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Ebrahimi SM, Tuunanen J, Saarela V, Honkamo M, Huotari N, Raitamaa L, Korhonen V, Helakari H, Järvelä M, Kaakinen M, Eklund L, Kiviniemi V. Synchronous functional magnetic resonance eye imaging, video ophthalmoscopy, and eye surface imaging reveal the human brain and eye pulsation mechanisms. Sci Rep 2024; 14:2250. [PMID: 38278832 PMCID: PMC10817967 DOI: 10.1038/s41598-023-51069-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 12/30/2023] [Indexed: 01/28/2024] Open
Abstract
The eye possesses a paravascular solute transport pathway that is driven by physiological pulsations, resembling the brain glymphatic pathway. We developed synchronous multimodal imaging tools aimed at measuring the driving pulsations of the human eye, using an eye-tracking functional eye camera (FEC) compatible with magnetic resonance imaging (MRI) for measuring eye surface pulsations. Special optics enabled integration of the FEC with MRI-compatible video ophthalmoscopy (MRcVO) for simultaneous retinal imaging along with functional eye MRI imaging (fMREye) of the BOLD (blood oxygen level dependent) contrast. Upon optimizing the fMREye parameters, we measured the power of the physiological (vasomotor, respiratory, and cardiac) eye and brain pulsations by fast Fourier transform (FFT) power analysis. The human eye pulsated in all three physiological pulse bands, most prominently in the respiratory band. The FFT power means of physiological pulsation for two adjacent slices was significantly higher than in one-slice scans (RESP1 vs. RESP2; df = 5, p = 0.045). FEC and MRcVO confirmed the respiratory pulsations at the eye surface and retina. We conclude that in addition to the known cardiovascular pulsation, the human eye also has respiratory and vasomotor pulsation mechanisms, which are now amenable to study using non-invasive multimodal imaging of eye fluidics.
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Affiliation(s)
- Seyed-Mohsen Ebrahimi
- 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
| | - Ville Saarela
- Department of Ophthalmology and Medical Research Center, Oulu University Hospital and Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
| | - Marja Honkamo
- Department of Ophthalmology and Medical Research Center, Oulu University Hospital and Research Unit of Clinical Medicine, University of Oulu, 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - Mika Kaakinen
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Lauri Eklund
- Oulu Center for Cell-Matrix Research, Faculty of Biochemistry and Molecular Medicine, Biocenter Oulu, University of Oulu, 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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Nobukawa S, Ikeda T, Kikuchi M, Takahashi T. Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease. Sci Rep 2024; 14:88. [PMID: 38167950 PMCID: PMC10761722 DOI: 10.1038/s41598-023-50265-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive functions produced by large-scale neural integrations are the most representative 'emergence phenomena' in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer's disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Research Center for Mathematical Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, 187-8661, Tokyo, Japan.
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Psychiatry and Behavioral Science, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuoka, Yoshida, 910-1193, Fukui, Japan
- Uozu Shinkei Sanatorium, 1784-1 Eguchi, Uozu, 937-0017, Toyama, Japan
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Ferdinando H, Moradi S, Korhonen V, Kiviniemi V, Myllylä T. Altered cerebrovascular-CSF coupling in Alzheimer's Disease measured by functional near-infrared spectroscopy. Sci Rep 2023; 13:22364. [PMID: 38102188 PMCID: PMC10724150 DOI: 10.1038/s41598-023-48965-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
In-vivo microscopical studies indicate that brain cerebrospinal fluid (CSF) transport driven by blood vessel pulsations is reduced in the early stages of Alzheimer's disease (AD). We hypothesized that the coupling pattern between cerebrovascular pulsations and CSF is altered in AD, and this can be measured using multi-wavelength functional near-infrared spectroscopy (fNIRS). To study this, we quantified simultaneously cerebral hemo- and CSF hydrodynamics in early AD patients and age-matched healthy controls. Physiological pulsations were analysed in the vasomotor very low frequency (VLF 0.008-0.1 Hz), respiratory (Resp. 0.1-0.6 Hz), and cardiac (Card. 0.6-5 Hz) bands. A sliding time window cross-correlation approach was used to estimate the temporal stability of the cerebrovascular-CSF coupling. We investigated how the lag time series variation of the coupling differs between AD patients and control. The couplings involving deoxyhemoglobin (HbR) and CSF water, along with their first derivative, in the cardiac band demonstrated significant difference between AD patients and controls. Furthermore, the lag time series variation of HbR-CSF in the cardiac band provided a significant relationship, p-value = 0.04 and r2 = 0.16, with the mini-mental state exam (MMSE) score. In conclusion, the coupling pattern between hemodynamics and CSF is reduced in AD and it correlates with MMSE score.
