Age-Specific Adult Rat Brain MRI Templates and Tissue Probability Maps

Abstract

Age-specific resources in human MRI mitigate processing biases that arise from structural changes across the lifespan. There are fewer age-specific resources for preclinical imaging, and they only represent developmental periods rather than adulthood. Since rats recapitulate many facets of human aging, it was hypothesized that brain volume and each tissue's relative contribution to total brain volume would change with age in the adult rat. Data from a longitudinal study of rats at 3, 5, 11, and 17 months old were used to test this hypothesis. Tissue volume was estimated from high resolution structural images using a priori information from tissue probability maps. However, existing tissue probability maps generated inaccurate gray matter probabilities in subcortical structures, particularly the thalamus. To address this issue, gray matter, white matter, and CSF tissue probability maps were generated by combining anatomical and signal intensity information. The effects of age on volumetric estimations were then assessed with mixed-effects models. Results showed that herein estimation of gray matter volumes better matched histological evidence, as compared to existing resources. All tissue volumes increased with age, and the tissue proportions relative to total brain volume varied across adulthood. Consequently, a set of rat brain templates and tissue probability maps from across the adult lifespan is released to expand the preclinical MRI community's fundamental resources.

Imaging Synaptic Density: The Next Holy Grail of Neuroscience?

“…For example, (Serrano et al, 2020) explored changes in synaptic density at different time-points of the rat lifespan (late puberty to adulthood) evaluating in parallel how epileptogenesis affects the brain. This study showed an increase in synaptic density throughout the lifespan of healthy animals, in line with the recently reported increases in gray and white matter volumes (MacNicol et al, 2022). These results support the idea of brain plasticity in which synapses are continuously being formed and strengthened, highlighting the potential of quantifying SV2A in vivo to detect aberrancies in brain development.…”

EiDA: A lossless approach for the dynamic analysis of connectivity patterns in signals; application to resting state fMRI of a model of ageing

“…We refer to a “group” as a set of recordings. In our case, as detailed in 2.9, the recordings are resting-state fMRI signals obtained during a two year study of brain ageing in rodents where the time-series in each acquisition were obtained from the parcellation of N = 44 anatomical brain regions ((32; 33)). The acquisitions were repeated for each animal four times during the life-span of the study.…”

“…We applied the methods described above to a longitudinal dataset of 48 ageing rats ((33; 32), MacNicol et al, in preparation). A cohort of 48 Sprague Dawley rats, Charles River, UK, were monitored across their full 2 years lifespan and scanned in up to 4 scanning sessions with a 9.4 T Bruker Biospec MR scanner, specifically at the ages of 3, 5, 11 and 17 months.…”

“…As an exemplar application of EiDA, we consider a longitudinal fMRI data-set acquired across the life-span of a cohort of rodents (four data-sets) (32; 33). This is a controlled experimental setting that challenges the methodology to recover the trajectories of expected loss of dynamical connectivity associated with ageing (31; 34; 35; 36; 37; 38; 39; 40; 41).…”

Automatic brain extraction and brain tissues segmentation on multi-contrast animal MRI

“…Nowadays, animal digital atlases are being created, that can be integrated in image processing pipelines. They are available for the dog [15][16][17][18][19][20] , the cat 21,22 , the domestic pig 23 , the sheep [24][25][26] , the horse 27 , the baboon 28,29 , the macaque 30,31 , the marmoset 32,33 and the rat brain 34 .…”