Local homogeneity of tonotopic organization in the primary auditory cortex of marmosets

Significance

Marmoset is a New World monkey rich in social interaction and vocal communication. We used in vivo two-photon fluorescence calcium imaging of sound-evoked responses of large neuronal populations at single-neuron resolution in the primary auditory cortex (A1) of anesthetized marmosets. We found that the pure tone-evoked responses of marmoset A1 neurons are highly homogeneous in their frequency preference within local cortical regions, in sharp contrast to that found in rodents. Thus, there is species-specific local tonotopic organization in A1, which imposes distinct neural circuitry constraints for the cortical integration of auditory information.

Abstract

Marmoset has emerged as a useful nonhuman primate species for studying brain structure and function. Previous studies on the mouse primary auditory cortex (A1) showed that neurons with preferential frequency-tuning responses are mixed within local cortical regions, despite a large-scale tonotopic organization. Here we found that frequency-tuning properties of marmoset A1 neurons are highly uniform within local cortical regions. We first defined the tonotopic map of A1 using intrinsic optical imaging and then used in vivo two-photon calcium imaging of large neuronal populations to examine the tonotopic preference at the single-cell level. We found that tuning preferences of layer 2/3 neurons were highly homogeneous over hundreds of micrometers in both horizontal and vertical directions. Thus, marmoset A1 neurons are distributed in a tonotopic manner at both macro- and microscopic levels. Such organization is likely to be important for the organization of auditory circuits in the primate brain.

In the auditory system, the most prominent topographic feature is tonotopic organization, in which adjacent cortical regions showed preferential responses to pure tones of nearby frequencies. In many species, in vivo electrophysiological recordings and imaging techniques have characterized the global tonotopic organization of the auditory cortex, revealing its division into separate fields and distinct tonotopic maps within each field (1–9). Although large-scale imaging and recording methods often yield global tonotopic maps, recent studies using two-photon calcium imaging to monitor the activity of individual neurons in mice showed that local populations of A1 neurons were highly heterogeneous in their frequency-tuning properties, although macroscopic imaging using intrinsic optical imaging over the entire auditory cortex showed an overall tonotopic organization (10–12). Thus, the macroscopic tonotopic map may reflect an averaged frequency preference for large populations of neurons with heterogeneous tuning properties (13–15).

Marmoset is a species of New World monkeys known to be highly vocal and social (16). It has a neocortex much closer to humans than the commonly used rodent models. The spatial distribution of cortical neurons with different frequency preferences is important for the organization of neural circuits that processes auditory signals, such as natural sounds comprising a complex mixture of frequencies. Thus, it is important to determine whether the local heterogeneity in neuronal frequency tuning found for mouse A1 neurons is a general property of mammalian auditory cortices, or alternatively, a property more unique to rodent brains. To address this issue, we used in vivo two-photon imaging to monitor pure tone-evoked responses of a few hundred A1 neurons simultaneously in anesthetized common marmosets (Callithrix jacchus). We found that A1 neurons within distances of a few hundred micrometers in both horizontal and vertical directions were highly homogeneous in their frequency-tuning properties. We also showed that this tonotopic organization in marmoset A1 is distinctly different from that found in rat A1 by the same imaging method. Such microarchitecture of the auditory cortex may be important for efficient coding of natural sounds in highly vocal animals such as marmosets.

Results

In Vivo Two-Photon Calcium Imaging in Marmoset A1.

