A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy

Nadkarni, Nachiket A.; Bougacha, Salma; Garin, Clément M.; Dhénain, Marc; Picq, Jean‐Luc

doi:10.1016/j.neuroimage.2018.10.010

Cited by 24 publications

(44 citation statements)

References 39 publications

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“…The study based on 35 SCs was selected for the final analysis as it highlighted either unilateral local functional regions or bilateral regions. Moreover, the extracted components matched well to anatomy (Nadkarni et al, 2018). The 35 SCs were used to create a 3D functional atlas of the mouse lemur brain.…”

Section: Methodsmentioning

confidence: 74%

“…1). Each component was manually classified using anatomical (Bons et al, 1998; Nadkarni et al, 2018) and Brodmann atlases (Brodmann, 1999 (original in 1909); Le Gros Clark, 1931). First, brain regions were classified based on their locations within the frontal, parietal, temporal and occipital lobes as well as subcortical and midbrain regions.…”

Section: Resultsmentioning

confidence: 99%

“…Up to now, description of mouse lemur functional organisation was based on cytoarchitectonic atlases (Bons et al, 1998; Le Gros Clark, 1931; Nadkarni et al, 2018). Here, using dictionary learning with a large number of components, we created a 3D map of 48 local functional regions.…”

Section: Discussionmentioning

confidence: 99%

“…Then spatial pre-processing was performed using the python module sammba-mri (SmAll MaMmals BrAin MRI; http://sammba-mri.github.io) which, using nipype for pipelining (Gorgolewski et al, 2011), leverages AFNI (Cox, 1996) for most steps and RATS (Oguz et al, 2014) for brain extraction. Anatomical images were mutually registered to create a study template, which was further registered to a high resolution anatomical mouse lemur template (Nadkarni et al, 2018). Resting state images were corrected for slice timing (interleaved), motion, and B0 distortion (per-slice registration to respective anatomicals), then all brought into the same space of the mouse lemur template by successive application of the individual anatomical to study template and study template to mouse lemur atlas transforms.…”

Section: Methodsmentioning

confidence: 99%

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Resting state cerebral networks in mouse lemur primates: from multilevel validation to comparison with humans

Garin

Nadkarni

Landeau

et al. 2019

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1Measures of resting-state functional connectivity allow the description of neuronal 2 networks in humans and provide a window on brain function in normal and 3 pathological conditions. Animal models are critical to further address experimentally 4 the function of brain networks and their roles in pathologies. Here we describe for the 5 first time brain network organization in the mouse lemur (Microcebus murinus), a 6 small primate attracting increased attention as a model for neuroscience. Resting-7 state functional MR images were recorded at 11.7 Tesla. Forty-eight functional 8 regions were identified and used to identify networks using graph theory, dictionary 9 learning and seed-based analyses. Comparison of results issued from these three 10 complementary methods allowed the description of the most robust networks from 11 mouse lemurs. Large scale networks were then identified from resting-state 12 functional MR images of humans using the same method as for lemurs. Strong 13 homologies were outlined between cerebral networks in mouse lemurs and humans. 14 15 Keywords 16 Brain function, Cerebral networks, Functional MRI, Graph theory, Human, Microcebus 17 murinus, Mouse lemur, Primate, Resting state 18Blood-oxygen level dependent (BOLD) functional magnetic resonance imaging 20 (fMRI) is largely used to investigate brain function in response to specific tasks. In the 21 absence of explicit tasks (i.e. in resting state conditions) patterns of oscillations of the 22 fMRI signal are similar in functionally connected brain structures (Biswal et al., 1995). 23 The detection of the synchronicity of BOLD signal in various brain regions in resting 24 state conditions can thus be used to describe cerebral network organization. In 25 particular this allows the characterization of i. local regions in which highly 26 coordinated neuronal activity occurs and ii. large scale networks composed of 27 widespread functional regions connected together (Biswal et al., 1995; Power et al., 28 2014). 29 Studies of brain networks have contributed to many breakthroughs in the 30 understanding of brain function in normal as well as in pathological conditions such 31 as Alzheimer's or Parkinson's diseases (Buckner et al., 2005; Gao and Wu, 2016). 32 However, many questions remain concerning both the technique and interpretation of 33 resting state fMRI. For example, both the role of resting state networks in cerebral 34 function, and the biological mechanisms underlying their activity, are still partly 35 unknown. Also, how their modulations impact behaviour and cognition in pathological 36 conditions is still debated (Mohan et al., 2016). 37Using animal models is critical to further address these questions. Indeed, in 38 animals it is possible to artificially stimulate neuronal activity to characterize biological 39 mechanisms underlying network function (Gerits et al., 2012). Another interest of 40 studying neuronal networks in animals is to evaluate how evolution has driven 41 network architecture and to assess to wha...

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Section: Methodsmentioning

confidence: 74%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Resting state cerebral networks in mouse lemur primates: from multilevel validation to comparison with humans

Garin

Nadkarni

Landeau

et al. 2019

Preprint

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“…Picq, A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy. NeuroImage, In press [1] .…”

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confidence: 99%

Digital templates and brain atlas dataset for the mouse lemur primate

et al. 2018

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We present a dataset made of 3D digital brain templates and of an atlas of the gray mouse lemur (Microcebus murinus), a small prosimian primate of growing interest for studies of primate biology and evolution. A template image was constructed from in vivo magnetic resonance imaging (MRI) data of 34 animals. This template was then manually segmented into 40 cortical, 74 subcortical and 6 cerebro-spinal fluid (CSF) regions. Additionally, the dataset contains probability maps of gray matter, white matter and CSF. The template, manual segmentation and probability maps can be downloaded in NIfTI-1 format at https://www.nitrc.org/projects/mouselemuratlas. Further construction and validation details are given in “A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy” (Nadkarni et al., 2018) [1], which also presents applications of the atlas such as automatic assessment of regional age-associated cerebral atrophy and comparative neuroanatomy studies.

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