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Optogenetics Comes to the Rat Brain

30 December 2011. Optogenetics is no longer just for the mouse. A team of researchers, led by Karl Deisseroth of Stanford University in Stanford, California and Patricia Janak, of the University of California San Francisco, reports the creation of several transgenic rat lines that allow for the optical control of specific catecholamine neurons.

Optogenetics has proved to be a revolutionary technique: By pairing genetics (insertion of a gene for light-activated proteins, or opsins, into specific cells) with optics (use of light to stimulate those cells), researchers can control defined cell populations in awake behaving mice with high temporal precision—on the scale of milliseconds. Deisseroth’s group has already used this tool to show that parvalbumin neurons are critical for gamma oscillations (SRF related news story) and that altering the excitatory/inhibitory balance of the mouse brain produces behavioral abnormalities reminiscent of schizophrenia (SRF related news story). Until now, however, this technique has been used exclusively in mice. In the current study, first author Ilana Witten and colleagues have created a series of transgenic rat lines expressing the DNA recombination enzyme Cre recombinase in specific cell types, enabling future optogenetics experiments in the rat, the rodent model of choice in many areas of neuroscience research.

Putting one of their novel transgeneic rat lines to work, Witten and colleagues used optogenetics to address an old question: does stimulation of dopamine neurons in the ventral tegmental area, a region long implicated in reward processing, underlie intracranial self-stimulation? Previous attempts to link dopamine neuron activation with self-stimulation behavior have proved difficult due to the GABAergic and glutamatergic interneurons that are also present and therefore activated during electrical stimulation of the VTA ( Fields et al., 2007). In the current study, however, optogenetics allowed the researchers to activate only dopamine neurons. Following injection of a Cre-dependent opsin virus into the VTA of transgenic rats expressing Cre in dopamine neurons, administration of blue light pulses resulted in vigourous self-stimulation behavior, indicating that activation of dopamine neurons is sufficient to produce such behavior.—Allison A. Curley.

Witten IB, Steinberg EE, Lee SY, Davidson TJ, Zalocusky KA, Brodsky M, Yizhar O, Cho SL, Gong S, Ramakrishnan C, Stuber GD, Tye KM, Janak PH, Deisseroth K. Recombinase-Driver Rat Lines: Tools, Techniques, and Optogenetic Application to Dopamine-Mediated Reinforcement. Neuron. 2011; 72: 721-733. Abstract

Comments on News and Primary Papers
Comment by:  Bryan Roth, SRF Advisor
Submitted 16 December 2011
Posted 21 December 2011
  I recommend the Primary Papers

This will be a valuable resource for those who use rats for neuropsychopharmacological research. Until now, the use of Cre-recombinase lines for expressing either optogenetic (Boyden et al., 2005) and pharmacogenetic (Armbruster et al., 2007) tools for modulating neuronal activity and signaling was limited to mice. Rats, of course, are superior to mice for many behavioral studies relevant to the pathogenesis and treatment of schizophrenia.

Now, Witten et al. (from the Deisseroth lab) provide rats which will allow for the Cre-mediated expression of a variety of genes. For this study, they utilized adeno-associated viral constructs, which allow for Cre-mediated expression of opsins (AAV-DIO; Tsai et al., 2009), although these rats should be useful for expression of nearly any protein.


Boyden ES, Zhang F, Bamberg E, Nagel G, Deisseroth K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat Neurosci . 2005 Sep 1 ; 8(9):1263-8. Abstract

Armbruster BN, Li X, Pausch MH, Herlitze S, Roth BL. Evolving the lock to fit the key to create a family of G protein-coupled receptors potently activated by an inert ligand. Proc Natl Acad Sci U S A . 2007 Mar 20 ; 104(12):5163-8. Abstract

Tsai HC, Zhang F, Adamantidis A, Stuber GD, Bonci A, de Lecea L, Deisseroth K. Phasic firing in dopaminergic neurons is sufficient for behavioral conditioning. Science . 2009 May 22 ; 324(5930):1080-4. Abstract

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Related News: Commentary Brief: Optogenetics Links Interneurons and γ Oscillations

Comment by:  Guillermo Gonzalez-Burgos
Submitted 24 July 2009
Posted 24 July 2009

Blue light, yellow light, and the role of parvalbumin-positive neurons in the pathophysiology of schizophrenia
Parvalbumin (PV)-positive cells are a prominent subtype of GABA neuron that via perisomatic synapses may strongly inhibit pyramidal cell activity (however, see Szabadics et al., 2006). In schizophrenia, PV neurons have reduced levels of mRNA for PV and for GAD67, the 67 kilodalton form of the GABA-synthesizing enzyme glutamate decarboxylase. The functional consequences of the PV reduction in schizophrenia are poorly understood, but one possibility is that decreased PV partially compensates for a deficit in GABA release caused by the GAD67 reduction. PV is a slow Ca2+ buffer, and so decreasing PV in nerve terminals may facilitate GABA release during repetitive PV cell firing (for a review, see Gonzalez-Burgos and Lewis, 2008).