To facilitate identification of various areas of marmoset auditory cortices, we performed intrinsic optical imaging (17) in anesthetized marmosets (Materials and Methods). We observed that pure tone stimuli could evoke intrinsic optical signals in three primary auditory regions, previously defined by electrophysiological recording and anatomical studies (18, 19) as the primary (A1), rostral field (R), and rostrotemporal field (RT) of the auditory cortex (Fig. 1 A–D and SI Appendix, Fig. S1). For A1 lying on the ventral bank of lateral sulcus (LS), we found that gradual increments of sound stimuli from low- to high-frequencies evoked responses in adjacent areas along LS from the the anteroventral region to the posterodorsal region of A1 (Fig. 1 A–C). The overall tonotopic map is shown for one example imaging plane in Fig. 1D, with different colors indicating frequency preferences. A similar map was observed in two other marmosets (SI Appendix, Fig. S1). This finding is consistent with previous results obtained by electrophysiological and optical imaging approaches (7, 8).

Fig. 1.

Large-scale imaging of neuronal responses in the marmoset auditory cortex. (A) Schematic diagram showing various regions of the marmoset auditory cortex. A1, primary auditory cortex; LS, lateral sulcus; R, rostral field; RT, rostrotemporal field. (B) Surface blood vessel map within the imaging window. (C) Intrinsic optical signals evoked by pure-tone stimuli at 6, 7, and 8 kHz, respectively. Dark areas show activated regions. (D) Summary of tonotopic maps (in the left hemisphere) revealed by intrinsic optical imaging using pure-tone stimuli, color-coded for five discrete frequencies (4–8 kHz). Note the gradual shift of the responses along the LS. (E) Imaging plane obtained using a 16× objective (with 2-μm pixel⁻¹). (F) Pure-tone responses (ΔF) for four different frequencies. Each image represents the average of five repeats. (G) Composite frequency-preference map for the imaging plane as in E. Colors indicate the preferred frequencies, with the brightness indicating the magnitude of the frequency selectivity. E–G were obtained with two-photon imaging. (Scale bars: 1 mm in B–D; 200 μm in E–G.)

Following obtaining the overall frequency-preference map of A1 with imaging of intrinsic optical signals, we further performed in vivo two-photon calcium imaging at selected local A1 regions by bulk loading with the fluorescent calcium indicator Cal-520 AM (20). Loading of the indicator reached an apparent plateau at ∼60 min after local injection, when hundreds of fluorescent cells could be detected at imaging depths that cover the major part of the cortical layers 2/3. Large-scale imaging over an area of about 1 mm × 1 mm with a 16× objective lens allowed us to measure global frequency tuning of A1 subregions, using a method similar to those reported previously for studying orientation and spatial frequency tuning in the primary visual cortex (21, 22). By measuring the fluorescence changes (ΔF) of all pixels within the imaging plane in response to pure tones at six discrete frequencies from 2.8 to 6.7 kHz, we found that activated regions were organized tonotopically (Fig. 1 E–G). Thus, large-scale imaging using both intrinsic and Ca²⁺ signals revealed similar tonotopic organization of marmoset A1, confirming previous electrophysiological findings.

Clustered Distribution of A1 Neurons with Similar Frequency Tuning.

Two-photon imaging of Ca²⁺ fluorescence signals at a higher resolution (with 40× objective, ∼0.6 μm/pixel) allowed us to monitor pure tone-evoked Cal-520 AM fluorescence changes in individual A1 cells within a given focal plane (Materials and Methods). We found that most cells exhibited maximal responses at a specific frequency [defined as the best frequency (BF)]. The percentage of responsive cells among all fluorescently loaded cells was 48 ± 6% (SE, n = 5 fields), and cells with similar BFs were found to localize together within the imaged field. In the example imaging field shown in Fig. 2A, 10 cells sampled within a distance of about 180 μm all showed the BF at 8 kHz. When fluorescence signals from all 68 responsive cells were measured over the entire imaged field, for pure-tone stimuli over discrete incremental frequencies from 0.5 to 32 kHz, we found most cells had BFs centered around 8 kHz (Fig. 2B). This frequency preference was also shown by the average ΔF/F tuning profile for all 68 cells (Fig. 2B, Top Inset). This homogeneity of BFs could also be visualized by the tonotopic map with BFs coded by discrete colors (Fig. 2C). As summarized by the histograms for the percentages of cells exhibiting different BFs, we observed such local uniformity of BFs for five different imaging planes (ranging from 180 to 520 μm in width) recorded from three marmosets over the frequency range from 1 to 9.5 kHz (Fig. 2D). Such homogeneity of tonotopic properties within each imaged area is in sharp contrast to that found in mice using Ca²⁺ imaging methods (10, 11), where cells within 50- to 100-μm distances showed much larger variability in BFs (up to four octaves).

