Areal extent quantification of functional representations using intrinsic signal optical imaging

Journal

of Neuroscience

Methods

68 (1996)

27-37

Area1 extent quantification of functional representations using intrinsic signal optical imaging Cynthia H. Chen-Bee, Michael C. Kwon, Susan A. Masino, Ron D. Frost& ‘Department

of Psychobiology

and the Centerfor Received

the Neurobiology

2 October

of Learning

1995: revised

21 March

and Memory,

Llnirersitv

1996: accepted 23 March

qf Culjfi,miu,

Iwinc’.

CA 92717 1!.TA

1996

Abstract An important parameter often investigated in the characterization of cortical functiona organization is the area1 extent of functional modules. Because it allows the visualization of functional modules with high spatial resolution in a noninvasive way to the cortex, intrinsic signal optical imaging (ISI) can be employed for the quantification of these area1extents. The present paper describes the use of ihe norm%ized threshold analysis of area1extent quantification for the objective assessment of single-whisker functional representations in the primary somatosensory cortex of adult rats. As the success of area1 extent quantification depends on the ability of lS1 to allow visualization of cortical representations with minimal stimulus-dependent blood vessel representations, which are commonly encountered by ISI, the present paper also describes the further development of the intratrial analysis of visualization for minimizing these vessel representations. Both analyses are discussed with respect to their advantages as well as their inherent limitations. Keywords:

Optical

imaging:

Intrinsic

signal; Rat somatosensory

cortex;

Barrel;

Whisker;

Vihrissa _.-..--

1. Introduction

The quantitative assessment of cortical organization has proved useful for addressing issues related to the stability, laterality, and plasticity of various brain functions. A relatively new technique which can be used for such an assessment is intrinsic signal optical imaging (ISI; for reviews see Grmvald et-al., 1991; Froitig, 1994a,b). IS1 detects activity-dependent changes in the optical properties of cortical tissue over large areas of the brain and has the spatial resolution necessary to allow visualization of discrete functional modules (e.g., representation of peripheral units). In addition, IS1 is noninvasive to the cortical tissue and may therefore provide opportunities not easily found with more traditional techniques for assessing functional organization such as single-unit recordings or 2-deoxyglucase autoradiography. By allowing the visualization of activity from the brain in response to peripheral stimulation, IS1 has successfully verified known characteristics of cortical functional organization (Ts’o et al., 1990) as well

* Corresponding author. Tel.: (1) 714 824-2883; E-mail: [email protected] 0165-0270/96/$15.00 Copyright PII SOlh5-0?70(96)00056-8

0 1996 Elsevier

Fax: (1) 714 824-2447;

Science

B.V. AlI rights

.-...-

as elucidated new organizational features !Bonhoeffer and Grinvald, 199 1). There are various approaches for attaining successful visualization of cortical functional modules. Several intertrial approaches which are traditionally employed for the optical imaging of visual cortex have been described by Bonhoeffer and Grinvald (1993). A different approach is to exploit data collected within the same trial (intratrial analysis). A common method of intratrial analysis is to divide data collected after stimulus onset by prestimulns data. This common method has been applied successfully for the visualization of single-whisker functional representations in rat barrel cortex (Masino

et al.. 1993: Peterson and

Goldreich, 1994). Thus far, IS1 has been implemented primarily for the visualization of cortical functional organization. In the present paper, we describe how IS1 can be used for the quantitative assessment of brain organization. We exploit the well-characterized rat whisker-to-barrel system (for reviews see Armstrong-James, 1995; Simons, 19951 to develop the normalized threshold analysis of areal extent quantification. While single-whisker representations have been visualized previously using TSI (Masino et al., 1993; Narayan et al.. 1994; Peterson and Goldreich, 19941, the reserved

