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Raitamaa L, Kautto J, Tuunanen J, Helakari H, Huotari N, Järvelä M, Korhonen V, Kiviniemi V. Association of body-mass index with physiological brain pulsations across adulthood - a fast fMRI study. Int J Obes (Lond) 2024; 48:1011-1018. [PMID: 38553569 PMCID: PMC11216984 DOI: 10.1038/s41366-024-01515-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND/OBJECTIVE Obesity is a risk factor for several brain-related health issues, and high body-mass index (BMI) is associated with an increased risk for several neurological conditions, including cognitive decline and dementia. Cardiovascular, respiratory, and vasomotor brain pulsations have each been shown to drive intracranial cerebrovascular fluid (CSF) flow, which is linked to the brain metabolite efflux that sustains homeostasis. While these three physiological pulsations are demonstrably altered in numerous brain diseases, there is no previous investigation of the association between physiological brain pulsations and BMI. SUBJECTS/METHODS We measured the amplitudes of the physiological brain pulsations using amplitude of low frequency fluctation (ALFF) based method with resting-state functional magnetic resonance imaging via high temporal resolution whole-brain magnetic resonance encephalography (MREG) in 115 healthy subjects. We next undertook multiple linear regression to model the BMI effect voxel-wise whole-brain on very low frequency (VLF), respiration, cardiovascular, and respiratory induced modulation of cardiovascular pulsation amplitudes with age, pulse pressure, and gender as nuisance variables. RESULTS In our study population, BMI was positively associated with the amplitudes of vasomotor, respiratory, and respiratory induced modulations of cardiovascular pulsations (p < 0.05), while negatively associated with the amplitudes of cardiovascular pulsations (p < 0.05). CONCLUSIONS The findings suggest that BMI is a significant factor in alterations of cardiovascular pulsation of neurofluids. As physiological pulsations are the drivers of CSF flow and subsequent metabolite clearance, these results emphasize the need for further research into the mechanisms through which obesity affects brain clearance.
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Affiliation(s)
- Lauri Raitamaa
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland.
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland.
| | - Joona Kautto
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Johanna Tuunanen
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Heta Helakari
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Matti Järvelä
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Diagnostic Imaging, Medical Research Center (MRC), Finland Oulu University Hospital, 90029, Oulu, Finland
- Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, 90220, Oulu, Finland
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
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Jin J, Zhou Y, Chen L, Chen Z. Ultrafast T 2 and T 2* mapping using single-shot spatiotemporally encoded MRI with reduced field of view and spiral out-in-out-in trajectory. Med Phys 2024. [PMID: 38896823 DOI: 10.1002/mp.17268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/15/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND T2 and T2* mapping are crucial components of quantitative magnetic resonance imaging, offering valuable insights into tissue characteristics and pathology. Single-shot methods can achieve ultrafast T2 or T2* mapping by acquiring multiple readout echo trains. However, the extended echo trains pose challenges, such as compromised image quality and diminished quantification accuracy. PURPOSE In this study, we develop a single-shot method for ultrafast T2 and T2* mapping with reduced echo train length. METHODS The proposed method is based on ultrafast single-shot spatiotemporally encoded (SPEN) MRI combined with reduced field of view (FOV) and spiral out-in-out-in (OIOI) trajectory. Specifically, a biaxial SPEN excitation scheme was employed to excite the spin signal into the spatiotemporal encoding domain. The OIOI trajectory with high acquisition efficiency was employed to acquire signals within targeted reduced FOV. Through non-Cartesian super-resolved (SR) reconstruction, 12 aliasing-free images with different echo times were obtained within 150 ms. These images were subsequently fitted to generate T2 or T2* mapping simultaneously using a derived model. RESULTS Accurate and co-registered T2 and T2* maps were generated, closely resembling the reference maps. Numerical simulations demonstrated substantial consistency (R2 > 0.99) with the ground truth values. A mean difference of 0.6% and 1.7% was observed in T2 and T2*, respectively, in in vivo rat brain experiments compared to the reference. Moreover, the proposed method successfully obtained T2 and T2* mappings of rat kidney in free-breathing mode, demonstrating its superiority over multishot methods lacking respiratory navigation. CONCLUSIONS The results suggest that the proposed method can achieve ultrafast and accurate T2 and T2* mapping, potentially facilitating the application of T2 and T2* mapping in scenarios requiring high temporal resolution.
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Affiliation(s)
- Junxian Jin
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Yang Zhou
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
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Helakari H, Järvelä M, Väyrynen T, Tuunanen J, Piispala J, Kallio M, Ebrahimi SM, Poltojainen V, Kananen J, Elabasy A, Huotari N, Raitamaa L, Tuovinen T, Korhonen V, Nedergaard M, Kiviniemi V. Effect of sleep deprivation and NREM sleep stage on physiological brain pulsations. Front Neurosci 2023; 17:1275184. [PMID: 38105924 PMCID: PMC10722275 DOI: 10.3389/fnins.2023.1275184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/02/2023] [Indexed: 12/19/2023] Open
Abstract
Introduction Sleep increases brain fluid transport and the power of pulsations driving the fluids. We investigated how sleep deprivation or electrophysiologically different stages of non-rapid-eye-movement (NREM) sleep affect the human brain pulsations. Methods Fast functional magnetic resonance imaging (fMRI) was performed in healthy subjects (n = 23) with synchronous electroencephalography (EEG), that was used to verify arousal states (awake, N1 and N2 sleep). Cardiorespiratory rates were verified with physiological monitoring. Spectral power analysis assessed the strength, and spectral entropy assessed the stability of the pulsations. Results In N1 sleep, the power of vasomotor (VLF < 0.1 Hz), but not cardiorespiratory pulsations, intensified after sleep deprived vs. non-sleep deprived subjects. The power of all three pulsations increased as a function of arousal state (N2 > N1 > awake) encompassing brain tissue in both sleep stages, but extra-axial CSF spaces only in N2 sleep. Spectral entropy of full band and respiratory pulsations decreased most in N2 sleep stage, while cardiac spectral entropy increased in ventricles. Discussion In summary, the sleep deprivation and sleep depth, both increase the power and harmonize the spectral content of human brain pulsations.
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Affiliation(s)
- Heta Helakari
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Matti Järvelä
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Tommi Väyrynen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Johanna Tuunanen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Johanna Piispala
- Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Mika Kallio
- Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Seyed Mohsen Ebrahimi
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Valter Poltojainen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Janne Kananen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Ahmed Elabasy
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Niko Huotari
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Timo Tuovinen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Maiken Nedergaard
- Center of Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark
- Center of Translational Neuromedicine, University of Rochester, Rochester, NY, United States
| | - Vesa Kiviniemi
- Oulu Functional Neuroimaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
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Increased very low frequency pulsations and decreased cardiorespiratory pulsations suggest altered brain clearance in narcolepsy. COMMUNICATIONS MEDICINE 2022; 2:122. [PMID: 36193214 PMCID: PMC9525269 DOI: 10.1038/s43856-022-00187-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background Narcolepsy is a chronic neurological disease characterized by daytime sleep attacks, cataplexy, and fragmented sleep. The disease is hypothesized to arise from destruction or dysfunction of hypothalamic hypocretin-producing cells that innervate wake-promoting systems including the ascending arousal network (AAN), which regulates arousal via release of neurotransmitters like noradrenalin. Brain pulsations are thought to drive intracranial cerebrospinal fluid flow linked to brain metabolite transfer that sustains homeostasis. This flow increases in sleep and is suppressed by noradrenalin in the awake state. Here we tested the hypothesis that narcolepsy is associated with altered brain pulsations, and if these pulsations can differentiate narcolepsy type 1 from healthy controls. Methods In this case-control study, 23 patients with narcolepsy type 1 (NT1) were imaged with ultrafast fMRI (MREG) along with 23 age- and sex-matched healthy controls (HC). The physiological brain pulsations were quantified as the frequency-wise signal variance. Clinical relevance of the pulsations was investigated with correlation and receiving operating characteristic analysis. Results We find that variance and fractional variance in the very low frequency (MREGvlf) band are greater in NT1 compared to HC, while cardiac (MREGcard) and respiratory band variances are lower. Interestingly, these pulsations differences are prominent in the AAN region. We further find that fractional variance in MREGvlf shows promise as an effective bi-classification metric (AUC = 81.4%/78.5%), and that disease severity measured with narcolepsy severity score correlates with MREGcard variance (R = −0.48, p = 0.0249). Conclusions We suggest that our novel results reflect impaired CSF dynamics that may be linked to altered glymphatic circulation in narcolepsy type 1. The flow of fluid surrounding and inside the human brain is thought to be caused by the movement of brain vessels, breathing and heart rate. These so called brain pulsations are linked to clearing waste from the brain. This process is increased during sleep and suppressed while we are awake. Narcolepsy is a neurological disease where the brain areas regulating being awake and asleep are affected. The diagnosis requires time-consuming hospital tests and is often delayed which has a prolonged negative impact on the patients. Here, we use brain imaging to investigate whether brain pulsations are altered in patients with narcolepsy, and if they can be utilized to differentiate patients with narcolepsy from healthy individuals. We find that narcolepsy affects all brain pulsations, and these findings show promise as an additional diagnostic tool that could help detect the disease earlier. Järvelä et al. investigate if narcolepsy is associated with altered brain pulsations using ultrafast fMRI. They find differences in the brain pulsations between narcolepsy type 1 patients and healthy controls that may link to altered brain clearance in narcolepsy, have diagnostic potential and correlate with the severity of narcolepsy.
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Graedel NN, Miller KL, Chiew M. Ultrahigh Resolution fMRI at 7T Using Radial-Cartesian TURBINE Sampling. Magn Reson Med 2022; 88:2058-2073. [PMID: 35785429 PMCID: PMC9546489 DOI: 10.1002/mrm.29359] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 04/21/2022] [Accepted: 05/23/2022] [Indexed: 12/05/2022]
Abstract
Purpose We investigate the use of TURBINE, a 3D radial‐Cartesian acquisition scheme in which EPI planes are rotated about the phase‐encoding axis to acquire a cylindrical k‐space for high‐fidelity ultrahigh isotropic resolution fMRI at 7 Tesla with minimal distortion and blurring. Methods An improved, completely self‐navigated version of the TURBINE sampling scheme was designed for fMRI at 7 Telsa. To demonstrate the image quality and spatial specificity of the acquisition, thin‐slab visual and motor BOLD fMRI at 0.67 mm isotropic resolution (16 mm slab, TRvol = 2.32 s), and 0.8 × 0.8 × 2.0 mm (whole‐brain, TRvol = 2.4 s) data were acquired. To prioritize the high spatial fidelity, we employed a temporally regularized reconstruction to improve sensitivity without any spatial bias. Results TURBINE images provide high structural fidelity with almost no distortion, dropout, or T2* blurring for the thin‐slab acquisitions compared to conventional 3D EPI owing to the radial sampling in‐plane and the short echo train used. This results in activation that can be localized to pre‐ and postcentral gyri in a motor task, for example, with excellent correspondence to brain structure measured by a T1‐MPRAGE. The benefits of TURBINE (low distortion, dropout, blurring) are reduced for the whole‐brain acquisition due to the longer EPI train. We demonstrate robust BOLD activation at 0.67 mm isotropic resolution (thin‐slab) and also anisotropic 0.8 × 0.8 × 2.0 mm (whole‐brain) acquisitions. Conclusion TURBINE is a promising acquisition approach for high‐resolution, minimally distorted fMRI at 7 Tesla and could be particularly useful for fMRI in areas of high B0 inhomogeneity. Click here for author‐reader discussions
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Affiliation(s)
- Nadine N Graedel
- Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom
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Raimondo L, Oliveira ĹAF, Heij J, Priovoulos N, Kundu P, Leoni RF, van der Zwaag W. Advances in resting state fMRI acquisitions for functional connectomics. Neuroimage 2021; 243:118503. [PMID: 34479041 DOI: 10.1016/j.neuroimage.2021.118503] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 01/21/2023] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.
