Animals Seven 4-6 month old male mice in a C57Bl6/J background were used for the first data set ( Figures 1 2 , and 3 ). Four VGAT-Cre mice were used for the optogenetic activation experiments and four others were used for the optogenetic inhibition ( Figure 6 ). A total of seven VGAT-Cre mice were used for histology experiments ( Figure S1 ). Three VGAT-Cre mice were used for visual thalamic optogenetic tagging and two mice used for anterior thalamic optogenetic tagging ( Figures 4 and 5 ). All research involving mice have been conducted according to the Institutional Animal Care and Use Committee (IACUC) guidelines at MIT. All procedures were approved by the IACUC.

Implant Design, Printing, and Loading Drive bodies were designed in 3D CAD software (SolidWorks, Concord, MA) and stereolithographically printed in Accura 55 plastic (American Precision Prototyping, Tulsa, OK). Each drive was loaded with 6-12 individual, independently movable microdrives. Each microdrive was loaded with 1-3, 12.5 micron nichrome stereotrodes or 25 micron tungsten stereotrodes (California Fine Wire Company, Grover Beach, CA), which were pinned to a custom-designed electrode interface board (EIB) (Sunstone Circuits, Mulino, OR). Two electromyography (EMG) wires, two electroencephalograph (EEG) wires and one ground wire (A-M systems, Carlsborg, WA), were also affixed to the EIB. An optical fiber targeting TRN (Doric Lenses, Quebec, Canada) was glued to the EIB. TRN targeting was achieved by guiding stereotrodes and optical fiber through a linear array (dimensions ∼1.1 × 1.8 mm) secured to the bottom of the drive by cyanoacrylate.

Drive Implantation Surgery Mice were anesthetized with 1% isofluorane and placed in a stereotaxic frame. For each animal, five stainless-steel screws were implanted in the skull to provide EEG contacts (a prefrontal site and a cerebellar reference), ground (cerebellar), and mechanical support for the hyperdrive. A craniotomy of size ∼3 × 2 mm was drilled with a center coordinate of (M/L 2.5mm, A/P −1.0mm) for experiments targeting the rostral TRN, and (M/L 2.5mm, A/P −2.0mm) for experiments targeting caudal TRN. The implant was attached to a custom-designed stereotaxic arm, rotated 15 degrees about the median and lowered to the craniotomy. Stereotrodes were lowered slightly at the time of implantation (<500 microns) and implanted into the brain.

Electrophysiological Recording Following recovery, each animal was connected to two 16-channel preamplifier headstages or a single, custom made 32-channel preamplifier headstage (Neuralynx, Bozeman, MT). All data were recorded using a Neuralynx Digilynx recording system. Signals from each stereotrode were amplified, filtered between 0.1 Hz and 9 kHz and digitized at approximately 30 kHz. Local field potentials (LFPs) were collected from a single channel on each stereotrode. The LFP and EEG traces were amplified and filtered between 0.1 Hz and 30 kHz. The EEG was acquired as a referential signal between the ipsilateral frontal lead (at approximately A/P: +0.5mm, M/L: 0.5mm, D/V, 0.1-0.2mm, directed at cingulate) and cerebellar reference. For experiments involving the tagging of visual neurons, the EEG was a referential signal between primary visual cortex and the cerebellum. Stereotrodes were slowly lowered (over several days) in 125-250 micron steps. Spike sorting was performed offline using the MClust toolbox ( http://redishlab.neuroscience.umn.edu/mclust/MClust.html ), based on spike amplitudes and energies on the two electrodes of each stereotrode. Units were separated by hand, and cross-correlation and autocorrelation analyses were used to confirm unit separation.

Sleep State Classification We classified behavioral epochs into three states: Wake, slow-wave sleep (SWS), and rapid eye movement (REM) sleep, using simultaneously recorded EEG and EMG. The wake epochs were identified by high EMG activity, and the REM epochs were determined by a low EMG activity and high EEG theta/delta power ratio. The remaining epochs were treated as SWS epochs. In all analyses, the scoring was further verified by visual inspection by going through the data in 4 s epochs as is commonly practiced. Minimum criteria for Wake and SWS were > 16 s and REM was > 5 s.

