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News Article | May 10, 2017
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A total of 43 male mice were used in this study and the figure contribution of each mouse is summarized in Supplementary Table 1. All mice tested were between 2 and 12 months of age. C57BL/6J mice were purchased from Taconic Biosciences and VGAT–channel rhodopsin-2 (ChR2) mice were obtained from the Jackson laboratories. VGAT-cre mice were backcrossed to C57BL/6J mice for at least six generations. All mice were kept on a 12 h–12 h light–dark cycle. All animal experiments were performed according to the guidelines of the US National Institutes of Health and the Institutional Animal Care and Use Committee at the New York University Langone Medical Center. Behavioural setup. Behavioural training and testing took place in gridded floor-mounted, custom-built enclosures made of sheet metal covered with a thin layer of antistatic coating for electrical insulation (dimensions in cm: length, 15.2; width, 12.7; height, 24). All enclosures contained custom-designed operant ports, each of which was equipped with an IR LED/IR phototransistor pair (Digikey) for nose-poke detection. Trial initiation was achieved through an ‘initiation port’ mounted on the grid floor 6 cm away from the ‘response ports’ located at the front of the chamber. Task rule cues and auditory sweeps were presented with millisecond precision through a ceiling-mounted speaker controlled by an RX8 Multi I/O processing system (Tucker-Davis Technologies). Visual stimuli were presented by two dimmable, white-light-emitting diodes (Mouser) mounted on each side of the initiation port and controlled by an Arduino Mega microcontroller (Ivrea). For the 2AFC and 4AFC tasks, two and four response ports were mounted at the angled front wall 7.5 or 5 cm apart, respectively. Response ports were separated by 1-cm divider walls and each was capable of delivering a milk reward (10 μl evaporated milk delivered by a single syringe pump (New Era Pump Systems) when a correct response was performed. For the auditory Go/No-go task environment, response and reward ports were dissociated, with the reward port placed directly underneath the response port. In the 4AFC, the two outermost ports were assigned for ‘select auditory’ responses, whereas the two innermost ports were assigned for ‘select visual’ responses. Access to all response ports was restricted by vertical sliding gates which were controlled by a servo motor (Tower Hobbies). The TDT Rx8 sound production system (Tucker Davis Technologies) was triggered through MATLAB (MathWorks), interfacing with a custom-written software running on an Arduino Mega (Ivrea) for trial logic control. Training. Prior to training, all mice were food restricted to and maintained at 85–90% of their ad libitum body weight. Training was largely similar to our previously described approach8. First, 10 μl of evaporated milk (reward) was delivered randomly to each reward port for shaping and reward habituation. Making response ports accessible signalled reward availability. Illumination of the LED at the spatially congruent side was used to establish the association with visual targets on half of the trials. On the other half, association was established with the auditory targets where an upsweep (10 to 14 kHz, 500 ms) indicated a left and a downsweep (16 to 12 kHz, 500 ms) indicated a right reward. An individual trial was terminated 20 s after reward collection, and a new trial became available 5 s later. Second, mice learned to poke in order to receive reward. All other parameters remained constant. An incorrect poke had no negative consequence. By the end of this training phase, all mice collected at least 20 rewards per 30-min session. Third, mice were trained to initiate trials. Initially, mice had to briefly (50 ms) break the infrared beam in the initiation port to trigger target stimulus presentation and render reward ports accessible. Trial rule (attend to vision or attend to audition) was indicated by 10-kHz low-pass filtered white noise (vision) or 11 kHz high-pass filtered white noise (audition) sound cues. Stimuli were presented in blocks of six trials consisting of single-modality stimulus presentation (no conflict). An incorrect response immediately rendered the response port inaccessible. Rewards were available for 15 s following correct poking, followed by a 5 s inter-trial interval (ITI). Incorrect poking was punished with a time-out, which consisted of a 30 s ITI. During an ITI, mice could not initiate new trials. Fourth, conflict trials were introduced, in which auditory and visual targets were co-presented indicating reward at opposing locations. Four different trial types were presented in repeating blocks: (1) three auditory-only trials; (2) three visual-only trials; (3) six conflict trials with auditory target; and (4) six conflict trials with visual target. The time that mice had to break the IR barrier in the initiation port was continuously increased over the course of this training stage (1–2 weeks) until it reached 0.5 s. At the same time, duration of the target stimuli was successively shortened to a final duration of 0.1 s. Once mice performed successfully on conflict trials, single-modality trials were removed and block length was reduced to three trials. Fifth, during the final stage of training, trial availability and task rule were dissociated. Broadband white noise indicated trial availability, which prompted a mouse to initiate a trial. Upon successful initiation, the white noise was immediately replaced by either low-pass or high-pass filtered noise for 0.1 s to indicate the rule. This was followed by a delay period (variable, but for most experiments it was 0.4 s) before target stimuli presentation. All block structure was removed and trial type was randomized. Particular steps were taken throughout the training and testing periods to ensure that mice used the rules for sensory selection (see Supplementary Discussion 2). The first two training steps were identical to the 2AFC training, except that auditory stimuli consisted of tone clouds (interleaved pure tones (50 ms per tone over 200 ms, 36 tones total) spanning a frequency range of 1–15 kHz) directed to the left or right ear of the mouse to indicate the