CC BY
102
REVIEW COMMUNICATIONS
NEUROSCIENCE
Optogenetics: Applications in neurobiology
Maria Mikhailova1, Alex Deal2, Evgeny Budygin12, and Raul Gainetdinov1
11nstitute of Translational Biomedicine, Saint Petersburg State University, Universitetskaya nab., 7-9, Saint Petersburg, 199034, Russian Federation 2Department of Neurobiology and Anatomy Wake Forest School of Medicine, USA, Medical Center Boulevard Winston-Salem, NC 27157-1010
Address correspondence and requests for materials to Maria Mikhailova, maria.m.vita@gmail.com
Abstract
Citation: Mikhailova, M., Deal, A., Budygin, E., and Gainetdinov, R. 2017. Optogenetics: Applications in neurobiology. Bio. Comm. 62(4): 261-271. https://doi. org/10.21638/11701/spbu03.2017.405
Author's information: Maria A. Mikhailova,
Ph.D. student, orcid.org/0000-0002-
8710-6806; Alex L. Deal, Ph.D. student,
orcid.org/0000-0003-0487-5720;
Evgeny A. Budygin, Ph.D., Associate
Professor, Principal Investigator,
orcid.org/0000-0002-5675-0279;
Raul R. Gainetdinov, M.D., Ph.D., Director,
SCOPUS ID 7006340278, ResearcherlD
G-5875-201 1
Manuscript Editor: Mikhail Kostylev, Department of Neurology, Yale University, USA;
Received: February 01, 2018;
Revised: April 05, 2018;
Accepted: April 13, 2018;
Copyright: © 2017 Mikhailova et al. This is an open-access article distributed under the terms of the License Agreement with Saint Petersburg State University, which permits to the authors an unrestricted distribution and self-archiving free of charge.
Funding: This work was supported by the Russian Science Foundation (Grant no. 14-50-00069).
Competing interests: The author has declared that no competing interests exist.
Commonly used neuromodulation techniques such as electrical stimulation or pharmacologic intervention have some technical limitations that preclude dissecting particular cell- or pathway-specific functions in the brain, which is composed of billions of neurons. An advancement of molecular genetics techniques has provided a novel method in neuroscience called optogenetics. Optogenetics uses a combination of genetic and optical methods that provide a means to, with great temporal precision, experimentally control the activation or suppression of specific neuronal sub-populations in heterogeneous brain regions where multiple neuronal subtypes exist; this approach can be performed even on freely moving animals. Thus, this tool can uniquely assist in establishing causality between the disorder and the underlying pathology. Ongoing exploration of pathological mechanisms in various animal models of neuropsychiatric disorders with precise tools such as optogenetics can provide significant advances in the development of more focused approaches to treatment of these disorders. Here, we selectively highlight the major advancements gained by the use of optogenetic tools to uncover at circuit levels mechanisms relevant to neuropsychiatric disorders.
Keywords: optogenetics, opsin, neuropsychiatric disorders, addiction, schizophrenia, stress, depression, Parkinson's disease, Alzheimer's disease.
Introduction
The brain is composed of billions of neurons which form uncountable connections, with each circuit having multiple cell types, firing properties, various signaling events, and wiring patterns. Pathways of various compositions communicate in such a way that they not only control neighboring networks, but they are themselves controlled by surrounding circuits. Multiple neurotransmitters released with various patterns to activate postsynaptic receptors on other neurons are simultaneously involved in this complicated interaction. Since brain tissue has a heterogeneous nature, it is very difficult to define the role of specific neuronal types in intact circuits. However, for complete understanding of the pathology of brain disorders, it is necessary to identify the underlying particular neural circuits using precise and specific methods and approaches. Despite the progress made in the last decades using various neuromodulation techniques (i.e. electrical stimulation or pharmacologic intervention), many questions remain regarding the exact circuitries and neurochemical mechanisms involved in certain pathological conditions. Indeed, it has been incredibly difficult to completely discern the underlying molecular mechanisms with these traditional tools, which are not able to dissect cell-type or pathway-specific function of a behavioral response. In fact, previous approaches either simultaneously affected multiple types of cells and processes in the targeted area, or had slow kinetics and reversibility.
Advancement of molecular genetics has created revolutionary improvements in approaches studying normal and pathological brain functioning. Since Karl Deisseroth and colleagues developed optogenetics via microbial opsin engineering by combining genetic methods and optical instruments for guiding light to activate or inhibit the specific neural circuits to manipulate behavior with temporal precision, it has been widely applied in fundamental research of neural circuits involved in different kinds of behaviors and pathogenic mechanisms of brain diseases (Boyden et al., 2005; Tye and Deisseroth, 2012). The recent emergence of these tools provides a new effective approach for establishing the causal relationships between selective neuronal activity and behavior. Optogenetics uses a combination of genetic and optical methods to control the activity of specific cells of living tissue even within freely moving animals. This provides a means to experimentally control the activation or inhibition of specific neurons in heterogeneous brain regions where multiple neuronal sub-populations exist, and to do so with precise temporal resolution (De-isseroth et al., 2006). Here, we will discuss the principal strengths of optogenetic manipulations to uncover at circuit levels the mechanisms relevant to certain neuro-psychiatric disorders.
Optogenetic tools
Multiple components are required for a successful opto-genetic study (Deisseroth, 2015). This technology most commonly involves three core features: microbial opsins (Berndt et al., 2011; Gradinaru et al., 2010; Mattis et al., 2011; Nagel et al., 2002, 2003; Zhang et al., 2007, 2011), specific opsin gene expression which targets well-defined cellular elements in the brain (Atasoy, Aponte, Su, and Sternson, 2008; Gradinaru et al., 2010; Kuhlman et al., 2008; Tsai et al., 2009), and neurochemical release in targeted brain regions elicited by certain wavelengths of light (Adamantidis et al., 2007; Aravanis et al., 2007; Bernstein et al., 2008; Diester et al., 2011; Grossman et al., 2010; Iwai et al., 2011; Schneider, Gradinaru, Zhang, and Deisseroth, 2008; Yizhar et al., 2011a).
Opsins, light-sensitive proteins, are a crucial tool for optogenetics. By inserting different exogenous light-activated ion channels and pumps, neural circuits can be activated or inhibited in response to light depending upon the ion selectivity of the protein. Currently, the most widely applied opsin is a codon optimized form of Channelrhodopsin-2, ChR2(H134R). ChR2 is a blue-light-activated cation channel from the single cell green alga Chlamydomonas reinhardtii (Nagel et al., 2003). When ChR2 is expressed in neurons, its activation by blue light opens the channel, thereby depolarizing the membrane, which allows sodium and potassium ions to flow down their electrochemical gradient into the
cell. This blue light-activated cation channel allows millisecond-precision excitation of neurons (Nagel et al. 2005; Towne and Thompson, 2016). Conversely, there is a microbial opsin that has been optimized to push the membrane voltage in the opposite direction and thus inhibit neural activity (Han et al., 2011; Stefaniket et al., 2013). Halorhodopsin, from the archaebacterium Natronomonas pharaonis (NpHR), is a yellow light-activated protein that pumps negative chloride ions into the cell, hyperpolarizing neural membranes and inhibiting activity (Zhang et al., 2007). Archaerhodopsin-3, from the archaebacterium Halorubrum sodomense (Arch), is a green light-activated protein that pumps protons out of the cell, also hyperpolarizing neural membranes and inhibiting activity (Chow et al., 2010). Together these three proteins comprise the basis of the majority of op-togenetic studies reported in the literature.
The optogenetic opsin protein family is rapidly expanding. Newly found opsins are sensitive to higher wavelengths: yellow/red-shifted ChR types such as chan-nelrhodopsin-1 from Volvox carteri (VChR1), a red-shifted channelrhodopsin (ReaChR), and Chrimson are some of the recent ones (Klapoetke et al., 2014; Lin et al., 2013; Zhang et al., 2008). These wavelengths are more suitable because they have less absorption and scattering in the brain tissue; therefore, they allow for the optical activation of particularly deep neurons (Lin et al., 2013).