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Affiliation(s)
- Hany Ferdinando
- Research Unit of Health Science and Technology, University of Oulu, Oulu, Finland.
| | - Sadegh Moradi
- Opto-Electronics and Measurement Technique Research Unit, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Research Unit of Health Science and Technology, University of Oulu, Oulu, Finland
- Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Vesa Kiviniemi
- Research Unit of Health Science and Technology, University of Oulu, Oulu, Finland
- Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Teemu Myllylä
- Research Unit of Health Science and Technology, University of Oulu, Oulu, Finland
- Opto-Electronics and Measurement Technique Research Unit, University of Oulu, Oulu, Finland
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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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Väyrynen T, Helakari H, Korhonen V, Tuunanen J, Huotari N, Piispala J, Kallio M, Raitamaa L, Kananen J, Järvelä M, Matias Palva J, Kiviniemi V. Infra-slow fluctuations in cortical potentials and respiration drive fast cortical EEG rhythms in sleeping and waking states. Clin Neurophysiol 2023; 156:207-219. [PMID: 37972532 DOI: 10.1016/j.clinph.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/09/2023] [Accepted: 10/23/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVE Infra-slow fluctuations (ISF, 0.008-0.1 Hz) characterize hemodynamic and electric potential signals of human brain. ISFs correlate with the amplitude dynamics of fast (>1 Hz) neuronal oscillations, and may arise from permeability fluctuations of the blood-brain barrier (BBB). It is unclear if physiological rhythms like respiration drive or track fast cortical oscillations, and the role of sleep in this coupling is unknown. METHODS We used high-density full-band electroencephalography (EEG) in healthy human volunteers (N = 21) to measure concurrently the ISFs, respiratory pulsations, and fast neuronal oscillations during periods of wakefulness and sleep, and to assess the strength and direction of their phase-amplitude coupling. RESULTS The phases of ISFs and respiration were both coupled with the amplitude of fast neuronal oscillations, with stronger ISF coupling being evident during sleep. Phases of ISF and respiration drove the amplitude dynamics of fast oscillations in sleeping and waking states, with different contributions. CONCLUSIONS ISFs in slow cortical potentials and respiration together significantly determine the dynamics of fast cortical oscillations. SIGNIFICANCE We propose that these slow physiological phases play a significant role in coordinating cortical excitability, which is a fundamental aspect of brain function.
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Affiliation(s)
- Tommi Väyrynen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), 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; MIPT group to: Research Unit of Health Sciences and Technology (HST), 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; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Johanna Tuunanen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), 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; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Johanna Piispala
- MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Mika Kallio
- MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Lauri Raitamaa
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Janne Kananen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Matti Järvelä
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, 02150 Espoo, Finland; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Centre for Cognitive Neuroimaging, University of Glasgow, United Kingdom
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu 90220, Finland
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Su WC, Colacot R, Ahmed N, Nguyen T, George T, Gandjbakhche A. The use of functional near-infrared spectroscopy in tracking neurodevelopmental trajectories in infants and children with or without developmental disorders: a systematic review. Front Psychiatry 2023; 14:1210000. [PMID: 37779610 PMCID: PMC10536152 DOI: 10.3389/fpsyt.2023.1210000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/24/2023] [Indexed: 10/03/2023] Open
Abstract
Understanding the neurodevelopmental trajectories of infants and children is essential for the early identification of neurodevelopmental disorders, elucidating the neural mechanisms underlying the disorders, and predicting developmental outcomes. Functional Near-Infrared Spectroscopy (fNIRS) is an infant-friendly neuroimaging tool that enables the monitoring of cerebral hemodynamic responses from the neonatal period. Due to its advantages, fNIRS is a promising tool for studying neurodevelopmental trajectories. Although many researchers have used fNIRS to study neural development in infants/children and have reported important findings, there is a lack of synthesized evidence for using fNIRS to track neurodevelopmental trajectories in infants and children. The current systematic review summarized 84 original fNIRS studies and showed a general trend of age-related increase in network integration and segregation, interhemispheric connectivity, leftward asymmetry, and differences in phase oscillation during resting-state. Moreover, typically developing infants and children showed a developmental trend of more localized and differentiated activation when processing visual, auditory, and tactile information, suggesting more mature and specialized sensory networks. Later in life, children switched from recruiting bilateral auditory to a left-lateralized language circuit when processing social auditory and language information and showed increased prefrontal activation during executive functioning tasks. The developmental trajectories are different in children with developmental disorders, with infants at risk for autism spectrum disorder showing initial overconnectivity followed by underconnectivity during resting-state; and children with attention-deficit/hyperactivity disorders showing lower prefrontal cortex activation during executive functioning tasks compared to their typically developing peers throughout childhood. The current systematic review supports the use of fNIRS in tracking the neurodevelopmental trajectories in children. More longitudinal studies are needed to validate the neurodevelopmental trajectories and explore the use of these neurobiomarkers for the early identification of developmental disorders and in tracking the effects of interventions.
