MRI-detectable changes in mouse brain structure induced by voluntary exercise

Cahill, Lindsay S.; Steadman, Patrick E.; Jones, Carly E.; Laliberté, Christine; Dazai, Jun; Lerch, Jason P.; Stefanović, Bojana; Sled, John G.

doi:10.1016/j.neuroimage.2015.03.036

Cited by 30 publications

(35 citation statements)

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“…Rodents models, such as transgenic mice, those with pharmacologically-induced neural lesions, and those selectively bred to engage in high or low amount of wheel running or other PA, have helped elucidate the brain regions and molecular mechanisms regulating PA. Because of the strong conservation in brain structure and physiology between rodents and humans (Howdeshell 2002; Rice and Barone 2000), the same brain areas likely facilitate PA in humans. The brain regions in rodents that are essential in modulating this behavior include the striatum, NAc, hypothalamus, amygdala, hippocampus, prefrontal cortex, locus coeruleus, cerebellum, and pons (Ambrogi Lorenzini et al 1991; Andrzejewski et al 2004; Basso and Morrell 2015; Beninger et al 2009; Bronikowski et al 2004; Cahill et al 2015; Dubreucq et al 2010; Kolb et al 2013; Korczynski and Fonberg 1979; Monroe et al 2014; Nonneman and Corwin 1981; Rhodes et al 2003; Roberts et al 2014; Ruegsegger et al 2016; Ruegsegger et al 2015; Tarr et al 2004; Teske et al 2013; Werme et al 2002; Yim and Mogenson 1989). The currently known identified genes and pathways in the brain that are particularly important in stimulating or suppressing this behavioral response include Fos/DeltaFosB (Correa et al 2016; Rhodes et al 2003; Werme et al 2002), CB1 receptors (Dubreucq et al 2010), NMDA receptor (Andrzejewski et al 2004; Yim and Mogenson 1989), dopaminergic (Monroe et al 2014; Roberts et al 2013; Roberts et al 2012; Waters et al 2013; Yang et al 2012)(Park et al Physiology & Behavior, Resubmitted )(Correa et al 2016), opioid (Ruegsegger et al 2015), and leptin (Matheny et al 2009; Ruegsegger et al 2016) signaling pathways.…”

Section: Discussionmentioning

confidence: 99%

“…In return, PA can alter brain plasticity, including promoting neuro-generative, -adpative, and – protective responses (Reviewed in (Dishman et al 2006)). Ex vivo high resolution 3D MR imaging analysis of mice that previously engaged in four weeks of voluntary exercise revealed that this behavior is associated with brain regions required for normal motor function and learning and memory, such as the hippocampus, dentate gyrus, stratum granulosum of the dentate gyrus, cingulate cortex, olivary complex, inferior cerebellar peduncles and other regions of the cerebellum (Cahill et al 2015). Prior to the initiation of PA, MRI analyses of select brain regions, including the striatum, hippocampus, and pons, are considered excellent predictors of later amount of time engaged in this behavior.…”

Section: Brain Regions Genes and Pathways Regulating Voluntary Physmentioning

confidence: 99%

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Sex‐dependent differences in voluntary physical activity

Rosenfeld

2016

J of Neuroscience Research

110

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There are increasing numbers of overweight and obese individuals in the US and globally, and correspondingly, the associated healthcare costs are rising dramatically. More than one-third of children are currently considered obese with a predisposition to type 2 diabetes, and it is likely that their metabolic conditions will worsen with age. Physical inactivity has also risen to be the leading cause of many chronic, non-communicable diseases (NCD). Children are more physically inactive now than they were in past decades, which may be due to intrinsic and extrinsic factors. In rodents, the amount of time engaged in spontaneous activity within the home cage is a strong predictor of later adiposity and weight gain. Thus, it is important to understand primary motivators stimulating physical activity (PA). There are normal sex differences in PA levels in rodents and humans. The perinatal environment can induce sex-dependent differences in PA disturbances. This review will consider the current evidence that there are sex differences in PA in rodents and humans. The rodent studies showing that early exposure to environmental chemicals can shape later adult PA responses will be discussed. Next, an exploration of whether there are different motivators stimulating exercise in male vs. female humans will be examined. Finally, the brain regions, genes, and pathways, that modulate PA in rodents, and possibly by translation humans, will be described. A better understanding of why each sex remains physically active through the lifespan could open up new avenues to prevent and treat obesity in children and adults.

