Redefining the Tonotopic Core of Rat Auditory Cortex: Physiological Evidence for a Posterior Field

Transcription

1 THE JOURNAL OF COMPARATIVE NEUROLOGY 453: (2002) Redefining the Tonotopic Core of Rat Auditory Cortex: Physiological Evidence for a Posterior Field NEOT N. DORON, JOSEPH E. LEDOUX, AND MALCOLM N. SEMPLE* W.M. Keck Laboratories of Neurobiology, Center for Neural Science, New York University, New York, New York ABSTRACT Previous physiological studies have identified a tonotopically organized primary auditory cortical field (AI) in the rat. Some of this prior research suggests that the rat, like other mammals, may have additional fields surrounding AI. We, therefore, recorded in the Sprague-Dawley rat extracellular responses of single neurons throughout AI, and continued posteriorly to verify the existence of a posterior field (P) and to compare the neuronal properties in the two regions. Acoustic stimuli, including tones, bandpass noise, broadband noise, and temporally modulated stimuli, were delivered dichotically via sealed systems. Consistent with previous findings, AI was characterized by an anteriorto-posterior tonotopic progression from high to low frequencies (ranging from 40 khz to 1 khz). A frequency reversal at the posterior border of AI marked entry into a second core tonotopic region, P, with progressively higher frequencies encountered further posteriorly, up to a point (approximately 8 khz) where cells were no longer tone responsive. Nevertheless, bandpass noise was an effective stimulus in P, enabling characterization of cells up to 15 khz. Compared with AI, the frequency tuning of response areas was relatively broader in P, the response latency was often longer and more variable, and the response magnitude was more commonly a nonmonotonic function of stimulus level. In both fields, most neurons were binaurally influenced. The presence of multiple auditory cortical fields in the rat is consistent with auditory cortical organization in other mammals. Moreover, the response properties of P relative to AI in the rat also resemble those found in other mammals. Finally, the physiological data suggest that core auditory cortex (temporal area TE1) is composed not only of AI as previously thought, but also of at least two other subdivisions, P and an anterior field (A). Furthermore, our physiological characterization of TE1 reveals that it is larger than suggested by previous anatomical characterizations. J. Comp. Neurol. 453: , Wiley-Liss, Inc. Indexing terms: tonotopic organization; core region; belt region; amplitude modulation; frequency modulation; rat atlas The only auditory field in the cortex that has been thoroughly characterized in the rat is the primary auditory region (AI; Sally and Kelly, 1988; Doron et al., 1996; Kilgard and Merzenich, 1999). In most mammals, however, ranging from small rodents to nonhuman primates, AI is one of several auditory cortical fields defined anatomicalally and physiologicalally (Merzenich and Brugge, 1973; Merzenich et al., 1975; Robertson and Irvine, 1987; for review see Aitkin, 1990; Clarey et al., 1992; Stiebler et al., 1997; Kaas and Hackett, 1999). In some mammals, more than one tonotopically organized core field, each receiving direct input from the tonotopically organized ventral nucleus of the medial geniculate nucleus (MGv), is surrounded by a belt of additional nontonotopic fields (Patterson, 1976). Grant sponsor: NIMH; Grant numbers: RO1-MH46516, R37-MH38774, Grant sponsor: ONR; Grant number: N ; Grant sponsor: the W.M. Keck Foundation. *Correspondence to: Malcolm N. Semple, Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY mal@cns.nyu.edu Received 28 February 2002; Revised 7 May 2002; Accepted 19 July 2002 DOI /cne Published online the week of October 14, 2002 in Wiley InterScience ( WILEY-LISS, INC.

2 346 N.N. DORON ET AL. These findings suggest that the rat may also have additional uncharacterized fields. Although detailed physiological evidence is still lacking, a few studies have provided anecdotal support for the existence of multiple fields in the rat, including anterior, ventral, and posterior fields (Azizi et al., 1985; Horikawa et al., 1988; Simpson and Knight, 1993; Budinger et al., 1999). In the current study, we recorded extracellularly from single neurons in rat cortex to determine whether a posterior field (P) exists. An auditory field located immediately posterior to AI in rat would be consistent with prior findings in other mammals (e.g., cat, Orman and Phillips, 1984; Phillips and Orman, 1984; Phillips et al., 1995, 1996; gerbil, Thomas et al., 1993; and European hedgehog, Batzri-Izraeli et al., 1990). We first characterize the properties of AI neurons in more detail than has been published previously and then compare the properties of AI neurons with cellular properties in P. We also provide some preliminary recordings from additional adjacent fields, and our use of bregma as a recording landmark allows comparison of the physiologicalally determined borders of the primary auditory cortex (temporal area TE1) with the borders found in existing anatomical maps. MATERIALS AND METHODS Animal All procedures were approved by the Institutional Animal Care and Use Committee of New York University. Twenty-two adult Sprague-Dawley rats (Hilltop Labs, Scottdale, PA; weight range, g) showing no signs of outer or middle ear pathology, were used in these experiments. The rats were anesthetized before surgery with sodium pentobarbital (60 mg/kg, i.p.), and during the recording session, anesthesia was maintained by supplemental doses to effect (based on regular monitoring of withdrawal reflexes, respiration, and heart rate). Additional solutions were administered as a precaution against dehydration (5% dextrose-saline, 5 ml/hour), to minimize mucous secretions (atropine sulfate, 0.15 mg/12 hours), and to reduce brain edema (dexamethasone, 0.10 mg/12 hours). Surgery All experiments were conducted in a double-wall sound attenuating room (IAC). The animal s body temperature during the experiment was regulated using a heating pad that maintained a constant rectal temperature of 37 C. After tracheal cannulation, the rat was supported in a modified stereotaxic frame with the external meatuses unobstructed. Sound delivery speculae were sealed to the temporal bone and muscle around the opening of the external auditory meatus. A small craniotomy in the right temporal bone provided access to the underlying dura, which was reflected to permit entry of a microelectrode into the cortex. Stimulation Tones, broadband noise, sinusoidal amplitude modulations (SAM) and sinusoidal frequency modulation (SFM) were generated by a two-channel synthesizer (MALab, Kaiser Instruments). All stimuli were shaped (70- to 100- msec duration, 5- to 10-msec cosine envelope rise/fall times) and were delivered dichotically at 700-msec intervals via short earpieces sealed to the transected external auditory canal and coupled to electrostatic earphones (Stax). Tympanic sound pressure levels (SPLs) for both ears were calibrated on-line from 60 Hz to 40 khz under computer control using a probe microphone assembly (Brüel and Kjær 0.5 in). Calibration for frequencies 40 khz was estimated by extrapolation; therefore, tuning in this range was only approximate. During data acquisition, attenuation was adjusted automatically to maintain a constant SPL as tone frequency was varied. SPLs reported here are expressed in decibels (db) re 20 Pa. For bandpass noise, the center frequency was set to the neuron s characteristic frequency (CF), as defined below and multiple (5 to 10) bandwidths were tested. For SAM and SFM stimuli, the carrier frequency was set to the cell s CF. SAM stimuli were typically delivered at db SPL and SFM at db SPL. The SAM depth was always 100%, and the SFM depth was typically 1,000 Hz. Recording techniques Because our initial goal in each animal was to map AI in an anterior to posterior (A-P) direction before continuing posteriorly to search for evidence of a field located posterior to AI, recordings began approximately 2.6 mm posterior to bregma. Note that all recording locations were measured relative to bregma, a measurement not taken in previous electrophysiological mapping studies in rats (see Discussion section). A platinum-plated tungsten microelectrode was advanced into the cortex in a dorsal to ventral direction by a stepping-motor microdrive (Cal Tech) controlled from outside the acoustic chamber. While the microelectrode was advanced into the cortical area, tones and/or bandpass noise were presented to the animal. When stimulus evoked activity was found, a cell was isolated and analyzed (see Data Analysis section). The microelectrode was then withdrawn and inserted into an adjacent, more posterior location of the cortex (0.1 mm apart in an A-P axis). The procedure was repeated so that the entire targeted region of the cortex could be analyzed. Typically, 50 or more penetrations were made per animal. Recording sessions lasted as long as healthy cortical neural activity could be recorded (times ranged from 7 to 57 hours per animal). When the cortical neural activity diminished (60% of the cases) or became epileptiform (25% of the cases), the recording session ended. Neural activity was amplified, filtered (commonly khz bandpass), and monitored in parallel via an oscilloscope and audiospeaker. Discharge properties were characterized under monaural (ipsilateral and then contralateral) and binaural conditions at the frequency for which the response threshold was lowest, the CF. Frequency tuning was assessed by determining the discharge rate at a constant SPL, chosen to be near saturation for the CF. In some cases, the frequency tuning function was repeated at other SPLs. The time of occurrence of each action potential, for successive presentations of each combination of frequency and sound pressure level, was digitized relative to the stimulus onset. Typically for both ears, the response to monaural stimulation at CF was recorded at stimulus levels incrementing in 10 db steps, up to 80 db SPL, to define threshold and saturating tone amplitude. Responses were then assessed with intervening tone levels, so that the form of the rate level function near both the threshold and the satu-

