Utility of quantitative MRI metrics in brain ageing research

. 2023 ; 15 () : 1099499. [epub] 20230309

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid36967815

Grantová podpora
P41 EB027061 NIBIB NIH HHS - United States
R01 AG055591 NIA NIH HHS - United States

The advent of new, advanced quantitative MRI metrics allows for in vivo evaluation of multiple biological processes highly relevant for ageing. The presented study combines several MRI parameters hypothesised to detect distinct biological characteristics as myelin density, cellularity, cellular membrane integrity and iron concentration. 116 healthy volunteers, continuously distributed over the whole adult age span, underwent a multi-modal MRI protocol acquisition. Scatterplots of individual MRI metrics revealed that certain MRI protocols offer much higher sensitivity to early adulthood changes while plateauing in higher age (e.g., global functional connectivity in cerebral cortex or orientation dispersion index in white matter), while other MRI metrics provided reverse ability-stable levels in young adulthood with sharp changes with rising age (e.g., T1ρ and T2ρ). Nonetheless, despite the previously published validations of specificity towards microstructural biology based on cytoarchitectonic maps in healthy population or alterations in certain pathologies, several metrics previously hypothesised to be selective to common measures failed to show similar scatterplot distributions, pointing to further confounding factors directly related to age. Furthermore, other metrics, previously shown to detect different biological characteristics, exhibited substantial intercorrelations, be it due to the nature of the MRI protocol itself or co-dependence of relevant biological microstructural processes. All in all, the presented study provides a unique basis for the design and choice of relevant MRI parameters depending on the age group of interest. Furthermore, it calls for caution in simplistic biological inferences in ageing based on one simple MRI metric, even though previously validated under other conditions. Complex multi-modal approaches combining several metrics to extract the shared subcomponent will be necessary to achieve the desired goal of histological MRI.

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Bartzokis G., Tishler T. A., Shin I. S., Lu P. H., Cummings J. L. (2004). Brain ferritin iron as a risk factor for age at onset in neurodegenerative diseases. Ann. N. Y. Acad. Sci. 1012, 224–236. doi: 10.1196/annals.1306.019, PMID: PubMed DOI

Benjamini Y., Hochberg Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B (Methodological). 57, 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x DOI

Bilgic B., Pfefferbaum A., Rohlfing T., Sullivan E. V., Adalsteinsson E. (2012). MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. NeuroImage 59, 2625–2635. doi: 10.1016/j.neuroimage.2011.08.077, PMID: PubMed DOI PMC

Castiglioni I., Gallivanone F., Soda P., Avanzo M., Stancanello J., Aiello M., et al. . (2019). AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur. J. Nucl. Med. Mol. Imaging 46, 2673–2699. doi: 10.1007/s00259-019-04414-4, PMID: PubMed DOI

Cox R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173. doi: 10.1006/cbmr.1996.0014, PMID: PubMed DOI

Craddock R. C., Clark D. J. (2016). Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI. Gigascience 5:s13742-016. doi: 10.1186/s13742-016-0147-0-d DOI

Does M. D. (2018). Inferring brain tissue composition and microstructure via MR relaxometry. NeuroImage 182, 136–148. doi: 10.1016/j.neuroimage.2017.12.087, PMID: PubMed DOI PMC

Drew P. J. (2019). Vascular and neural basis of the BOLD signal. Curr. Opin. Neurobiol. 58, 61–69. doi: 10.1016/j.conb.2019.06.004, PMID: PubMed DOI PMC

Dyrby T. B., Innocenti G. M., Bech M., Lundell H. (2018). Validation strategies for the interpretation of microstructure imaging using diffusion MRI. NeuroImage 182, 62–79. doi: 10.1016/j.neuroimage.2018.06.049, PMID: PubMed DOI

Ekstrom A. (2010). How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res. Rev. 62, 233–244. doi: 10.1016/j.brainresrev.2009.12.004, PMID: PubMed DOI PMC

Filip P., Bareš M. (2021). “Cerebellum—aging of the neuronal machine” in Factors Affecting Neurological Aging. eds. Martin C. R., Preedy V. R., Rajendram R. (Netherlands: Elsevier; ), 281–288.