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area1 extents of these representations have not been quantified in an objective manner. In order to achieve successful quantification, it is essential that IS1 allows the visualization of functional representations with minimal blood vessel representations. One of the main challenges encountered by IS1 has been to overcome intrinsic signals (IS) originating from local blood vessels (Fig. 4)-which appear as blood vessel representations on images of brain activity (Fig. 5). The consistent presence of blood vessel representations is not surprising since IS from local blood vessels has been observed to increase in response to peripheral stimulation (Grinvald et al., 1986; Masino et al., 1993; Narayan et al., 1994). By exploiting the spatiotemporal differences between stimulus-dependent cortical IS and stimulus-dependent vessel IS, we have further developed the intratrial analysis of visualization which minimizes blood vessel representations. The present paper provides the rationale and application of an alternative method for the intratrial analysis and describes the normalized threshold analysis employed in our laboratory to determine the area1 extent of singlewhisker functional representations.

2. Materials

and general methods

Some of the details have been described elsewhere (Masino et al., 1993). A brief summary as well as additional details are provided below. 2.1. Subjects, surgical preparation, tion

and whisker stimula-

Each subject, 21 male and female Sprague-Dawley rats (200-685 g), was anesthetized with sodium pentobarbital (Nembutal, 50 mg/kg). The skull overlying either the left or the right somatosensory cortex was thinned to permit imaging that is noninvasive to the cortical tissue. During stimulation trials, one contralateral whisker was deflected for either 1 or 1.2 s at 5 Hz. The amplitude of the rostral-to-caudal deflection was 0.5 mm at a distance of 15 mm from the snout so that adjacent whiskers were undisturbed. 2.2. Prior to data collection The experimental set-up is illustrated in Frostig (1994a). In each experiment, an image of the vasculature was obtained for landmark reference. The camera was then defocused 300 km below the cortical surface during imaging sessions. The cortex was illuminated with light driven by a Kepco power supply (ATE 15-15M), passed through a 630 nm light filter (Omega Optical, Brattleboro, VT; bandpass, 30 nm), and guided by two fiber-optic light guides. The light reflected from the cortex was collected by a slow-scan CCD camera. Unless otherwise noted, the

Methods

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present paper refers to the data in terms of stimulus-dependent IS (traces seen in Figs. l-3 and Fig. 5A) or stimulusdependent activity (images seen in Fig. 4, Fig. 5B,C and Fig. 6). Prior to data collection, the amount of illumination was calibrated to the same baseline values. Although different regions of the cortex varied in their baseline values after such a calibration (uneven illumination due to curvature of cortical surface), its effect on data analysis was eliminated by treating data with respect to local baseline values (see Section 2.4). 2.3. During data collection As illustrated in Fig. 1, the slow changes in the spontaneous IS from barrel cortex are larger typically by an order of magnitude ( u 10-3, Fig. 1A) as compared to the faster changes in the stimulus-dependent IS (- 10p4, Fig. 1B). Presumably, the large amplitude of the slow fluctuations in the spontaneous IS hindered our attempts to visualize single-whisker functional representations via more traditional analyses employed for optical imaging of the visual cortex (for details see Bonhoeffer and Grinvald, 1993). Thus, we structured the data collection process to develop further the intratrial analysis of visualization since it avoids incorporating the large and slow changes observed for the spontaneous IS from barrel cortex. The details of the data collection apply to all data sessions except for those illustrated in Figs. 1 and 5 (see figure legends for differences in protocol). Each imaging session contained both stimulation and nonstimulation trials (control trials; 10% of total trials) interlaced randomly, with each trial lasting 4.5 s and with a 15 s intertrial interval. The determination of the trial and intertrial intervals was based on previous findings of IS within visual cortex in response to short-lasting (- 1 s) stimulation (Grinvald et al., 1986). These results included: (i) a maximum increase in IS which occurs within 2 s of stimulus onset; (ii) a return to prestimulus level; and (iii) a large and slow undershoot lasting 10 s. As illustrated in Fig. lB, and in agreement with previous findings (Grinvald et al., 1986; Masino et al., 19931, stimulus-dependent cortical IS within barrel cortex followed a similar temporal profile. Since the information contained within the first return to baseline (- 3 s after stimulus onset) was sufficient for visualization, we do not exploit the large undershoot in the present study. The camera collected IS continuously and integrated the data over fixed time intervals (250,300, or 500 ms frames). Since the 6.8 mm X 5.1 mm camera view (CCD camera combined with a 50 mm AF Nikkor lens and an extender) was translated into a 192 X 144 pixel array, each pixel represented IS collected over a 35 p,rn X 35 p,rn cortical area. A complete data session contained a summed stimulation trial and a summed control trial. The number of data sessions collected per animal ranged between 1 and 10 for a total of 48 data sessions.