3 TONOTOPIC FIELDS IN RAT TE1 ration point could be defined with a resolution of 5 db. Some stimulus levels were retested to monitor response reliability. Binaural responsiveness at CF was measured by fixing the contralateral stimulus level at just above threshold and increasing the ipsilateral stimulus level in 5 or 10 db steps from below threshold up to 80 db SPL. In some cases, the response to monaural stimulation at CF was retested with incrementing stimulus levels to monitor response reliability. Data analysis Auditory responsiveness and latency. Activity evoked at the CF was compared with spontaneous firing rates for each cell to determine auditory responsiveness. Onset responses were defined by significantly elevated activity in the window from 0 to 49 msec after stimulus onset (three or more standard deviations above the mean spontaneous rate). Offset responses were defined in a similar manner, using a window from 0 to 49 msec after stimulus offset. Delayed offset responses were measured using a window of 49 msec in length, adjusted to capture the response in a range 28 to 322 msec after stimulus offset. Response latencies were generally determined by the first spike after stimulus onset for each trial. In some cases, especially for cells with high spontaneous rates, a temporal window was used to exclude spike times that differed significantly from the modal initial-spike latency. However, in most cases, there was little to no spontaneous activity, and consequently, the first-spike latency distribution was very narrow. Rate level and frequency tuning functions. Functions relating discharge rate to SPL typically increased to a peak and then either saturated (monotonic cells) or rolled over (nonmonotonic cells) at higher SPLs. The degree of turnover (T) was calculated: T (R peak R 80dB )/R peak, where R peak is the evoked activity (poststimulus minus spontaneous rates firing rates) at the SPL that yielded the peak response, and R 80dB is the evoked activity at the highest SPL tested, 80 db SPL. The resulting ratio, therefore, ranged from zero (no turnover) to one (complete turnover). Frequency tuning breadth was characterized from the function relating discharge rate to frequency, by determining the bandwidth associated with a discharge rate midway between the spontaneous and maximum rates (the 50% bandwidth). Because sporadic spontaneous firing near threshold often complicated the task of determining a threshold tuning function, we analyzed frequency tuning at the SPL at which the monaural rate level function at CF reached a plateau or peaked. Tuning breadth was expressed as the ratio of CF to the 50% bandwidth. Facilitative and suppressive interactions. Binaural interactions were considered facilitative when a sound presented to either the contralateral or ipsilateral ear alone produced excitation, and stimulation of both ears together resulted in greater excitation (EE according to the classification scheme of Aitkin et al. 1975). Interactions were considered suppressive when contralateral stimulation alone produced excitation and ipsilateral stimulation alone had no apparent effect, but the binaural response magnitude was less than the contralateral magnitude (EI). Cells were considered not to be binaurally influenced if a sound presented to the contralateral ear alone produced excitation and no ipsilateral influence 347 could be detected under binaural or monaural conditions (EO). Responses to modulated stimuli. The best modulation frequency (BMF) for SAM and SFM stimuli, as reported here, is based on synchrony rather than discharge rate: vector strength was used as the index of synchronization to the modulation period. When the vector strength was the same for more than one modulation frequency for a given cell, the BMF was chosen based on the greater discharge rate. Significance of synchronization was assessed using the Rayleigh test of the uniform distribution on a circle, a standard test in circular statistics (e.g., Batschelet, 1981). Histology After each experiment, electrolytic lesions (2 A 15 sec) were placed at several recording sites. Lesions were always made at locations where frequency reversals were observed. The rat was given a lethal dose of sodium pentobarbital (100 mg/kg, i.p.) and subsequently perfused through the heart with heparinized saline followed by phosphate-buffered formalin. Serial frozen sections were cut at 40 m in a coronal plane and stained with cresyl violet. Locations of electrode penetrations were then visualized and reconstructed under the microscope, based on both the electrolytic lesions and the measurements relative to bregma recorded for each penetration. There was no discernible difference between AI and P in the Nisslstained sections. For presentation purposes, frontal sections stained for Nissl were acquired via a Sony CCD camera (model XC- 77) directly to the computer using NIH Image. Representative sections and penetration tracts were traced, using Canvas, to illustrate the area of recordings. In addition, a high resolution digital photomicrograph of a representative section at the point of frequency reversal between AI and P was acquired via a CoolSnap digital camera (Roper Scientific, Inc.) directly to the computer using CoolSnap software. Finally, whole brain images were acquired by scanning photographic slides and photographs. Images contrasts were adjusted before printing. RESULTS Recordings of isolated single-unit activity were successfully made from 339 neurons in 22 animals. The frequency organization in the rat s temporal cortex revealed two adjacent, tonotopically organized fields: AI (199 cells from 22 animals) and P (74 cells from 12 animals). All cells in AI and P were acoustically responsive. In addition, 67 cells (of which 30 were acoustically responsive) were recorded in regions surrounding AI and P from 15 animals, including 7 cells in an area just anterior to AI (A), 30 cells in a belt area just ventral to AI (V belt), 9 cells in a belt area just posterior to P (P belt), and 21 cells from other surrounding regions. Primary auditory cortex and posterior field AI and P were identified and distinguished from each other based on their tonotopic gradients, as well as their firing properties (discussed below). AI showed a clear anterior to posterior progression from high (ranging up to 40 khz) to low (ranging down to 1 khz) frequencies, with frequencies higher than 4 khz occupying a larger area of the cortex than low frequencies. AI extended ap-