Detection of Sleep Spindles We filtered the EEG or LFP signal within the spindle frequency band (9-15 Hz) and computed its Hilbert transform (MATLAB function “hilbert”). The envelope of the signal (1 s smoothing) was used as a basis for spindle detection. A threshold of one standard deviation (SD) was applied and each threshold crossing, with parameters of > 0.5 s and < 3 s, were initially included. These events were subsequently visually inspected before being included in the analysis. Visualization was done aided by a time-frequency plot of the EEG or LFP signal.

State-Associated TRN Unit Firing Rate During Wake, SWS and REM states, we computed the firing rate of individual TRN units with 1 s bin size and computed the mean of all instantaneous binned firing rates as a measure of arousal-related modulation of TRN unit firing rate.

TRN Unit Burst Structure Quantification Marlinski et al., 2012 Marlinski V.

Sirota M.G.

Beloozerova I.N. Differential gating of thalamocortical signals by reticular nucleus of thalamus during locomotion. Vaingankar et al., 2012 Vaingankar V.

Sanchez Soto C.

Wang X.

Sommer F.T.

Hirsch J.A. Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features. Vaingankar et al., 2012 Vaingankar V.

Sanchez Soto C.

Wang X.

Sommer F.T.

Hirsch J.A. Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features. We used the method described in () to compute the normalized burst interspike interval (ISI) shape. For examining the accelerando-decelerando burst structure, bursts were ≥ 6 spikes spaced with ≤ 30 ms window following ≥ 70 ms of silence. Based on the burst ISI sequences of each TRN unit, we used a spline function to interpolate the ISI shape with 21 points (MATLAB function “interp1”) ().

TRN Unit Burst Index Quantification For each TRN unit we computed the ISI and constructed the state space map for ISI(t) versus ISI(t+1). For a spike train with N spikes, there are N-1 ISI points in the state space map. A burst was detected if two consecutive ISIs: ISI(t) and ISI(t+1), were both smaller than 5 ms. The burst index was then computed as the ratio between the number of burst events (or ISI points) and the total number of ISI points whose values were between 10 and 100 ms. Normalization to that specific ISI range was used to control for the differences in firing rate between cells with minimal concerns about behavioral occupancy.

Unit Rate-EEG Power Correlation Smith et al., 2010 Smith A.C.

Scalon J.D.

Wirth S.

Yanike M.

Suzuki W.A.

Brown E.N. State-space algorithms for estimating spike rate functions. r(t) = [r(t), r(t+1), …, r(t+60)], and P(t) = [P(t), P(t+1), …, P(t+60)], the instantaneous Pearson correlation between r(t) and P(t) is R r , P ( t ) = cov ( r , P ) σ r σ P = E [ ( r − μ r ) ( P − μ P ) ] σ r σ P

where μ and σ denote the mean and SD, respectively. In addition, we shuffled the spike times (by randomly jittering spike trains uniformly in time with a range of [-30, 30] s) and computed the shuffled unit rate-EEG power correlation statistics and associated confidence intervals based on 500 Monte Carlo trials. Significant correlations were assigned as above or below 3SD of the shuffled unit rate-EEG power correlations (with zero mean). EEG delta (1-4 Hz) and spindle (9-15 Hz) power was computed using a Fourier transform of the broadband signal in 500-ms overlapping windows (MATLAB function: “spectrum”). Next, we applied a state-space algorithm to estimate the underlying Poisson spike rate function of binned TRN unit spikes (bin size 500 ms) (). We computed the Pearson correlation between the TRN unit’s spike rate and LFP delta power in 30 s window. Specifically, at time t, for two vectors,t) = [r(t), r(t+1), …, r(t+60)], and(t) = [P(t), P(t+1), …, P(t+60)], the instantaneous Pearson correlation betweent) and(t) iswhere μ and σ denote the mean and SD, respectively. In addition, we shuffled the spike times (by randomly jittering spike trains uniformly in time with a range of [-30, 30] s) and computed the shuffled unit rate-EEG power correlation statistics and associated confidence intervals based on 500 Monte Carlo trials. Significant correlations were assigned as above or below 3SD of the shuffled unit rate-EEG power correlations (with zero mean).

