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Bolt T, Wang S, Nomi JS, Setton R, Gold BP, deB Frederick B, Thomas Yeo BT, Jean Chen J, Picchioni D, Spreng RN, Keilholz SD, Uddin LQ, Chang C. Widespread Autonomic Physiological Coupling Across the Brain-Body Axis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.19.524818. [PMID: 39131291 PMCID: PMC11312447 DOI: 10.1101/2023.01.19.524818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The brain is closely attuned to visceral signals from the body's internal environment, as evidenced by the numerous associations between neural, hemodynamic, and peripheral physiological signals. We show that these brain-body co-fluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo fMRI data acquisition sequences, we identify widespread co-fluctuations in the low-frequency range (0.01 - 0.1 Hz) between resting-state global fMRI signals, neural activity, and a host of autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body co-fluctuations observed at rest are elicited by arousal induced by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Further, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide (PETCO2) variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot explain the origin of these signals in the brain. These findings establish the global fMRI signal as a significant component of the arousal response governed by the autonomic nervous system.
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2
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Luppi AI, Rosas FE, Noonan MP, Mediano PAM, Kringelbach ML, Carhart-Harris RL, Stamatakis EA, Vernon AC, Turkheimer FE. Oxygen and the Spark of Human Brain Evolution: Complex Interactions of Metabolism and Cortical Expansion across Development and Evolution. Neuroscientist 2024; 30:173-198. [PMID: 36476177 DOI: 10.1177/10738584221138032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Scientific theories on the functioning and dysfunction of the human brain require an understanding of its development-before and after birth and through maturation to adulthood-and its evolution. Here we bring together several accounts of human brain evolution by focusing on the central role of oxygen and brain metabolism. We argue that evolutionary expansion of human transmodal association cortices exceeded the capacity of oxygen delivery by the vascular system, which led these brain tissues to rely on nonoxidative glycolysis for additional energy supply. We draw a link between the resulting lower oxygen tension and its effect on cytoarchitecture, which we posit as a key driver of genetic developmental programs for the human brain-favoring lower intracortical myelination and the presence of biosynthetic materials for synapse turnover. Across biological and temporal scales, this protracted capacity for neural plasticity sets the conditions for cognitive flexibility and ongoing learning, supporting complex group dynamics and intergenerational learning that in turn enabled improved nutrition to fuel the metabolic costs of further cortical expansion. Our proposed model delineates explicit mechanistic links among metabolism, molecular and cellular brain heterogeneity, and behavior, which may lead toward a clearer understanding of brain development and its disorders.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences and Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - MaryAnn P Noonan
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
- Department of Computing, Imperial College London, London, UK
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Robin L Carhart-Harris
- Psychedelics Division-Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences and Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Anthony C Vernon
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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3
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Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [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: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
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Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
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4
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Huck J, Jäger A, Schneider U, Grahl S, Fan AP, Tardif C, Villringer A, Bazin P, Steele CJ, Gauthier CJ. Modeling venous bias in resting state functional MRI metrics. Hum Brain Mapp 2023; 44:4938-4955. [PMID: 37498014 PMCID: PMC10472917 DOI: 10.1002/hbm.26431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 04/12/2023] [Accepted: 05/11/2023] [Indexed: 07/28/2023] Open
Abstract
Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.