4 348 N.N. DORON ET AL. Fig. 1. The distribution of characteristic frequencies (CFs) of neurons within the temporal cortex (i.e., anterior field [A], primary auditory cortical field [AI], posterior field [P], and belt area just posterior to P [P belt]) relative to their distance from bregma (in millimeters). The frequency organization in the rat s brain reveals two tonotopic fields. An anterior to posterior (3 mm) progression from high ( 40 khz) to low ( 1 khz) frequencies is characteristic of AI. A frequency reversal at the posterior border of AI (arrow at 5.8 mm posterior to bregma) marks entry into a second core tonotopic region (P) with progressively higher frequencies encountered further posteriorly. Each circle indicates the CF for one or more individual neurons (n 7, 199, 74 and 9 for A, AI, P, and P belt, respectively). Filled gray and black circles mark data points taken from two different individual animals. Black dots within circles indicate data points taken from cells used in Figure 5. Arrows at 2.68 and 6.52 mm posterior to bregma mark the borders between A to P and between P to P belt, respectively. These data are consistent with previously published tonotopic organization of rat AI (dashed line, derived from Kelly, 1990). proximately 3.1 mm in anterior to posterior length, from 2.7 to 5.8 mm posterior to bregma. At the posterior border of AI, a frequency reversal marked entry into P (Fig. 1). In this second core tonotopic region, progressively higher frequencies were encountered further posteriorly, up to a point (approximately 8 khz) where cells were typically unresponsive to tones. In this region of P, response areas were characterized using bandpass noise, which revealed a continuation of the tonotopic progression up to at least 15 khz. Further posteriorly, cells only responded to broadband noise and, therefore, were not assigned CFs and were not considered to be part of the tonotopically organized P. Note that it is possible that the tonotopic progression and, hence, the border of P may extend further posteriorly, but cells may require more complex stimuli than were available here. P extended approximately 0.6 mm in anterior to posterior length, from 5.8 to 6.4 mm posterior to bregma, based on the present data. The existence of P was evident not only in the group data but was also clear in individual cases. In fact, every individual animal from which cells in P were recorded (n 12) displayed the field s characteristic frequency gradient, with increasing CFs in an anterior to posterior direction. Note that bregma was a very reliable landmark to use in locating these fields, as there was a relatively small variability in the average distance between bregma and the transition from AI to P ( mm SEM; maximum error was 0.4 mm). To compare data across cases, anterior-posterior distances were always aligned based on this transition point; in most figures, these distances are converted into the average distance from bregma. In both fields, cells found along single dorsoventral (D-V) penetrations had similar CFs, revealing the D-V orientation of isofrequency contours (Fig. 2). The isofrequency contours of AI were more distinct and slightly larger in the D-V dimensions than the contours of P: AI contours ranged from 3.1 to 5.4 mm ventral to bregma, whereas P contours ranged from 3.1 to 5.0 mm ventral to bregma, depending on the A-P level. The ventral part of the isofrequency contours in AI was slightly wider than its dorsal part. Some firing properties were similar in P and AI, but in many respects, the properties differed. Many of these characteristics are summarized in Table 1. In both fields, the most robust auditory responses were typically found in layers III IV (Fig. 3), and all cells reported here were recorded from those layers. Spontaneous activity was similar in AI (mean SEM: Hz, 158 cells) and P ( Hz, 47 cells). These estimates exclude a few recordings that showed bursting periods of much higher spontaneous activity. Pure tones typically elicited a transient burst of action potentials at stimulus onset in AI (99% of 158 cells), and P (94% of 47 cells). The magnitude of this initial burst was similar in both fields; the onset response was an average of and standard deviations above the mean spontaneous rates (the z score, as reported in Table 1) in AI and P, respectively. Additional short latency bursts locked to stimulus offset were elicited both in AI (37%) and P (34%). In some neurons from both fields (31% in AI; 38% in P), there were weaker, delayed offset responses that typically began 30 msec (range, 28 to 322 msec) after stimulus offset (Fig. 4). Responsiveness in both AI and P was clearly influenced by the stimulus duty cycle, resulting from the combination of pip duration and stimulus onset asynchrony (SOA, the period from the start of the stimulus to the start of the next stimulus). Our most common stimulus parameters, which included pip durations of msec (typically 70 msec) and SOAs of 700 msec, consistently yielded onset responses in both AI and P, and offset and/or delayed offset responses in some cells in both fields, as described above. However, longer pip durations of msec, with SOAs of 700 1,000 msec, never yielded onset responses in either field, except in response to the first pip. Perhaps an inhibitory process is activated by the longer pip durations, or higher duty cycles, preventing onset responses to subsequent pips. In addition, most AI neurons exhibited offset (35%, 7 of 20) or delayed offset (60%, 12 of 20) responses to these longer pip durations, suggesting that the inhibition was temporally limited. In contrast, only 25% (3 of 12) of cells in P had either offset or delayed offset responses to the longer pip durations. Fi-