side of reward delivery. In the third stage, mice were trained to recognize the difference between visual and auditory response port positions. Initially, only two reward ports were available while access to the response ports associated with the non-target modality was restricted. All other parameters were as previously described in the 2AFC. Once mice successfully oriented to both target types (about two weeks), all four response ports were made available for subsequent training. Choosing a response port of the wrong modality was punished by a brief air puff delivered directly to the response port. Mice remained on this paradigm until they reached a performance criterion of 70% accuracy on both modalities. During the fourth training stage, sensory conflict trials were introduced using the same parameters as in the 2AFC. Trial types and locations were randomized (spatial conflict was also random). Responses were scored as correct or one of three different error types (see confusion matrix in Extended Data Fig. 2e). A total of four mice were trained. A pair of electrostatic speakers (Tucker Davis Technologies) producing the auditory stimuli were placed outside of the training apparatus and sound stimuli were conveyed by cylindrical tubes to apertures located at either side of the initiation port, allowing stereotypical delivery of stimuli across trials. Trial availability was indicated by a light positioned at the top of the box and trial initiation required a 200-ms continuous interruption of the IR beam in the initiation port to ensure that the animals head was properly positioned to hear the stimuli. Following trial initiation, a second port (the response port) was opened and a pure tone stimulus was played. A 20-kHz tone signalled a ‘Go’ response, whereas frequencies above or below 20 kHz signalled a ‘No-go’ response. The pure tone stimuli were presented for 300 ms before response time, and were pseudo-randomly varied on a trial-by-trial basis, with trials divided between the Go stimulus (approximately 40% of trials) and two No-go stimuli (16 and 24 kHz, appproximately 30% of trials per frequency). After stimulus presentation, the response port was made accessible for a 3-s period. In Go trials, correct poking within the trial period (hit) rendered the reward port accessible, and reward was subsequently delivered upon poking. For a ‘miss’ in which the mouse failed to poke within the 3-s period, the reward port remained inaccessible. For a ‘correct rejection’, which involved withholding a response when No-go stimuli were played, the reward port was made accessible at the end of the 3-s period. For a ‘false alarm’, which involved a poke in the response port on a No-go trial, the reward port remained inaccessible and the next trial was delayed by a 15-s time-out, as opposed to the regular 10-s inter-trial interval. For electrophysiological recordings and experiments with optical manipulation, testing conditions were equivalent to the final stage of training. The first cohort of PFC recordings involving ‘manipulation-free mice’ included three C57BL/6 wild-type mice and one VGAT-cre mouse. The VGAT-cre mouse in this cohort, which was also used for experiments involving PFC manipulations, was initially run for an equivalent number of laser-free sessions as the three wild-type mice before any manipulation. This design was used to confirm equivalence in electrophysiological findings across genotypes, and to strengthen the overall conclusions drawn by using transgenic animals. Equivalence across genotypes can be readily appreciated by comparing the four principal component analysis (PCA) plots in Extended Data Fig. 1j. For laser sessions, laser pulses of either blue (473 nm for ChR2 activation) or yellow (560 nm for eNpHR3.0 activation) light at an intensity of 4–5 mW (measured at the tip of the optic fibres) were delivered pseudo-randomly on 50% of the trials. During most optogenetic experiments, laser stimulation occurred during the whole delay period (500 ms) of the task. For temporal-specific manipulations concurrent with electrophysiological recordings (Fig. 1k, l, 2e, f and Extended Data Figs 4, 6), laser pulses were delivered for 250 ms either during the first half, after 100 ms (following cue presentation) or the latter half of the delay period. In the high-resolution optogenetic inactivation experiment (Fig. 2h) laser pulses were 100 ms long, dividing the 500-ms delay period equally into five periods. During a session, only one condition was tested. For stabilized step function opsin (SSFO, hChR2(C128S/D156A)) experiments (Figs 3, 4 and Extended Data Fig. 8), a 50-ms pulse of blue (473 nm, 4 mW intensity) light at the beginning of the delay period was delivered to activate the opsins and a 50-ms pulse of red (603 nm, 8 mW intensity) light to terminate activation at the end of the delay period. Similarly, for MGB manipulations (Extended Data Fig. 10), SSFO was activated by a 50-ms pulse of blue (473 nm, 4 mW intensity) light before stimulus delivery and its activity was terminated by a 50-ms pulse of red (603 nm, 8 mW intensity) at stimulus offset. An Omicron-Laserage lighthub system (Dudenhofen) was used for all optogenetic manipulations. For all experiments with optogenetic manipulations, only sessions where baseline performance was ≥65% correct were included in the analysis. For all behavioural testing, single-mouse statistics were initially used to evaluate significance and effect size followed by statistical comparisons across sessions. Performance on the auditory Go/No-go task was assessed on the basis of the number of correct responses to Go stimuli (hit rate) relative to No-go stimuli (false alarm rate) and was considered sufficient if the overall discrimination index (d′ = Z − Z )) was greater than 2 for the baseline condition. In cases where multiple groups were compared, a Kruskal–Wallis one-way analysis of variance (ANOVA) was used to assess variance across groups, followed by post hoc testing. For pairwise comparisons a Wilcoxon rank-sum test was used. Data are presented as mean ± s.e.m. and significance levels were set to P < 0.05. Injections were performed using a quintessential stereotactic injector (QSI, Stoelting). All viruses were obtained through UNC Chapel Hill, virus-vector core. For PFC manipulation during