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Affiliation(s)
- Luisa Raimondo
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Ĺcaro A F Oliveira
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Jurjen Heij
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | | | - Prantik Kundu
- Hyperfine Research Inc, Guilford, CT, United States; Icahn School of Medicine at Mt. Sinai, New York, United States
| | - Renata Ferranti Leoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
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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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Raitamaa L, Huotari N, Korhonen V, Helakari H, Koivula A, Kananen J, Kiviniemi V. Spectral analysis of physiological brain pulsations affecting the BOLD signal. Hum Brain Mapp 2021; 42:4298-4313. [PMID: 34037278 PMCID: PMC8356994 DOI: 10.1002/hbm.25547] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/17/2022] Open
Abstract
Physiological pulsations have been shown to affect the global blood oxygen level dependent (BOLD) signal in human brain. While these pulsations have previously been regarded as noise, recent studies show their potential as biomarkers of brain pathology. We used the extended 5 Hz spectral range of magnetic resonance encephalography (MREG) data to investigate spatial and frequency distributions of physiological BOLD signal sources. Amplitude spectra of the global image signals revealed cardiorespiratory envelope modulation (CREM) peaks, in addition to the previously known very low frequency (VLF) and cardiorespiratory pulsations. We then proceeded to extend the amplitude of low frequency fluctuations (ALFF) method to each of these pulsations. The respiratory pulsations were spatially dominating over most brain structures. The VLF pulsations overcame the respiratory pulsations in frontal and parietal gray matter, whereas cardiac and CREM pulsations had this effect in central cerebrospinal fluid (CSF) spaces and major blood vessels. A quasi‐periodic pattern (QPP) analysis showed that the CREM pulsations propagated as waves, with a spatiotemporal pattern differing from that of respiratory pulsations, indicating them to be distinct intracranial physiological phenomenon. In conclusion, the respiration has a dominant effect on the global BOLD signal and directly modulates cardiovascular brain pulsations.
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Affiliation(s)
- Lauri Raitamaa
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Niko Huotari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Vesa Korhonen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Heta Helakari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Anssi Koivula
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Janne Kananen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
| | - Vesa Kiviniemi
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu
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9
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Engel M, Kasper L, Wilm B, Dietrich B, Vionnet L, Hennel F, Reber J, Pruessmann KP. T-Hex: Tilted hexagonal grids for rapid 3D imaging. Magn Reson Med 2020; 85:2507-2523. [PMID: 33270941 DOI: 10.1002/mrm.28600] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 10/20/2020] [Accepted: 10/22/2020] [Indexed: 01/09/2023]
Abstract
PURPOSE The purpose of this work is to devise and demonstrate an encoding strategy for 3D MRI that reconciles high speed with flexible segmentation, uniform k-space density, and benign T 2 ∗ effects. METHODS Fast sampling of a 3D k-space is typically accomplished by 2D readouts per shot using EPI trains or spiral readouts. Tilted hexagonal (T-Hex) sampling is a way of acquiring more k-space volume per excitation while maintaining uniform sampling density and a smooth T 2 ∗ filter. The k-space volume covered per shot is controlled by the tilting angle. Image reconstruction is performed with a 3D extension of the iterative SENSE approach, incorporating actual field dynamics and static off-resonance. T-Hex imaging is compared with established 3D schemes in terms of speed and noise performance. RESULTS Tilted hexagonal acquisition is found to achieve greater imaging speed than known alternatives, particularly in combination with spiral trajectories. The interplay of the proposed 3D trajectories, array detection, and off-resonance is successfully addressed by iterative inversion of the full signal model. Enhanced coverage per shot is of greatest utility for high speed in an intermediate resolution regime of 1 to 4 mm. T-Hex EPI combines the benefits of extended coverage per shot with increased robustness against off-resonance effects. CONCLUSION Sampling of tilted hexagonal grids is a feasible means of gaining 3D imaging speed with near-optimal SNR efficiency and benign depiction properties. It is a particularly promising technique for time-resolved applications such as fMRI.
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Affiliation(s)
- Maria Engel
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Lars Kasper
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.,Translational Neuromodeling Unit, IBT, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Bertram Wilm
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Benjamin Dietrich
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Laetitia Vionnet
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Franciszek Hennel
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Jonas Reber
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Klaas P Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
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10
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Kotila A, Järvelä M, Korhonen V, Loukusa S, Hurtig T, Ebeling H, Kiviniemi V, Raatikainen V. Atypical Inter-Network Deactivation Associated With the Posterior Default-Mode Network in Autism Spectrum Disorder. Autism Res 2020; 14:248-264. [PMID: 33206471 DOI: 10.1002/aur.2433] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 12/13/2022]
Abstract
Previous studies have suggested that atypical deactivation of functional brain networks contributes to the complex cognitive and behavioral profile associated with autism spectrum disorder (ASD). However, these studies have not considered the temporal dynamics of deactivation mechanisms between the networks. In this study, we examined (a) mutual deactivation and (b) mutual activation-deactivation (i.e., anticorrelated) time-lag patterns between resting-state networks (RSNs) in young adults with ASD (n = 20) and controls (n = 20) by applying the recently defined dynamic lag analysis (DLA) method, which measures time-lag variations peak-by-peak between the networks. In order to achieve temporally accurate lag patterns, the brain imaging data was acquired with a fast functional magnetic resonance imaging (fMRI) sequence (TR = 100 ms). Group-level independent component analysis was used to identify 16 RSNs for the DLA. We found altered mutual deactivation timings in ASD in (a) three of the deactivated and (b) two of the transiently anticorrelated (activated-deactivated) RSN pairs, which survived the strict threshold for significance of surrogate data. Of the significant RSN pairs, 80% included the posterior default-mode network (DMN). We propose that temporally altered deactivation mechanisms, including timings and directionality, between the posterior DMN and RSNs mediating processing of socially relevant information may contribute to the ASD phenotype. LAY SUMMARY: To understand autistic traits on a neural level, we examined temporal fluctuations in information flow between brain regions in young adults with autism spectrum disorder (ASD) and controls. We used a fast neuroimaging procedure to investigate deactivation mechanisms between brain regions. We found that timings and directionality of communication between certain brain regions were temporally altered in ASD, suggesting atypical deactivation mechanisms associated with the posterior default-mode network.
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Affiliation(s)
- Aija Kotila
- Research Unit of Logopedics, the Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Matti Järvelä
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, the Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Tuula Hurtig
- Research Unit of Clinical Neuroscience, Psychiatry, University of Oulu, Oulu, Finland.,Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Hanna Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Ville Raatikainen
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
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11
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Hennig J, Kiviniemi V, Riemenschneider B, Barghoorn A, Akin B, Wang F, LeVan P. 15 Years MR-encephalography. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 34:85-108. [PMID: 33079327 PMCID: PMC7910380 DOI: 10.1007/s10334-020-00891-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/02/2020] [Accepted: 09/29/2020] [Indexed: 02/07/2023]
Abstract
Objective This review article gives an account of the development of the MR-encephalography (MREG) method, which started as a mere ‘Gedankenexperiment’ in 2005 and gradually developed into a method for ultrafast measurement of physiological activities in the brain. After going through different approaches covering k-space with radial, rosette, and concentric shell trajectories we have settled on a stack-of-spiral trajectory, which allows full brain coverage with (nominal) 3 mm isotropic resolution in 100 ms. The very high acceleration factor is facilitated by the near-isotropic k-space coverage, which allows high acceleration in all three spatial dimensions. Methods The methodological section covers the basic sequence design as well as recent advances in image reconstruction including the targeted reconstruction, which allows real-time feedback applications, and—most recently—the time-domain principal component reconstruction (tPCR), which applies a principal component analysis of the acquired time domain data as a sparsifying transformation to improve reconstruction speed as well as quality. Applications Although the BOLD-response is rather slow, the high speed acquisition of MREG allows separation of BOLD-effects from cardiac and breathing related pulsatility. The increased sensitivity enables direct detection of the dynamic variability of resting state networks as well as localization of single interictal events in epilepsy patients. A separate and highly intriguing application is aimed at the investigation of the glymphatic system by assessment of the spatiotemporal patterns of cardiac and breathing related pulsatility. Discussion MREG has been developed to push the speed limits of fMRI. Compared to multiband-EPI this allows considerably faster acquisition at the cost of reduced image quality and spatial resolution.
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Affiliation(s)
- Juergen Hennig
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany. .,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Bruno Riemenschneider
- Department of Radiology, Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, NY, USA
| | - Antonia Barghoorn
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Burak Akin
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fei Wang
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Departments of Radiology and Paediatrics, Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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12
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Järvelä M, Raatikainen V, Kotila A, Kananen J, Korhonen V, Uddin LQ, Ansakorpi H, Kiviniemi V. Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy. Cereb Cortex Commun 2020; 1:tgaa073. [PMID: 34296133 PMCID: PMC8153076 DOI: 10.1093/texcom/tgaa073] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/29/2020] [Accepted: 09/29/2020] [Indexed: 11/12/2022] Open
Abstract
Narcolepsy is a chronic neurological disease characterized by dysfunction of the hypocretin system in brain causing disruption in the wake-promoting system. In addition to sleep attacks and cataplexy, patients with narcolepsy commonly report cognitive symptoms while objective deficits in sustained attention and executive function have been observed. Prior resting-state functional magnetic resonance imaging (fMRI) studies in narcolepsy have reported decreased inter/intranetwork connectivity regarding the default mode network (DMN). Recently developed fast fMRI data acquisition allows more precise detection of brain signal propagation with a novel dynamic lag analysis. In this study, we used fast fMRI data to analyze dynamics of inter resting-state network (RSN) information signaling between narcolepsy type 1 patients (NT1, n = 23) and age- and sex-matched healthy controls (HC, n = 23). We investigated dynamic connectivity properties between positive and negative peaks and, furthermore, their anticorrelative (pos-neg) counterparts. The lag distributions were significantly (P < 0.005, familywise error rate corrected) altered in 24 RSN pairs in NT1. The DMN was involved in 83% of the altered RSN pairs. We conclude that narcolepsy type 1 is characterized with delayed and monotonic inter-RSN information flow especially involving anticorrelations, which are known to be characteristic behavior of the DMN regarding neurocognition.