5 TONOTOPIC FIELDS IN RAT TE1 349 Fig. 2. Distribution of characteristic frequencies (CFs) of neurons within the temporal cortex (i.e., primary auditory cortical field [AI], posterior field [P], anterior field [A], belt area just posterior to P [P belt], and belt area just ventral to AI [V belt]) and surrounding regions relative to their location from bregma (abscissa shows distance in millimeters posterior to bregma, ordinate shows distance in millimeters ventral to bregma). This distribution was reconstructed from 22 animals (n 339); data from two individual animals are marked with either one or two underscores (which correspond to the data represented by filled gray and black circles, respectively, in Fig. 1). The thin lines mark the transitions between each decade of frequencies in AI. The anterior thick line delineates the border between AI and A, whereas the posterior thick line delineates the border between AI and P. N, responsive only to noise; NR, no response. Inset: side view of the rat brain with the recording area and frequency transitions outlined. nally, when a few cells in AI were presented with longer SOAs of 2,000 msec, using the same longer pip durations of msec, onset responses were restored, suggesting that the duration of inhibition initiated by these longer pips is between 1,000 and 2,000 msec. A few P neurons were tested with 2,000 msec SOAs, but onset responses were still not observed, suggesting that inhibition might last longer in P than in AI. Frequency tuning also differed in these two core fields. In general, individual tuning functions tended to be sharp in AI, with clearly defined best frequencies, whereas in P they were broader and sometimes rather irregular (Fig. 5A). In Figure 5B, an index of tuning breadth is plotted as a function of CF. The sharpness index shows a marked frequency dependence for the full frequency range in AI, so we restricted our statistical analysis to the frequency range ( khz) in which tuning was found in both cortical fields and in which tuning sharpness increased only marginally. For cells in this range, the mean tuning breadth index was higher in AI ( ; n 83) than

6 350 N.N. DORON ET AL. TABLE 1. Summary of Cellular Properties by Cortical Region 1 AI P V belt Spontaneous rates 2 (Hz) Response to pure tones 2 Tonotopicity (anterior to High to low Low to high None posterior) Onset responses (% cells) Offset responses (% cells) Delayed offset responses (% cells) Onset strength (z score) Frequency tuning 3 Sharp Broad Broad First-spike latency 4 (ms) SD of first-spike latency 4 (ms) % Nonmonotonic cells Binaural interactions 6 62% EI 10 62% EE 10 See text Response to noise 7 Broad band noise (% cells) Broad band noise and NR to tone (% cells) Band pass noise (% cells) Band pass noise and NR to tone (% cells) Response to AM stimuli 8 Entrainment (% cells, Raleigh test) Vector strength, at BMF Synchronization BMF (Hz) Response to FM stimuli 9 Entrainment (% cells, Raleigh test) See text for details of analyses. AI, primary auditory cortical field; P, posterior field; V belt, a belt area just ventral to AI; NR, no response; AM, amplitude modulation; FM, frequency modulation; BMF, best modulation frequency. 2 n 158, 47, 11 for AI, P, and V belt, respectively, unless otherwise specified. 3 For details of frequency tuning in AI and P, refer to Fig n 138, 50, 14 for AI, P, and V belt, respectively. 5 n 142, 45, 14 for AI, P, and V belt, respectively. 6 n 111, 45, 14 for AI, P, and V belt, respectively. 7 n 175, 52, 14 for AI, P, and V belt, respectively. 8 n 66, 17, 7 for AI, P, and V belt, respectively. 9 n 51, 16, 4 for AI, P, and V belt, respectively. 10 Values where AI and P are significantly different from each other. 11 Values where V belt differs significantly from one or both of the other two regions (see text for details of statistical comparisons). in P ( ; n 43). This difference was statistically significant (P ; t test), confirming the sharper frequency tuning of AI. Bandpass and broadband noise were often very effective stimuli in both fields: 92% (161 of 175) and 98% (51 of 52) of cells in AI and P, respectively, responded to broadband noise, whereas 91% and 96% of cells in AI and P, respectively, responded to bandpass noise. In fact, some P cells (29%) responded to broadband noise but not to pure tones, and 13% responded to bandpass noise but not to pure tones. In contrast, none of the cells in AI responded exclusively to broadband or bandpass noise. It should be noted that our test for bandpass noise sensitivity was based on a center frequency equivalent to CF. When tones were ineffective, we chose a noise center frequency equivalent to the CF of the nearest tone-responsive cell. The minimum mean discharge latency was typically longer and more variable in P than in AI (Figs. 6, 7A,B). The mean first-spike latency was significantly lower (P 0.001, t test) in AI ( msec) than in P ( msec). Similarly, the SD of mean first-spike latency was smaller (P 0.001; t test) in AI ( msec; n 138) than in P ( msec; n 50). In the inset of Figure 6, SD is plotted as a function of mean first-spike latency for cells in both fields. Note that both axes are logarithmic, which reduces the apparent variance in the data but has the advantage of spatially dispersing the lowest values. The populations of AI and P neurons overlap, but firstspike latencies in AI are typically shorter and more precise than in P. Even within fields, there was a gradual tendency toward longer and more variable latencies at more posterior locations (Fig. 6), but rare exceptions could be found in both fields (e.g, Fig. 7C). Nonmonotonic discharge-rate/stimulus level functions were more common in P. Examples of both monotonic and nonmonotonic rate level functions are illustrated in Figure 8. The degree of turnover served as a quantitative index of monotonicity, ranging from 0 (no turnover, monotonic) to 1 (complete turnover, nonmonotonic; see Materials and Methods section). The degree of turnover is shown in Figure 9 as a function of anterior to posterior cortical location relative to bregma; greater turnover was most common at posterior locations. Adopting a criterion of 25% turnover, the proportion of neurons judged nonmonotonic was 76% (34 of 45) in P, significantly more than the 25% (35 of 142) found in AI (P 0.001; ; see Fig. 10). A more stringent criterion of 50% turnover would lead to estimates of 50% and 8% nonmonotonic cells in P and AI, respectively. Almost all units in both fields were contralaterally excitable, and most were binaurally influenced. Suppressive interactions (EI cells) were more common (62%; 69 of 111) in AI, whereas a significantly different proportion of binaural types was found in P, where facilitative interactions (EE) were more common (62%; 28 of 45; P 0.001, ). In addition, there was also some suggestion of clustering of binaural type (Fig. 11); that is, whereas EI cells were fairly evenly distributed throughout AI, EE cells were more commonly found at the posterior (lowfrequency) part of AI. EO cells were not commonly found in either AI or P. The distribution of binaural types for AI and P is summarized in the inset of Figure 11. Some cells (12 and 7 in AI and P, respectively) from both fields exhibited a mixture of facilitative and suppression binaural interactions reminiscent of properties previously described for cat AI (Semple and Kitzes, 1993). Responses of many cells in AI (79%, 52 of 66 tested) and P (77%, 13 of 17 tested) were significantly entrained to SAM tones (P 0.01, Rayleigh test); vector strengths for such cells were similar at the BMF, averaging and in AI and P, respectively. Overall, cells typically responded best to SAM rates of 2 5 Hz in AI and 2 10 Hz in P (Fig. 12) and were generally unresponsive to SAM rates above 20 Hz. When SAM rates were calculated on a cell by cell basis, the average BMF was and Hz in AI and P, respectively. There was no discernible topographic organization of SAM BMF in either AI or P (Fig. 13). Many cells in both AI (73%, 37 of 51) and P (75%, 12 of 16) also responded in synchrony with SFM stimuli (P 0.01, Rayleigh test), although the range of SFM frequencies tested quantitatively for each cell was very limited, typically including 1 and 10 Hz only. Vector strengths at BMF were in AI, and in P. This difference was primarily due to the well-entrained responses of P cells to 10 Hz SFM stimuli (mean vector strength ). Surrounding regions Although the primary focus of this study was to compare properties in AI and P, electrode penetrations were sometimes made in areas surrounding these two fields. In the area just anterior to AI (anterior to 2.7 mm posterior to