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Affiliation(s)
| | | | | | | | | | - Amir Gandjbakhche
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, United States
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Thakkar RN, Kioutchoukova IP, Griffin I, Foster DT, Sharma P, Valero EM, Lucke-Wold B. Mapping the Glymphatic Pathway Using Imaging Advances. J 2023; 6:477-491. [PMID: 37601813 PMCID: PMC10439810 DOI: 10.3390/j6030031] [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] [Indexed: 08/22/2023] Open
Abstract
The glymphatic system is a newly discovered waste-clearing system that is analogous to the lymphatic system in our central nervous system. Furthermore, disruption in the glymphatic system has also been associated with many neurodegenerative disorders (e.g., Alzheimer's disease), traumatic brain injury, and subarachnoid hemorrhage. Thus, understanding the function and structure of this system can play a key role in researching the progression and prognoses of these diseases. In this review article, we discuss the current ways to map the glymphatic system and address the advances being made in preclinical mapping. As mentioned, the concept of the glymphatic system is relatively new, and thus, more research needs to be conducted in order to therapeutically intervene via this system.
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Affiliation(s)
- Rajvi N. Thakkar
- College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | | | - Ian Griffin
- College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Devon T. Foster
- College of Medicine, Florida International University, Miami, FL 33199, USA
| | | | | | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, 1600 SW Archer Rd., Gainesville, FL 32610, USA
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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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Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors. Brain Topogr 2022; 35:282-301. [PMID: 35142957 PMCID: PMC9098592 DOI: 10.1007/s10548-022-00891-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 02/01/2023]
Abstract
Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.
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Huotari N, Tuunanen J, Raitamaa L, Raatikainen V, Kananen J, Helakari H, Tuovinen T, Järvelä M, Kiviniemi V, Korhonen V. Cardiovascular Pulsatility Increases in Visual Cortex Before Blood Oxygen Level Dependent Response During Stimulus. Front Neurosci 2022; 16:836378. [PMID: 35185462 PMCID: PMC8853630 DOI: 10.3389/fnins.2022.836378] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 01/13/2022] [Indexed: 11/13/2022] Open
Abstract
The physiological pulsations that drive tissue fluid homeostasis are not well characterized during brain activation. Therefore, we used fast magnetic resonance encephalography (MREG) fMRI to measure full band (0–5 Hz) blood oxygen level-dependent (BOLDFB) signals during a dynamic visual task in 23 subjects. This revealed brain activity in the very low frequency (BOLDVLF) as well as in cardiac and respiratory bands. The cardiovascular hemodynamic envelope (CHe) signal correlated significantly with the visual BOLDVLF response, considered as an independent signal source in the V1-V2 visual cortices. The CHe preceded the canonical BOLDVLF response by an average of 1.3 (± 2.2) s. Physiologically, the observed CHe signal could mark increased regional cardiovascular pulsatility following vasodilation.
