Bradley Voytek's Research on Neural Oscillations
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Through careful measurement and consideration of the brain signals we record, we can build massive databases of brain activity. We can link these with other open neuroscience data sources to mine for links between brain activity, connectivity, gene expression, cell type, and function in new ways. We can leverage data mining to generate new hypotheses *for us*. Our relevant papers: oscillation parameterization: https://www.biorxiv.org/content/early/2018/04/11/299859 waveform shape analysis: https://www.biorxiv.org/content/early/2018/04/16/302000 hypothesis generation: http://voyteklab.com/wp-content/uploads/Voytek-JNeurosciMethods2012.pdf open data ecosystems: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005037
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Bradley Voytek's Research on Neural Oscillations
- 1. BradleyVoytek, Ph.D. UC San Diego Department of Cognitive Science Neurosciences Graduate Program Halıcıoğlu Data Science Institute bvoytek@ucsd.edu @bradleyvoytek
- 3. Support • Alfred P. Sloan Research Fellowship in Neuroscience • Whitehall Foundation • NIMH R01 MH095984 • NSF BCS COGNEURO 1736028 (Neuroscience) • NSF DGE NRT 1735234 (Data Science) • Kavli Innovative Research Grant • NIHT32 UCSD Institute for Neural Computation Cognitive NeuroscienceTraining Grant • NIHT32 UCSD Genetics Training Program
- 4. The state of the Voyteklab - 2017
- 5. The state of the Voyteklab - 2017 What are oscillations??!
- 6. Oscillations Source: Berger, Über das Elektroenkephalogramm des Menschen 1929
- 7. Oscillatory topography Source:Voytek, et al., Front Hum Neurosci 2010
- 8. So what might oscillations be good for?
- 9. Oscillations influence perception Source: Donoghue et al. (in preparation)
- 10. Communication through coherence Source: Fries, Trends Cogn Sci 2005
- 11. But seriously. What’s an oscillation?
- 13. Oscillations are bumps “In a power spectrum, brain oscillations appear as bumps on top of this 1/f slope…” - He B, Trends Cogn Sci 2014 “[when] particular oscillation frequencies become dominant… a peak (bump) appears in the respective frequency band.” - Buzsáki el al., Neuron 2013
- 14. Oscillations are correlated with everything
- 15. Oscillations are correlated with everything Tens-of-thousands of oscillation papers
- 16. Oscillations are correlated with everything Tens-of-thousands of oscillation papers How do we measure oscillations?
- 18. This is what we implicitly model when we see a “power decrease”
- 19. But there’s a problem
- 21. Obrist 1954 ; Bullock 1981
- 22. Mukamel 2005; Manning 2009; Miller 2012 Obrist 1954 ; Bullock 1981
- 23. Freeman 2008; Gao 2017; Podvalny 2017 Mukamel 2005; Manning 2009; Miller 2012 Obrist 1954 ; Bullock 1981
- 24. All of these give the same result using standard analysis methods!
- 25. We can’t adjudicate between these without careful parameterization
- 26. Misinterpretations? “Theta/alpha and gamma operate in a push-pull fashion”
- 27. Misinterpretations? “Theta/alpha and gamma operate in a push-pull fashion”
- 28. Misinterpretations? “Theta/alpha and gamma operate in a push-pull fashion”
- 29. Misinterpretations? “Theta/alpha and gamma operate in a push-pull fashion”
- 30. Misinterpretations? “Theta/alpha and gamma operate in a push-pull fashion”
- 31. Misinterpretations? “Theta/alpha and gamma operate in a push-pull fashion”
- 32. Misinterpretations? Aperiodic slope change or alpha/gamma push pull!
- 33. We can’t adjudicate between these without careful parameterization
- 34. But there’s another problem
- 37. Even with no oscillation present, narrowband analyses give results!
- 38. This is an even bigger problem
- 39. “If there’s power in the band, something is oscillating.”
- 41. “If there’s power in the band, something is oscillating.”
- 43. Aperiodic shifts in aging Source:Voytek et al., J Neurosci 2015
- 44. What does the aperiodic slope reflect? Source: Gao et al., NeuroImage 2017
- 45. Aperiodic slope :: EI balance Source: Gao et al., NeuroImage 2017
- 46. Aperiodic slope modulated by phase Source: Gao et al., NeuroImage 2017
- 47. Aperiodic slope tracks consciousness Source: Gao et al., NeuroImage 2017
- 48. EI imbalance Implicated in nearly every major neurological and psychiatric disorder Source:Voytek & Knight, Biol Psychiatry 2015
- 49. But wait, there’s more!