electrophysiological recordings, 200 nl of AAV2-hSyn-DIO-ChR2 was injected bilaterally into the PFC of VGAT-cre mice. Bilateral injections of AAV1-hSyn-eNpHR3.0-eYFP (300 nl) were used for mediodorsal thalamus and LGN manipulations. For SSFO experiments, AAV1-CamKIIa-SSFO-GFP was injected bilateral either into PFC (200 nl) or mediodorsal thalamus (400 nl). To test the effect of mediodorsal activation on functional cortical connectivity we injected the mediodorsal thalamus with AAV1-CamKIIa-SSFO-GFP (400 nl) ipsilateral and the PFC with AAV1-hSyn-ChR2-eYFP (200 nl) contralateral to the recording site. Following virus injection, animals were allowed to recover for at least two weeks for virus expression to take place before the start of behavioural testing or tissue collection. Mice were deeply anaesthetized using 1% isoflurane. For each mouse, up to three pairs of optic fibres (Doric Lenses) were used in behavioural optogenetic experiments and stereotactically inserted at the following coordinates (in mm from Bregma): PFC, AP 2.6, ML ± 0.25, DV −1.25; mediodorsal thalamus, AP −1.4, ML ± 0.6, DV −1.5; LGN, AP −2.2, ML 2.15, DV 2.6. Up to three stainless-steel screws were used to anchor the implant to the skull and everything was bonded together with dental cement. Mice were allowed to recover with ad libitum access to food and water for one week, after which they were brought back to food regulation and behavioural training resumed. A 473-nm laser was used for ChR2 activation, whereas eNpHR3.0 activation was achieved with a laser with a wavelength of 561 nm. Laser intensities were adjusted to be 4–5 mW measured at the tip of the optic fibre, which was generally the minimum intensity required to produce behavioural effects. Custom multi-electrode array scaffolds (drive bodies) were designed using 3D CAD software (SolidWorks) and printed in Accura 55 plastic (American Precision Prototyping) as described previously21. Prior to implantation, each array scaffold was loaded with 12–18 independently movable microdrives carrying 12.5-μm nichrome (California Fine Wire Company) stereotrodes or tetrodes. Electrodes were pinned to custom-designed, 96-channel electrode interface boards (EIB, Sunstone Circuits) along with a common reference wire (A-M systems). For combined optogenetic manipulations and electrophysiological recordings of the PFC, optic fibres delivering the light beam lateral (45° angled tips) were embedded adjacent to the electrodes (Extended Data Fig. 3g). In the case of combined optogenetic PFC manipulations with mediodorsal recordings, the optic fibre was placed away from the electrodes at the appropriate spatial offset. For combined unilateral multi-site recordings of PFC and mediodorsal (four mice) with SSFO manipulations, two targeting arrays (0.5 × 0.5 mm for PFC and 0.5 × 0.35 mm for mediodorsal) where separated by 3.2 mm in the AP axis. For SSFO manipulations, optic fibres delivering a lateral light beam were implanted directly next to the array targeting either PFC or mediodorsal thalamus. To test the effect of mediodorsal activation on functional cortical connectivity, a single electrode array was targeted to the PFC unilaterally, whereas a 400-μm core optic fibre (Doric Lenses) was targeted to the contralateral PFC. In addition, a 200-μm core optic fibre was placed 2.8 mm behind the electrode array for activating SSFO in the ipsilateral mediodorsal thalamus. Similarly, to interrogate the same question in a sensory thalamocortical circuit, an electrode array was implanted unilaterally into V1 and an additional 400-μm core optic fibre (Doric Lenses) was targeted to the contralateral V1. In addition, a 200-μm core optic fibre was placed 0.5 mm anterior to the electrode array for activating SSFO in the ipsilateral LGN. During implantation, mice were deeply anaesthetized with 1% isofluorane and mounted on a stereotaxic frame. A craniotomy was drilled centred at AP 2 mm, ML 0.6 mm for PFC recordings (approximately 1 × 2.5 mm), at AP −3 mm, ML 2.5 mm for V1 (1.5 × 1.5 mm) or at AP −1 mm, ML 1.2 mm for mediodorsal recordings (approximately 2 × 2 mm). The dura was carefully removed and the drive implant was lowered into the craniotomy using a stereotaxic arm until stereotrode tips touched the cortical surface. Surgilube (Savage Laboratories) was applied around electrodes to guard against fixation through dental cement. Stainless-steel screws were implanted into the skull to provide electrical and mechanical stability and the entire array was secured to the skull using dental cement. Signals from stereotrodes (cortical recordings) or tetrodes (thalamic recordings) were acquired using a Neuralynx multiplexing digital recording system (Neuralynx) through a combination of 32- and 64-channel digital multiplexing headstages plugged into the 96-channel EIB of the implant. Signals from each electrode were amplified, filtered between 0.1 Hz and 9 kHz and digitized at 30 kHz. For thalamic recordings, tetrodes were lowered from the cortex into the mediodorsal thalamus over the course of 1–2 weeks where recording depths ranged from −2.8 to −3.2 mm DV. For PFC recordings, adjustments accounted for the change of depth of PFC across the AP axis. Thus, in anterior regions, unit recordings were obtained –1.2 to −1.7 mm DV, whereas for more posterior recordings electrodes were lowered −2 to −2.4 mm DV. Following acquisition, spike sorting was performed offline on the basis of the relative spike amplitude and energy within electrode pairs using the MClust toolbox (http://redishlab.neuroscience.umn.edu/mclust/MClust.html). Units were divided into fast spiking and regular spiking on the basis of the waveform characteristics as previously described21. In brief, the peak to trough time was measured in all spike waveforms, and showed a distinct bimodal distribution (Hartigan’s dip test, P < 10−5). These distributions separated at 210 μs, and cells with peak to trough times above this threshold were considered regular-spiking neurons and those with peak to trough times below this threshold were considered fast-spiking cells (Extended data Fig. 1g). The majority of cells (2,727) in PFC recordings were categorized as regular spiking, whereas approximately one-third (909) was categorized as fast spiking. For histological verification of electrode position, drive-implanted