Abstract
Evidence from magnetic resonance imaging (MRI) studies shows that healthy aging is associated with profound changes in cortical and subcortical brain structures. The reliable delineation of cortex and basal ganglia using automated computational anatomy methods based on T1-weighted images remains challenging, which results in controversies in the literature. In this study we use quantitative MRI (qMRI) to gain an insight into the microstructural mechanisms underlying tissue ageing and look for potential interactions between ageing and brain tissue properties to assess their impact on automated tissue classification. To this end we acquired maps of longitudinal relaxation rate R1, effective transverse relaxation rate R2* and magnetization transfer – MT, from healthy subjects (n=96, aged 21–88years) using a well-established multi-parameter mapping qMRI protocol. Within the framework of voxel-based quantification we find higher grey matter volume in basal ganglia, cerebellar dentate and prefrontal cortex when tissue classification is based on MT maps compared with T1 maps. These discrepancies between grey matter volume estimates can be attributed to R2* - a surrogate marker of iron concentration, and further modulation by an interaction between R2* and age, both in cortical and subcortical areas. We interpret our findings as direct evidence for the impact of ageing-related brain tissue property changes on automated tissue classification of brain structures using SPM12. Computational anatomy studies of ageing and neurodegeneration should acknowledge these effects, particularly when inferring about underlying pathophysiology from regional cortex and basal ganglia volume changes.
Highlights
The detailed in vivo assessment of brain anatomy and the challenging delineation of subcortical structures are crucial for understanding the biological processes of brain development and healthy ageing
The multi-parameter (MPM) quantitative MRI (qMRI) protocol used in our study provides estimates of the magnetic resonance imaging (MRI) parameters MT, R1 (=1/T1), R2* (=1/ T2*) and proton density (PD) (Weiskopf et al, 2013)
There was a significantly higher grey matter (GM) volume derived from MT maps in pallidum, putamen, lateral geniculate body of thalamus, substantia nigra, cerebellar dentate, cingulate and prefrontal cortex compared to estimates derived from R1 maps, as reported in Table 1
Summary
The detailed in vivo assessment of brain anatomy and the challenging delineation of subcortical structures are crucial for understanding the biological processes of brain development and healthy ageing. In this context, the accuracy of automated brain morphometry methods is important for the comprehension of the pathophysiology of neurological and neuropsychiatric disorders with basal ganglia involvement, such as Parkinson's syndrome, Huntington's disease, dystonia, various tremor forms, Tourette's syndrome and schizophrenia (Utter and Basso, 2008). The majority of these studies assessed basal ganglia anatomy using T1-weighted (T1w) imaging data, which turned out to be methodologically challenging (Wonderlick et al, 2009; Babalola et al, 2009). The difficulties in assuring accurate and robust computer-based delineation of the basal ganglia are mainly due to the presence of a high amount of iron (Hallgren and Sourander, 1958), which results in poor and variable contrast on T1w magnetic resonance images (Patenaude et al, 2011; Callaghan et al, 2014)
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