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Affiliation(s)
- Niko Huotari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
- *Correspondence: Niko Huotari,
| | - Johanna Tuunanen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Lauri Raitamaa
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Ville Raatikainen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Janne Kananen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Heta Helakari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Timo Tuovinen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Matti Järvelä
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Vesa Kiviniemi
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), 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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Zienkiewicz A, Favre M, Ferdinando H, Iring S, Serrador J, Myllylä T. Blood pressure wave propagation - a multisensor setup for cerebral autoregulation studies. Physiol Meas 2021; 42. [PMID: 34731844 DOI: 10.1088/1361-6579/ac3629] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/03/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Cerebral autoregulation is critically important to maintain proper brain perfusion and supply the brain with oxygenated blood. Non-invasive measures of blood pressure (BP) are critical in assessing cerebral autoregulation. Wave propogation velocity may be a useful technique to estimate BP but the effect of the location of the sensors on the readings has not been thoroughly examined. In this paper, we were interested to study if propagation velocity of the pressure wave in the direction from the heart to the brain may differ compared with propagation from the heart to the periphery, as well as across different physiological tasks and/or health conditions. Using non-invasive sensors simultaneously placed on different locations of the human body allow for the study of how propagation velocity of the pressure wave, based on pulse transit time (PTT), varies across different directions. APPROACH We present multi-sensor BP wave propagation measurement setup aimed for cerebral autoregulation studies. The presented sensor setup consists of three sensors, one each placed on the neck, chest and finger, allowing simultaneous measurement of changes in BP propagation velocity towards the brain and to the periphery. We show how commonly tested physiological tasks affect the relative changes of PTT and correlations with BP. MAIN RESULTS We observed that during maximal blow, valsalva and breath hold breathing tasks, the relative changes of PTT were higher when PTT was measured in the direction from the heart to the brain than from the heart to the peripherals. In contrast, during a deep breathing task, the relative change in PTT from the heart to the brain was lower. In addition, we present a short literature review of PTT methods used in brain research. SIGNIFICANCE These preliminary data suggest that physiological task and direction of PTT measurement may affect relative PTT changes. Presented three-sensor setup provides an easy and neuroimaging compatible method for cerebral autoregulation studies by allowing to measure BP wave propagation velocity towards the brain vs. towards the periphery.
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Affiliation(s)
- Aleksandra Zienkiewicz
- Optoelectronics and Measurement Techniques Research Unit, University of Oulu, Oulu, FINLAND
| | - Michelle Favre
- Department of Pharmacology, Physiology & Neuroscience, Rutgers The State University of New Jersey, Newark, New Jersey, UNITED STATES
| | - Hany Ferdinando
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Pohjois-Pohjanmaa, FINLAND
| | - Stephanie Iring
- Department of Pharmacology, Physiology & Neuroscience, Rutgers The State University of New Jersey, Newark, New Jersey, UNITED STATES
| | - Jorge Serrador
- Department of Pharmacology, Physiology & Neuroscience, Rutgers The State University of New Jersey, Newark, New Jersey, UNITED STATES
| | - Teemu Myllylä
- Optoelectronics and Measurement Techniques Research Unit, University of Oulu, Oulu, FINLAND
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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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13
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Yang D, Hong KS. Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach. J Alzheimers Dis 2021; 80:647-663. [PMID: 33579839 DOI: 10.3233/jad-201163] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. OBJECTIVE This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. METHODS In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. RESULTS There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. CONCLUSION The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.
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Affiliation(s)
- Dalin Yang
- School of Mechanical Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea
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14
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Abreu R, Jorge J, Leal A, Koenig T, Figueiredo P. EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States. Brain Topogr 2021; 34:41-55. [PMID: 33161518 DOI: 10.1007/s10548-020-00805-1/figures/5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 10/23/2020] [Indexed: 05/25/2023]
Abstract
Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.
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Affiliation(s)
- Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), ICNAS, University of Coimbra, Coimbra, Portugal
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
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15
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Abreu R, Jorge J, Leal A, Koenig T, Figueiredo P. EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States. Brain Topogr 2020; 34:41-55. [PMID: 33161518 DOI: 10.1007/s10548-020-00805-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 10/23/2020] [Indexed: 12/14/2022]
Abstract
Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.
