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Domínguez D JF, Stewart A, Burmester A, Akhlaghi H, O'Brien K, Bollmann S, Caeyenberghs K. Improving quantitative susceptibility mapping for the identification of traumatic brain injury neurodegeneration at the individual level. Z Med Phys 2024:S0939-3889(24)00001-1. [PMID: 38336583 DOI: 10.1016/j.zemedi.2024.01.001] [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: 06/02/2023] [Revised: 12/19/2023] [Accepted: 01/07/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Emerging evidence suggests that traumatic brain injury (TBI) is a major risk factor for developing neurodegenerative disease later in life. Quantitative susceptibility mapping (QSM) has been used by an increasing number of studies in investigations of pathophysiological changes in TBI. However, generating artefact-free quantitative susceptibility maps in brains with large focal lesions, as in the case of moderate-to-severe TBI (ms-TBI), is particularly challenging. To address this issue, we utilized a novel two-pass masking technique and reconstruction procedure (two-pass QSM) to generate quantitative susceptibility maps (QSMxT; Stewart et al., 2022, Magn Reson Med.) in combination with the recently developed virtual brain grafting (VBG) procedure for brain repair (Radwan et al., 2021, NeuroImage) to improve automated delineation of brain areas. We used QSMxT and VBG to generate personalised QSM profiles of individual patients with reference to a sample of healthy controls. METHODS Chronic ms-TBI patients (N = 8) and healthy controls (N = 12) underwent (multi-echo) GRE, and anatomical MRI (MPRAGE) on a 3T Siemens PRISMA scanner. We reconstructed the magnetic susceptibility maps using two-pass QSM from QSMxT. We then extracted values of magnetic susceptibility in grey matter (GM) regions (following brain repair via VBG) across the whole brain and determined if they deviate from a reference healthy control group [Z-score < -3.43 or > 3.43, relative to the control mean], with the aim of obtaining personalised QSM profiles. RESULTS Using two-pass QSM, we achieved susceptibility maps with a substantial increase in quality and reduction in artefacts irrespective of the presence of large focal lesions, compared to single-pass QSM. In addition, VBG minimised the loss of GM regions and exclusion of patients due to failures in the region delineation step. Our findings revealed deviations in magnetic susceptibility measures from the HC group that differed across individual TBI patients. These changes included both increases and decreases in magnetic susceptibility values in multiple GM regions across the brain. CONCLUSIONS We illustrate how to obtain magnetic susceptibility values at the individual level and to build personalised QSM profiles in ms-TBI patients. Our approach opens the door for QSM investigations in more severely injured patients. Such profiles are also critical to overcome the inherent heterogeneity of clinical populations, such as ms-TBI, and to characterize the underlying mechanisms of neurodegeneration at the individual level more precisely. Moreover, this new personalised QSM profiling could in the future assist clinicians in assessing recovery and formulating a neuroscience-guided integrative rehabilitation program tailored to individual TBI patients.