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Affiliation(s)
- Julia Huck
- Department of PhysicsConcordia UniversityMontrealQuebecCanada
- PERFORM CenterMontrealQuebecCanada
| | - Anna‐Thekla Jäger
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité ‐ Universitätsmedizin BerlinBerlinGermany
| | - Uta Schneider
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Sophia Grahl
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Audrey P. Fan
- Department of Biomedical EngineeringUniversity of CaliforniaDavisCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Christine Tardif
- Faculty of Medicine and Health Sciences, Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- McConnell Brain Imaging CentreMontreal Neurological InstituteMontrealQuebecCanada
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité ‐ Universitätsmedizin BerlinBerlinGermany
- Clinic for Cognitive NeurologyUniversity of LeipzigLeipzigGermany
- IFB Adiposity DiseasesLeipzig University Medical CentreLeipzigGermany
| | - Pierre‐Louis Bazin
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Faculty of Social and Behavioural SciencesUniversity of AmsterdamAmsterdamThe Netherlands
| | - Christopher J. Steele
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Department of PsychologyConcordia UniversityMontrealQuebecCanada
| | - Claudine J. Gauthier
- Department of PhysicsConcordia UniversityMontrealQuebecCanada
- PERFORM CenterMontrealQuebecCanada
- Montreal Heart InstituteMontrealQuebecCanada
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5
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Biondetti E, Cho J, Lee H. Cerebral oxygen metabolism from MRI susceptibility. Neuroimage 2023; 276:120189. [PMID: 37230206 PMCID: PMC10335841 DOI: 10.1016/j.neuroimage.2023.120189] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 05/27/2023] Open
Abstract
This article provides an overview of MRI methods exploiting magnetic susceptibility properties of blood to assess cerebral oxygen metabolism, including the tissue oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2). The first section is devoted to describing blood magnetic susceptibility and its effect on the MRI signal. Blood circulating in the vasculature can have diamagnetic (oxyhemoglobin) or paramagnetic properties (deoxyhemoglobin). The overall balance between oxygenated and deoxygenated hemoglobin determines the induced magnetic field which, in turn, modulates the transverse relaxation decay of the MRI signal via additional phase accumulation. The following sections of this review then illustrate the principles underpinning susceptibility-based techniques for quantifying OEF and CMRO2. Here, it is detailed whether these techniques provide global (OxFlow) or local (Quantitative Susceptibility Mapping - QSM, calibrated BOLD - cBOLD, quantitative BOLD - qBOLD, QSM+qBOLD) measurements of OEF or CMRO2, and what signal components (magnitude or phase) and tissue pools they consider (intravascular or extravascular). Validations studies and potential limitations of each method are also described. The latter include (but are not limited to) challenges in the experimental setup, the accuracy of signal modeling, and assumptions on the measured signal. The last section outlines the clinical uses of these techniques in healthy aging and neurodegenerative diseases and contextualizes these reports relative to results from gold-standard PET.
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Affiliation(s)
- Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy
| | - Junghun Cho
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, New York, USA
| | - Hyunyeol Lee
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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6
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Garcia-Garcia B, Mattern H, Vockert N, Yakupov R, Schreiber F, Spallazzi M, Perosa V, Haghikia A, Speck O, Düzel E, Maass A, Schreiber S. Vessel Distance Mapping: A novel methodology for assessing vascular-induced cognitive resilience. Neuroimage 2023; 274:120094. [PMID: 37028734 DOI: 10.1016/j.neuroimage.2023.120094] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 03/30/2023] [Accepted: 04/04/2023] [Indexed: 04/09/2023] Open
Abstract
The association between cerebral blood supply and cognition has been widely discussed in the recent literature. One focus of this discussion has been the anatomical variability of the circle of Willis, with morphological differences being present in more than half of the general population. While previous studies have attempted to classify these differences and explore their contribution to hippocampal blood supply and cognition, results have been controversial. To disentangle these previously inconsistent findings, we introduce Vessel Distance Mapping (VDM) as a novel methodology for evaluating blood supply, which allows for obtaining vessel pattern metrics with respect to the surrounding structures, extending the previously established binary classification into a continuous spectrum. To accomplish this, we manually segmented hippocampal vessels obtained from high-resolution 7T time-of-flight MR angiographic imaging in older adults with and without cerebral small vessel disease, generating vessel distance maps by computing the distances of each voxel to its nearest vessel. Greater values of VDM-metrics, which reflected higher vessel distances, were associated with poorer cognitive outcomes in subjects affected by vascular pathology, while this relation was not observed in healthy controls. Therefore, a mixed contribution of vessel pattern and vessel density is proposed to confer cognitive resilience, consistent with previous research findings. In conclusion, VDM provides a novel platform, based on a statistically robust and quantitative method of vascular mapping, for addressing a variety of clinical research questions.