Fitting Mixtures of Gaussians and Model Selection Hastie et al., 2009 Hastie T.

Tibshirani R.

Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. x = (x 1 ,x 2 ,…,x n ) be a sample of n independent data observations. For the K-mixtures of Gaussians, the likelihood function of the data x is given by L ( θ ; x , z ) = P ( x , z | θ ) = ∏ i = 1 n ∑ k = 1 K I ( z i = k ) π k p ( x i | μ k , Σ k )

where z i ∈ { 1,2 , ⋯ , K } denotes the latent variable for each data point, 0 < π k < 1 denotes the k-th mixing coefficient for specific Gaussian; μ k and Σ k denote, respectively, the mean vector and variance (or covariance) for the k-th Gaussian, and θ = { π k , μ k , Σ k } k = 1 K ; and I ( z i = k ) is an indicator function, which is equal to 1 when the argument z i = k holds and 0 otherwise. Given an initial condition of θ , the EM algorithm iteratively runs the E-step and M-step until the likelihood function reaches a local maximum. To capture the multi-modal nature of data distributions, we use the maximum likelihood method to fit the data with mixtures of Gaussians. A common likelihood inference approach is the expectation-maximization (EM) algorithm (). Without loss of generality, let= (,…,) be a sample of n independent data observations. For the K-mixtures of Gaussians, the likelihood function of the datais given bywheredenotes the latent variable for each data point,denotes the k-th mixing coefficient for specific Gaussian;anddenote, respectively, the mean vector and variance (or covariance) for the k-th Gaussian, and; andis an indicator function, which is equal to 1 when the argument z= k holds and 0 otherwise. Given an initial condition of, the EM algorithm iteratively runs the E-step and M-step until the likelihood function reaches a local maximum. For model selection (i.e., in order to select the number of mixtures K), we use the well-established statistical criteria, such as Akaike’s information criterion (AIC), Bayesian information criterion (BIC), or the likelihood ratio test (LRT). Upon the convergence of the EM algorithm, let LL k denote the final log-likelihood value of the data from fitting k-mixtures of Gaussians. Comparing two models, say k-mixtures versus (k-1)-mixtures, we select the k-mixtures (bigger model) if the following rule holds. LL k - LL k-1 > Critical value. Critical value = { q AIC ( q / 2 ) log n BIC χ q , ( 1 − α ) 2 LRT

where q = dim ( θ b i g ) − dim ( θ s m a l l ) , and χ q , ( 1 − α ) 2 is the ( 1 − α ) th quantile of the Chi-square distribution with q degrees of freedom. We use all three criteria to fit the data in where the critical value depends on the specific statistical criterion being usedwhere, andis theth quantile of the Chi-square distribution with q degrees of freedom. We use all three criteria to fit the data in Figure 2 B (α = 0.05), and all criteria favor the two mixtures of Gaussians.

Pairwise TRN Unit Rate Correlation r 1 (t) = [r 1 (t), r 1 (t+1), …, r 1 (t+20)], and r 2 (t) = [r 2 (t), r 2 (t+1), …, r 2 (t+20)], the instantaneous Pearson correlation between r 1 (t) and r 2 (t) was calculated by R 12 ( t ) = cov ( r 1 , r 2 ) σ r 1 σ r 2 = E [ ( r 1 − μ r 1 ) ( r 2 − μ r 2 ) ] σ r 1 σ r 2

Spikes from individual TRN units were first binned with 500 ms bin size. We computed the instantaneous spike rates of two selected TRN units and then used a 10 s moving window to compute the Pearson correlation between two spike rate traces. Lett) = [r(t), r(t+1), …, r(t+20)], andt) = [r(t), r(t+1), …, r(t+20)], the instantaneous Pearson correlation betweent) andt) was calculated by To assess the statistical significance, we also created shuffled spike data (by randomly jittering two spike trains uniformly in time) and computed the shuffled correlation statistics and associated confidence intervals based on 500 Monte Carlo trials. Significant correlations were assigned as above or below 2SD of the shuffled unit rate correlations (with zero mean).

