Abstract
This article contains a spreadsheet computing estimates of the expected subcortical regional volumes of an individual based on its characteristics and the scanner characteristics, in addition to supplementary results related to the article “Normative data for subcortical regional volumes over the lifetime of the adult human brain” (O. Potvin, A. Mouiha, L. Dieumegarde, S. Duchesne, 2016) [1] on normative data for subcortical volumes. Data used to produce normative values was obtained by anatomical magnetic resonance imaging from 2790 healthy individuals aged 18–94 years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer. The spreadsheet includes formulas in order to compute for a new individual, significance test for volume abnormality, effect size and estimated percentage of the normative population with a smaller volume while taking into account age, sex, estimated intracranial volume (eTIV), and scanner characteristics. Detailed R-squares of each predictor for all formula are also reported as well as the difference of subcortical volumes segmented by FreeSurfer on two different computer hardware setups.
Keywords: Neuroimaging, Age, Sex, Magnetic resonance, Normality, Normal aging, Morphometry
Specifications Table
| Subject area | Neuroscience, Neurology, Neurobiology |
| More specific subject area | Volumetric subcortical normative values |
| Type of data | Tables, Excel file |
| How data was acquired | MRI images from open databases, data analyses and normative values generated by statistical models |
| Data format | Analyzed |
| Experimental factors | The sociodemographics, the scanner manufacturer and magnetic field strength |
| Experimental features | Subcortical volumes extracted using FreeSurfer |
| Data source location | Australia, Austria, Belgium, Canada, Finland, Germany, Ireland, Italy, Netherlands, United Kingdom, and USA |
| Data accessibility | Data is with this article |
Value of the data
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The data provides the first subcortical regional normative values in a very large sample of healthy individuals with a wide age range and diversity of scanner manufacturer and magnetic field strength.
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The calculator can be used to assess deviation from normality for any given individual patient or healthy control.
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These values can be useful for multicenter studies using various scanner manufacturers and magnetic field strengths.
1. Data
A Microsoft Excel spreadsheet computing expected subcortical regional volumes for an individual according to his age, sex, intracranial volume and the scanner characteristics is provided (see Subcortical_Norms_Calculator.xlsm file online). Table 1 reports detailed R-squares of each predictor for all models predicting subcortical volumes. Table 2 shows the difference of subcortical volumes segmented by FreeSurfer on two different computer hardware setups.
Table 1.
Percentage of the variance explained (R2) by each predictor in models predicting subcortical regional volumes.