Fig. 2.

Marmoset A1 cells with similar frequency preference were highly clustered within local cortical regions. (A, Left) In vivo two-photon image of Ca²⁺ fluorescence at a focal plane, at ∼150 μm below the pia surface. (Scale bar: 50 μm.) (A, Right) Single trials (gray lines, n = 8) and mean (red line) fluorescence changes (ΔF/F) evoked by pure-tone stimuli (7–10 kHz) at 10 sampled cells (marked by red circles on the Left). (Scale bar: 0.2 s and 20% ΔF/F.) (B) ΔF/F with time for 68 pure tone-selective cells during sequential stepwise application of discrete pure tones from 0.5 to 32 kHz (black bar, tone duration of 0.2 s), with the magnitude of ΔF/F color coded by the continuous scale shown on the Right. Red trace on Top shows the averaged ΔF/F for all 68 cells. Note that the BF for the vast majority of cells was located at 8 kHz. (Scale bar: 0.5 s and 20% ΔF/F.) (C) Spatial distribution of all 68 cells shown in A, with their BFs (kHz) color coded by the scheme shown on the Bottom. (Scale bar: 50 μm.) (D) The percentage of cells with different BFs observed in five imaged planes from three marmosets. Top corresponds to the example imaging plane shown in A–C. (E) Cumulative percentage plot for the distribution of nearest-neighbor distances for all cells in each of five different imaging planes as that in D (same color coding; black indicates data for 68 cells shown in A–C and colored indicates data collected from four other imaging planes observed in three different marmosets). Gray diagonal dashed line depicts theoretically uniform distribution. All curves were significantly different from the diagonal line (P < 0.001 for all curves, Kolmogorov–Smirnov test). (F) Fluorescence images of two adjacent regions in marmoset A1 after Cal-520 loading of the same marmoset in two experiments performed 9 d apart. (Scale bar: 1 mm.) (G) Distribution of cells with different BFs, in the two regions of interest (ROIs) boxed in F. (Scale bar: 100 μm.) (H) Histograms of BF distributions for all tone-selective cells recorded in the two ROIs. (n = 39 in ROI 1; n = 57 in ROI 2).

We also quantified this clustering of cells with the same BFs by calculating the nearest-neighbor distance, defined as the distance of the nearest cell showing the same BF, for all responsive cells in the imaging plane (Fig. 2E). The cumulative percentage plot of the distribution of nearest-neighbor distances of all five imaged planes showed steep slopes at small distances, with median distance (50%) of 30 ± 7 μm (SE, n = 5). These distributions were significantly different from the random uniform distribution of nearest-neighbor distances (P < 0.001, Kolmogorov–Smirnov test), consistent with the homogeneity of BFs within the imaged plane.

In the experiments above, we have shown a tonotopic organization of A1 at the millimeter scale using imaging of intrinsic optical signals and local homogeneous distribution of cells with the same BFs. To further confirm that the local homogeneous tonotopy occurred at different A1 regions in the same marmoset, we performed measurements on two adjacent A1 areas which were separated by about 500 μm (Fig. 2F), in two separate experiments 9 d apart. We observed a clear difference in the dominant BFs of 6.7 and 9.5 kHz in the two regions, respectively, as shown by the BF distribution histograms (Fig. 2H).

Tonotopic Homogeneity in A1 Is Sound-Intensity Invariant.