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Affiliation(s)
- Juan F Domínguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Ashley Stewart
- School of Information Technology and Electrical Engineering, Faculty of Engineering, Architecture, and Information Technology, The University of Queensland, Brisbane, Australia
| | - Alex Burmester
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Hamed Akhlaghi
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Department of Emergency Medicine, St. Vincent's Hospital, Melbourne, Australia
| | - Kieran O'Brien
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- School of Information Technology and Electrical Engineering, Faculty of Engineering, Architecture, and Information Technology, The University of Queensland, Brisbane, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
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2
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Gogola A, Cohen AD, Snitz B, Minhas D, Tudorascu D, Ikonomovic MD, Shaaban CE, Doré V, Matan C, Bourgeat P, Mason NS, Leuzy A, Aizenstein H, Mathis CA, Lopez OL, Lopresti BJ, Villemagne VL. Implementation and Assessment of Tau Thresholds in Non-Demented Individuals as Predictors of Cognitive Decline in Tau Imaging Studies. J Alzheimers Dis 2024; 100:S75-S92. [PMID: 39121123 DOI: 10.3233/jad-240543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2024]
Abstract
Background Tau accumulation in Alzheimer's disease is associated with short term clinical progression and faster rates of cognitive decline in individuals with high amyloid-β deposition. Defining an optimal threshold of tau accumulation predictive of cognitive decline remains a challenge. Objective We tested the ability of regional tau PET sensitivity and specificity thresholds to predict longitudinal cognitive decline. We also tested the predictive performance of thresholds in the proposed new NIA-AA biological staging for Alzheimer's disease where multiple levels of tau positivity are used to stage participants. Methods 18F-flortaucipir scans from 301 non-demented participants were processed and sampled. Four cognitive measures were assessed longitudinally. Regional standardized uptake value ratios were split into infra- and suprathreshold groups at baseline using previously derived thresholds. Survival analysis, log rank testing, and Generalized Estimation Equations assessed the relationship between the application of regional sensitivity/specificity thresholds and change in cognitive measures as well as tau threshold performance in predicting cognitive decline within the new NIA-AA biological staging. Results The meta temporal region was best for predicting risk of short-term cognitive decline in suprathreshold, as compared to infrathreshold participants. When applying multiple levels of tau positivity, each subsequent level of tau identified cognitive decline at earlier timepoints. Conclusions When using 18F-flortaucipir, meta temporal suprathreshold classification was associated with increased risk of cognitive decline, suggesting that abnormal tau deposition in the cortex predicts decline. Likewise, the application of multiple levels of tau clearly predicts the distinctive cognitive trajectories in the new NIA-AA biological staging framework.
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Affiliation(s)
- Alexandra Gogola
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beth Snitz
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Davneet Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Milos D Ikonomovic
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
- Geriatric Research Education and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - C Elizabeth Shaaban
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vincent Doré
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
- Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Melbourne, VIC, Australia
| | - Cristy Matan
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Pierrick Bourgeat
- Commonwealth Scientific and Industrial Research Organisation Health & Biosecurity, Melbourne, VIC, Australia
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Antoine Leuzy
- Critical Path for Alzheimer's Disease (CPAD) Consortium, Critical Path Institute, Tucson, AZ, USA
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Oscar L Lopez
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Alzheimer's Disease Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Molecular Imaging & Therapy, Austin Health, Melbourne, VIC, Australia
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3
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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4
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Gogola A, Lopresti BJ, Tudorascu D, Snitz B, Minhas D, Doré V, Ikonomovic MD, Shaaban CE, Matan C, Bourgeat P, Mason NS, Aizenstein H, Mathis CA, Klunk WE, Rowe CC, Lopez OL, Cohen AD, Villemagne VL. Biostatistical Estimation of Tau Threshold Hallmarks (BETTH) Algorithm for Human Tau PET Imaging Studies. J Nucl Med 2023; 64:1798-1805. [PMID: 37709531 PMCID: PMC10626371 DOI: 10.2967/jnumed.123.265941] [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: 04/26/2023] [Revised: 08/03/2023] [Indexed: 09/16/2023] Open
Abstract
A methodology for determining tau PET thresholds is needed to confidently detect early tau deposition. We compared multiple threshold-determining methods in participants who underwent either 18F-flortaucipir or 18F-MK-6240 PET scans. Methods: 18F-flortaucipir (n = 798) and 18F-MK-6240 (n = 216) scans were processed and sampled to obtain regional SUV ratios. Subsamples of the cohorts were based on participant diagnosis, age, amyloid-β status (positive or negative), and neurodegeneration status (positive or negative), creating older-adult (age ≥ 55 y) cognitively unimpaired (amyloid-β-negative, neurodegeneration-negative) and cognitively impaired (mild cognitive impairment/Alzheimer disease, amyloid-β-positive, neurodegeneration-positive) groups, and then were further subsampled via matching to reduce significant differences in diagnostic prevalence, age, and Mini-Mental State Examination score. We used the biostatistical estimation of tau threshold hallmarks (BETTH) algorithm to determine sensitivity and specificity in 6 composite regions. Results: Parametric double receiver operating characteristic analysis yielded the greatest joint sensitivity in 5 of the 6 regions, whereas hierarchic clustering, gaussian mixture modeling, and k-means clustering all yielded perfect joint specificity (2.00) in all regions. Conclusion: When 18F-flortaucipir and 18F-MK-6240 are used, Alzheimer disease-related tau status is best assessed using 2 thresholds, a sensitivity one based on parametric double receiver operating characteristic analysis and a specificity one based on gaussian mixture modeling, delimiting an uncertainty zone indicating participants who may require further evaluation.
