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Sci Rep. 2016 Nov 25;6:36851. doi: 10.1038/srep36851.

Characterization of Structural Connectivity of the Default Mode Network in Dogs using Diffusion Tensor Imaging.

Author information

AU MRI Research Center, Dept. of Electrical &Computer Engineering, Auburn University, Auburn, AL, USA.
Dept. of Psychology, Auburn University, Auburn, AL, USA.
Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA.
Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Canine Detection Research Institute, Auburn University, Auburn, AL, USA.
Dept. of Anatomy, Physiology &Pharmacology, Auburn University, Auburn, AL, USA.
MR R&D, Siemens Healthcare, Malvern, PA, USA.


Diffusion tensor imaging (DTI) provides us an insight into the micro-architecture of white-matter tracts in the brain. This method has proved promising in understanding and investigating the neuronal tracts and structural connectivity between the brain regions in primates as well as rodents. The close evolutionary relationship between canines and humans may have spawned a unique bond in regard to social cognition rendering them useful as an animal model in translational research. In this study, we acquired diffusion data from anaesthetized dogs and created a DTI-based atlas for a canine model which could be used to investigate various white matter diseases. We illustrate the application of this atlas by calculating DTI tractography based structural connectivity between the anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC) regions of the default mode network (DMN) in dogs. White matter connectivity was investigated to provide structural basis for the functional dissociation observed between the anterior and posterior parts of DMN. A comparison of the integrity of long range structural connections (such as in the DMN) between dogs and humans is likely to provide us with new perspectives on the neural basis of the evolution of cognitive functions.

[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

Author N.S. is an employee of Siemens Healthcare, Malvern, PA who is stationed at the MRI Research Center in Auburn University. She contributed by optimizing the sequences used to acquire data in this study. However, note that she had no role in data analysis and its interpretation. These competing interests do not alter the authors’ adherence to common policies on sharing data and materials.

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