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Affiliation(s)
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany; Biomedical Magnetic Resonance, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany
| | - Niklas Vockert
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Frank Schreiber
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany; Department of Neurology, Otto-von-Guericke University, 39120, Magdeburg, Germany
| | - Marco Spallazzi
- Department of Medicine and Surgery, Unit of Neurology, Azienda Ospedalierouniversitaria, 43126 Parma, Italy
| | - Valentina Perosa
- Department of Neurology, Otto-von-Guericke University, 39120, Magdeburg, Germany; J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Aiden Haghikia
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany; Department of Neurology, Otto-von-Guericke University, 39120, Magdeburg, Germany
| | - Oliver Speck
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany; Biomedical Magnetic Resonance, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany; Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany; Department of Neurology, Otto-von-Guericke University, 39120, Magdeburg, Germany; Institute of Cognitive Neuroscience, University College London, London WCIN 3AZ, UK
| | - Anne Maass
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany; Department of Neurology, Otto-von-Guericke University, 39120, Magdeburg, Germany
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7
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Mesoscopic in vivo human T 2* dataset acquired using quantitative MRI at 7 Tesla. Neuroimage 2022; 264:119733. [PMID: 36375782 DOI: 10.1016/j.neuroimage.2022.119733] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/15/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022] Open
Abstract
Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for advancing neuroscience and bridging the resolution gap with animal models. Despite the variety of MRI contrasts measured in recent years at the mesoscopic scale, in vivo quantitative imaging of T2* has not been performed. Here we provide a dataset containing empirical T2* measurements acquired at 0.35 × 0.35 × 0.35 mm3 voxel resolution using 7 Tesla MRI. To demonstrate unique features and high quality of this dataset, we generate flat map visualizations that reveal fine-scale cortical substructures such as layers and vessels, and we report quantitative depth-dependent T2* (as well as R2*) values in primary visual cortex and auditory cortex that are highly consistent across subjects. This dataset is freely available at https://doi.org/10.17605/OSF.IO/N5BJ7, and may prove useful for anatomical investigations of the human brain, as well as for improving our understanding of the basis of the T2*-weighted (f)MRI signal.
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8
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Bernal J, Valdés-Hernández MDC, Escudero J, Duarte R, Ballerini L, Bastin ME, Deary IJ, Thrippleton MJ, Touyz RM, Wardlaw JM. Assessment of perivascular space filtering methods using a three-dimensional computational model. Magn Reson Imaging 2022; 93:33-51. [PMID: 35932975 DOI: 10.1016/j.mri.2022.07.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/19/2022] [Accepted: 07/30/2022] [Indexed: 10/31/2022]
Abstract
Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all "vesselness" filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.
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Affiliation(s)
- Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany; German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Maria D C Valdés-Hernández
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK.
| | - Javier Escudero
- Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK
| | - Roberto Duarte
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Lucia Ballerini
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
| | | | - Rhian M Touyz
- Research Institute of the McGill University Health Centre, McGill University, Montréal, Canada
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
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9
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Bollmann S, Mattern H, Bernier M, Robinson SD, Park DJ, Speck O, Polimeni JR. Imaging of the pial arterial vasculature of the human brain in vivo using high-resolution 7T time-of-flight angiography. eLife 2022; 11:71186. [PMID: 35486089 PMCID: PMC9150892 DOI: 10.7554/elife.71186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
The pial arterial vasculature of the human brain is the only blood supply to the neocortex, but quantitative data on the morphology and topology of these mesoscopic arteries (diameter 50–300 µm) remains scarce. Because it is commonly assumed that blood flow velocities in these vessels are prohibitively slow, non-invasive time-of-flight magnetic resonance angiography (TOF-MRA)—which is well suited to high 3D imaging resolutions—has not been applied to imaging the pial arteries. Here, we provide a theoretical framework that outlines how TOF-MRA can visualize small pial arteries in vivo, by employing extremely small voxels at the size of individual vessels. We then provide evidence for this theory by imaging the pial arteries at 140 µm isotropic resolution using a 7 Tesla (T) magnetic resonance imaging (MRI) scanner and prospective motion correction, and show that pial arteries one voxel width in diameter can be detected. We conclude that imaging pial arteries is not limited by slow blood flow, but instead by achievable image resolution. This study represents the first targeted, comprehensive account of imaging pial arteries in vivo in the human brain. This ultra-high-resolution angiography will enable the characterization of pial vascular anatomy across the brain to investigate patterns of blood supply and relationships between vascular and functional architecture.
