Age-related differences in myeloarchitecture measured at 7 T

Decoding Brain Development and Aging: Pioneering Insights From MRI Techniques

ABSTRACT

The aging process induces a variety of changes in the brain detectable by magnetic resonance imaging (MRI). These changes include alterations in brain volume, fluid-attenuated inversion recovery (FLAIR) white matter hyperintense lesions, and variations in tissue properties such as relaxivity, myelin, iron content, neurite density, and other microstructures. Each MRI technique offers unique insights into the structural and compositional changes occurring in the brain due to normal aging or neurodegenerative diseases. Age-related brain volume changes encompass a decrease in gray matter and an increase in ventricular volume, associated with cognitive decline. White matter hyperintensities, detected by FLAIR, are common and linked to cognitive impairments and increased risk of stroke and dementia. Tissue relaxometry reveals age-related changes in relaxivity, aiding the distinction between normal aging and pathological conditions. Myelin content, measurable by MRI, changes with age and is associated with cognitive and motor function alterations. Iron accumulation, detected by susceptibility-sensitive MRI, increases in certain brain regions with age, potentially contributing to neurodegenerative processes. Diffusion MRI provides detailed insights into microstructural changes such as neurite density and orientation. Neurofluid imaging, using techniques like gadolinium-based contrast agents and diffusion MRI, reveals age-related changes in cerebrospinal and interstitial fluid dynamics, crucial for brain health and waste clearance. This review offers a comprehensive overview of age-related brain changes revealed by various MRI techniques. Understanding these changes helps differentiate between normal aging and pathological conditions, aiding the development of interventions to mitigate age-related cognitive decline and other symptoms. Recent advances in machine learning and artificial intelligence have enabled novel methods for estimating brain age, offering also potential biomarkers for neurological and psychiatric disorders.

Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease

While iron over-accumulation has been reported in late stage Alzheimer's disease (AD), whether this occurs early in the asymptomatic stage of AD remains unknown. We aimed to assess brain iron levels in asymptomatic AD using quantitative MR relaxometry of effective transverse relaxation rate (R2*) and longitudinal relaxation rate (R1), and recruited 118 participants comprised of three groups including healthy young participants, and cognitively normal older individuals without or with positive AD biomarkers based on cerebrospinal fluid (CSF) proteomics analysis. Compared with the healthy young group, increased R2* was found in widespread cortical and subcortical regions in the older groups. Further, significantly higher levels of R2* were found in the cognitively normal older subjects with positive CSF AD biomarker (i.e., asymptomatic AD) compared with those with negative AD biomarker in subcortical regions including the left and right caudate, left and right putamen, and left and right globus pallidus (p < .05 for all regions), suggesting increased iron content in these regions. Subcortical R2* of some regions was found to significantly correlate with CSF AD biomarkers and neuropsychological assessments of visuospatial functions. In conclusion, R2* could be a valuable biomarker for studying early pathophysiological changes in AD.

An atlas for human brain myelin content throughout the adult life span

Myelin water imaging is a quantitative neuroimaging technique that provides the myelin water fraction (MWF), a metric highly specific to myelin content, and the intra-/extra-cellular T₂ (IET2), which is related to water and iron content. We coupled high-resolution data from 100 adults with gold-standard methodology to create an optimized anatomical brain template and accompanying MWF and IET2 atlases. We then used the MWF atlas to characterize how myelin content relates to demographic factors. In most brain regions, myelin content followed a quadratic pattern of increase during the third decade of life, plateau at a maximum around the fifth decade, then decrease during later decades. The ranking of mean myelin content between brain regions remained consistent across age groups. These openly available normative atlases can facilitate evaluation of myelin imaging results on an individual basis and elucidate the distribution of myelin content between brain regions and in the context of aging.

Beyond volumetry: Considering age-related changes in brain shape complexity using fractal dimensionality

Gray matter volume for cortical, subcortical, and ventricles all vary with age. However, these volumetric changes do not happen on their own, there are also age-related changes in cortical folding and other measures of brain shape. Fractal dimensionality has emerged as a more sensitive measure of brain structure, capturing both volumetric and shape-related differences. For subcortical structures it is readily apparent that segmented structures do not differ in volume in isolation-adjacent regions must also vary in shape. Fractal dimensionality here also appears to be more sensitive to these age-related differences than volume. Given these differences in structure are quite prominent in structure, caution should be used when examining comparisons across age in brain function measures, as standard normalisation methods are not robust enough to adjust for these inter-individual differences in cortical structure.

Age-related decrements in cortical gyrification: Evidence from an accelerated longitudinal dataset

Cortical gyrification has been found to decrease due to aging, but thus far this has only been examined in cross-sectional samples. Interestingly, the topography of these age-related differences in gyrification follows a distinct gradient along the cortex relative to age effects on cortical thickness, likely suggesting a different underlying neurobiological mechanism. Here I examined several aspects of gyrification in an accelerated longitudinal dataset of 280 healthy adults aged 45-92 with an interval between first and last MRI sessions of up to 10 years (total of 815 MRI sessions). Results suggest that age changes in sulcal morphology underlie these changes in gyrification.