Abstract
We present NOMIS (https://github.com/medicslab/NOMIS), a comprehensive open MRI tool to assess morphometric deviation from normality in the adult human brain. Based on MR anatomical images from 6,909 cognitively healthy individuals aged 18-100 years, we modeled 1,344 measures computed using the open access FreeSurfer pipeline, considering account personal characteristics (age, sex, intracranial volume) and image quality (resolution, contrast-to-noise ratio and surface reconstruction defect holes), and providing expected values for any new individual. Then, for each measure, the NOMIS tool was built to generate Z-score effect sizes denoting the extent of deviation from the normative sample. Depending on the user need, NOMIS offers four versions of Z-score adjusted on different sets of variables. While all versions consider head size and image quality, they can also incorporate age and/or sex, thereby facilitating multi-site neuromorphometric research across adulthood.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵* Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
↵** Part of the data used in this article were obtained from the Consortium pour l’identification précoce de la maladie Alzheimer - Québec (CIMA-Q; cima-q.ca). As such, the investigators within the CIMA-Q contributed to the design, the implementation, the acquisition of clinical, cognitive, and neuroimaging data and biological samples. A list of the CIMA-Q investigators is available on www.cima-q.ca.
↵*** Part of the data used in this article were obtained from the Canadian Consortium on Neurodegeneration in Aging (CCNA; www.ccna-ccnv.ca).
Two major updates were made. First, we modified the procedure to develop the norms in order to avoid as much as possible potential biases that could be introduced by differences in participants characteristics at each site/scanner. Thus, instead of using magnetic field strength and scanner vendor that can induce a potential bias due to known or unknown differences in individuals characteristics within each combination of scanner strength/vendor (which could arise due to recruitment in specific studies), NOMIS solely uses information from the images themselves. Second, we aded a comparison between NOMIS most basic version (i.e. adjusting only for image quality and head size) to two post-hoc scaling harmonization procedures.