Filip P., Dufek M., Mangia S., Michaeli S., Bareš M., Schwarz D., et al. . (2021). Alterations in sensorimotor and mesiotemporal cortices and diffuse white matter changes in primary progressive multiple sclerosis detected by adiabatic relaxometry. Front. Neurosci. 15:711067. doi: 10.3389/fnins.2021.711067 PubMed DOI PMC

Filip P., Gallea C., Lehéricy S., Lungu O., Bareš M. (2019). Neural scaffolding as the Foundation for Stable Performance of aging cerebellum. Cerebellum [internet]. Cerebellum 18, 500–510. doi: 10.1007/s12311-019-01015-7 PubMed DOI

Filip P., Svatkova A., Carpenter A. F., Eberly L. E., Nestrasil I., Nissi M. J., et al. . (2020a). Rotating frame MRI relaxations as markers of diffuse white matter abnormalities in multiple sclerosis. NeuroImage Clin. 1:102234. doi: 10.1016/j.nicl.2020.102234 PubMed DOI PMC

Filip P., Vojtíšek L., Baláž M., Mangia S., Michaeli S., Šumec R., et al. . (2020b). Differential diagnosis of tremor syndromes using MRI relaxometry. Parkinsonism Relat. Disord. 81, 190–193. doi: 10.1016/j.parkreldis.2020.10.048, PMID: PubMed DOI

Glasser M. F., Sotiropoulos S. N., Wilson J. A., Coalson T. S., Fischl B., Andersson J. L., et al. . (2013). The minimal preprocessing pipelines for the human Connectome project. NeuroImage 80, 105–124. doi: 10.1016/j.neuroimage.2013.04.127, PMID: PubMed DOI PMC

Glasser M. F., Van Essen D. C. (2011). Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI. J. Neurosci. 31, 11597–11616. doi: 10.1523/JNEUROSCI.2180-11.2011, PMID: PubMed DOI PMC

Grydeland H., Vértes P. E., Váša F., Romero-Garcia R., Whitaker K., Alexander-Bloch A. F., et al. . (2019). Waves of maturation and senescence in micro-structural MRI markers of human cortical myelination over the lifespan. Cereb. Cortex 29, 1369–1381. doi: 10.1093/cercor/bhy330, PMID: PubMed DOI PMC

Hakkarainen H., Sierra A., Mangia S., Garwood M., Michaeli S., Gröhn O., et al. . (2016). MRI relaxation in the presence of fictitious fields correlates with myelin content in normal rat brain. Magn. Reson. Med. 75, 161–168. doi: 10.1002/mrm.25590, PMID: PubMed DOI PMC

Kessler L. G., Barnhart H. X., Buckler A. J., Choudhury K. R., Kondratovich M. V., Toledano A., et al. . (2015). The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat. Methods Med. Res. 24, 9–26. doi: 10.1177/0962280214537333, PMID: PubMed DOI

Liimatainen T., Sorce D. J., O’Connell R., Garwood M., Michaeli S. (2010). MRI contrast from relaxation along a fictitious field (RAFF). Magn. Reson. Med. 64, 983–994. doi: 10.1002/mrm.22372, PMID: PubMed DOI PMC

Michaeli S., Burns T. C., Kudishevich E., Harel N., Hanson T., Sorce D. J., et al. . (2009). Detection of neuronal loss using T1ρ MRI assessment of 1H2O spin dynamics in the aphakia mouse. J. Neurosci. Methods 177, 160–167. doi: 10.1016/j.jneumeth.2008.10.025, PMID: PubMed DOI PMC

Michaeli S., Oez G., Sorce D. J., Garwood M., Ugurbil K., Majestic S., et al. . (2007). Assessment of brain iron and neuronal integrity in patients with Parkinson’s disease using novel MRI contrasts. Mov. Disord. 22, 334–340. doi: 10.1002/mds.21227, PMID: PubMed DOI

Mitsumori F., Watanabe H., Takaya N. (2009). Estimation of brain iron concentration in vivo using a linear relationship between regional iron and apparent transverse relaxation rate of the tissue water at 4.7 T. Magn. Reson. Med. 62, 1326–1330. doi: 10.1002/mrm.22097, PMID: PubMed DOI

Möller H. E., Bossoni L., Connor J. R., Crichton R. R., Does M. D., Ward R. J., et al. . (2019). Iron, myelin, and the brain: neuroimaging meets neurobiology. Trends Neurosci. 42, 384–401. doi: 10.1016/j.tins.2019.03.009, PMID: PubMed DOI