Currently, genetic engineering methods enable the expression of these opsins in specific cell types and desired subcellular locations. While the tools for achieving genetic specificity to opsin delivery are constantly improving, there are currently three general methods to transport opsins to neurons in vivo. The use of transgen-ic mice, which stably express an opsin under the control of a specific promoter, is one of the common methods (Liske et al., 2013). The limitations of this method are the lack of spatial specificity of opsin expression and the time and effort required to generate new mouse lines for new opsins and promoters. Another approach is the use of viral vectors with neural tropism, such as lentivi-rus and adeno-associated virus (AAV) (Fenno, Yizhar, and Deisseroth, 2011). These vectors can be engineered to express an opsin under the control of a specific promoter, then injected into the brain region of interest, where they can be light-activated from a fiber optic cable guided to the area of infusion via a surgical implant. An advantage of this approach is that transgenic animals are not required, allowing delivery into species that are not easily amenable to transgenesis, such as rats and primates. The disadvantage of using vectors is that AAV is relatively small with a limited packaging capability, often cited as 4.7-5.4 kilobases (Grieger and Samulski, 2005), and cannot be used in targeting strategies that require large cell-type-specific promoters (Fitzsimons, Bland, and During, 2002). The final approach integrates the ad-
vantages of transgenic mice with the brevity and precision targeting of viral methods. A large variety of mouse lines has been generated that expresses Cre-recombi-nase under different cell-type-specific promoters. This recombinase recognizes loxP sites in DNA that flank a gene and can flip the sequence into the correct orientation to allow transcription (or vice versa). When a virus expressing an inverted and floxed (flanked by loxP sites) opsin is introduced into a Cre-recombinase expressing cell, the enzyme inverts the opsin into the correct orientation to allow gene expression. Adjacent cells that do not express the Cre-recombinase are infected by the virus but do not express the opsin. By combining this with projection-based strategies, highly specific circuit targeting can be achieved (Towne and Thompson, 2016).
Optogenetic applications
The innovative application of opsin proteins for opto-genetics has revolutionized many fields of biomedical research and particularly neuroscience. Therefore, it is not surprising that Nature Methods considered this approach the "Method of the Year" in 2010. The application of optogenetic methods has allowed us a more sophisticated understanding of neuronal circuits underlying complex behaviors, such as those involved in psychiatric disorders.
Schizophrenia
Cortical inhibitory interneuron dysfunction may play an important role in the pathophysiology of certain psychiatric disorders, including autism, schizophrenia and various cognitive disorders that are associated with social deficits (Benes, 2010; Marin, 2012; Rubenstein and Merzenich, 2003; Uhlhaas and Singer, 2010; Yizhar et al., 2011b; Yizhar, 2012). Schizophrenia symptoms include hallucinations, delusions, disorganized speech and catatonic behavior, as well as negative symptoms such as emotional flatness, apathy and lack of speech (Endicott and Spitzer, 1978; Kay, Fszbein, and Opler, 1987).
With its complex cytoarchitecton structure, the neocortex contains multiple neuronal subtypes, including distinct classes of inhibitory interneurons and excitatory pyramidal neurons (Freund, 2003; Isaacson and Scanziani, 2011; Markram et al., 2004). Optogenetics allows for a clearly defined way to distinguish between these functionally discrete cell types and to record and manipulate neural activity in a precise way, unlike electrical stimulation. The breakdown in the transmission or processing of neural information is one symptom of schizophrenia, which is thought to be correlated to an imbalance between excitation and inhibition, therefore altering gamma oscillations, which are considered to have a potential role in the transfer of information
between brain regions (Kehrer, Maziashvili, Dugladze, and Gloveli, 2008; Lewis, Hashimoto, and Volk, 2005). A specific class of GABA interneurons that express the calcium-binding protein parvalbumin (PV) and have fast-spiking (FS) electrophysiological properties is widely believed to generate cortical gamma oscillations (Freund T., 2003; Fuchs et al., 2007; Sohal, 2012; Tamas, Buhl, Lorincz, and Somogyi, 2000; Whittington, Traub, and Jefferys, 1995; Ylinen et al., 1995). Traditional neu-robiological methods made it difficult to selectively manipulate PV neurons; however, optogenetic tools allow us to control PV neuron activity on a millisecond time scale.
Two recent studies used optogenetics to directly test the role of PV neurons. Cardin et al. showed that optically-induced activation of PV interneurons at 4050 Hz is sufficient to induce gamma frequency local field potential (LFP) oscillations, while activation of pyramidal neurons enhanced only lower frequency oscillations and did not elicit LFP gamma oscillations in the barrel cortex of PV-Cre mice (Cardin et al., 2009). Gamma frequency modulation of excitatory input was also found to heighten cortical circuit performance by reducing circuit noise (Cardin et al., 2009; Sohal et al., 2009). Additionally, this study showed that gamma frequency LFP oscillations generated by PV interneurons can gate sensory inputs, demonstrating a potential role for interneu-ron-driven oscillations in cortical processing. Next, using an array of optogenetic technologies (Aravanis et al., 2007; Boyden et al., 2005; Zhang et al., 2007), Deisseroth and his group have demonstrated that inhibition of PV interneurons via halorhodopsin (eNpHR) suppresses gamma oscillations, whereas activation of these neurons drives gamma oscillations in vivo (Sohal et al., 2009). These two studies have clearly revealed the ability of PV neurons to generate cortical gamma rhythms and their role in information processing in the neocortex, which may help to understand information processing deficits in schizophrenia (Cardin et al., 2009; Sohal et al., 2009).
Severe behavioral deficits in psychiatric diseases such as schizophrenia have been hypothesized to arise from a disruption in the homeostatic balance between cortical excitation and inhibition (E/I balance), possibly reflecting mechanisms such as impairments in PV interneurons within neural microcircuitry (Kehrer, Ma-ziashvili, Dugladze, and Gloveli, 2008; Markram et al., 2010; Rubenstein and Merzenich, 2003; Rubenstein et al., 2010; Vattikuti et al., 2010). Yizhar et al. tested this hypothesis in freely moving mice using several optoge-netic tools. This study found that increasing activity in excitatory neurons within the medial prefrontal cortex disrupted the social behavior of mice, whereas optoge-netically elevating activity within prefrontal interneu-rons did not. Thus, elevation, but not reduction, of cellular E/I balance elicits a profound impairment in cellular
information processing associated with specific behavioral impairments (Yizhar et al., 2011b).
Addiction
Optogenetics has importantly contributed to investigation of the neuronal pathways related to reward seeking, and has allowed identification of the adaptations that take place in these circuits following chronic exposure to drugs of abuse (Cao, Burdakov, and Sarnyai, 2011; Lobo, 2012; Stuber, Britt, and Bonci, 2012).
Two highly interconnected brain regions play critical roles in mediating reward: the ventral tegmental area (VTA) and the nucleus accumbens (NAc). The VTA is a heterogeneous brain structure that contains different neuronal populations, which include dopamine (DA), GABA and glutamate neurons. DA neurons in the VTA are the main effectors of reward (Berke and Hyman, 2000; Grace, 2000; Gonzales, Job, and Doyon, 2004; Stuber, Britt, and Bonci, 2012; Weiss and Porrino, 2002). It is well known that DA can be released naturally with two main patterns: phasic and tonic (Grace, 1991; Grace, 2000; Schultz, 1998; Wightman and Robinson, 2002). Tonic DA firing occurs at a low frequency of 5-10 Hz and results in steady-state DA concentrations that are lower than 50 nM (Justice, 1993; Parsons and Justice, 1992). In contrast, phasic, or burst, firing of DA neurons at frequencies of more than 30 Hz leads to large, transient increases in DA concentrations, which may significantly exceed 50 nM (Aragona et al., 2008; Freeman, Meltzer, and Bunney, 1985; Grace and Bunney, 1983, 1984; Hyland et al., 2002; Wightman and Robinson, 2002; Wightman and Zimmerman, 1990). These distinct characteristics of phasic and tonic DA release suggest divergent roles of each pattern in the control of DA-related behaviors. Recently, the emergence of optogenetics has allowed us to better explore the causal relationship between accumbal DA and addictive behavior. Optogenetic activation of VTA DA neurons can selectively induce DA release in accumbal terminal fields with very high temporal and spatial precision. Moreover, optical stimulation of the VTA was shown to mimic phasic and tonic DA release (Bass et al., 2013; Tsai et al., 2009). Using a combinatorial viral-mediated gene delivery approach to express ChR2 in mesolimbic DA neurons in rats, our group revealed that tonic optogenetic stimulation of VTA-nucleus accumbens DA release significantly decreased reward consummatory behavior, possibly by masking phasic activity that is thought to be essential for reward-based behaviors (Bass et al., 2013; Mikhailova et al., 2016). Using the same approach, Tsai and his group showed that phasic, but not tonic, DA release is solely responsible for the development of conditional place preference (CPP) (Tsai et al., 2009).