Spike-Phase Modulation Index We applied a Hilbert transform to compute an analytic signal and its instantaneous phase value (MATLAB function “hilbert”) for the cortical EEG. During SWS we band-passed the EEG within the spindle frequency band (9-15 Hz). For each TRN unit, we constructed a spike-phase histogram (24 bins within 0-360°; MATLAB function “rose”). To quantify phase preference for each TRN neuronal subtype, we first aligned individual spike-phase histograms to their respective peak values (spike phase modulation curve or SPMC) and then calculated each group’s spike-phase modulation (SPM) using a weighted mean of SPMCs from all units (weighted by the number of contributed spikes from each unit).

Cortical Slow Wave-Triggered PETH To characterize the TRN unit firing patterns relative to the cortical EEG slow wave (1-4 Hz), we first band-passed the cortical EEG to obtain the slow wave signal. To identify the TRN unit firing “frame,” we searched for the slow wave onset. It is known that cortical slow wave onset triggers the cortical up states (accompanied with elevated synchronous multiunit activity bursts) during SWS. To do that, we searched for the local peak values of the slow wave during SWS epochs (MATLAB function “findpeaks” with visually-guided threshold for each data set). We then used the peak as a trigger to compute the peri-event time histogram (PETH) for each TRN unit (time lag [-200, 180] ms, bin size 20 ms). Finally, we computed the group-averaged PETHs for SC and AC neurons ( Figure 3 E).

Pairwise Spike Time Synchrony We computed the spike-triggered synchrony between paired TRN units (time lag [-500, 500] ms, bin size 10 ms). We then computed the mean and SD of the correlation profile (which is nonnegative and nonsymmetric, Figure 3 G). The correlation value above or below 2SD was considered significant. We integrated the significant correlation value in a small window ([-50, 50] ms, shaded area of Figure 3 G) and computed the averaged Z-score as a measure of synchrony.

Measuring the Similarity of Phase Synchrony between Two TRN Units a and b. We used the cosine similarity to measure the similarity between these two vectors s i m i l a r i t y = a · b ‖ a ‖ × ‖ b ‖ = ∑ i a i × b i ∑ i ( a i ) 2 × ∑ i ( b i ) 2

The spike phase histogram (in delta or spindle band) measures the degree of phase synchrony of a TRN unit firing with respect to specific cortical EEG phase. To measure the similarity of phase synchrony between two TRN units, we computed the normalized (spindle or delta) phase histograms and represented them as two vectorsand. We used the cosine similarity to measure the similarity between these two vectors The similarity ranges from −1 meaning exactly opposite, to 1 meaning exactly the same, with 0 usually indicating independence, and in-between values indicating intermediate similarity or dissimilarity. In Figure S3 B, we computed the Pearson correlation between the spike-time synchrony and the similarity of delta phase synchrony among all spindle-correlated (SC) TRN unit pairs.

Two-Choice Task Setup Experiments were conducted in a standard modular test chamber (Med Associates, env-008). The chamber was modified to form an isosceles triangle: 23 × 24cm (base × height). The front wall contained two white light emitting diodes (Digikey 511-1638-ND), 6.5cm apart, mounted below two nose-pokes. A third nose-poke with response detector was centrally located on the grid floor, 6cm away from the base wall and two small Plexiglas walls (3 × 5cm), opening at an angle of 20°, served as a guide to the poke. All nosepokes contained an IR LED/IR phototransistor pair (Digikey 160-1030-ND/160-1028-ND) for response detection. At the level of the floor-mounted poke, two headphone speakers (AUVIO, 3300669) were introduced into each sidewall of the box, allowing for the delivery of sound cues. Access to the two wall-mounted nose-pokes was regulated by a rotating disk (radius 7cm) containing two holes that could be aligned with the nose-pokes underneath via a servo motor (Tower Hobbies, TS-53). Trial logic was controlled by custom software running on an Arduino Mega 2560 microcontroller. Liquid reward consisting of 10μl of evaporated milk (Nestle) was delivered directly to the lateral nose-pokes via a single-syringe pump (New Era Pump Systems, NE-1000).