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Affiliation(s)
- Alexandra Gogola
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania;
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Dana Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Beth Snitz
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Davneet Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Vincent Doré
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
| | - Milos D Ikonomovic
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Geriatric Research Education and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania; and
| | - C Elizabeth Shaaban
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Cristy Matan
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Pierrick Bourgeat
- Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
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Strogulski NR, Portela LV, Polster BM, Loane DJ. Fundamental Neurochemistry Review: Microglial immunometabolism in traumatic brain injury. J Neurochem 2023; 167:129-153. [PMID: 37759406 PMCID: PMC10655864 DOI: 10.1111/jnc.15959] [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: 07/05/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023]
Abstract
Traumatic brain injury (TBI) is a devastating neurological disorder caused by a physical impact to the brain that promotes diffuse damage and chronic neurodegeneration. Key mechanisms believed to support secondary brain injury include mitochondrial dysfunction and chronic neuroinflammation. Microglia and brain-infiltrating macrophages are responsible for neuroinflammatory cytokine and reactive oxygen species (ROS) production after TBI. Their production is associated with loss of homeostatic microglial functions such as immunosurveillance, phagocytosis, and immune resolution. Beyond providing energy support, mitochondrial metabolic pathways reprogram the pro- and anti-inflammatory machinery in immune cells, providing a critical immunometabolic axis capable of regulating immunologic response to noxious stimuli. In the brain, the capacity to adapt to different environmental stimuli derives, in part, from microglia's ability to recognize and respond to changes in extracellular and intracellular metabolite levels. This capacity is met by an equally plastic metabolism, capable of altering immune function. Microglial pro-inflammatory activation is associated with decreased mitochondrial respiration, whereas anti-inflammatory microglial polarization is supported by increased oxidative metabolism. These metabolic adaptations contribute to neuroimmune responses, placing mitochondria as a central regulator of post-traumatic neuroinflammation. Although it is established that profound neurometabolic changes occur following TBI, key questions related to metabolic shifts in microglia remain unresolved. These include (a) the nature of microglial mitochondrial dysfunction after TBI, (b) the hierarchical positions of different metabolic pathways such as glycolysis, pentose phosphate pathway, glutaminolysis, and lipid oxidation during secondary injury and recovery, and (c) how immunometabolism alters microglial phenotypes, culminating in chronic non-resolving neuroinflammation. In this basic neurochemistry review article, we describe the contributions of immunometabolism to TBI, detail primary evidence of mitochondrial dysfunction and metabolic impairments in microglia and macrophages, discuss how major metabolic pathways contribute to post-traumatic neuroinflammation, and set out future directions toward advancing immunometabolic phenotyping in TBI.