These results demonstrate that phasic DA activity is sufficient to mediate behavioral conditioning (Tsai et
al., 2009). Furthermore, phasic activation of DA neurons causally enhances positive reinforcing actions in a food-seeking task (Adamantidis et al., 2011). Moreover, phasic activation was sufficient to reactivate previously extinguished food-seeking behavior in the absence of external cues (Adamantidis et al., 2011) and to enhance the initiation of approach behavior without long-term motivational regulation (Ilango et al., 2014). Optoge-netic activation of DA neurons, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited, reward-seeking behavior (Steinberg et al., 2013).
The notion that changes in excitatory drive onto NAc neurons is central to drug effects is further strengthened by studies that have directly manipulated the activity of D1 dopamine receptor (D1R) or D2 dopamine receptor (D2R) containing MSNs (Bock et al., 2013; Lobo et al., 2010) or the cholinergic interneuron populations within the NAc (Witten et al., 2010). Lobo et al. used optoge-netic tools to selectively control the firing rate of D1R and D2R neurons in the NAc and studied consequent effects on cocaine reward. This study showed that D1R and D2R containing neurons in the NAc have distinctly different effects on cocaine conditioned place preference: activating D1R-expressing neurons enhances cocaine CPP, whereas activating D2R-expressing neurons suppresses cocaine CPP (Lobo et al., 2010). These results provide insight into the molecular control of D1R and D2R neuronal activity as well as the circuit level contribution of these cell types to cocaine reward (Lobo et al., 2010). Optogenetic methods helped to establish the role of cholinergic cells, despite the fact that they represent less than 1% of NAc neurons (Witten et al., 2010). In related work that also combined optogenetics with the use of CPP, it has been shown that light-induced silencing of cholinergic neurons in the NAc blocked cocaine conditioning in freely moving mammals.
Stuber et al. demonstrated that optical stimulation of the excitatory projections from the BLA to the NAc, but not cortical projections, in mice produced robust self-stimulation. Moreover, this self-stimulation of BLA-NAc projections was dependent on D1R signalling. Brief optical inhibition of BLA-to-NAc fibers reduced cue-evoked intake of sucrose, demonstrating an important role of this specific pathway in controlling naturally occurring reward-related behavior. These data suggest that while the BLA is important for processing both positive and negative effects, the BLA-to-NAc glutamate pathway in conjunction with DA signaling in the NAc promotes motivated behavioral responding (Stuber, Britt, and Bonci, 2011). Optogenetic self-stimulation of DA neurons in the VTA has also been demonstrated (Adaman-tidis et al., 2011; Witten et al., 2011). Cohen et al. showed that activation of VTA GABA neurons inhibits VTA DA neurons in vivo and counteracts excitatory drive from
primary reward when the reward is expected. In addition, these neurons were excited by aversive stimuli, potentially contributing to suppression of firing in some DA neurons in response to aversive events (Cohen et al., 2012). Research is also underway to understand why drug-seeking behaviors persist despite severe adverse consequences, which is a hallmark of addiction (Chen et al., 2013; Seif et al., 2013). Chen et al. indicated that cocaine self-administration decreases ex vivo intrinsic excitability of deep layer pyramidal neurons in the pre-limbic cortex, which was significantly more pronounced in compulsive drug-seeking animals. Furthermore, compensating for hypoactive prelimbic cortex neurons with in vivo optogenetic prelimbic cortex stimulation significantly prevented compulsive cocaine seeking, whereas optogenetic prelimbic cortex inhibition significantly increased compulsive cocaine seeking (Chen et al., 2013). Together, these studies clearly demonstrate how optogenetics has revealed the causality of changes in brain neurotransmission for addictive behaviors and has begun to identify related circuitries.
Stress and Anxiety
Commonly comorbid with addiction, stress-related disorders can influence reward seeking and reward taking behavior and have been similarly explored using optogenetics. Anxiety disorders are a result of aberrant stress responses characterized by a hyper-alert state of arousal which can be exhibited as a multitude of varied symptoms (Lieb, 2005). Due to the diverse manifestations of anxiety disorders, understanding the underlying circuitry driving these stress-related behaviors is an important step towards effectively treating them.
A group of regions that has garnered close examination in stress and anxiety disorders includes the shell region of the NAc, the bed nucleus of the stria terminalis (BNST), and the medial (MeA), central (CeA), and basolateral (BLA) nuclei of the amygdala (sometimes refered to as the "extended amygdala"; Sparta, Jennings, Ung, and Stuber, 2013). The BNST, in particular, has been the focus of numerous optogenetic studies (GarciaGarcia et al., 2017; Partridge et al., 2016; Xu et al., 2016) due to its connectivity to both stress regions, particularly the amygdala, and regions involved in reward, such as the mesolimbic pathway (Sparta Jennings, Ung, and Stuber, 2013). For example, it was shown that modulation of BNST GABA inputs can occur when optically stimulated in the presence of an agonist for kappa opioid receptors, which play a role in stress-induced behavioral changes (Li et al., 2012). While some studies show that stress and stress-induced signaling can lead to changes in neuronal activity, other regions studied have demonstrated that neuronal activation can induce stress and anxiety-like behavior. It was recently found that optical
activation of ^-adrenergic receptor signaling via Gas in BLA excitatory neurons was able to induce anxiety-like behaviors in mice (Siuda et al., 2016). These experiments show that the application of optogenetics can enable scientists to explore the effects of stressful stimuli on neuronal function but also the effects of neuronal function on behavior displays, whether in the presence of a stressful stimuli or not.
Other regions and pathways have also been examined in stress and anxiety disorders, including structures with connections to the amygdala such as the medial pre-frontal cortex (mPFC), locus coeruleus (LC), and hippocampus (Felix-Ortiz et al., 2016; Luthi and Luscher, 2014; McCall et al., 2015; Seo et al, 2016; Vialou et al., 2014), as well as other circuitry such as the septohypo-thalamic circuit (Anthony et al., 2015). The LC is of particular interest because of its prominent role in the stress response. It was found that optically-induced corticotropin releasing hormone (CRH) release from projections from the amygdala to the LC resulted in anxiogenic behavior (McCall et al., 2015). Alternatively, optical stimulation of LC noradrenergic terminals in the BLA also induced anxiety-like behavior (McCall et al., 2017). Interestingly, studies have been able to take advantage of the specificity of optogenetics to target distinct efferent and afferent projections between the two regions. For example, optical stimulation of projections from the mPFC to the BLA inhibited cholecystokinin-induced anxiety (Vialou et al., 2014), while optical stimulation of inputs from the BLA to the mPFC was also anxiogenic (Felix-Ortiz et al., 2016). This is important because the ability to isolate projections and induce precise activation (or inhibition) can help establish causality in connecting the circuit of activity within these regions of interest with the related stress and anxiety-like behavior.
Depression
Depression is a common mental disorder characterized by persistent sadness and a loss of interest in activities (Kessler et al., 2003). The major behavioral symptoms include anhedonia and deficits in several aspects of reward. This suggests a certain degree of overlap among the brain regions affected by depression and drug addiction (Russo et al., 2013).
One experiment elicited a depression-related phe-notype (increased social avoidance and decreased sucrose preference) in mice undergoing a subthreshold social defeat paradigm with optogenetic phasic activation of VTA DA neurons (Chaudhury et al., 2013). Activation of these neurons also quickly induced a similar phenotype in previously resilient mice that had been subjected to repeated social defeat stress (Chaudhury et al., 2013). Furthermore, the differences in projection pathway-specificity in promoting stress susceptibility
were shown: optical activation of NAc-projecting or inhibition of PFC-projecting VTA DA neurons induced susceptibility to social-defeat stress (Chaudhury et al., 2013). In contrast to these results, following a chronic mild stress protocol, which is different from the acute social defeat stress, optogenetic phasic activation of VTA DA neurons acutely rescued the chronic mild stress-induced depression-like phenotype and alleviated deficits in sucrose preference assays while selective inhibition of VTA DA neurons acutely produced depression-related behavior in freely moving rodents (Tye et al., 2012).
