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It has not yet been peer reviewed by a journal.

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[Preprint]. 2024 May 1:2024.04.29.591421.

doi: 10.1101/2024.04.29.591421.

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

René Labounek¹, Monica T Bondy¹, Amy L Paulson¹, Sandrine Bédard², Mihael Abramovic³, Eva Alonso-Ortiz^{2

4}, Nicole T Atcheson⁵, Laura R Barlow⁶, Robert L Barry^{7

8

9}, Markus Barth^{5

10}, Marco Battiston¹¹, Christian Büchel¹², Matthew D Budde^{13

14}, Virginie Callot^{15

16}, Anna Combes¹¹, Benjamin De Leener^{2

4

17}, Maxime Descoteaux¹⁸, Paulo Loureiro de Sousa¹⁹, Marek Dostál^{20

21}, Julien Doyon²², Adam V Dvorak²³, Falk Eippert²⁴, Karla R Epperson²⁵, Kevin S Epperson²⁵, Patrick Freund^{26

27

28}, Jürgen Finsterbusch¹², Alexandru Foias², Michela Fratini^{29

30}, Issei Fukunaga³¹, Claudia A M Gandini Wheeler-Kingshott^{11

32}, GianCarlo Germani³³, Guillaume Gilbert³⁴, Federico Giove^{30

35}, Francesco Grussu^{11

36}, Akifumi Hagiwara³¹, Pierre-Gilles Henry³⁷, Tomáš Horák^{38

39

40}, Masaaki Hori^{31

41}, James M Joers³⁷, Kouhei Kamiya⁴¹, Haleh Karbasforoushan⁴², Miloš Keřkovský²⁰, Ali Khatibi^{43

44

45}, Joo-Won Kim^{46

47

48}, Nawal Kinany^{49

50}, Hagen Kitzler⁵¹, Shannon Kolind^{6

23

52}, Yazhuo Kong^{53

54}, Petr Kudlička^{40

55}, Paul Kuntke⁵¹, Nyoman D Kurniawan⁵, Slawomir Kusmia⁵⁶, Maria Marcella Laganà⁵⁷, Cornelia Laule^{6

23

58

59}, Christine S W Law²⁵, Tobias Leutritz²⁸, Yaou Liu⁶⁰, Sara Llufriu⁶¹, Sean Mackey⁶², Allan R Martin⁶³, Eloy Martinez-Heras^{61

64}, Loan Mattera⁶⁵, Kristin P O'Grady^{66

67}, Nico Papinutto⁴², Daniel Papp^{2

68}, Deborah Pareto⁶⁴, Todd B Parrish⁶⁹, Anna Pichiecchio^{32

33}, Ferran Prados^{11

70

71}, Àlex Rovira⁶⁴, Marc J Ruitenberg⁷², Rebecca S Samson¹¹, Giovanni Savini^{73

74}, Maryam Seif^{26

28}, Alan C Seifert⁴⁶, Alex K Smith⁶⁸, Seth A Smith^{66

67

75}, Zachary A Smith⁷⁶, Elisabeth Solana⁶¹, Yuichi Suzuki⁷⁷, George W Tackley⁷⁸, Alexandra Tinnermann¹², Jan Valošek^{2