We next examined whether the BF of the same A1 cell depends on the intensity of the sound stimuli. Using pure tones of three sound intensities (60, 70, and 80 dB), we found that many A1 cells exhibited higher responses as the sound intensity was increased, while others showed similar responses at all three intensities, and a few showed reduced responses at higher intensities (Fig. 3 E and F). Despite the variation in the dependence on sound intensity, the vast majority of A1 cells within the same imaging plane were narrowly tuned to the same frequency at all three sound levels, as shown by the cell-based tonotopic maps (Fig. 3A, BFs around 8 kHz). As shown for the example cell in Fig. 3B, similar averaged response profiles and BF distributions were observed for the responses at three different sound intensities (all peaked at 8 kHz, Fig. 3C). This is largely consistent with the previous finding using electrophysiological recording that sound-evoked responses of awake marmoset A1 neurons are sound-level invariant (23). Further analysis indicates that the distributions of nearest-neighbor distances of the same BFs were all significantly different from the distribution expected for uniform distribution (Fig. 3D, P < 0.001, Kolmogorov–Smirnov test).

Fig. 3.

Local homogeneity in frequency tuning did not depend on the sound intensity. (A) Sound-evoked responses of the same population of A1 cells at three different sound intensities (SPL, sound pressure level; 60, 70, and 80 dB), with BFs of the cells color coded. Data were collected for the same imaging plane as that shown in Fig. 2. (Scale bar: 50 μm.) (B) The averaged frequency-tuning profiles derived from an example cell (indicated by red arrows in A) at three different sound intensities (60, 70, and 80 dB). (C) Total number of cells showing different BFs for three different sound intensities. (D) Cumulative percentage plot for the distribution of the nearest-neighbor distance for all cells at three different sound intensities. All curves were significantly different from the diagonal line (P < 0.001 for all curves, Kolmogorov–Smirnov test). (E) Tonal receptive fields of four example cells from the same imaging plane in A are shown. Plots below depict maximum mean fluorescence changes to pure tone stimuli with increasing intensities, showing diverse changes in cellular responses at three different sound intensities. (F) Cells in one imaging plane (as in A) that responded to the pure tone at a particular sound intensity. The brightness of the dots indicates the response magnitude (maximum mean ΔF/F, color coded by the scale bar). (Scale bar: 50 μm.) (G) Color-coded intensity map derived by combining the three distribution plots shown in F. Colors of cells indicate whether the cells showed responses to one, two, or three intensities. Note that most cells were activated at all three intensities (white dots).

Previous single-unit recordings from A1 of several species have suggested a patchy organization for intensity tuning along the isofrequency axis (24–26). Thus, we have examined the existence of clustering of cells based on the preferred intensity. Although some cells were best activated at different intensities, the numbers of cells activated were similar, with 68, 51, and 54 (out of 109) activated at three intensities tested (Fig. 3F), respectively. We noted that most cells could be activated by all three intensities (Fig. 3G; 40 white dots) and a small fraction of cells showed intensity selectivity (Fig. 3G; 14 red dots, 5 green dots, 2 blue dots). However, there was neither apparent clustering of cells that respond selectively to one particular intensity nor an apparent gradient of best intensity across the isofrequency axis.

Vertical Organization of A1 Tonotopic Maps at Superficial Cortical Layers.

A general organization principle in many sensory cortices is that neurons for processing similar functional features are organized into vertical columns (27, 28). We thus further examine whether tonotopic maps are also homogeneous along the vertical axis of A1. Due to the technical limitation of our two-photon Ca²⁺ imaging method for the marmoset, we were only able to address this issue by exploring the tonotopic properties of A1 neurons with a depth up to 370 μm from the pial surface, covering the major part of layer 2/3. Fig. 4A shows the spatial distribution of A1 cells, color coded with their BFs in the example experiment shown in Fig. 2. Different groups of cells at five different focal depths (140, 170, 200, 230, and 260 μm) were all found to exhibit BFs predominately at 8 or 10 kHz, with clear clustering of cells of similar BFs.