Two other key brain areas of depression-related circuitry that have been targeted using optogenetics are the mPFC and dorsal raphe (DR). The mPFC contains different neuronal populations, and it is believed that this structure plays an important role in various neuro-psychiatric diseases, including depression. However, an understanding of the mechanisms by which the mPFC neurons are involved in this disorder has remained elusive. A series of optogenetic studies were carried out to investigate the role of different classes of mPFC neurons in depression-related behavior. Covington et al. (2010) performed the first optogenetic study of the mPFC in a mouse model of depression and showed that optogenetic activation of the mPFC induced an antidepressant-like effect in susceptible mice following chronic social defeat stress. Activation of mPFC terminals in the brainstem DR nucleus increased motor activity during the forced swim test, but did not affect general locomotor activity in the open field test, suggesting that this projection is particularly important for responding to behavioral challenges (Warden et al., 2012). Further work revealed that activation of mPFC terminals in the NAc generated antidepressant-like effects following social defeat stress, whereas mPFC terminal activation in the BLA had anx-iolytic effects (Vialou et al., 2014).
Parkinson's disease
Parkinson's disease (PD), a common neurodegenera-tive disease, is characterized by rigidity, tremor, postural instability and slow movement (Gelb, Oliver, and Gil-man, 1999; Hughes, Daniel, Kilford, and Lees, 1992). PD results from the death of dopamine neurons in the substantia nigra (SN). Pharmacological approaches to elevate DA levels and deep brain stimulation (DBS) have been the conventional treatments for PD for many decades. Pharmacological interventions, however, have various limitations and side effects following long-term use. Although DBS in the subgenual cingulate cortex has been used in humans to treat depression (Mayberg et al., 2005), DBS in the subthalamic nucleus (STN), a part of the basal ganglia circuit, and in other targets, has shown some therapeutic effects for treatment of the motor mainfestations of Parkinson's disease (Benabid, 2003;
Deuschl et al., 2006; Kumar et al., 1998). However, the precise cellular mechanisms of this therapy have been unclear and highly controversial. Because of the heterogeneity of brain tissue where electrodes are placed, it has been challenging to elucidate the major target cell types involved or the underlying mechanisms of DBS.
In 2009, it was reported that therapeutic effects could be accounted for by direct and selective stimulation of afferent axons projecting into the STN using optogenetics and solid-state optics to drive or inhibit an array of distinct circuit elements in freely moving parkinsonian rodents (Gradinaru et al., 2009). High-frequency stimulation of afferent fibers projecting from the motor cortex to the STN ameliorated motor symptoms, while optogenetic excitation and inhibition of subthalamic nucleus neurons had no effect. Thus, these results demonstrated an optical approach for dissection of disease circuitry and provided a new way for systematic decon-struction of disease circuits by selectively controlling individual components (Gradinaru et al., 2009). These data suggested a model for DBS treatment in which white matter tracts or axonal pathways are the most effective direct target of control. These data were later confirmed by other studies (Li et al., 2012).
Optogenetics has also been used to probe the mechanisms underlying the efficacy of grafting human pluripotent stem cell-derived DA neurons in disease models. Animals with striatal 6-OHDA lesions exhibit motor impairments that are strongly reversed following engraftment of DA neurons in the lesioned area (Kriks et al., 2011). To probe the mechanisms underlying the efficacy of this effect, Steinbeck et al. optogenetically inhibited engrafted DA stem cells after motor recovery. They reported that inhibiting DA neurons resulted in the reappearance of motor deficits, indicating the essential role of DA neurons at the lesion site (Steinbeck et al., 2015).
Alzheimer's disease
Alzheimer's disease (AD) is the debilitating neurodegen-erative disorder most commonly observed in the elderly, in which patients have problems with language, disorientation, and impaired declarative memory that affects neuronal activity at many levels. AD is the most common form of dementia that results from damage of neurons and local circuits in specific brain regions (Palop et al., 2006). The aggregation of amyloid в (Ap) peptides is considered one of the hallmarks of this pathological dysfunction in the brains of AD patients (Selkoe et al., 2012). Studies using electrical or pharmacological stimulations have shown that certain patterns of neuronal activity can modulate the formation and secretion of Ap plaques from neurons (Cirrito et al., 2005, 2008; Kamenetz et al., 2003), however, the exact mechanism of this phenom-
enon remains largely unknown. Optogenetics was used to selectively activate a specific neuronal pathway in APP transgenic mice in order to observe the causative role between a certain type of synaptic activation and Ap pathology (Yamamoto et al., 2015). Their findings suggest a connection between overactivity of the specific projection pathway and augmentation in Ap deposition. This study also provided the foundation for further research using optogenetics for chronic stimulation in animal models of neurodegenerative disorders.
Long-term memory is essential for cognition, and its disruption is pivotal to multiple neurolpsychiatric disorders, especially AD. Bero et al. found that optogenetic excitation of mPFC neurons inhibits the activation of ento-rhinal-hippocampal circuits and impairs the encoding of long-term associative memory (Bero et al., 2014). Since the development of optogenetics, there have also been some new discoveries about the mechanism of memory retrieval. It was found that contextual fear memory recall could be abolished by optogenetic inhibition of the hippocampus, but could not be influenced by pharmacological inhibition. It indicated that long-term memory retrieval, which is dependent on the hippocampus, could shift to other structures like the anterior cingulate cortex (Goshen et al., 2011; Rajasethupathy et al., 2015).
Conclusion
The use of optogenetics has greatly improved the precision with which we are able to examine discrete neuronal subpopulations in a cell- and time-specificic manner unattainable through conventional methods. Furthermore, this tool can uniquely be used to establish causality between the disorder and the underlying biology. As we examine different neuropsychiatric disorders with precise tools such as optogenetics, the accumulated data will guide the future development of new therapeutic approaches that will become more focused.
References
Adamantidis, A. R., Tsai, H-C., Boutrel, B., Zhang, F., Stuber, G. D., Budygin, E.A., Tourino, C., Bonci, A., Deisseroth, K., and de Lecea, L. 2011. Optogenetic interrogation of dopaminergic modulation of the multiple phases of reward-seeking behavior. Journal of Neuroscience 31:10829-10835. https://doi.org/10.1523/JNEUROSCI.2246-11.2011
Adamantidis, A. R., Zhang, F., Aravanis, A. M., Deisseroth, K., and de Lecea, L. 2007. Neural substrates of awakening probed with optogenetic control of hypocretin neurons. Nature 450: 420-424. https://doi.org/10.1038/ nature06310