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Affiliation(s)
- Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), ICNAS, University of Coimbra, Coimbra, Portugal
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
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16
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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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Raitamaa L, Korhonen V, Huotari N, Raatikainen V, Hautaniemi T, Kananen J, Rasila A, Helakari H, Zienkiewicz A, Myllylä T, Borchardt V, Kiviniemi V. Breath hold effect on cardiovascular brain pulsations - A multimodal magnetic resonance encephalography study. J Cereb Blood Flow Metab 2019; 39:2471-2485. [PMID: 30204040 PMCID: PMC6893986 DOI: 10.1177/0271678x18798441] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Ultra-fast functional magnetic resonance encephalography (MREG) enables separate assessment of cardiovascular, respiratory, and vasomotor waves from brain pulsations without temporal aliasing. We examined effects of breath hold- (BH) related changes on cardiovascular brain pulsations using MREG to study the physiological nature of cerebrovascular reactivity. We used alternating 32 s BH and 88 s resting normoventilation (NV) to change brain pulsations during MREG combined with simultaneously measured respiration, continuous non-invasive blood pressure, and cortical near-infrared spectroscopy (NIRS) in healthy volunteers. Changes in classical resting-state network BOLD-like signal and cortical blood oxygenation were reproduced based on MREG and NIRS signals. Cardiovascular pulsation amplitudes of MREG signal from anterior cerebral artery, oxygenated hemoglobin concentration in frontal cortex, and blood pressure decreased after BH. MREG cardiovascular pulse amplitudes in cortical areas and sagittal sinus increased, while cerebrospinal fluid and white matter remained unchanged. Respiratory centers in the brainstem - hypothalamus - thalamus - amygdala network showed strongest increases in cardiovascular pulsation amplitude. The spatial propagation of averaged cardiovascular impulses altered as a function of successive BH runs. The spread of cardiovascular pulse cycles exhibited a decreasing spatial similarity over time. MREG portrayed spatiotemporally accurate respiratory network activity and cardiovascular pulsation dynamics related to BH challenges at an unpreceded high temporal resolution.
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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, Finland
| | - Vesa Korhonen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Niko Huotari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Ville Raatikainen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Taneli Hautaniemi
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Janne Kananen
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Aleksi Rasila
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Heta Helakari
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Aleksandra Zienkiewicz
- Biomedical Sensors and Measurement Systems Group, Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
| | - Teemu Myllylä
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland.,Biomedical Sensors and Measurement Systems Group, Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
| | - Viola Borchardt
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Oulu Functional Neuro Imaging Group, Research Unit of Medical Imaging Physics and Technology (MIPT), University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
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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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Zhang S, Hennig J, LeVan P. Direct modelling of gradient artifacts for EEG-fMRI denoising and motion tracking. J Neural Eng 2019; 16:056010. [PMID: 31216524 DOI: 10.1088/1741-2552/ab2b21] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Simultaneous electroencephalography and functional magnetic resonance imaging recording (EEG-fMRI) has been widely used in neuroscientific and clinical research. The artifacts in the recorded EEG resulting from rapidly switching magnetic field gradients are usually corrected by average-artifact subtraction (AAS) due to their repetitive nature. But the performance of AAS is often disrupted by altered artifact waveforms across epochs, notably due to head motion. APPROACH Here, a method is proposed to make use of the known MR sequence gradient waveforms for a direct modelling of gradient artifacts. After accounting for filtering effects on the gradient artifacts, a continuous modulation of the gradient waveforms superimposed on the EEG signal is obtained. MAIN RESULTS Although a moving AAS template can adjust to slow drifts in gradient artifact variation, it fails to adapt to abrupt motion, resulting in residual noise. We demonstrate how this modelling approach can reduce motion-affected gradient artifacts without distorting the underlying neuronal signals. Moreover, the method provides useful head motion information highly correlated with motion tracked by an optical camera. SIGNIFICANCE Our work provides a novel way to improve gradient artifact removal in EEG-fMRI, and shows a potential to detect head motion without requiring additional hardware.