Animal Training Mice were food restricted to 85%–90% of their ad libitum body weight prior to training. Mice were subsequently habituated to the task box and allowed to collect reward (10 μl evaporated milk, Nestle) freely, one session daily, for two days. A session consisted of several trials, in which reward were predelivered to a right or left nose-poke. The ability to collect reward was signaled by the rotation of a disk that had previously blocked access to the reward nose-pokes ( Figure 5 F). The appropriate nose-poke was assigned by continuous illumination of an LED directly below that nose-poke. Visual stimulus presentation was terminated upon reward collection. This training stage was introduced to teach the mice the association between the visual stimulus (LED illumination) and reward (evaporated milk). An individual trial was terminated 20 s after reward collection, and a new trial became available 10 s later. On the following two training days the animals had to poke into the correctly-assigned nose-poke for the reward to be delivered. All other parameters stayed the same. A poke into the incorrect nose-poke had no consequences. By the end of this training phase, all mice collected at least 30 reward per session. For the next stage of training, mice were trained to initiate individual trials, allowing for the establishment of a temporal window in which mice could anticipate subsequent delivery of the visual stimulus. Mice were informed about trial availability by white noise delivered through speakers surrounding an initiation nose-poke. The initiation nose-poke was placed on the box floor, 6 cm away from the front wall, midway between the two aforementioned reward nose-pokes. Initially, it was sufficient for the mice to break the infra-red beam in the initiation nose-poke momentarily in order to trigger both the wall-mounted disk rotation (to grant access to the reward nose-pokes) and simultaneous delivery of the visual stimulus (20 s). Correct poking resulted in reward delivery, while incorrect poking resulted in immediate termination of the trial by disk rotation to block access to the reward nose-pokes. Reward were available for 15 s following correct poking, followed by 5 s intertrial interval (ITI). Incorrect poking had a time-out, which consisted of a 20 s ITI. All animals initiated at least 25 times in 30 min at the end of a 3-7 day training period. The next stage of training required the mice to consistently hold their snouts in the initiation nose-poke, breaking the infra-red beam continuously for increasing time intervals (from 100 ms to 500 ms). If an animal removed its snout from the nose-poke prior to fulfilling the required time, it was counted as an interrupted initiation and the process had to be repeated. Once the mice performed at a level of at least 70% correct responses within a session, the visual stimulus was shortened consecutively to 3, 1 and finally to 0.5 s. This training phase took 5-10 days for mice to reach 70% response accuracy. Each trial contained a left or right visual stimulus which was delivered randomly.

Animal Testing During electrophysiological recordings, parameters were equivalent to the final training stage except that the required holding times were randomized, ranging between 0.5-0.7 s, rendering the precise visual stimulus presentation time unpredictable. Mice generally performed at ∼85% accuracy. For experiments with optical stimulation, one tenth of the trials contained no visual stimulus (catch trial). Test sessions were ∼1.5h in duration with no manipulation occurring during the first and last 20 min. In the middle period, laser trains were delivered every fourth trial. Laser trains consisted of 50Hz, 2-ms pulses for 1.2 s (or 500 ms), of either blue (for ChR2 activation) or yellow (for eNpHR3.0 activation) light at an intensity of 4-6mW. Laser trains started either upon initiation (attention stimulation) or visual stimulus presentation (control stimulation). Testing of ChR2 expressing mice occurred between zeitgeber time (ZT) 7-10. eNpHR3.0 expressing mice were tested during ZT 1-5, after being kept awake for 1-3h.