Anthony, T.E., Dee, N., Bernard, A., Lerchner, W., Heintz, N., and Anderson, D.J. 2014. Control of stress-induced persistent anxiety by an extra-amygdala septohypothalam-ic circuit. Cell 156(3):522-536. https://doi.org/10.1016/j. cell.2013.12.040
Aragona, B.J., Cleaveland, N.A., Stuber, G.D., Day, J.J, Carel-li, R.M., and Wightman, R.M. 2008. Preferential enhancement of dopamine transmission within the nucleus accumbens shell by cocaine is attributable to a direct increase in phasic dopamine release events. Journal of Neuroscience 28:8821-8831. https://doi.org/10.1523/ JNEUROSCI.2225-08.2008 Aravanis, A.M., Wang, L.-P., Zhang, F., Meltzer, L.A., Mogri, M.Z., Schneider, M.B., and Deisseroth, K. 2007. An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology. Journal of Neural Engineering 4:S143-S156. https://doi.org/10.1088/1741-2560/4Z3/S02 Atasoy, D., Aponte, Y., Su, H.H., and Sternson, S.M. 2008. A FLEX switch targets Channelrhodopsin-2 to multiple cell types for imaging and long-range circuit mapping. Journal of Neuroscience 28:7025-7030. https://doi.org/10.1523/JNEUROSCI.1954-08.2008 Bass, C.E., Grinevich, V.P., Gioia D., Day-Brown, J.D., Bonin, K. D., Stuber, G. D., Weiner, J. L., and Budygin, E.A. 2013. Optogenetic stimulation of VTA dopamine neurons reveals that tonic but not phasic patterns of dopamine transmission reduce ethanol self-administration. Frontiers in Behavioral Neuroscience 7:173. https://doi.org/10.3389/fnbeh.2013.00173 Benabid, A.L.2003. Deep brain stimulation for Parkinson's
disease. Current Opinion in Neurobiology 13:696-706. Benes, F.M.2010. Amygdalocortical circuitry in schizophrenia: from circuits to molecules. Neuropsychopharmacol-ogy 35:239-257. https://doi.org/10.1038/npp.2009.116 Berke, J. D., and Hyman, S. E. 2000. Addiction, dopamine, and the molecular mechanisms of memory. Neuron 25:515532. https://doi.org/10.1016/S0896-6273(00)81056-9 Berndt, A., Schoenenberger, P., Mattis, J., Tye, K.M., Deisseroth, K., Hegemann, P., and Oertner, T. G. 2011. High-efficiency channelrhodopsins for fast neuronal stimulation at low light levels. Proceedings of the National Academy of Sciences of the United States of America 108:75957600. https://doi.org/10.1073/pnas.1017210108 Bernstein, J.G., Han, X., Henninger, M.A., Ko, E.Y., Qian, X., Franzesi, G.T., McConnell, J.P., Stern, P., Desimone, R., and Boyden, E.S.2008. Prosthetic systems for therapeutic optical activation and silencing of genetically-targeted neurons. Proceedings — Society of Photo-Optical Instrumentation Engineers 6854:68540H. https://doi.org/10.1117/12.768798 Bero, A. W., Meng, J., Cho, S., Shen, A. H., Canter, R. G., Ericsson, M., and Tsai, L.H.2014. Early remodeling of the neocortex upon episodic memory encoding. Proceedings of the National Academy of Sciences of the United States of America 111:11852-11857. https://doi.org/10.1073/ pnas.1408378111 Bock, R., Shin, J. H., Kaplan, A. R., Dobi, A., Markey, E., Kramer, P. F., Gremel, C. M., Christensen, C. H., Adrover, M. F., and Alvarez, V.A.2013. Strengthening the accum-bal indirect pathway promotes resilience to compulsive cocaine use. Nature Neuroscience 16:632-638. https://doi.org/10.1038/nn.3369 Boyden, E.S., Zhang, F., Bamberg, E., Nagel, G., and Deisseroth, K.2005. Millisecond-timescale, genetically targeted optical control of neural activity. Nature Neuroscience 8:1263-1268. https://doi.org/10.1038/nn1525 Cao, Z. F., Burdakov, D., and Sarnyai, Z. 2011. Optogenetics: potentials for addiction research. Addiction Biology 16:519531. https://doi.org/10.1111/j.1369-1600.2011.00386.x Cardin, J. A., Carlen, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.-H., and Moore, C.I.2009. Driving fast-spiking cells induces у rhythm and controls
sensory responses. Nature 459:663-667. https://doi. org/10.1038/nature08002 Chaudhury, D., Walsh, J.J., Friedman, A. K., Juarez, B., Ku, S. M., Koo, J.W., Ferguson, D., Tsai, H., Pomeranz, L., Christoffel, D.J., et al. 2013. Rapid regulation of depression-related behaviours by control of midbrain dopamine neurons. Nature 493:532-536. https://doi.org/10.1038/ nature11713
Chen, B.T., Yau, H.J., Hatch, C., Kusumoto-Yoshida, I., Cho, S.L., Hopf, F.W., and Bonci, A.2013. Rescuing cocaine-induced prefrontal cortex hypoactivity prevents compulsive cocaine seeking. Nature 496:359-362. https://doi. org/10.1038/nature12024 Chow, B.Y., Han, X., Dobry, A. S., Qian, X., Chuong, A. S., Li, M., Henninger, M.A., Belfort, G. M., Lin, Y., Monahan, P. E., and Boyden, E.S.2010. High-performance genetically tar- getable optical neural silencing by light-driven proton pumps. Nature 463:98-102. https://doi.org/10.1038/ nature08652
Cirrito, J.R., Kang, J.E., Lee, J., Stewart, F.R., Verges, D.K., Silverio, L. M., Bu, G., Mennerick, S., and Holtzman, D.M.2008. Endocytosis is required for synaptic activity-dependent release of amyloid-beta in vivo. Neuron 58:42-51. https://doi.org/10.1016Zj.neuron.2008.02.003 Cirrito, J. R., Yamada, K. A., Finn, M. B., Sloviter, R. S., Bales, K. R., May, P. C., Schoepp, D. D., Paul, S. M., Mennerick, S. and Holtzman, D.M.2005. Synaptic activity regulates interstitial fluid amyloid-beta levels in vivo. Neuron 48:913922. https://doi.org/10.1016/j.neuron.2005.10.028 Cohen, J.Y., Haesler, S., Vong, L., Lowell, B.B., and Uchida, N.2012. Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature 482:85-88. https://doi.org/10.1038/nature10754 Covington, H.E., III, Lobo, M.K., Maze, I., Vialou, V., Hy-man, J. M., Zaman, S., LaPlant, Q., Mouzon, E., Ghose, S., Tamminga, C.A., Neve, R.L., Deisseroth, K., and Nestler, E.J. 2010. Antidepressant effect of optogenetic stimulation of the medial prefrontal cortex. Journal of Neuroscience 30:16082-16090. https://doi.org/10.1523/ JNEUROSCI.1731-10.2010 Deisseroth, K.2015. Optogenetics: 10 years of microbial opsins in neuroscience. Nature Neuroscience 18(9):1213-1225. https://doi.org/10.1038/nn.4091 Deisseroth, K., Feng, G., Majewska, A. K., Miesenbock, G., Ting, A., and Schnitzer, M.J.2006. Next-generation optical technologies for illuminating genetically targeted brain circuits. Journal of Neuroscience 26(41 ):10380-10386. https://doi.org/10.1523/JNEUROSCI.3863-06.2006 Deuschl, G., Schade-Brittinger, C., Krack, P., et al. 2006. A randomized trial of deep-brain stimulation for Parkinson's disease. New England Journal of Medicine 355:896-908. https://doi.org/10.1056/NEJMoa060281 Diester, I., Kaufman, M.T, Mogri, M., Pashaie, R., Goo, W., Yizhar, O., Ramakrishnan, C., Deisseroth, K., and She-noy, K.V. 2011. An optogenetic toolbox designed for primates. Nature Neuroscience 14:387-397. https://doi. org/10.1038/nn.2749 Endicott, J., and Spitzer, R. L. 1918. A diagnostic interview: the schedule for affective disorders and schizophrenia. Archives of General Psychiatry 35:837-844. Felix-Ortiz, A. C., Burgos-Robles, A., Bhagat, N. D., Leppla, C. A., and Tye, K. M. 2016. Bidirectional modulation of anxiety-related and social behaviors by amygdala projections to the medial prefrontal cortex. Neuroscience 321:197-209. https://doi.org/10.1016/j.neuroscience.2015.07.041 Fenno, L., Yizhar, O., and Deisseroth, K.2011. The development and application of optogenetics. Annual Review
