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Controlling conscious visual perception with light

Final Report Summary - A LIGHT ON VISION (Controlling conscious visual perception with light.)

1. Publishable summary report

Illusions can tell us a great deal about how our brain works1,2. More than being mere entertainment, their misdirection of neural processing lifts the veil covering the elementary computations that continuously take place when we examine a visual scene. Sensory illusions come in many flavors, but context-dependent optical illusions stand out by their simplicity and prevalence in our daily lives (Fig. 1a-c3-5). These illusions are characterized by a dramatically changed appearance or interpretation of a visual stimulus depending on the embedding context. The neural correlate underlying these contextual illusions is the modulation of the neuronal response to part of an image by the visual features surrounding it (Fig. 1d)6.
Contextual modulations have been extensively described and perturbed in humans (e.g.7,8) and primates(e.g.9-11) but a full understanding of the circuitry has remained elusive because of technical limits in spatial12 and temporal resolution13. Recently, the arrival of optogenetics has revolutionized neuroscience14,15. By use of light gated ion channels and pumps, optogenetics allow us exquisite spatiotemporal control of neural activity. This has suddenly created the opportunity to study the neurobiological mechanisms of contextual modulation at a resolution previously impossible.
The best documented example of contextual modulation in the cerebral cortex is that of surround suppression (Fig. 1e). Typically, neurons in the primary visual cortex demonstrate a rapid decline in response when an increase in stimulus size exceeds an optimal value16-18. This process contributes to a relative enhancement of responses to smaller stimuli and could aid in figure-ground segmentation and perceptual pop-out. How center/surround interactions, probes for contextual modulations and proxies to contextual illusions, come about in the cerebral cortex is the main focus of this project proposal.
There are a number of hypotheses for the cause of surround suppression in primary visual cortex (V1) (Fig. 1f). It could arise by (1) relaying suppression in earlier stages of visual processing19,20, (2a) by intracortical computation either via circuits across layers within the same cortical column, i.e. intralaminar21 or (2b) via horizontal connections across a larger cortical spread mediated by a subclass of inhibitory interneurons18 or by (3) long range intercortical communication (ICC) between different brain regions constituting an effective network11,22. However, heavy corticogeniculate feedback23 (1), sluggish and spatially coarse cortical inactivation methods (1, 2a and 3; e.g. ablation24, pharmacology21, cooling11,13 respectively) and undetermined inputs to the specific subclass of interneurons (2b25) have prevented corroboration of any of these hypotheses. Furthermore, the strength of the suppression is dependent on the orientation of the surround elements26,27. Surrounds which are iso-oriented with respect to the center produce stronger suppression than cross-oriented surrounds. This mechanism could play an important role in figure-ground segregation.
We addressed these questions by recording neural activity in the different layers of V1 cortex in anesthetized mice using laminar electrodes while simultaneously modulating feedback information from higher visual areas to V1 by inhibiting activity in these higher areas using optogenetic intervention.

Our electrophysiological recordings in V1 (Fig. 2a-b) revealed a consistent temporal pattern whereby surround suppression only developed after the initial response (50-150 ms), suggesting recurrent processing. We also observed clear laminar differences (Fig. 2c-d). [Objective 1: manuscript submitted to The Journal of Neuroscience, IF 7]
To test whether intercortical communication is important during this recurrent processing, we used optogenetic interruption of the feedback signals from higher visual areas while recording in V1. We did so by expressing the light-gated cation channel channelrhodopsin-2 (ChR2) in inhibitory neurons of specific higher visual areas, through local viral injections of a Cre-dependent ChR2 vector into GAD2-Cre mice, which express Cre-recombinase in all inhibitory neurons28. Within this preparation, illumination of the cortical surface with blue light efficiently activates all transduced GABAergic interneurons. This increase in inhibition silences the injected area of cortex.
Viral injections were targeted using transcranial imaging of the intrinsic signal during retinotopic mapping29 (Fig. 2e: upper middle and right panel). This allowed us to delineate the borders of V1 and chart the potentially interesting higher visual regions, e.g. lateral areas AL or LM or medial area PM30,31 (Fig. 2e: upper left panel). Next, we superimposed epi-fluorescence images (eYFP-fused to the virus) and functional retinotopy maps to determine success of our injections (Fig. 2e: lower panel).
Our preliminary optogenetic intervention results bear evidence in favor of feedback signals from higher visual areas to V1 providing the suppressive surround causing a decline in response with increasing stimulus sizes (Fig. 2f: right lower panel). In the absence of cortical feedback (Fig. 2f: right upper panel), mainly from area AL, responses of V1 neurons to large visual stimuli display a release of suppression, while responses to small visual stimuli are retained or even facilitated, with some laminar differences. Data obtained by inactivating area LM and data recorded using single-contact electrodes, yielding perfectly isolated single cell responses, are in agreement with these results. We are currently collecting more data and running more sophisticated analyses to corroborate these preliminary findings. To exclude the possibility that we are directly modulating activity in V1 by aberrant locally transfected neurons, histological sections were made encompassing both the virally expressing higher visual slab of cortex and the V1 recording site marked by coating the electrode with a lipophilic dye (DiI) (Fig. 2f: left panel). These showed an absence of any virally expressing neurons anywhere near the electrode track within V1. [Objective 2]
Although these preliminary data are promising, we will need to do many more experiments and controls, to convincingly conclude that intercortical communication is vital for the surround suppression in V1. Importantly, given a strong dependency of surround suppression on anesthetic state (Fig. 2g) we will need to supplement these results obtained in anesthetized mice with results obtained in the awake and behaving mouse, which is currently our main aim. [Objective 3; Objectives 2 & 3: manuscript in preparation]
Given their cell-specificity, spatial and temporal precision, optogenetic interventions harbor an important and influential instrument to disentangle the basic processing mechanisms of cortical and subcortical functioning. Moreover clinical use in remediating psychiatric dysfunctions will also be an achievable goal in the coming decennium.