Fig. 4.

(A) Spatial distribution of A1 cells with distinct BFs at five different cortical depths from 140 to 260 μm, with the BF of each cell color coded by the scheme shown on the Right. (B) A 3D composite plot of the distribution of cells with the same BFs, with cells of the same BFs in different depths within 50 μm linked by lines of the same color as that coding BFs. (C) Projected 2D maps of the composite plot onto two planes at azimuth angles of 0° and 45°, showing vertical alignment of cells with the same BFs at different depths. (Scale bar: 50 μm.)

A composite 3D plot (cell pairs sharing the same BFs from adjacent imaging planes with a distance <50 μm were connected by lines) of the cell distribution showed that clusters of cells with the same BFs are well aligned vertically among imaged planes, although there appeared to be more cells with BFs deviated from 10 kHz at deeper cortical regions (Fig. 4B). In a separate experiment on a different marmoset, we imaged five planes over the cortical depths between 250 and 370 μm; similar clustering and vertical alignment of cells of the same BFs were also observed. These findings support the existence of a columnar structure of A1 tonotopic maps at the microscopic level.

Local Tonotopic Organization Is More Heterogeneous in Rat A1.

The above studies showed that the tonotopic organization in the marmoset A1 is highly homogeneous. Given the previous findings showing marked local heterogeneity in the frequency tuning of mouse A1 neurons, we further examine the tonotopic organization of rat A1 neurons using the same Cal-520 imaging method as that used in the marmoset studies above. We found that, for anesthetized Sprague-Dawley rats (∼300 g), A1 cells within a local area (∼307 × 324 μm) in general showed BFs from a low (2 kHz) to high frequencies (32 kHz), with a range that covered four octaves (Fig. 5A). By contrast, over a larger size of imaging area (∼524 × 554 μm) of marmoset A1, the BFs ranged less than two octaves (Fig. 5B). Rough inspection of the local tonotopic maps of rat A1 revealed many regions exhibited “salt-and-pepper” distributions of BFs (Fig. 5A), although small regions with clustered distribution of similar BFs could also be found (Fig. 5A).

Fig. 5.

Local tonotopic organization in rat A1 is more heterogeneous than that in marmoset A1. (A) Spatial distribution of all tone-selective cells in an example imaging plane in the rat A1, with their BFs (kHz) color coded by the scheme below. (Scale bar: 50 μm.) (B) Spatial distribution of all tone-selective cells in an example imaging plane from a marmoset. Note the uniform distribution of BFs. (C and D) Cumulative percentage plots for the distribution of nearest-neighbor distances for all cells (in C) and for the distribution of ΔBF for all cell pairs within the same imaging plane (in D), respectively. Red, marmoset; black, rat. Dashed lines indicate data for the example imaging plane as shown in A and B; solid lines indicate pooled data from 9 imaging planes from two rats and 11 imaging planes from two marmosets; diagonal dashed line indicates theoretically random (uniform) distribution. All curves were significantly different from the diagonal line, which represents the theoretically random distribution of neurons with different BFs (P < 0.001 for all curves, Kolmogorov–Smirnov test). Curves for rats and marmosets were significantly different (***P < 0.001 for all curves, Kolmogorov–Smirnov test). (E) Spatial distribution of A1 cells with various BFs at different cortical depths from 200 to 350 μm. Data are from three different rats. The percentages of cells with different BFs in each imaging region are shown in the Bottom histograms. Note that in all three rats, similar A1 regions (with dominant BF of 2 kHz) were imaged.