of Neuroscience 34:389-412. https://doi.org/10.1146/ annurev-neuro-061010-1 13817 Fitzsimons, H.L., Bland, R.J., and During, M.J.2002. Promoters and regulatory elements that improve adeno-associated virus transgene expression in the brain. Methods 28:227-236. https://doi.org/10.1016/S1046-2023(02)00227-X Freeman, A.S., Meltzer, L.T., and Bunney, B.S.1985. Firing properties of substantia nigra dopaminergic neurons in freely moving rats. Life Sciences 36:1983-1994 Freund, T.F.2003. Interneuron diversity series: rhythm and mood in perisomatic inhibition. Trends in Neurosciences 26:489-495. https://doi.org/10.1016/S0166-2236(03)00227-3 Fuchs, E. C., Zivkovic, A. R., Cunningham, M. O., et al. 2007. Recruitment of parvalbumin-positive interneurons determines hippocampal function and associated behavior. Neuron 53:591-604. https://doi.org/10.1016/j. neuron.2007.01.031 Garcia-Garcia, A.L., Canetta, S., Stujenske, J.M., Burghardt, N.S., Ansorge, M.S., Dranovsky, A., and Leonardo, E.D. 2017. Serotonin inputs to the dorsal BNST modulate anxiety in a 5-HT1A receptor-dependent manner. Molecular Psychiatry [Epub ahead of print] https://doi.org/10.1038/mp.2017.165 Gelb, D.J., Oliver, E., and Gilman, S. 1999. Diagnostic criteria for parkinson disease. Archives of Neurology 56(1):33-39. https://doi.org/10.1001/archneur.56.133 Gonzales, R.A., Job, M. O., and Doyon, W. M. 2004. The role of mesolimbic dopamine in the development and maintenance of ethanol reinforcement. Pharmacology and Therapeutics 103:121-146. https://doi.org/10.1016/j. pharmthera.2004.06.002 Goshen, I., Brodsky, M., Prakash, R., Wallace, J., Gradinaru, V., Ramakrishnan, C., and Deisseroth, K. 2011. Dynamics of retrieval strategies for remote memories. Cell 147:678689. https://doi.org/10.1016/j.cell.2011.09.033 Grace, A.A.1991. Phasic versus tonic dopamine release and the modulation of dopamine system responsivity: a hypothesis for the etiology of schizophrenia. Neuroscience 41:1-24. https://doi.org/10.1016/0306-4522(91)90196-U Grace, A.A.2000. The tonic/phasic model of dopamine system regulation and its implications for understanding alcohol and psychostimulant craving. Addiction 95:119128. https://doi.org/10.1046/j.1360-0443.95.8s2.1.x Grace, A.A., and Bunney, B.S.1983 Intracellular and extracellular electrophysiology of nigral dopaminergic neurons-1. Identification and characterization. Neuroscience 10:301-315. https://doi.org/10.1016/0306-4522(83)90135-5 Grace, A.A., and Bunney, B.S. 1984. The control of firing pattern in nigral dopamine neurons: burst firing. The Journal of Neuroscience 4:2877-2890. Gradinaru, V., Zhang, F., Ramakrishnan, C., Mattis, J., Prakash, R., Diester, I., Goshen, I., Thompson K.R., Deisseroth, K.2010. Molecular and cellular approaches for diversifying and extending optogenetics. Cell 141:154-165. https://doi.org/10.1016/j.cell.2010.02.037 Gradinaru, V., Mogri, M., Thompson, K. R., Henderson, J.M., and Deisseroth, K.2009. Optical deconstruction of parkinsonian neural circuitry. Science 324:354-359. https://doi.org/10.1126/science.1 167093 Grieger, J.C., and Samulski, R.J.2005. Packaging capacity of adeno-associated virus serotypes: impact of larger genomes on infectivity and postentry steps. Journal of Virology 79:9933-9944. https://doi.org/10.1128/ jvi.79.15.9933-9944.2005
Grossman, N., Poher, V., Grubb, M.S. et al. 2010. Multisite optical excitation using ChR2 and micro-LED array. Journal of Neural Engineering 7:016004. https://doi.org/10.1088/1741-2560/7/1/016004 Han, X., Chow, B.Y., Zhou, H., Klapoetke, N.C., Chuong, A., Rajimehr, R., Yang, A., Baratta M.V., Winkle, J., Desimone, R., and Boyden, E.S.2011. A high-light sensitivity optical neural silencer: development and application to optogenetic control of non-human primate cortex. Frontiers in Systems Neuroscience 5:18. https://doi.org/10.3389/fnsys.201 1.00018 Hughes, A.J., Daniel, S. E., Kilford, L., and Lees, A.J.1992. Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. Journal of Neurology, Neurosurgery, and Psychiatry 55:181-184. Hyland, B.I., Reynolds, J.N., Hay, J., Perk, C.G., and Miller, R. 2002. Firing modes of midbrain dopamine cells in the freely moving rat. Neuroscience 114:475-492. https://doi.org/10.1016/S0306-4522(02)00267-1 Ilango, A., Kesner, A.J., Broker, C.J., Wang, D.V., and Ikemo-to, S. 2014. Phasic excitation of ventral tegmental dopamine neurons potentiates the initiation of conditioned approach behavior: parametric and reinforcement-schedule analyses. Frontiers in Behavioral Neuroscience 8:155. https://doi.org/10.3389/fnbeh.2014.00155 Isaacson, J.S., and Scanziani, M. 2011. How inhibition shapes cortical activity. Neuron 72:231-243. https://doi.org/10.1016/j.neuron.201 1.09.027 Iwai, Y., Honda, S., Ozeki, H., Hashimoto, M., and Hirase, H. 2011. A simple head-mountable LED device for chronic stimulation of optogenetic molecules in freely moving mice. Neuroscience Research 70:124-127. https://doi.org/10.1016/j.neures.2011.01.007 Justice, J. B., Jr. 1993. Quantitative microdialysis of neurotransmitters. Journal of Neuroscience Methods 48:263-276. Kamenetz, F., Tomita, T., Hsieh, H., Seabrook, G., Borchelt, D., Iwatsubo, T., Sisodia, S., and Malinow, R.2003. APP processing and synaptic function. Neuron 37:925-937. https://doi.org/10.1016/S0896-6273(03)00124-7 Kay, S. R., Fiszbein, A., and Opler, L.A. 1987. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13:261-276. Kehrer, C., Maziashvili, N., Dugladze, T., and Gloveli, T. 2008. Altered excitatory-inhibitory balance in the NMDA-hypofunction model of schizophrenia. Frontiers in Molecular Neuroscience 1:6. https://doi.org/10.3389/ neuro.02.006.2008 Kessler, R.C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K.R., Rush, A.J., Walters, E.E., and Wang, P.S.2003. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association 289:3095-3105. https://doi.org/10.1001/ jama.289.23.3095 Klapoetke, N.C., Murata, Y., Kim, S.S., Pulver, S.R., Birdsey-Benson, A., Cho, Y. K., et al. 2014. Independent optical excitation of distinct neural populations. Nature Methods 11:338-346. https://doi.org/10.1038/nmeth.2836 Kriks, S., Shim, J.W., Piao, J., et al. 2011. Dopamine neurons derived from human ES cells efficiently engraft in animal models of Parkinson's disease. Nature 480(7378):547-551. https://doi.org/10.1038/nature10648 Kuhlman, S.J., and Huang, Z.J. 2008. High-resolution labeling and functional manipulation of specific neuron types in mouse brain by Cre-activated viral gene expression. PLoS ONE 3(4):e2005. https://doi.org/10.1371/journal. pone.0002005
Kumar, R., Lozano, A. M., Kim, Y.J., Hutchison, W. D., Sime, E., Halket, E., Lang, A.E.1998. Double-blind evaluation of subthalamic nucleus deep brain stimulation in advanced Parkinson's disease. Neurology 51:850-855. Lewis, D.A., Hashimoto, T., and Volk, D.W.2005. Cortical inhibitory neurons and schizophrenia. Nature Reviews Neuroscience 6:312-324. https://doi.org/10.1038/nrn1648 Li, C., Pleil, K.E., Stamatakis, A.M., Busan, S., Vong, L., Lowell, B.B., Stuber, G.D., Kash, T.L.2012. Presynaptic inhibition of GABA release in the BNST by kappa opioid receptor signaling. Biological Psychiatry 71(8):725-732. https://doi.org/10.1016/j.biopsych.2011.11.015 Li, Q., Ke, Y., Chan, D. C., Qian, Z. M., Yung, K. K., Ko, H., Ar-buthnott, G.W., and Yung, W.H.2012. Therapeutic deep brain stimulation in parkinsonian rats directly influences motor cortex. Neuron 76:1030-1041. https://doi.org/10.1016Zj.neuron.2012.09.032 Lieb, R. 2005. Anxiety disorders: clinical presentation and epidemiology; pp. 405-432 in: J.E.Barrett (ed.), Handbook of Experimental Pharmacology. Berlin: Springer-Verlag GmbH.
