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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Delli Pizzi S, Gambi F, Di Pietro M, Caulo M, Sensi SL, Ferretti A. BOLD cardiorespiratory pulsatility in the brain: from noise to signal of interest. Front Hum Neurosci 2024; 17:1327276. [PMID: 38259340 PMCID: PMC10800549 DOI: 10.3389/fnhum.2023.1327276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as "physiological noise" and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.
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Affiliation(s)
- Stefano Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Francesco Gambi
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
| | | | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University, Chieti, Italy
| | - Stefano L. Sensi
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University, Chieti, Italy
| | - Antonio Ferretti
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University, Chieti, Italy
- UdA-TechLab, Research Center, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
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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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Jensen KHR, Dam VH, Ganz M, Fisher PM, Ip CT, Sankar A, Marstrand-Joergensen MR, Ozenne B, Osler M, Penninx BWJH, Pinborg LH, Frokjaer VG, Knudsen GM, Jørgensen MB. Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort. BMC Psychiatry 2023; 23:151. [PMID: 36894940 PMCID: PMC9999625 DOI: 10.1186/s12888-023-04618-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/19/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging. METHODS All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [11C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome. DISCUSSION The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients. TRIAL REGISTRATION Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).
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Affiliation(s)
- Kristian Høj Reveles Jensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Cheng-Teng Ip
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Anjali Sankar
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Maja Rou Marstrand-Joergensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Merete Osler
- Center for Clinical Research and Prevention, Bispebjerg & Frederiksberg Hospitals, Copenhagen, Denmark.,Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Lars H Pinborg
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibe Gedsø Frokjaer
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin Balslev Jørgensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. .,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. .,Psychiatric Centre Copenhagen, Copenhagen, Denmark.
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5
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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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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Colenbier N, Marino M, Arcara G, Frederick B, Pellegrino G, Marinazzo D, Ferrazzi G. WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions. J Neural Eng 2022; 19:10.1088/1741-2552/ac8bff. [PMID: 35998568 PMCID: PMC9673276 DOI: 10.1088/1741-2552/ac8bff] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 08/23/2022] [Indexed: 11/12/2022]
Abstract
Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions.Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency (1/TR, with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods.Main results.WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods.Significance.WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available athttps://github.com/gferrazzi/WHOCARES.
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Affiliation(s)
- Nigel Colenbier
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | - Marco Marino
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, 3001, Belgium
| | - Giorgio Arcara
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | - Blaise Frederick
- Brain Imaging Center, McLean Hospital, 115 Mill St., Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard University Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | | | - Daniele Marinazzo
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Giulio Ferrazzi
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
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Abstract
We review theoretical and numerical models of the glymphatic system, which circulates cerebrospinal fluid and interstitial fluid around the brain, facilitating solute transport. Models enable hypothesis development and predictions of transport, with clinical applications including drug delivery, stroke, cardiac arrest, and neurodegenerative disorders like Alzheimer’s disease. We sort existing models into broad categories by anatomical function: Perivascular flow, transport in brain parenchyma, interfaces to perivascular spaces, efflux routes, and links to neuronal activity. Needs and opportunities for future work are highlighted wherever possible; new models, expanded models, and novel experiments to inform models could all have tremendous value for advancing the field.
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Paasonen E, Paasonen J, Lehto LJ, Pirttimäki T, Laakso H, Wu L, Ma J, Idiyatullin D, Tanila H, Mangia S, Michaeli S, Gröhn O. Event-recurring multiband SWIFT functional MRI with 200-ms temporal resolution during deep brain stimulation and isoflurane-induced burst suppression in rat. Magn Reson Med 2022; 87:2872-2884. [PMID: 34985145 PMCID: PMC9160777 DOI: 10.1002/mrm.29154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop a high temporal resolution functional MRI method for tracking repeating events in the brain. METHODS We developed a novel functional MRI method using multiband sweep imaging with Fourier transformation (SWIFT), termed event-recurring SWIFT (EVER-SWIFT). The method is able to image similar repeating events with subsecond temporal resolution. Here, we demonstrate the use of EVER-SWIFT for detecting functional MRI responses during deep brain stimulation of the medial septal nucleus and during spontaneous isoflurane-induced burst suppression in the rat brain at 9.4 T with 200-ms temporal resolution. RESULTS The EVER-SWIFT approach showed that the shapes and time-to-peak values of the response curves to deep brain stimulation significantly differed between downstream brain regions connected to the medial septal nucleus, resembling findings obtained with traditional 2-second temporal resolution. In contrast, EVER-SWIFT allowed for detailed temporal measurement of a spontaneous isoflurane-induced bursting activity pattern, which was not achieved with traditional temporal resolution. CONCLUSION The EVER-SWIFT technique enables subsecond 3D imaging of both stimulated and spontaneously recurring brain activities, and thus holds great potential for studying the mechanisms of neuromodulation and spontaneous brain activity.