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Affiliation(s)
- Nathan R. Strogulski
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Luis V. Portela
- Neurotrauma and Biomarkers Laboratory, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Brian M. Polster
- Department of Anesthesiology and Shock, Trauma and Anesthesiology Research Center, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - David J. Loane
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
- Department of Anesthesiology and Shock, Trauma and Anesthesiology Research Center, University of Maryland School of Medicine, Baltimore, Maryland, USA
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6
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [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: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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7
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Varlow C, Vasdev N. Evaluation of Tau Radiotracers in Chronic Traumatic Encephalopathy. J Nucl Med 2023; 64:460-465. [PMID: 36109185 PMCID: PMC10071800 DOI: 10.2967/jnumed.122.264404] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/06/2022] [Accepted: 09/06/2022] [Indexed: 11/16/2022] Open
Abstract
Chronic traumatic encephalopathy (CTE) is a neurologic disorder associated with head injuries, diagnosed by the perivascular accumulation of hyperphosphorylated tau protein (phospho-tau) identified at autopsy. Tau PET radiopharmaceuticals developed for imaging Alzheimer disease are under evaluation for brain injuries. The goal of this study was to conduct a head-to-head in vitro evaluation of 5 tau PET radiotracers in subjects pathologically diagnosed with CTE. Methods: Autoradiography was used to assess the specific binding and distribution of 3H-flortaucipir (also known as Tauvid, AV-1451, and T807), 3H-MK-6240 (also known as florquinitau), 3H-PI-2620, 3H-APN-1607 (also known as PM-PBB3 and florzolotau), and 3H-CBD-2115 (also known as 3H-OXD-2115) in fresh-frozen human postmortem CTE brain tissue (stages I-IV). Immunohistochemistry was performed for phospho-tau with AT8, 3R tau with RD3, 4R tau with RD4 and amyloid-β with 6F/3D antibodies. Tau target density (maximum specific binding) was quantified by saturation analysis with 3H-flortaucipir in tissue sections. Results: 3H-flortaucipir demonstrated a positive signal in all CTE cases examined, with varying degrees of specific binding (68.7% ± 10.5%; n = 12) defined by homologous blockade and to a lesser extent by heterologous blockade with MK-6240 (27.3% ± 13.6%; n = 12). The 3H-flortaucipir signal was also displaced by the monoamine oxidase (MAO)-A inhibitor clorgyline (43.9% ± 4.6%; n = 3), indicating off-target binding to MAO-A. 3H-APN-1607 was moderately displaced in homologous blocking studies and was not displaced by 3H-flortaucipir; however, substantial displacement was observed when blocking with the β-amyloid-targeting compound NAV-4694. 3H-MK-6240 and 3H-PI-2620 had negligible binding in all but 2 CTE IV cases, and binding may be attributed to pathology severity or mixed Alzheimer disease/CTE pathology. 3H-CBD-2115 showed moderate binding, displaced under homologous blockade, and aligned with 4R-tau immunostaining. Conclusion: In human CTE tissues, 3H-flortaucipir and 3H-APN-1607 revealed off-target binding to MAO-A and amyloid-β, respectively, and should be considered if these radiotracers are used in PET imaging studies of patients with brain injuries. 3H-MK-6240 and 3H-PI-2620 bind to CTE tau in severe- or mixed-pathology cases, and their respective 18F PET radiotracers warrant further evaluation in patients with severe suspected CTE.
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Affiliation(s)
- Cassis Varlow
- Azrieli Centre for Neuro-Radiochemistry, Brain Health Imaging Centre, Centre for Addiction and Mental Health, and Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Neil Vasdev
- Azrieli Centre for Neuro-Radiochemistry, Brain Health Imaging Centre, Centre for Addiction and Mental Health, and Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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8
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Gogola A, Minhas DS, Villemagne VL, Cohen AD, Mountz JM, Pascoal TA, Laymon CM, Mason NS, Ikonomovic MD, Mathis CA, Snitz BE, Lopez OL, Klunk WE, Lopresti BJ. Direct Comparison of the Tau PET Tracers 18F-Flortaucipir and 18F-MK-6240 in Human Subjects. J Nucl Med 2022; 63:108-116. [PMID: 33863821 PMCID: PMC8717175 DOI: 10.2967/jnumed.120.254961] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 04/06/2021] [Indexed: 11/16/2022] Open
Abstract
Tau PET tracers exhibit varying levels of specific signal and distinct off-target binding patterns that are more diverse than amyloid PET tracers. This study compared 2 frequently used tau PET tracers, 18F-flortaucipir and 18F-MK-6240, in the same subjects. Methods:18F-flortaucipir and 18F-MK-6240 scans were collected within 2 mo in 15 elderly subjects varying in clinical diagnosis and cognition. FreeSurfer, version 5.3, was applied to 3-T MR images to segment Braak pathologic regions (I-VI) for PET analyses. Off-target binding was assessed in the choroid plexus, meninges, and striatum. SUV ratio (SUVR) outcomes were determined over 80-100 min (18F-flortaucipir) or 70-90 min (18F-MK-6240) normalized to cerebellar gray matter. Masked visual interpretation of images was performed by 5 raters for both the medial temporal lobe and the neocortex, and an overall (majority) rating was determined. Results: Overall visual ratings