mice were lightly anaesthetized using isoflorane and small electrolytic lesions were generated by passing current (10 μA for 20 s) through the electrodes. All mice were then deeply anaesthetized and transcardially perfused using phosphate-buffered saline (PBS) followed by 4% paraformaldehyde. Brains were dissected and postfixed overnight at 4 °C. Brain sections (50 μm) were cut using a vibratome (LEICA) and fluorescent images were obtained on a confocal microscope (LSM800, Zeiss). Confocal images are shown as maximal projection of 10 confocal planes, 20 μm thick. For all PFC and mediodorsal neurons, changes in firing rate associated task performance were assessed using peri-stimulus time histograms (PSTHs). PSTHs were computed using a 10-ms bin width for individual neurons in each recording session4 convolved with a Gaussian kernel (25 ms full-width at half-maximum) to create a spike density function (SDF)31, 32, which was then converted to a z score by subtracting the mean firing rate in the baseline (500 ms before event onset) and dividing by the variance over the same period. For comparison of overall firing rates across conditions, trial number and window size were matched between groups. Homogeneity of variance for firing rates across conditions was determined using the Fligner–Killeen test for homoscedasticity33. For comparisons of multiple groups, a Kruskal–Wallis one-way ANOVA was used to assess variance across groups before pairwise comparisons. A total of 3,444 single units were recorded within the PFC and 974 single units were recorded in the mediodorsal across animals. Overall assessment of firing rates during the task delay period showed that individual regular-spiking PFC neurons did not exhibit sustained increases in spiking relative to baseline (population shown in Extended Data Fig. 1) and a comparison of variance homoscedasticity (Fligner–Killeen test) did not reveal changes in variance. In a subset of cells, however, a brief enhancement of spike-timing consistency at a defined moment in the delay period was observed (Fig. 1b). To formally identify these neurons we used the following steps. First, periods of increased consistency in spike-timing across trials were identified using a matching-minimization algorithm34. This approach was used to determine the best moments of spike time alignment across trials (candidate tuning peaks). The number of these candidate tuning peaks (n) was based on firing rate values during the delay period for each neurons. n was obtained by minimizing the equation: Where n is the number of observed spikes in a trial k. As such, the initial (and maximum) number of candidate peaks is equal to the median number of spikes observed across trials. With an initial number of candidate peaks in hand, their times were subsequently estimated. These times were initially placed randomly within the delay window, and iteratively adjusted to obtain the set of final candidate peak times. The result of this iterative process was the solution to the equation: Where the set of final candidate peak times S is obtained by iteratively minimizing the temporal distance between candidate peak times (in each iteration) C and the observed spike times across trials S on the basis of a penalty associated with increased temporal distance, computed across all trials k. In the first step, temporal adjustment for each candidate peak time was based on finding the local minimum of the temporal distance function, d (as described in ref. 34) after which spikes were adjusted by linear interpolation. In brief, neighbouring spike times across trials were sorted by their temporal offset to a given candidate peak time, and their linear fit was computed. Each candidate peak time was then moved to the midpoint of that fitted line, to achieve a local minimum. In a second step, cost minimization was jointly computed for all putative peaks using the Lagrange multiplier solution to the global minimization equation34 and intervals between peak times were adjusted on the basis of this global minimum. Both the local and the global minimization steps were iterated until the spike-time variance, defined as the sum of the squared distances between spikes across trials, converged and a set of final candidate peak times were determined. Next, to identify genuine tuning peaks, we applied two further conditions. First, for 75% of the trials, at least one spike was required to fall within ±25 ms of each final candidate peak time. This conservative threshold was based on the median firing rates observed during the delay (around 10 Hz) predicting that inter-trial spike distances will be greater than 50 ms if spikes were randomly distributed, making it highly improbable to fulfill this condition by chance. Second, these candidate peaks needed to have z-score values of >1.5 (equivalent to a one-sided test of significance) to be considered genuine tuning peaks. The z score of spiking across trials during the delay was computed relative to the pre-delay 500-ms baseline (10-ms binning, convolved with a 25-ms full-width at half-maximum Gaussian kernel). Obtaining a genuine tuning peak identified a unit as task-modulated, which was subsequently used for most analyses in this study. The vast majority of units only showed a single tuning peak using this method. Independent validation of this method’s validity is discussed in Supplementary Discussion 1. To estimate the extent to which task modulated units differentially encode task rules, a PCA was first performed as described previously10. Next, linear regression was applied to define the two orthogonal, task-related axes of rule type and movement direction. These analyses were performed on neural z-core time-series, separately for each comparison (trials separated by rule type or movement direction). In brief, a data matrix X of size N  × (N  × T), was constructed in which columns corresponded to the z-scored population response vectors for a given task rule or movement direction at a particular time (T) within the 1-s window following task initiation. This window size was chosen to provide sufficient samples for analysis, but only the delay period data were examined for this study. The contribution of each principal component to the population response across time was quantified by projecting the trial-type-specific