Simon Wilton Davis
Assistant Professor in Neurology
My research centers around the use of structural and functional imaging measures to study the shifts in network architecture in the aging brain. I am specifically interested in changes in how changes in structural and functional connectivity associated with aging impact the semantic retrieval of word or fact knowledge. Currently this involves asking why older adults have particular difficulty in certain kinds of semantic retrieval, despite the fact that vocabularies and knowledge stores typically improve with age.
A second line of research involves asking questions about how this semantic system is organized in young adults, understanding which helps form a basis for asking questions about older adults. To what degree are these semantic retrieval processes lateralized? What cognitive factors affect this laterality? How are brain structures like the corpus callosum involved in mediating distributed activation patterns associated with semantic retrieval?
Evaluating State-Based Network Dynamics in a Transdiagnostic Sample of Patients with Anhedonia awarded by National Institutes of Health (Co-Sponsor). 2020 to 2023
Using network-guided TMS to ameliorate memory deficits in early Alzheimer's disease awarded by National Institutes of Health (Principal Investigator). 2020 to 2022
Impact of Timing, Targeting, and Brain State on rTMS of Human and Non-Human Primates awarded by National Institutes of Health (Co Investigator). 2017 to 2021
Bilateral Brain Dynamics Supporting Cognition in Normal Aging and Dementia awarded by National Institutes of Health (Principal Investigator). 2017 to 2021
Using fMRI-guided TMS to increase central executive function in older adults awarded by National Institutes of Health (Investigator). 2015 to 2021
Role of white-matter connectivity on age-related reorganization of brain networks awarded by National Institutes of Health (Graduate Student). 2008 to 2011
Stanley, M. L., et al. “Toward a more integrative cognitive neuroscience of episodic memory.” Connectomics: Applications to Neuroimaging, 2018, pp. 199–218. Scopus, doi:10.1016/B978-0-12-813838-0.00011-X. Full Text
Hovhannisyan, Mariam, et al. “The visual and semantic features that predict object memory: Concept property norms for 1,000 object images.” Memory & Cognition, Jan. 2021. Epmc, doi:10.3758/s13421-020-01130-5. Full Text
Davis, Simon W., et al. “Visual and Semantic Representations Predict Subsequent Memory in Perceptual and Conceptual Memory Tests.” Cereb Cortex, vol. 31, no. 2, Jan. 2021, pp. 974–92. Pubmed, doi:10.1093/cercor/bhaa269. Full Text
Deng, Lifu, et al. “Age-Related Compensatory Reconfiguration of PFC Connections during Episodic Memory Retrieval.” Cerebral Cortex (New York, N.Y. : 1991), vol. 31, no. 2, Jan. 2021, pp. 717–30. Epmc, doi:10.1093/cercor/bhaa192. Full Text
Cooper, Rose, et al. Mapping the organization and dynamics of the posterior medial network during movie watching. Oct. 2020. Epmc, doi:10.1101/2020.10.21.348953. Full Text
Beynel, L., et al. The effect of functionally-guided-connectivity-based rTMS on amygdala activation. Oct. 2020. Epmc, doi:10.1101/2020.10.13.338483. Full Text
Gamboa Arana, Olga Lucia, et al. “Intensity- and timing-dependent modulation of motion perception with transcranial magnetic stimulation of visual cortex.” Neuropsychologia, vol. 147, Oct. 2020, p. 107581. Pubmed, doi:10.1016/j.neuropsychologia.2020.107581. Full Text Open Access Copy
Powers, John P., et al. “Examining the Role of Lateral Parietal Cortex in Emotional Distancing Using TMS.” Cogn Affect Behav Neurosci, vol. 20, no. 5, Oct. 2020, pp. 1090–102. Pubmed, doi:10.3758/s13415-020-00821-5. Full Text Open Access Copy
Crowell, C. A., et al. “Older adults benefit from more widespread brain network integration during working memory.” Neuroimage, vol. 218, Sept. 2020, p. 116959. Pubmed, doi:10.1016/j.neuroimage.2020.116959. Full Text Open Access Copy
Beynel, Lysianne, et al. “Structural Controllability Predicts Functional Patterns and Brain Stimulation Benefits Associated with Working Memory.” J Neurosci, vol. 40, no. 35, Aug. 2020, pp. 6770–78. Pubmed, doi:10.1523/JNEUROSCI.0531-20.2020. Full Text
Gamboa, Olga Lucia, et al. “Application of long-interval paired-pulse transcranial magnetic stimulation to motion-sensitive visual cortex does not lead to changes in motion discrimination.” Neurosci Lett, vol. 730, June 2020, p. 135022. Pubmed, doi:10.1016/j.neulet.2020.135022. Full Text Open Access Copy
Davis, Simon, et al. “F113. Hippocampal Connectivity Insulates High-Risk Adolescents From the Relationship Between Stress and Depressive Symptoms.” Biological Psychiatry, vol. 83, no. 9, Elsevier BV, 2018, pp. S281–S281. Crossref, doi:10.1016/j.biopsych.2018.02.726. Full Text
Deng, Zhi-De, et al. “T176. Controllability of Structural Brain Networks in Depressed Patients Receiving Repetitive Transcranial Magnetic Stimulation.” Biological Psychiatry, vol. 83, no. 9, Elsevier BV, 2018, pp. S196–S196. Crossref, doi:10.1016/j.biopsych.2018.02.513. Full Text
Brooks, Jeffrey, et al. “NEURAL CORRELATES OF THE OWN-AGE BIAS IN YOUNGER AND OLDER ADULTS.” Journal of Cognitive Neuroscience, MIT PRESS, 2013, pp. 35–35.
Hall, Shana, et al. “AN FMRI INVESTIGATION OF THE NEURAL BASIS OF INVOLUNTARY MEMORY: HOW DO THEY DIFFER FROM ESTABLISHED VOLUNTARY MEMORY NETWORKS?” Journal of Cognitive Neuroscience, MIT PRESS, 2013, pp. 110–110.
Madden, David, et al. “AGE-RELATED DIFFERENCES IN THE FUNCTIONAL NEUROANATOMY OF TOP-DOWN ATTENTIONAL CONTROL DURING VISUAL SEARCH.” Journal of Cognitive Neuroscience, MIT PRESS, 2013, pp. 62–63.
Yanovsky, I., et al. “Quantifying deformation using information theory: The log-unbiased nonlinear registration.” 2007 4th Ieee International Symposium on Biomedical Imaging: From Nano to Macro Proceedings, 2007, pp. 13–16. Scopus, doi:10.1109/ISBI.2007.356776. Full Text
Leow, A., et al. “Inverse consistent mapping in 3D deformable image registration: Its construction and statistical properties.” Lecture Notes in Computer Science, vol. 3565, 2005, pp. 493–503.
Price, J. C., et al. “Quantitative and statistical analyses of PET imaging studies of amyloid deposition in humans.” Ieee Nuclear Science Symposium Conference Record, vol. 5, 2004, pp. 3161–64.
Liu, Y., et al. “Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification.” Lecture Notes in Computer Science, vol. 3216, no. PART 1, 2004, pp. 393–401. Scopus, doi:10.1007/978-3-540-30135-6_48. Full Text