liu Q, Del Castello M, Corsi C, Wenderoth N, Mantini D. Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI. Brain Topogr 2018; 31:337-345. [PMID: 29427251 DOI: 10.1007/s10548-018-0631-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 02/06/2018] [Indexed: 01/02/2023]
Abstract
The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject's scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.
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Affiliation(s)
- Marco Marino
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium
| | - Quanying Liu
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium
| | - Mariangela Del Castello
- Department of Electrical, Electronic, and Information Engineering "Gugliemo Marconi", University of Bologna, 40136, Bologna, Italy
| | - Cristiana Corsi
- Department of Electrical, Electronic, and Information Engineering "Gugliemo Marconi", University of Bologna, 40136, Bologna, Italy
| | - Nicole Wenderoth
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium
| | - Dante Mantini
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland.
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK.
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium.
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15
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Abreu R, Leal A, Figueiredo P. EEG-Informed fMRI: A Review of Data Analysis Methods. Front Hum Neurosci 2018; 12:29. [PMID: 29467634 PMCID: PMC5808233 DOI: 10.3389/fnhum.2018.00029] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 01/18/2018] [Indexed: 01/17/2023] Open
Abstract
The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.
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Affiliation(s)
- Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal
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16
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Rajkumar R, Rota Kops E, Mauler J, Tellmann L, Lerche C, Herzog H, Shah NJ, Neuner I. Simultaneous trimodal PET-MR-EEG imaging: Do EEG caps generate artefacts in PET images? PLoS One 2017; 12:e0184743. [PMID: 28902890 PMCID: PMC5597218 DOI: 10.1371/journal.pone.0184743] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 08/30/2017] [Indexed: 11/24/2022] Open
Abstract
Trimodal simultaneous acquisition of positron emission tomography (PET), magnetic resonance imaging (MRI), and electroencephalography (EEG) has become feasible due to the development of hybrid PET-MR scanners. To capture the temporal dynamics of neuronal activation on a millisecond-by-millisecond basis, an EEG system is appended to the quantitative high resolution PET-MR imaging modality already established in our institute. One of the major difficulties associated with the development of simultaneous trimodal acquisition is that the components traditionally used in each modality can cause interferences in its counterpart. The mutual interferences of MRI components and PET components on PET and MR images, and the influence of EEG electrodes on functional MRI images have been studied and reported on. Building on this, this study aims to investigate the influence of the EEG cap on the quality and quantification of PET images acquired during simultaneous PET-MR measurements. A preliminary transmission scan study on the ECAT HR+ scanner, using an Iida phantom, showed visible attenuation effect due to the EEG cap. The BrainPET-MR emission images of the Iida phantom with [18F]Fluordeoxyglucose, as well as of human subjects with the EEG cap, did not show significant effects of the EEG cap, even though the applied attenuation correction did not take into account the attenuation of the EEG cap itself.
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Affiliation(s)
- Ravichandran Rajkumar
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA – BRAIN – Translational Medicine, Juelich, Germany
| | - Elena Rota Kops
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
| | - Jörg Mauler
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
| | - Lutz Tellmann
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
| | - Christoph Lerche
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
| | - Hans Herzog
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
- JARA – BRAIN – Translational Medicine, Juelich, Germany
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- Department of Electrical and Computer Systems Engineering, and Monash Biomedical Imaging, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4 (INM4), Forschungszentrum Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA – BRAIN – Translational Medicine, Juelich, Germany
- * E-mail:
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17
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Mano M, Lécuyer A, Bannier E, Perronnet L, Noorzadeh S, Barillot C. How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI. Front Neurosci 2017; 11:140. [PMID: 28377691 PMCID: PMC5359276 DOI: 10.3389/fnins.2017.00140] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 03/07/2017] [Indexed: 01/18/2023] Open
Abstract
Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies.
