Barrel Cortex


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The dotted lines are the results of template matching. B Left: the F d and A F were plotted for each trial. Right: these points were clustered into two groups red, successes; blue, failures.

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Tactile Experience Induces c-fos Expression in Rat Barrel Cortex

The green and pink dots are from the two representative trials shown as the green and pink traces in A , respectively. C—E Analysis for three different cells. C Well-separated cell. D Marginally separated cell. E Example of a cell in which successes and failures overlapped. Since spikes can occur up to about 50 ms after whisker deflection [ 32 , 53 ], we also computed the difference between the first and second poststimulus frames.

F d was defined as the larger of these values. Second, the amplitude, A F , of the fluorescence transient was derived from template matching [ 54 ]. The template was as follows:.

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We plotted F d and A F for all trials Figure 3 B and applied hierarchical clustering, using the Euclidean distance to define the distance between points. For many cells, the trials fell into two groups, corresponding to failures and successes. For imaging, the focal plane was positioned at the equator center of the soma of the recorded cell. This analysis shows that successes and failures can be accurately separated in two-thirds of randomly selected neurons.

Furthermore, neurons in which successes and failures are separable can be unambiguously identified using a simple analysis of F d versus A F plots. We next examined the factors limiting reliable spike detection in a subset of cells. We noticed that in the F d versus A F plots, the points corresponding to failures were not uniformly distributed around zero; even during failures, the fluorescence signal tended to increase after whisker deflection Figure 3 B— 3 E. The performance of our spike detection algorithms should therefore depend on the relative positions of the focal plane and the target soma: in cases in which the focal plane overlaps the equator of the cell, most of the excitation volume will be inside the soma, and the neuropil signal will contribute minimally, implying optimal spike detection; when the focal plane is closer to the edge of the cell, the neuropil signal will contribute more signal, which in turn will lead to a degradation of spike detection.

We tested this hypothesis directly by measuring the overlap between the failure and success ellipses while changing the focal plane. In addition to the neuropil signal, other factors limit the separation of successes and failures. The amplitudes of the fluorescence changes following APs differ from cell to cell Figure 2 F.

Even among the cells that exhibited clear segregations, A F varied from neuron to neuron 10th percentile, Under typical conditions for multicell imaging, the focal plane overlaps in a random manner with imaged neurons, and the fraction of neurons in which successes and failures can be accurately separated is expected to be somewhat less than two-thirds. For the rest of the study, we focused on the neurons in which failures and successes could be clearly distinguished. Out of neurons we imaged, neurons On average, the response probability P r was 0.

We calculated the selectivity index SI , which represents the relative response probability for the two whiskers see Materials and Methods. For the analysis of SI, we will focus on the neurons in which, in addition to the separability criterion, at least 20 successes were detected out of neurons. What could be the sources of variability in whisker dominance? The SI is known to depend on the position of the neuron within the barrel [ 30 ]. It is possible that somatotopy varies smoothly within a barrel column: neurons nearer to the border with the barrel column dominated by the SW would respond relatively more to the SW, at the expense of the PW, and thus have lower SI values.

We measured SI as a function of distance across whisker rows rostral—caudal. In the example of Figure 6 , we imaged 16 neurons in the C3 barrel. The largest response was evoked by the C3 whisker; the C4 barrel is to the right of the C3 whisker Figure 6 A. Seven out of 16 neurons responded to whisker stimulation and produced signals that allowed us to separate failures and successes. The SI varied greatly from cell to cell, even for adjacent cells Figure 6 B.

A Left: the location of the imaged area in CO-stained barrels. Right: Fluo-4 AM image showing the locations of the analyzed cells. The imaged area was centered on the C3 barrel, with the C4 barrel to the right. B The SI of neurons in A as a function of their location. C Spatial changes in the selectivity index across 33 experiments. To overlay different experiments the SI and location were shifted so that the center of gravity of the data points was on the origin.

