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Subnetwork-Specific Homeostatic Plasticity in Mouse Visual Cortex In Vivo

Summary Homeostatic regulation has been shown to restore cortical activity in vivo following sensory deprivation, but it is unclear whether this recovery is uniform across all cells or specific to a subset of the network. To address this issue, we used chronic calcium imaging in behaving adult mice to examine the activity of individual excitatory and inhibitory neurons in the same region of the layer 2/3 monocular visual cortex following enucleation. We found that only a fraction of excitatory neurons homeostatically recover activity after deprivation and inhibitory neurons show no recovery. Prior to deprivation, excitatory cells that did recover were more likely to have significantly correlated activity with other recovering excitatory neurons, thus forming a subnetwork of recovering neurons. These network level changes are accompanied by a reduction in synaptic inhibition onto all excitatory neurons, suggesting that both synaptic mechanisms and subnetwork activity are important for homeostatic recovery of activity after deprivation.


Surgery
Cranial windows were surgically implanted over the right hemisphere of monocular primary visual cortex, according to previously described methods (Holtmaat et al., 2009). Briefly, mice were administered with a pre-surgery analgesic (buprenorphine s.c. 0.1 µg/g of body weight) and then anesthetized with an i.p. injection of ketamine (0.15 mg/g of body weight) and xylazine (0.015 mg/g of body weight). A craniotomy was made over monocular visual cortex. A glass cover slip was positioned over the cortex and sealed to the surrounding bone with dental cement. For functional imaging experiments and experiments involving acute slices prepared from mice expressing GCaMP5, mice were injected with AAV2/1-ef1α-GCaMP5 before the glass cover slip was positioned. In both structural and functional imaging experiments, mice were allowed to recover for at least 18 days before the commencement of the imaging period. Monocular enucleation involved topical application of lidocaine to the area around the left eye under anaesthesia induced by ketamine-xylazine injection (dose as above) prior to surgical removal of the eye. Control 'sham-enucleated' mice were given time-matched anaesthesia. For functional imaging, we used intrinsic signal imaging to localize the monocular visual cortex as described previously (Keck et al., 2013). For functional imaging, brief isoflurane sedation (approximately 10 seconds) was used to head-fix the mouse. The animal was then allowed to habituate to the setup before data acquisition commenced.

In vivo structural and functional imaging
Structural data were collected using repeated two-photon imaging (Denk et al., 1990).
The illumination source was a Spectra Physics MaiTai BB laser with a DeepSee prechirp unit (Newport/Spectra Physics) set to 909 nm. Excitation and emitted light were delivered and collected through an Olympus 40× 0.8 NA water immersion objective. The average laser power delivered to the brain was < 50 mW. Scanning and image acquisition were conducted using Scanimage freeware (VidrioTech). Image acquisition parameters were: 64 × 64 µm field of view, 512 × 512 pixels, 0.5 µm step in depth between frames.
Functional calcium imaging was performed with a custom built two-photon microscope. The illumination source was a MaiTai eHP laser with a DeepSee prechirp unit (Newport/Spectra Physics) or a Chameleon Vision S (Coherent). The excitation wavelength was set to 910 nm. The scanhead was based on an 8 kHz resonant scanner (Cambridge Technology), used in bidirectional mode. This enabled frame rates of 40 Hz at 400 × 600 pixels. A high power objective Z-piezo stage (Physik Instrumente) was used in order to move the objective down in steps of approximately 20 µm between frames and return to the initial position after four frames. With this system, we acquired data at four different depths, reducing the effective frame rate from 40 Hz to 10 Hz. Laser power was < 50 mW. We used a Nikon 16× 0.8 NA objective.
Data were acquired with a 250 MHz digitizer (National Instruments) and preprocessed with a custom programmed FPGA (National Instruments).

Treadmill and visual stimulation
The awake paradigm, treadmill and visual stimuli have been described previously (Dombeck et al., 2007;Keller et al., 2012). Briefly, head-fixed animals ran on a spherical treadmill while visual stimuli were presented on two screens arranged at 60 degrees relative to each other in front of the mouse. This arrangement is intended to simulate visual flow similar to that experienced when running between two walls.
Visual stimuli were full field gratings, the motion of which was controlled by rotation of the spherical treadmill. Experiments consisted of alternating 3 minute blocks in which the mouse either received coupled visual feedback or the screens were switched off. Each condition was repeated twice, followed by drifting gratings in eight directions (0-360 degrees by 45 degree steps) presented in a random order, with a spatial frequency of 0.04 cycles/degree and a temporal frequency of 2 Hz. The same visual stimulus paradigm was used at each time point before and after enucleation.

