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From Brains to BRAINs: Neuroscience at the Cutting Edge

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Since we are in the midst of the Decade of the Mind (not to be confused with the Decade of the Brain), we are constantly inundated by reports of new neuroscience. From direct brain to brain communication, to memory manipulation, to cognition enhancing cybernetics and genetics, cutting edge neuroscience often sounds more like science fiction than actual science. This talk will separate the fact from the fiction of modern neuroscience. We'll discuss the science behind cutting edge neuroscience techniques like Brainbow, expansion microscopy, and functional connectomics and how widely publicized advances in building artificial brains and using apps to detect and affect mental states is more fiction than fact.

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From Brains to BRAINs: Neuroscience at the Cutting Edge

  1. 1. From Brains to BRAINS: Neuroscience at the Cutting Edge John Borghi, PhD @JohnBorghi @BoldSignalsPod
  2. 2. Open Connectome Project
  3. 3. McCabe, D. P., & Castel, A. D. (2008). Seeing is believing: The effect of brain images on judgments of scientific reasoning. Cognition, 107(1), 343-352. Superfluous Neuroscience
  4. 4. Ariely, D., & Berns, G. S. (2010). Neuromarketing: the hope and hype of neuroimaging in business. Nature Reviews Neuroscience, 11(4), 284-292.
  5. 5. Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., ... & Sowell, E. R. (2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18, 773–778.
  6. 6. Adapted From: Brodmann K (1909). Vergleichende Lokalisationslehre der Grosshirnrinde.
  7. 7. Lafer-Sousa, R., Hermann, K.L., & Conway, B.R. (2015). Striking individual differences in color perception uncovered by ‘the dress’ photograph. Current Biology.
  8. 8. Park, H. J., & Friston, K. (2013). Structural and functional brain networks: from connections to cognition. Science, 342(6158), 1238411.
  9. 9. Wickersham, I. R., Lyon, D. C., Barnard, R. J., Mori, T., Finke, S., Conzelmann, K. K., ... & Callaway, E. M. (2007). Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron, 53(5), 639-647. Transsynaptic Tracing
  10. 10. Chen, F., Tillberg, P. W., & Boyden, E. S. (2015). Expansion microscopy. Science, 347(6221), 543-548 Expansion Microscopy
  11. 11. Chung, K., Wallace, J., Kim, S. Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., ... & Deisseroth, K. (2013). Structural and molecular interrogation of intact biological systems. Nature, 497(7449), 332-337. CLARITY
  12. 12. Chung, K., Wallace, J., Kim, S. Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., ... & Deisseroth, K. (2013). Structural and molecular interrogation of intact biological systems. Nature, 497(7449), 332-337. CLARITY
  13. 13. Lichtman, J. W., Livet, J., & Sanes, J. R. (2008). A technicolour approach to the connectome. Nature Reviews Neuroscience, 9(6), 417-422. Golgi Stain Brainbow
  14. 14. Chung, K., Wallace, J., Kim, S. Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., ... & Deisseroth, K. (2013). Structural and molecular interrogation of intact biological systems. Nature, 497(7449), 332-337.
  15. 15. Allen Cell Types Database
  16. 16. Optogenetic actuators (e.g. channelrhodopsin) Optogenetic sensors (e.g. Clomeleon, Mermaid) Genetic Construct inserted into virus Virus is injected into animal Laser light is used to Control activity of infected neurons Activity of infected neurons is measured via optic sensor Optogenetics
  17. 17. Baratta, M. V., Nakamura, S., Dobelis, P., Pomrenze, M. B., Dolzani, S. D., & Cooper, D. C. (2012). Optogenetic control of genetically-targeted pyramidal neuron activity in prefrontal cortex. arXiv preprint arXiv:1204.0710.
  18. 18. Ramirez, S., Liu, X., Lin, P. A., Suh, J., Pignatelli, M., Redondo, R. L., ... & Tonegawa, S. (2013). Creating a false memory in the hippocampus. Science,341(6144), 387-391. Image: Evan Wondolowski of Collective Next
  19. 19. Tanaka, K. Z., Pevzner, A., Hamidi, A. B., Nakazawa, Y., Graham, J., & Wiltgen, B. J. (2014). Cortical Representations Are Reinstated by the Hippocampus during Memory Retrieval. Neuron, 84(2), 347-354.
  20. 20. Grosenick, L., Marshel, J. H., & Deisseroth, K. (2015). Closed-Loop and Activity-Guided Optogenetic Control. Neuron, 86(1), 106-139. “Closed Loop” Optogenetics