| Regions | Age | Age2 | Age3 | Sex | eTIV | eTIV2 | eTIV3 | MFS | GE / Siemens | Philips / Siemens | GE X MFS | Philips X MFS | eTIV X MFS | Age X Sex | eTIV X GE | eTIV X Philips | Total R2 | Validation R2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Accumbens L | 25.6 | 1.1 | – | 1.6 | 0.2 | 0.0 | – | 1.0 | 0.4 | 1.4 | 1.8 | 5.1 | – | 0.4 | – | – | 38.5 | 34.2 |
| Accumbens R | 28.7 | 0.3 | 0.1 | 2.6 | 0.1 | – | – | 1.0 | 0.1 | 1.4 | 0.5 | 3.0 | – | 0.2 | – | – | 37.8 | 28.6 |
| Amygdala L | 14.0 | 1.1 | 0.1 | 13.1 | 4.2 | 0.1 | – | 8.1 | 0.0 | 0.0 | 0.5 | 0.0 | – | 0.1 | 0.1 | 0.1 | 41.4 | 39.0 |
| Amygdala R | 9.6 | 0.1 | 0.2 | 12.7 | 3.5 | – | – | 4.4 | 0.1 | 0.0 | 0.0 | 0.4 | – | – | – | – | 31.1 | 33.9 |
| Brainstem | 3.1 | 0.9 | 0.3 | 21.5 | 26.7 | 0.2 | – | 0.0 | 0.3 | 0.0 | 0.9 | 0.1 | – | 0.2 | – | – | 54.1 | 61.1 |
| Caudate L | 12.8 | 3.7 | 0.1 | 7.1 | 15.1 | 0.2 | 0.0 | – | 0.0 | 2.0 | – | – | – | 0.2 | 0.0 | 0.1 | 41.2 | 37.0 |
| Caudate R | 9.0 | 7.2 | – | 6.8 | 11.7 | 0.0 | – | 0.0 | 0.4 | 5.5 | 0.0 | 0.5 | 0.0 | 0.2 | 0.0 | 0.2 | 41.7 | 31.4 |
| Hippocampus L | 21.4 | 5.8 | 0.0 | 6.9 | 10.6 | 0.2 | – | 3.3 | 0.7 | 1.6 | 0.2 | 0.0 | 0.0 | 0.2 | – | – | 50.9 | 48.2 |
| Hippocampus R | 18.0 | 6.7 | 0.1 | 7.2 | 11.1 | – | – | 5.3 | 0.4 | 0.6 | 0.2 | 0.1 | – | 0.2 | – | – | 49.7 | 51.6 |
| Pallidum L | 14.5 | 3.0 | 0.1 | 8.8 | 8.6 | 0.2 | – | 1.4 | 0.6 | 0.6 | 0.0 | 1.8 | – | 0.6 | – | – | 40.0 | 37.8 |
| Pallidum R | 19.5 | 1.5 | 0.5 | 8.5 | 6.9 | 0.1 | – | 1.1 | 0.1 | 3.6 | 0.3 | 1.1 | – | 0.3 | – | – | 43.4 | 42.4 |
| Putamen L | 34.6 | 1.9 | – | 6.2 | 3.3 | 0.0 | 0.0 | 0.1 | 0.1 | 3.8 | 0.1 | 1.5 | 0.2 | 0.3 | – | – | 52.0 | 41.9 |
| Putamen R | 34.7 | 2.9 | 0.0 | 7.3 | 3.2 | 0.0 | – | 0.2 | 0.1 | 3.4 | 0.0 | 2.1 | – | 0.5 | – | – | 54.2 | 47.2 |
| Thalamus L | 27.3 | 1.8 | 0.4 | 10.8 | 17.1 | 0.5 | – | 1.7 | 0.0 | 0.8 | 0.1 | 0.5 | 0.2 | 0.3 | – | – | 61.5 | 57.3 |
| Thalamus R | 34.8 | 0.4 | 0.3 | 12.1 | 17.0 | 0.3 | – | 0.6 | 0.0 | 0.5 | 0.0 | 0.1 | – | 0.3 | 0.0 | 0.2 | 66.6 | 66.3 |
| Ventral DC L | 17.9 | 0.6 | 0.6 | 17.9 | 20.5 | 0.4 | – | 1.3 | 0.0 | 1.3 | 0.1 | 0.0 | – | 0.1 | – | – | 60.8 | 66.9 |
| Ventral DC R | 26.2 | 0.2 | – | 16.7 | 17.9 | 0.3 | – | 0.6 | 0.0 | 0.6 | 0.1 | 0.0 | – | 0.2 | 0.1 | 0.0 | 62.8 | 64.1 |
| Ventricles1 | 40.2 | 3.3 | – | 4.9 | 7.6 | – | – | 0.0 | 0.1 | 0.4 | – | – | 0.0 | 0.3 | 0.0 | 0.1 | 56.9 | 66.9 |
| Lateral L1 | 39.3 | 2.3 | – | 3.6 | 7.4 | – | – | – | 0.1 | 0.5 | – | – | – | 0.3 | 0.0 | 0.0 | 53.4 | 61.7 |
| Lateral R1 | 38.6 | 2.8 | – | 4.0 | 6.9 | – | – | – | 0.1 | 0.3 | – | – | – | 0.2 | 0.0 | 0.1 | 53.0 | 65.2 |
| Inferior lateral L1 | 21.7 | 9.0 | 0.0 | 4.4 | 1.0 | 0.2 | 0.0 | 1.5 | 0.1 | 1.1 | 0.7 | 0.0 | – | 0.3 | 0.1 | 0.2 | 40.4 | 43.4 |
| Inferior lateral R1 | 16.0 | 8.9 | 0.4 | 3.3 | 0.2 | – | – | 1.9 | 0.0 | 1.4 | – | – | 0.2 | 0.5 | 0.0 | 0.2 | 33.0 | 32.6 |
| 3rd1 | 42.6 | 3.5 | 0.0 | 7.5 | 5.3 | 0.1 | – | 0.1 | 0.2 | 0.1 | 0.2 | 0.1 | 0.1 | 0.2 | – | – | 59.9 | 64.1 |
| 4th | 0.2 | 0.7 | – | 6.4 | 5.1 | 0.0 | 0.1 | 0.8 | 0.5 | 0.0 | – | – | – | – | 0.1 | 0.1 | 13.9 | 11.4 |
| Corpus callosum | 17.7 | 5.0 | 0.2 | 2.0 | 6.5 | 0.0 | 0.1 | 2.4 | 0.2 | 0.0 | – | – | 0.1 | – | 0.4 | 0.1 | 34.8 | 32.7 |
| Subcortical GM | 41.0 | 0.1 | 0.0 | 15.5 | 16.8 | 0.2 | – | 0.8 | 0.0 | 0.5 | 0.1 | 0.3 | – | 0.4 | – | – | 75.6 | 72.0 |
1Log10 transformed. MFS: Magnetic field strength, eTIV: Estimated total intracranial volume. GM: gray matter.