Analysis of structural imaging data
In vivo two-photon images of dendritic structures were analyzed in ImageJ (US National Institutes of Health) and custom written software in MATLAB (Mathworks).
All analysis was done blind to the experimental condition on raw data in threedimensions. To calculate spine density, all spines that were clearly distinguishable in any imaging plane (including z) were counted. Density measurements were made from 31 branches (20 deprived and 11 control). Spine size was calculated as previously described Keck et al., 2013). Briefly, only spines that extended from the dendrite in the x-y plane were included. The spine intensity was background subtracted and normalized by the intensity of the adjacent dendrite to account for differences in overall intensity between imaging sessions. Only spines present at all time points were included in the analysis of size. The survival fraction was calculated as the fraction of spines that were present at the time point -1 hour prior to enucleation that were still present at each subsequent time point after enucleation.

Analysis of functional imaging data
Analysis of functional imaging data was conducted as described previously (Keck et al., 2013;Keller et al., 2012). Briefly, data were full-frame registered using a custom written registration algorithm. Cells were selected based on mean and maximum projections of the data by hand (typically the nucleus was excluded from the selection) and using high-resolution depth stacks to ensure that cells did not have a filled nucleus (Fig. S2G,H). Use of the maximum projection ensured the inclusion of all the active cells, even those that were not visible in the mean projection. Note however, that this biased our cell selection towards active cells. Fluorescence traces were calculated as the average fluorescence of pixels lying within the cell in each frame. To remove slow signal changes in raw fluorescence traces, the 8-percentile value of the fluorescence distribution in a ± 15 second window was subtracted from the raw fluorescence signal. ∆F/F 0 signals were calculated by dividing the raw fluorescence signal by the median of each cell's fluorescence distribution. Cellular activity was calculated using integrated fluorescence as described previously (Keck et al., 2013).

Classification of visually responsive neurons
Neurons were considered visually responsive if they had significantly more activity during visual stimulation (light) than in darkness, in the absence of locomotion. These neurons accounted for 20% of the acquired neurons (215/1081), but their activity constituted 87% of the total activity of all the acquired neurons, under all recording conditions. GCaMP5 detection limits are several action potentials per second with saturation at a few tens of action potentials per second (Akerboom et al., 2012).
Visually driven responses below these rates may not be detected in our measurements.
Activity was normalized to the average of the two baseline sessions for individual cells. The day-to-day variability of neurons in control animals was calculated as the percentage of neurons that was active (or inactive) on a given day, but not active (or inactive) on the subsequent imaging day. Following sensory deprivation, cells that were not active in the last 72 hour imaging session were classified as inactive, other cells were considered recovering. Orientation selectivity was calculated as previously described (Ko et al., 2011), and all orientations and directions were presented prior to and following enucleation to account for any potential changes in selectivity. In figure   S2D, visually evoked responses occurred within 500ms of the onset of the stimuli.
The frequency of the random responses was measured by superimposing the timing of the onset of the visual stimuli on periods when the animal was in darkness.

Classification of excitatory and inhibitory neurons
We sectioned and reconstructed the neurons previously imaged in vivo and performed immunohistochemistry to identify their cell type as GABA positive or GABA negative (Kerlin et al., 2010). At the end of the functional imaging experiment, we injected fluorescent beads adjacent to the imaged cortical regions. Animals were then sacrificed and the brain was fixed in 4% paraformaldehyde (PFA). In a separate set of mice, we injected the same AAV virus used in our functional imaging experiments, sacrificed the animals and performed immunohistochemistry to determine which specific inhibitory subtypes (i.e. somatostatin (SST), vasoactive intestinal peptide (VIP), calbindin (CB), reelin, parvalbumin (PV), calretinin (CR)) were labeled by our AAV virus. We found that all subtypes tested were labeled by our AAV virus (of We used an adapted version of a previously described protocol for immunohistochemistry (Kreczko et al., 2009). Horizontal brain slices (80-100 µm) were prepared from the visual cortex, washed 4 times in PBS (pH 7.4) and then incubated overnight at -20 ºC in 30% sucrose (in PBS pH 7.4). The following day sections were defrosted at room temperature and then rinsed 4 times (5 minutes each) in PBS. Sections were next transferred to -20 ºC methanol for 10 minutes before being rinsed 4 times (5 minutes each) in PBS. Sections were incubated in blocking serum (10% normal goat serum or 10% donkey serum, as appropriate) for two hours at room temperature. Blocking serum was then replaced with primary antibody (concentrations as above, in blocking serum) and slices incubated for one week at 4˚C.
Imaged regions were tiled using ImageJ to reconstruct the functionally imaged cortical regions, using the fluorescent beads for guidance. Neurons were mapped to the in vivo images from the functional imaging experiments by three independent experimenters or by using a custom written algorithm in Matlab. The presence of the GABA antibody in neurons in an identified region was quantified as a ratio of somatic signal intensity over background intensity, so that GABA positive neurons had a 20% greater somatic labeling than background levels. Cells were determined to be GABA positive or negative without knowledge of the in vivo activity profiles.
The calcium transient kinetics of known inhibitory and excitatory neurons were then used to train a regression tree classification algorithm. To prevent any contamination from colliding calcium transients, we used isolated calcium events to train the classifier, which consisted of a mean of 13 ± 1 events per cell. We distinguished these isolated events by taking the first derivative of the smoothed calcium transient and identified a positive peak (with a threshold of 10% above baseline) that was not followed by a second positive peak for 10 seconds. Calcium transient shape parameters were first normalized to the mean of all cells in a given animal, then these values were log-normalized. We trained the classification tree on a randomly selected half of the dataset and then cross-validated it on the other half of the dataset, which was novel to the classifier. Performance on this novel dataset was 91% correct.

