'Pscore' - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics
BACKGROUND: Quantitative 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: 961 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<5(%),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 (e.g., mean and radial 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%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.
| Errataetall: |
UpdateIn: J Magn Reson Imaging. 2024 Jan 30;:. - PMID 38291798 |
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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: |
bioRxiv : the preprint server for biology - (2024) vom: 09. 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: Electronic UpdateIn: J Magn Reson Imaging. 2024 Jan 30;:. - PMID 38291798 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2023.12.10.571016 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365966940 |
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| 100 | 1 | |a Hafiz, Rakibul |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a 'Pscore' - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics |
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| 500 | |a UpdateIn: J Magn Reson Imaging. 2024 Jan 30;:. - PMID 38291798 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a BACKGROUND: Quantitative 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: 961 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<5(%),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 (e.g., mean and radial 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%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 | ||
| 650 | 4 | |a Preprint | |
| 650 | 4 | |a Diffusion MRI | |
| 650 | 4 | |a Extreme Values | |
| 650 | 4 | |a Individual Deviations | |
| 650 | 4 | |a Normative Distribution | |
| 650 | 4 | |a Skewness | |
| 650 | 4 | |a Zscores | |
| 700 | 1 | |a Irfanoglu, M Okan |e verfasserin |4 aut | |
| 700 | 1 | |a Nayak, Amritha |e verfasserin |4 aut | |
| 700 | 1 | |a Pierpaoli, Carlo |e verfasserin |4 aut | |
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