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Affiliation(s)
- Shuoyue Zhang
- Department of Radiology - Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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20
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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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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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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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23
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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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24
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Myllylä T, Harju M, Korhonen V, Bykov A, Kiviniemi V, Meglinski I. Assessment of the dynamics of human glymphatic system by near-infrared spectroscopy. JOURNAL OF BIOPHOTONICS 2018; 11:e201700123. [PMID: 28802090 DOI: 10.1002/jbio.201700123] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 07/25/2017] [Accepted: 08/10/2017] [Indexed: 06/07/2023]
Abstract
Fluctuations in brain water content has attracted increasing interest, particularly as regards studies of the glymphatic system, which is connected with the complex organization of dural lymphatic vessels, responsible for cleaning tissue. Disturbances of glymphatic circulation are associated with several brain disorders, including dementia. This article introduces an approach to noninvasive measurement of water dynamics in the human brain utilizing near-infrared spectroscopy (NIRS). We demonstrate the possibility to sense dynamic variations of water content between the skull and grey matter, for instance, in the subarachnoid space. Measured fluctuations in water content, especially in the cerebrospinal fluid (CSF), are assumed to be correlated with the dynamics of glymphatic circulation. The sampling volume for the NIRS optode was estimated by Monte Carlo modelling for the wavelengths of 660, 740, 830 and 980 nm. In addition, using combinations of these wavelengths, this article presents the calculation models for quantifying water and haemodynamics. The presented NIRS technique allows long-term functional brain monitoring, including sleeping time. Furthermore, it is used in combination with different magnetic neuroimaging techniques, particularly magnetic resonance encephalography. Using the combined setup, we report the preliminary results on the interaction between CSF and blood oxygen level-dependent fluctuations.
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Affiliation(s)
- Teemu Myllylä
- Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
- Oulu Functional Neuroimaging Group, Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Markus Harju
- Inverse Problems Group, Department of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional Neuroimaging Group, Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Alexander Bykov
- Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
- Department of Photonics and Optical Information Technology, ITMO University, St Petersburg, Russia
| | - Vesa Kiviniemi
- Oulu Functional Neuroimaging Group, Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Igor Meglinski
- Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
- Department of Photonics and Optical Information Technology, ITMO University, St Petersburg, Russia
- Institute of Biology, Irkutsk State University, Irkutsk, Russia
- Institute of Engineering Physics for Biomedicine, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia
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25
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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: 13] [Impact Index Per Article: 2.2] [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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26
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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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27
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Grooms JK, Thompson GJ, Pan WJ, Billings J, Schumacher EH, Epstein CM, Keilholz SD. Infraslow Electroencephalographic and Dynamic Resting State Network Activity. Brain Connect 2018; 7:265-280. [PMID: 28462586 DOI: 10.1089/brain.2017.0492] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.
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Affiliation(s)
- Joshua K Grooms
- 1 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Garth J Thompson
- 2 Magnetic Resonance Research Center (MRRC) and Radiology and Biomedical Imaging, Yale University , New Haven, Connecticut
| | - Wen-Ju Pan
- 1 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia
| | - Jacob Billings
- 3 Department of Neuroscience, Emory University , Atlanta, Georgia
| | - Eric H Schumacher
- 4 Department of Psychology, Georgia Institute of Technology , Atlanta, Georgia
| | - Charles M Epstein
- 5 Department of Neurology, Emory University School of Medicine , Atlanta, Georgia
| | - Shella D Keilholz
- 1 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia .,3 Department of Neuroscience, Emory University , Atlanta, Georgia
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28
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Gao C, Sun J, Yang X, Gong H. Gender differences in brain networks during verbal Sternberg tasks: A simultaneous near-infrared spectroscopy and electro-encephalography study. JOURNAL OF BIOPHOTONICS 2018; 11:e201700120. [PMID: 28921863 DOI: 10.1002/jbio.201700120] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 09/04/2017] [Accepted: 09/08/2017] [Indexed: 06/07/2023]
Abstract
Gender differences in psychological processes have been of great interest in a variety of fields including verbal fluency, emotion processing and working memory. Previous studies suggested that women outperform men in verbal working memory (VWM). However, the inherent mechanisms are still unclear. To obtain a deeper insight into the gender differences in brain networks in VWM, this study used near-infrared spectroscopy (NIRS) and electro-encephalography (EEG) simultaneously to investigate gender-related brain networks during verbal Sternberg tasks. NIRS results confirmed that women surpass men in VWM from the perspective of both brain activation and connectivity. Results of EEG (effective connectivity and event-related spectral power) showed that men tend to use a more visuospatial strategy to encode memory. In addition, novel analysis methods of brain networks can provide useful information about the gender specifics of brain functions. Gender-related pseudo-color maps constructed from all channels of average HbO2 activity during low- and high-load tasks (from 0 to 6 seconds after beginning).
