Large-scale topology and the default mode network in the mouse connectome
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- James M. Stafford
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY 10016;
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- Benjamin R. Jarrett
- Departments of bBehavioral Neuroscience and
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- Oscar Miranda-Dominguez
- Departments of bBehavioral Neuroscience and
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- Brian D. Mills
- Departments of bBehavioral Neuroscience and
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- Nicholas Cain
- Allen Institute for Brain Science, Seattle, WA 98103; and
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- Stefan Mihalas
- Allen Institute for Brain Science, Seattle, WA 98103; and
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- Garet P. Lahvis
- Departments of bBehavioral Neuroscience and
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- K. Matthew Lattal
- Departments of bBehavioral Neuroscience and
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- Suzanne H. Mitchell
- Departments of bBehavioral Neuroscience and
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- Stephen V. David
- Departments of bBehavioral Neuroscience and
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- John D. Fryer
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL 32224
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- Joel T. Nigg
- Departments of bBehavioral Neuroscience and
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- Damien A. Fair
- Departments of bBehavioral Neuroscience and
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説明
<jats:title>Significance</jats:title><jats:p>Noninvasive brain imaging holds great promise for expanding our capabilities of treating human neurologic and psychiatric disorders. However, key limitations exist in human-only studies, and the ability to use animal models would greatly advance our understanding of human brain function. Mice offer sophisticated genetic and molecular methodology, but correlating these data to functional brain imaging in the mouse brain has remained a major hurdle. This study is the first, to our knowledge, to use whole-brain functional imaging to show large-scale functional architecture with structural correlates in the mouse. Perhaps more important is the finding of conservation in brain topology and default network among rodents and primates, thereby clearing the way for a bridge measurement between human and mouse models.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 111 (52), 18745-18750, 2014-12-15
Proceedings of the National Academy of Sciences