Two types of quantitative analyses were formed to examine the uniformity in the BF distribution within A1. First, we measured the nearest-neighbor distance of the cell with the same BFs for all responsive A1 cells monitored in the same image plane (as that done earlier for marmoset, Fig. 2). This analysis showed that the distribution of nearest-neighbor distances in rat A1 was much closer to the random distribution, as shown by the cumulative percentage curves for the example cases and averaged data from two rats (9 imaging planes) and two marmosets (11 imaging planes) (Fig. 5C, 9 imaging planes from two rats and 11 imaging planes from two marmosets). In the second analysis, we measured the ΔBF for all cell pairs within the imaged plane, and plotted the cumulative percentage curves for both rat and marmoset A1, based on the same dataset as above. This analysis showed that the distributions of ΔBF for rat A1 was significantly different from those of marmoset (P < 0.001, Kolmogorov–Smirnov test), much closer to the random distribution (diagonal line), although both marmoset and rat distributions were significantly different from random (P < 0.001, Kolmogorov–Smirnov test) (Fig. 5D). Taken together, our results indicate that, over distances of hundreds of micrometers, the local A1 tonotopic map in rats exhibited a much higher heterogeneity than that in marmosets.

To examine whether the nonuniform local tonotopic organization exists along the vertical axis of rat A1, we have also imaged the cellular tuning responses within a local region (∼307 × 324 μm) at different cortical depths (210–350 μm from the pia surface) in three rats examined in this study. We found that for a similar imaging area in A1, cells with diverse BFs were distributed in similar salt-and-pepper distribution in all three rats and there was a dominant population of cells with the same BF (2 kHz) in all three rats (Fig. 5E). These findings indicate there was a reproducible tonotopic map among different rats, and the existence of predominant BF in the local A1 region could account for the global tonotopic property in rodents, despite the presence of local heterogeneity in BFs.

Tonotopic Responses of A1 Cells Using Genetically Encoded Ca²⁺ Indicators.

The above results were obtained by using the fluorescent Ca²⁺ indicator Cal-520 that was acutely loaded into neurons. It is known that due to differential Ca²⁺ affinities of different indicators, the recorded neuronal activities may be biased by the extent of subthreshold responses included in the fluorescence signal (11, 29). Although Cal-520 has a K_d (320 nM) similar to that of Fluo-4 (350 nM), which is known to respond only to suprathreshold activities (11), we decided to perform imaging experiments using a genetically encoded ultrasensitive fluorescent protein GCaMP6f (30), which can reliably detect single action potentials in marmoset neurons (31, 32).

Marmosets were injected with AAV vectors encoding human synapsin promoter-driven GCaMP6f in A1 at 3 wk before the imaging experiment (Materials and Methods). In a large focal plane (∼520 × 550 μm, Fig. 6A), A1 cells expressing GCaMP6f showed robust responses to single pure-tone stimuli, as shown by the tone-evoked fluorescence changes in four example cells (Fig. 6B) as well as by the heat map of florescent changes recorded from 20 responsive cells in one imaging plane (Fig. 6C). These results indicate that the BFs were homogeneous over large distances in the imaging plane. The spatial distribution of cells with different BFs also showed homogeneous distribution of BFs within local regions of A1, as shown in Fig. 6D. In constructing this distribution map, we have combined the data for cells activated by sound stimuli at three different intensities, due to the relatively low number of cells expressing GCaMP6f, compared with that observed with Cal-520 loading. We also examined the tonotopic responses of A1 cells evoked by sounds at various intensities and found the BFs were largely invariant over three levels of sound intensities (40, 60, and 80 dB). When imaging planes at three different cortical depths (180, 220, and 250 μm) were examined for their tonotopic organization, we found again similar clustering of cells with the same BFs at three different depths. These results were summarized by the 3D plots, with cell pairs sharing similar BFs from adjacent imaging planes within a distance of <100 μm being connected by lines (Fig. 6E). Taken together, our experiments using GCaMP6f have largely confirmed the findings using Cal-520, showing both horizontal and vertical local homogeneity in sound frequency preference and intensity invariance of tonotopic properties in marmoset A1 neurons.

Fig. 6.