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Affiliation(s)
- Chenyang Gao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, P. R. China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Huazhong University of Science and technology, Wuhan, P. R. China
| | - Jinyan Sun
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan, P. R. China
| | - Xiaoquan Yang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, P. R. China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Huazhong University of Science and technology, Wuhan, P. R. China
| | - Hui Gong
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, P. R. China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Huazhong University of Science and technology, Wuhan, P. R. China
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29
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Thompson GJ. Neural and metabolic basis of dynamic resting state fMRI. Neuroimage 2017; 180:448-462. [PMID: 28899744 DOI: 10.1016/j.neuroimage.2017.09.010] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 08/30/2017] [Accepted: 09/06/2017] [Indexed: 02/07/2023] Open
Abstract
Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.
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Affiliation(s)
- Garth J Thompson
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China.
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30
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Myllylä T, Zacharias N, Korhonen V, Zienkiewicz A, Hinrichs H, Kiviniemi V, Walter M. Multimodal brain imaging with magnetoencephalography: A method for measuring blood pressure and cardiorespiratory oscillations. Sci Rep 2017; 7:172. [PMID: 28282963 PMCID: PMC5412650 DOI: 10.1038/s41598-017-00293-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 02/17/2017] [Indexed: 11/25/2022] Open
Abstract
Studies with magnetoencephalography (MEG) are still quite rarely combined simultaneously with methods that can provide a metabolic dimension to MEG investigations. In addition, continuous blood pressure measurements which comply with MEG compatibility requirements are lacking. For instance, by combining methods reflecting neurovascular status one could obtain more information on low frequency fluctuations that have recently gained increasing interest as a mediator of functional connectivity within brain networks. This paper presents a multimodal brain imaging setup, capable to non-invasively and continuously measure cerebral hemodynamic, cardiorespiratory and blood pressure oscillations simultaneously with MEG. In the setup, all methods apart from MEG rely on the use of fibre optics. In particular, we present a method for measuring of blood pressure and cardiorespiratory oscillations continuously with MEG. The potential of this type of multimodal setup for brain research is demonstrated by our preliminary studies on human, showing effects of mild hypercapnia, gathered simultaneously with the presented modalities.
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Affiliation(s)
- Teemu Myllylä
- University of Oulu, Optoelectronics and Measurement Techniques Research Unit, Health & Wellness Measurements Group, Oulu, Finland.
| | - Norman Zacharias
- Leibniz Institute for Neurobiology, Magdeburg, Germany.,Charité - Universitätsmedizin Berlin, Department of Anesthesiology, Neuroimaging Research Group, Berlin, Germany
| | - Vesa Korhonen
- Oulu University Hospital, Department of Diagnostic Radiology, Oulu, Finland.,University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu Functional NeuroImaging Group, Oulu, Finland
| | - Aleksandra Zienkiewicz
- University of Oulu, Optoelectronics and Measurement Techniques Research Unit, Health & Wellness Measurements Group, Oulu, Finland
| | - Hermann Hinrichs
- Leibniz Institute for Neurobiology, Magdeburg, Germany.,University Hospital Magdeburg, Clinic for Neurology, Magdeburg, Germany
| | - Vesa Kiviniemi
- Oulu University Hospital, Department of Diagnostic Radiology, Oulu, Finland.,University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu Functional NeuroImaging Group, Oulu, Finland
| | - Martin Walter
- Leibniz Institute for Neurobiology, Magdeburg, Germany.,University of Tübingen, Department of Psychiatry, Tübingen, Germany
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31
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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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32
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Kiviniemi V, Wang X, Korhonen V, Keinänen T, Tuovinen T, Autio J, LeVan P, Keilholz S, Zang YF, Hennig J, Nedergaard M. Ultra-fast magnetic resonance encephalography of physiological brain activity - Glymphatic pulsation mechanisms? J Cereb Blood Flow Metab 2016; 36:1033-45. [PMID: 26690495 PMCID: PMC4908626 DOI: 10.1177/0271678x15622047] [Citation(s) in RCA: 225] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 11/06/2015] [Indexed: 11/16/2022]
Abstract
The theory on the glymphatic convection mechanism of cerebrospinal fluid holds that cardiac pulsations in part pump cerebrospinal fluid from the peri-arterial spaces through the extracellular tissue into the peri-venous spaces facilitated by aquaporin water channels. Since cardiac pulses cannot be the sole mechanism of glymphatic propulsion, we searched for additional cerebrospinal fluid pulsations in the human brain with ultra-fast magnetic resonance encephalography. We detected three types of physiological mechanisms affecting cerebral cerebrospinal fluid pulsations: cardiac, respiratory, and very low frequency pulsations. The cardiac pulsations induce a negative magnetic resonance encephalography signal change in peri-arterial regions that extends centrifugally and covers the brain in ≈1 Hz cycles. The respiratory ≈0.3 Hz pulsations are centripetal periodical pulses that occur dominantly in peri-venous areas. The third type of pulsation was very low frequency (VLF 0.001-0.023 Hz) and low frequency (LF 0.023-0.73 Hz) waves that both propagate with unique spatiotemporal patterns. Our findings using critically sampled magnetic resonance encephalography open a new view into cerebral fluid dynamics. Since glymphatic system failure may precede protein accumulations in diseases such as Alzheimer's dementia, this methodological advance offers a novel approach to image brain fluid dynamics that potentially can enable early detection and intervention in neurodegenerative diseases.