79

80

81}, Dimitri Van De Ville^{49

50}, Marios C Yiannakas¹¹, Kenneth A Weber 2nd⁶², Nikolaus Weiskopf^{27

28

82}, Richard G Wise^{78

83

84}, Patrik O Wyss³, Junqian Xu^{46

47

48}, Julien Cohen-Adad^{2

4

79

85}, Christophe Lenglet³⁷, Igor Nestrašil^{1

37}

Affiliations

¹ Division of Clinical Behavioral Neuroscience, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
² NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
³ Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland.
⁴ Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada.
⁵ Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Australia.
⁶ Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
⁷ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
⁸ Harvard Medical School, Boston, Massachusetts, USA.
⁹ Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, Massachusetts, USA.
¹⁰ School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Australia.
¹¹ NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK.
¹² Department for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
¹³ Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA.
¹⁴ Clement J. Zablocki Veteran's Affairs Medical Center, Milwaukee, WI, USA.
¹⁵ Aix-Marseille Univ, CNRS, CRMBM, Marseille, France.
¹⁶ APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France.
¹⁷ Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada.
¹⁸ Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, QC, Canada.
¹⁹ Université de Strasbourg, CNRS, ICube, Strasbourg, France.
²⁰ Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic.
²¹ Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
²² McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
²³ Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
²⁴ Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
²⁵ Stanford University, Stanford, CA, USA.
²⁶ Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
²⁷ Wellcome Trust Centre for Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK.
²⁸ Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
²⁹ Institute of Nanotechnology, CNR, Rome, Italy.
³⁰ IRCCS Santa Lucia Foundation, Neuroimaging Laboratory, Rome, Italy.
³¹ Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan.
³² Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
³³ Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy.
³⁴ MR Clinical Science, Philips Healthcare Canada, Mississauga, Canada.
³⁵ CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy.
³⁶ Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.
³⁷ Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
³⁸ Faculty of Medicine, Masaryk University, Brno, Czech Republic.
³⁹ Department of Neurology, University Hospital Brno, Brno, Czech Republic.
⁴⁰ Multimodal and Functional Imaging Laboratory, Central European Institute of Technology, Brno, Czech Republic.
⁴¹ Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan.
⁴² Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
⁴³ Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), University of Birmingham, Birmingham, UK.
⁴⁴ Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
⁴⁵ Institute for Mental Health, University of Birmingham, Birmingham, UK.
⁴⁶ Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
⁴⁷ Department of Radiology, Baylor College of Medicine, Houston, Texas, USA.
⁴⁸ Department of Psychiatry, Baylor College of Medicine, Houston, Texas, USA.
⁴⁹ Neuro-X Institute, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland.
⁵⁰ Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland.
⁵¹ Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and Carl Gustav Carus University Hospital, Technische Universität Dresden, Germany.
⁵² Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
⁵³ CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Science, Beijing, 100101, China.
⁵⁴ Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
⁵⁵ First Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic.
⁵⁶ IBM Poland, Department of Content Design, Cracow, Poland.
⁵⁷ Canon Medical Systems srl, Rome, Italy.
⁵⁸ Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada.
⁵⁹ International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada.
⁶⁰ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, China.
⁶¹ Neuroimmunology and Multiple Sclerosis Unit, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic Barcelona, Fundació de Recerca Clínic Barcelona-IDIBAPS and Universitat de Barcelona. Barcelona, Spain.
⁶² Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
⁶³ Department of Neurological Surgery, University of California, Davis, CA, USA.
⁶⁴ Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
⁶⁵ Fondation Campus Biotech Geneva, Genève, Switzerland.
⁶⁶ Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶⁷ Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶⁸ Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
⁶⁹ Department of Radiology, Northwestern University, Chicago, IL 60611, USA.
⁷⁰ e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain.
⁷¹ Centre for Medical Image Computing, University College London, London, UK.
⁷² School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, Australia.
⁷³ Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele (MI), Italy.
⁷⁴ Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089, Rozzano (MI), Italy.
⁷⁵ Department of Biomedical Engineering, Vanderbilt University, Nashville, TN USA.
⁷⁶ Department of Neurosurgery, University of Oklahoma, Oklahoma City, OK, USA.
⁷⁷ The University of Tokyo Hospital, Radiology Center, Tokyo, Japan.
⁷⁸ Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, Wales, UK.
⁷⁹ Mila - Quebec AI Institute, Montreal, QC, Canada.
⁸⁰ Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic.
⁸¹ Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic.
⁸² Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany.
⁸³ Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy.
⁸⁴ Institute for Advanced Biomedical Technologies, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy.
⁸⁵ Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Canada.

PMID: 38746371
PMCID: PMC11092490
DOI: 10.1101/2024.04.29.591421

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

René Labounek et al. bioRxiv. 2024.

[Preprint]. 2024 May 1:2024.04.29.591421.

doi: 10.1101/2024.04.29.591421.

