Unveiling Normative Trajectories of Lifespan Brain Maturation Using Quantitative MRI
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908851345817600 |
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| author | Chen, Xinjie Ocampo-Pineda, Mario Lu, Po-Jui Ekerdt, Clara Weigel, Matthias Jansen, Michelle G. Cagol, Alessandro Chan, Kwok-Shing Schädelin, Sabine Zwiers, Marcel Oosterman, Joukje M. Norris, David G. Bayer, Johanna M. M. Marquand, Andre F. Menks, Willeke M. Kuhle, Jens Kappos, Ludwig Melie-Garcia, Lester Granziera, Cristina Marques, José P. |
| author_facet | Chen, Xinjie Ocampo-Pineda, Mario Lu, Po-Jui Ekerdt, Clara Weigel, Matthias Jansen, Michelle G. Cagol, Alessandro Chan, Kwok-Shing Schädelin, Sabine Zwiers, Marcel Oosterman, Joukje M. Norris, David G. Bayer, Johanna M. M. Marquand, Andre F. Menks, Willeke M. Kuhle, Jens Kappos, Ludwig Melie-Garcia, Lester Granziera, Cristina Marques, José P. |
| contents | Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal aging from pathological deviations.
Methods: We conducted a multicentre study using qMRI metrics (R1, R2*, and Quantitative Susceptibility Mapping) to model age trajectories across brain structures, including tractography-based white matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). MRI data from 537 healthy subjects, aged 8 to 79 years, were harmonized using two independent methods. We modeled age trajectories and performed regional analyses to capture maturation patterns and aging effects across the lifespan.
Findings: Our findings revealed a distinct brain maturation gradient, with early qMRI peak values in TWMB, followed by SWM, and culminating in CGM regions. This gradient was observed as a posterior-to-anterior maturation pattern in the cortex and an inferior-to-superior maturation pattern in white matter tracts. R1 demonstrated the most robust age trajectories, while R2* and susceptibility exhibited greater variability and different patterns. The normative modeling framework confirmed the reliability of our age-modelled trajectories across datasets.
Interpretation: Our study highlights the potential of multiparametric qMRI to capture complex, region-specific brain development patterns, addressing the need for comprehensive, age-spanning studies across multiple brain structures. Various harmonization strategies can merge qMRI cohorts, improving the robustness of qMRI-based age models and facilitating the understanding of normal patterns and disease-associated deviations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00661 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unveiling Normative Trajectories of Lifespan Brain Maturation Using Quantitative MRI Chen, Xinjie Ocampo-Pineda, Mario Lu, Po-Jui Ekerdt, Clara Weigel, Matthias Jansen, Michelle G. Cagol, Alessandro Chan, Kwok-Shing Schädelin, Sabine Zwiers, Marcel Oosterman, Joukje M. Norris, David G. Bayer, Johanna M. M. Marquand, Andre F. Menks, Willeke M. Kuhle, Jens Kappos, Ludwig Melie-Garcia, Lester Granziera, Cristina Marques, José P. Medical Physics Neurons and Cognition Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal aging from pathological deviations. Methods: We conducted a multicentre study using qMRI metrics (R1, R2*, and Quantitative Susceptibility Mapping) to model age trajectories across brain structures, including tractography-based white matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). MRI data from 537 healthy subjects, aged 8 to 79 years, were harmonized using two independent methods. We modeled age trajectories and performed regional analyses to capture maturation patterns and aging effects across the lifespan. Findings: Our findings revealed a distinct brain maturation gradient, with early qMRI peak values in TWMB, followed by SWM, and culminating in CGM regions. This gradient was observed as a posterior-to-anterior maturation pattern in the cortex and an inferior-to-superior maturation pattern in white matter tracts. R1 demonstrated the most robust age trajectories, while R2* and susceptibility exhibited greater variability and different patterns. The normative modeling framework confirmed the reliability of our age-modelled trajectories across datasets. Interpretation: Our study highlights the potential of multiparametric qMRI to capture complex, region-specific brain development patterns, addressing the need for comprehensive, age-spanning studies across multiple brain structures. Various harmonization strategies can merge qMRI cohorts, improving the robustness of qMRI-based age models and facilitating the understanding of normal patterns and disease-associated deviations. |
| title | Unveiling Normative Trajectories of Lifespan Brain Maturation Using Quantitative MRI |
| topic | Medical Physics Neurons and Cognition |
| url | https://arxiv.org/abs/2411.00661 |