Two-photon imaging of marmoset A1 using genetically encoded Ca indicator GCaMP6f. (A, Top) Fluorescence image of a region in marmoset A1, 3 wk after AAV-GCaMP6f injection. (A, Bottom) Two-photon fluorescence image of a ∼520 × 550 μm imaging area in A1 superficial layer at a depth of 220 μm, corresponding to that marked by the red box at the Top. (Scale bars: 100 μm.) (B) Mean fluorescence changes (ΔF/F) evoked by three pure-tone stimuli (0.8, 1.0, and 1.2 kHz; duration 0.2 s, eight trials each) for four example cells (marked by red circles in A). (Scale bar: 0.5 s and 20% ΔF/F.) (C) ΔF/F with time for 20 pure tone-responsive cells during sequential stepwise application of pure tones from 0.5 to 16 kHz (black bar, tone duration 0.2 s), with the magnitude of ΔF/F color coded by the continuous scale shown on the Right. Red trace on Top shows the averaged ΔF/F for all 20 cells. Note that the BF for the vast majority of cells was located at 1.2 kHz. (Scale bar: 0.5 s and 20% ΔF/F.) (D) Spatial distribution of all tone-selective cells in the example imaging plane as that shown in A, Bottom, including data recorded at three different sound levels (40, 60, and 80 dB) of the same frequency. (E) Three-dimensional distribution of A1 cells with similar BFs at three different cortical depths, with sound stimuli of three different intensities applied.

Discussion

Using in vivo two-photon calcium imaging to monitor the activities of a large population of A1 neurons in responses to pure tones of different frequencies, we found that the frequency preference of marmoset A1 neurons is homogeneous over distances of hundreds of micrometers in both horizontal and vertical directions relative to the pia surface. This was shown by using either acute loading of fluorescence Ca²⁺ indicator Cal-520 or in vivo expression of genetically encoded Ca²⁺ indicator GCaMP6f. These results demonstrated the feasibility of simultaneous recording of large populations of cortical neurons in the marmoset brain at single-cell resolution. Furthermore, macroscopic tonotopic maps previously observed by electrophysiological recording (7) and intrinsic optical signals (8) directly reflect homogeneous neuronal tuning properties within local regions of marmoset A1.

Neuropil contamination can be a potential problem under dense labeling of neurons. It is difficult to fully exclude the contamination because the extent of neuropil contamination in signals recorded at somata is unknown. However, we have attempted to subtract the neuropil signal by using a method previously reported (33) and found that the local homogeneity of tonotopic organization observed in marmoset A1 was not affected (SI Appendix, Fig. S2). Furthermore, the clear difference in tonotopic organization between rats and marmosets is unlikely to be caused by neuropil contamination, since such interference presumably occurred in both cases.

The precision of tonotopic organization at the cellular level has recently become a controversial issue (34–39), based mostly on studies from mice. Although a tonotopic map of monkey A1 at the cellular level has not been reported, previous studies using extracellular electrophysiological recording of neuronal activities have suggested that the tonotopic preference of cortical cells within local regions of A1 is homogeneous, consistent with a smooth tonotopic organization. Since both electrophysiological recording and intrinsic optical imaging have a spatial resolution of 50–100 μm (7, 8, 40), the smooth tonotopic maps may result from the averaged responses over many neurons. Extracellular recordings could also be biased toward highly active neurons in either multi- or single-unit recordings (41). The in vivo two-photon calcium imaging approach offers cellular resolution that could unequivocally address the issue of local homogeneity in the frequency tuning properties of A1 neurons.