z-score time-series (for example, attend to vision rule) onto individual principal components and computing the variance. The first principal component was used for all subsequent analyses as subsequent principal components were found to be uninformative in the initial analysis. Multi-variable linear regression was applied to determine the contribution of task rule and subsequent movement to principal component divergence across time for the corresponding trial-type comparisons. Specifically, linear analysis related the response of unit i at time t to a linear combination of these two task variables using the following equation: Where r (k) is the z-score response for a neuron in trial set (k) for each task variable; movement and rule. The regression coefficients (β) were used to describe the extent to which z-score time-series variation in the firing rate of the unit at a given time point describes a particular task variable. This analysis was generally only applied to correct responses. Regression coefficients were then used to identify dimensions in state space corresponding to variance across neural response data for the two task variables. Vectors of these coefficients across z-score time-series matrices separated by trial types (for example, rule1 versus rule 2) were projected onto subspaces spanned by the previously identified principal component. We next constructed task-variable axes ( ) using QR-decomposition to identify principal component separation associated with each task variable (v). To identify movement along these axes for each population response, their associated z-score time-series were projected onto these axes across time as follows: Where X is the population vector for trial type c. This projection resulted in two time-series vectors p for each task variable that compared movement across trial types (rule 1 versus rule 2; right versus left) on their corresponding axes. The difference between these two time-series was used as the main metric for information (task rule or movement) in this study. For evaluating rule information in error trials when their number permitted analysis (>20 error trials; based on empirical assessment of minimum trial numbers required for principal component divergence), trial type axes obtained from correct trials were multiplied by −1 to reverse directionality. The significance bounds for all time-series were obtained using random subsampling and bootstrapping (around 60% of total neurons per bootstrap, 200 replications). The 95% confidence bounds at each time point were then estimated on the basis of the resulting distribution. To determine whether our inference that rule information was related to tuning peaks, task-modulated spike times were randomly jittered by 500 ms and the PCA repeated. This resulted in loss of rule-information-related principal component divergence, validating our inference. To obtain a quantitative estimate of peak fidelity across multiple trials, an internal neural synchrony measurement35 was modified for short-term synchrony, which was associated with identified peaks. This approach was applied to spike trains associated with differing task conditions and responses. Each spike within the train was convolved with a Gaussian kernel with a 9-ms half width. Trials were then summed and divided by the kernel peak size and trial number giving a maximum value (for perfect alignment) of one at any point. Convolution vector values around the tuning in the baseline condition were compared to the value within the same time window in the other condition. To compute cross-correlation histograms (cross-correlograms), the MATLAB function ‘crosscorr’ was applied to whole-session spike trains from pairs of cells. Continuous traces at a 1-kHz sampling rate were first generated on the basis of the spike times, with times at which spikes occurred set to one and all other times to zero. Crosscorr was then applied to trains from all possible cell pairs, using a maximum lag time of ±50 ms. The significance of a cross-correlogram was determined by randomly jittering all spike times independently and re-computing the cross-correlogram. Jitter values were drawn from a Gaussian distribution centred at zero with a s.d. of 3 ms. This process was repeated 100 times for each pair, and if the observed peak cleared the 95% confidence bounds of all shuffled sets, the pair was determined to have a significant cross-correlation. Pairs of cells were grouped as follows: the control group was composed of cells in which only the first cell was rule-tuned. The test group was composed of pairs in which both cells were tuned. This test group was further broken down into two subgroups: one in which both cells responded to the same rule and one in which the cells responded to different rules. Within these groups, co-modulation was defined as the number of significant cross-correlograms divided by the total number of cross-correlograms. After overall group comparison using a χ2 test, proportion differences were statistically evaluated in a post hoc pairwise fashion using binomial proportion tests. To examine the effect of tuning to the same rule on co-modulation strength, the distributions of cross-correlogram peak heights were also compared for the groups of pairs described above. An empirical CDF (cumulative distribution function) was constructed using the peak heights of each group, and these distributions were compared using a signed-rank test. Finally, the relationship between cross-correlogram peak height and inter-alignment time was explored. The inter-alignment times among neuronal pairs tuned to the same rule were calculated by taking difference in spike alignment times of each pair. To more effectively assess putative monosynaptic connections, the significant cross-correlograms between tuned pairs were also re-computed at a 50-μs resolution. Significance thresholding at this resolution was repeated by determining whether a sequence of two or more successive bins of the adjusted trace, which exceeded two standard deviations of the overall trace, occurred within 10 ms of the centre bin19. Cross-correlograms containing such outliers were further characterized on the basis of their peak times. Those with peaks at 300 μs or later were categorized as putative monosynaptic connections18, 19. Among