Attention Trial Analysis A total of 66 TRN neurons from 3 animals were recorded while animals performed the task at criterion for the first data set ( Figure S4 ). We computed the PETH relative to the imitation nose-poke of TRN units from multiple trials (short versus long latencies). To improve visualization, each row was scaled between 0 and 1, with 0 and 1 corresponding to the minimum and maximum firing rates, respectively. Based on the short latency trials, units were then sorted based on the firing rate increase between two windows, [-1, 0] s and [0.2, 1.2] s, with 0 representing the initiation nose-poke ( Figure S4 F). For optogenetic tagging experiments ( Figure 5 ), a total of 52 neurons were recorded from 3 mice (visual) and 31 neurons were recorded from 2 mice (anterior). A PETH for each neuron was generated (visual-tagged: 37 neurons; anterior-tagged: 31 neurons), aligned to the initiation nose-poke. This was done for both short- and long-latency trials. Average PETHs for all neurons within each group was generated and shown in Figure 5 G. Long latency trial PETH for both groups did not show significant modulation in the task (data not shown).

Two-Sample Proportion Test 1 and p 2 based on sample sizes n 1 and n 2 . The null hypothesis H 0 is assumed to be p 1 = p 2 . We then computed the z-score using the formula z = p 1 − p 2 p 1 ( 1 − p 1 ) n 1 + p 2 ( 1 − p 2 ) n 2

where the denominator denotes the standard error (SE). The confidence intervals (CIs) for the difference of two odds are (p 1 -p 2 ) ± z SE. Then the one-sided or two-sided P-valued associated with the z-value can be computed (z = 1.96 for a 95% CI and z = 2.58 for a 99% CI). We reject the null hypothesis H 0 if p < 0.05, otherwise we do not reject the null hypothesis. When comparing two odds ratios from two independent sample groups, we first compute the sample proportions pand pbased on sample sizes nand n. The null hypothesis His assumed to be p= p. We then computed the z-score using the formulawhere the denominator denotes the standard error (SE). The confidence intervals (CIs) for the difference of two odds are (p-p) ± z SE. Then the one-sided or two-sided P-valued associated with the z-value can be computed (z = 1.96 for a 95% CI and z = 2.58 for a 99% CI). We reject the null hypothesis Hif p < 0.05, otherwise we do not reject the null hypothesis.

Virus Injections For anatomical tracing experiments, AAV-hSyn-DIO-EGFP (serotype 2) was injected at multiple volumes (200nL – 1 μL) into thalamus of VGAT-Cre animals (A/P, −0.6mm to −1.0mm, M/L: 0.9mm; D/V −3.5mm) unilaterally. Animals were allowed to recover for at least 3 weeks for optimal virus expression, after which they were prepared for histological experiments. Cardin et al., 2009 Cardin J.A.

Carlén M.

Meletis K.

Knoblich U.

Zhang F.

Deisseroth K.

Tsai L.H.

Moore C.I. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Sohal et al., 2009 Sohal V.S.

Zhang F.

Yizhar O.

Deisseroth K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. 12 VG/mL. Viruses (250-350nl) were injected bilaterally into TRN of VGAT-cre mice (A/P, −0.6mm; ± M/L: 0.9mm; D/V −3.5mm) using a quintessential stereotactic injector (Stoelting, #53311). Mice were allowed to recover for 2-4 weeks following injection to allow for virus expression. For retrograde histological tracing and optogenetic tagging experiments ( Dittgen et al., 2004 Dittgen T.

Nimmerjahn A.

Komai S.

Licznerski P.

Waters J.

Margrie T.W.

Helmchen F.

Denk W.

Brecht M.

Osten P. Lentivirus-based genetic manipulations of cortical neurons and their optical and electrophysiological monitoring in vivo. Kato et al., 2011a Kato S.

Kobayashi K.

Inoue K.

Kuramochi M.

Okada T.

Yaginuma H.

Morimoto K.

Shimada T.

Takada M.

Kobayashi K. A lentiviral strategy for highly efficient retrograde gene transfer by pseudotyping with fusion envelope glycoprotein. Kato et al., 2011b Kato S.

