Converging measures of neural change at the microstructural, informational, and cortical network levels in the hippocampus during the learning of the structure of organic compounds
The critical role of the hippocampus in human learning has been illuminated by neuroimaging studies that increasingly improve the detail with which hippocampal function is understood. However, the hippocampal information developed with different types of imaging technologies is seldom integrated within a single investigation of the neural changes that occur during learning. Here, we show three different ways in which a small hippocampal region changes as the structures and names of a set of organic compounds are being learned, reflecting changes at the microstructural, informational, and cortical network levels. The microstructural changes are sensed using measures of water diffusivity. The informational changes are assessed using machine learning of the neural representations of organic compounds as they are encoded in the fMRI-measured activation levels of a set of hippocampal voxels. The changes in cortical networks are measured in terms of the functional connectivity between hippocampus and parietal regions. The co-location of these three hippocampal changes reflects that structure’s involvement in learning at all three levels of explanation, consistent with the multiple ways in which learning brings about neural change.
KeywordsDiffusion imaging fMRI Functional connectivity Learning Hippocampus Multi-voxel pattern analysis
The authors thank Zachary Anderson for assistance in designing and conducting the experiment, Theodore Depietro Jr. for machine learning analyses of the data, and Vladimir Cherkassky and Robert Mason for comments on earlier versions of the manuscript.
This research was supported by the Office of Naval Research (Grant number N00014-16-1-2694).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
Research involving human participants
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
All participants gave signed informed written consent on a consent form approved by the Carnegie Mellon Institutional Review Board.
All procedures performed were approved by the Institutional Review Board of Carnegie Mellon University and were in accordance with the ethical standards of this committee and with the 1964 Helsinki declaration and its later amendments. This article does not contain any studies with animals performed by any of the authors.
- Bai Y, Alexander DC (2008) Model-based registration to correct for motion between acquisitions in diffusion MR imaging. In: 2008 IEEE international symposium on biomedical imaging: from nano to macro. Paris, France, pp 947–950Google Scholar
- Cohen J (1969) Statistical power analysis for the behavioral sciences. Academic, New YorkGoogle Scholar
- Jung K-J (2010) Sensitivity of motion estimation to the anisotropic diffusion of white matter in diffusion MRI. In: Proceedings of the 18th Annual Meeting of the ISMRM. Stockholm, Sweden, p 4036Google Scholar
- Jung K-J, Kohli N, Yeh F-C, Keller TA, Zhao T (2013) Motion correction in diffusion spectrum imaging using simulated diffusion images at multiple b bands. In: Proceedings of the 21st Annual Meeting of the ISMRM. Salt Lake City, Utah, USA, p 3192Google Scholar
- O’Keefe J, Nadel L (1978) The hippocampus as a cognitive map. Oxford University Press, New YorkGoogle Scholar