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Affiliation(s)
- Marsel Mano
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Anatole Lécuyer
- Institut National de Recherche en Informatique et en Automatique (INRIA)Rennes, France; Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France
| | - Elise Bannier
- Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France; CHU PontchaillouRennes, France
| | - Lorraine Perronnet
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Saman Noorzadeh
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Christian Barillot
- Institut National de Recherche en Informatique et en Automatique (INRIA)Rennes, France; Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France; Institut National de la Santé et de la Recherche MédicaleRennes, France
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18
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Arrubla J, Farrher E, Strippelmann J, Tse DHY, Grinberg F, Shah NJ, Neuner I. Microstructural and functional correlates of glutamate concentration in the posterior cingulate cortex. J Neurosci Res 2017; 95:1796-1808. [PMID: 28117486 DOI: 10.1002/jnr.24010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 12/06/2016] [Accepted: 12/08/2016] [Indexed: 12/15/2022]
Abstract
Glutamate is the major excitatory neurotransmitter in the human brain and has a central role in both intrinsic and stimulus-induced activity. We conducted a study in a cohort of healthy, male volunteers in which glutamate levels were measured in the posterior cingulate cortex (PCC) using 1H magnetic resonance spectroscopy at 3T. The advantages of simultaneous electroencephalography and magnetic resonance imaging (EEG-MRI) were exploited and the subjects were measured in the same session and under the same physiological conditions. Diffusion tensor imaging (DTI), functional MRI (fMRI) and EEG were measured in order to investigate the functional and microstructural correlates of glutamate. The concentration of glutamate (institute units) was calculated and those values were tested for correlation with the metrics of resting state fMRI, DTI, and EEG electrical sources. Our results showed that the concentration of glutamate in the PCC had a significant negative correlation with the tissue mean diffusivity in the same area. The analysis of resting state networks did not show any relationship between the concentration of glutamate and the intrinsic activity of the resting state networks. The concentration of glutamate showed a positive correlation with the electrical generators of α-1 frequency and a negative correlation with the generators of α-2 and β-1 electrical generators. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Jorge Arrubla
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany
| | - Johanna Strippelmann
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Desmond H Y Tse
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany.,Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany.,JARA - BRAIN - Translational Medicine, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine 11, Forschungszentrum Jülich, Jülich, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA - BRAIN - Translational Medicine, RWTH Aachen University, Aachen, Germany
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19
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Jorge J, Grouiller F, Gruetter R, van der Zwaag W, Figueiredo P. Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion. Neuroimage 2015; 120:143-53. [PMID: 26169325 DOI: 10.1016/j.neuroimage.2015.07.020] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 07/03/2015] [Accepted: 07/07/2015] [Indexed: 11/16/2022] Open
Abstract
The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7 T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.
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Affiliation(s)
- João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Institute for Systems and Robotics/Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | | | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, University of Geneva, Geneva, Switzerland; Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - Wietske van der Zwaag
- Biomedical Imaging Research Center, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
| | - Patrícia Figueiredo
- Institute for Systems and Robotics/Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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20
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Abolghasemi V, Ferdowsi S. EEG–fMRI: Dictionary learning for removal of ballistocardiogram artifact from EEG. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.01.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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Removal of pulse artefact from EEG data recorded in MR environment at 3T. Setting of ICA parameters for marking artefactual components: application to resting-state data. PLoS One 2014; 9:e112147. [PMID: 25383625 PMCID: PMC4226479 DOI: 10.1371/journal.pone.0112147] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Accepted: 10/13/2014] [Indexed: 11/21/2022] Open
Abstract
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.
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22
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Simultaneous EEG-fMRI at ultra-high field: artifact prevention and safety assessment. Neuroimage 2014; 105:132-44. [PMID: 25449743 DOI: 10.1016/j.neuroimage.2014.10.055] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Revised: 10/20/2014] [Accepted: 10/24/2014] [Indexed: 11/21/2022] Open
Abstract
The simultaneous recording of scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can provide unique insights into the dynamics of human brain function, and the increased functional sensitivity offered by ultra-high field fMRI opens exciting perspectives for the future of this multimodal approach. However, simultaneous recordings are susceptible to various types of artifacts, many of which scale with magnetic field strength and can seriously compromise both EEG and fMRI data quality in recordings above 3T. The aim of the present study was to implement and characterize an optimized setup for simultaneous EEG-fMRI in humans at 7 T. The effects of EEG cable length and geometry for signal transmission between the cap and amplifiers were assessed in a phantom model, with specific attention to noise contributions from the MR scanner coldheads. Cable shortening (down to 12 cm from cap to amplifiers) and bundling effectively reduced environment noise by up to 84% in average power and 91% in inter-channel power variability. Subject safety was assessed and confirmed via numerical simulations of RF power distribution and temperature measurements on a phantom model, building on the limited existing literature at ultra-high field. MRI data degradation effects due to the EEG system were characterized via B0 and B1(+) field mapping on a human volunteer, demonstrating important, although not prohibitive, B1 disruption effects. With the optimized setup, simultaneous EEG-fMRI acquisitions were performed on 5 healthy volunteers undergoing two visual paradigms: an eyes-open/eyes-closed task, and a visual evoked potential (VEP) paradigm using reversing-checkerboard stimulation. EEG data exhibited clear occipital alpha modulation and average VEPs, respectively, with concomitant BOLD signal changes. On a single-trial level, alpha power variations could be observed with relative confidence on all trials; VEP detection was more limited, although statistically significant responses could be detected in more than 50% of trials for every subject. Overall, we conclude that the proposed setup is well suited for simultaneous EEG-fMRI at 7 T.