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Affiliation(s)
- M Järvelä
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - V Raatikainen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - A Kotila
- Research Unit of Logopedics, University of Oulu, 90014 Oulu, Finland
| | - J Kananen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - V Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
| | - L Q Uddin
- Department of Psychology, University of Miami, Coral Gables, 33124 FL, USA
| | - H Ansakorpi
- Research Unit of Clinical Neuroscience, Neurology, University of Oulu, 90014 Oulu, Finland
| | - V Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, 90220 Oulu, Finland
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13
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Kananen J, Helakari H, Korhonen V, Huotari N, Järvelä M, Raitamaa L, Raatikainen V, Rajna Z, Tuovinen T, Nedergaard M, Jacobs J, LeVan P, Ansakorpi H, Kiviniemi V. Respiratory-related brain pulsations are increased in epilepsy-a two-centre functional MRI study. Brain Commun 2020; 2:fcaa076. [PMID: 32954328 PMCID: PMC7472909 DOI: 10.1093/braincomms/fcaa076] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/29/2020] [Accepted: 05/05/2020] [Indexed: 01/03/2023] Open
Abstract
Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11-0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01-0.1 Hz) and cardiovascular (0.8-1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.
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Affiliation(s)
- Janne Kananen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Heta Helakari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Matti Järvelä
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Lauri Raitamaa
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Ville Raatikainen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Zalan Rajna
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu 90014, Finland
| | - Timo Tuovinen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
| | - Maiken Nedergaard
- Center for Translational Neuromedicine, Department of Neurosurgery, University of Rochester Medical Center, Rochester, NY 14642, USA
- Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Julia Jacobs
- Department of Pediatric Neurology and Muscular Disease, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg 79110, Germany
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Pierre LeVan
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg 79110, Germany
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Hanna Ansakorpi
- Medical Research Center (MRC), Oulu 90220, Finland
- Research Unit of Neuroscience, Neurology, University of Oulu, Oulu 90220, Finland
- Department of Neurology, Oulu University Hospital, Oulu 90029, Finland
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland
- Medical Imaging, Physics and Technology (MIPT), Faculty of Medicine, University of Oulu, Oulu 90220, Finland
- Medical Research Center (MRC), Oulu 90220, Finland
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14
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Wang F, Hennig J, LeVan P. Time-domain principal component reconstruction (tPCR): A more efficient and stable iterative reconstruction framework for non-Cartesian functional MRI. Magn Reson Med 2020; 84:1321-1335. [PMID: 32068309 DOI: 10.1002/mrm.28208] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 12/27/2019] [Accepted: 01/19/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE To improve the reconstruction efficiency (i.e., computational load) and stability of iterative reconstruction for non-Cartesian fMRI when using high undersampling rates and/or in the presence of strong off-resonance effects. THEORY AND METHODS The magnetic resonance encephalography (MREG) sequence with 3D non-Cartesian trajectory and 0.1s repetition time (TR) was applied to acquire fMRI datasets. Different from a conventional time-point-by-time-point sequential reconstruction (SR), the proposed time-domain principal component reconstruction (tPCR) performs three steps: (1) decomposing the k-t-space fMRI datasets into time-domain principal component space using singular value decomposition, (2) reconstructing each principal component with redistributed computation power according to their weights, and (3) combining the reconstructed principal components back to image-t-space. The comparison of reconstruction accuracy was performed by simulation experiments and then verified in real fMRI data. RESULTS The simulation experiments showed that the proposed tPCR was able to significantly reduce reconstruction errors, and subsequent functional activation errors, relative to SR at identical computational cost. Alternatively, at fixed reconstruction accuracy, computation time was greatly reduced. The improved performance was particularly obvious for L1-norm nonlinear reconstructions relative to L2-norm linear reconstructions and robust to different regularization strength, undersampling rates, and off-resonance effects intensity. By examining activation maps, tPCR was also found to give similar improvements in real fMRI experiments. CONCLUSION The proposed proof-of-concept tPCR framework could improve (1) the reconstruction efficiency of iterative reconstruction, and (2) the reconstruction stability especially for nonlinear reconstructions. As a practical consideration, the improved reconstruction speed promotes the application of highly undersampled non-Cartesian fast fMRI.
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Affiliation(s)
- Fei Wang
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany.,Departments of Radiology and Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
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15
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Raatikainen V, Korhonen V, Borchardt V, Huotari N, Helakari H, Kananen J, Raitamaa L, Joskitt L, Loukusa S, Hurtig T, Ebeling H, Uddin LQ, Kiviniemi V. Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder. Autism Res 2019; 13:244-258. [PMID: 31637863 PMCID: PMC7027814 DOI: 10.1002/aur.2218] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 08/28/2019] [Accepted: 09/16/2019] [Indexed: 02/06/2023]
Abstract
This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. Lay Summary Autism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra‐fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default‐mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.
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Affiliation(s)
- Ville Raatikainen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Viola Borchardt
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Niko Huotari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Heta Helakari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Janne Kananen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Leena Joskitt
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Tuula Hurtig
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Hanna Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Vesa Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
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16
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Rajna Z, Raitamaa L, Tuovinen T, Heikkila J, Kiviniemi V, Seppanen T. 3D Multi-Resolution Optical Flow Analysis of Cardiovascular Pulse Propagation in Human Brain. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2028-2036. [PMID: 30892202 DOI: 10.1109/tmi.2019.2904762] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The brain is cleaned from waste by glymphatic clearance serving a similar purpose as the lymphatic system in the rest of the body. Impairment of the glymphatic brain clearance precedes protein accumulation and reduced cognitive function in Alzheimer's disease (AD). Cardiovascular pulsations are a primary driving force of the glymphatic brain clearance. We developed a method to quantify cardiovascular pulse propagation in the human brain with magnetic resonance encephalography (MREG). We extended a standard optical flow estimation method to three spatial dimensions, with a multi-resolution processing scheme. We added application-specific criteria for discarding inaccurate results. With the proposed method, it is now possible to estimate the propagation of cardiovascular pulse wavefronts from the whole brain MREG data sampled at 10 Hz. The results show that on average the cardiovascular pulse propagates from major arteries via cerebral spinal fluid spaces into all tissue compartments in the brain. We present an example, that cardiovascular pulsations are significantly altered in AD: coefficient of variation and sample entropy of the pulse propagation speed in the lateral ventricles change in AD. These changes are in line with the theory of glymphatic clearance impairment in AD. The proposed non-invasive method can assess a performance indicator related to the glymphatic clearance in the human brain.
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17
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The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution. Neuroimage 2019; 194:228-243. [PMID: 30910728 DOI: 10.1016/j.neuroimage.2019.03.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/14/2019] [Accepted: 03/19/2019] [Indexed: 11/20/2022] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) enables the update of various brain-activity measures during an ongoing experiment as soon as a new brain volume is acquired. However, the recorded Blood-oxygen-level dependent (BOLD) signal also contains physiological artifacts such as breathing and heartbeat, which potentially cause misleading false positive effects especially problematic in brain-computer interface (BCI) and neurofeedback (NF) setups. The low temporal resolution of echo planar imaging (EPI) sequences (which is in the range of seconds) prevents a proper separation of these artifacts from the BOLD signal. MR-Encephalography (MREG) has been shown to provide the high temporal resolution required to unalias and correct for physiological fluctuations and leads to increased specificity and sensitivity for mapping task-based activation and functional connectivity as well as for detecting dynamic changes in connectivity over time. By comparing a simultaneous multislice echo planar imaging (SMS-EPI) sequence and an MREG sequence using the same nominal spatial resolution in an offline analysis for three different experimental fMRI paradigms (perception of house and face stimuli, motor imagery, Stroop task), the potential of this novel technique for future BCI and NF applications was investigated. First, adapted general linear model pre-whitening which accounts for the high temporal resolution in MREG was implemented to calculate proper statistical results and be able to compare these with the SMS-EPI sequence. Furthermore, the respiration- and cardiac pulsation-related signals were successfully separated from the MREG signal using independent component analysis which were then included as regressors for a GLM analysis. Only the MREG sequence allowed to clearly separate cardiac pulsation and respiration components from the signal time course. It could be shown that these components highly correlate with the recorded respiration and cardiac pulsation signals using a respiratory belt and fingertip pulse plethysmograph. Temporal signal-to-noise ratios of SMS-EPI and MREG were comparable. Functional connectivity analysis using partial correlation showed a reduced standard error in MREG compared to SMS-EPI. Also, direct time course comparisons by down-sampling the MREG signal to the SMS-EPI temporal resolution showed lower variance in MREG. In general, we show that the higher temporal resolution is beneficial for fMRI time course modeling and this aspect can be exploited in offline application but also, is especially attractive, for real-time BCI and NF applications.
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18
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Abbott DF, Archer JS, Carney PW, Vaughan DN, Jackson GD. Editorial: Functional Brain Mapping of Epilepsy Networks: Methods and Applications. Front Neurosci 2019; 13:417. [PMID: 31164798 PMCID: PMC6536687 DOI: 10.3389/fnins.2019.00417] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 04/11/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- David F Abbott
- The Florey Institute of Neuroscience and Mental Health, Austin Hospital, Melbourne, VIC, Australia.,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - John S Archer
- The Florey Institute of Neuroscience and Mental Health, Austin Hospital, Melbourne, VIC, Australia.,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Patrick W Carney
- The Florey Institute of Neuroscience and Mental Health, Austin Hospital, Melbourne, VIC, Australia.,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.,Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia
| | - David N Vaughan
- The Florey Institute of Neuroscience and Mental Health, Austin Hospital, Melbourne, VIC, Australia.,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Graeme D Jackson
- The Florey Institute of Neuroscience and Mental Health, Austin Hospital, Melbourne, VIC, Australia.,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
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19
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Huotari N, Raitamaa L, Helakari H, Kananen J, Raatikainen V, Rasila A, Tuovinen T, Kantola J, Borchardt V, Kiviniemi VJ, Korhonen VO. Sampling Rate Effects on Resting State fMRI Metrics. Front Neurosci 2019; 13:279. [PMID: 31001071 PMCID: PMC6454039 DOI: 10.3389/fnins.2019.00279] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 03/08/2019] [Indexed: 01/21/2023] Open
Abstract
Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.