Recent anatomical studies have shown that L2 and L3 cells in the mouse barrel cortex are part of distinct thalamocortical circuits [ 29 ]. L2 cells are excited by lemniscal L3 cells and by L5A cells which, like L3 cells, are excited by L4 cells. However, L5A cells also received strong direct input from the posterior medial nucleus POm , which is part of the paralemniscal circuit. L2 cells therefore have a mixed character lemniscal and paralemniscal.

The thalamocortical projection originating in POm is broad [ 29 ], suggesting that if L2 cells are primarily driven by POm, we would expect smaller SI gradients in L2 than L3. These findings suggest that both L3 and L2 are functionally primarily lemnsical under our experimental conditions. Therefore, somatotopy varies relatively smoothly across the borders between barrel columns.

Changes in somatotopy accounted for only a small fraction of the heterogeneity in responses to whisker deflection. We compared the SIs of a large number of neuronal pairs as a function of distance between the neurons. This indicates that the response properties of nearby neurons are highly heterogeneous, even if they are intermingled in the same cortical column.

D The difference in SI for pairs of neurons as a function of the distance between the neurons. The distance was calculated as the projection of the position vector connecting pairs of neurons onto the line that connects the two neighboring barrels. Essentially indistinguishable data were obtained using the absolute distance length of the position vector; see Figure S3. Red circles indicate pairs of neurons whose SIs were significantly different. RF, receptive field. We next examined the trial-to-trial variability across neurons. Although neurons exhibited different whisker selectivities, they tended to respond on the same trials Figure 7 B.

To quantify the strengths of trial-to-trial correlations, a commonly used method is to calculate the correlation coefficient of two vectors corresponding to each neuron [ 57 , 58 ]. However, the correlation coefficient is determined in part by the differences in the response probabilities of the cells. For example, if one neuron fires frequently and the other rarely, then the correlation coefficient is low, even if they were highly correlated.

We thus used a different measure of correlation that corrects for differences in response probabilities. We define the correlation between two neurons simply as the number of trials in which both of the compared neurons fired, divided by the number of trials in which the less-responsive neuron fired. Using this definition, we found that the firing correlation was very high PW: 0. In other words, if the less-responsive cell fires in a particular trial, the more-responsive neuron will also fire with high probability. The highly correlated activity was also observed under isoflurane anesthesia 0.

A The trial-to-trial correlation between the response patterns for each pair of neurons imaged simultaneously to PW stimulation was plotted as a function of the distance between the two neurons. B The same analysis as depicted in A , but with the paired correlations plotted from the response pattern following SW stimulation.

Membrane potential correlates of sensory perception in mouse barrel cortex

C Top: raw response pattern of 11 simultaneously imaged neurons to PW stimulation. Each row represents one neuron, and each column represents one trial. The neurons were sorted based on response probability Cell 1 responded the least, and Cell 11 responded the most. Bottom: the response patterns from above were sorted so that all responsive trials were clustered towards the bottom of the chart, regardless of cellular identity and origin of that response. The number of responsive neurons in each trial remained the same. The red arrow indicates the actual value. We further quantified trial-to-trial fluctuations to test whether the cells that are more responsive likely fire if the less-responsive cells fire.

We constructed a response matrix based on the raw data Figure 8 C : In the top panel, neurons are ordered based on their response probability bottom, highest; top, lowest. It can be seen in this example that in most of the trials in which Cell 3 fired see Figure 8 C, top trials 1 , 5 , 9 , 10 , 11 , 13 , 17 , 21 , 23 , 24 , and 29 , cells 4—11 also fired, with few exceptions. We then rearranged the same dataset by assigning the responses on each trial to the most-responsive neurons preserving the total number of responses.

