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The power of sample size through a multi-scanner approach in MR neuroimaging regression analysis: evidence from Alzheimer’s disease with and without depression

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Abstract

The inconsistency of volumetric results often seen in MR neuroimaging studies can be partially attributed to small sample sizes and variable data analysis approaches. Increased sample size through multi-scanner studies can tackle the former, but combining data across different scanner platforms and field-strengths may introduce a variability factor capable of masking subtle statistical differences. To investigate the sample size effect on regression analysis between depressive symptoms and grey matter volume (GMV) loss in Alzheimer’s disease (AD), a retrospective multi-scanner investigation was conducted. A cohort of 172 AD patients, with or without comorbid depressive symptoms, was studied. Patients were scanned with different imaging protocols in four different MRI scanners operating at either 1.5 T or 3.0 T. Acquired data were uniformly analyzed using the computational anatomy toolbox (CAT12) of the statistical parametric mapping (SPM12) software. Single- and multi-scanner regression analyses were applied to identify the anatomical pattern of correlation between GM loss and depression severity. A common anatomical pattern of correlation between GMV loss and increased depression severity, mostly involving sensorimotor areas, was identified in all patient subgroups imaged in different scanners. Analysis of the pooled multi-scanner data confirmed the above finding employing a more conservative statistical criterion. In the retrospective multi-scanner setting, a significant correlation was also exhibited for temporal and frontal areas. Increasing the sample size by retrospectively pooling multi-scanner data, irrespective of the acquisition platform and parameters employed, can facilitate the identification of anatomical areas with a strong correlation between GMV changes and depression symptoms in AD patients.

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Acknowledgements

There is not any external funding source for this work and authors do not declare any potential financial conflicts-of-interest.

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Authors and Affiliations

  1. Second Department of Radiology, University General Hospital ‘Attikon’, Medical School, National and Kapodistrian University of Athens, 19 Papadiamantopoulou Street, Athens, Greece

    Efstratios Karavasilis

  2. Department of Medical Physics, Medical School, Democritus University of Thrace, Alexandroupolis, Greece

    Efstratios Karavasilis & Ioannis Seimenis

  3. 3rd Age Day Care Center IASIS, Glyfada, Greece

    Theodore P. Parthimos & John D. Papatriantafyllou

  4. First Department of Neurology, Aeginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece

    Foteini Christidi

  5. Second Department of Neurology, University General Hospital ‘Attikon’, Medical School, National and Kapodistrian University of Athens, Athens, Greece

    Sokratis G. Papageorgiou

  6. Department of Radiology, 424 General Military Hospital, Thessaloníki, Greece

    George Kapsas

  7. Division of Clinical Neurosciences, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, USA

    Andrew C. Papanicolaou

  8. Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, USA

    Andrew C. Papanicolaou

  9. Neuroscience Institute, Le Bonheur Children’s Hospital, Memphis, USA

    Andrew C. Papanicolaou

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  1. Efstratios Karavasilis
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  2. Theodore P. Parthimos
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  4. Foteini Christidi
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  7. Andrew C. Papanicolaou
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Correspondence to Efstratios Karavasilis.

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Karavasilis, E., Parthimos, T.P., Papatriantafyllou, J.D. et al. The power of sample size through a multi-scanner approach in MR neuroimaging regression analysis: evidence from Alzheimer’s disease with and without depression. Australas Phys Eng Sci Med 42, 563–571 (2019). https://doi.org/10.1007/s13246-019-00758-1

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  • DOI: https://doi.org/10.1007/s13246-019-00758-1

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