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Affiliation(s)
- Niko Huotari
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Lauri Raitamaa
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Heta Helakari
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Janne Kananen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Ville Raatikainen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Aleksi Rasila
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Timo Tuovinen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Jussi Kantola
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Viola Borchardt
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Vesa J Kiviniemi
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Vesa O Korhonen
- Oulu Functional NeuroImaging Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland
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20
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Ye JC. Compressed sensing MRI: a review from signal processing perspective. BMC Biomed Eng 2019; 1:8. [PMID: 32903346 PMCID: PMC7412677 DOI: 10.1186/s42490-019-0006-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 02/04/2019] [Indexed: 11/27/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires multi-dimensional k-space data through 1-D free induction decay or echo signals. This often limits the use of MRI, especially for high resolution or dynamic imaging. Accordingly, many investigators has developed various acceleration techniques to allow fast MR imaging. For the last two decades, one of the most important breakthroughs in this direction is the introduction of compressed sensing (CS) that allows accurate reconstruction from sparsely sampled k-space data. The recent FDA approval of compressed sensing products for clinical scans clearly reflect the maturity of this technology. Therefore, this paper reviews the basic idea of CS and how this technology have been evolved for various MR imaging problems.
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Affiliation(s)
- Jong Chul Ye
- Department of Bio and Brain Engineering, Korea Adv. Inst. of Science & Technology (KAIST), 291 Daehak-ro, Daejeon, Korea
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21
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Spectral entropy indicates electrophysiological and hemodynamic changes in drug-resistant epilepsy - A multimodal MREG study. NEUROIMAGE-CLINICAL 2019; 22:101763. [PMID: 30927607 PMCID: PMC6444290 DOI: 10.1016/j.nicl.2019.101763] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 02/01/2019] [Accepted: 03/10/2019] [Indexed: 12/20/2022]
Abstract
Objective Epilepsy causes measurable irregularity over a range of brain signal frequencies, as well as autonomic nervous system functions that modulate heart and respiratory rate variability. Imaging dynamic neuronal signals utilizing simultaneously acquired ultra-fast 10 Hz magnetic resonance encephalography (MREG), direct current electroencephalography (DC-EEG), and near-infrared spectroscopy (NIRS) can provide a more comprehensive picture of human brain function. Spectral entropy (SE) is a nonlinear method to summarize signal power irregularity over measured frequencies. SE was used as a joint measure to study whether spectral signal irregularity over a range of brain signal frequencies based on synchronous multimodal brain signals could provide new insights in the neural underpinnings of epileptiform activity. Methods Ten patients with focal drug-resistant epilepsy (DRE) and ten healthy controls (HC) were scanned with 10 Hz MREG sequence in combination with EEG, NIRS (measuring oxygenated, deoxygenated, and total hemoglobin: HbO, Hb, and HbT, respectively), and cardiorespiratory signals. After pre-processing, voxelwise SEMREG was estimated from MREG data. Different neurophysiological and physiological subfrequency band signals were further estimated from MREG, DC-EEG, and NIRS: fullband (0–5 Hz, FB), near FB (0.08–5 Hz, NFB), brain pulsations in very-low (0.009–0.08 Hz, VLFP), respiratory (0.12–0.4 Hz, RFP), and cardiac (0.7–1.6 Hz, CFP) frequency bands. Global dynamic fluctuations in MREG and NIRS were analyzed in windows of 2 min with 50% overlap. Results Right thalamus, cingulate gyrus, inferior frontal gyrus, and frontal pole showed significantly higher SEMREG in DRE patients compared to HC. In DRE patients, SE of cortical Hb was significantly reduced in FB (p = .045), NFB (p = .017), and CFP (p = .038), while both HbO and HbT were significantly reduced in RFP (p = .038, p = .045, respectively). Dynamic SE of HbT was reduced in DRE patients in RFP during minutes 2 to 6. Fitting to the frontal MREG and NIRS results, DRE patients showed a significant increase in SEEEG in FB in fronto-central and parieto-occipital regions, in VLFP in parieto-central region, accompanied with a significant decrease in RFP in frontal pole and parietal and occipital (O2, Oz) regions. Conclusion This is the first study to show altered spectral entropy from synchronous MREG, EEG, and NIRS in DRE patients. Higher SEMREG in DRE patients in anterior cingulate gyrus together with SEEEG and SENIRS results in 0.12–0.4 Hz can be linked to altered parasympathetic function and respiratory pulsations in the brain. Higher SEMREG in thalamus in DRE patients is connected to disturbances in anatomical and functional connections in epilepsy. Findings suggest that spectral irregularity of both electrophysiological and hemodynamic signals are altered in specific way depending on the physiological frequency range. Simultaneous imaging methods indicate spectral irregularity in neurovascular and electrophysiological brain pulsations in DRE. Altered spectral entropy in EEG, NIRS and BOLD indicate dysfunctional brain pulsations in respiratory frequency in epilepsy. Spectral irregularity (0-5 Hz) of BOLD in right thalamus supports previous structural and functional findings in epilepsy.
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22
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Tao S, Shu Y, Trzasko JD, Huston J, Bernstein MA. Partial fourier shells trajectory for non-cartesian MRI. Phys Med Biol 2019; 64:04NT01. [PMID: 30625455 DOI: 10.1088/1361-6560/aafcc5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Non-Cartesian MRI acquisition has demonstrated various advantages in many clinical applications. The shells trajectory is a 3D non-Cartesian MRI acquisition technique that samples the k-space using a series of concentric shells to achieve efficient 3D isotropic acquisition. Partial Fourier acquisition is an acceleration technique that is widely used in Cartesian MRI. It exploits the conjugate symmetry of k-space measurement to reduce the number of k-space samples compared to full-k-space acquisition, without loss of spatial resolution. For a Cartesian MRI acquisition, the direction of partial Fourier acceleration is aligned either with the phase encoded or frequency encoded direction. In those cases, the underlying image matrix can be reconstructed from the undersampled k-space data using a non-iterative, homodyne reconstruction framework. However, designing a non-Cartesian acquisition trajectory that is compatible with non-iterative homodyne reconstruction is not nearly as straightforward as in the Cartesian case. One reason is the non-iterative homodyne reconstruction requires (slightly over) half of the k-space to be fully sampled. Since the direction of partial Fourier acceleration varies throughout the acquisition in the non-Cartesian trajectory, directly applying the same partial Fourier acquisition pattern (as in Cartesian acquisitions) to a non-Cartesian trajectory does not necessarily yield a continuous, physically-achievable trajectory. In this work, we develop an asymmetric shells trajectory with fully-automated trajectory and gradient waveform design to achieve partial Fourier acquisition for the shells trajectory. We then demonstrate a non-iterative image reconstruction framework for the proposed trajectory. Phantom and in vivo brain scans based on spoiled gradient echo (SPGR) shells and magnetization-prepared shells (MP-shells) were performed to test the proposed trajectory design and reconstruction method. Our phantom and in vivo results demonstrate that the proposed partial Fourier shells trajectory maintains the desirable image contrast and high sampling efficiency from the fully sampled shells, while further reducing data acquisition time.
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Affiliation(s)
- Shengzhen Tao
- Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States of America
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23
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Rettenmeier C, Maziero D, Qian Y, Stenger VA. A circular echo planar sequence for fast volumetric fMRI. Magn Reson Med 2018; 81:1685-1698. [PMID: 30273963 DOI: 10.1002/mrm.27522] [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: 03/26/2018] [Revised: 08/03/2018] [Accepted: 08/15/2018] [Indexed: 11/07/2022]
Abstract
PURPOSE To demonstrate a circular EPI (CEPI) sequence as well as a generalized EPI reconstruction for fast fMRI with parallel imaging acceleration. METHODS The CEPI acquisition was constructed using variable readout lengths and maximum ramp sampling as well as blipped-CAIPI z-gradient encoding for simultaneous multislice (SMS) and 3D volumetric imaging. A signal equation model with constant and linear phase terms was used to iteratively reconstruct images with low ghosting. Simulation, phantom, and human imaging experiments including audio/visual fMRI were performed at 3T using a 52-channel coil. RESULTS Application of CEPI gradients with duration of 27 ms covering a 22-cm FOV at a 64 × 64 pixel resolution in SMS and 3D acquisitions resulted in images with comparable quality to those of standard Cartesian EPI. With parallel imaging techniques robust detection of BOLD fMRI activation with temporal sampling down to 275 ms was possible. The high temporal resolution enabled higher activation statistics at a penalty in increased noise and residual aliasing. The un-accelerated 3D acquisition showed large temporal instability compared with a standard 2D acquisition. CONCLUSION Nonuniform sampling and generalized image reconstructions can be applied to EPI acquisitions including those with blipped-CAIPI z gradients. The same gradients can be used for either SMS or 3D acquisitions providing identical coverage.
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Affiliation(s)
- Christoph Rettenmeier
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii
| | - Danilo Maziero
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii
| | - Yongxian Qian
- Department of Radiology, New York University School of Medicine, New York, New York
| | - V Andrew Stenger
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii
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24
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Fast imaging for mapping dynamic networks. Neuroimage 2018; 180:547-558. [DOI: 10.1016/j.neuroimage.2017.08.029] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 07/21/2017] [Accepted: 08/09/2017] [Indexed: 01/22/2023] Open
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25
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Riemenschneider B, LeVan P, Hennig J. Targeted partial reconstruction for real-time fMRI with arbitrary trajectories. Magn Reson Med 2018; 81:1118-1129. [PMID: 30230016 DOI: 10.1002/mrm.27478] [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: 04/10/2018] [Revised: 06/19/2018] [Accepted: 07/11/2018] [Indexed: 11/10/2022]
Abstract
PURPOSE A partial image reconstruction formalism is introduced for the targeted extraction of real-time feedback from arbitrary trajectories when full image reconstruction in real time is computationally too demanding. METHODS Explicit calculation and storage of linear combinations of lines of the reconstruction matrix by an incomplete basis change in spatial coordinates lead to translation of the expensive full reconstruction from a frame-wise application to a region of interest (ROI)-wise application. This step is independent from signal data and can be executed before the experiment. Subsequently, the results of the sum over fully reconstructed voxels can be evaluated directly. Data from a high-speed fMRI acquisition was used to investigate the targeted partial reconstruction of a functional ROI atlas, incorporating an intravolume dephasing correction. The same data and ROIs were used for a comparison of the time series obtained with those obtained from already existing methods for compartment-wise reconstruction. To examine real-time feasibility, the reconstruction was implemented and tested for online reconstruction performance. RESULTS The reconstruction yields results that are virtually identical to the standard reconstruction (i.e., the magnitude sums over the ROIs), with negligible discrepancies even after termination of the conjugate gradient algorithm at a feasible number of iterations. Notably, more discrepancies arise with existing compartment-wise reconstructions. The online real-time implementation evaluated 1 ROI within 2.8 ms in the case of a highly parallel 3D whole brain acquisition. CONCLUSION The high reconstruction fidelity and speed are satisfying for the exemplary application of real-time functional feedback using a highly parallel 3D whole brain acquisition.
