Lifespan normative data on rates of brain volume changes

Marco Battaglini¹, Giordano Gentile¹, Ludovico Luchetti¹, Antonio Giorgio¹, Hugo Vrenken², Frederik Barkhof³, Keith S Cover⁴, Rohit Bakshi⁵, Renxin Chu⁵, Maria Pia Sormani⁶, Christian Enzinger⁷, Stefan Ropele⁸, Olga Ciccarelli⁹, Claudia Wheeler-Kingshott¹⁰, Marios Yiannakas¹¹, Massimo Filippi¹², Maria Assunta Rocca¹², Paolo Preziosa¹², Antonio Gallo¹³, Alvino Bisecco¹³, Jacqueline Palace¹⁴, Yazhuo Kong¹⁵, Dana Horakova¹⁶, Manuela Vaneckova¹⁷, Claudio Gasperini¹⁸, Serena Ruggieri¹⁸, Nicola De Stefano¹⁹; MAGNIMS Study Group

Affiliations

¹ Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
² Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam, the Netherlands.
³ Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam, the Netherlands; Institutes of Neurology and Healthcare Engineering, UCL London, UK; National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
⁴ Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam, the Netherlands; Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, the Netherlands.
⁵ Laboratory for Neuroimaging Research, Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶ Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
⁷ Department of Neurology, Medical University of Graz, Graz, Austria; Division of Neuroradiology, Vascular & Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria.
⁸ Department of Neurology, Medical University of Graz, Graz, Austria.
⁹ Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College, London, UK; National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
¹⁰ Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College, London, UK; Brain MRI 3T, UK Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
¹¹ Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College, London, UK.
¹² Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
¹³ Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania Luigi Vanvitelli, Naples, Italy.
¹⁴ Nuffield Department of Clinical Neurosciences, Oxford University Hospitals NHS Trust, University of Oxford, Oxford, UK.
¹⁵ CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
¹⁶ Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
¹⁷ Department of Radiodiagnostics, First Faculty of Medicine Charles University and General University Hospital in Prague, Prague, Czech Republic.
¹⁸ Department of Neurosciences S Camillo Forlanini Hospital, Rome, Italy.
¹⁹ Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy. Electronic address: destefano@unisi.it.

PMID: 31207467
DOI: 10.1016/j.neurobiolaging.2019.05.010

Lifespan normative data on rates of brain volume changes

Marco Battaglini et al. Neurobiol Aging. 2019 Sep.

. 2019 Sep:81:30-37.

doi: 10.1016/j.neurobiolaging.2019.05.010. Epub 2019 May 22.

Authors

Affiliations

¹ Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
² Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam, the Netherlands.
³ Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam, the Netherlands; Institutes of Neurology and Healthcare Engineering, UCL London, UK; National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
⁴ Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam, the Netherlands; Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, the Netherlands.
⁵ Laboratory for Neuroimaging Research, Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶ Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
⁷ Department of Neurology, Medical University of Graz, Graz, Austria; Division of Neuroradiology, Vascular & Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria.
⁸ Department of Neurology, Medical University of Graz, Graz, Austria.
⁹ Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College, London, UK; National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK.
¹⁰ Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College, London, UK; Brain MRI 3T, UK Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
¹¹ Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, University College, London, UK.
¹² Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
¹³ Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania Luigi Vanvitelli, Naples, Italy.
¹⁴ Nuffield Department of Clinical Neurosciences, Oxford University Hospitals NHS Trust, University of Oxford, Oxford, UK.
¹⁵ CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
¹⁶ Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
¹⁷ Department of Radiodiagnostics, First Faculty of Medicine Charles University and General University Hospital in Prague, Prague, Czech Republic.
¹⁸ Department of Neurosciences S Camillo Forlanini Hospital, Rome, Italy.
¹⁹ Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy. Electronic address: destefano@unisi.it.

PMID: 31207467
DOI: 10.1016/j.neurobiolaging.2019.05.010

Abstract

We provide here normative values of yearly percentage brain volume change (PBVC/y) as obtained with Structural Imaging Evaluation, using Normalization, of Atrophy, a widely used open-source software, developing a PBVC/y calculator for assessing the deviation from the expected PBVC/y in patients with neurological disorders. We assessed multicenter (34 centers, 11 acquisition protocols) magnetic resonance imaging data of 720 healthy participants covering the whole adult lifespan (16-90 years). Data of 421 participants with a follow-up > 6 months were used to obtain the normative values for PBVC/y and data of 392 participants with a follow-up <1 month were selected to assess the intrasubject variability of the brain volume measurement. A mixed model evaluated PBVC/y dependence on age, sex, and magnetic resonance imaging parameters (scan vendor and magnetic field strength). PBVC/y was associated with age (p < 0.001), with 60- to 70-year-old participants showing twice more volume decrease than participants aged 30-40 years. PBVC/y was also associated with magnetic field strength, with higher decreases when measured by 1.5T than 3T scanners (p < 0.001). The variability of PBVC/y normative percentiles was narrower as the interscan interval was longer (e.g., 80th normative percentile was 50% smaller for participants with 2-year than with 1-year follow-up). The use of these normative data, eased by the freely available calculator, might help in better discriminating pathological from physiological conditions in the clinical setting.

Keywords: Aging; Atrophy; Brain volume; MRI; Normative data.

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Lifespan normative data on rates of brain volume changes

Affiliations

Lifespan normative data on rates of brain volume changes

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Abstract

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Medical