In other words, we shifted the responses downward in the matrix. This procedure produces a matrix of spiking activity in which the least-active cell always predicts a spike in more-active cells Figure 8 C, bottom. Across 23 experiments in which more than five neurons responded in more than 20 trials, the correlation coefficient was 0. Thus, in most cases, whenever a particular neuron fires, there is a very high probability that other neurons with the same or greater overall response properties will also fire.

Previous studies on the barrel cortex have found that the magnitude of the sensory-evoked response depends on whether the cortex is in an UP or DOWN state [ 59 , 60 ]. Therefore, the variability in the sensory responses is at least in part due to fluctuations in the cortical state. In many areas of the cerebral cortex, the AP rates are low around 1 Hz , and sensory information is encoded by the presence or absence, or the timing, of individual APs [ 12 , 32 , 61 ]. Reading out the neural code therefore demands the detection of single spikes in multiple individual neurons.

In about half of the neurons, it was possible to detect single APs, with negligible error rates. The difference in the detection efficiency between Kerr et al. First, Kerr et al. With optimal sectioning, we were able to detect spikes reliably in two-thirds of the recorded cells. Second, species-specific differences likely play a role. Kerr et al. We note that our approach for detecting single APs relies on the low firing rates seen in the barrel cortex [ 32 ].

Moreover, although our method distinguishes between trials with and without APs, it cannot reliably distinguish the number of APs in a short burst. Therefore, our approach cannot be generally applied to the analysis of spike trains [ 64 ]. Maps of response selectivity are thought to vary continuously across the cortical surface, with some notable discontinuities, such as fractures and pinwheels [ 7 , 65 , 66 ]. Additional technical refinements facilitating single-spike detection have allowed us to analyze the microstructure of the mouse somatosensory cortex.

Single-unit recordings from rat and mouse barrel cortex under various conditions have reported that single-whisker deflections evoke approximately one AP [ 26 , 27 , 30 , 67 ]. Recent studies using in vivo whole-cell or loose cell—attached recordings reported much lower levels of activity 0. This discrepancy is most likely due to the sampling biases of single-unit recordings, which are insensitive to nonspiking cells [ 13 , 14 ]. In the current study, the response probability to the PW was 0. The difference between our results and previous studies using whole-cell recordings [ 12 , 32 ] is likely due to differences in the anesthesia protocols; in our preparation, the sensory-evoked response is stronger under ketamine—xylazine anesthesia than under urethane or isoflurane anesthesia unpublished data.

The response probability to the PW and SW varied greatly across neurons. At the level of neuronal populations, receptive fields changed gradually with distance within barrels. This is consistent with previous single-unit studies in the rat that have shown that the SW response depends on the location of the recording electrode within the barrel; at a particular location, the SW dominating the nearest surround column tends to evoke the largest surround response [ 30 ].

In addition, receptive fields changed gradually across borders between barrel columns, demarcated by the narrow septa in L4. This is consistent with the functional anatomy in the mouse barrel cortex, in which L3 L2 cells above septa and barrels are coupled to L4 L5A cells in a similar manner [ 29 ]. The wiring differs in the rat: L2 and L3 neurons in barrel-related columns receive strong input from L4 barrels [ 68 — 72 ], with a minor input from L5A. In contrast, L2 neurons in septum-related columns receive strong input from regions in L5A below septa [ 68 ].

L3 neurons in septum-related columns are only weakly coupled to intracortical circuitry in brain slices [ 40 ]. The spatial gradients associated with somatotopy only explained a small fraction of the cell-to-cell variability in response selectivity. Previous studies using extracellular recordings have reported that pairs of neurons recorded on the same electrode can exhibit different response selectivities [ 57 , 58 ].

Our findings are analogous to mapping studies in the rodent visual system, where neighboring neurons can have highly distinct orientation selectivity [ 24 , 73 , 74 ]. What features of cortical circuits could underlie locally heterogeneous response properties? Recently, several studies in brain slices have revealed that cortical columns contain highly specific, fine-scale subnetworks [ 71 , 75 — 77 ]. It is likely that the heterogeneous and spatially intermingled response properties that we observe are a reflection of this fine-scale specificity. Even the earliest single-unit studies have noted that cortical responses to sensory stimulation vary substantially from trial to trial [ 79 — 82 ].