Several recent studies (10, 11, 42, 43) using the in vivo two-photon imaging approach have cast doubt on the existence of strictly tonotopic maps in A1. It was found that neurons in mice A1 with diverse frequency preferences were highly mixed locally, despite the presence of apparent macroscopic tonotopy. These previous studies revealed that the BF for evoking neuronal responses was highly variable among neurons within 50- to 100-μm distances, with differences in BFs as large as two to four octaves (44). In the present study of marmosets, we found that differences of BFs among cells within 250 μm were less than one octave. To ensure that the discrepancy between our results and previous findings on mice was not caused by the difference in the Ca²⁺ imaging method, we also examined the local tonotopic property of rat A1 cells using our Cal-520 loading method. Quantitative comparison of our marmoset and rat results showed that rat A1 cells with different BFs were distributed in a much more mixed manner than that found in the marmoset A1, implicating different A1 organizations in rodents vs. marmosets. Nevertheless, in both rat and marmoset A1, cells with the same BFs were distributed in a manner that was far from random. Furthermore, we found that in each local rat A1 region, there was a large majority of cells with a particular BF, with other cells of diverse BFs interspersed among them. The presence of a dominant population of cells with the same BF could account for the global tonotopic maps found previously in rodents (6, 9, 26) and in our rat results (SI Appendix, Fig. S3), as well as the previously reported “clustered” and salt-and-pepper distributions of cells in different local regions in mouse A1 (43, 44) and rat A1 (45).

In highly visual animals such as monkeys and cats, neurons with similar receptive field properties in the primary visual cortex (V1) are well organized into local columns. By contrast, in rodents with poor vision, neurons with different receptive field properties are mixed locally in a salt-and-pepper manner (21, 22, 46–48). By analogy, highly uniform tuning properties of A1 neurons locally in marmosets may reflect an organization principle favorable for auditory processing in animal species that are rich in vocal communication and social interaction. Natural sounds with syllables in marmoset calls need to be first decomposed into frequency-specific signals and then reintegrated for auditory perception. Previous studies have shown that some neurons in marmoset A1 selectively respond to the “twitter” sound syllables, but not to the time-reversed one (49, 50). Integration of frequency-specific signals into syllables may depend on intracortical connections among A1 neurons, in addition to potential contributions from thalamocortical inputs. Given the local homogeneity in the frequency tuning of A1 neurons of marmoset, long-range intracortical circuitry among neurons in different tonotopic A1 domains may play a significant role in the integration of auditory signals of different frequencies. The current results thus pave the way for further analysis of A1 circuitry underlying natural sound processing in marmosets.

Materials and Methods

Animal care and experimental procedures were approved by the Animal Care Committee of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (Shanghai, China). Four adult common marmosets (C. jacchus; one male and three females; body weight: 260–400 g) obtained from the nonhuman primate facility of the Institute of Neuroscience were used in this study. All acoustic stimuli were generated using MATLAB (MathWorks) and the sound delivery system was calibrated using a B&K (2669-L) calibrator. For two-photon imaging, the duration of pure-tone stimuli was 0.2 s (including 5-ms ON and 5-ms OFF linear ramps), with an interstimulus interval of 1–1.5 s. Cal-520 AM (AAT Bioquest) was injected into layer 2/3 auditory cortex as previously described (51). Cells were identified manually on the basis of size, shape, and brightness. Fluorescence changes with time of individual cells were extracted by averaging pixel intensity values within each cell in each frame. Correction for neuropil contamination was not applied (see however SI Appendix, Fig. S2). Average fluorescence intensity level (F_t) evoked by each stimulus (measurement window 0.4 s; 0.2-s stimulus duration and 0.2-s poststimulus duration) was compared with average prestimulus baseline fluorescence (F₀) over a 0.5-s window. Cells showing F_t that was significantly larger than F₀ (five to eight repeats, P < 0.05, ANOVA) were defined as “responsive cells.” Of these, BF of the responsive cell was determined by the tone frequency that evoked the largest responses over the frequency range tested. In our dataset, nearly all responsive cells were selective to pure tones. Error bars indicate SEM. Frequency vector maps were calculated using the vector-summation method (52). Full description of materials and methods can be found in SI Appendix, Materials and Methods.