Palombo M., Ianus A., Guerreri M., Nunes D., Alexander D. C., Shemesh N., et al. . (2020). SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. NeuroImage 215:116835. doi: 10.1016/j.neuroimage.2020.116835, PMID: PubMed DOI PMC

Peters A. (2002). The effects of normal aging on myelin and nerve fibers: a review. J. Neurocytol. 31, 581–593. doi: 10.1023/A:1025731309829 PubMed DOI

Peters A., Morrison J. H., Rosene D. L., Hyman B. T. (1998). Are neurons lost from the primate cerebral cortex during normal aging? Cerebral Cortex (New York, NY: 1991) 8, 295–300. doi: 10.1093/cercor/8.4.295 PubMed DOI

Peters A., Sethares C. (2003). Is there remyelination during aging of the primate central nervous system? J. Comp. Neurol. 460, 238–254. doi: 10.1002/cne.10639 PubMed DOI

Reisberg D. The Oxford Handbook of Cognitive Psychology. Oxford University Press; (2013), England

Salimi-Khorshidi G., Douaud G., Beckmann C. F., Glasser M. F., Griffanti L., Smith S. M. (2014). Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468. doi: 10.1016/j.neuroimage.2013.11.046, PMID: PubMed DOI PMC

Satzer D., DiBartolomeo C., Ritchie M. M., Storino C., Liimatainen T., Hakkarainen H., et al. . (2015). Assessment of dysmyelination with RAFFn MRI: application to murine MPS I. PLoS One 10:e0116788. doi: 10.1371/journal.pone.0116788, PMID: PubMed DOI PMC

Schmierer K., Scaravilli F., Altmann D. R., Barker G. J., Miller D. H. (2004). Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407–415. doi: 10.1002/ana.20202, PMID: PubMed DOI

Smith S. M., Beckmann C. F., Andersson J., Auerbach E. J., Bijsterbosch J., Douaud G., et al. . (2013). Resting-state fMRI in the human connectome project. NeuroImage 80, 144–168. doi: 10.1016/j.neuroimage.2013.05.039, PMID: PubMed DOI PMC

Stüber C., Morawski M., Schäfer A., Labadie C., Wähnert M., Leuze C., et al. . (2014). Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. NeuroImage 93, 95–106. doi: 10.1016/j.neuroimage.2014.02.026, PMID: PubMed DOI

Tariq M., Schneider T., Alexander D. C., Gandini Wheeler-Kingshott C. A., Zhang H. (2016). Bingham–NODDI: mapping anisotropic orientation dispersion of neurites using diffusion MRI. NeuroImage 133, 207–223. doi: 10.1016/j.neuroimage.2016.01.046 PubMed DOI

Uddin M. N., Figley T. D., Marrie R. A., Figley C. R. (2018). Can T1w/T2w ratio be used as a myelin-specific measure in subcortical structures? Comparisons between FSE-based T1w/T2w ratios, GRASE-based T1w/T2w ratios and multi-echo GRASE-based myelin water fractions. NMR Biomed. 31:e3868. doi: 10.1002/nbm.3868, PMID: PubMed DOI

Weiskopf N., Edwards L. J., Helms G., Mohammadi S., Kirilina E. (2021). Quantitative magnetic resonance imaging of brain anatomy and in vivo histology. Nat. Rev. Phys. 3, 570–588. doi: 10.1038/s42254-021-00326-1 DOI

Yankner B. A., Lu T., Loerch P. (2008). The aging brain. Annu. Rev. Pathol. 3, 41–66. doi: 10.1146/annurev.pathmechdis.2.010506.092044 PubMed DOI

Zaidi H. Quantitative Analysis in Nuclear Medicine Imaging. Springer; (2006), Berlin

Zang Y. F., He Y., Zhu C. Z., Cao Q. J., Sui M. Q., Liang M., et al. . (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development 29, 83–91. doi: 10.1016/j.braindev.2006.07.002, PMID: PubMed DOI

Zang Y., Jiang T., Lu Y., He Y., Tian L. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage 22, 394–400. doi: 10.1016/j.neuroimage.2003.12.030 PubMed DOI

Zhang Y. D., Dong Z., Wang S. H., Yu X., Yao X., Zhou Q., et al. . (2020). Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation. Inf. Fusion. 64, 149–187. doi: 10.1016/j.inffus.2020.07.006 PubMed DOI PMC

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