Response variability is often thought of as noise. A common assumption is that the brain averages over neurons to reduce noise and improve signal detection [ 57 , 83 ]. However, averaging works only if individual neurons respond independently. We find remarkably high correlations between the responses of individual neurons.

The highly correlated sensory responses observed in our study suggest that population averaging is unlikely to aid signal detection in the barrel cortex of mice. Consistent with previous studies [ 59 , 60 , 84 — 87 ], we found that the response variability of individual neurons is reflected in the response pattern of the local cortical network.

Particularly, in the barrel cortex, it has been reported that the magnitude of the sensory-evoked response is smaller when the cortex is in the UP state compared to when the cortex is in the DOWN state [ 59 , 60 ]. Our findings are consistent with these studies. However, these previous studies were not able to assess whether all neurons, or a subset of neurons, contribute to the correlations. Our dense sampling of neurons revealed that all responding neurons show similar trial-to-trial fluctuations. Our study, as well as most of the previous work on response variability in population of neurons, was carried out in anesthetized animals.

The behavioral significance of our findings will require future studies with awake animals performing sensory tasks [ 83 , 88 , 89 ]. All experimental protocols were conducted according to the National Institutes of Health guidelines for animal research and were approved by the Institutional Animal Care and Use Committee at Cold Spring Harbor Laboratory. The data in Figures 5 — 8 were collected under ketamine—xylazine anesthesia.

The response probability remained constant during the experiments Figure S2. Imaging windows were installed above the barrel cortex. A small craniotomy diameter, 1—2 mm was made 1 mm posterior from bregma and 3. The cover glass was then sealed in place using dental acrylic, leaving one side open [ 49 , 90 ].

To record the electrocorticogram EcoG , a thin 0. In preliminary experiments in an illuminated environment , it was verified that exposure to such a cage caused an active exploratory behavior in the animals, involving intense sniffing and whisking. Little c-Fos expression was observed in control animals. Figure 7 shows that c-Fos activation was strongest in layer IV 6.

Similarly, as in the previous experimental situation, the elevation of c-Fos expression was not significant in layer VIb 1. Immunocytochemistry showing an increase in c-Fos expression in the PMBSF after placing rats in a new environment experiment 2. Shown are both left and right PMBSFs of two rats: The control naive rat top panels and the experimental rat bottom panels.

Calibration, 0. This study presents two novel approaches to produce whisker stimulation-responsive changes in gene expression in the barrel cortex.

We have focused on c-Fos, a transcription factor protein, as a prototypical marker for gene expression. Both of these approaches, mechanical whisker stimulation and exposure to a new environment, differ from those described previously. Notably, the previous experimental systems involved anesthesia Mack and Mack , artificial mode of stimulation Welker ; Welker et al.

Our experience shows that it is not just the presence of new objects, but their nature especially the presence of holes between bars that elicits whisker-dependent c-Fos expression R. Filipkowski and L. Kaczmarek, unpubl. This is in accordance with the findings of Lipp and Van der Loos who observed that mice appear to use their whiskers for detecting openings in their surroundings rather than for texture discrimination. In the case of the new environment, a natural tendency of rats is to explore Eilam and Golani and this tendency was used in combination with a novelty element completely new cage.

The increase of c- fos expression after exposure to a new cage was also shown before in chicks Anokhin et al. Immunocytochemistry provides good spatial resolution. We have used the power of this approach to investigate laminar cortical distribution of c-Fos activation in detail. All of these layers process sensory information Waite and Tracey On the other hand, no changes were observed in sublayer VIb.

