Tag Archives: neuroanatomy

Mouse Lemur Brain

The gray mouse lemur (Microcebus murinus) is a small Madagascan primate, averaging 12 cm length and weighing between 60-120 grams. Despite the diminutive size, mouse lemurs are increasingly used in medical studies of Alzheimer’s disease and similar neurological disease processes found in humans. Mouse lemurs often live to 12 years or more in the wild, which combined with torpor (a form of short-term hibernation) may be associated with the longer lifespans. Considering mouse lemurs have prolonged lifespans, it is probably not surprising that they also experience age-related brain atrophy. Nadkarni et al. (2019) address the absence of a dedicated mouse lemur brain atlas through in-vivo MRI scanning 34 mouse lemurs, investigating age-related brain atrophy and the neuroanatomy of Microcebus murinus in a comparative context. Results showed that most of the cerebral cortex was affected with age-related brain atrophy including the primary visual cortex and, although the remainder of the primary sensory areas were unaffected by atrophy, an even higher amount of atrophy was found in the sub-cortical brain regions including the thalamus, hippocampus and amygdala. All previous studies of mouse lemur neuroanatomy have been conducted with histological atlases. However, Nadkarni and colleagues compared mouse lemur cerebral to cortical volumes using high-quality MRI, finding that contrary to histology studies, mouse lemurs had similar cortical to cerebral volume indices to other primate species and were not to be considered a “lesser primate” species as has been previously argued. The proportion of cerebral white matter was the highest in humans, before a continual decrease in macaques and smaller monkeys with the lowest white matter volumes observed in mouse lemurs. The trend for increasing white matter volumes in primates, culminating with the highest values in humans, has often been argued as necessary for reinforcing intra-cerebral connectivity, hypothesized as an important process in primate brain evolution.

Included with this study of mouse lemurs, Nadkarni and colleagues also produced an accompanying MRI in-vivo brain atlas which includes 120 labelled brain structures specific to Microcebus murinus which to-date, has been unavailable. The accessibility of a brain atlas specific for mouse lemurs removes the time-consuming process of manual MRI segmentation, allowing quick and direct comparison of brain regions with other primates for a comparative evolutionary context and in medical research for Alzheimer’s disease.

Alannah Pearson

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From Fossils to Function

From Fossils to Function
Integrative and Taxonomically-Inclusive Approaches to Vertebrate Evolutionary Neuroscience

Brain, Behavior and Evolution, 91
(2018)


Primate brain volumes

Comparative neurobiology has traditionally been used to describe and quantify the macro-and micro-anatomical changes to the brain between human and non-human primates. Research literature commonly refers to the Stephen dataset with brain volumes measured from a small sample of ex-vivo non-human primate brains with species often represented by only one or two individuals. Although this is common limitation inherent to many physical neuroanatomy collections, caution should be used with such limited samples not representative of a species and the actual variation unknown. The consequences for such assumptions on quantifying the neuroanatomical differences between humans and non-human primates have broader implications for human evolution. Despite the increasing accessibility of primate neuroimaging datasets, many comparative studies still rely on the Stephen brain volumes.  This is despite the necessary factoring of numerous bias including cerebral tissue damage from the delay between the post-mortem interval and brain preservation, potential introduction of artifacts from tissue preservation processes causing shrinkage, some cellular destruction and occasional damage during brain extraction.

Navarette and colleagues recently compared digital neuroanatomical volumes from ex-vivo brain MRI with the Stephen data using the same primate species but including an extra 20 species. Results showed differences between the Stephen data and those obtained by Navarrete et al. with larger brain volumes measured in the pre-fixed state versus post-fixed, indicating fixation did noticeably affect brain volume measurement. Although Navarrete et al. aimed to quantify primate brain volume variation by increasing the number of primate species to 39, there were still 29 species represented by only one individual, while the maximum of for the entire sample was never greater than three individuals per species. Although Navarrete et al. argued a lack of larger in-vivo primate brain neuroimaging datastes, several are accessible as part of the National Chimpanzee Research Center. More broadly, Navarrete and colleagues have shown quantifiable differences between pre- and post-fixed brain volumes and emphasised the need for caution in the suitability of ex-vivo brain collections to provide reliable volumetric measurements for comparative primate neuroanatomy.

Alannah Pearson


Advances in brain imaging

Klein et al 2017The diagnosis of human brain abnormalities depends on knowing the norm and yet defining the range of normal variation is still far from resolved. Understanding what is within the normal human range has been limited by samples and the constraints of producing accurate brain mapping. Access to large brain imaging databases has been possible for a while but producing reliable atlases of key structures including folding patterns (sulci, gyri and fundii), volumes and major shape changes has not had large enough sample sizes to reliably grasp the range of normal brain variation. Current approaches have relied on highly skilled professionals to assess neuroanatomy. While this approach is adequate, it does introduce an inherent level of subjectivity and potential bias with each neuroanatomist dependent on the individual level of experience. To begin reducing this error while increasing sample sizes, new computational technologies allow more automated imaging processes that combine speed and quality.

Mindboggle is a new software platform recently released after development through a long-term research project addressing a need for integrating morphometry (measurements of morphology) to assess the quantitative differences in brain structure. Mindboggle relies on specially developed algorithms to segment brain tissue in MRI images, produce volumetric and structural parallelization of the brain and asses shape variation. Klein and colleagues highlighted issues with similar algorithm-based software that produced errors in segmenting brain from non-brain tissue. Freesurfer was shown to underestimate grey matter while overestimating white matter, while ANTs included more grey matter yet sometimes excluded white matter that extended deep in gyral folds. To resolve this issue, Mindboggle employed a hybrid algorithm that overlays the Freesurfer and ANTs segmentation imaging then combines these to produce a more faithful imaging set negating any errors in volume estimates, folding patterns or shape differences. Further results indicated the geodesic algorithm produced an exaggerated depth for brain regions like the insula, while the time depth algorithm unique to Mindboggle produced more valid results for shallow brain structures than other comparable algorithms. Finally, Mindboggle was shown to be reliable with minimal error estimate showing a consistently greater shape difference between left and right hemispheres than the difference between repeated scans of the same individuals.

Mindboggle also introduced many new and innovative features for extracting and measuring fundii but these algorithms have not yet been thoroughly evaluated. Additionally, the Mindboggle algorithms are developed for human brain anatomy and expansion into non-human neuroanatomy has not yet been fully developed. The potential of Mindboggle and similar platforms lies in the allowance to expand knowledge of normal human brain variation by using much larger samples to more accurately capture the normal range in human neuroanatomy to better inform diagnoses of brain abnormalities.

Alannah Pearson