Why is PV cell-mediated inhibition significant to brain function? What deficits in cortical circuit function may be compensated for (at least partially) by a decrease of PV in schizophrenia? The answers to these questions depend on our knowledge of the functional role of PV neurons in cortical circuits. A leading hypothesis in this regard suggests that PV cells are essential for the production of synchronized oscillations in cortex, particularly in the γ frequency band (Bartos et al., 2007). γ oscillations are thought to be important for the transmission of information within and between neocortical areas (Salinas and Sejnowski, 2001). If so, then γ activity must be important for cognition, which is critically dependent on the flow of information across the neocortex (Singer, 1999; Fuster, 2004). Therefore, cognitive deficits (a key feature of schizophrenia) may result from the impairment of γ oscillations reported in the illness, which in turn could be a consequence of deficits in PV cell-mediated inhibition.

How is PV cell-mediated inhibition linked to the production of γ oscillations? PV neurons alone are not sufficient to produce network oscillations because, although PV cell membranes resonate at γ frequency (Pike et al., 2000), PV neurons actually lack intrinsic pacemaker activity. Therefore, whereas PV cells may be necessary to produce γ oscillations, synaptic interactions with other cell types in the cortical circuit must be necessary as well.

Studying the link between PV neuron activity and γ oscillations is complicated by the lack of tools available to selectively manipulate PV cell activity. Interestingly, two recent studies (Sohal et al., 2009; Cardin et al., 2009) employed novel “optogenetic” methods in mice, to further advance our understanding of how PV neurons are involved in γ oscillations.

Using viral vectors to drive cell type-specific expression of recombinant genes, Sohal and colleagues, as well as Cardin et al., produced expression of microbial opsins (which are light-sensitive ion channel proteins) selectively in specific populations of cortical neurons. Briefly described, modulation of neuronal activity using such optogenetic techniques works as follows: in cells expressing channelrhodopsin-2 (ChR2), illumination with blue light produces a depolarizing current that has excitatory effects (increases cell firing). On the other hand, cells expressing halorhodopsin (eNpHR) respond to yellow light with a hyperpolarizing current that has inhibitory effects (decreases cell firing). In this way, light of different wavelengths can be used to either inhibit or excite PV cells or pyramidal (PYR) neurons in the mouse cortex (in vivo or in vitro) in different experimental designs.

Sohal and colleagues first demonstrated that inhibiting the activity of eNpHR-expressing PV neurons with yellow light suppresses γ oscillations generated in vivo by rhythmic flashes of blue light applied to stimulate nearby ChR2-expressing PYR neurons. Furthermore, they show that non-rhythmic excitation of PYR cells produces non-rhythmic PYR cell firing. However, if the non-rhythmic PYR spikes are used to trigger feedback inhibition by PV neurons (driven by blue light flashes that stimulate ChR2-expressing PV cells), the addition of feedback inhibition induces a γ rhythm in PYR cell output. These results show that by means of recurrent interactions with pyramidal cells (consistent with the so-called PING models of γ rhythms; Whittington et al., 2000), PV cell activity is crucial for γ oscillations.

Finally, the experiments performed by Sohal et al. suggest that γ activity may selectively enhance the flow of information in cortical circuits. For example, their experiments demonstrate that the gain of the neuronal input-output relation (that is, the slope of the curve describing the transformation of inputs into outputs) is specifically enhanced when excitatory inputs onto a PYR cell are modulated rhythmically at γ frequency. Moreover, the amount of information flowing across synapses during interactions between PYR and PV neurons was estimated using concepts derived from information theory. The estimates from Sohal et al. showed that when network activity was driven by trains of blue flashes delivered at γ frequency, information transmission was markedly enhanced.

