Spatial comparisons of mammalian brain connectivity
Poster No:
1795
Submission Type:
Late-Breaking Abstract Submission
Authors:
Aidan Murphy1, Kimberley Phillips2, Sarah Brosnan3, Lisa Parr4, Justin Pargeter5, Dietrich Stout6, Chet Sherwood7, Sophie Barton1, Erin Hecht1
Institutions:
1Harvard University, Cambridge, MA, 2Trinity University, San Antonio, TX, 3Georgia State University, Atlanta, GA, 4Yerkes National Primate Research Center, Atlanta, GA, 5New York University, New York City, NY, 6Emory University, Atlanta, GA, 7The George Washington University, Washington, WA
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Introduction:
Our goal in this project is in quantifying how brain connectivity evolves. We investigate two evolutionarily grounded hypotheses, the rostrocaudal gradient hypothesis (Cahalane, 2012) and the association network hypothesis (Buckner, 2013). The rostrocaudal gradient hypothesis predicts that as one moves from the caudal to the rostral end of the brain, the number of long distance connections to/from other areas will increase. The association network hypothesis predicts that as one moves away from primary sensory areas the amount of long-range connections will increase, and furthermore that this effect becomes more pronounced going from small to large brains.
Methods:
In order to quantify anatomical convergence we will be utilizing diffusion tensor imaging (DTI). We have acquired structural DTI images of a variety of mammals, with large samples of humans, chimpanzees, capuchins and domesticated dogs. DTI scans are used for probabilistic tractography (FSL), as well as for grey/white matter segmentation when structural MRI scans do not exist. We use a voxelwise approach by seeding streamlines in white matter and tracking streamlines into cerebral cortical grey matter. By doing so we are able to identify long-distance cortico-cortical connections between each voxel of grey matter and every other voxel. The resulting connectivity matrix is used to compute a metric for anatomical convergence called "Convergence Index" (CI), which quantifies the number of long-distance connections to/from a given grey matter voxel to other grey matter voxels (Figure 1). CI is high for voxels with a high number of long-distance connections, and low for voxels that have few or mostly local connections.
Having assigned each voxel a convergence index, we then calculate the rostrocaudal position and the sensorimotor distance of each voxel. Rostrocaudal position indicates the location of a voxel along the rostrocaudal axis of the brain, low for locations near the back of the brain and high for regions in the front of the brain. Sensorimotor distance indicates the euclidean distance between a voxel and the nearest primary sensory region (V1, A1, S1). We create models of each hypothesis' predictions (Figure 2), which we then compare with observed data in our results.
Having assigned each voxel a convergence index, we then calculate the rostrocaudal position and the sensorimotor distance of each voxel. Rostrocaudal position indicates the location of a voxel along the rostrocaudal axis of the brain, low for locations near the back of the brain and high for regions in the front of the brain. Sensorimotor distance indicates the euclidean distance between a voxel and the nearest primary sensory region (V1, A1, S1). We create models of each hypothesis' predictions (Figure 2), which we then compare with observed data in our results.
·Figure 1. Convergence Index is calculated for each voxel in cerebral cortex of a human brain. Results are of an example human subject.
·Figure 2. Simulations of the two patterns of connectivity being investigated in a human brain. Cool colors (blue) signify a low CI, whereas hot colors (red) signify a high CI.
Results:
Since these hypotheses are not mutually exclusive and may be statistically masked by other spatial features of brain connectivity, our primary metric is the difference in image similarity between a computed model of each effect within the brain and the observed data. We use the MeasureImageSimilarity function in the ANTS package to calculate the correlation between the average CI results in a species with each simulated hypothesis. For capuchins, the observed data is more similar to the RC model than the AN model (index of 0.26 vs 0.22), whereas in chimpanzees, humans, and domesticated dogs, the observed data is less similar to the RC model and more similar to the AN model (0.18 vs 0.21, 0.17 vs 0.19, 0.22 vs 0.27). These results line up with regressions of CI against these spatial variables, with capuchins showing the only statistically significant result with rostrocaudal position (p<0.025).
Conclusions:
In this project we were interested in the quantifying two hypothesized patterns of spatial connectivity across a variety of mammals, including both primates and the independently large-brained carnivore taxa. By doing so, we are able to identify to what extent each pattern exists within a given species, and whether there seems to be a relationship between these hypotheses and brain size or taxa.
We found that the rostrocaudal gradient seems relatively more pronounced in the smallest-brained primate in our dataset, whereas the association network is more pronounced in the great apes and humans. In addition, dogs and humans show what may be a case of convergence evolution of posteriorly situated hubs of high CI, which might underlie their capacity for overimitation.
We found that the rostrocaudal gradient seems relatively more pronounced in the smallest-brained primate in our dataset, whereas the association network is more pronounced in the great apes and humans. In addition, dogs and humans show what may be a case of convergence evolution of posteriorly situated hubs of high CI, which might underlie their capacity for overimitation.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Novel Imaging Acquisition Methods:
Diffusion MRI 2
Keywords:
Cortex
Cross-Species Homologues
Development
MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
2. Buckner, R.L. and F.M. Krienen, The evolution of distributed association networks in the human brain. Trends in Cognitive Sciences, 2013. 17(12): p. 648-665.