these putative connections, the pairs were split into two groups: those that were tuned to the same rule, and those that were tuned to opposite rules. To compare peak strength, spike probability was estimated by subtracting a shuffled distribution of spike times with the same average firing rate as the postsynaptic neuron and dividing by the number of spikes in the presynaptic neuron17. The distributions of the resulting peak strengths among same rule and opposite rule putative monosynaptic connections were compared using the Kolmogorov–Smirnov test. Finally, the peak strengths of these pairs were plotted against their inter-alignment time. As in the above analysis, only same rule pairs were included. To further assess the degree of rule representation in the PFC and mediodorsal thalamus, we applied two population decoding approaches, the maximum correlation coefficient (MCC) and Poisson naive Bayes (PNB) classifiers as implemented in the neural decoding toolbox36. These analyses were applied to all tuned neurons recorded from either structure, each of which were pooled into a pseudo-population for each structure (n = 604 neurons in the PFC and n = 156 neurons in the mediodorsal thalamus). For MCC decoding, firing rate response profiles in individual correct trials associated with each rule were preprocessed by converting them to a z score using the mean and variance in the corresponding trial to prevent baseline spike-rate differences from affecting classification37. For PNB classification, neuron spiking activity was modelled as a Poisson random variable with each neuron’s activity assumed to be independent. Trial-specific z scores (MCC) or spike counts (PNB) from these pseudo-populations were then repeatedly and randomly subsampled (200 resampling runs) and divided into training and test subsets (six training and two test trials per recording session across n = 360 PFC and n = 116 mediodorsal sessions). For each subsampling, the classifier was trained using the training subset to produce a predictive mean response template for each rule (i). Templates were constructed separately for 100-ms overlapping windows across the trace (step size = 20 ms) and classifiers trained for each template. The windowed classifiers allowed us to estimate the temporal evolution of information in the population. In the cross-validation step, these templates were used to predict the class for each test trial in the test set (x*) by maximizing the correlation decision function in the case of MCC or the log-likelihood decision function in the case of the PNB classifier38. Finally, we estimated the predictive strength of population activity at each time point, that is, the extent to which activity in that time bin predicts the trial type, as the average of the correct predictions in the test set. To determine the variability of this estimate, a bootstrapping procedure was applied in which 25% of neurons were subsampled from the overall population and the same procedure was repeated (50 resampling runs). The resulting traces were used to estimate the 95% confidence intervals of the initial estimate from the full population. To determine the degree of causal connectivity in the ensemble of recorded neurons within the PFC or their counterpart in our simulated network, we used the Weiner–Granger vector autoregressive (VAR) causality analysis as implemented in the multivariate Granger causality toolbox (MVGC)25. Spike train data from each recorded or simulated neuron within a session was converted to a continuous signal by binning in 1-ms increments39, 40 and convolving the resulting signal with a Gaussian filter (half width 5 ms). For all neurons in individual sessions, this analysis used 500-ms segments either within the delay period (delay) or just before (task engagement) along with an equal number of randomly selected segments recorded outside of the behavioural environment (out of task). For assessment of laser effects, a matched number of correct trials in the laser and non-laser condition were compared for each recording session across neurons. To improve stationarity in the signal, segments were adjusted by subtracting the mean and dividing by the s.d. of each segment39, 41 and stationarity was checked by determining whether the spectral radius of the estimated full model was less than one25. All models met this stationarity criteria. Model order was estimated empirically for each subset using Bayesian information criteria after which VAR model parameters were determined for the selected model order. On the basis of the resulting parameters, time-domain conditional Granger causality measurements were calculated for each cell pair across all trials. Causal density for a given condition in each session was taken as the mean pairwise-conditional causality25. To assess the effect of changes in thalamic excitability on cortical connection strength, we measured intra-cortical responses evoked by ChR2-mediated activation of the contralateral cortex for V1/LGN (94 neurons in two mice) and PFC/mediodorsal thalamus (96 neurons in three mice). Responses to either cortical stimulation alone (10 ms ChR2 activation to the contralateral cortex), thalamic activation alone (500 ms SSFO activation in ipsilateral LGN or mediodorsal thalamus) or the combination were recorded in V1 and PFC (100 interleaved trials per condition). For the combined condition, thalamic activation preceded cortical stimulation by 100 ms. Network structure and dynamics. We constructed a model that consisted of excitatory (regular-spiking) and inhibitory (fast-spiking) PFC neurons as well as mediodorsal neurons. Within the PFC, regular-spiking cells formed subnetworks representing each task rule consisting of multiple interconnected chains. Neurons in each of these chains were locally connected to their nearest neighbour within the chain as well as to other chains within the same subnetwork. While neurons representing different rules were connected, connections were made stronger within each subnetwork (for example, among neurons representing the same rule) on the basis of our cross-correlation experimental data. Regular-spiking neurons of either rule sent overlapping projections to mediodorsal neurons and received reciprocal inputs from the mediodorsal thalamus. Mediodorsal inputs were modulatory with a