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). 2019 to 2023
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
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
Wolpe, N., et al. “Age-related reduction in motor adaptation: brain structural correlates and the role of explicit memory.” Neurobiology of Aging, vol. 90, June 2020, pp. 13–23. Scopus, doi:10.1016/j.neurobiolaging.2020.02.016. Full Text
Crowell, C. A., et al. “Older adults benefit from more widespread brain network integration during working memory.” Neuroimage, vol. 218, May 2020, p. 116959. Pubmed, doi:10.1016/j.neuroimage.2020.116959. Full Text Open Access Copy
Beynel, Lysianne, et al. “Site-Specific Effects of Online rTMS during a Working Memory Task in Healthy Older Adults.” Brain Sci, vol. 10, no. 5, Apr. 2020. Pubmed, doi:10.3390/brainsci10050255. Full Text Open Access Copy
Strömmer, Juho M., et al. “Physical Activity Predicts Population-Level Age-Related Differences in Frontal White Matter.” J Gerontol a Biol Sci Med Sci, vol. 75, no. 2, Jan. 2020, pp. 236–43. Pubmed, doi:10.1093/gerona/gly220. Full Text
Beynel, Lysianne, et al. “Effects of online repetitive transcranial magnetic stimulation (rTMS) on cognitive processing: A meta-analysis and recommendations for future studies.” Neurosci Biobehav Rev, vol. 107, Dec. 2019, pp. 47–58. Pubmed, doi:10.1016/j.neubiorev.2019.08.018. Full Text Open Access Copy
Deng, Lifu, et al. Age-related compensatory reconfiguration of PFC connections during episodic memory retrieval. Nov. 2019. Epmc, doi:10.1101/858357. Full Text
Bruffaerts, Rose, et al. “Perceptual and conceptual processing of visual objects across the adult lifespan.” Sci Rep, vol. 9, no. 1, Sept. 2019, p. 13771. Pubmed, doi:10.1038/s41598-019-50254-5. Full Text
Fuhrmann, Delia, et al. “Strong and specific associations between cardiovascular risk factors and white matter micro- and macrostructure in healthy aging.” Neurobiol Aging, vol. 74, Feb. 2019, pp. 46–55. Pubmed, doi:10.1016/j.neurobiolaging.2018.10.005. Full Text
Beynel, L., et al. “Online repetitive transcranial magnetic stimulation during working memory in younger and older adults: A randomized within-subject comparison.” Plos One, vol. 14, no. 3, 2019, p. e0213707. Pubmed, doi:10.1371/journal.pone.0213707. 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
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.
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.
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.