c-Fos immunohistochemistry
We sectioned and reconstructed the neurons previously imaged in vivo to use immunohistochemistry to identify GCAMP5 positive neurons as either c-Fos positive or c-Fos negative. At the end of the functional imaging experiment 72 hours after enucleation, we injected fluorescent beads adjacent to the imaged cortical regions.
Acute horizontal slices of visual cortex were prepared from these animals and after recovery for four hours in artificial cerebral spinal fluid (ACSF) with 1 µM tetrodotoxin (TTX) saturated with 95% O 2 / 5% CO 2 , the slices were moved to 1% PFA for 24 hours and then incubated overnight at 4 ºC in mouse anti-c-Fos (1:500, Santa Cruz). Following primary incubation, slices were washed three times (for 20 minutes each) with PBS containing 0.25% triton-X. Slices were then incubated for two hours at room temperature in the anti-mouse AF 568 secondary antibody (1:1000, Invitrogen). For slices following electrophysiology experiments the secondary antibody was changed to anti-mouse AF 488 (1:1000, Invitrogen). After this, sections were washed three times in PBS (15 minutes) before mounting in Mowiol (Calbiochem). Slices were imaged and reconstructed as described above for GABAergic reconstructions.

c-Fos Quantification
The presence of c-Fos antibody in neurons in an identified region was quantified as a ratio of somatic signal intensity over background intensity, so that c-Fos positive neurons had a somatic signal that was at least 20% greater than background levels.
Cells were classified as c-Fos positive or c-Fos negative blind to the in vivo activity profile for that cell. In a subset of experiments to quantify the density of c-Fos positive neurons, we counted the number of c-Fos positive cells in a known volume of visual cortex.

Measuring neuronal correlations
We computed the pairwise correlations between calcium traces of all active neurons in the same cortical region. Cells were considered to be correlated if the correlation coefficient was positive and statistically significant (p<0.05). The average and summed correlation strength, as well as the percentage of correlated neurons was calculated from statistically significant correlations. Persistent correlations were those that were significant both in the baseline and 72 hours after (sham) enucleation.
We did not correct for the liquid junction potential. Patch pipettes (4 -7 MΩ) contained the following in mM: 130 KMeSO 4 , 8 NaCl, 2 KH 2 PO 4 , 2 D-glucose and 10 HEPES. mEPSP recordings were made at resting membrane potential with 1 µM TTX and 10 mM Gabazine in the bath solution. Recordings were discarded if the series resistance varied by ± 15% or if the resting membrane potential or input resistance varied by ± 10% across the recording period. mEPSP recordings were filtered at 3 kHz and digitized at 20 kHz and analyzed blind to the experimental condition in the MiniAnalysis programme (Synaptosoft) according to previous criteria (Clements and Bekkers, 1997) so that the smallest detected event was 2.5 times the root mean square of the baseline noise. mIPSCs and mEPSCs were collected and analyzed as described previously (Keck et al., 2013). The ratio of the inhibitory to excitatory drive at L2/3 excitatory neurons was assessed in whole-cell mode using stimulation of horizontal L2/3 inputs with a concentric bipolar stimulating electrode.
L2/3 neurons were held at either -70 mV or +10 mV and extracellular stimulation consisted of 1 ms current pulses with a stimulation intensity set to 50% of the intensity that evoked the maximum EPSP amplitude. Neuronal excitability was recorded in the presence of bath-applied synaptic blockers (20 µM 6-cyano-7-nitroquinoxaline2,3-dione (CNQX), 50 µM 2-amino-5-phosphonopentanoate acid (APV), and 10 mM Gabazine) by somatic injection of a 500 ms depolarizing current step in increments of 10 pA. The slope was calculated from the linear component of the input-output function. Passive membrane properties were estimated from -100 pA current injections, based on the membrane deflection and time-course of decay to baseline membrane potential. For c-Fos excitability experiments, current injection was increased in 10 pA increments until a single action potential was recorded, at which point the pipette was withdrawn. For all c-Fos electrophysiology experiments, following recording, the slice was fixed overnight in 1% PFA and immunohistochemistry was performed the following day.

Statistics
All statistical analysis was performed either in Matlab or SigmaPlot v.12.
Comparisons were made using parametric (t-test, ANOVA with Holm-Sidak post-hoc test, repeated measures ANOVA with post-hoc test) or non-parametric (Mann-Whitney Rank Sum, ANOVA on ranks) statistics where appropriate. Correlation coefficients were calculated with a Spearman's rank correlation coefficient.