7 TONOTOPIC FIELDS IN RAT TE1 351 Fig. 3. A: Nissl-stained coronal section through the cortex of the rat showing lesion sites at the border between primary auditory cortical field (AI) and posterior field (P). The arrowhead points to the lesion that marks the site responsive to pure tones (layers III IV), in this case, in the coronal plane where the frequency reversal between AI and P was observed. The arrow points to the lesion located beyond the responsive area of the cortex (in layers V VI). Dashed lines adjacent to the section mark borders based on Paxinos and Watson (1998). Dashed line within the section marks the dorsal border of temporal cortex, area 1 (TE1) based on the physiology in this study. B: Drawings of coronal sections in a rostral to caudal direction through the cortex, depicting the borders of TE1 as found in this study (shaded region). Labels beside each section mark the borders of TE1, TE2, TE3, and perirhinal cortex (PRh) according to Paxinos and Watson, (1998). Numbers above each section refer to the distance posterior to bregma (in millimeters). Note that, whereas TE1 in the rat atlas does not continue anteriorly and posteriorly to levels 2.6 and 6.6 mm posterior to bregma, respectively, it does based on the physiology map obtained in this study (see Discussion section). Asterisks next to levels 2.6 and 6.6 mm posterior to bregma indicate areas from which recordings were not made; therefore, the nature of these regions was not determined (see Discussion section). Note that the dorsal border of TE1 in A and B, based on Paxinos and Watson, is more ventral than what was found in this study (see Discussion section). Note also that the lesions seen in A are also drawn in B (5.8 mm posterior to bregma). d, dorsal; m, medial. Scale bar 0.5 mm in A, 2.0 mm in B. bregma), a reversal in the frequency map from high (in AI) to low was observed, as CFs dropped to around khz (Figs. 1, 2). However, other firing characteristics were similar to those found in AI. For example, although only a limited number of cells were characterized in this region, these cells, like in AI, were all binaurally influenced, sharply tuned, and had short latency responses and low thresholds. Further research is needed to evaluate these similarities and to determine the tonotopic organization of this anterior region (see Discussion section). In contrast, cells just posterior to P ( 6.4 mm posterior to bregma) were only responsive to broadband noise. Cells from regions ventral to AI (V belt; below approximately 5.4 mm ventral to bregma, depending on the A-P level) were typically unresponsive to pure tones (18 of 23 cells, 78%) but were very broadly tuned, often responding to over three octaves of frequencies (AI cells typically responded to one octave or less). In contrast to the relatively weak responses to pure tones, broadband noise, which was a moderately effective stimulus throughout rat auditory cortex, was often particularly effective in the V belt. For example, 64% of cells in this region responded to broadband noise and not to tones. Additional characteristics also distinguished V belt from AI and P, including long mean first-spike latencies ( msec; significantly greater than AI; P 0.001; F 56.4) and SDs ( msec; significantly greater than AI and P; P 0.001; F 75.2; see inset of Figure 6), a large percentage of delayed offset responses (72%; P 0.05, ), and robust responses to more complex stimuli (e.g., a vector

8 352 N.N. DORON ET AL. Fig. 4. Representative single-unit discharge patterns shown as raster plots and peristimulus time histograms illustrating typical examples of onset followed by offset responses (A) and onset followed by weaker delayed offset responses (B), for primary auditory cortical field (AI) and posterior field (P) cells. Typical onset responses from both fields are illustrated in Figure 7. Bars below each histogram represent the pip duration (70 msec). strength at SAM BMF of ; significantly greater than AI and P; P 0.001; F 9.37; Fig. 14). DISCUSSION Posterior field The present study provides the first characterization of cells in P, a core auditory field extending posterior to AI. Like AI, P is tonotopically organized. High frequencies are represented caudally and low frequencies rostrally, creating a reversal of the tonotopic organization found in AI. In addition, single neurons in P are characterized by longer response latencies than are typically recorded in AI, a finding that seems consistent with a previous report of longer latency peak auditory evoked potentials in a region just posterior to AI (Simpson and Knight, 1993). A comparable posterior field has been described in other mammals, including the cat (Reale and Imig, 1980), the European hedgehog (Batzri-Izraeli et al., 1990), and the gerbil, in which this area is called ventroposterior (VP; Thomas et al., 1993). The rat, cat, and gerbil share a similar tonotopic gradient in this field (i.e., anterior to posterior representation of high-to-low frequencies), whereas in the hedgehog, the tonotopic gradient is organized in the opposite direction. The functional significance and cause of this variability in direction are not known. It is interesting to note that, although there is a large degree of consistency in the organization of tonotopic gradients in Fig. 5. Frequency tuning breadth compared in primary auditory cortical field (AI) and posterior field (P). A: Frequency tuning functions from single cells in AI (solid line) and P (dashed line), chosen from a similar frequency range. The P cell s relatively broader and less regular tuning function illustrates a common finding in our sample. The 50% bandwidth is indicated by the thick horizontal bar on each function. B: A tuning breadth index (CF/BW, where CF is the characteristic frequency and BW is the 50% bandwidth as explained in the Materials and Methods section and illustrated in A) is plotted as a function of frequency for individual cells in AI (n 83) and P (n 43). For the frequency range over which P cells exhibited frequency tuning ( khz), tuning was significantly sharper in AI. mammalian auditory systems, because rats, cats, gerbils, and mice, all share the same tonotopic relationships among A, AI, and P (where studied), other mammals show different relationships, such as squirrels, guinea pigs, rabbits, and monkeys (Imig et al., 1977; McMullen and Glaser, 1982; Aitkin et al., 1986; Luethke et al., 1988; Redies et al., 1989a; Morel et al., 1993). It is possible that some of these discrepancies may be due to mislabeling of structures across species, particularly given the existence of multiple tonotopic fields, with alternating tonotopic gradients. Future research is needed to better evaluate these differences.