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Affiliation(s)
- Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Hendrik Mattern
- Department of Biomedical Magnetic Resonance, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Michaël Bernier
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
| | - Simon D Robinson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Daniel J Park
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
| | - Oliver Speck
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
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10
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Matheson GJ, Ogden RT. Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data. Neuroimage 2022; 256:119195. [PMID: 35452807 PMCID: PMC9470242 DOI: 10.1016/j.neuroimage.2022.119195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 03/24/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022] Open
Abstract
Positron emission tomography (PET) is an in vivo imaging method essential for studying the neurochemical pathophysiology of psychiatric and neurological disease. However, its high cost and exposure of participants to radiation make it unfeasible to employ large sample sizes. The major shortcoming of PET imaging is therefore its lack of power for studying clinically-relevant research questions. Here, we introduce a new method for performing PET quantification and analysis called SiMBA, which helps to alleviate these issues by improving the efficiency of PET analysis by exploiting similarities between both individuals and regions within individuals. In simulated [11C]WAY100635 data, SiMBA greatly improves both statistical power and the consistency of effect size estimation without affecting the false positive rate. This approach makes use of hierarchical, multifactor, multivariate Bayesian modelling to effectively borrow strength across the whole dataset to improve stability and robustness to measurement error. In so doing, parameter identifiability and estimation are improved, without sacrificing model interpretability. This comes at the cost of increased computational overhead, however this is practically negligible relative to the time taken to collect PET data. This method has the potential to make it possible to test clinically-relevant hypotheses which could never be studied before given the practical constraints. Furthermore, because this method does not require any additional information over and above that required for traditional analysis, it makes it possible to re-examine data which has already previously been collected at great expense. In the absence of dramatic advancements in PET image data quality, radiotracer development, or data sharing, PET imaging has been fundamentally limited in the scope of research hypotheses which could be studied. This method, especially combined with the recent steps taken by the PET imaging community to embrace data sharing, will make it possible to greatly improve the research possibilities and clinical relevance of PET neuroimaging.
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Affiliation(s)
- Granville J Matheson
- Department of Psychiatry, Columbia University, New York, NY 10032, USA; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
| | - R Todd Ogden
- Department of Psychiatry, Columbia University, New York, NY 10032, USA; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA
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11
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Bhogal AA. Medullary vein architecture modulates the white matter BOLD cerebrovascular reactivity signal response to CO 2: Observations from high-resolution T2* weighted imaging at 7T. Neuroimage 2021; 245:118771. [PMID: 34861395 DOI: 10.1016/j.neuroimage.2021.118771] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/13/2021] [Accepted: 11/29/2021] [Indexed: 01/24/2023] Open
Abstract
Brain stress testing using blood oxygenation level-dependent (BOLD) MRI to evaluate changes in cerebrovascular reactivity (CVR) is of growing interest for evaluating white matter integrity. However, even under healthy conditions, the white matter BOLD-CVR response differs notably from that observed in the gray matter. In addition to actual arterial vascular control, the venous draining topology may influence the WM-CVR response leading to signal delays and dispersions. These types of alterations in hemodynamic parameters are sometimes linked with pathology, but may also arise from differences in normal venous architecture. In this work, high-resolution T2*weighted anatomical images combined with BOLD imaging during a hypercapnic breathing protocol were acquired using a 7 tesla MRI system. Hemodynamic parameters including base CVR, hemodynamic lag, lag-corrected CVR, response onset and signal dispersion, and finally ΔCVR (corrected CVR minus base CVR) were calculated in 8 subjects. Parameter maps were spatially normalized and correlated against an MNI-registered white matter medullary vein atlas. Moderate correlations (Pearson's rho) were observed between medullary vessel frequency (MVF) and ΔCVR (0.52; 0.58 for total WM), MVF and hemodynamic lag (0.42; 0.54 for total WM), MVF and signal dispersion (0.44; 0.53 for total WM), and finally MVF and signal onset (0.43; 0.52 for total WM). Results indicate that, when assessed in the context of the WM venous architecture, changes in the response shape may only be partially reflective of the actual vascular reactivity response occurring further upstream by control vessels. This finding may have implications when attributing diseases mechanisms and/or progression to presumed impaired WM BOLD-CVR.
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Affiliation(s)
- Alex A Bhogal
- Radiology, University Medical Center Utrecht, Heidelberglaan 100, , Utrecht 3584 CX, the Netherland.
