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1 Department of Bioengineering, Faculty of Engineering, Kagoshima University, Kagoshima 890-0065; 2 Department of Public Health, Faculty of Medicine, Kagoshima University, Kagoshima 890-8250, Japan
Submitted 6 March 2003; accepted in final form 13 August 2003
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ABSTRACT |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
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). Cells recorded along electrode penetrations perpendicular to the cortical layers were found to prefer the same stimulus orientation, whereas a gradual shift of orientation preference was observed along tangential penetrations. The two-dimensional layout of orientations, the orientation preference map, was subsequently confirmed by 2-deoxyglucose labeling (Albus 1979; Albus and Sieber 1984; Hubel et al. 1978; Singer 1981; Tootel et al. 1988) and in vivo optical imaging experiments (Bartfeld and Grinvald 1992; Blasdel 1992; Bonhoeffer and Grinvald 1991; Obermayer and Blasdel 1993; Swindale et al. 2003). The nature of the orientation map and its spatial relationship to other functional maps such as ocular dominance and spatial frequency have been the subject of extensive study.
Several recent studies reported unequal representation of cardinal and oblique contours in the primary visual cortex. Investigators reported that in both developing and adult ferrets, larger areas were activated by horizontal and vertical contours than by oblique contours (Chapman and Bonhoeffer 1998; Chapman et al. 1996; Coppola et al. 1998; Rao et al. 1997; White et al. 2001). In cats and kittens, some electrophysiological (Albus 1975; Bauer and Jordan 1993; De Valois et al. 1982; Fregnac and Imbert 1978; Kennedy and Orban 1979; Leventhal and Hirsch 1977; Mansfield and Ronner 1978; Payne and Berman 1983; Pettigrew et al. 1968) and optical imaging (Dragoi et al. 2001; Muller et al. 2000) studies have suggested that more cortical cells tend to display horizontal or vertical preferences, and cortical cells preferring horizontal or vertical orientations are more narrowly tuned (Kennedy and Orban 1979; Rose and Blakemore 1974) in the primary visual cortex. However, investigators have largely ignored the fundamental question of how much the representation of a given angle overlaps with the representation of nearby angles. In this study, we applied optical imaging based on intrinsic signals to cat area 17. An alternative approach to this problem was utilized by evaluating the tuning width of cell populations based on optical signals evoked by contours with various orientation angles.
Optical imaging was shown to be powerful tools for mapping the cortical functional organizations. One critical problem with this technique is the extraction of signals from a single stimulus condition. To date, the methods used to determine the layout of orientation domains all require knowledge of responses to many stimulus orientations, not to only one orientation. The amplitude of optical changes represents the only criteria used to extract cortical region of activation. This placed a technical constraint on quantitative analysis of the size of the response domain to a single orientation. In this study, attempts were first made to develop a new signal extraction method to quantitatively analyze the area of response to a single orientation and to understand the orientation tuning for cell populations.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
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Twelve cats ranging in size from 2.5 to 4.3 kg were used in this study. Anesthesia was induced using ketamine hydrochloride (10–20 mg/kg body weight, im). Throughout the recording session, animals were immobilized using pancuronium bromide, and anesthesia was maintained under artificial ventilation with a 70:30 mixture of N2O and O2, supplemented with isoflurane (
2%). Level of anesthesia was monitored under electrocardiography. Atropine sulfate was administered subcutaneously to reduce salivation. End-tidal CO2 was maintained at 3.8–4.2%. Body temperature was kept constant at 38°C using a thermostatically controlled heating pad. Continuous infusion of glucose-Ringer solution via a gastric catheter compensated for fluid loss. All wound margins were infiltrated with xylocaine. For optical imaging, a chamber 18 mm ID was fixed to the skull and centered on the midline (Horsley-Clarke P5). The optics of the eyes were measured to select appropriate contact lenses, and photographs of the fundus were taken to determine the position of the fovea prior to recording. Pupils were dilated with local application of 0.5% tropicamide and 0.5% phenylephrine. Corneas were covered with contact lenses of appropriate power to focus the image on a 22-inch MultiSync FP1350 CRT display (NEC) positioned 57 cm from the eyes, onto the retina. The skull and dura in the chamber were removed to expose the region for imaging. The chamber was sealed using a glass coverslip and filled with silicone oil to minimize movement of the brain during recording. Response images were obtained using light tuned to 605 nm (bandwidth: 10 nm). Light from a tungsten lamp was directed through an interference filter and guided to the chamber through a fiberoptic bundle. Reflected images from the cortical surface were obtained using an Imager 2001 (Optical Imaging) equipped with a charge-coupled device (CCD). Two commercially available lenses with large numerical apertures were connected face-to-face to focus the image onto the detector chip of the CCD camera (Ratzlaff and Grinvald 1991). Size of the imaged area was adjusted by selecting an appropriate combination of lenses with differing focal distances: 35 and 50 mm. The CCD camera was focused on a plane 300 µm below the cortical surface.