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Affiliation(s)
- Ekaterina Paasonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.,Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jaakko Paasonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Lauri J Lehto
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Tiina Pirttimäki
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.,Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
| | - Hanne Laakso
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Lin Wu
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jun Ma
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Djaudat Idiyatullin
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Heikki Tanila
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Silvia Mangia
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Shalom Michaeli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Olli Gröhn
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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Poltojainen V, Kemppainen J, Keinänen N, Bode M, Isokangas JM, Kuitunen H, Nikkinen J, Sonkajärvi E, Korhonen V, Tuovinen T, Järvelä M, Huotari N, Raitamaa L, Kananen J, Korhonen T, Tetri S, Kuittinen O, Kiviniemi V. Physiological instability is linked to mortality in primary central nervous system lymphoma: A case-control fMRI study. Hum Brain Mapp 2022; 43:4030-4044. [PMID: 35543292 PMCID: PMC9374894 DOI: 10.1002/hbm.25901] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 04/07/2022] [Accepted: 04/26/2022] [Indexed: 11/07/2022] Open
Abstract
Primary central nervous system lymphoma (PCNSL) is an aggressive brain disease where lymphocytes invade along perivascular spaces of arteries and veins. The invasion markedly changes (peri)vascular structures but its effect on physiological brain pulsations has not been previously studied. Using physiological magnetic resonance encephalography (MREGBOLD ) scanning, this study aims to quantify the extent to which (peri)vascular PCNSL involvement alters the stability of physiological brain pulsations mediated by cerebral vasculature. Clinical implications and relevance were explored. In this study, 21 PCNSL patients (median 67y; 38% females) and 30 healthy age-matched controls (median 63y; 73% females) were scanned for MREGBOLD signal during 2018-2021. Motion effects were removed. Voxel-by-voxel Coefficient of Variation (CV) maps of MREGBOLD signal was calculated to examine the stability of physiological brain pulsations. Group-level differences in CV were examined using nonparametric covariate-adjusted tests. Subject-level CV alterations were examined against control population Z-score maps wherein clusters of increased CV values were detected. Spatial distributions of clusters and findings from routine clinical neuroimaging were compared [contrast-enhanced, diffusion-weighted, fluid-attenuated inversion recovery (FLAIR) data]. Whole-brain mean CV was linked to short-term mortality with 100% sensitivity and 100% specificity, as all deceased patients revealed higher values (n = 5, median 0.055) than surviving patients (n = 16, median 0.028) (p < .0001). After adjusting for medication, head motion, and age, patients revealed higher CV values (group median 0.035) than healthy controls (group median 0.024) around arterial territories (p ≤ .001). Abnormal clusters (median 1.10 × 105 mm3 ) extended spatially beyond FLAIR lesions (median 0.62 × 105 mm3 ) with differences in volumes (p = .0055).
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Affiliation(s)
- Valter Poltojainen
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Janette Kemppainen
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Cancer and Translational Medicine Research Unit, University of Oulu, Oulu, Finland
| | - Nina Keinänen
- Department of Anaesthesiology, Oulu University Hospital, Oulu, Finland
| | - Michaela Bode
- Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | | | - Hanne Kuitunen
- Department of Oncology and Haematology, Oulu University Hospital, Oulu, Finland
| | - Juha Nikkinen
- Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland
| | - Eila Sonkajärvi
- Department of Anaesthesiology, Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Timo Tuovinen
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Matti Järvelä
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Niko Huotari
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Lauri Raitamaa
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Janne Kananen
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
| | - Tommi Korhonen
- Medical Research Center, University of Oulu/Oulu University Hospital, Oulu, Finland.,Department of Clinical Neuroscience, University of Oulu, Oulu, Finland
| | - Sami Tetri
- Medical Research Center, University of Oulu/Oulu University Hospital, Oulu, Finland.,Department of Clinical Neuroscience, University of Oulu, Oulu, Finland
| | - Outi Kuittinen
- Department of Oncology and Haematology, Oulu University Hospital, Oulu, Finland.,Cancer Center, Kuopio University Hospital, Kuopio, Finland.,Faculty of Health Medicine, Institute of Clinical Medicine, University of Eastern Finland, Oulu, Finland
| | - Vesa Kiviniemi
- Oulu Functional Neuroimaging, University of Oulu/Oulu University Hospital, Oulu, Finland.,Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Department of Radiology, Oulu University Hospital, Oulu, Finland
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The Bidirectional Link Between Sleep Disturbances and Traumatic Brain Injury Symptoms: A Role for Glymphatic Dysfunction? Biol Psychiatry 2022; 91:478-487. [PMID: 34481662 PMCID: PMC8758801 DOI: 10.1016/j.biopsych.2021.06.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 12/14/2022]
Abstract
Mild traumatic brain injury (mTBI), often referred to as concussion, is a major cause of morbidity and mortality worldwide. Sleep disturbances are common after mTBI. Moreover, subjects who develop subjective sleep complaints after mTBI also report more severe somatic, mental health, and cognitive impairment and take longer to recover from mTBI sequelae. Despite many previous studies addressing the role of sleep in post-mTBI morbidity, the mechanisms linking sleep to recovery after mTBI remain poorly understood. The glymphatic system is a brainwide network that supports fluid movement through the cerebral parenchyma and the clearance of interstitial solutes and wastes from the brain. Notably, the glymphatic system is active primarily during sleep. Clearance of cellular byproducts related to somatic, mental health, and neurodegenerative processes (e.g., amyloid-β and tau, among others) depends in part on intact glymphatic function, which becomes impaired after mTBI. In this viewpoint, we review the current knowledge regarding the association between sleep disturbances and post-mTBI symptoms. We also discuss the role of glymphatic dysfunction as a potential link between mTBI, sleep disruption, and posttraumatic morbidity. We outline a model where glymphatic dysfunction and sleep disruption caused by mTBI may have an additive effect on waste clearance, leading to cerebral dysfunction and impaired recovery. Finally, we review the novel techniques being developed to examine glymphatic function in humans and explore potential interventions to alter glymphatic exchange that may offer a novel therapeutic approach to those experiencing poor sleep and prolonged symptoms after mTBI.
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