- 52. sinusoid Neural oscillations are not sinusoidal Source: Cole &Voytek Trends Cogn Sci 2017 not sinusoids
- 53. Nonsinusoidality is deceiving Source: Cole &Voytek bioRxiv 2017
- 54. Nonsinusoidality is deceiving Source: Cole &Voytek bioRxiv 2017 n:m synchrony (cf Palva & Palva, Trends Cogn Sci 2012)
- 55. We can’t adjudicate between these without careful parameterization
- 56. Source: de Hemptinne et al., PNAS 2013; de Hemptinne et al., Nature Neurosci 2015 Nonsinusoidality can look like PAC
- 57. Source: Cole et al., J Neurosci 2017 Nonsinusoidality tracks DSB efficacy PD beta is sharp
- 58. Source: Cole et al., J Neurosci 2017 Nonsinusoidality tracks DSB efficacy PD beta is sharp DBS unsharpens it
- 59. Source: Cole et al., J Neurosci 2017 Nonsinusoidality tracks DSB efficacy PD beta is sharp DBS unsharpens it it looks like PAC
- 60. Not throwing the baby out with the bathwater! Source:Vaz et al., NeuroImage 2017
- 61. Where does sharpness come from? Source: Cole &Voytek Trends Cogn Sci 2017; cf Stephanie Jones’ work
- 62. Nonsinusoids, aperiodic slope, (and bears? Oh my!)
- 63. This is actually a problem…
- 64. …and problems are fun!
- 66. Parameterizing neural power spectra Source: Haller, Donoghue, Peterson, et al., bioRxiv
- 67. fit aperiodic (1/f) in PSD frequency (Hz) power 10 20 30 40 remove aperiodic signal A B iteratively fit and remove Gaussians C (halt fitting at noise floor) multi-Gaussian fit using iteration parametersD remove Gaussians from original PSDE re-fit aperiodicF combine assess goodness of fit G H peak peak peak 1/f fit final fit Haller, Donoghue, Peterson, et al., bioRxiv
- 68. Parameterizing neural power spectra Source: Haller, Donoghue, Peterson, et al., bioRxiv Code: https://github.com/voytekresearch/fooof
- 69. Cycle-by-cycle waveform analyses Source: Cole &Voytek, et al., bioRxiv
- 70. What next?
- 72. Origin of oscillations Source: Nagreas, Gao et al., under review
- 73. Origin of oscillations Source: Nagreas, Gao et al., under review
- 74. Origin of oscillations Source: Nagreas, Gao et al., under review
- 75. Origin of oscillations Source: Nagreas, Gao et al., under review
- 77. “Voltage budget” Source: Peterson &Voytek (in preparation)
- 79. I - interface with online data II - preprocess data III – project to cor4cal sources IV – compute power spectra Source: Donoghue et al. (in preparation)
- 80. theta alpha Oscillatory topography theta (4-8 Hz) alpha (8-12 Hz) beta (18-30 Hz) Source: Donoghue et al. (in preparation)
- 81. beta low gamma NeuroSynth Source:Yarkoni et al., Nature Methods 2011
- 82. beta low gamma Multimodal comparisons • cell types? • gene expression (ABA) • WM connectivity (HCP) • functional terms (NeuroSynth) • waveforms (shape) • oscillations (FOOOF)
- 83. beta low gamma Function~oscillation associations Source: Donoghue et al. (in preparation)
- 84. beta low gamma Function~gene associations Source: Donoghue et al. (in preparation)
- 85. beta low gamma Source: Donoghue et al. (in preparation) dopamine receptor D2 Function~gene associations
- 86. beta low gamma Source: Donoghue et al. (in preparation) dopamine receptor D2 Function~gene associations dopamine receptor D2
- 87. Source:Voytek &Voytek, J Neurosci Methods 2012
- 88. Source:Voytek &Voytek, J Neurosci Methods 2012 Data mining for hypothesis generation
- 89. We can integrate diverse datasets: cell type, gene expression, WM, function, waveform, oscillations, aperiodic signal
- 90. Putting them into the same space —“brain space”—allows us to ask new questions:
- 91. Are there genes statistically over/ under-expressed in “attention” regions of the brain?
- 92. Are distant brain regions that oscillate at similar frequencies more likely to be connected by WM tracts?
- 93. Are there certain cell densities across lamina that give rise to particular waveform shapes?
- 94. But we can go further.
- 95. We can look for connections between all of these—cell types, gene expression, function, oscillations, connectivity—to see how they all interrelate.
- 96. And we can find gaps—missing connections where topics should be related but aren’t.
- 97. We can mine through these connections to find potential missing links.
- 98. That is, we can use the data themselves to generate new hypotheses for us.
- 99. BradleyVoytek, Ph.D. UC San Diego Department of Cognitive Science Neurosciences Graduate Program Halıcıoğlu Data Science Institute bvoytek@ucsd.edu @bradleyvoytek