Cats were cared for in accordance with the Guiding Principles for the Care and Use of Animals in the Field of Neuroscience of the Japan Neuroscience Society.
Single-cell recordings
In 2 of the 12 cats, after optical imaging, unit recording was applied to confirm the position of visual field center and the feasibility of the proposed optical signal extraction method. Extracellular recording with microelectrodes of single-cell activities were made according to the methods described previously (Kobatake et al. 1998). Topography of visual field representations within the striate cortex was determined by relating positions of receptive fields for single neurons or small clusters of neurons to locations of corresponding recording sites. Receptive field positions were determined by moving small rectangles of light of various sizes and orientations. Once visual responses were evoked at a given location, receptive field location, size, and preferred orientation were mapped. Penetrations were made in a quasiregular grid pattern with a mean spacing of about 300 µm.
After the receptive field was mapped to confirm the feasibility of the proposed method, the same stimulus set used in optical imaging was presented in two cats to check the single cell activity. Stimuli were intermixed and presented 10 times, for durations of 1 s, with 2-s blank intervals between trials. Magnitude of the responses was determined as mean firing rate during stimulus presentation minus the spontaneous firing rate during the 1-s period immediately preceding stimulus presentation. Considering response latency, the time window for response was shifted within a range of 50–250 ms from the precise onset of stimulus presentation to maximize mean firing rate. The statistical significance of responses was determined by comparing mean firing rates within the window for 10 individual responses with 10 spontaneous firing rates immediately preceding each stimulus presentation, using the Kolmogorov-Smirnov test.
Visual stimuli
Large field (40° x 40°) binocular stimulation was performed. Stimuli comprised full-screen square-wave gratings drifting back and forth at 1.5°/s, presented in eight different orientations: starting from horizontal at 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°. Spatial frequency ranged from 0.5 to 2.0 cycles/°. The same spatial frequency was used for all orientations in a given recording session. Stimuli were randomly interleaved, and each was generally presented 40 times. In one trial, a uniform gray screen was displayed on the monitor for the first 3 s, followed by stimulus for 5 s. After that, the screen was changed to the uniform gray screen again as the acquisition of data was completed. To prevent intertrial influence from previous stimulus presentation, the gray screen was displayed between trials for about 13 s. All stimuli were presented on a 22-in MultiSync FP1350 CRT display (NEC) under control of a personal computer with a spatial resolution of 1600 x 1200 pixels and a frame rate of 85 Hz.
Data analysis
Data analysis was performed using MATLAB (The MathWorks). Raw images from individual trials were visually inspected to remove distorted images in which signals had overshot the dynamic range of the amplifier. Binning (2 x 2 pixels combined into a single pixel) was performed in some cases, but no further spatial filtering was performed prior to statistical evaluation of activation points. The cumulative mean was obtained for stimulus image data recorded 500–3,000 ms after presentation to allow for delay of the intrinsic signal after stimulus presentation and optical signal self-sustaining time. To compute the t-map, the response image for each orientation was first paired with the image obtained just before presentation of the stimulus. The t value was computed on the pixel base between the two data sets, and intensity values were replaced with the corresponding t values to construct t-maps.
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RESULTS |
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Evaluation of activity map for a single orientation
To facilitate quantitative comparisons, we propose a new method based on the statistical parameter t to extract activation regions. To examine the validity of normal distribution assumption, we computed the skewness and kurtosis based on the data obtained at each of stimulus repetitions. Figure 1 shows a typical example. Pixels that showed statistically significant (with 95% confidence level) difference from the normal distribution were plotted in blue. Only 0.3% of the pixels in the imaged area showed significant difference from the normal distribution. The distributions of optical intensity values at more than 99% of the pixels can be considered as normal distribution.