Lin, J.Y., Knutsen, P.M., Muller, A., Kleinfeld, D., and Tsi-en, R.Y. 2013. ReaChR: A red-shifted variant of chan-nelrhodopsin enables deep transcranial optoge-netic excitation. Nature Neuroscience 16:1499-1508. https://doi.org/10.1038/nn.3502 Liske, H, Qian, X., Anikeeva, P., Deisseroth, K., and Delp, S.2013. Optical control of neuronal excitation and inhibition using a single opsin protein, ChR2. Scientific Reports 3:3110. https://doi.org/10.1038/srep03110 Lobo, M. K. 2012. Lighting up the brain's reward circuitry. Annals of the New York Academy of Sciences 1260:24-33. https://doi.org/10.1111/j.1749-6632.2011.06368.x Lobo, M.K., Covington, H.E., III, Chaudhury, D., Friedman, A. K., Sun, H., Damez-Werno, D., Dietz, D. M., Zaman, S., Koo, J.W., Kennedy, P.J., Mouzon, E., Mogri, M., Neve, R.L., Deisseroth, K., Han, M.H., and Nestler, E.J.2010. Cell type-specific loss of BDNF signaling mimics optogenetic control of cocaine reward. Science 330:385-390. https://doi.org/10.1126/science.1188472 Luthi, A., and Luscher, C.2014. Pathological circuit function underlying addiction and anxiety disorders. Nature Neuroscience 17(12):1635-1643. https://doi.org/10.1038/ nn.3849
Marin, O. 2012. Interneuron dysfunction in psychiatric disorders. Nature Reviews Neuroscience 13:107-120. https://doi.org/10.1038/nrn3155 Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., and Wu, C.2004. Interneurons of the neo-cortical inhibitory system. Nature Reviews Neuroscience 5:793-807. https://doi.org/10.1038/nrn1519 Markram, K., and Markram, H. 2010. The intense world theory — a unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience 4:224. https://doi.org/10.3389/fnhum.2010.00224 Mattis, J., Tye, K. M., Ferenczi, E. A. et al. 2011. Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins. Nature Methods 9:159-172. https://doi.org/10.1038/nmeth.1808 Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Semi-nowicz, D., Hamani, C., Schwalb, J. M., and Kennedy, S. H. 2005. Deep brain stimulation for treatment-resistant depression. Neuron 45:651-660. https://doi.org/10.1016/j. neuron.2005.02.014 McCall, J. G., Al-Hasani, R., Siuda, E. R., Hong, D. Y., Norris, A.J., Ford, C.P., and Bruchas, M.R. 2015. CRH engagement of the locus coeruleus noradrenergic system medi-
ates stress-induced anxiety. Neuron 87(3):605-620. https://doi.Org/10.1016/j.neuron.2015.07.002 McCall, J.G., Siuda, E.R., Bhatti, D.L., Lawson, L.A., McElli-gott, Z. A., Stuber, G. D., and Bruchas, M. R. 2017. Locus coeruleus to basolateral amygdala noradrenergic projections promote anxiety-like behavior. eLife 6:e18247. https://doi.org/10.7554/eLife.18247 Mikhailova, M.A., Bass, C. E., Grinevich, V. P., Chappell, A. M., Deal, A. L., Bonin, K. D., Weiner, J. L., Gainetdinov, R. R., and Budygin, E.A.2016. Optogenetically-induced tonic dopamine release from VTA-nucleus accumbens projections inhibits reward consummatory behaviors. Neuroscience 333:54-64. https://doi.org/10.1016/j.neurosci-ence.2016.07.006 Nagel, G., Ollig, D., Fuhrmann, M., Kateriya, S., Musti, A.M., Bamberg, E., Hegemannet, P.2002. Channelrhodop-sin-1: a light-gated proton channel in green algae. Science 296:2395-2398. https://doi.org/10.1126/sci-ence.1072068
Nagel, G., Brauner, M., Liewald, J. F., Adeishvili, N., Bamberg, E., and Gottschalk, A.2005. Light activation of chan-nelrhodopsin-2 in excitable cells of Caenorhabditis elegans triggers rapid behavioral responses. Current Biology 15(24):2279-2284. https://doi.org/10.1016/j. cub.2005.11.032 Nagel, G., Szellas, T., Huhn, W., Kateriya, S., Adeishvili, N., Berthold, P., Ollig, D., Hegemann, P., and Bamberg, E. 2003. Channelrhodopsin-2, a directly light-gated cation-selective membrane channel. Proceedings of the National Academy of Sciences of the United States of America 100:1394013945. https://doi.org/10.1073/pnas.1936192100 Palop, J.J., and Mucke, L., 2010. Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks. Nature Neuroscience 13:812818. https://doi.org/10.1038/nn.2583 Parsons, L.H., and Justice, J.B., Jr. 1992. Extracellular concentration and in vivo recovery of dopamine in the nucleus accumbens using microdialysis. Journal of Neurochemistry 58:212-218. https://doi. org/10.1111/j.1471-4159.1992.tb09298.x Partridge, J.G., Forcelli, P.A., Luo, R., Cashdan, J. M., Schulkin, J., Valentino, R.J., and Vicini, S.2016. Stress increases GABAergic neurotransmission in CRF neurons of the central amygdala and bed nucleus stria terminalis. Neu-ropharmacology 107:239-250. https://doi.org/10.1016/j. neuropharm.2016.03.029 Rajasethupathy, P., Sankaran, S., Marshel, J. H., Kim, C. K., Fe-renczi, E., Lee, S.Y., Berndt, A., Ramakrishnan, C., Jaffe, A., Lo, M., Liston, C., and Deisseroth, K. 2015. Projections from neocortex mediate top-down control of memory retrieval. Nature 526:653-659. https://doi.org/10.1038/nature15389 Rubenstein, J.L.2010. Three hypotheses for developmental defects that may underlie some forms of autism spectrum disorder. Current Opinion in Neurology 23:118-123. https://doi.org/10.1097/WCO.0b013e328336eb13 Rubenstein, J. L., and Merzenich, M. M.2003. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain and Behavior 2:255-267. Russo, S.J., and Nestler, E.J. 2013. The brain reward circuitry in mood disorders. Nature Reviews. Neuroscience 14:609625. https://doi.org/10.1038/nrn3381 Schneider, M.B., Gradinaru, V., Zhang, F., and Deisseroth, K.2008. Controlling neuronal activity. American Journal of Psychiatry 165:562. https://doi.org/10.1176/appi. ajp.2008.08030444 Schultz, W. 1998. Predictive reward signal of dopamine neurons. Journal of Neurophysiology 80:1-27. https://doi. org/10.1152/jn.1998.80.1.1
Seif, T., Chang, S.J., Simms, J.A., Gibb, S.L., Dadgar, J., Chen, B.T., Harvey, B.K., Ron, D., Messing, R.O., Bonci, A., and Hopf, F.W.2013. Cortical activation of accumbens hyperpolarization-active NMDARs mediates aversion-resistant alcohol intake. Nature Neuroscience 16:1094-1100. https://doi.org/10.1038/nn.3445 Selkoe, D., Mandelkow, E., and Holtzman D. 2012. Deciphering Alzheimer disease. Cold Spring Harbor Perspectives in Medicine 2:a011460. https://doi.org/10.1101/cshper-spect.a011460
Seo, D-o., Funderburk, S.C., Bhatti, D.L., Motard, L.E., New-bold, D., Girven, K.S., et al. 2016. A GABAergic projection from the centromedial nuclei of the amygdala to ventromedial prefrontal cortex modulates reward behavior. The Journal of Neuroscience 36(42):10831-10842. https://doi.org/10.1523/JNEUROSCI.1164-16.2016 Siuda, E. R., Al-Hasani, R., McCall, J. G., Bhatti, D. L., and Bruchas, M.R. 2016. Chemogenetic and optogenetic activation of Gas signaling in the basolateral amygdala induces acute and social anxiety-like states. Neuropsycho-pharmacology 41(8):201 1-2023. https://doi.org/10.1038/ npp.2015.371