  21. 21. Transcranial Direct Current Stimulation foc.us tDCS v2 Ghostbusters (1984)
  22. 22. Grau, C., Ginhoux, R., Riera, A., Nguyen, T. L., Chauvat, H., Berg, M., ... & Ruffini, G. (2014). Conscious brain-to-brain communication in humans using non- invasive technologies. PloS one, 9(8), e105225. Brain-to-Brain Communication
  23. 23. Park, H. J., & Friston, K. (2013). Structural and functional brain networks: from connections to cognition. Science, 342(6158), 1238411.
  24. 24. Szigeti, B., Gleeson, P., Vella, M., Khayrulin, S., Palyanov, A., Hokanson, J., ... & Larson, S. (2014). OpenWorm: an open-science approach to modeling Caenorhabditis elegans. Frontiers in computational neuroscience, 8. OpenWorm
  25. 25. Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H., & Chklovskii, D. B. (2011). Structural properties of the Caenorhabditis elegans neuronal network. PLoS computational biology, 7(2), e1001066. Robot Worms
  26. 26. Oh, S. W., Harris, J. A., Ng, L., Winslow, B., Cain, N., Mihalas, S., ... & Zeng, H. (2014). A mesoscale connectome of the mouse brain. Nature, 508(7495), 207-214.
  27. 27. EyeWire
  28. 28. The Human Brain Project
  29. 29. The Human Connectome Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex.PLoS biology, 6(7), e159.
  30. 30. Irimia, A., Chambers, M. C., Torgerson, C. M., & Van Horn, J. D. (2012). Circular representation of human cortical networks for subject and population-level connectomic visualization. Neuroimage, 60(2), 1340-1351.
  31. 31. Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159-172. The Human Connectome
  32. 32. Fox, C. J., Iaria, G., & Barton, J. J. (2009). Defining the face processing network: optimization of the functional localizer in fMRI. Human brain mapping,30(5), 1637-1651. The Face Processing Network
  33. 33. Glahn, D. C., Winkler, A. M., Kochunov, P., Almasy, L., Duggirala, R., Carless, M. A., ... & Blangero, J. (2010). Genetic control over the resting brain.Proceedings of the National Academy of Sciences, 107(3), 1223-1228. The Default Mode Network
  34. 34. Brain Training Games Kesler, S. R., Sheau, K., Koovakkattu, D., & Reiss, A. L. (2011). Changes in frontal-parietal activation and math skills performance following adaptive number sense training: Preliminary results from a pilot study.Neuropsychological rehabilitation, 21(4), 433-454. Owen, A. M., Hampshire, A., Grahn, J. A., Stenton, R., Dajani, S., Burns, A. S., ... & Ballard, C. G. (2010). Putting brain training to the test. Nature,465(7299), 775-778.
  35. 35. Jones-Hagata, L. B., Ortega, B. N., Zaiko, Y. V., Roach, E. L., Korgaonkar, M. S., Grieve, S. M., ... & Etkin, A. (2015). Identification of a Common Neurobiological Substrate for Mental Illness. JAMA Psychiatry Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J., & Davidson, R. J. (2011). The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience, 12(3), 154-167.
  36. 36. Kaplan, J. T., Man, K., & Greening, S. G. (2015). Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations. Frontiers in human neuroscience, 9. Multi-Voxel Patten Analysis
  37. 37. Woo, C. W., Koban, L., Kross, E., Lindquist, M. A., Banich, M. T., Ruzic, L., ... & Wager, T. D. (2014). Separate neural representations for physical pain and social rejection. Nature communications, 5. Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302(5643), 290-292. Multi-Voxel Patten Analysis
  38. 38. Schoenmakers, S., Barth, M., Heskes, T., & van Gerven, M. (2013). Linear reconstruction of perceived images from human brain activity. NeuroImage, 83, 951- 961. Neural Decoding with MVPA
  39. 39. Cowen, A. S., Chun, M. M., & Kuhl, B. A. (2014). Neural portraits of perception: reconstructing face images from evoked brain activity. Neuroimage, 94, 12-22.
  40. 40. fMRI and Lie Detection? Langleben, D. D., Loughead, J. W., Bilker, W. B., Ruparel, K., Childress, A. R., Busch, S. I., & Gur, R. C. (2005). Telling truth from lie in individual subjects with fast event‐related fMRI. Human brain mapping, 26(4), 262-272.
  41. 41. Sentiment Analysis by Emotient
  42. 42. Clarity by Neurokky Neuroscience Fiction
  43. 43. Thanks! John Borghi, PhD John.Borghi@Gmail.com @JohnBorghi Bold Signals Podcast BoldSignalsi@Gmail.com @BoldSignalsPod

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