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Affiliation(s)
- Vesa Kiviniemi
- Oulu Functional NeuroImaging, Department of Diagnostic Radiology, MRC, Oulu University Hospital, Oulu, Finland Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Xindi Wang
- Oulu Functional NeuroImaging, Department of Diagnostic Radiology, MRC, Oulu University Hospital, Oulu, Finland Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Vesa Korhonen
- Oulu Functional NeuroImaging, Department of Diagnostic Radiology, MRC, Oulu University Hospital, Oulu, Finland Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Tuija Keinänen
- Oulu Functional NeuroImaging, Department of Diagnostic Radiology, MRC, Oulu University Hospital, Oulu, Finland Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Timo Tuovinen
- Oulu Functional NeuroImaging, Department of Diagnostic Radiology, MRC, Oulu University Hospital, Oulu, Finland Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Joonas Autio
- Oulu Functional NeuroImaging, Department of Diagnostic Radiology, MRC, Oulu University Hospital, Oulu, Finland Functional Architecture Team, Center for Life Science Technologies, RIKEN, Japan
| | - Pierre LeVan
- Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA, USA
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
| | - Jürgen Hennig
- Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Maiken Nedergaard
- School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, USA
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33
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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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34
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Thompson GJ, Pan WJ, Keilholz SD. Different dynamic resting state fMRI patterns are linked to different frequencies of neural activity. J Neurophysiol 2015; 114:114-24. [PMID: 26041826 DOI: 10.1152/jn.00235.2015] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 06/02/2015] [Indexed: 01/31/2023] Open
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) results have indicated that network mapping can contribute to understanding behavior and disease, but it has been difficult to translate the maps created with rsfMRI to neuroelectrical states in the brain. Recently, dynamic analyses have revealed multiple patterns in the rsfMRI signal that are strongly associated with particular bands of neural activity. To further investigate these findings, simultaneously recorded invasive electrophysiology and rsfMRI from rats were used to examine two types of electrical activity (directly measured low-frequency/infraslow activity and band-limited power of higher frequencies) and two types of dynamic rsfMRI (quasi-periodic patterns or QPP, and sliding window correlation or SWC). The relationship between neural activity and dynamic rsfMRI was tested under three anesthetic states in rats: dexmedetomidine and high and low doses of isoflurane. Under dexmedetomidine, the lightest anesthetic, infraslow electrophysiology correlated with QPP but not SWC, whereas band-limited power in higher frequencies correlated with SWC but not QPP. Results were similar under isoflurane; however, the QPP was also correlated to band-limited power, possibly due to the burst-suppression state induced by the anesthetic agent. The results provide additional support for the hypothesis that the two types of dynamic rsfMRI are linked to different frequencies of neural activity, but isoflurane anesthesia may make this relationship more complicated. Understanding which neural frequency bands appear as particular dynamic patterns in rsfMRI may ultimately help isolate components of the rsfMRI signal that are of interest to disorders such as schizophrenia and attention deficit disorder.
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Affiliation(s)
- Garth John Thompson
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - Wen-Ju Pan
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - Shella Dawn Keilholz
- Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
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