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Affiliation(s)
- Bruno Riemenschneider
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Germany
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26
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Kananen J, Tuovinen T, Ansakorpi H, Rytky S, Helakari H, Huotari N, Raitamaa L, Raatikainen V, Rasila A, Borchardt V, Korhonen V, LeVan P, Nedergaard M, Kiviniemi V. Altered physiological brain variation in drug-resistant epilepsy. Brain Behav 2018; 8:e01090. [PMID: 30112813 PMCID: PMC6160661 DOI: 10.1002/brb3.1090] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 07/04/2018] [Accepted: 07/08/2018] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION Functional magnetic resonance imaging (fMRI) combined with simultaneous electroencephalography (EEG-fMRI) has become a major tool in mapping epilepsy sources. In the absence of detectable epileptiform activity, the resting state fMRI may still detect changes in the blood oxygen level-dependent signal, suggesting intrinsic alterations in the underlying brain physiology. METHODS In this study, we used coefficient of variation (CV) of critically sampled 10 Hz ultra-fast fMRI (magnetoencephalography, MREG) signal to compare physiological variance between healthy controls (n = 10) and patients (n = 10) with drug-resistant epilepsy (DRE). RESULTS We showed highly significant voxel-level (p < 0.01, TFCE-corrected) increase in the physiological variance in DRE patients. At individual level, the elevations range over three standard deviations (σ) above the control mean (μ) CVMREG values solely in DRE patients, enabling patient-specific mapping of elevated physiological variance. The most apparent differences in group-level analysis are found on white matter, brainstem, and cerebellum. Respiratory (0.12-0.4 Hz) and very-low-frequency (VLF = 0.009-0.1 Hz) signal variances were most affected. CONCLUSIONS The CVMREG increase was not explained by head motion or physiological cardiorespiratory activity, that is, it seems to be linked to intrinsic physiological pulsations. We suggest that intrinsic brain pulsations play a role in DRE and that critically sampled fMRI may provide a powerful tool for their identification.
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Affiliation(s)
- Janne Kananen
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Timo Tuovinen
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Hanna Ansakorpi
- Research Unit of Neuroscience, Neurology, University of Oulu, Oulu, Finland.,Department of Neurology and Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
| | - Seppo Rytky
- Department of Clinical Neurophysiology, Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
| | - Heta Helakari
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Niko Huotari
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Ville Raatikainen
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Aleksi Rasila
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Viola Borchardt
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - Pierre LeVan
- Faculty of Medicine, Department of Radiology - Medical Physics, University Medical Center Freiburg, University of Freiburg, Freiburg, Germany
| | - Maiken Nedergaard
- Center for Translational Neuromedicine, Department of Neurosurgery, University of Rochester, Rochester, New York.,Faculty of Health and Medical Sciences, Center for Basic and Translational Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | - Vesa Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital, Oulu, Finland.,Oulu Functional NeuroImaging-Group, Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
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27
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Keinänen T, Rytky S, Korhonen V, Huotari N, Nikkinen J, Tervonen O, Palva JM, Kiviniemi V. Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network. J Neurosci Res 2018; 96:1689-1698. [PMID: 29761531 DOI: 10.1002/jnr.24257] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 04/20/2018] [Accepted: 04/23/2018] [Indexed: 01/14/2023]
Abstract
Both functional magnetic resonance imaging (fMRI) and electrophysiological recordings have revealed that resting-state functional connectivity is temporally variable in human brain. Combined full-band electroencephalography-fMRI (fbEEG-fMRI) studies have shown that infraslow (<.1 Hz) fluctuations in EEG scalp potential are correlated with the blood-oxygen-level-dependent (BOLD) fMRI signals and that also this correlation appears variable over time. Here, we used simultaneous fbEEG-fMRI to test the hypothesis that correlation dynamics between BOLD and fbEEG signals could be explained by fluctuations in the activation properties of resting-state networks (RSNs) such as the extent or strength of their activation. We used ultrafast magnetic resonance encephalography (MREG) fMRI to enable temporally accurate and statistically robust short-time-window comparisons of infra-slow fbEEG and BOLD signals. We found that the temporal fluctuations in the fbEEG-BOLD correlation were dependent on RSN connectivity strength, but not on the mean signal level or magnitude of RSN activation or motion during scanning. Moreover, the EEG-fMRI correlations were strongest when the intrinsic RSN connectivity was strong and close to the pial surface. Conversely, weak fbEEG-BOLD correlations were attributable to periods of less coherent or spatially more scattered intrinsic RSN connectivity, or RSN activation in deeper cerebral structures. The results thus show that the on-average low correlations between infra-slow EEG and BOLD signals are, in fact, governed by the momentary coherence and depth of the underlying RSN activation, and may reach systematically high values with appropriate source activities. These findings further consolidate the notion of slow scalp potentials being directly coupled to hemodynamic fluctuations.
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Affiliation(s)
- Tuija Keinänen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.,Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Seppo Rytky
- Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Juha Nikkinen
- Department of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland
| | - Osmo Tervonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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28
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Lennartz C, Schiefer J, Rotter S, Hennig J, LeVan P. Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra. Front Neurosci 2018; 12:287. [PMID: 29867310 PMCID: PMC5951985 DOI: 10.3389/fnins.2018.00287] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 04/11/2018] [Indexed: 01/01/2023] Open
Abstract
In functional magnetic resonance imaging (fMRI), functional connectivity is conventionally characterized by correlations between fMRI time series, which are intrinsically undirected measures of connectivity. Yet, some information about the directionality of network connections can nevertheless be extracted from the matrix of pairwise temporal correlations between all considered time series, when expressed in the frequency-domain as a cross-spectral density matrix. Using a sparsity prior, it then becomes possible to determine a unique directed network topology that best explains the observed undirected correlations, without having to rely on temporal precedence relationships that may not be valid in fMRI. Applying this method on simulated data with 100 nodes yielded excellent retrieval of the underlying directed networks under a wide variety of conditions. Importantly, the method did not depend on temporal precedence to establish directionality, thus reducing susceptibility to hemodynamic variability. The computational efficiency of the algorithm was sufficient to enable whole-brain estimations, thus circumventing the problem of missing nodes that otherwise occurs in partial-brain analyses. Applying the method to real resting-state fMRI data acquired with a high temporal resolution, the inferred networks showed good consistency with structural connectivity obtained from diffusion tractography in the same subjects. Interestingly, this agreement could also be seen when considering high-frequency rather than low-frequency connectivity (average correlation: r = 0.26 for f < 0.3 Hz, r = 0.43 for 0.3 < f < 5 Hz). Moreover, this concordance was significantly better (p < 0.05) than for networks obtained with conventional functional connectivity based on correlations (average correlation r = 0.18). The presented methodology thus appears to be well-suited for fMRI, particularly given its lack of explicit dependence on temporal lag structure, and is readily applicable to whole-brain effective connectivity estimation.
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Affiliation(s)
- Carolin Lennartz
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Jonathan Schiefer
- BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Bernstein Center Freiburg & Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Stefan Rotter
- BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.,Bernstein Center Freiburg & Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
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Albers F, Schmid F, Wachsmuth L, Faber C. Line scanning fMRI reveals earlier onset of optogenetically evoked BOLD response in rat somatosensory cortex as compared to sensory stimulation. Neuroimage 2018; 164:144-154. [DOI: 10.1016/j.neuroimage.2016.12.059] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 11/17/2016] [Accepted: 12/20/2016] [Indexed: 12/20/2022] Open
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30
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van der Zwaag W, Reynaud O, Narsude M, Gallichan D, Marques JP. High spatio-temporal resolution in functional MRI with 3D echo planar imaging using cylindrical excitation and a CAIPIRINHA undersampling pattern. Magn Reson Med 2017; 79:2589-2596. [PMID: 28905414 DOI: 10.1002/mrm.26906] [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: 03/20/2017] [Revised: 08/17/2017] [Accepted: 08/17/2017] [Indexed: 12/24/2022]
Abstract
PURPOSE The combination of 3D echo planar imaging (3D-EPI) with a 2D-CAIPIRINHA undersampling scheme provides high flexibility in the optimization for spatial or temporal resolution. This flexibility can be increased further with the addition of a cylindrical excitation pulse, which exclusively excites the brain regions of interest. Here, 3D-EPI was combined with a 2D radiofrequency pulse to reduce the brain area from which signal is generated, and hence, allowing either reduction of the field of view or reduction of parallel imaging noise amplification. METHODS 3D-EPI with cylindrical excitation and 4 × 3-fold undersampling in a 2D-CAIPIRINHA sampling scheme was used to generate functional MRI (fMRI) data with either 2-mm or 0.9-mm in-plane resolution and 1.1-s temporal resolution over a 5-cm diameter cylinder placed over both temporal lobes for an auditory fMRI experiment. RESULTS Significant increases in image signal-to-noise ratio (SNR) and temporal SNR (tSNR) were found for both 2-mm isotropic data and the high-resolution protocol when using the cylindrical excitation pulse. Both protocols yielded highly significant blood oxygenation level-dependent responses for the presentation of natural sounds. CONCLUSION The higher tSNR of the cylindrical excitation 3D-EPI data makes this sequence an ideal choice for high spatiotemporal resolution fMRI acquisitions. Magn Reson Med 79:2589-2596, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Wietske van der Zwaag
- Spinoza Centre for Neuroimaging, Amsterdam, Netherlands.,Centre d'Imagerie BioMédicale, EPFL, Lausanne, Switzerland
| | | | | | - Daniel Gallichan
- Centre d'Imagerie BioMédicale, EPFL, Lausanne, Switzerland.,Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - José P Marques
- Donders Institute for Brain Behaviour and Cognition, Radboud University, Nijmegen, Netherlands
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31
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Shu Y, Tao S, Trzasko JD, Huston J, Weavers PT, Bernstein MA. Magnetization-prepared shells trajectory with automated gradient waveform design. Magn Reson Med 2017; 79:2024-2035. [PMID: 28833440 DOI: 10.1002/mrm.26863] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/14/2017] [Accepted: 07/16/2017] [Indexed: 01/19/2023]
Abstract
PURPOSE To develop a fully automated trajectory and gradient waveform design for the non-Cartesian shells acquisition, and to develop a magnetization-prepared (MP) shells acquisition to achieve an efficient three-dimensional acquisition with improved gray-to-white brain matter contrast. METHODS After reviewing the shells k-space trajectory, a novel, fully automated trajectory design is developed that allows for gradient waveforms to be automatically generated for specified acquisition parameters. Designs for two types of shells are introduced, including fully sampled and undersampled/accelerated shells. Using those designs, an MP-Shells acquisition is developed by adjusting the acquisition order of shells interleaves to synchronize the center of k-space sampling with the peak of desired gray-to-white matter contrast. The feasibility of the proposed design and MP-Shells is demonstrated using simulation, phantom, and volunteer subject experiments, and the performance of MP-Shells is compared with a clinical Cartesian magnetization-prepared rapid gradient echo acquisition. RESULTS Initial experiments show that MP-Shells produces excellent image quality with higher data acquisition efficiency and improved gray-to-white matter contrast-to-noise ratio (by 36%) compared with the conventional Cartesian magnetization-prepared rapid gradient echo acquisition. CONCLUSION We demonstrated the feasibility of a three-dimensional MP-Shells acquisition and an automated trajectory design to achieve an efficient acquisition with improved gray-to-white matter contrast. Magn Reson Med 79:2024-2035, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Yunhong Shu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Shengzhen Tao
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.,Mayo Graduate School, Mayo Clinic, Rochester, Minnesota, USA
| | | | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul T Weavers
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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32
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Kasper L, Engel M, Barmet C, Haeberlin M, Wilm BJ, Dietrich BE, Schmid T, Gross S, Brunner DO, Stephan KE, Pruessmann KP. Rapid anatomical brain imaging using spiral acquisition and an expanded signal model. Neuroimage 2017; 168:88-100. [PMID: 28774650 DOI: 10.1016/j.neuroimage.2017.07.062] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Revised: 06/22/2017] [Accepted: 07/29/2017] [Indexed: 11/30/2022] Open
Abstract
We report the deployment of spiral acquisition for high-resolution structural imaging at 7T. Long spiral readouts are rendered manageable by an expanded signal model including static off-resonance and B0 dynamics along with k-space trajectories and coil sensitivity maps. Image reconstruction is accomplished by inversion of the signal model using an extension of the iterative non-Cartesian SENSE algorithm. Spiral readouts up to 25 ms are shown to permit whole-brain 2D imaging at 0.5 mm in-plane resolution in less than a minute. A range of options is explored, including proton-density and T2* contrast, acceleration by parallel imaging, different readout orientations, and the extraction of phase images. Results are shown to exhibit competitive image quality along with high geometric consistency.
