Iron Content in Deep Gray Matter as a Function of Age Using Quantitative Susceptibility Mapping: A Multicenter Study

Abstract

PurposeTo evaluate the effect of resolution on iron content using quantitative susceptibility mapping (QSM); to verify the consistency of QSM across field strengths and manufacturers in evaluating the iron content of deep gray matter (DGM) of the human brain using subjects from multiple sites; and to establish a susceptibility baseline as a function of age for each DGM structure using both a global and regional iron analysis.MethodsData from 623 healthy adults, ranging from 20 to 90 years old, were collected across 3 sites using gradient echo imaging on one 1.5 Tesla and two 3.0 Tesla MR scanners. Eight subcortical gray matter nuclei were semi-automatically segmented using a full-width half maximum threshold-based analysis of the QSM data. Mean susceptibility, volume and total iron content with age correlations were evaluated for each measured structure for both the whole-region and RII (high iron content regions) analysis. For the purpose of studying the effect of resolution on QSM, a digitized model of the brain was applied.ResultsThe mean susceptibilities of the caudate nucleus (CN), globus pallidus (GP) and putamen (PUT) were not significantly affected by changing the slice thickness from 0.5 to 3 mm. But for small structures, the susceptibility was reduced by 10% for 2 mm thick slices. For global analysis, the mean susceptibility correlated positively with age for the CN, PUT, red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN). There was a negative correlation with age in the thalamus (THA). The volumes of most nuclei were negatively correlated with age. Apart from the GP, THA, and pulvinar thalamus (PT), all the other structures showed an increasing total iron content despite the reductions in volume with age. For the RII regional high iron content analysis, mean susceptibility in most of the structures was moderately to strongly correlated with age. Similar to the global analysis, apart from the GP, THA, and PT, all structures showed an increasing total iron content.ConclusionA reasonable estimate for age-related iron behavior can be obtained from a large cross site, cross manufacturer set of data when high enough resolutions are used. These estimates can be used for correcting for age related iron changes when studying diseases like Parkinson’s disease, Alzheimer’s disease, and other iron related neurodegenerative diseases.

R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years

“…Previous studies including subjects across the lifespan also found steep changes with age in the globus pallidus during childhood that subsequently leveled off in adulthood, and shallower but more protracted curves for the caudate and putamen (Aquino et al, 2009; Li et al, 2014). Likewise, the globus pallidus has been shown to have the steepest slope of change with age relative to other structures in early development (Ning et al, 2019), and minimal (Persson et al, 2015) or no significant change with age during adulthood (Acosta‐Cabronero et al, 2016; Li et al, 2020). Although the majority of change observed in the globus pallidus in this study occurred in childhood, we also observe a late life increase of R2* and QSM that is not consistent with other studies.…”

Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging

“…In SWI, iron depositions exhibit negative regional phase, resulting in decreased image intensity in the vicinity of iron ( Wang et al, 2012 ; Pfefferbaum et al, 2009 ; Xu et al, 2008 ; Haacke et al, 2004 ; Haacke et al, 2007 ). SWI, and its advanced version, namely, quantitative susceptibility mapping (QSM), have been shown to be sensitive techniques for investigating age-dependent iron deposition in normal aging, neurodegeneration, and dementia ( Wang et al, 2012 ; Pfefferbaum et al, 2009 ; Xu et al, 2008 ; C Liu et al, 2015; Sehgal et al, 2005 ; EM Haacke et al, 2009; Harder et al, 2008 ; van der Weijden et al, 2019 ; Acosta-Cabronero et al, 2016 ; Betts et al, 2016 ; Keuken et al, 2017 ; Zhang et al, 2018 ; Bilgic et al, 2012 ; Lin et al, 2015 ; Killiany et al, 2020 ; Li et al, 2021 ; Darki et al, 2016 ; Chen et al, 2021 ). Furthermore, MRI mapping of myelin water fraction (MWF), a proxy of myelin content, provides important insights for understanding brain maturation and neurodegeneration ( Bouhrara and Spencer, 2016 ; MacKay and Laule, 2016 ; Whittall et al, 1997 ; Does, 2018 ; Alonso-Ortiz et al, 2015 ; Piredda et al, 2021 ; MacKay et al, 1994 ; Vavasour et al, 2006 ).…”

Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging

“…However, unlike structural, diffusion, and fMRI data that have been collected in existing large-scale imaging studies consisting of thousands of subjects (for example, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium 79 ), QSM has only been used in smaller-scale studies with clinically-defined cohorts. This new UK Biobank QSM resource is by approximately 2 orders of magnitude the largest QSM dataset that has ever been collected 9 , which is particularly powerful as a result of the combination of the number of subjects and the breadth of linked data including genetics, extensive phenotyping and health outcomes. At the time of scanning, most UK Biobank subjects are largely healthy, with the cohort age range chosen such that health outcomes will begin to accumulate in the coming decades.…”

“…Unlike the other brain imaging modalities in UK Biobank, which have been (or are being) collected or collated previously in thousands of subjects [73][74][75][76] , QSM has previously been limited to smaller-scale studies. This UK Biobank QSM resource is approximately 2 orders of magnitude greater than the largest existing QSM dataset 77 . The number of subjects, coupled with the breadth of linked data, including genetics, extensive phenotyping and health outcomes, is expected to open up new avenues of investigation for QSM.…”

“…The impact of QSM to date has partially limited by the size and scope of available datasets, typically consisting of tens or hundreds of subjects 9 . The dominant strategy in these studies has been to use clinically-defined cohorts to maximize statistical power.…”