Sparta, D. R., Jennings, J.H., Ung, R.L., and Stuber, G.D. 2013. Optogenetic strategies to investigate neural circuitry engaged by stress. Behavioural Brain Research 255:19-25. https://doi.org/10.1016Zj.bbr.2013.05.007 Sohal, V. S. 2012. Insights into cortical oscillations arising from optogenetic studies. Biological Psychiatry 71:1039-1045. https://doi.org/10.1016/j.biopsych.2012.01.024 Sohal, V.S., Zhang, F., Yizhar, O., and Deisseroth, K. 2009. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature 459:698-702. https://doi.org/10.1038/nature07991 Stefanik, M.T., Moussawi, K., Kupchik, Y.M., Smith, K.C., Miller, R.L., Huff, M.L., Deisseroth, K., Kalivas, P.W., and LaLumiere, R.T. 2013. Optogenetic inhibition of cocaine seeking in rats. Addiction Biology 18:50-53. https://doi.org/10.1111/j.1369-1600.2012.00479.x Steinbeck, J.A., Choi, S.J., Mrejeru, A., et al. 2015. Opto-genetics enables functional analysis of human embryonic stem cell-derived grafts in a Parkinson's disease model. Nature Biotechnology 33(2):204-209. https://doi.org/10.1038/nbt.3124 Steinberg, E. E., Keiflin, R., Boivin, J. R., Witten, I. B., Deisseroth, K., and Janak, P. H. 2013. A causal link between prediction errors, dopamine neurons and learning. Nature Neuroscience 16:966-973. https://doi.org/10.1038/nn.3413 Stuber, G.D., Britt, J.P., and Bonci, A. 2012. Optogenet-ic modulation of neural circuits that underlie reward seeking. Biological Psychiatry 71:1061-1067. https://doi.org/10.1016/j.biopsych.2011.11.010 Stuber, G.D., Sparta, D.R., Stamatakis, A.M., van Leeuw-en, W.A., Hardjoprajitno, J. E., Cho, S., Tye, K. M., Kempa-doo, K. A., Zhang, F., Deisseroth, K., et al. 2011. Excitatory transmission from the amygdala to nucleus accumbens facilitates reward seeking. Nature 475:377-380. https://doi.org/10.1038/nature10194 Tamas, G., Buhl, E.H., Lorincz, A., and Somogyi, P. 2000. Proximally targeted GABAergic synapses and gap junctions synchronize cortical interneurons. Nature Neuroscience 3:366-371. https://doi.org/10.1038/73936 Towne, C., and Thompson, K.R. 2016. Overview on research and clinical applications of optogenetics. Current Protocols in Pharmacology 75:11.19.1-11.19.21. https://doi.org/10.1002/cpph.13 Tsai, H. C., Zhang, F., Adamantidis, A., Stuber, G. D., Bonci, A., de Lecea, L., and Deisseroth, K. 2009. Phasic firing in dopaminergic neurons is sufficient for behavioral condi-
tioning. Science 324:1080-1084. https://doi.org/10.1126/ science.1168878 Tye, K.M., and Deisseroth, K. 2012. Optogenetic investigation of neural circuits underlying brain disease in animal models. Nature Reviews. Neuroscience 13:251-266. https://doi.org/10.1038/nrn3171 Tye, K.M., Mirzabekov, J.J., Warden, M.R., Ferenczi, E.A., Tsai, H., Finkelstein, J., Kim, S., Adhikari, A., Thompson, K. R., Andalman, A. S., et al. 2012. Dopamine neurons modulate neural encoding and expression of depression-related behaviour. Nature 493:537-541. https://doi.org/10.1038/nature11740 Uhlhaas, P.J., and Singer, W. 2010. Abnormal neural oscillations and synchrony in schizophrenia. Nature Reviews Neuroscience 11:100-113. https://doi.org/10.1038/nrn2774 Vattikuti, S., and Chow, C.C. 2010. A computational model for cerebral cortical dysfunction in autism spectrum disorders. Biological Psychiatry 67:672-678. https://doi.org/10.1016/j.biopsych.2009.09.008 Vialou, V., Bagot, R. C., Cahill, M.E., Ferguson, D., Robison, A.J., Dietz, D.M., Fallon, B., Mazei-Robison, M., Ku, S.M., Harrigan, E., Winstanley, C.A., Joshi, T., Feng, J., Berton, O., and Nestler, E.J.2014. Prefrontal cortical circuit for depression- and anxiety-related behaviors mediated by cholecys-tokinin: role of AfosB. Journal of Neuroscience 34(11):3878-3887. https://doi.org/10.1523/JNEUROSCI.1787-13.2014 Warden, M.R., Selimbeyoglu, A., Mirzabekov, J.J., Lo, M., Thompson, K.R., Kim, S.Y., Adhikari, A., Tye, K.M., Frank, L. M., and Deisseroth, K. 2012. A prefrontal cortex-brainstem neuronal projection that controls response to behavioural challenge. Nature 492(7429):428-432. https://doi.org/10.1038/nature11617 Weiss, F., and Porrino, L.J. 2002. Behavioral neurobiology of alcohol addiction: recent advances and challenges. Journal of Neuroscience 22:3332-3337. https://doi.org/20026359 Whittington, M.A., Traub, R.D., and Jefferys, J.G. 1995. Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation. Nature 373:612-615. https://doi.org/10.1038/373612a0 Wightman, R. M., and Robinson, D. L. 2002. Transient changes in mesolimbic dopamine and their association with 'reward'. Journal of Neurochemistry 82:721-735. https://doi.org/10.1046/j.1471-4159.2002.01005.x Wightman, R. M., and Zimmerman, J.B.1990. Control of dopamine extracellular concentration in rat striatum by impulse flow and uptake. Brain Research. Brain Research Reviews 15:135-144. Witten, I. B., Lin, S. C., Brodsky, M., Prakash, R., Diester, I., Anike-eva, P., Gradinaru, V., Ramakrishnan, C., and Deisseroth, K. 2010. Cholinergic interneurons control local circuit activity and cocaine conditioning. Science 330:1677-1681. https://doi.org/10.1126/science.1193771
Witten, I.B., Steinberg, E.E., Lee, S.Y., Davidson, T.J., Zalo-cusky, K.A., Brodsky, M., Yizhar, O., Cho, S. L., Gong, S., Ramakrishnan, C., Stuber, G. D., Tye, K. M., Janak, P. H., and Deisseroth, K.2011. Recombinase-driver rat lines: tools, techniques, and optogenetic application to do-pamine-mediated reinforcement. Neuron 72:721-733. https://doi.org/10.1016/j.neuron.2011.10.028 Xu, X., Ikrar, T., Sun, Y., Santos, R., Holmes, T.C., Francesco-ni, W., and Berton, F. 2016. High-resolution and cell-type-specific photostimulation mapping shows weak excitatory vs. strong inhibitory inputs in the bed nucleus of the stria terminalis. Journal of Neurophysioogy 115(6):3204-3216. https://doi.org/10.1152/jn.01148.2015 Yamamoto, K., Tanei, Z., Hashimoto, T., Wakabayashi, T., Oku-no, H., Naka, Y., Yizhar, O., Fenno, L.E., Fukayama, M., Bito, H., Cirrito, J. R., Holtzman, D. M., Deisseroth, K., and Iwatsubo, T.2015. Chronic optogenetic activation augments aß pathology in a mouse model of Alzheimer disease. Cell Reports 11:859-865. https://doi.org/10.1016/j. celrep.2015.04.017 Yizhar, O. 2012. Optogenetic insights into social behavior function. Biological Psychiatry 71:1075-1080. https://doi.org/10.1016/j.biopsych.2011.12. 029 Yizhar, O., Fenno, L.E., Davidson, T.J., Mogri, M., and Deisseroth, K. 2011a. Optogenetics in neural systems. Neuron 71:9-34. https://doi.org/10.1016Zj.neuron.2011.06.004 Yizhar, O., Fenno, L. E., Prigge, M., Schneider, F., Davidson, T.J., O'Shea, D.J., Sohal, V.S., Goshen, I., Finkelstein, J., Paz, J.T., et al. 2011b. Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature 477:171-178. https://doi.org/10.1038/na-ture10360
Ylinen, A., Soltész, I., Bragin, A., Penttonen, M., Sik, A., and Buzsaki, G. 1995. Intracellular correlates of hippo-campal theta rhythm in identified pyramidal cells, granule cells, and basket cells. Hippocampus 5:78-90. https://doi.org/10.1002/hipo.450050110 Zhang, F., Vierock, J., Yizhar, O., Fenno, L. E., Tsunoda, S., Ki-anianmomeni, A., Prigge, M., Berndt, A., Cushman, J., Polle, J., Magnuson, J., Hegemann, P., and Deisseroth, K. 2011. The microbial opsin family of optogenetic tools. Cell 147:1446-1457. https://doi.org/10.1016/j. cell.2011.12.004 Zhang, F., Prigge, M., Beyrière, F., Tsunoda, S.P., Mattis, J., Yizhar, O., Hegemann, P., and Deisseroth, K.2008. Red-shifted optogenetic excitation: A tool for fast neural control derived from Volvox carteri. Nature Neuroscience 1 1:631-633. https://doi.org/10.1038/nn.2120 Zhang, F., Wang, L.P., Brauner, M., Liewald, J.F., Kay, K., Watzke, N., Wood, P.G., Bamberg, E., Nagel, G., Gottschalk, A., and Deisseroth, K. 2007. Multimodal fast optical interrogation of neural circuitry. Nature 446:633-639. https://doi.org/10.1038/nature05744