showed complete concordance between radiotracers for both the medial temporal lobe and the neocortex. SUVR outcomes were highly correlated (r2 > 0.92; P ≪ 0.001) for all Braak regions except Braak II. The dynamic range of SUVRs in target regions was approximately 2-fold higher for 18F-MK-6240 than for 18F-flortaucipir. Cerebellar SUVs were similar for 18F-MK-6240 and 18F-flortaucipir, suggesting that differences in SUVRs are driven by specific signals. Apparent off-target binding was observed often in the striatum and choroid plexus with 18F-flortaucipir and most often in the meninges with 18F-MK-6240. Conclusion: Both 18F-MK-6240 and 18F-flortaucipir are capable of quantifying signal in a common set of brain regions that develop tau pathology in Alzheimer disease; these tracers perform equally well in visual interpretations. Each also shows distinct patterns of apparent off-target binding. 18F-MK-6240 showed a greater dynamic range in SUVR estimates, which may be an advantage in detecting early tau pathology or in performing longitudinal studies to detect small interval changes.
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Affiliation(s)
- Alexandra Gogola
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Davneet S Minhas
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Victor L Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - James M Mountz
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Charles M Laymon
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania; and
| | - N Scott Mason
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Milos D Ikonomovic
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Chester A Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Beth E Snitz
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Brian J Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania;
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9
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Risacher SL, West JD, Deardorff R, Gao S, Farlow MR, Brosch JR, Apostolova LG, McAllister TW, Wu Y, Jagust WJ, Landau SM, Weiner MW, Saykin AJ. Head injury is associated with tau deposition on PET in MCI and AD patients. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12230. [PMID: 34466653 PMCID: PMC8383323 DOI: 10.1002/dad2.12230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/04/2021] [Indexed: 01/20/2023]
Abstract
INTRODUCTION Head injuries (HI) are a risk factor for dementia, but the underlying etiology is not fully known. Understanding whether tau might mediate this relationship is important. METHODS Cognition and tau deposition were compared between 752 individuals with (impaired, n = 302) or without cognitive impairment (CN, n = 450) with amyloid and [18F]flortaucipir positron emission tomography, HI history information, and cognitive testing from the Alzheimer's Disease Neuroimaging Initiative and the Indiana Memory and Aging Study. RESULTS Sixty-three (38 CN, 25 impaired) reported a history of HI. Higher neuropsychiatric scores and poorer memory were observed in those with a history of HI. Tau was higher in individuals with a history of HI, especially those who experienced a loss of consciousness (LOC). Results were driven by impaired individuals, especially amyloid beta-positive individuals with history of HI with LOC. DISCUSSION These findings suggest biological changes, such as greater tau, are associated with HI in individuals with cognitive impairment. Small effect sizes were observed; thus, further studies should replicate and extend these results.
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Affiliation(s)
- Shannon L. Risacher
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - John D. West
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachael Deardorff
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of BiostatisticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Martin R. Farlow
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Jared R. Brosch
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Liana G. Apostolova
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Thomas W. McAllister
- Department of PsychiatryIndiana University School of MedicineIndianapolisIndianaUSA
| | - Yu‐Chien Wu
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | - William J. Jagust
- Helen Wills Neuroscience InstituteUniversity of California, BerkeleyBerkeleyCaliforniaUSA
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California, BerkeleyBerkeleyCaliforniaUSA
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Michael W. Weiner
- Departments of RadiologyMedicine and PsychiatryUniversity of California‐San FranciscoSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
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Ayubcha C, Moghbel M, Borja AJ, Newberg A, Werner TJ, Alavi A, Revheim ME. Tau Imaging in Head Injury. PET Clin 2021; 16:249-260. [PMID: 33648666 DOI: 10.1016/j.cpet.2020.12.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Tau proteins play a significant role in a variety of degenerative neurologic conditions. Postmortem neuropathology studies of victims of repeat and severe head trauma have defined a unique spatial expression of neurologic tauopathies in these individuals, known as chronic traumatic encephalopathy. Established and newly developed radiotracers are now being applied to head injury populations with the intent of diagnosis and disease monitoring. This review assesses the role of tau in head injury, the state of tau radiotracer development, and the potential clinical value of tau-PET as derived from head injury studies.
