@article {162, title = {Chronically implantable LED arrays for behavioral optogenetics in primates}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {Jan-09-2021}, pages = {1112 - 1116}, abstract = {

Optogenetic methods have been widely used in rodent brains, but remain relatively under-developed for nonhuman primates such as rhesus macaques, an animal model with a large brain expressing sophisticated sensory, motor and cognitive behaviors. To address challenges in behavioral optogenetics in large brains, we developed Opto-Array, a chronically implantable array of light-emitting diodes for high-throughput optogenetic perturbation. We demonstrated that optogenetic silencing in the macaque primary visual cortex with the help of the Opto-Array results in reliable retinotopic visual deficits in a luminance discrimination task. We separately confirmed that Opto-Array illumination results in local neural silencing, and that behavioral effects are not due to tissue heating. These results demonstrate the effectiveness of the Opto-Array for behavioral optogenetic applications in large brains.

}, issn = {1548-7091}, doi = {10.1038/s41592-021-01238-9}, url = {https://www.nature.com/articles/s41592-021-01238-9}, author = {Rajalingham, Rishi and Sorenson, Michael and Azadi, Reza and Bohn, Simon and DiCarlo, James J. and Afraz, Arash} } @article {149, title = {Chronically implantable LED arrays for behavioral optogenetics in primates}, journal = {bioRxiv}, year = {2020}, month = {9/11/2020}, type = {preprint}, abstract = {

Challenges in behavioral optogenetics in large brains demand development of a chronically implantable platform for light delivery. We have developed Opto-Array, a chronically implantable array of LEDs for high-throughput optogenetic perturbation in non-human primates. We tested the Opto-Array in the primary visual cortex of a macaque monkey, and demonstrated that optogenetic cortical silencing by the Opto-Array results in reliable retinotopic visual deficits on a luminance discrimination task.

}, doi = {10.1101/2020.09.10.291583}, url = {https://www.biorxiv.org/content/10.1101/2020.09.10.291583v1.abstract}, author = {Rajalingham, Rish and Sorenson, Michael and Azadi, Reza and Bohn, Simon and DiCarlo, James J. and Afraz, Arash} } @article {152, title = {An Open Resource for Non-human Primate Optogenetics}, journal = {Neuron}, year = {2020}, month = {January 10, 2020}, type = {NeuroResource}, abstract = {

Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.

}, issn = {08966273}, doi = {10.1016/j.neuron.2020.09.027}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0896627320307510}, author = {Tremblay, Sebastien and Acker, Leah and Afraz, Arash and Albaugh, Daniel L. and Amita, Hidetoshi and Andrei, Ariana R. and Angelucci, Alessandra and Aschner, Amir and Balan, Puiu F. and Basso, Michele A. and Benvenuti, Giacomo and Bohlen, Martin O. and Caiola, Michael J. and Calcedo, Roberto and Cavanaugh, James and Chen, Yuzhi and Chen, Spencer and Chernov, Mykyta M. and Clark, Andrew M. and Dai, Ji and Debes, Samantha R. and Deisseroth, Karl and Desimone, Robert and Dragoi, Valentin and Egger, Seth W. and Eldridge, Mark A.G. and El-Nahal, Hala G. and Fabbrini, Francesco and Federer, Frederick and Fetsch, Christopher R. and Fortuna, Michal G. and Friedman, Robert M. and Fujii, Naotaka and Gail, Alexander and Galvan, Adriana and Ghosh, Supriya and Gieselmann, Marc Alwin and Gulli, Roberto A. and Hikosaka, Okihide and Hosseini, Eghbal A. and Hu, Xing and {\"u}er, Janina and Inoue, Ken-ichi and Janz, Roger and Jazayeri, Mehrdad and Jiang, Rundong and Ju, Niansheng and Kar, Kohitij and Klein, Carsten and Kohn, Adam and Komatsu, Misako and Maeda, Kazutaka and Martinez-Trujillo, Julio C. and Matsumoto, Masayuki and Maunsell, John H.R. and Mendoza-Halliday, Diego and Monosov, Ilya E. and Muers, Ross S. and Nurminen, Lauri and Ortiz-Rios, Michael and {\textquoteright}Shea, Daniel J. and Palfi, {\'e}phane and Petkov, Christopher I. and Pojoga, Sorin and Rajalingham, Rishi and Ramakrishnan, Charu and Remington, Evan D. and Revsine, Cambria and Roe, Anna W. and Sabes, Philip N. and Saunders, Richard C. and Scherberger, {\"o}rg and Schmid, Michael C. and Schultz, Wolfram and Seidemann, Eyal and Senova, Yann-Suhan and Shadlen, Michael N. and Sheinberg, David L. and Siu, Caitlin and Smith, Yoland and Solomon, Selina S. and Sommer, Marc A. and Spudich, John L. and Stauffer, William R. and Takada, Masahiko and Tang, Shiming and Thiele, Alexander and Treue, Stefan and Vanduffel, Wim and Vogels, Rufin and Whitmire, Matthew P. and Wichmann, Thomas and Wurtz, Robert H. and Xu, Haoran and Yazdan-Shahmorad, Azadeh and Shenoy, Krishna V. and DiCarlo, James J. and Platt, Michael L.} } @article {30, title = {Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination}, journal = {Proceedings of the National Academy of Sciences}, year = {2015}, month = {05/2015}, pages = {6730 - 6735}, abstract = {