Table 2.
Subcortical volumes differences between segmentation on two different computer hardware setups (n=50).
| Regions | Mean difference (%) | t | p |
|---|---|---|---|
| Accumbens L | 0.05 | −0.22 | 0.825 |
| Accumbens R | 1.10 | 0.97 | 0.339 |
| Amygdala L | 0.95 | 1.71 | 0.094 |
| Amygdala R | 0.96 | 1.79 | 0.080 |
| Brainstem | 0.09 | 0.6 | 0.552 |
| Caudate L | 0.02 | −0.05 | 0.961 |
| Caudate R | 0.07 | 0.18 | 0.854 |
| Hippocampus L | 0.41 | 1.48 | 0.144 |
| Hippocampus R | −0.55 | −2.01 | 0.049 |
| Pallidum L | −0.35 | −0.46 | 0.645 |
| Pallidum R | −0.37 | −0.73 | 0.471 |
| Putamen L | 0.52 | 1.3 | 0.200 |
| Putamen R | 0.18 | 0.65 | 0.519 |
| Thalamus L | −0.03 | −0.17 | 0.862 |
| Thalamus R | −0.11 | −0.44 | 0.658 |
| Ventral DC L | −0.07 | −0.19 | 0.851 |
| Ventral DC R | 0.11 | 0.36 | 0.723 |
| Ventricles | |||
| All | 0.00 | −0.18 | 0.858 |
| Lateral L | −0.01 | −0.22 | 0.830 |
| Lateral R | 0.00 | 0.16 | 0.874 |
| Inferior lateral L | −0.20 | 0.65 | 0.521 |
| Inferior lateral R | 0.15 | −0.02 | 0.984 |
| 3rd | −0.06 | −0.74 | 0.461 |
| 4th | −0.09 | −0.09 | 0.928 |
| Corpus callosum | 0.34 | 0.91 | 0.366 |
| Subcortical GM | 0.10 | 0.90 | 0.375 |
Bonferroni-corrected critical value for significance: .002.
2. Experimental design, materials and methods
2.1. Participants and segmentation
A detailed description of the participants and segmentation procedure can be found in Potvin et al. [1].
2.2. Statistical analyses
Regression models predicting subcortical regional volumes were built using age, sex, eTIV, MFS, and scanner manufacturer as predictors. The details about model building can be found in Potvin et al. [1]. Individual predictors׳ weight was measured by squared semi-partial correlations.
The impact of the hardware setup on the volumes generated by FreeSurfer was tested by dependent one-sample t-tests with Bonferroni correction.
Detailed information about the normative statistics included in the Excel spreadsheet can be found in Potvin et al. [1] and in the work of Crawford and colleagues [2], [3].
Acknowledgements
We gratefully acknowledge financial support from Alzheimer׳s Society of Canada (#13–32), Canadian Foundation for Innovation (#30469), Fonds de recherche du Québec – Santé / Pfizer Canada - Pfizer-FRQS Innovation Fund (#25262), and Canadian Institute for Health Research (#117121). S.D. is a Research Scholar from Fonds de recherche du Québec – Santé (#30801).
This study comprises multiple samples of healthy individuals. We wish to thank all principal investigators who collected these datasets and agreed to let them accessible. The list of those acknowledged is available as a supplementary file in the online version of this article and also listed in [1].
Footnotes
Transparency data associated with this article can be found in the online version at doi:10.1016/j.dib.2016.10.001.
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2016.10.001.
Transparency document. Supplementary material
Supplementary material
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Appendix A. Supplementary material
Supplementary material
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Supplementary material
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References
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Supplementary Materials
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