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Optical change in response to visual stimulation was much smaller compared with changes caused by cardiovascular pulsation or breathing, which become more serious at the region covered by the blood vessels. As shown in Fig. 2A, amplitude criteria were enough to separate pixels showing small optical change from those showing large optical change— e.g., at pixels labeled with * and +. However, at pixel x, located just above a relatively large blood vessel, the mean value was substantially biased by several large values recorded in a few trials, even though most trials displays no significant changes. This implies that amplitude criteria may extract some regions that are particularly problematic in regions covered with relatively big blood vessels.
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We attempted to use both amplitude of optical changes and statistical significance of resulting data to evaluate activation (Wang et al. 1996, 1998). An amplitude map was obtained by dividing responses to a single orientation by a composite map constructed using mean responses to all eight orientations. To quantitatively define responsive areas, a threshold was selected based on the threshold versus the extracted area curve shown in Fig. 2B. With increasing threshold from minimum (Min) to maximum (Max) intensity among all eight images obtained during presentation of contours with the eight orientations, the extracted area changed from 0 to 100% of the cortical area. In the range 40–70% of (Max – Min), the relationship between threshold and extracted area was almost linear. For quantitative evaluation of the size of the response area in this study, (1 – 1/e) of (Max – Min) was used to calculate threshold. We calculated the (1 – 1/e)(Max – Min) intensity value in each image and delineated points displaying this value. Contours delineated by these spots were referred to as 1/e-drop contours. Figure 2C illustrates the 1/e-drop contour obtained during the presentation of grating with an orientation. However, as shown, the amplitude map is highly sensitive to fluctuations in the cortical surface, such as those occurring with pulsations or movement.
In addition to the amplitude map, this study introduced a statistical parametric map, t-map, to evaluate response. Figure 2D shows the curve of the threshold at the level of significance versus extracted area for all cases. With decreasing level of statistical confidence, corresponding to a lower t value, the extracted area changed from 0 to 100% of the cortical area. In the range from t = –4.5 to t = –1.0, the change in area size was close to linear. In this study, the widely accepted value of P < 0.05, corresponding to t = –1.68 (stimulus repetitions = 40) was selected as threshold for the t-map to denote the significance of optical changes. The contours shown in Fig. 2E were delineated by the significance of darkening in individual pixels (t-test, P < 0.05) compared with images obtained using a gray screen. A comparison of the two contours shows that noisy areas in the vicinity of larger blood vessels were clearly excluded from the t-map.
The activation area was that both included in the 1/e-drop contour and displaying a t value with P < 0.05 (Fig. 2, F and G). Performance of the method was evaluated by comparison with the method using only the amplitude criteria and with the results of electrophysiological recording. Noise in the optical imaging experiment often occurred in regions near blood vessels or with lower luminance. In regions without clear blood vessels or those covered by blood vessels with diameter <80 µm and demonstrating luminance in the upper two-thirds of maximum intensity in a recording image, the combination of 1/e-drop and t-map produced features comparable to 1/e-drop contours. The overlap index between areas extracted using the two methods, which was calculated by dividing the area of overlap by the mean of each total area, was 81.70 ± 6.47%. In the remaining regions, overlap was as low as 26.01 ± 5.62%, and changes in optical intensity frequently exceeded the amplitude threshold level, although the statistical significance of the change was low.
To confirm the results obtained by this method, single-cell recordings were made using microelectrodes in two animals after optical imaging experiments. Microelectrode penetration was targeted to regions in 1/e-drop contours and to regions of overlap of the 1/e-drop and t-maps. The statistical significance of responses was determined by comparing mean firing rates within the window for 10 individual responses, with 10 spontaneous firing rates immediately preceding each stimulus presentation, using the Kolmogorov-Smirnov test. On average, for contours with orientation in the region extracted using the proposed method, a significant increase in spiking rate was observed from cells in 79.8% of the total 70 cells obtained in 14 penetrations when the same stimulus was shown. In the area extracted solely using the amplitude map, only 48.9% of 61 cells obtained in 10 penetrations displayed a significant increase in spiking rate. In all response cells, mean spiking rate of cells in the area extracted using the proposed method (25.47 ± 1.16 spikes/s) was significantly (t = 2.095, df = 129, P < 0.05) higher than that in the area defined using amplitude only (21.94 ± 1.21 spikes/s). This demonstrated a high level of consistency between the results obtained using our method and spiking cells.