longer time constant than for the PFC (1 ms versus 10 ms), and resulted in increased spiking of fast-spiking neurons (direct synaptic drive, w = 0.6) while providing an amplifying input (factor, 1.6×) to connections between regular-spiking neurons (regardless of rule tuning). During rule encoding, the arrival of input attributed to one rule simultaneously activated the starter neuron (first neuron in a chain) in chains encoding that rule, engaging mediodorsal neurons and enhancing their firing through synaptic convergence. In turn, mediodorsal neurons enabled signal propagation that was specific to that rule by amplifying currently active regular-spiking neuronal connections, while preventing irrelevant synchrony elsewhere through augmented inhibition. Spiking neuron model. We employed the leaky integrate-and-fire (LIF) model to simulate both of the network paradigms described above. LIF is a simplified spiking neuron model that is frequently used to mathematically model the electrical activity of neurons. The evolution of the membrane voltage of neuron j using the LIF equation is as follows: where C is the membrane capacitance, V is the jth neuron’s membrane voltage, α is the leak conductance (α = 0.95). Iext is an externally applied current with amplitude taken independently for each neuron from a uniform distribution (μ = 0.825, s.d. = 0.25 for PFC and mediodorsal neurons). I syn is the synaptic input to cell j, and this is defined as follows: where ω represents the strength of the connection between presynaptic cell i and the postsynaptic neuron j; A is the connectivity matrix that denotes the connectivity map. τ is the spike duration (1 ms in our simulations) and the H(t) is a Heaviside function that is zero for negative values (t < τ) and one for positive values (t > τ). In this model the voltage across the cell membrane grows, and after it reaches a certain threshold (Vth = 1), the cell fires an action potential, and its membrane potential is reset to the reset voltage. Here, the resting potential (E) and reset-potential Vreset are set to zero. The neuron enters a refractory period (Tref = 1.5 ms) immediately after it reaches the threshold (V = Vth) and spikes. To integrate the LIF equation, we used the Euler method with a step size of Δt = 0.01 ms. To reproduce the spontaneous activity of the network, we introduced a noise that arrives randomly at each cell with a predefined probability (f  = 10 Hz). For each statistical analysis provided in the manuscript, an appropriate statistical comparison was performed. For large sample sets, the Kolmogorov–Smirnov normality test was first performed on the data to determine whether parametric or non-parametric tests were required. Variance testing for analysis involving comparisons of firing rates under differing behavioural conditions and following optogenetic manipulations was done using the Fligner–Killeen test of variance homoscedasticity. For small sample sizes (n < 5) non-parametric tests were used by default. Two different approaches were used to calculate the required sample size. For studies in which sufficient information on response variables could be estimated, power analyses were performed to determine the number of mice needed. For sample size estimation in which effect size could be estimated, the sample number needed was estimated using power analysis in MATLAB (sampsizepwr) with a β of 0.7 (70%). For studies in which the behavioural effect of the manipulation could not be prespecified, including optogenetic experiments, we used a sequential stopping rule42. This method enables null-hypothesis tests to be performed in sequential stages, by analysing the data at several experimental points using non-parametric pairwise testing. In these cases, the experiment initially uses a small cohort of mice which are tested over multiple behavioural sessions. If the P value for the trial comparison across mice falls below 0.05, the effect is considered significant and the cohort size is not increased. If the P value is greater than 0.36 following four sessions that met criteria, the investigator stopped the experiment and retained the null hypothesis. Using this strategy, the required number of animals was determined to be between three and five animals per cohort across testing conditions. For multiple comparisons, a non-parametric ANOVA (Kruskal–Wallis H-test) was performed followed by pairwise post hoc analysis. All post hoc pairwise comparisons were two-sided. No randomization or investigator blinding was done for experiments involving electrophysiology. Blinding was used for experiments involving SSFO and behaviour (mediodorsal versus PFC). All computer code used for analysis and simulation in this study was implemented in MATLAB computing software (MathWorks). Code will be made freely available to any party upon request. Requests should be directed to the corresponding author. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Gagnon-Turcotte G.,Laval University | LeChasseur Y.,Laval University | Bories C.,Doric Lenses Inc | De Koninck Y.,Doric Lenses Inc | Gosselin B.,Laval University
IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings | Year: 2015

This paper presents a multichannel wireless op-Togenetic headstage providing neural recording and optical stimulation capabilities simultaneously. The proposed headstage, which is entirely built using commercial off-The-shelf components, includes 32 electrophysiological recording channels and up to 32 high-power optical stimulation channels. It can process 32 neuronal signals in real-Time with high compression ratio using an embedded digital signal processor performing spike detection and data compression in-situ. The presented headstage is small and lightweight rendering it suitable for conducting in-vivo experiments with freely moving transgenic rodents. We report results obtained from in-vivo experiments showing that the proposed wireless headstage can collect, detect and compress the microvolts amplitude neuronal signals evoked by light stimulation with a high averaged peak-signal-To-noise ratio of 22.4 dB and a high averaged signal-To-noise distortion ratio of 17.0 dB. The design of this headstage is using a rigid-flex printed circuit board, resulting into a lightweight (2.8 g) and compact device (17×18×10 mm3). © 2015 IEEE.