1. Macknick, S., Martinez-Conde, S., & Blakeslee, S. Sleights of mind: What the neuroscience of magic reveals about our everyday deceptions. New York: Henry Holt. (2010)
2. Eagleman, D. Visual illusions and neurobiology. Nat Rev Neurosci, 2, 920-6 (2001)
3. Chubb, C., Sperling, G., & Solomon, JA. Texture interactions determine perceived contrast. Proc Natl Acad Sci USA. 86, 9631-5 (1989)
4. Roberts, B., Harris, MG., & Yates, TA. The roles of inducer size and distance in the Ebbinghaus illusion (Titchener circles). Perception, 34, 847-56 (2005)
5. Boring, EG. A new ambiguous figure. Am J Psychol, 42, 444-5 (1930)
6. Roelfsema, PR. Cortical algorithms for perceptual grouping. Annu Rev Neurosci. 29, 203-27 (2006)
7. Murray, S., Boyaci, H., & Kersten, D. The representation of perceived angular size in human primary visual cortex. Nat Neurosci, 9, 429-34 (2006)
8. Schwarzkopf, DS., Song, C., & Rees, G. The surface area of human V1 predicts the subjective experience of object size. Nat Neurosci. 14, 28-30 (2011)
9. Lamme, VAF. The neurophysiology of figure-ground segregation in primary visual cortex. J Neurosci, 15, 1605- 15 (1995)
10. Zipser, K., Lamme, VAF., & Schiller, P. Contextual modulation in primary visual cortex. J Neurosci, 16, 7376-89 (1996)
11. Hupé, J.M. James, A.C. Payne, B.R. Lomber, S.G. Girard, P., & Bullier, J. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature. 394, 784-7 (1998)
12. Parkkonen, L., Andersson, J., Hämäläinen, M., & Hari, R. Early visual brain areas reflect the percept of an ambiguous scence. Proc Natl Acad Sci USA, 105, 20500-4 (2008)
13. Nassi, JJ., Lomber, SG., & Born, RT. Reversible inactivation of cortico-cortical feedback in awake primate visual cortex. Abstract Ann Meeting Soc Neurosci, Chicago (2009)
14. Boyden, ES., Zhang, F., Bamberg, E., Nagel, G., & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat Neurosci. 8, 1263-8 (2005)
15. Deisseroth, K., Feng, G., Majewska, AK., Miesenböck, G., Ting, A., & Schnitzer, MJ. Next-generation optical technologies for illuminating genetically targeted brain circuits. J Neurosci. 26, 10380-6 (2006)
16. Nelson, J., & Frost B. Orientation-selective inhibition from beyond the classic visual receptive field. Brain Res. 13, 359-65 (1978)
17. Levitt, JB., & Lund, JS. Contrast dependence of contextual effects in primate visual cortex. Nature. 387, 73-6 (1997)
18. Adesnik, H., Burns, W., Taniguchi, H., Huang, Z., & Scanziani, M. A neural circuit for spatial summation in visual cortex. Nature. 490, 226-31 (2012)
19. Priebe, NJ., Ferster, D. Mechanisms underlying cross-orientation suppression in cat visual cortex. Nat Neurosci, 9, 552-61 (2006)
20. Priebe, NJ. & Ferster, D. Inhibition, spike threshold, and stimulus selectivity in primary visual cortex. Neuron. 57, 482-97 (2008)
21. Bolz, J., & Gilbert, C.D. Generation of end-inhibition in the visual cortex via interlaminar connections. Nature. 320, 362-5 (1986)
22. Angelucci, A., & Bressloff, PC. Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons. Prog Brain Res, 154, 93-120 (2006)
23. Alitto, H. & Usrey, M. Corticothalamic feedback and sensory processing. Curr Opinion in Neurobiology. 13, 440-5 (2003)
24. Murphy, PC., & Sillito, AM. Corticofugal feedback influences the generation of length tuning in the visual pathway. Nature, 329, 727-9.
25. Ma, W., Liu, B., Li, Y., Huang, J., Zhang, L., Tao, H. Visual representations by cortical somatostatin inhibitory neurons – selective but with weak and delayed responses. J Neurosci. 30, 14371-9 (2012)
26. Lee, S., Kwan, A., Zhang, S., Phoumthipphavong, V., Flannery, JG., Masmanidis, SC., Taniguchi, H., Huang, ZJ., Zhang, F., Boyden, ES., Deisseroth, K., & Dan, Y. Activation of specific interneurons improves V1 feature selectivity and visual perception. Nature, 488, 379-83 (2012)
27. Wilson, N., Runyan, C. Wang, F., & Sur, M. Division and subtraction by distinct cortical inhibitory networks in vivo. Nature. 488, 343-8 (2012)
28. Kätzel, D., Zemelman, BV., Buetfering, C., Wölfel, M., & Miesenböck, G. The columnar and laminar organization of inhibitory connections to neocortical excitatory cells. Nat Neurosci, 14, 100-7 (2010)
29. Heimel, JA., Hartman, RJ., Hermans, JM., & Levelt, CN. Screening mouse vision with intrinsic signal optical imaging. Eur J Neurosci, 25, 795-804 (2006)
30. Andermann, ML., Kerlin, AM., Roumis, DK., Glickfeld, LL., & Reid, CL. Functional specialization of mouse higher visual cortical areas. Neuron, 72, 1025-39 (2011)
31. Marshel, JH., Garrett, ME., Nauhaus, I., & Callaway, EM. Functional specialization of seven mouse visual cortical areas. Neuron, 72, 1040-54 (2011)