"Pscore" : A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics
Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine..
BACKGROUND: Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed.
PURPOSE: To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue.
STUDY TYPE: Retrospective cohort.
POPULATION: Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project.
FIELD STRENGTH/SEQUENCE: 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE.
ASSESSMENT: The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values.
STATISTICAL TESTS: ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95 (%), PEV<5 (%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations.
RESULTS: The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (average PEV > 95 ¯ = PEV < 5 ¯ = 5 ± 0 % $$ \overline{{\mathrm{PEV}}_{>95}}=\overline{{\mathrm{PEV}}_{<5}}=5\pm 0\% $$ [SD]).
DATA CONCLUSION: The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.
LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Journal of magnetic resonance imaging : JMRI - (2024) vom: 30. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hafiz, Rakibul [VerfasserIn] |
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| Links: |
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| Themen: |
Diffusion MRI |
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| Anmerkungen: |
Date Revised 26.02.2024 published: Print-Electronic UpdateOf: bioRxiv. 2024 Jan 09;:. - PMID 38105995 Citation Status Publisher |
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| doi: |
10.1002/jmri.29248 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM36782163X |
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| 245 | 1 | 0 | |a "Pscore" |b A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics |
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| 500 | |a Date Revised 26.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a UpdateOf: bioRxiv. 2024 Jan 09;:. - PMID 38105995 | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. | ||
| 520 | |a BACKGROUND: Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed | ||
| 520 | |a PURPOSE: To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue | ||
| 520 | |a STUDY TYPE: Retrospective cohort | ||
| 520 | |a POPULATION: Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project | ||
| 520 | |a FIELD STRENGTH/SEQUENCE: 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE | ||
| 520 | |a ASSESSMENT: The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values | ||
| 520 | |a STATISTICAL TESTS: ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95 (%), PEV<5 (%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations | ||
| 520 | |a RESULTS: The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (average PEV > 95 ¯ = PEV < 5 ¯ = 5 ± 0 % $$ \overline{{\mathrm{PEV}}_{>95}}=\overline{{\mathrm{PEV}}_{<5}}=5\pm 0\% $$ [SD]) | ||
| 520 | |a DATA CONCLUSION: The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets | ||
| 520 | |a LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1 | ||
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| 650 | 4 | |a diffusion MRI | |
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| 650 | 4 | |a individual deviations | |
| 650 | 4 | |a normative distribution | |
| 650 | 4 | |a skewness | |
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| 700 | 1 | |a Nayak, Amritha |e verfasserin |4 aut | |
| 700 | 1 | |a Pierpaoli, Carlo |e verfasserin |4 aut | |
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