This latter region is known for its specific role during the early phases of cortical development. Cells in layer VIb are among the first to differentiate. They show specific patterns of gene expression during development and throughout the adulthood Valverde et al. It would be very difficult to directly compare the numerical results between experiments 1 and 2 for technical reasons as well as the inability to quantitate the amount of stimulation applied in each case.

However, we noted that there was little difference in the overall levels of increases in c-Fos protein in any layer, with layer IV being the most responsive. Exposure to a novel environment is an important model of plastic changes in the brain. This is especially true in the cortex, where major changes were documented in thickness and weight of the cortex; the size of neurons, their nuclei, cell bodies, synapses, dendritic branches, density of axons, dendrites, and synapses; glial cells; and blood vessels.

Most of these results were obtained following long usually 30—90 days exposure to specific conditions Rosenzweig ; Rosenzweig and Bennett ; Kolb and Whishaw , although some of the aforementioned changes were seen after only 4 days Wallace et al. In this study we show that even a very brief exposure of animals to a new environment results in an activation of expression of a protein forming transcription factor, thus suggesting a trigger of long-term neuronal changes Kaczmarek We have observed that only some of the PMBSF neurons show elevated c-Fos expression after mechanical stimulation of vibrissae.

Similar findings have been presented by Melzer and Steiner who showed that not all of the barrel cortex neurons exhibit high level of c- fos mRNA expression after stimulation in a Lausanne apparatus. We have extended this observation to show that the majority of cells expressing c-Fos after mechanical stimulation turned out to be parvalbumin-negative. Parvalbumin has often been taken as a marker of certain inhibitory interneurons Celio ; Ren et al. This finding is in agreement with the results obtained by Chaudhuri et al.

2-Minute Neuroscience: Primary Somatosensory Cortex

Therefore, it remains to be clarified in further studies why excitatory neocortical neurons display increased c-Fos expression, while the large, fast-spiking, nonspiny interneurons on layer IV do not show this increase. In conclusion, two new models of whisker stimulation are presented. They lead to the induction of c-Fos protein expression in the rat barrel cortex. This expression is observed in layers of the cortex known to receive and convey sensory stimulation, and in cells that are, presumably, excitatory neurons.

Results presented in this paper are based on material collected from a total of 19 adult Wistar male rats obtained from Nencki Institute Animal House. To minimize animal suffering, the rules established by the Ethical Committee on Animal Research of Nencki Institute, based on disposition of the President of Polish Republic, were followed strictly in all experiments.

To habituate nonspecific responses, four rats were placed on the top of a copper cylinder 50 cm high, 8 cm diam. If the rats jumped off, they were gently placed back on the top of the cylinder. On the experimental day, the rats were placed on the top of the cylinder and their whiskers were stimulated for 20 min Fig.

Large whiskers were stimulated on either the left two animals or the right two animals side of the snout with three objects a brush, a piece of Styrofoam with holes, and a piece of plastic. Two hours after the end of the brushing, the rat brains were processed for immunocytochemistry. The rats were divided into two groups. No manipulations were performed on the naive animals that served as controls seven rats , and they were housed side by side with the experimental animals.

Two hours after exposure to the new cage, the brains of experimental animals along with those of controls, were processed for immunocytochemistry. The stained sections were again washed in PBS, mounted on slides, and air-dried 2—4 days. The slides were then washed in PBS, dehydrated, and mounted as described above.

The expression of c-Fos was assessed as described by Kaminska et al. Briefly, the sections were washed three times in PBS pH 7. The staining reaction was stopped by two to three washes with PBS. The sections were mounted on gelatin-covered slides, air-dried, dehydrated in ethanol solutions and xylene, and embedded in Entellan Merck.

For immunohistochemical detection of parvalbumin, monoclonal mouse antiserum Sigma, was used. Primary and secondary antibodies were diluted in PBS containing 0.


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These sections were evaluated using the fluorescent microscope with appropriate filter set. The position of the regions and layers was determined by Nissl and cytochrome oxidase staining.

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