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Affiliation(s)
- Lars Kasper
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland; Translational Neuromodeling Unit, IBT, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Maria Engel
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Christoph Barmet
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland; Skope Magnetic Resonance Technologies AG, Zurich, Switzerland
| | - Maximilian Haeberlin
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Bertram J Wilm
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Benjamin E Dietrich
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Thomas Schmid
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Simon Gross
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - David O Brunner
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Klaas E Stephan
- Translational Neuromodeling Unit, IBT, University of Zurich and ETH Zurich, Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Klaas P Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
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33
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Improving temporal resolution in fMRI using a 3D spiral acquisition and low rank plus sparse (L+S) reconstruction. Neuroimage 2017; 157:660-674. [PMID: 28684333 DOI: 10.1016/j.neuroimage.2017.06.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 04/18/2017] [Accepted: 06/01/2017] [Indexed: 11/22/2022] Open
Abstract
Rapid whole-brain dynamic Magnetic Resonance Imaging (MRI) is of particular interest in Blood Oxygen Level Dependent (BOLD) functional MRI (fMRI). Faster acquisitions with higher temporal sampling of the BOLD time-course provide several advantages including increased sensitivity in detecting functional activation, the possibility of filtering out physiological noise for improving temporal SNR, and freezing out head motion. Generally, faster acquisitions require undersampling of the data which results in aliasing artifacts in the object domain. A recently developed low-rank (L) plus sparse (S) matrix decomposition model (L+S) is one of the methods that has been introduced to reconstruct images from undersampled dynamic MRI data. The L+S approach assumes that the dynamic MRI data, represented as a space-time matrix M, is a linear superposition of L and S components, where L represents highly spatially and temporally correlated elements, such as the image background, while S captures dynamic information that is sparse in an appropriate transform domain. This suggests that L+S might be suited for undersampled task or slow event-related fMRI acquisitions because the periodic nature of the BOLD signal is sparse in the temporal Fourier transform domain and slowly varying low-rank brain background signals, such as physiological noise and drift, will be predominantly low-rank. In this work, as a proof of concept, we exploit the L+S method for accelerating block-design fMRI using a 3D stack of spirals (SoS) acquisition where undersampling is performed in the kz-t domain. We examined the feasibility of the L+S method to accurately separate temporally correlated brain background information in the L component while capturing periodic BOLD signals in the S component. We present results acquired in control human volunteers at 3T for both retrospective and prospectively acquired fMRI data for a visual activation block-design task. We show that a SoS fMRI acquisition with an acceleration of four and L+S reconstruction can achieve a brain coverage of 40 slices at 2mm isotropic resolution and 64 x 64 matrix size every 500ms.
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Raatikainen V, Huotari N, Korhonen V, Rasila A, Kananen J, Raitamaa L, Keinänen T, Kantola J, Tervonen O, Kiviniemi V. Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data. Neuroimage 2017; 148:352-363. [PMID: 28088482 DOI: 10.1016/j.neuroimage.2017.01.024] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 01/10/2017] [Accepted: 01/11/2017] [Indexed: 01/30/2023] Open
Abstract
This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach. A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs. Mean time lag of 0.6s (± 0.5 std) and mean absolute correlation of 0.69 (± 0.08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2min window analysis, there was large variability over subjects as ranging between 1-10sec. Directionality varied between these highly correlating sources an average 28.8% of the possible number of direction changes. The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0.0s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.
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Affiliation(s)
- Ville Raatikainen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Niko Huotari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Aleksi Rasila
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Janne Kananen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Tuija Keinänen
- Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland; Department of Clinical Neurophysiology, Oulu University Hospital, Oulu, Finland
| | - Jussi Kantola
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Osmo Tervonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland; Research unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
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Akin B, Lee HL, Hennig J, LeVan P. Enhanced subject-specific resting-state network detection and extraction with fast fMRI. Hum Brain Mapp 2016; 38:817-830. [PMID: 27696603 DOI: 10.1002/hbm.23420] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 08/26/2016] [Accepted: 09/21/2016] [Indexed: 12/16/2022] Open
Abstract
Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Burak Akin
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Hsu-Lei Lee
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Jürgen Hennig
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
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36
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Chen Z, Xue R, Zhang P, Sun K, Zuo Z, An J, Chen J, He S, Chen L, Wang DJJ. Multi-phase passband balanced SSFP fMRI with 50ms sampling rate at 7Tesla enables high precision in resolving 100ms neuronal events. Magn Reson Imaging 2016; 35:20-28. [PMID: 27580519 DOI: 10.1016/j.mri.2016.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 08/09/2016] [Accepted: 08/20/2016] [Indexed: 01/09/2023]
Abstract
Passband balanced steady state free precession (b-SSFP) fMRI employs the flat portion of the SSFP off-resonance response to obtain microscopic susceptibility changes elicited by changes in blood oxygenation following enhancement in neuronal activity. This technique can reduce geometric distortion and signal dropout while maintaining rapid acquisition and high signal-to-noise ratio (SNR) compared with traditional fMRI techniques. In the study, we developed a novel multi-phase passband b-SSFP fMRI technique that can achieve a spatial resolution of a few mm3 and a high temporal sampling rate of 50ms per slice at 7Tesla. This technique was further applied for an event-related (ER) fMRI paradigm. As a comparison, gradient-echo echo-planar imaging (GE-EPI) with similar spatial resolution and temporal sampling rate was carried out for the same ER-fMRI experiment. Experiments with visual cortex stimulation were carried out at 7Tesla to demonstrate whether the multi-phase b-SSFP technique and GE-EPI are able to differentiate temporal delays in hemodynamic response function (HRF) separated by 100ms in stimulus onset. Consistent with ERP results, the upslope of the HRF of both techniques can differentiate 100ms delay in stimulus onset, with the former showing a lower level of intersubject variability. The present study demonstrated that the multi-phase passband b-SSFP fMRI technique can be applied for resolving neuronal events on the order of 100ms at ultrahigh magnetic fields.
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Affiliation(s)
- Zhongwei Chen
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA
| | - Rong Xue
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA; Beijing Institute for Brain Disorders, Beijing, China.
| | - Peng Zhang
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA
| | - Kaibao Sun
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA
| | - Jing An
- Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Jing Chen
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA
| | - Sheng He
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA; Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Lin Chen
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA; Beijing Institute for Brain Disorders, Beijing, China.
| | - Danny J J Wang
- UCLA-Beijing Joint Center for Advanced Brain Imaging, Beijing, China; UCLA-Beijing Joint Center for Advanced Brain Imaging, Los Angeles, CA, USA; Laboratory of FMRI Technology (LOFT), Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA; Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
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Körbl K, Jacobs J, Herbst M, Zaitsev M, Schulze-Bonhage A, Hennig J, LeVan P. Marker-based ballistocardiographic artifact correction improves spike identification in EEG-fMRI of focal epilepsy patients. Clin Neurophysiol 2016; 127:2802-2811. [PMID: 27417056 DOI: 10.1016/j.clinph.2016.05.361] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 04/20/2016] [Accepted: 05/22/2016] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Ballistocardiographic (BCG) artifacts resemble interictal epileptic discharges (IEDs) and can lead to incorrect IED identification in EEG-fMRI. This study investigates IEDs marked in EEGs corrected using information from a moiré phase tracking (MPT) marker. METHODS EEG-fMRI from 18 patients was processed with conventional methods for BCG removal, while 9 patients used a MPT marker. IEDs were marked first without ECG information. In a second review, suspicious IEDs synchronous with the BCG were discarded. After each review, an event-related fMRI analysis was performed on the marked IEDs. RESULTS No difference was found in the proportion of suspicious IEDs in the 2 patient groups. However, the distribution of IED timings was significantly related to the cardiac cycle in 11 of 18 patients recorded without MPT marker, but only 2 of 9 patients with marker. In patients recorded without marker, failing to discard suspicious IEDs led to more inaccurate fMRI maps and more distant activations. CONCLUSIONS BCG artifact correction based on MPT recordings allowed a more straightforward identification of IEDs that did not require ECG information in the large majority of patients. SIGNIFICANCE Marker-based ballistocardiographic artifact correction greatly facilitates the study of the generators of interictal discharges with EEG-fMRI.
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Affiliation(s)
- Katharina Körbl
- Dept. Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.
| | - Julia Jacobs
- Dept. Neuropediatrics and Muscular Diseases, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Michael Herbst
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Dept. Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, USA
| | - Maxim Zaitsev
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Jürgen Hennig
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Pierre LeVan
- Dept. Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
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Rajna Z, Keskinarkaus A, Kiviniemi V, Seppanen T. Speeding up the file access of large compressed NIfTI neuroimaging data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:654-7. [PMID: 26736347 DOI: 10.1109/embc.2015.7318447] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A method and implementation are presented to achieve a thousand fold speed-up for seeking of large files in a commonly used compressed neuroimaging data format NIfTI. Such technologies are not currently available in this research field while they would make the everyday work for hundreds of researchers and experts much smoother and faster. The method includes the creation of a novel index structure for the compressed data in order to achieve the speed-up. With random seek simulations, we demonstrate that a speed-up of over hundred up to even five thousand can be reached compared to the currently available implementations. By configuring the index structure properly, one can set an operating point which optimizes the efficiency as speed-up versus index size according to the requirements by the user. For example, a thousand fold speed-up can be achieved with an index size of only about two percent of the original compressed data.