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12
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Elliott C, Momayyezsiahkal P, Arnold DL, Liu D, Ke J, Zhu L, Zhu B, George IC, Bradley DP, Fisher E, Cahir-McFarland E, Stys PK, Geurts JJG, Franchimont N, Gafson A, Belachew S. Abnormalities in normal-appearing white matter from which multiple sclerosis lesions arise. Brain Commun 2021; 3:fcab176. [PMID: 34557664 PMCID: PMC8453433 DOI: 10.1093/braincomms/fcab176] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 11/24/2022] Open
Abstract
Normal-appearing white matter is far from normal in multiple sclerosis; little is known about the precise pathology or spatial pattern of this alteration and its relation to subsequent lesion formation. This study was undertaken to evaluate normal-appearing white matter abnormalities in brain areas where multiple sclerosis lesions subsequently form, and to investigate the spatial distribution of normal-appearing white matter abnormalities in persons with multiple sclerosis. Brain MRIs of pre-lesion normal-appearing white matter were analysed in participants with new T2 lesions, pooled from three clinical trials: SYNERGY (NCT01864148; n = 85 with relapsing multiple sclerosis) was the test data set; ASCEND (NCT01416181; n = 154 with secondary progressive multiple sclerosis) and ADVANCE (NCT00906399; n = 261 with relapsing-remitting multiple sclerosis) were used as validation data sets. Focal normal-appearing white matter tissue state was analysed prior to lesion formation in areas where new T2 lesions later formed (pre-lesion normal-appearing white matter) using normalized magnetization transfer ratio and T2-weighted (nT2) intensities, and compared with overall normal-appearing white matter and spatially matched contralateral normal-appearing white matter. Each outcome was analysed using linear mixed-effects models. Follow-up time (as a categorical variable), patient-level characteristics (including treatment group) and other baseline variables were treated as fixed effects. In SYNERGY, nT2 intensity was significantly higher, and normalized magnetization transfer ratio was lower in pre-lesion normal-appearing white matter versus overall and contralateral normal-appearing white matter at all time points up to 24 weeks before new T2 lesion onset. In ASCEND and ADVANCE (for which normalized magnetization transfer ratio was not available), nT2 intensity in pre-lesion normal-appearing white matter was significantly higher compared to both overall and contralateral normal-appearing white matter at all pre-lesion time points extending up to 2 years prior to lesion formation. In all trials, nT2 intensity in the contralateral normal-appearing white matter was also significantly higher at all pre-lesion time points compared to overall normal-appearing white matter. Brain atlases of normal-appearing white matter abnormalities were generated using measures of voxel-wise differences in normalized magnetization transfer ratio of normal-appearing white matter in persons with multiple sclerosis compared to scanner-matched healthy controls. We observed that overall spatial distribution of normal-appearing white matter abnormalities in persons with multiple sclerosis largely recapitulated the anatomical distribution of probabilities of T2 hyperintense lesions. Overall, these findings suggest that intrinsic spatial properties and/or longstanding precursory abnormalities of normal-appearing white matter tissue may contribute to the risk of autoimmune acute demyelination in multiple sclerosis.
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Affiliation(s)
| | - Parya Momayyezsiahkal
- NeuroRx Research, Montreal, QC H2X 3P9, Canada.,McGill University, Montreal, QC H3A 0G4, Canada
| | - Douglas L Arnold
- NeuroRx Research, Montreal, QC H2X 3P9, Canada.,McGill University, Montreal, QC H3A 0G4, Canada
| | - Dawei Liu
- Biogen Digital Health, Biogen, Cambridge, MA 02142, USA
| | - Jun Ke
- Biogen, Cambridge, MA 02142, USA
| | - Li Zhu
- Biogen, Cambridge, MA 02142, USA
| | - Bing Zhu
- Biogen, Cambridge, MA 02142, USA
| | - Ilena C George
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | | | | | - Peter K Stys
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC, 1081 HV Amsterdam, Netherlands
| | | | - Arie Gafson
- Biogen Digital Health, Biogen, Cambridge, MA 02142, USA
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13
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Xifra-Porxas A, Kassinopoulos M, Mitsis GD. Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability. eLife 2021; 10:e62324. [PMID: 34342582 PMCID: PMC8378847 DOI: 10.7554/elife.62324] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
Human brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.