Authors

Affiliations

¹ Division of Clinical Behavioral Neuroscience, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
² NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
³ Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland.
⁴ Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada.
⁵ Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Australia.
⁶ Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
⁷ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
⁸ Harvard Medical School, Boston, Massachusetts, USA.
⁹ Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, Massachusetts, USA.
¹⁰ School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Australia.
¹¹ NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK.
¹² Department for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
¹³ Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA.
¹⁴ Clement J. Zablocki Veteran's Affairs Medical Center, Milwaukee, WI, USA.
¹⁵ Aix-Marseille Univ, CNRS, CRMBM, Marseille, France.
¹⁶ APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France.
¹⁷ Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada.
¹⁸ Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, QC, Canada.
¹⁹ Université de Strasbourg, CNRS, ICube, Strasbourg, France.
²⁰ Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic.
²¹ Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
²² McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
²³ Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
²⁴ Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
²⁵ Stanford University, Stanford, CA, USA.
²⁶ Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
²⁷ Wellcome Trust Centre for Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK.
²⁸ Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
²⁹ Institute of Nanotechnology, CNR, Rome, Italy.
³⁰ IRCCS Santa Lucia Foundation, Neuroimaging Laboratory, Rome, Italy.
³¹ Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan.
³² Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
³³ Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy.
³⁴ MR Clinical Science, Philips Healthcare Canada, Mississauga, Canada.
³⁵ CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy.
³⁶ Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.
³⁷ Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
³⁸ Faculty of Medicine, Masaryk University, Brno, Czech Republic.
³⁹ Department of Neurology, University Hospital Brno, Brno, Czech Republic.
⁴⁰ Multimodal and Functional Imaging Laboratory, Central European Institute of Technology, Brno, Czech Republic.
⁴¹ Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan.
⁴² Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
⁴³ Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), University of Birmingham, Birmingham, UK.
⁴⁴ Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
⁴⁵ Institute for Mental Health, University of Birmingham, Birmingham, UK.
⁴⁶ Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
⁴⁷ Department of Radiology, Baylor College of Medicine, Houston, Texas, USA.
⁴⁸ Department of Psychiatry, Baylor College of Medicine, Houston, Texas, USA.
⁴⁹ Neuro-X Institute, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland.
⁵⁰ Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland.
⁵¹ Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and Carl Gustav Carus University Hospital, Technische Universität Dresden, Germany.
⁵² Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
⁵³ CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Science, Beijing, 100101, China.
⁵⁴ Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
⁵⁵ First Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic.
⁵⁶ IBM Poland, Department of Content Design, Cracow, Poland.
⁵⁷ Canon Medical Systems srl, Rome, Italy.
⁵⁸ Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada.
⁵⁹ International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada.
⁶⁰ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, China.
⁶¹ Neuroimmunology and Multiple Sclerosis Unit, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic Barcelona, Fundació de Recerca Clínic Barcelona-IDIBAPS and Universitat de Barcelona. Barcelona, Spain.
⁶² Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
⁶³ Department of Neurological Surgery, University of California, Davis, CA, USA.
⁶⁴ Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
⁶⁵ Fondation Campus Biotech Geneva, Genève, Switzerland.
⁶⁶ Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶⁷ Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶⁸ Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
⁶⁹ Department of Radiology, Northwestern University, Chicago, IL 60611, USA.
⁷⁰ e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain.
⁷¹ Centre for Medical Image Computing, University College London, London, UK.
⁷² School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, Australia.
⁷³ Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele (MI), Italy.
⁷⁴ Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089, Rozzano (MI), Italy.
⁷⁵ Department of Biomedical Engineering, Vanderbilt University, Nashville, TN USA.
⁷⁶ Department of Neurosurgery, University of Oklahoma, Oklahoma City, OK, USA.
⁷⁷ The University of Tokyo Hospital, Radiology Center, Tokyo, Japan.
⁷⁸ Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, Wales, UK.
⁷⁹ Mila - Quebec AI Institute, Montreal, QC, Canada.
⁸⁰ Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic.
⁸¹ Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic.
⁸² Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany.
⁸³ Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy.
⁸⁴ Institute for Advanced Biomedical Technologies, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy.
⁸⁵ Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Canada.

PMID: 38746371
PMCID: PMC11092490
DOI: 10.1101/2024.04.29.591421

Abstract

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

Keywords: BMI; body size; brain; human; in vivo neuroimaging; magnetic resonance imaging; spinal cord; structure.