Kuramochi M.

Takasumi K.

Kobayashi K.

Inoue K.

Takahara D.

Hitoshi S.

Ikenaka K.

Shimada T.

Takada M.

Kobayashi K. Neuron-specific gene transfer through retrograde transport of lentiviral vector pseudotyped with a novel type of fusion envelope glycoprotein. 2) for 2.5 hr, resuspended in PBS, washed and concentrated using Amicon Ultra 4. Titers were between 108-109 VG/mL. Mice were allowed 4-6 weeks of recovery following surgery to allow for retrograde virus expression. For optogenetic manipulation experiments, AAV-EF1α-DIO-ChR2-EYFP and AAV-EF1α-DIO-eNpHR3.0-EYFP (all serotype 2) were used (). These viruses were produced by the vector core at UNC Chapel Hill with titers around 10VG/mL. Viruses (250-350nl) were injected bilaterally into TRN of VGAT-cre mice (A/P, −0.6mm; ± M/L: 0.9mm; D/V −3.5mm) using a quintessential stereotactic injector (Stoelting, #53311). Mice were allowed to recover for 2-4 weeks following injection to allow for virus expression. For retrograde histological tracing and optogenetic tagging experiments ( Figures 4 and 5 ), pseudotyped retrograde lentiviruses (RG-LV) were used. Visually connected TRN neurons were labeled through virus injections (0.5-0.8 μl) into visual thalamus (AP, −2.1mm, ML, 2mm, DV, 2.5mm) whereas anterior thalamic connected TRN neurons were targeted through injections into the anterior complex (AP, −0.7mm, ML, 0.65mm, DV, −2.6mm). RG-LV contained the EF1α promoter, followed by a double flox cassette in which the floxed gene (in reverse orientation) was either EGFP, channelrhodopsin (ChR2), or halorhodopsin (eNpHR3.0), and followed by the woodchuck posttranscriptional regulatory element (WPRE). All vectors were modified from the original lentivector pFCGW (). For production of the viral vector, the expression plasmid along with two helper plasmids Δ8.9 and FuG–B2 (a chimeric envelope protein composed of the extracellular and transmembrane domains of rabies virus glycoprotein (RG) and the cytoplasmic domain of VSV-G; pCAGGS–FuG–B2; a gift from Kazuto Kobayashi, Fukushima Medical University, Fukushima, Japan) (), were transfected into HEK293T cells with Lipofectamine2000 (Invitrogen). Viral particles were collected from the cell culture medium, pelleted by ultracentrifugation at 65,000 × g (m/s) for 2.5 hr, resuspended in PBS, washed and concentrated using Amicon Ultra 4. Titers were between 10-10VG/mL. Mice were allowed 4-6 weeks of recovery following surgery to allow for retrograde virus expression.

Optic Fiber Implantation for Behavioral Experiments Two optic fibers, 4-5mm long, were inserted bilaterally above the TRN (A/P, −0.6mm; ± M/L: 1.4mm; D/V −2.8mm) using a stereotactic arm. Two to four stainless-steel screws were implanted into the skull to anchor the implant and fixed with dental cement. Animals were allowed to recover and training resumed one week later. For ChR2 activation a 473 nm laser and for eNpHR3.0 activation a 579nm laser were used (Opto Engine, Midvale, UT).

Immunofluorescence Coronal, 50 μm thick, free-floating sections were incubated in 0.05 M glycine and preblocked in 10% bovine serum albumin with 0.2% Triton X-100. An antibody against GFP was used to enhance tracer signal. To identify TRN inhibitory neurons, we used an antibody against the calcium binding protein parvalbumin (PV), which labels the TRN. The tissue was incubated overnight in primary antibody for GFP (1:1000, chicken polyclonal, Abcam) and/or PV (1:2000; mouse monoclonal, Swant). The sections were rinsed in 0.01 M PBS, incubated for 4 hr with a goat antichicken (for GFP polyclonal) or antimouse IgG (for PV monoclonal) conjugated with the fluorescent probes Alexa Fluor 488 (green) or 568 (red; 1:100; Invitrogen), and thoroughly rinsed with PBS. To exclude nonspecific immunoreactivity, we performed control experiments with sections adjacent to those used in the experiments described above. These included omission of the primary antibodies and incubation with all secondary antisera. Control experiments resulted in no immunohistochemical labeling.