Gagnon-Turcotte G.,Laval University | Lechasseur Y.,Doric Lenses Inc. | Bories C.,Laval University | De Koninck Y.,Laval University | Gosselin B.,Laval University
Proceedings - IEEE International Symposium on Circuits and Systems | Year: 2016

This paper presents the in vivo performances of a resource-optimized digital action potential (AP) detector featuring an adaptive threshold based on a new Sigma-delta control loop. The proposed AP detector is optimized for utilizing low hardware resources, which makes it suitable for real-time implementation on most common low-power microcontroller units (MCU). The adaptive threshold is calculated using a digital control loop based on a Sigma-delta modulator that precisely estimates the standard deviation of the neuronal signal amplitude. The detector was demonstrated using a common MCU from MSP430 family, incorporated into a small wireless platform for combined optogenetics and neura recording. The system has been fully characterized experimentally within in vivo experiments on a freely-moving transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line4. The results demonstrate that the proposed AP detector can be used to achieve overall data reduction ratios above 11 hen transmitting only the detected APs. A comparison of the obtained results with other thresholding approaches shows that the pr posed detector provides similar performances to those significantly more resource demanding approaches. © 2016 IEEE.

Ameli R.,Laval University | Mirbozorgi A.,Laval University | Neron J.-L.,Doric Lenses Inc. | Lechasseur Y.,Doric Lenses Inc. | Gosselin B.,Laval University
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2013

This paper presents a miniature Optogenetics headstage for wirelessly stimulating the brain of rodents with an implanted LED while recording electrophysiological data from a two-channel custom readout. The headstage is powered wirelessly using an inductive link, and is built using inexpensive commercial off-the-shelf electronic components, including a RF microcontroller and a printed antenna. This device has the capability to drive one light-stimulating LED and, at the same time, capture and send back neural signals recorded from two microelectrode readout channels. Light stimulation uses flexible patterns that allow for easy tuning of light intensity and stimulation periods. For driving the LED, a low-pass filtered digitally-generated PWM signal is employed for providing a flexible pulse generation method that alleviates the need for D/A converters. The proposed device can be powered wirelessly into an animal chamber using inductive energy transfer, which enables compact, light-weight and cost-effective smart animal research systems. The device dimensions are 15×25×17 mm; it weighs 7.4 grams and has a data transmission range of more than 2 meters. Different types of LEDs with different power consumptions can be used for this system. The power consumption of the system without the LED is 94.52 mW. © 2013 IEEE.

Gagnon-Turcotte G.,Laval University | Kisomi A.A.,Laval University | Ameli R.,Laval University | Camaro C.-O.D.,Laval University | And 8 more authors.
Sensors (Switzerland) | Year: 2015

We present a small and lightweight fully wireless optogenetic headstage capable of optical neural stimulation and electrophysiological recording. The headstage is suitable for conducting experiments with small transgenic rodents, and features two implantable fiber-coupled light-emitting diode (LED) and two electrophysiological recording channels. This system is powered by a small lithium-ion battery and is entirely built using low-cost commercial off-the-shelf components for better flexibility, reduced development time and lower cost. Light stimulation uses customizable stimulation patterns of varying frequency and duty cycle. The optical power that is sourced from the LED is delivered to target light-sensitive neurons using implantable optical fibers, which provide a measured optical power density of 70 mW/mm2at the tip. The headstage is using a novel foldable rigid-flex printed circuit board design, which results into a lightweight and compact device. Recording experiments performed in the cerebral cortex of transgenic ChR2 mice under anesthetized conditions show that the proposed headstage can trigger neuronal activity using optical stimulation, while recording microvolt amplitude electrophysiological signals. © 2015 by the authors; licensee MDPI, Basel, Switzerland.

PubMed | Doric Lenses Inc., University of Québec and Laval University
Type: Journal Article | Journal: Sensors (Basel, Switzerland) | Year: 2015

We present a small and lightweight fully wireless optogenetic headstage capable of optical neural stimulation and electrophysiological recording. The headstage is suitable for conducting experiments with small transgenic rodents, and features two implantable fiber-coupled light-emitting diode (LED) and two electrophysiological recording channels. This system is powered by a small lithium-ion battery and is entirely built using low-cost commercial off-the-shelf components for better flexibility, reduced development time and lower cost. Light stimulation uses customizable stimulation patterns of varying frequency and duty cycle. The optical power that is sourced from the LED is delivered to target light-sensitive neurons using implantable optical fibers, which provide a measured optical power density of 70 mW/mm at the tip. The headstage is using a novel foldable rigid-flex printed circuit board design, which results into a lightweight and compact device. Recording experiments performed in the cerebral cortex of transgenic ChR2 mice under anesthetized conditions show that the proposed headstage can trigger neuronal activity using optical stimulation, while recording microvolt amplitude electrophysiological signals.

Doric Lenses Inc. | Date: 2011-11-23

The hybrid fiber-optic cannula can have a body having an implant end, a light passage extending through the body, and a light inlet end coinciding with the light passage, an optical fiber held in the body in coincidence with the light passage and oriented out the implant end, the light inlet end being opposite the implant end relative to the light passage, and a conduit extending through the body between a conduit outlet located at the implant end and a conduit inlet. A fluid and/or electrical wires, can be conveyed by the conduit, for instance.

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