Unequal area for cardinal and oblique orientations
In all maps, extracted regions were typically circular or elliptical (Fig. 3A). Quantitative comparisons were performed to evaluate activation areas for each orientation. Figure 3A shows the activation areas extracted based on the above definition; activation areas for horizontal and vertical gratings occupied 38.1 and 37.3% of the exposed cortical area, respectively. The two oblique orientations activated 28.9 and 31.6% of the exposed cortical area, respectively. The difference in activation area was 0.8% between horizontal and vertical and 2.7% between the two oblique orientations. In contrast to this small difference, the difference between cardinal orientation and oblique was 
5.7%. The area activated by cardinal orientations was much larger than that activated by oblique orientations.
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A profile of mean orientation versus cortical area in all 12 cats is shown in Fig. 3B. Peaks occurred around the cardinal orientations, and cortical areas for oblique orientations were lower compared with other orientations. Angles with cardinal, and oblique axes were pooled separately. On average, cardinal orientations (0° and 90°) activated 36.8 ± 2.0% (SD) of the exposed cortical area, whereas oblique orientations (45° and 135°) activated 28.9 ± 2.3%. The area induced with the presentation of 0° or 90° orientation was significantly larger than that for oblique presentations (t = 9.195, df = 11, P < 0.0001). On average, 7.81% more cortical area responded preferentially to cardinal angles over oblique angles. Of the 12 cats, 9 displayed larger areas for cardinal orientations. Areas responding to oblique orientations were larger in the other three cats, although their differences were <4%.
This study demonstrated overrepresentation of cardinal orientations compared with oblique orientations. We considered the possibility that artifacts might arise from the characteristics of the CRT monitor used for stimulus presentation, for example, aliasing problems that are seen only in oblique lines, not in cardinal lines. To exclude this possibility, additional experiments were performed with the monitor tilted at 45° relative to the cat. In this case, the current oblique stimuli drove the cardinal columns, and cardinal stimuli drove the oblique columns. From four hemispheres of two cats, optical imaging was performed during presentation of 0°-, 45°-, 90°-, and 135°-oriented lines. Results from all four hemispheres revealed a consistent tendency for a large area to respond to cardinal lines compared with oblique lines. Activated areas responding to 0°, 45°, 90°, and 135° lines displayed high correlation coefficients (0.984 for cat 1 left hemisphere; 0.995 for cat 1 right hemisphere; 0.997 for cat 2 left hemisphere; 0.991 for cat 2 right hemisphere) between the four activated area values obtained with upright and tilted monitor. In response to identical stimuli in a hemisphere, the difference in the cortical sizes between those obtained with upright and tilted monitor ranged from approximately 1.2% (0.225 ± 0.577), representing a significantly smaller difference (F = 0.106, P < 0.0032) than that between cardinal and oblique responses (7.113 ± 1.768). Taken together, these control experiments indicate that overrepresentation of cardinal orientations cannot be attributed to differences in the efficacy of different stimulus grating.
Orientation selectivity of cells responding to cardinal or oblique contours
The area activated by contours with orientation always overlapped areas activated by other orientations. Orientation selectivity of domains displaying responses to the four principle orientations, two cardinal and two oblique, was investigated in detail. Figure 4A shows the percent overlap of each orientation domain (0°, 45°, 90°, 135°) with each of eight other orientation maps (0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 157.5°) in the area activated by each of the four principal angles. The graph was reorganized depending on the separation of angles (Fig. 4B). Both cardinal and oblique displayed similar patterns in tuning. A large part of the area evoked by a cardinal or oblique orientation demonstrated responses to contours with orientations separated by 22.5°, irrespective of the direction of difference. Overlap was minimum at 67.5° and 90° separation. The area showing response to 45° separated orientation was in between.
