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Hangel G, Niess E, Lazen P, Bednarik P, Bogner W, Strasser B. Emerging methods and applications of ultra-high field MR spectroscopic imaging in the human brain. Anal Biochem 2022; 638:114479. [PMID: 34838516 DOI: 10.1016/j.ab.2021.114479] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/15/2021] [Accepted: 11/16/2021] [Indexed: 12/21/2022]
Abstract
Magnetic Resonance Spectroscopic Imaging (MRSI) of the brain enables insights into the metabolic changes and fluxes in diseases such as tumors, multiple sclerosis, epilepsy, or hepatic encephalopathy, as well as insights into general brain functionality. However, the routine application of MRSI is mostly hampered by very low signal-to-noise ratios (SNR) due to the low concentrations of metabolites, about 10000 times lower than water. Furthermore, MRSI spectra have a dense information content with many overlapping metabolite resonances, especially for proton MRSI. MRI scanners at ultra-high field strengths, like 7 T or above, offer the opportunity to increase SNR, as well as the separation between resonances, thus promising to solve both challenges. Yet, MRSI at ultra-high field strengths is challenged by decreased B0- and B1-homogeneity, shorter T2 relaxation times, stronger chemical shift displacement errors, and aggravated lipid contamination. Therefore, to capitalize on the advantages of ultra-high field strengths, these challenges must be overcome. This review focuses on the challenges MRSI of the human brain faces at ultra-high field strength, as well as the possible applications to this date.
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Affiliation(s)
- Gilbert Hangel
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Austria
| | - Eva Niess
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Philipp Lazen
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Petr Bednarik
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Bernhard Strasser
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria.
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Tamaki M, Watanabe T, Sasaki Y. Coregistration of magnetic resonance spectroscopy and polysomnography for sleep analysis in human subjects. STAR Protoc 2021; 2:100974. [PMID: 34901890 PMCID: PMC8637650 DOI: 10.1016/j.xpro.2021.100974] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
We developed a protocol for simultaneous magnetic resonance spectroscopy (MRS) and polysomnography (PSG) recordings while subjects are in sleep. The approach is useful to estimate plasticity-stability balances by measuring neurochemical changes in the brain during sleep. We detail the steps needed to minimize artifacts in PSG recordings and the setup and coregistration of MRS data to sleep stages. We also describe useful information for various types of electroencephalogram (EEG) experiments in magnetic resonance imaging (MRI) environments. For complete details on the use and execution of this protocol, please refer to Tamaki et al. (2020b). Avoid instructions that may cause subjects to feel pressured to sleep well Introduce an adaptation sleep session before a main sleep session Segment PSG data and align them with MRS data for MRI artifact removal Assign sleep stage to each MRS segment to obtain E/I balance for the stage
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Affiliation(s)
- Masako Tamaki
- Cognitive Somnology RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research, Saitama 3510198, Japan.,RIKEN Center for Brain Science, Saitama 3510198, Japan
| | - Takeo Watanabe
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence 02912, USA
| | - Yuka Sasaki
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence 02912, USA
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Tamaki M, Wang Z, Barnes-Diana T, Guo D, Berard AV, Walsh E, Watanabe T, Sasaki Y. Complementary contributions of non-REM and REM sleep to visual learning. Nat Neurosci 2020; 23:1150-1156. [PMID: 32690968 PMCID: PMC7483793 DOI: 10.1038/s41593-020-0666-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 06/11/2020] [Indexed: 02/07/2023]
Abstract
Sleep is beneficial for learning. However, it remains unclear whether learning is facilitated by non-REM (NREM) sleep or by REM sleep, whether it results from plasticity increases or stabilization, and whether facilitation results from learning-specific processing. Here, we trained volunteers on a visual task, and measured the excitatory and inhibitory (E/I) balance in early visual areas during subsequent sleep as an index of plasticity. E/I balance increased during NREM sleep irrespective of whether pre-sleep learning occurred, but it was associated with post-sleep performance gains relative to pre-sleep performance. By contrast, E/I balance decreased during REM sleep but only after pre-sleep training, and the decrease was associated with stabilization of pre-sleep learning. These findings indicate that NREM sleep promotes plasticity, leading to performance gains independent of learning, while REM sleep decreases plasticity to stabilize learning in a learning-specific manner.
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Affiliation(s)
- Masako Tamaki
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.,National Institute of Occupational Safety and Health, Kawasaki, Japan
| | - Zhiyan Wang
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
| | - Tyler Barnes-Diana
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
| | - DeeAnn Guo
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Aaron V Berard
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
| | - Edward Walsh
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Takeo Watanabe
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
| | - Yuka Sasaki
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.
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