9 TONOTOPIC FIELDS IN RAT TE1 353 Fig. 6. A scatter plot showing the mean first-spike latency (open circles, left axis) and standard deviation (SD; gray circles, right axis) as a function of cortical location relative to bregma (in millimeters; n 6, 138, 50 and 4 for anterior field [A], primary auditory cortical field [AI], posterior field [P], and belt area just posterior to P [P belt], respectively). The inset scatter plot shows SD as a function of minimum mean first-spike latency for cells in AI (open circles; n 138), P (plus signs; n 50), and belt area just ventral to AI (V belt; grayed circles; n 14). Each circle indicates one or more individual neurons. Black dots within circles indicate data points taken from cells used in Figure 7. Arrows are the same as in Figure 1. Note the progression to longer and more variable latencies at more posterior locations, both within and between fields. Also note that the data in the inset are strikingly similar to what was reported in AI and P of the cat (Phillips et al., 1995, Fig. 9). Primary auditory cortex The present study also provides an extensive examination of response characteristics of cells in AI of the rat. In general, these characteristics are similar to what has previously been found in rat and other mammals, particularly the cat and gerbil. These response properties include the characteristic frequency organization of AI, the relatively sharp frequency tuning, the preponderance of binaurally influenced cells, with short mean first-spike latencies, and low thresholds (Merzenich and Brugge, 1973; Merzenich et al., 1975, 1976; Hellweg et al., 1977; Reale and Imig, 1980; Phillips and Irvine, 1981a; Brugge, 1982; Gates and Aitkin, 1982; McMullen and Glaser, 1982; Kelly, 1990; Thomas et al., 1993; Phillips et al., 1994; Doron et al., 1996; Stiebler et al., 1997; Kilgard and Merzenich, 1999). In addition, some of the present findings in AI provide new insights and help clarify some previous controversies regarding the characteristics of AI in the rat. For example, the isofrequency contours in the present study were found to run in a D-V direction with an average length of 2 mm, which is in agreement with the study by Sally and Kelly (1988) but contradicts the finding of concentrically organized contours as reported by Horikawa et al. (1988). In addition, although the synchronous firing of AI cells in response to temporally modulated (SAM and SFM) stimuli Fig. 7. A,B: Representative single-unit discharge patterns shown as raster plots and peristimulus time histogram, illustrate the typical properties of onset responses observed in primary auditory cortical field (AI) and posterior field (P). Note the longer and more variable latencies typical of P neurons. C: Examples of exceptions from both fields, showing that some AI cells responded with longer, more variable latencies, and some P responses, are more temporally precise. First-spike latency means, standard deviations, and distance from bregma are indicated in Figure 6. Insets in C show responses using expanded time scales. is in agreement with previous work (Gaese and Ostwald, 1995), in that study BMFs to SAM stimuli were higher ( 10 Hz) than observed in the present study (2 5 Hz). Furthermore, the anterior to posterior length of AI reported here (3.1 mm) is smaller than the length (3.9 mm) described by Kelly (1990; see dashed line in Fig. 1). One possible explanation for this discrepancy is that we recorded from the right hemisphere, whereas Kelly (1990) recorded from the left hemisphere. In mice, based on similar physiological mapping techniques, the area of the auditory cortical field in the left hemisphere was larger by a factor of 1.3 than the field in the right hemisphere (Stiebler et al., 1997). Another difference between the present study and Kelly (1990) is that our response curve of CF vs. A-P location (Fig. 1) appears shifted slightly toward higher CFs than the data from Kelly. Disparities between the two studies

10 354 N.N. DORON ET AL. Fig. 8. Typical monotonic (top row) and nonmonotonic (bottom row) rate/level functions. The functions were normalized based on maximum spike counts per 20 trials; actual maxima are indicated next to each plot. Percent turnover and distance from bregma are indicated in Figure 9. SPL, sound pressure level. Fig. 10. The proportion of cells exhibiting nonmonotonic rate level functions was much greater in posterior field (P; 76%, n 45), than in primary auditory cortical field (AI; 25%, n 142). Nonmonotonicity was based on a criterion of 25% turnover. Fig. 9. Scatter plot showing the degree of turnover as a function of cortical location relative to bregma (in millimeters), in anterior field (A), primary auditory cortical field (AI), and posterior field (P). The degree of turnover (see schematic inset) was calculated as (R peak R 80dB )/R peak. Dashed line indicates criterion level of 25% turnover, used for distinguishing between monotonic (below line) and nonmonotonic (above line) cells (see Materials and Methods section). Each circle indicates one or more individual neurons. Black dots within circles indicate data points taken from cells used in Figure 8. Arrows are the same as in Figure 1. Note that cells at more posterior locations tended to have greater turnover. n 5, 142, and 45 for A, AI, and P, respectively. could conceivably reflect differences between the stimulus delivery systems for high frequencies. It should be noted, however, that the anterior and posterior AI borders as Fig. 11. The distribution of characteristic frequencies relative to distance from bregma (in millimeters), coded by binaural attributes, in anterior field (A) and posterior field (P), reveals that suppressive interactions are more common in primary auditory cortical field (AI), whereas facilitative interactions predominate in P (see inset). Each circle indicates one or more individual neuron (n 111 and 45 for AI and P, respectively). Arrow is the same as in Figure 1. EE, EI, EO according to the classification scheme of Aitkin et al. (1975). reported here are verified by recordings from adjacent fields (P and A), which have cells that respond to low frequencies, and in the case of the shifted CF curves, the shift exists for frequencies both above and below 40 khz (the upper limit of calibration in our system).

