Bradley Voytek, Ph.D.

Address:
UCSF MC 2240
Mission Bay – Genentech Hall Room N474
600 16th Street
San Francisco, CA 94158

email: Bradley.Voytek@ucsf.edu
website: http://darb.ketyov.com/
lab: 415-476-2164
fax: 415-502-1655

Curriculum Vitae [pdf]

Biography: 

Brad is an NIH-funded neuroscience researcher making use of big data, mapping, and mathematics to figure out cognition. His research has appeared in peer-reviewed scientific publications such as PNAS, Neuron, the Journal of Cognitive Neuroscience, and others. His research in cognition, brain-computer interfaces, and data analysis has been featured in The Washington Post, Wired, and The New York Times.

He earned his PhD in neuroscience from Berkeley in 2010 where he studied the role of neuroplasticity in human cognition. He applies this research to problems in cognitive neuroscience, recovery from brain injury, brain-computer interfacing, and other domains.

He's interested in leveraging data to modernize cognitive neuroscientific research, the majority of which is conducted using techniques from 1960s psychological experimentation (only with bigger, more expensive toys). To that end he's created several research tools, most notably the neuroscience literature meta-analytic resource brainSCANr.com with his wife, Jessica Bolger Voytek.

His public science writing has been featured in Forbes, The New Yorker, The Guardian, The Atlantic, and Scientific American. His non-academic… uh... interests, include explaining the zombie brain with the Zombie Research Society. Really. He has been interviewed by National Geographic, Wired, New York Magazine, and the American Academy of Neurology because of this “research”.

He’s an avid science teacher and outreach advocate and he’s spoken at events ranging from elementary schools to venues such as TEDxBerkeley, @GoogleTalks, and SciFoo. He runs the blog Oscillatory Thoughts (http://blog.ketyov.com) and occasionally writes for the Scientific American blog.

In 2006 he split the Time Person of the Year award.

ResearchDescription:

Brad’s research focuses on bridging basic neuronal physiology and human behavior to understand how the brain coordinates information to give rise to complex cognition. He uses a variety of techniques to study and manipulate cognitive processes, including real-time EEG as well as intracranial human electrocorticography. His research has guided his interests toward methodological endeavors where he as published several papers on electrophysiological analytic techniques. He also created brainscanr.com, an online resource for researchers that scans PubMed for the probability of relationships between neuroscientific topics and displays these relationships for a quick meta-analytic overview.

Selected Publications:

•    Voytek B& Knight RT (2010). Prefrontal cortex and basal ganglia contributions to visual working memory. Proc Natl Acad Sci USA 107(42), 18167-18172.
•    Voytek B, Davis M, Yago E, Barceló F, Vogel EK, Knight RT (2010). Dynamic neuroplasticity after human prefrontal cortex damage. Neuron 68(3), 401-408.
•    Voytek B,Secundo L, Bidet-Caulet A, Scabini D, Stiver S, Gean AD, Manley G, Knight RT (2010). Hemicraniectomy: A new model for human electrophysiology with high spatio-temporal resolution. J Cogn Neurosci 22(11), 2491-2502.
•    Voytek B, Canolty RT, Shestyuk A, Crone NE, Parvizi J, Knight RT (2010). Shifts in gamma phase-amplitude coupling frequency from theta to alpha over posterior cortex during visual tasks. Front Hum Neurosci 4(191), 1-9.
•    Voytek B& Knight RT (2010). Dynamic communication and connectivity in frontal networks. In Mind and the Frontal Lobes: Cognition, Behavior, and Brain Imaging (editors, Levine B & Craik FIM) New York (USA): Oxford University Press.
•    Løvstad M, Funderud I, Lindgren M, Endestad T, Due-Tønnessen P, Meling T, Voytek B, Knight RT, Solbakk AK. Orbitofrontal and lateral prefrontal cortex contributions to novelty processing: An Event Related Potentials (ERP) study. J Cogn Neurosci
•    Voytek B (2006). Emergent basal ganglia pathology within computational models. J Neurosci 26(28), 7317-7318.