Using somewhat different genetic engineering approaches, Cardin and colleagues produced cell type-specific expression of ChR2 in PV cells or PYR neurons. Expression of ChR2 in PV cells of the mouse barrel (somatosensory) cortex allowed the activation of PV neurons with rhythmic flashes of light. Such manipulation produced rhythmic population activity (as detected recording local field potentials) more strongly when PV cells were driven at γ frequency compared with other frequencies. They report, in addition, some data showing that manipulation of PV cell activity has an impact on γ oscillations intrinsically generated by the cortical circuits, as opposed to γ rhythms induced by rhythmic stimuli applied by the investigators. For example, brief flashes of blue light applied to stimulate firing of ChR2-expressing PV neurons during spontaneous γ activity were able to reset the phase of the γ rhythm. Cardin et al. also demonstrate that activation of ChR2-expressing PV neurons can suppress the somatosensory response (to whisker stimulation) of nearby PYR cells. Then they go on to test an important functional role predicted for γ oscillations: that cells in a local network engaged in γ oscillations may respond differently to incoming inputs, depending on the timing of the incoming inputs relative to the phase of the γ cycle (Fries et al., 2007). In an elegant experiment, the investigators paired brief whisker stimulation with rhythmic flashes of blue light which, by stimulating ChR2-expressing PV cells, generate a γ rhythm locally. The crucial finding from this experiment is that the excitatory power of whisker stimulation was strongly dependent on the γ cycle phase at which whisker stimulation was delivered.

The data from these two recent studies briefly summarized above further consolidate the notion that PV-positive GABA neurons are key players in the mechanisms of γ synchrony in cortex. The data from these studies confirm that rhythmic PV neuron firing at γ frequency is sufficient to generate a γ rhythm in the population of postsynaptic neurons. This is not the same, however, as saying that PV neurons alone are sufficient to generate γ. Indeed, the data from these two studies point to the idea that PV neurons work in close interaction, via feedback loops, with nearby pyramidal cells. This makes sense, given that PV neurons are not intrinsic pacemakers. γ rhythms, therefore, seem to originate in complex network interactions that require the coordinated activity of pyramidal neurons, PV cells, and possibly other GABA neuron subtypes as well.

These two studies also highlight the similar importance of PV cell-dependent γ oscillations across different regions of cortex primarily involved in very different functions: Sohal and colleagues studied the role of PV neurons in frontal cortical areas, whereas Cardin et al. manipulated PV cell activity in the primary somatosensory cortex. The similarity of the findings regarding PV neuron function suggests that these neurons probably play a very similar role, at the microcircuit level, in these two very different cortical areas. Interestingly, deficits in GABA transmission, as assessed in postmortem brain studies, appear to be found in multiple areas of cortex simultaneously (Hashimoto et al., 2008). If this is indeed the case, then an impairment of γ oscillations may be present in most cortical areas. Thus, deficits in PV cell-dependent γ rhythms may explain the impairment of not only complex cognitive functions (for example, working memory), but also of more basic sensory processing which, as reviewed elsewhere (Javitt, 2009), is also impaired in schizophrenia. Finding a global deficit of GABA transmission and γ oscillations, as opposed to a deficit restricted to a single cortical area, increases the probability of success in developing pharmacological treatments.

A better understanding of the functional role of PV neurons in normal cortical circuits is crucial to developing better models that could explain how alterations of PV cells (and of other GABA neurons subtypes) originate in schizophrenia. As highlighted elsewhere (Lewis and Gonzalez-Burgos, 2008), any given alteration observed in schizophrenia may represent 1) cause, an upstream factor related to the disease pathogenesis; 2) consequence, a deleterious effect of a cause; 3) compensation, a response to either cause or consequence that helps restore homeostasis; or 4) confound, a product of factors frequently associated with, but not a part of, the disease process, or an artifact of the approach used to obtain the measure of interest. A major challenge for schizophrenia research is therefore determining to which of the four “C” categories each alteration belongs (Lewis and Gonzalez-Burgos, 2008). Certainly, information from basic neuroscience research studies such as those of Sohal et al. and Cardin et al. and many other past and future studies is extremely helpful in achieving this goal.


Szabadics J, Varga C, Molnar G, Olah S, Barzo P, Tamas G: Excitatory effect of GABAergic axo-axonic cells in cortical microcircuits. Science 311:233-235, 2006. Abstract

Gonzalez-Burgos G, Lewis DA: GABA Neurons and the Mechanisms of Network Oscillations: Implications for Understanding Cortical Dysfunction in Schizophrenia. Schizophr Bull 34:944-961, 2008. Abstract

Bartos M, Vida I, Jonas P: Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nat Rev Neurosci 8:45-56, 2007.Abstract

Salinas E, Sejnowski TJ: Correlated neuronal activity and the flow of neural information. Nat Rev Neurosci 2:539-550, 2001. Abstract

Singer W: Neuronal synchrony: a versatile code for the definition of relations? Neuron 24:111-25, 1999. Abstract

Fuster JM: Upper processing stages of the perception-action cycle. Trends Cogn Sci 8:143-145, 2004. Abstract