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Affiliation(s)
- Alba Xifra-Porxas
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | - Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada
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14
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Berg RC, Preibisch C, Thomas DL, Shmueli K, Biondetti E. Investigating the effect of flow compensation and quantitative susceptibility mapping method on the accuracy of venous susceptibility measurement. Neuroimage 2021; 240:118399. [PMID: 34273528 DOI: 10.1016/j.neuroimage.2021.118399] [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] [Received: 03/24/2021] [Revised: 06/15/2021] [Accepted: 07/13/2021] [Indexed: 11/25/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a promising non-invasive method for obtaining information relating to oxygen metabolism. However, the optimal acquisition sequence and QSM reconstruction method for reliable venous susceptibility measurements are unknown. Full flow compensation is generally recommended to correct for the influence of venous blood flow, although the effect of flow compensation on the accuracy of venous susceptibility values has not been systematically evaluated. In this study, we investigated the effect of different acquisition sequences, including different flow compensation schemes, and different QSM reconstruction methods on venous susceptibilities. Ten healthy subjects were scanned with five or six distinct QSM sequence designs using monopolar readout gradients and different flow compensation schemes. All data sets were processed using six different QSM pipelines and venous blood susceptibility was evaluated in whole-brain segmentations of the venous vasculature and single veins. The quality of vein segmentations and the accuracy of venous susceptibility values were analyzed and compared between all combinations of sequences and reconstruction methods. The influence of the QSM reconstruction method on average venous susceptibility values was found to be 2.7-11.6 times greater than the influence of the acquisition sequence, including flow compensation. The majority of the investigated QSM reconstruction methods tended to underestimate venous susceptibility values in the vein segmentations that were obtained. In summary, we found that multi-echo gradient-echo acquisition sequences without full flow compensation yielded venous susceptibility values comparable to sequences with full flow compensation. However, the QSM reconstruction method had a great influence on susceptibility values and thus needs to be selected carefully for accurate venous QSM.
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Affiliation(s)
- Ronja C Berg
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany.
| | - Christine Preibisch
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaninger Str. 22, 81675 Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Clinic for Neurology, Ismaninger Str. 22, 81675 Munich, Munich, Germany.
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, United Kingdom.
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.
| | - Emma Biondetti
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom; Institut du Cerveau - ICM, Centre de NeuroImagerie de Recherche - CENIR, Team "Movement Investigations and Therapeutics" (MOV'IT), INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France.
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15
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Abstract
Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block 12, room 1220, 01-938, Warsaw, Poland.
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16
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Automatic collateral circulation scoring in ischemic stroke using 4D CT angiography with low-rank and sparse matrix decomposition. Int J Comput Assist Radiol Surg 2020; 15:1501-1511. [PMID: 32662055 DOI: 10.1007/s11548-020-02216-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 06/11/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Sufficient collateral blood supply is crucial for favorable outcomes with endovascular treatment. The current practice of collateral scoring relies on visual inspection and thus can suffer from inter and intra-rater inconsistency. We present a robust and automatic method to score cerebral collateral blood supply to aid ischemic stroke treatment decision making. The developed method is based on 4D dynamic CT angiography (CTA) and the ASPECTS scoring protocol. METHODS The proposed method, ACCESS (Automatic Collateral Circulation Evaluation in iSchemic Stroke), estimates a target patient's unfilled cerebrovasculature in contrast-enhanced CTA using the lack of contrast agent due to clotting. To do so, the fast robust matrix completion algorithm with in-face extended Frank-Wolfe optimization is applied on a cohort of healthy subjects and a target patient, to model the patient's unfilled vessels and the estimated full vasculature as sparse and low-rank components, respectively. The collateral score is computed as the ratio of the unfilled vessels to the full vasculature, mimicking existing clinical protocols. RESULTS ACCESS was tested with 46 stroke patients and obtained an overall accuracy of 84.78%. The optimal threshold selection was evaluated using a receiver operating characteristics curve with the leave-one-out approach, and a mean area under the curve of 85.39% was obtained. CONCLUSION ACCESS automates collateral scoring to mitigate the shortcomings of the standard clinical practice. It is a robust approach, which resembles how radiologists score clinical scans, and can be used to help radiologists in clinical decisions of stroke treatment.