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23
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Reese B, Habel U, Neuner I. [Simultaneous EEG-fMRI measurements: insights in applications and challenges]. DER NERVENARZT 2014; 85:671-9. [PMID: 24817636 DOI: 10.1007/s00115-014-4012-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND The following article presents an introduction to simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) measurements which have undergone a huge development during the last few years. OBJECTIVES The idea behind combining both non-invasive methods is to join the excellent temporal resolution of EEG (ms) together with the superior spatial resolution of fMRI (mm). In this article the status quo of the method and perspectives regarding multimodal imaging are discussed. MATERIAL AND METHODS Simultaneous EEG-fMRI measurements are affected by scanner and cardioballistic artifacts. We present common artifact subtraction methods in order to achieve a feasible data quality and outline what to consider when planning and recording EEG and fMRI simultaneously. Moreover, we discuss different analysis strategies. RESULTS Combined EEG-fMRI measurements have already increased our knowledge about the underlying relationships between the blood oxygenation level-dependent (BOLD) response and the EEG signal and are applied to answer widespread research questions. Simultaneous measurements are an essential part of multimodal imaging in investigating the underlying processing mechanisms of the brain as well as in advancing our understanding of neuropsychiatric diseases. CONCLUSIONS Current developments in multimodal imaging focus on the combination of electrophysiological and MRI parameters within ultra-high field MRI as well as on positron emission tomography (PET) in a trimodal approach.
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Affiliation(s)
- B Reese
- Klinik für Psychiatrie, Psychotherapie und Psychosomatik, Medizinische Fakultät, RWTH Aachen, Universitätsklinikum Aachen, Pauwelsstr. 30, 52074, Aachen, Deutschland
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24
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Kay B, Szaflarski JP. EEG/fMRI contributions to our understanding of genetic generalized epilepsies. Epilepsy Behav 2014; 34:129-35. [PMID: 24679893 PMCID: PMC4008674 DOI: 10.1016/j.yebeh.2014.02.030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2014] [Accepted: 02/26/2014] [Indexed: 12/26/2022]
Abstract
The first reports of combined EEG and fMRI used for evaluation of epileptic spikes date back to the mid-90s. At that time, the technique was called EEG-triggered fMRI--the "triggered" corresponded to an epilepsy specialist reviewing live EEG while the patient was located in the scanner; after the spike was identified, a scan was initiated to collect the data. Since then major progress has been made in combined EEG/fMRI data collection and analyses. These advances allow studying the electrophysiology of genetic generalized epilepsies (GGEs) in vivo in greater detail than ever. In addition to continuous data collection, we now have better methods for removing physiologic and fMRI-related artifacts, more advanced understanding of the hemodynamic response functions, and better computational methods to address the questions regarding the origins of the epileptiform discharge generators in patients with GGEs. These advances have allowed us to examine numerous cohorts of children and adults with GGEs while not only looking for spike and wave generators but also examining specific types of GGEs (e.g., juvenile myoclonic epilepsy or childhood absence epilepsy), drug-naïve patients, effects of medication resistance, or effects of epileptiform abnormalities and/or seizures on brain connectivity. While the discussion is ongoing, the prevailing thought is that the GGEs as a group are a network disorder with participation from multiple nodes including the thalami and cortex with the clinical presentation depending on which node of the participating network is affected by the disease process. This review discusses the contributions of EEG/fMRI to our understanding of GGEs.
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Affiliation(s)
- Benjamin Kay
- Graduate Program in Neuroscience, University of Cincinnati Academic Health Center, Cincinnati, OH, USA,Department of Neurology, University of Cincinnati Academic Health Center, Cincinnati, OH, USA
| | - Jerzy P. Szaflarski
- Department of Neurology, University of Cincinnati Academic Health Center, Cincinnati, OH, USA,Department of Neurology and the University of Alabama at Birmingham (UAB) Epilepsy Center, UAB, Birmingham, AL, USA
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