Analysis of Anterogradely Labeled Axon Terminals Zikopoulos and Barbas, 2006 Zikopoulos B.

Barbas H. Prefrontal projections to the thalamic reticular nucleus form a unique circuit for attentional mechanisms. We analyzed anterograde labeling in the thalamus after injections in the rostral third of TRN at high magnification (1000 × ) using unbiased methods as described previously (). Using systematic, random sampling we examined 1/6 of the total volume of the thalamus in two cases, which resulted in plotting > 40000 labeled bouton profiles in each case, with the aid of a semiautomated commercial system (Neurolucida; MicroBrightField). In four other cases, we qualitatively evaluated labeling in the thalamus to extend and cross-validate our quantitative results. We normalized data by expressing the relative proportion of labeled boutons in each nucleus or region of interest as a percentage of the total number of all boutons mapped in each case.

Analysis of Retrogradely Labeled Neurons and Their Overlap Fiala, 2005 Fiala J.C. Reconstruct: a free editor for serial section microscopy. Zikopoulos and Barbas, 2006 Zikopoulos B.

Barbas H. Prefrontal projections to the thalamic reticular nucleus form a unique circuit for attentional mechanisms. Zikopoulos and Barbas, 2012 Zikopoulos B.

Barbas H. Pathways for emotions and attention converge on the thalamic reticular nucleus in primates. In five cases, we mapped the distribution and overlap of retrogradely labeled neurons in TRN after injections of tracers in visual or anterior thalamic nuclei and marked their stereotaxic coordinates. Using systematic, random sampling we examined 1/6 of the total volume of the thalamus, we outlined brain sections, placed cytoarchitectonic borders of TRN, and mapped labeled pathways in each case with the aid of a commercial computerized microscope system and motorized stage (Neurolucida; MicroBrightField). The procedure involves setting a reference point for every brain hemisphere analyzed, and as a result the outlines are automatically registered and aligned to the actual corresponding sections, retaining information about the three-dimensional (3D) coordinates of every mark or trace. To compare the distribution and overlap of retrograde labeling across cases in TRN, we reconstructed in three dimensions the entire nucleus using the free, open source software Reconstruct (). The stereotypy of TRN among animals facilitated the use of the reconstructed nucleus from a representative case as reference, as described previously (). We first imported the reference outlines and traces containing 3D information about the topography of labeling from all cases in Reconstruct, and then coregistered and aligned them to generate 3D models. This resulted in the stereotactic registration of all markers and traces that were superimposed on the 3D models. This method made it possible to compare the relative distribution of labeled TRN neurons that project to different anterior or visual thalamic nuclei. To assess the accuracy of the relative overlaps, we injected DLG and AD of the same hemisphere in two cases and mapped the two pathways in TRN using absolute stereotaxic coordinates. The independent analyses yielded similar results.

Imaging We viewed sections under high magnification (x200 – x1000) using epifluorescence or confocal laser microscopes (Olympus BX63 or Olympus Fluoview) and captured stacks of images. We acquired image stacks of several focal planes in each area of interest resulting in pictures with high depth of field of 50-μm-thick sections focused throughout the extent of their z-axes. The stacks were collapsed into single images using the maximum z-projection of stacks function in ImageJ. To image large regions of the thalamus we captured multiple adjacent high resolution images with a minimum overlap of 20% at high magnification, and compiled them into photomontages by using the automatic photomerge function in Photoshop. We applied 3D-deconvolution algorithms to images before analysis with the aid of Autodeblur (Media Cybernetics). Photomicrographs were prepared with Adobe Photoshop (Adobe Systems), and overall brightness and contrast were adjusted without retouching.