11 TONOTOPIC FIELDS IN RAT TE1 355 Fig. 13. Scatter plot showing the distribution of cells entrained to sinusoidal amplitude modulations (SAM) stimuli relative to distance from bregma (in millimeters) for primary auditory cortical field (AI) and posterior field (P). The cells are coded by the best modulation frequency. NR, cells with no response. Each circle indicates one or more individual neurons. n 66 and 17 for AI and P cells, respectively. Arrows are as in Figure 1. Fig. 12. Histogram showing cellular responses, by cortical region, to sinusoidal amplitude modulations (SAM) stimuli. A: The percentage of cells significantly entrained (P 0.01, Raleigh test) to SAM stimuli, as a function of SAM frequency. B: The average vector strength of all cells that significantly responded to SAM stimuli, as a function of SAM frequency. Total number of cells tested for each bar in A, from left to right, primary auditory cortical field (AI): 61, 64, 21, 39, 65, 57; posterior field (P): 17, 17, 8, 9, 17, 17; belt area just ventral to AI (V belt): 5, 7, 6, 7, 6, 3. Total number of cells tested for each bar in B, from left to right, AI: 27, 24, 15, 20, 31, 3; P: 7, 8, 7, 7, 10, 3; V belt: 3, 6, 5, 6, 3, 0. Finally, the present physiological data from AI, as well as other fields, help to clarify some discrepancies regarding the anatomical borders of these fields, as presented in various atlases. These details will be given further consideration in a subsequent section below. Relation of AI organization to thalamocortico projection patterns It is difficult to compare our physiology-based map of AI with maps based on thalamocortical tract tracing studies, because tracing studies typically do not present their cortical results relative to bregma. Nevertheless, two studies (Scheel, 1988; Roger and Arnault, 1989) showed that the topographic organization of thalamocortical projections is in general agreement with the tonotopic organization of AI. These studies showed that the core area receives its projections from throughout the ventral nucleus of the medial geniculate nucleus (MGv) and from the caudal part of the medial nucleus of the medial geniculate nucleus (MGm). Although there were no apparent variations in the topographical organization of these thalamocortical projections from the MGv along its anterior to posterior axis, these projections are topographically organized in coronal planes. Specifically, the cells of origin of projections to cortical targets ranging from high to low frequencies are organized within the MGv in a circular pattern, from medial to ventral to lateral to dorsal. Whether these areas of MGv display the same frequency tuning characteristics as their target cortical regions remains to be seen, as detailed tonotopic mapping of MGv in the rat has yet to be done. For comparison, in the cat, this correspondence between the tuning properties of MGv neurons and their AI target regions is maintained. Specifically, in the cat, high frequencies are represented in the medial part of the MGv, and low frequencies are found in the lateral and dorsal parts (Aitkin and Webster, 1972; De Ribaupierre and Toros, 1976; Calford and Webster, 1981; Imig and Morel, 1985; Morel et al., 1987; Rodrigues-Dagaeff et al., 1989). This finding suggests that the rat MGv may be organized in a similar manner, because the thalamocortical auditory pathways of the rat and cat share many features, including a similar frequency map of AI (Reale and Imig, 1980; Sally and Kelly, 1988; this study) and similar connectivity between MGv and AI (Scheel, 1988; Rodrigues-Dagaeff et al., 1989).

12 356 N.N. DORON ET AL. Fig. 14. Example of the responses of a ventral belt cell after sinusoidal amplitude modulations (SAM) stimuli. A: The cell s response to an SAM stimulus, with a carrier of 35 khz and SAM frequency of 2 Hz, at 70 db sound pressure level (SPL). B: The cell s response to the same SAM stimulus as in A, by using a higher resolution time scale and summed over the course of one period of the stimulus. The zero time point represents the beginning of each new period of the stimulus. C: The synchronization index (vector strength) plotted versus amplitude modulated (AM) frequency, all using a carrier frequency of 35 khz at 70 db SPL. Note that the response was most entrained to an SAM frequency of 2 Hz (the cell s best modulation frequency). Comparing the physiological characteristics of AI and P Several properties of AI and P indicate that these regions in the rat are quite similar to those of the cat: in both species P was characterized by broader and more irregular frequency tuning, longer and more variable discharge latencies, a greater incidence of nonmonotonic rate level functions, a greater incidence of binaural summation, and a greater proportion of EE (rather than EI) binaural interactions (Merzenich et al., 1973, 1975; Phillips and Irvine, 1981b; Orman and Phillips, 1984; Phillips and Orman, 1984; Phillips et al., 1995). One difference is that, whereas EE responses are rare in rat AI, they are common in cat AI (e.g., Middlebrooks et al., 1980). Given these similarities between the cat and rat auditory cortex and the similar connectivity between MGv and AI in the two species (Scheel, 1988; Rodrigues-Dagaeff et al., 1989), it is likely that the thalamic input to P is similar in the two species. In the cat, P (unlike AI) receives a considerable projection from posterior MGv (Rodrigues- Dagaeff et al., 1989). Future experiments will help determine whether this pattern also exists in rat. Response properties in the anterior parts of the cat MGv (Rodrigues-Dagaeff et al., 1989) and cat and rat AI (Merzenich et al., 1973, 1975; Reale and Imig, 1980; present study), suggest that processing along this anterior auditory system is less complex than along the posterior system, involving P and presumably posterior MGv, which seem to be involved with a higher order of information processing. For example, in both cat (Phillips et al., 1985) and rat (present study), cells in P are more frequently nonmonotonic, and exhibit the longest latency and most variable responses. The present data also suggest a similar gradient exists even within AI. These properties could all reflect a greater degree of temporal filtering in the posterior aspect of the auditory cortex of both species. Additional fields Horikawa and his colleagues (1988) have tentatively identified three fields in addition to AI in the rat: the anteroventral (AV), posterodorsal (PD), and anterior auditory (A) fields. Based on their brief report, one could speculate that A is a tonotopically organized third component of the core auditory cortex (i.e., in addition to AI and P). Insufficient data are provided to determine whether fields AV and PD are also tonotopically organized. Our preliminary findings reveal a frequency reversal at the anterior border of AI. The anterior region appears to exhibit sharp tuning, short latency responses, and binaural influence all characteristic of field A in the cat (Phillips and Irvine, 1982). Because A and AI in the cat both receive projections from similar areas of MGv (Reale and Imig, 1980; Morel and Imig, 1987; Rodrigues-Dagaeff et al., 1989), it is likely that this putative A in the rat, based on its similarity to A in the cat, and the similarity between AI in rat and cat, also receives projections from the same areas of MGv that project to AI. Characterization of the complete core and belt auditory cortical regions in the rat is beyond the scope of our current study. Nevertheless, our recordings ventral to AI (i.e., in the V belt) are likely to correspond to the few cells in this region reported previously by Sally and Kelly (1988). Although samples in both studies were small, at least some of these cells show a clear discontinuity with the