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Affiliation(s)
- Cyrus Ayubcha
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Mateen Moghbel
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Austin J Borja
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA
| | - Andrew Newberg
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, USA; Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Thomas J Werner
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Mona-Elisabeth Revheim
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Sognsvannsveien 20, Oslo 0372, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Problemveien 7, Oslo 0315, Norway.
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Tamil Selvan S, Ravichandar R, Kanta Ghosh K, Mohan A, Mahalakshmi P, Gulyás B, Padmanabhan P. Coordination chemistry of ligands: Insights into the design of amyloid beta/tau-PET imaging probes and nanoparticles-based therapies for Alzheimer’s disease. Coord Chem Rev 2021. [DOI: 10.1016/j.ccr.2020.213659] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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A critical review of radiotracers in the positron emission tomography imaging of traumatic brain injury: FDG, tau, and amyloid imaging in mild traumatic brain injury and chronic traumatic encephalopathy. Eur J Nucl Med Mol Imaging 2020; 48:623-641. [DOI: 10.1007/s00259-020-04926-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 06/11/2020] [Indexed: 12/14/2022]
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13
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Abrahamson EE, Ikonomovic MD. Brain injury-induced dysfunction of the blood brain barrier as a risk for dementia. Exp Neurol 2020; 328:113257. [PMID: 32092298 DOI: 10.1016/j.expneurol.2020.113257] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 01/31/2020] [Accepted: 02/20/2020] [Indexed: 02/06/2023]
Abstract
The blood-brain barrier (BBB) is a complex and dynamic physiological interface between brain parenchyma and cerebral vasculature. It is composed of closely interacting cells and signaling molecules that regulate movement of solutes, ions, nutrients, macromolecules, and immune cells into the brain and removal of products of normal and abnormal brain cell metabolism. Dysfunction of multiple components of the BBB occurs in aging, inflammatory diseases, traumatic brain injury (TBI, severe or mild repetitive), and in chronic degenerative dementing disorders for which aging, inflammation, and TBI are considered risk factors. BBB permeability changes after TBI result in leakage of serum proteins, influx of immune cells, perivascular inflammation, as well as impairment of efflux transporter systems and accumulation of aggregation-prone molecules involved in hallmark pathologies of neurodegenerative diseases with dementia. In addition, cerebral vascular dysfunction with persistent alterations in cerebral blood flow and neurovascular coupling contribute to brain ischemia, neuronal degeneration, and synaptic dysfunction. While the idea of TBI as a risk factor for dementia is supported by many shared pathological features, it remains a hypothesis that needs further testing in experimental models and in human studies. The current review focusses on pathological mechanisms shared between TBI and neurodegenerative disorders characterized by accumulation of pathological protein aggregates, such as Alzheimer's disease and chronic traumatic encephalopathy. We discuss critical knowledge gaps in the field that need to be explored to clarify the relationship between TBI and risk for dementia and emphasize the need for longitudinal in vivo studies using imaging and biomarkers of BBB dysfunction in people with single or multiple TBI.
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Affiliation(s)
- Eric E Abrahamson
- Geriatric Research Education and Clinical Center, VA Pittsburgh Healthcare System, University of Pittsburgh, Pittsburgh, PA, United States; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Milos D Ikonomovic
- Geriatric Research Education and Clinical Center, VA Pittsburgh Healthcare System, University of Pittsburgh, Pittsburgh, PA, United States; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States.
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