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Conflict of interest statement

Declaration of interests Since June 2022, Dr. A.K. Smith has been employed by GE HealthCare. This article was co-authored by Dr. Smith in his personal capacity. The opinions expressed in the article are his in and do not necessarily reflect the views of GE HealthCare. Since August 2022, Dr. M. M. Laganà has been employed by Canon Medical Systems srl, Rome, Italy. This article was co-authored by Dr. M. M. Laganà in her personal capacity. The opinions expressed in the article are her own and do not necessarily reflect the views of Canon Medical Systems. Since September 2023, Dr. Papp has been an employee of Siemens Healthcare AB, Sweden. This article was co-authored by Dr. Papp in his personal capacity. The views and opinions expressed in this article are his own and do not necessarily reflect the views of Siemens Healthcare AB, or Siemens Healthineers AG. Since January 2024, Dr. Barry has been employed by the National Institute of Biomedical Imaging and Bioengineering at the NIH. This article was co-authored by Robert Barry in his personal capacity. The opinions expressed in the article are his own and do not necessarily reflect the views of the NIH, the Department of Health and Human Services, or the United States government. Guillaume Gilbert is an employee of Philips Healthcare. S Llufriu received compensation for consulting services and speaker honoraria from Biogen Idec, Novartis, Bristol Myer Squibb Genzyme, Sanofi Jansen and Merck. The Max Planck Institute for Human Cognitive and Brain Sciences and Wellcome Centre for Human Neuroimaging have institutional research agreements with Siemens Healthcare. NW holds a patent on acquisition of MRI data during spoiler gradients (US 10,401,453 B2). NW was a speaker at an event organized by Siemens Healthcare and was reimbursed for the travel expenses. The other authors declare no competing interests.

Figures

**Figure 1:. Cross-sectional area of spinal cord white matter correlates with body height and weight.**
*Abbreviations:* CSA - cross-sectional area; SC - spinal cord; WM - white matter; GM - gray matter; r - Pearson correlation coefficient; ⍴ - Spearman correlation coefficient. All spinal cord measurements were averaged from cervical C3–4 levels. Regression lines (i.e., the dashed black lines) were estimated from all available data points. Plots with statistically significant correlation (p_FWE<0.05) are highlighted with yellow background, and corresponding r and ⍴ values are highlighted with black bold font.

**Figure 2:. Interactions between body height and morphology of the central nervous system.**
**Panel a)** Representative image of brain and spinal cord (SC) anatomy. The brain scan shows cortical gray matter (GM), cerebral white matter (WM), subcortical GM structures, brainstem and cerebellum. The axial SC scan shows the WM and GM anatomy at the C3/C4 level. Image orientation is described in **panel a)**: A - anterior, P - posterior, S - superior, I - Inferior, L - left and R - right. **Panel b)** Pearson correlation coefficient between body height and (i) cortical GM volume; (ii) cerebral WM volume; (iii) subcortical GM structure volume; (iv) brainstem volume; (v) cerebellar volume; and (vi) cross-sectional area (CSA) of cervical SC WM at C3/C4 level. The colormap for the correlation values is shown in the left bottom corner of the figure. All correlations are significant (p_FWE<0.05). Regarding the investigated list of structures, body height demonstrated the strongest correlation with the cortical GM volume. **Panel c)** Pearson correlation coefficient between the CSA of cervical WM at C3/C4 level and (i) cortical GM volume; (ii) cerebral WM volume; (iii) subcortical GM structure volume; (iv) brainstem volume; and (v) cerebellar volume. The colormap for the correlation values is shown in the left bottom corner of the figure. All correlations are significant (p_FWE<0.05). The correlation map shows a descending gradient from the brainstem through subcortical GM structures and cerebral WM to cortical GM. The gradient may be driven by the increasing distance to the cervical SC level and decreasing relative volume of common tract pathways. The cerebellum shows the lowest, yet significant, correlation level. This finding may be explained by the fact that the cerebrum is more strongly and directly interconnected to the peripheral nervous system via SC than the cerebellum, with spinocerebellar tracts as the primary direct connections.^,