Pike FG, Goddard RS, Suckling JM, Ganter P, Kasthuri N, Paulsen O: Distinct frequency preferences of different types of rat hippocampal neurones in response to oscillatory input currents. J Physiol 529 Pt 1:205-213, 2000. Abstract

Sohal VS, Zhang F, Yizhar O, Deisseroth K: Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature 2009 Jun 4;459(7247):698-702. Abstract

Cardin JA, Carlen M, Meletis K, Knoblich U, Zhang F, Deisseroth K, Tsai LH, Moore CI: Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 2009 Jun 4;459(7247):663-7. Abstract

Whittington MA, Traub RD, Kopell N, Ermentrout B, Buhl EH: Inhibition-based rhythms: experimental and mathematical observations on network dynamics. Int J Psychophysiol 38:315-336, 2000. Abstract

Fries P, Nikolic D, Singer W: The gamma cycle. Trends Neurosci 30:309-316, 2007. Abstract

Hashimoto T, Bazmi HH, Mirnics K, Wu Q, Sampson AR, Lewis DA: Conserved Regional Patterns of GABA-Related Transcript Expression in the Neocortex of Subjects With Schizophrenia. American Journal of Psychiatry 162:479-489, 2008. Abstract

Javitt DC: When doors of perception close: bottom-up models of disrupted cognition in schizophrenia. Annu Rev Clin Psychol 5:249-275, 2009. Abstract

Lewis DA, Gonzalez-Burgos G: Neuroplasticity of neocortical circuits in schizophrenia. Neuropsychopharmacology 33:141-165, 2008. Abstract

View all comments by Guillermo Gonzalez-Burgos

Related News: Study Probes Brain Circuitry of Anhedonia

Comment by:  Deanna M. Barch
Submitted 11 January 2016
Posted 11 January 2016

Ferenczi and colleagues performed a series of elegant and informative experiments that mechanistically tie together multiple key aspects of anhedonia potentially related to depression, including alterations in the activity of the medial prefrontal cortex (often increased in individuals with depression), reductions in striatal responses to rewarding outcomes (often decreased among individuals with depression), and the presence of anhedonia (altered sucrose preference and social interaction in rats; reduced self-reported pleasure and pleasure responsive behaviors among individuals with depression). Further, the results of Ferenczi et al. also highlight the importance of examining alterations in the functional connectivity of brain regions, in addition to a focus on activity in any individual brain region. Such causal connections, if replicated in humans, provide additional clues for targeted interventions or prevention related to depression.

However, it is also critical to keep in mind that the mechanisms leading to "anhedonia" may differ across different forms of mental illness, and that the same endpoint (reduced motivated behaviors) may arise through different pathways. As such, this pattern of interrelationships may be more relevant for understanding anhedonia in the context of depression rather than in the context of psychosis, where there is less evidence for alterations in the experience of reward (e.g., analogy to sucrose preference) and more evidence for alterations in the anticipation of rewards or other goal-directed aspects of reward processing.

It will be important in future work to determine whether mPFC excitability modulates additional reward-related behaviors that may also be influenced by the dopamine system, such as effort-related decision making or the ability to engage cognition control in response to rewards. Further, it will be important to determine what effects modulation of activation in other prefrontal targets (e.g., regions in rats that may be more homologous to the dorsolateral prefrontal cortex or anterior cingulate in humans) have on connectivity, dopamine, and striatal function, and anhedonia-relevant behaviors, as there is also evidence for altered activity and connectivity in these regions related to anhedonia-relevant functions in humans with both depression and psychosis.


Barch DM, Pagliaccio D, Luking K. Mechanisms Underlying Motivational Deficits in Psychopathology: Similarities and Differences in Depression and Schizophrenia. Curr Top Behav Neurosci. 2015 May 31. Abstract

Collins AG, Brown JK, Gold JM, Waltz JA, Frank MJ. Working memory contributions to reinforcement learning impairments in schizophrenia. J Neurosci. 2014 Oct 8; 34(41):13747-56. Abstract

Gold JM, Strauss GP, Waltz JA, Robinson BM, Brown JK, Frank MJ. Negative symptoms of schizophrenia are associated with abnormal effort-cost computations. Biol Psychiatry. 2013 Jul 15; 74(2):130-6. Abstract

Treadway MT, Buckholtz JW, Schwartzman AN, Lambert WE, Zald DH. Worth the 'EEfRT'? The effort expenditure for rewards task as an objective measure of motivation and anhedonia. PLoS One. 2009; 4(8):e6598. Abstract

Treadway MT, Peterman JS, Zald DH, Park S. Impaired effort allocation in patients with schizophrenia. Schizophr Res. 2015 Feb; 161(2-3):382-5. Abstract

View all comments by Deanna M. Barch