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39
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Ngo GC, Holtrop JL, Fu M, Lam F, Sutton BP. High temporal resolution functional MRI with partial separability model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:7482-5. [PMID: 26738022 DOI: 10.1109/embc.2015.7320122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Even though the hemodynamic response is a slow phenomenon, high temporal resolution in functional fMRI can enable better differentiation between the signal of interest and physiological noise or increase the statistical power of functional studies. To increase the temporal resolution, several methods have been developed to decrease the repetition time, TR, such as simultaneous multi-slice imaging and MR encephalography approaches. In this work, a method using a fast acquisition and a partial separability model is presented to achieve a multi-slice fMRI protocol at a temporal resolution of 75 ms. The method is demonstrated on a visual block task.
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40
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Jäger V, Dümpelmann M, LeVan P, Ramantani G, Mader I, Schulze-Bonhage A, Jacobs J. Concordance of Epileptic Networks Associated with Epileptic Spikes Measured by High-Density EEG and Fast fMRI. PLoS One 2015; 10:e0140537. [PMID: 26496480 PMCID: PMC4619722 DOI: 10.1371/journal.pone.0140537] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Accepted: 09/28/2015] [Indexed: 11/18/2022] Open
Abstract
Objective The present study aims to investigate whether a newly developed fast fMRI called MREG (magnetic resonance encephalography) measures metabolic changes related to interictal epileptic discharges (IED). For this purpose BOLD changes are correlated with the IED distribution and variability. Methods Patients with focal epilepsy underwent EEG-MREG using a 64 channel cap. IED voltage maps were generated using 32 and 64 channels and compared regarding their correspondence to the BOLD response. The extents of IEDs (defined as number of channels with >50% of maximum IED negativity) were correlated with the extents of positive and negative BOLD responses. Differences in inter-spike variability were investigated between interictal epileptic discharges (IED) sets with and without concordant positive or negative BOLD responses. Results 17 patients showed 32 separate IED types. In 50% of IED types the BOLD changes could be confirmed by another independent imaging method. The IED extent significantly correlated with the positive BOLD extent (p = 0.04). In 6 patients the 64-channel EEG voltage maps better reflected the positive or negative BOLD response than the 32-channel EEG; in all others no difference was seen. Inter-spike variability was significantly lower in IED sets with than without concordant positive or negative BOLD responses (with p = 0.04). Significance Higher density EEG and fast fMRI seem to improve the value of EEG-fMRI in epilepsy. The correlation of positive BOLD and IED extent could suggest that widespread BOLD responses reflect the IED network. Inter-spike variability influences the likelihood to find IED concordant positive or negative BOLD responses, which is why single IED analysis may be promising.
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Affiliation(s)
- Vera Jäger
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Section for Epileptology, University Medical Center Freiburg, Freiburg, Germany
| | - Pierre LeVan
- Medical Physics, University Medical Center Freiburg, Freiburg, Germany
| | - Georgia Ramantani
- Section for Epileptology, University Medical Center Freiburg, Freiburg, Germany
| | - Irina Mader
- Department for Neuroradiology, University Medical Center Freiburg, Freiburg, Germany
| | | | - Julia Jacobs
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Freiburg, Germany
- * E-mail:
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Zahneisen B, Assländer J, LeVan P, Hugger T, Reisert M, Ernst T, Hennig J. Quantification and correction of respiration induced dynamic field map changes in fMRI using 3D single shot techniques. Magn Reson Med 2015; 71:1093-102. [PMID: 23716298 DOI: 10.1002/mrm.24771] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
PURPOSE Respiration induced dynamic field map changes in the brain are quantified and the influence on the magnitude signal (physiological noise) is investigated. Dynamic off-resonance correction allows to reduce the signal fluctuations overlaying the blood oxygenation level dependent signal in T2*-weighted functional imaging. THEORY AND METHODS A single-shot whole brain imaging technique with 100 ms temporal resolution was used to measure dynamic off-resonance maps that were calculated from the incremental changes of the image phase. These off-resonance maps are then used to dynamically update the off-resonance corrected reconstruction. RESULTS A global resonance offset and a pronounced gradient in head-foot direction were identified as the main components of the change during a respiration cycle. On average, correction for these fluctuations decreases the magnitude fluctuations by around 30%. CONCLUSION Single shot 3D imaging allows for a robust quantification of dynamic off-resonance changes in the brain. Correction for these fluctuations removes the physiological noise component associated with dynamic point spread function changes.
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Affiliation(s)
- Benjamin Zahneisen
- Department of Medicine, University of Hawaii, John A. Burns School of Medicine, Honolulu, Hawaii, USA
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Rajna Z, Kananen J, Keskinarkaus A, Seppänen T, Kiviniemi V. Detection of short-term activity avalanches in human brain default mode network with ultrafast MR encephalography. Front Hum Neurosci 2015; 9:448. [PMID: 26321936 PMCID: PMC4531800 DOI: 10.3389/fnhum.2015.00448] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Accepted: 07/27/2015] [Indexed: 11/13/2022] Open
Abstract
Recent studies pinpoint visually cued networks of avalanches with MEG/EEG data. Co-activation pattern (CAP) analysis can be used to detect single brain volume activity profiles and hemodynamic fingerprints of neuronal avalanches as sudden high signal activity peaks in classical fMRI data. In this study, we aimed to detect dynamic patterns of brain activity spreads with the use of ultrafast MR encephalography (MREG). MREG achieves 10 Hz whole brain sampling, allowing the estimation of spatial spread of an avalanche, even with the inherent hemodynamic delay of the BOLD signal. We developed a novel computational method to separate avalanche type fast activity spreads from motion artifacts, vasomotor fluctuations, and cardio-respiratory noise in human brain default mode network (DMN). Reproducible and classical DMN sources were identified using spatial ICA prior to advanced noise removal in order to assure that ICA converges to reproducible networks. Brain activity peaks were identified from parts of the DMN, and normalized MREG data around each peak were extracted individually to show dynamic avalanche type spreads as video clips within the DMN. Individual activity spread video clips of specific parts of the DMN were then averaged over the group of subjects. The experiments show that the high BOLD values around the peaks are mostly spreading along the spatial pattern of the particular DMN segment detected with ICA. With also the spread size and lifetime resembling the expected power law distributions, this indicates that the detected peaks are parts of activity avalanches, starting from (or crossing) the DMN. Furthermore, the split, one-sided sub-networks of the DMN show different spread directions within the same DMN framework. The results open possibilities to follow up brain activity avalanches in the hope to understand more about the system wide properties of diseases related to DMN dysfunction.
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Affiliation(s)
- Zalán Rajna
- Biomedical Engineering Research Group, Department of Computer Science and Engineering, Faculty of Information Technology and Electrical Engineering, University of Oulu Oulu, Finland
| | - Janne Kananen
- Oulu Functional Neuroimaging Research Group, Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital Oulu, Finland
| | - Anja Keskinarkaus
- Biomedical Engineering Research Group, Department of Computer Science and Engineering, Faculty of Information Technology and Electrical Engineering, University of Oulu Oulu, Finland
| | - Tapio Seppänen
- Biomedical Engineering Research Group, Department of Computer Science and Engineering, Faculty of Information Technology and Electrical Engineering, University of Oulu Oulu, Finland
| | - Vesa Kiviniemi
- Oulu Functional Neuroimaging Research Group, Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital Oulu, Finland
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Chen G, Ward BD, Chen G, Li SJ. Decreased effective connectivity from cortices to the right parahippocampal gyrus in Alzheimer's disease subjects. Brain Connect 2015; 4:702-8. [PMID: 25132215 DOI: 10.1089/brain.2014.0295] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The purpose of this study was to detect effective connectivity (EC) changes in the default mode network and hippocampus network in 20 patients with Alzheimer's disease (AD) and 20 cognitively normal (CN) subjects, using multivariate Granger causality. The authors used the maximum coefficients in the multivariate autoregression model to quantitatively measure the different EC strength levels between the CN and AD groups. It was demonstrated that the EC strength difference can classify AD from CN subjects. Further, the right parahippocampal gyrus (PHP_R) showed imbalanced bidirectional EC connections. The PHP_R received weaker input connections from the neocortices, but its output connections were significantly increased in AD. These findings may provide neural physiological mechanisms for interpreting AD subjects' memory deficits during the encoding processes.
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Affiliation(s)
- Guangyu Chen
- Department of Biophysics, Medical College of Wisconsin , Milwaukee, Wisconsin
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Safi-Harb M, Proulx S, von Ellenrieder N, Gotman J. Advantages and disadvantages of a fast fMRI sequence in the context of EEG-fMRI investigation of epilepsy patients: A realistic simulation study. Neuroimage 2015; 119:20-32. [PMID: 26093328 DOI: 10.1016/j.neuroimage.2015.06.039] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Revised: 05/26/2015] [Accepted: 06/13/2015] [Indexed: 10/23/2022] Open
Abstract
EEG-fMRI is an established technique to allow mapping BOLD changes in response to interictal discharges recorded in the EEG of epilepsy patients. Traditional fMRI experiments rely on an echo planar imaging (EPI) sequence with a time resolution given by its time-to-repetition (TR) of ~2 s. Recently, multiple fast fMRI sequences have been developed to get around the limited temporal resolution of the EPI sequence, and achieved a TR in the 100 ms range or lower. One such sequence is called Magnetic Resonance EncephaloGraphy (MREG). Its high temporal resolution should offer increased detection sensitivity and statistical power in the context of epilepsy studies and in fMRI experiments in general. The aim of this work was to investigate the advantages and disadvantages offered by MREG. This was done by superimposing artificial event-related BOLD responses on EPI and MREG background signals, from 5 epileptic patients, that were free of epileptic discharges (spikes) on simultaneously recorded EEG. These functional datasets simulated different spiking rates and hemodynamic response amplitudes, and were analyzed with the commonly used General Linear Model (GLM) with the canonical hemodynamic response function (HRF) as a fixed model of the response. Robustness to violation of the assumptions of the GLM was additionally assessed with similar simulations using variable spike-to-spike response amplitudes and 8 non-canonical HRFs. Consistent with previous work, MREG yields higher maximum statistical t-values than EPI, but our simulations showed these statistics to be inflated, as the false positive rate at a standard threshold was high. At thresholds set to appropriately control specificity, EPI showed better true positive rate and larger cluster size than MREG. However, the lack of an appropriate calibration of the amplitude of the responses across the sequences precludes definitive judgment on their relative sensitivity. In addition, we show that a mismatch between the assumed and actual HRF impairs more MREG detection performance, but that EPI is more affected by non-modeled spike-to-spike variations of response amplitude. Filtering-out physiological noise, which is not aliased at the fast sampling rate of MREG, and the modeling of temporal autocorrelation are advantageous in increasing the detection power of MREG. This simulation study 1) warrants care when interpreting statistical t-values from fast fMRI sequences, 2) proposes thresholds for valid inferences and processing methods for maximal sensitivities, and 3) demonstrates the relative robustness/susceptibility of MREG and EPI to violation of the GLM's assumptions.