**Figure 3:. Mean diffusivity and magnetization transfer ratio in spinal cord white matter correlates with body weight.**
*Abbreviations:* GM - gray matter; WM - white matter; SC - spinal cord; FA - fractional anisotropy; MD - mean diffusivity; MTR - magnetization transfer ratio; r - Pearson correlation coefficient; ⍴ - Spearman correlation coefficient. All spinal cord measurements were averaged from cervical C2–5 levels. Black dashed regression lines were estimated from the Siemens and Philips scanners’ data points. Red dotted regression lines were estimated from the GE scanner’s data points. Plots with statistically significant correlation (p_FWE<0.05) are highlighted with yellow background, and corresponding r and ⍴ values are highlighted with black bold font.

**Figure 4:. Brain morphology strongly correlates with body size but weakly with age.**
*Abbreviations:* GM - gray matter; WM - white matter; Vol - volume; SubCort - subcortical; r - Pearson correlation coefficient; ⍴ - Spearman correlation coefficient. Regression lines (i.e., the dashed black lines) were estimated from all available data points. Plots with statistically significant correlation (p_FWE<0.05) are highlighted with yellow background, and corresponding r and ⍴ values are highlighted with black bold font.

**Figure 5:. Cortical morphology correlates with body size, age, and cross-sectional area of the spinal cord white matter.**
*Abbreviations:* CSA - cross-sectional area; SC - spinal cord; WM - white matter; GM - gray matter; PrecentralG - precentral gyrus; PostcentralG - postcentral gyrus; Vol - volume; r - Pearson correlation coefficient; ⍴ - Spearman correlation coefficient. Regression lines (i.e., the dashed black lines) were estimated from all available data points. Plots with statistically significant correlation (p_FWE<0.05) are highlighted with yellow background, and corresponding r and ⍴ values are highlighted with black bold font. a) Graphs demonstrate correlations with body size and age. b) Graphs demonstrate correlation with CSA measured in the SC region as averages from cervical C3–4 levels.

**Figure 6:. Brain morphology correlates with spinal cord morphology.**
*Abbreviations:* CSA - cross-sectional area; SC - spinal cord; WM - white matter; GM - gray matter; Vol - volume; SubCort - subcortical; r - Pearson correlation coefficient; ⍴ - Spearman correlation coefficient. All SC measurements were averaged from cervical C3–4 levels. Regression lines (i.e., the dashed black lines) were estimated from all available data points. Plots with statistically significant correlation (p_FWE<0.05) are highlighted with yellow background, and corresponding r and ⍴ values are highlighted with black bold font.

**Figure 7:. Exploratory visualization using biplot projections of principal components.**
a) biplot projection of 1^st and 2^nd principal components (PCs); b) biplot projection of 1^st and 3^rd PCs; c) biplot projection of 1^st and 4^th PCs; d) biplot projection of 1^st and 5^th PCs. Variable vectors are visualized in each biplot projection with a color-coding characteristic for a corresponding variable group. Variable name abbreviations and variable color codings are described as follows. *Variable abbreviations:* MD - mean diffusivity; RD - radial diffusivity; MTR - magnetization transfer ratio; SC - spinal cord; WM - white matter; GM - gray matter; CSA - cross-sectional area; Vol - volume; PrecentralG - precentral gyrus; PostcentralG - postcentral gyrus. *Variable color coding*: demography - green; cerebral volumes - light blue; cortical thicknesses - yellow; SC morphometry - magenta; SC WM microstructure - red. *How to read a biplot:* The overall domain of each component axis is <−1,1>. Each variable is characterized as a vector of magnitude in the range of <0,1> in the biplot space. Angle 0° between the component axis and variable vector with magnitude 1 (or between two variable vectors both with magnitude 1) is proportional to Pearson correlation coefficient 1. Under the same vector magnitude circumstances, an angle of 180° equals Pearson correlation coefficient −1, and angles of 90° and 270° equal Pearson correlation coefficient 0. The lower magnitude of variable vectors proportionally decreases the overall linear dependence between vector angles close to 0° or 180°, respectively. Similarly, angle deviation from 0° or 180° also decreases the level of linear dependence between pairs of vectors in the biplot.

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Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

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Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

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