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17
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Bause J, Polimeni JR, Stelzer J, In MH, Ehses P, Kraemer-Fernandez P, Aghaeifar A, Lacosse E, Pohmann R, Scheffler K. Impact of prospective motion correction, distortion correction methods and large vein bias on the spatial accuracy of cortical laminar fMRI at 9.4 Tesla. Neuroimage 2020; 208:116434. [DOI: 10.1016/j.neuroimage.2019.116434] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 11/08/2019] [Accepted: 12/02/2019] [Indexed: 01/24/2023] Open
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18
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Bazin PL, Alkemade A, van der Zwaag W, Caan M, Mulder M, Forstmann BU. Denoising High-Field Multi-Dimensional MRI With Local Complex PCA. Front Neurosci 2019; 13:1066. [PMID: 31649500 PMCID: PMC6794471 DOI: 10.3389/fnins.2019.01066] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 09/24/2019] [Indexed: 11/13/2022] Open
Abstract
Modern high field and ultra high field magnetic resonance imaging (MRI) experiments routinely collect multi-dimensional data with high spatial resolution, whether multi-parametric structural, diffusion or functional MRI. While diffusion and functional imaging have benefited from recent advances in multi-dimensional signal analysis and denoising, structural MRI has remained untouched. In this work, we propose a denoising technique for multi-parametric quantitative MRI, combining a highly popular denoising method from diffusion imaging, over-complete local PCA, with a reconstruction of the complex-valued MR signal in order to define stable estimates of the noise in the decomposition. With this approach, we show signal to noise ratio (SNR) improvements in high resolution MRI without compromising the spatial accuracy or generating spurious perceptual boundaries.
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Affiliation(s)
- Pierre-Louis Bazin
- Integrative Model-Based Cognitive Neuroscience Research Unit, Department of Psychology, Universiteit van Amsterdam, Amsterdam, Netherlands
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Anneke Alkemade
- Integrative Model-Based Cognitive Neuroscience Research Unit, Department of Psychology, Universiteit van Amsterdam, Amsterdam, Netherlands
| | | | - Matthan Caan
- Brain Imaging Centre, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Martijn Mulder
- Integrative Model-Based Cognitive Neuroscience Research Unit, Department of Psychology, Universiteit van Amsterdam, Amsterdam, Netherlands
- Department of Psychology, Universiteit Utrecht, Utrecht, Netherlands
| | - Birte U. Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, Department of Psychology, Universiteit van Amsterdam, Amsterdam, Netherlands
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19
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Kay K, Jamison KW, Vizioli L, Zhang R, Margalit E, Ugurbil K. A critical assessment of data quality and venous effects in sub-millimeter fMRI. Neuroimage 2019; 189:847-869. [PMID: 30731246 PMCID: PMC7737092 DOI: 10.1016/j.neuroimage.2019.02.006] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 02/02/2019] [Accepted: 02/04/2019] [Indexed: 01/07/2023] Open
Abstract
Advances in hardware, pulse sequences, and reconstruction techniques have made it possible to perform functional magnetic resonance imaging (fMRI) at sub-millimeter resolution while maintaining high spatial coverage and acceptable signal-to-noise ratio. Here, we examine whether sub-millimeter fMRI can be used as a routine method for obtaining accurate measurements of fine-scale local neural activity. We conducted fMRI in human visual cortex during a simple event-related visual experiment (7 T, gradient-echo EPI, 0.8-mm isotropic voxels, 2.2-s sampling rate, 84 slices), and developed analysis and visualization tools to assess the quality of the data. Our results fall along three lines of inquiry. First, we find that the acquired fMRI images, combined with appropriate surface-based processing, provide reliable and accurate measurements of fine-scale blood oxygenation level dependent (BOLD) activity patterns. Second, we show that the highly folded structure of cortex causes substantial biases on spatial resolution and data visualization. Third, we examine the well-recognized issue of venous contributions to fMRI signals. In a systematic assessment of large sections of cortex measured at a fine scale, we show that time-averaged T2*-weighted EPI intensity is a simple, robust marker of venous effects. These venous effects are unevenly distributed across cortex, are more pronounced in gyri and outer cortical depths, and are, to a certain degree, in consistent locations across subjects relative to cortical folding. Furthermore, we show that these venous effects are strongly correlated with BOLD responses evoked by the experiment. We conclude that sub-millimeter fMRI can provide robust information about fine-scale BOLD activity patterns, but special care must be exercised in visualizing and interpreting these patterns, especially with regards to the confounding influence of the brain's vasculature. To help translate these methodological findings to neuroscience research, we provide practical suggestions for both high-resolution and standard-resolution fMRI studies.
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Affiliation(s)
- Kendrick Kay
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, USA.
| | - Keith W Jamison
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, USA
| | - Ruyuan Zhang
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, USA
| | - Eshed Margalit
- Stanford Neurosciences Institute, Stanford University, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, USA
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