Neurons that respond more to images of faces over nonface objects were identified in the inferior temporal (IT) cortex of primates three decades ago. Although it is hypothesized that perceptual discrimination between faces depends on the neural activity of IT subregions enriched with \“face neurons,\” such a causal link has not been directly established. Here, using optogenetic and pharmacological methods, we reversibly suppressed the neural activity in small subregions of IT cortex of macaque monkeys performing a facial gender-discrimination task. Each type of intervention independently demonstrated that suppression of IT subregions enriched in face neurons induced a contralateral deficit in face gender-discrimination behavior. The same neural suppression of other IT subregions produced no detectable change in behavior. These results establish a causal link between the neural activity in IT face neuron subregions and face gender-discrimination behavior. Also, the demonstration that brief neural suppression of specific spatial subregions of IT induces behavioral effects opens the door for applying the technical advantages of optogenetics to a systematic attack on the causal relationship between IT cortex and high-level visual perception.

}, issn = {0027-8424}, doi = {10.1073/pnas.1423328112}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1423328112}, author = {Afraz, Arash and Boyden, Edward S. and DiCarlo, James J.} } @article {33, title = {Neural Mechanisms Underlying Visual Object Recognition}, journal = {Cold Spring Harbor Symposia on Quantitative Biology}, volume = {79}, year = {2014}, month = {2014}, pages = {99 - 107}, abstract = {

Invariant visual object recognition and the underlying neural representations are fundamental to higher-level human cognition. To understand these neural underpinnings, we combine human and monkey psychophysics, large-scale neurophysiology, neural perturbation methods, and computational modeling to construct falsifiable, predictive models that aim to fully account for the neural encoding and decoding processes that underlie visual object recognition. A predictive encoding model must minimally describe the transformation of the retinal image to population patterns of neural activity along the entire cortical ventral stream of visual processing and must accurately predict the responses to any retinal image. A predictive decoding model must minimally describe the transformation from those population patterns of neural activity to observed object recognition behavior (i.e., subject reports), and, given that population pattern of activity, it must accurately predict behavior for any object recognition task. To date, we have focused on core object recognition-a remarkable behavior that is accomplished with image viewing durations of \<200 msec. Our work thus far reveals that the neural encoding process is reasonably well explained by a largely feed-forward, highly complex, multistaged nonlinear neural network-the current best neuronal simulation models predict approximately one-half of the relevant neuronal response variance across the highest levels of the ventral stream (areas V4 and IT). Remarkably, however, the decoding process from IT to behavior for all object recognition tasks tested thus far is very accurately predicted by simple direct linear conversion of the inferior temporal neural population state to behavior choice. We have recently examined the behavioral consequences of direct suppression of IT neural activity using pharmacological and optogenetic methods and find them to be well-explained by the same linear decoding model.

}, issn = {0091-7451}, doi = {10.1101/sqb.2014.79.024729}, url = {http://symposium.cshlp.org/content/79/99.full.pdf+html}, author = {Afraz, Arash and Yamins, Daniel L.K. and DiCarlo, James J.} }