TY - JOUR TI - The power of sample size through a multi-scanner approach in MR neuroimaging regression analysis: evidence from Alzheimer’s disease with and without depression AU - Karavasilis, E. AU - Parthimos, T.P. AU - Papatriantafyllou, J.D. AU - Christidi, F. AU - Papageorgiou, S.G. AU - Kapsas, G. AU - Papanicolaou, A.C. AU - Seimenis, I. JO - Australasian Physical and Engineering Sciences in Medicine PY - 2019 VL - 42 TODO - 2 SP - 563-571 PB - SPRINGER NETHERLANDS SN - null TODO - 10.1007/s13246-019-00758-1 TODO - Magnetic resonance imaging; Neuroimaging; Regression analysis; Sampling, Alzheimer; Computational anatomy; Depression; Regression; Sample sizes; Statistical criterion; Statistical differences; Statistical parametric mapping, Scanning, aged; Alzheimer disease; Article; brain atrophy; brain size; cohort analysis; comorbidity; controlled study; depression; disease association; disease severity; female; frontal cortex; gray matter; human; image analysis; major clinical study; male; neuroimaging; nuclear magnetic resonance imaging; regression analysis; retrospective study; sample size; sensorimotor cortex; symptom; temporal cortex; Alzheimer disease; complication; depression; diagnostic imaging; neuroimaging; organ size; pathology; regression analysis; sample size, Aged; Alzheimer Disease; Depression; Female; Gray Matter; Humans; Magnetic Resonance Imaging; Male; Neuroimaging; Organ Size; Regression Analysis; Sample Size TODO - 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. © 2019, Australasian College of Physical Scientists and Engineers in Medicine. ER -