Non-parametric MRI Brain Atlas for the Polish Population

• Published in: Frontiers in neuroinformatics Vol. 15; p. 684759
• Main Authors: Borys, Damian, Kijonka, Marek, Psiuk-Maksymowicz, Krzysztof, Gorczewski, Kamil, Zarudzki, Lukasz, Sokol, Maria, Swierniak, Andrzej
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 06-10-2021; Frontiers Media S.A
• Subjects: Age; Brain mapping; Brain research; Cerebrospinal fluid; Ethnicity; Magnetic resonance imaging; Morphology; MRI; Nervous system; Neuroimaging; Neuroscience; non-parametric atlases; Nonparametric statistics; Population; Population studies; Segmentation; Spatial discrimination; Standard deviation; Statistical analysis; statistical parametric mapping; sub-population brain template; Substantia alba; Substantia grisea; tissue probability maps; Poland
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2021.684759

Introduction:
The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology
in vivo
is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence.
Material and Methods:
The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed.
Results:
The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller.
Discussion and Conclusion:
To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.