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Affiliation(s)
- Mouna Safi-Harb
- Montréal Neurological Institute, McGill University, Montréal, Canada.
| | - Sébastien Proulx
- Montréal Neurological Institute, McGill University, Montréal, Canada
| | | | - Jean Gotman
- Montréal Neurological Institute, McGill University, Montréal, Canada
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45
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Jacobs J, Menzel A, Ramantani G, Körbl K, Assländer J, Schulze-Bonhage A, Hennig J, LeVan P. Negative BOLD in default-mode structures measured with EEG-MREG is larger in temporal than extra-temporal epileptic spikes. Front Neurosci 2014; 8:335. [PMID: 25477775 PMCID: PMC4235409 DOI: 10.3389/fnins.2014.00335] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 10/05/2014] [Indexed: 11/13/2022] Open
Abstract
Introduction: EEG-fMRI detects BOLD changes associated with epileptic interictal discharges (IED) and can identify epileptogenic networks in epilepsy patients. Besides positive BOLD changes, negative BOLD changes have sometimes been observed in the default-mode network, particularly using group analysis. A new fast fMRI sequence called MREG (Magnetic Resonance Encephalography) shows increased sensitivity to detect IED-related BOLD changes compared to the conventional EPI sequence, including frequent occurrence of negative BOLD responses in the DMN. The present study quantifies the concordance between the DMN and negative BOLD related to IEDs of temporal and extra-temporal origin. Methods: Focal epilepsy patients underwent simultaneous EEG-MREG. Areas of overlap were calculated between DMN regions, defined as precuneus, posterior cingulate, bilateral inferior parietal and mesial prefrontal cortices according to a standardized atlas, and significant negative BOLD changes revealed by an event-related analysis based on the timings of IED seen on EEG. Correlation between IED number/lobe of origin and the overlap were calculated. Results: 15 patients were analyzed, some showing IED over more than one location resulting in 30 different IED types. The average overlap between negative BOLD and DMN was significantly larger in temporal (23.7 ± 19.6 cm3) than extra-temporal IEDs (7.4 ± 5.1 cm3, p = 0.008). There was no significant correlation between the number of IEDs and the overlap between DMN structures and negative BOLD areas. Discussion: MREG results in an increased sensitivity to detect negative BOLD responses related to focal IED in single patients, with responses often occurring in DMN regions. In patients with high overlap with the DMN, this suggests that epileptic IEDs may be associated with a brief decrease in attention and cognitive ability. Interestingly this observation was not dependent on the frequency of IED but more common in IED of temporal origin.
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Affiliation(s)
- Julia Jacobs
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg Freiburg, Germany ; Epilepsy Center, University Medical Center Freiburg Freiburg, Germany
| | - Antonia Menzel
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg Freiburg, Germany
| | - Georgia Ramantani
- Epilepsy Center, University Medical Center Freiburg Freiburg, Germany
| | - Katharina Körbl
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg Freiburg, Germany
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Kasper L, Haeberlin M, Dietrich BE, Gross S, Barmet C, Wilm BJ, Vannesjo SJ, Brunner DO, Ruff CC, Stephan KE, Pruessmann KP. Matched-filter acquisition for BOLD fMRI. Neuroimage 2014; 100:145-60. [DOI: 10.1016/j.neuroimage.2014.05.024] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 04/25/2014] [Accepted: 05/10/2014] [Indexed: 11/15/2022] Open
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Korhonen V, Hiltunen T, Myllylä T, Wang X, Kantola J, Nikkinen J, Zang YF, LeVan P, Kiviniemi V. Synchronous multiscale neuroimaging environment for critically sampled physiological analysis of brain function: hepta-scan concept. Brain Connect 2014; 4:677-89. [PMID: 25131996 DOI: 10.1089/brain.2014.0258] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Functional connectivity of the resting-state networks of the brain is thought to be mediated by very-low-frequency fluctuations (VLFFs <0.1 Hz) in neuronal activity. However, vasomotor waves and cardiorespiratory pulsations influence indirect measures of brain function, such as the functional magnetic resonance imaging blood-oxygen-level-dependent (BOLD) signal. How strongly physiological oscillations correlate with spontaneous BOLD signals is not known, partially due to differences in the data-sampling rates of different methods. Recent ultrafast inverse imaging sequences, including magnetic resonance encephalography (MREG), enable critical sampling of these signals. In this study, we describe a multimodal concept, referred to as Hepta-scan, which incorporates synchronous MREG with scalp electroencephalography, near-infrared spectroscopy, noninvasive blood pressure, and anesthesia monitoring. Our preliminary results support the idea that, in the absence of aliased cardiorespiratory signals, VLFFs in the BOLD signal are affected by vasomotor and electrophysiological sources. Further, MREG signals showed a high correlation coefficient between the ventromedial default mode network (DMNvmpf) and electrophysiological signals, especially in the VLF range. Also, oxy- and deoxyhemoglobin and vasomotor waves were found to correlate with DMNvmpf. Intriguingly, usage of shorter time windows in these correlation measurements produced significantly (p<0.05) higher positive and negative correlation coefficients, suggesting temporal nonstationary behavior between the measurements. Focus on the VLF range strongly increased correlation strength.
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Affiliation(s)
- Vesa Korhonen
- 1 Department of Diagnostic Radiology, Institute of Diagnostics , Medical Research Center of Oulu, Oulu, Finland
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Testud F, Gallichan D, Layton KJ, Barmet C, Welz AM, Dewdney A, Cocosco CA, Pruessmann KP, Hennig J, Zaitsev M. Single-shot imaging with higher-dimensional encoding using magnetic field monitoring and concomitant field correction. Magn Reson Med 2014; 73:1340-57. [PMID: 24687529 DOI: 10.1002/mrm.25235] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Revised: 02/12/2014] [Accepted: 03/11/2014] [Indexed: 11/09/2022]
Abstract
PURPOSE PatLoc (Parallel Imaging Technique using Localized Gradients) accelerates imaging and introduces a resolution variation across the field-of-view. Higher-dimensional encoding employs more spatial encoding magnetic fields (SEMs) than the corresponding image dimensionality requires, e.g. by applying two quadratic and two linear spatial encoding magnetic fields to reconstruct a 2D image. Images acquired with higher-dimensional single-shot trajectories can exhibit strong artifacts and geometric distortions. In this work, the source of these artifacts is analyzed and a reliable correction strategy is derived. METHODS A dynamic field camera was built for encoding field calibration. Concomitant fields of linear and nonlinear spatial encoding magnetic fields were analyzed. A combined basis consisting of spherical harmonics and concomitant terms was proposed and used for encoding field calibration and image reconstruction. RESULTS A good agreement between the analytical solution for the concomitant fields and the magnetic field simulations of the custom-built PatLoc SEM coil was observed. Substantial image quality improvements were obtained using a dynamic field camera for encoding field calibration combined with the proposed combined basis. CONCLUSION The importance of trajectory calibration for single-shot higher-dimensional encoding is demonstrated using the combined basis including spherical harmonics and concomitant terms, which treats the concomitant fields as an integral part of the encoding.
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Affiliation(s)
- Frederik Testud
- Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany
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Zahneisen B, Poser BA, Ernst T, Stenger AV. Simultaneous Multi-Slice fMRI using spiral trajectories. Neuroimage 2014; 92:8-18. [PMID: 24518259 DOI: 10.1016/j.neuroimage.2014.01.056] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Revised: 01/22/2014] [Accepted: 01/31/2014] [Indexed: 11/25/2022] Open
Abstract
Parallel imaging methods using multi-coil receiver arrays have been shown to be effective for increasing MRI acquisition speed. However parallel imaging methods for fMRI with 2D sequences show only limited improvements in temporal resolution because of the long echo times needed for BOLD contrast. Recently, Simultaneous Multi-Slice (SMS) imaging techniques have been shown to increase fMRI temporal resolution by factors of four and higher. In SMS fMRI multiple slices can be acquired simultaneously using Echo Planar Imaging (EPI) and the overlapping slices are un-aliased using a parallel imaging reconstruction with multiple receivers. The slice separation can be further improved using the "blipped-CAIPI" EPI sequence that provides a more efficient sampling of the SMS 3D k-space. In this paper a blipped-spiral SMS sequence for ultra-fast fMRI is presented. The blipped-spiral sequence combines the sampling efficiency of spiral trajectories with the SMS encoding concept used in blipped-CAIPI EPI. We show that blipped spiral acquisition can achieve almost whole brain coverage at 3mm isotropic resolution in 168 ms. It is also demonstrated that the high temporal resolution allows for dynamic BOLD lag time measurement using visual/motor and retinotopic mapping paradigms. The local BOLD lag time within the visual cortex following the retinotopic mapping stimulation of expanding flickering rings is directly measured and easily translated into an eccentricity map of the cortex.
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Affiliation(s)
- Benjamin Zahneisen
- University of Hawaii, Department of Medicine, John A. Burns School of Medicine, Honolulu, HI, USA.
| | | | - Thomas Ernst
- University of Hawaii, Department of Medicine, John A. Burns School of Medicine, Honolulu, HI, USA
| | - Andrew V Stenger
- University of Hawaii, Department of Medicine, John A. Burns School of Medicine, Honolulu, HI, USA
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Jacobs J, Stich J, Zahneisen B, Assländer J, Ramantani G, Schulze-Bonhage A, Korinthenberg R, Hennig J, LeVan P. Fast fMRI provides high statistical power in the analysis of epileptic networks. Neuroimage 2013; 88:282-94. [PMID: 24140936 DOI: 10.1016/j.neuroimage.2013.10.018] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 09/27/2013] [Accepted: 10/08/2013] [Indexed: 10/26/2022] Open
Abstract
EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution.
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Affiliation(s)
- Julia Jacobs
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Mathildenstrasse 1, 79106 Freiburg, Germany.
| | - Julia Stich
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Mathildenstrasse 1, 79106 Freiburg, Germany
| | - Benjamin Zahneisen
- Medical Physics, University Medical Center Freiburg, Breisacher Straße 60a, 79106 Freiburg, Germany
| | - Jakob Assländer
- Medical Physics, University Medical Center Freiburg, Breisacher Straße 60a, 79106 Freiburg, Germany
| | - Georgia Ramantani
- Section for Epileptology, University Medical Center Freiburg, Breisacher Strasse 64, 79106 Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Section for Epileptology, University Medical Center Freiburg, Breisacher Strasse 64, 79106 Freiburg, Germany
| | - Rudolph Korinthenberg
- Department of Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Mathildenstrasse 1, 79106 Freiburg, Germany
| | - Jürgen Hennig
- Medical Physics, University Medical Center Freiburg, Breisacher Straße 60a, 79106 Freiburg, Germany
| | - Pierre LeVan
- Medical Physics, University Medical Center Freiburg, Breisacher Straße 60a, 79106 Freiburg, Germany
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