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Section: Discussionmentioning

confidence: 99%

Section: Brain Regions Genes and Pathways Regulating Voluntary Physmentioning

confidence: 99%

Sex‐dependent differences in voluntary physical activity

Rosenfeld

2016

J of Neuroscience Research

110

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“…http://dx.doi.org/10.1101/183004 doi: bioRxiv preprint first posted online MR images were obtained in-house at the Mouse Imaging Centre in a multiple mouse imaging setup (Lerch et al, 2011a) and as part of other studies' wildtype groups (Ellegood et al, 2015;Cahill et al, 2015). All 153 images were T2-weighted and obtained ex-vivo on a 7T Varian MR scanner, with brains perfused with a gadolinium-based contrast agent 150 before imaging (de Guzman et al, 2016).…”

Section: Animals and Imagingmentioning

confidence: 99%

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

Yee

Fernandes

French

et al. 2017

Preprint

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An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings.Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed.We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance * Corresponding author Email address: yohan.yee@sickkids.ca (Yohan Yee) Preprint submitted to BioRxivAugust 31, 2017 . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/183004 doi: bioRxiv preprint first posted online (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

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“…High resolution sMRI in rodents can provide an excellent readout of anatomical brain changes in mice following nerve injury, housing in an enriched environment, or maze training and such changes are also associated with cellular and molecular changes (Seminowicz et al, 2009; Lerch et al, 2011b; Cahill et al, 2015; Scholz et al, 2015a). However, it is still unclear whether sMRI can be utilized in mice to reveal volumetric regional brain changes following tooth extraction, whether it can detect volumetric regional brain differences in mice of different genetic background, and whether it can be utilized as a phenotyping method to identify genetic sources for inter-individual differences in brain plasticity following tooth loss.…”

Section: Introductionmentioning

confidence: 99%

Widespread Volumetric Brain Changes following Tooth Loss in Female Mice

et al. 2017

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Tooth loss is associated with altered sensory, motor, cognitive and emotional functions. These changes vary highly in the population and are accompanied by structural and functional changes in brain regions mediating these functions. It is unclear to what extent this variability in behavior and function is caused by genetic and/or environmental determinants and which brain regions undergo structural plasticity that mediates these changes. Thus, the overall goal of our research program is to identify genetic variants that control structural and functional plasticity following tooth loss. As a step toward this goal, here our aim was to determine whether structural magnetic resonance imaging (sMRI) is sensitive to detect quantifiable volumetric differences in the brains of mice of different genetic background receiving tooth extraction or sham operation. We used 67 adult female mice of 7 strains, comprising the A/J (A) and C57BL/6J (B) strains and a randomly selected sample of 5 of the 23 AXB-BXA strains (AXB1, AXB4, AXB24, BXA14, BXA24) that were produced from the A and B parental mice by recombinations and inbreeding. This panel of 25 inbred strains of genetically diverse inbred strains of mice is used for mapping chromosomal intervals throughout the genome that harbor candidate genes controlling the phenotypic variance of any trait under study. Under general anesthesia, 39 mice received extraction of 3 right maxillary molar teeth and 28 mice received sham operation. On post-extraction day 21, post-mortem whole-brain high-resolution sMRI was used to quantify the volume of 160 brain regions. Compared to sham operation, tooth extraction was associated with a significantly reduced regional and voxel-wise volumes of cortical brain regions involved in processing somatosensory, motor, cognitive and emotional functions, and increased volumes in subcortical sensorimotor and temporal limbic forebrain regions including the amygdala. Additionally, comparison of the 10 BXA14 and 21 BXA24 mice revealed significant volumetric differences between the two strains in several brain regions. These findings highlight the utility of high-resolution sMRI for studying tooth loss-induced structural brain plasticity in mice, and provide a foundation for further phenotyping structural brain changes following tooth loss in the full AXB-BXA panel to facilitate mapping genes that control brain plasticity following orofacial injury.

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MRI-detectable changes in mouse brain structure induced by voluntary exercise

Cited by 30 publications

References 58 publications

Sex‐dependent differences in voluntary physical activity

Sex‐dependent differences in voluntary physical activity

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

Widespread Volumetric Brain Changes following Tooth Loss in Female Mice

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