Freesurfer cortical normative data for adults using Desikan-Killiany-Tourville and ex vivo protocols

&NA; We recently built normative data for FreeSurfer morphometric estimates of cortical regions using its default atlas parcellation (Desikan‐Killiany or DK) according to individual and scanner characteristics. We aimed to produced similar normative values for Desikan‐Killianny‐Tourville (DKT) and ex vivo‐based labeling protocols, as well as examine the differences between these three atlases. Surfaces, thicknesses, and volumes of cortical regions were produced using cross‐sectional magnetic resonance scans from the same 2713 healthy individuals aged 18–94 years as used in the reported DK norms. Models predicting regional cortical estimates of each hemisphere were produced using age, sex, estimated intracranial volume (eTIV), scanner manufacturer and magnetic field strength (MFS) as predictors. The DKT and DK models generally included the same predictors and produced similar R2. Comparison between DK, DKT, ex vivo atlases normative cortical measures showed that the three protocols generally produced similar normative values. HighlightsNormative data for morphometric estimates of cortical brain regions are provided.Segmentation was performed using FreeSurfer's 5.3 DKT and ex vivo atlases.Models include age, sex, eTIV and scanner's OEM and strength.Normative Z scores and R‐squares are compared between DK, DKT, and ex vivo atlases.

[1]  C. Rowe,et al.  The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease , 2009, International Psychogeriatrics.

[2]  J. Yesavage,et al.  Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. , 1986 .

[3]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[4]  Simon Duchesne,et al.  Normative data for subcortical regional volumes over the lifetime of the adult human brain , 2016, NeuroImage.

[5]  André J. W. van der Kouwe,et al.  Predicting the location of human perirhinal cortex, Brodmann's area 35, from MRI , 2013, NeuroImage.

[6]  Jens C. Pruessner,et al.  Operationalizing protocol differences for EADC-ADNI manual hippocampal segmentation , 2015, Alzheimer's & Dementia.

[7]  Olivier Potvin,et al.  FreeSurfer subcortical normative data , 2016, Data in brief.

[8]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[9]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[10]  Paul H Garthwaite,et al.  Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression. , 2012, Psychological assessment.

[11]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[12]  Abraham Z. Snyder,et al.  A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume , 2004, NeuroImage.

[13]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[14]  D. Louis Collins,et al.  The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: Evidence of validity , 2015, Alzheimer's & Dementia.

[15]  M. Mallar Chakravarty,et al.  Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: Towards a harmonized segmentation protocol , 2015, NeuroImage.

[16]  Olivier Potvin,et al.  Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain , 2017, NeuroImage.

[17]  Bruce R. Rosen,et al.  Predicting the location of entorhinal cortex from MRI , 2009, NeuroImage.