13 TONOTOPIC FIELDS IN RAT TE1 isofrequency contours in AI, and according to the present study, they show a reliable response to complex stimuli. In our limited recordings, we found no evidence of sharp tuning and tonotopic organization in the V belt. Similarly, cells found at the posterior end of P would only respond to broadband noise and, hence, apparently mark the posterior limit of the tonotopically organized core. As noted in the results, it is possible that more complex stimuli than were available here may reveal that P extends further posteriorly. Defining the borders of auditory cortical fields Whereas previous physiology studies of AI measured recording locations relative to cortical surface markings, we used bregma as a landmark for all cellular recordings, which allowed the reconstruction of tonotopic maps relative to bregma. This in turn allows comparison of the physiology results obtained here with anatomy-based atlases of the rat brain. Thus, brain sections taken at the reversal point that marked the border between AI and P, which occurred at an average of 5.8 mm posterior to bregma (e.g., Fig. 3), were found to correspond well to sections at similar A-P levels in several common older and newer rat atlases (Sprague-Dawley, Swanson, 1992, 1998; and Wistar, Zilles, 1985; Paxinos and Watson, 1986, 1998), despite that these atlases are not consistent in all respects, as will be discussed below. All the common rat atlases divide the rat temporal cortex into three main areas: TE1, TE2, and TE3. Whereas TE1 is tonotopically organized and mainly receives its input from the tonotopically organized division of the medial geniculate nucleus (MGN), that is, the MGv (Scheel, 1988; Redies et al., 1989b; Roger and Arnault, 1989; Brandner and Redies, 1990; Clerici and Coleman, 1990; Morel et al., 1993; Romanski and LeDoux, 1993a; Shi and Cassell, 1997), the other two regions are not tonotopically organized, and receive input from the nontonotopically organized divisions of the MGN, including polymodal input from other thalamic and cortical nonauditory modalities (Deacon et al., 1983; Guldin and Markowitsch, 1983; Shi and Cassell, 1997). Although previous studies included only AI as a part of TE1 (e.g., Zilles et al., 1980; Zilles, 1985; Zilles and Wree, 1985), it seems reasonable to propose that both P and A should also be considered part of TE1 (Fig. 15). Like AI, P and A exhibit tonotopic organization (present study; Horikawa et al., 1988) and, based on cat data, share thalamic projections deriving at least in part from MGv (Morel and Imig, 1987; Rodrigues-Dagaeff et al., 1989). Because TE3 is located ventral to TE1, and the cells ventral to AI in the present study were not tonotopically organized, this ventral region (i.e., the V belt) is probably part of TE3. However, the possibility also exists that V belt is part of the ventral region of TE1 known as TE1v, which has different afferent and efferent projections than the rest of TE1 (Romanski and LeDoux 1993a,b), and whose tonotopic organization has not yet been determined. With these borders in mind, it is possible to compare the A-P and D-V borders of the auditory fields as found in the present study with those of rat atlases (Fig. 16). Note that there was essentially no difference in the AI borders between the older and newer Swanson atlases (1992 and 1998, respectively), so only the newer one is shown in Figure 16 and discussed below. 357 Fig. 15. Outline of a side view of brain depicting the borders of temporal cortex, area 1 (TE1) as found in this study, including anterior field (A), primary auditory cortical field (AI), and posterior field (P) (see Discussion section). PRh, perirhinal cortex. In general, the borders of TE1 in the atlases align quite well with the borders of AI and P as determined electrophysiologicalally in the present study. Three main differences bear pointing out. First, the most glaring difference involves the dorsal border of two atlases, the Swanson (1998) and new Paxinos and Watson (1998) atlases, whose dorsal borders are much too ventral based on the current results (Fig. 16). Of interest, in the old Paxinos and Watson atlas (1986), the dorsal border fits well with the present data (Fig. 16). In their newer atlas, Paxinos and Watson (1998) cite Swanson (1998) as to why they lowered the dorsal border of AI and added a new region in its place, the secondary auditory cortex, dorsal area (Fig. 16, inset). The Swanson atlas (1998), in turn, cites three studies for their determination of the AI border. The first two are the physiology studies by Sally and Kelly (1988) and Kelly and Sally (1988); however, as mentioned previously, these studies mark cell locations relative to the blood vessels, whose locations vary widely from brain to brain, so it is difficult to determine where the dorsal border of AI relative to bregma should be in such cases. In most other respects, including the dorsal to ventral length of AI ( 2 mm in each study), a measurement that is independent of cell location relative to bregma, the findings of the Sally and Kelly studies display a remarkable level of agreement with those of the present study. In the third study cited by Swanson (1998), AI borders were based on granule cell layer density, threshold intensity differences in evoking responses in the paraflocculus, and tracer labeling differences in the pons after cortical injections (Azizi et al., 1985). However, that definition of AI includes a ventral border extending nearly 8 mm ventral to bregma 2 mm below AI as defined in the atlases (Fig. 16), suggesting that these methods may not be well suited for accurate determination of AI borders. In other studies, the borders of AI have usually been defined based on tonotopic mapping and connections with the thalamus (Patterson, 1976; for review, see Webster, 1992, 1995; Winer, 1992). The results of our current study suggest that the dorsal border of AI is better depicted in the old Paxinos and Watson (1986) and Zilles (1985) atlases than

14 358 N.N. DORON ET AL. Fig. 16. Photomicrograph of a side view of the rat brain, showing the distribution of cells from which recordings were made (as in Fig. 2). The dorsal and ventral borders of temporal cortex, area 1 (TE1) are illustrated, as derived from several rat atlases, including those of Zilles (1985), Swanson (1992 and 1998), and Paxinos and Watson (1986 and 1998). In addition, the borders of TE2, TE3, and perirhinal cortex (PRh), derived from Paxinos and Watson (1998), are shown for reference. Axes on the top and right indicate anterior-posterior and dorsal-ventral distance (millimeters) posterior and ventral to bregma, respectively. Note that, in general, the physiological map agrees with the anatomical maps (see Discussion section). Left inset: lines illustrate the dorsal borders of secondary auditory cortex, dorsal area (see Discussion section), as found in Swanson (1998, 1992) and Paxinos and Watson (1988). in the new Paxinos and Watson (1998) and Swanson (1998) atlases. A second discrepancy between the atlases (Fig. 16) and the present results is that, although TE1 terminates at a similar A-P level at its posterior border in all atlases (excluding Swanson, 1998), a level that corresponds well with the posterior border of P found in the present study, the dorsal and ventral borders at this posterior end vary among the atlases. At the dorsal part of the posterior border, we found cells that physiologicalally belong in the P at coordinates that were more dorsal than the border of TE1 in all atlases. Along the ventral border of the posterior portion of TE1, the two Paxinos and Watson atlases (1986, 1998) conform well to where we found cells in P, whereas the Zilles (1985) border appears a bit too dorsal and the Swanson (1998) border appears too ventral. Finally, some differences are evident near the anterior border of TE1. For example, AI extends further anteriorly in the present study compared with all of the atlases, although this difference is greatest for the Zilles and older Paxinos and Watson atlases. Moreover, the current study is consistent with the view that the tonotopic core extends even further anteriorly to include field A. Overall, these differences between the anatomicalally and physiologicalally defined boundaries merit further study, and reveal the importance of using comparable landmark systems in physiological and anatomical studies. ACKNOWLEDGMENTS We thank J.C. Repa for critical reading of the article. NYU received a grant from the W.M. Keck Foundation.