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Figure 4C shows the graph of overlap versus separation angle, organized by cardinal and oblique categories. In the area responding to cardinal-oriented contours, 56.4 ± 11.3% of the area responded to 22.5°-separated contours. In the area responding to oblique contours, 67.4 ± 13.1% of the area responded to 22.5°-separated contours. No statistical significant difference was confirmed between the overlap of oblique orientation with neighboring contours compared with cardinal contours. In cases where angles were separated by <45°, the percentage area responding to other orientations was higher in area responding to oblique contours. Interestingly, in cases where angles were separated by 67.5° and 90°, percentage area responding to other orientations was higher in the area responding to cardinal orientation. This may mean that single cardinal orientation domains contain more mixed clustering of oriented cells.
Overlap of contour presentation with a cardinal or oblique orientation and presentation of nearby orientations
As shown in Fig. 4, no significant difference was found when angles 22.5° around the test angle were compared. To understand this property, we showed additional contours with orientations 10° separated from each of the four contours. Figure 5 shows the graph of overlap versus separation angle of 10° and 22.5°, organized by cardinal and oblique categories. In the area responding to cardinal-oriented contours, 75.7 ± 8.5% of the area responded to 10°-separated contours. In the area responding to oblique contours, 91.5 ± 5.6% of the area responded to 10°-separated contours. Cardinal orientations were with statistically significant less overlap with 10°-separated contours (t = 5.93.40, df = 11, P < 0.0001).
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In Fig. 6, contours of cortical areas activated by horizontal, vertical, and the two oblique oriented gratings were superimposed on contours obtained by the presentation of gratings at angles of +10° or –10° separation. For each of the four principle axes, overlap with neighboring angles was investigated. The areas activated by horizontal and vertical contours displayed less overlap with neighboring angles than those for the two oblique contours (Fig. 6). For quantitative evaluations, means were calculated for both overlap area of each of the four angles and the +10° angle, and overlap area and the –10° angle. Areas activated by the two oblique orientations were largely shared with the 10°-separated counterparts. Cardinal orientations displayed less overlap with neighboring orientations.
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These results were consistent across animals. The overlap index between areas was extracted using the two methods, which were calculated by dividing the area of overlap by the mean of each total area. As shown in Fig. 7A, overlap index for the 12 animals was 38.5 ± 1.8% (SE) and 35.1 ± 2.8% between the horizontal and vertical orientation and their neighboring orientations, respectively, as high as 46.1 ± 2.9 and 45.4 ± 4.0% between oblique and neighboring orientations. Fisher's PLSD test revealed that overlap indices for both horizontal and vertical orientations were significantly smaller than those at oblique orientations (0° vs. 135°, P < 0.0386; 0° vs. 45°, P < 0.0240; 90° vs. 135°, P < 0.0028; 90° vs. 45°, P < 0.0016). No significant difference was observed between 0° and 90° and 45° and 135°. Mean values for cardinal and oblique were 36.8 ± 1.6% and 45.7 ± 2.7%, respectively (Fig. 7B). A statistically significant difference was identified between these values (t = 3.40, df = 11, P < 0.0059).
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Trends were confirmed by examining the number of cats showing significant differences between cardinal and oblique orientations in the overlap index to orientations separated by 10°. The difference in each cat is summarized in Fig. 7C. Overlap indices between oblique and ±10° displayed larger values than cardinal in 10 of the 12 animals.
In each of the areas responding to the four contours, areas not responding to +10° or –10° contours were also investigated. Figure 7D shows the results of these investigations. For cardinal areas, 8.4 ± 0.2% (horizontal) and 8.2 ± 0.2% (vertical) areas responded to none of their neighbors. For oblique areas, only 5.7 ± 0.5%, and 6.3 ± 0.3% areas displayed no response. Statistically significant differences were found between 0° and 45°, 0° and 135°, 90° and 45°, and 90° and 135° (Fisher's PLSD test, 0° vs. 135°, P < 0.0001; 0° vs. 45°, P < 0.0001; 90° vs. 135°, P < 0.0001; 90° vs. 45°, P < 0.0001). A significant difference was identified between cardinal and oblique orientations (8.3 ± 0.2 vs. 6.0 ± 0.3; df = 11, t = 5.259, P < 0.0003). Responses to cardinal orientation were found to be more independent than those to oblique orientation.
