Using high-field functional and quantitative structural MRI to reveal the dynamics of auditory statistical learning and representational change

Fred Dick (primary)
Psychological Sciences
Birkbeck
Martina Callaghan (secondary)
Institute of Neurology
UCL

Abstract

Our brains are exceptionally good at extracting important information from the sea of unfamiliar complex sounds around us. However, we are only beginning to how the brain restructures itself to accomplish this learning, and how particular stages of learning may be supported by different neurobiological mechanisms. This PhD project tackles these issues by combining intensive, computationally-informed auditory training paradigms with functional and structural MRI at high field strengths. The project will test hypotheses about the emergence and consolidation of new auditory representations, as well as the relationship of learning success to transient or sustained changes in brain microstructure.


References

Carey, D., Caprini, F., Allen, M., Lutti, A., Weiskopf, N., Rees, G.,Callaghan, M, & Dick, F. (2018). Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure. NeuroImage, 182, 429–440. http://doi.org/10.1016/j.neuroimage.2017.11.066

Carey, D., Rosen, S., Krishnan, S., Pearce, M. T., Shepherd, A., Aydelott, J., & Dick, F. (2015). Generality and specificity in the effects of musical expertise on perception and cognition. Cognition, 137, 81–105. http://doi.org/10.1016/j.cognition.2014.12.005

Dick, F. K., Lehet, M. I., Callaghan, M. F., Keller, T. A., Sereno, M. I., & Holt, L. L. (2017). Extensive Tonotopic Mapping across Auditory Cortex Is Recapitulated by Spectrally Directed Attention and Systematically Related to Cortical Myeloarchitecture. Journal of Neuroscience, 37(50), 12187–12201. http://doi.org/10.1523/JNEUROSCI.1436-17.2017

Holt, L. L., Tierney, A. T., Guerra, G., Laffere, A., & Dick, F. (2018). Dimension-selective attention as a possible driver of dynamic, context-dependent re-weighting in speech processing. Hearing Research, 366, 50–64. http://doi.org/10.1016/j.heares.2018.06.014

Callaghan, M. F., Helms, G., Lutti, A., Mohammadi, S., & Weiskopf, N. (2014). A general linear relaxometry model of R1 using imaging data. Magnetic Resonance in Medicine, 73(3), 1309–1314. http://doi.org/10.1002/mrm.25210


BBSRC Area
Animal disease, health and welfare
Area of Biology
Neurobiology
Techniques & Approaches
BiophysicsImage ProcessingMathematics / StatisticsSimulation / Modelling