The findings reported herein primarily depend on data obtained from binocular stimulus presentation. A control experiment identified no difference from monocular stimulation. Using the same methods described in this study, we compared binocular and monocular stimulation in two hemispheres in two different cats. For each of the four principle axes (0°, 45°, 90°, 135°), overlap with 10°-separated neighboring angles was investigated. Overlap index values for cardinal and oblique were 38.7% and 46.8% for cat 1 left hemisphere and 37.5% and 44.7% for cat 2 right hemisphere, respectively. Cardinal orientations displayed less overlap with neighboring orientations.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONDISCLOSURESREFERENCES |
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; Bauer and Jordan 1993; De Valois et al. 1982; Fregnac and Imbert 1978; Kennedy and Orban 1979; Leventhal and Hirsch 1977; Mansfield and Ronner 1978; Payne and Berman 1983; Pettigrew et al. 1968). Second, cortical cells preferring horizontal or vertical orientations may display narrower tuning (Kennedy and Orban 1979; Rose and Blakemore 1974). This study found, on average, 5.38% more cortical area responding preferentially to cardinal angles than to oblique angles. This result tends to support the first theory. At the same time, by comparing areas activated by cardinal or oblique contours with those separated from the contours by 10°, a significantly reduced degree of overlap was identified between areas activated by the presentation of cardinal contours and areas activated by neighboring orientations compared with overlap between oblique orientations and neighboring orientations. In addition, the area activated only by cardinal contours was significantly larger than the area activated only by oblique contours. This result suggests the validity of the second theory. Therefore in total, our results may indicate the validity of both theories. More cells prefer horizontal or vertical orientations, and these cells are tuned more sharply than oblique selective cells. This unequal representation of cardinal and oblique contours provides the neuronal basis for phenomena such as the oblique effect.
In addition to inequalities in number of cells and sharpness of tuning, a previous report demonstrated that overrepresentation of horizontally or vertically tuned cells is limited to central area 17 (Kennedy and Orban 1979). Our preliminary experiments showed similar trends. In the periphery area, the bias of representation for cardinal orientations over oblique orientations is not as strong as in the central area. This is the reason the central area of area 17 was selected in this study.
A significant difference was identified in the size of areas responding to cardinal and oblique contours in adult cats. This finding is consistent with results from developing and adult ferrets (Chapman and Bonhoeffer 1998; Chapman et al. 1996; Coppola et al. 1998). In those studies, investigators reported overrepresentation of horizontal and vertical orientation preferences in area 17. This suggests that more circuitry in the visual cortex is devoted to processing contours oriented along cardinal axes than to oblique contours. In addition, our results revealed oblique orientations share significantly more neuronal circuitry with the orientations separated from them by 10° than cardinal orientations do. The present results suggest that larger and more isolated neuronal circuitry is used for processing horizontal and vertical contours. This may contribute to the oblique effect, which was confirmed several decades ago in various animal models (Appelle 1972).
To facilitate quantitative comparisons, this study utilized a new method based on the statistical parameter t to extract activation regions. There is a huge amount of literature on the mathematics and applicability of t score analysis in functional MRI (fMRI) images, as what is for intrinsic signal imaging still a unique method, is for fMRI the standard method of analysis. Single-cell recordings reveal that, in most cases, cells in the extracted region respond to the same stimulus that evokes the optical change. This represents a significant improvement with respect to previous methods. In addition, the proposed method was found to be particularly useful in areas displaying poor signal/noise ratio. Previously, manual masking was required for areas covered by large blood vessels, making analysis of optical image data complicated and time consuming. Using the methods described herein, analysis of data can be completed much more rapidly, with fewer operational artifacts.
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FOOTNOTES |
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Address for reprint requests and other correspondence: G. Wang, Dept. of Bioengineering, Faculty of Engineering, Kagoshima Univ., 1-21-40 Korimoto, Kagoshima 890-0065, Japan (E-mail: gwang{at}be.kagoshima-u.ac.jp).
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t-map.Overlap between C and E was plotted. G: extracted activation area. White bar in each figure indicates orientation of stimulation. LCF, longitudinal cerebral fissure. Scale bar = 1 mm.