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3

fractional change in IS relative to the prestimulus level (first frame) according to the formula AR/R,, where R = light reflectance coIlected during an individual frame X and A R = R, - R,. This calculation was performed for each pixel in order to normalize the temporal profile with respect to local baseline levels (so as to correct for uneven illumination; see Section 2.2). As i&&rated in Fig. 3A, an array of temporal profiles was created by averaging spa-

0

5

10 15 20 25 30 35 40 Time (s)

b2 E 9 o2 -22 -4‘“u * - 6i! -8IA -lO-12.’

stimulus

0 12

34

5 6 7 8 91011 Time (s)

Fig. I. Temporal profile of intrinsic signal (IS) obtained from the rat barrel cortex. IS was collected in either 500 ms (A) or 300 ms (II) frames and plotted on the y-axis in fractional change units relative to the first collected frame (see Section 2.4 for calculation details). By convention, decreasing light reflectance is plotted as upgoing so that increasing IS may be plotted as upgoing. (A) Spontaneous cortical IS from the right cortex of a 300 g female rat. IS was sampled over a 0.16 mm? cortical area in 500 ms frames during a single 45.5 s trial On the x-axis, the timepoint of 0 contains IS collected during O-499 ms while the timepoint of 5 s contains IS collected during 5000-5499 ms, and so forth. The magnitude of changes in IS remained similar when the frame duration is increased to 5 s and the trial interval is increased to 455 s (data not shown). (B) Stimulus-dependent cortical IS from the left cortex of a 380 g male rat. IS was sampled over a 0.16 mm’ cortical area in 300 ms frames after averaging 32 trials. On the x-axis, the timepoint of 0 contains IS collected during O-299 ms, with timepoints 1.2-2.4 s containing IS collected during the 1.2 s stimulation of whisker C2 at 5 Hz. An initial increase was observed within w 0.5 s after stimulus onset while a return to baseline was observed within - 2 s after stimulus onset. As compared to the slow changes in the spontaneous IS, the amplitude of the fast changes in the stimulus-dependent cortical IS (i.e. prior to the large undershoot) was smaller by an order of magnitude.

2.4. Intrinsic si,gnal (IS): array

qf temporal profiles

One traditional method of following the time course of IS over large areas of the brain is to create an array of temporal profiles (Orbach et al., 1985). We calculated the

Fig. 2. Spatiotemporal characteristics of IS within the rtght barrel cortex of a 420 g male rat as assessed with an array of temporal praftles. The above composite images contain arrays of temporal profiles (for details see Section 2.4) superimposed on an image of the surface vasculature. As in Figs. 2 and 1. decreasing light reflectance is plotted as upgoing (vertical arrow bar = 1 X lO-fractional change, time course of activity = 4.5 s in 500 ms frames). Medial is towards the top in the present figure as well as in subsequent figures. Horizontal scale bar = 1 mm and applies to both images. (A) A large variability in the temporal profile of spontaneous IS typically is observed within the same data session (average of 16 control trials1 as well as across data sessions (data not shown). Note that the spatial organization of the temporal profiles mirrored certain regions of the cortical vasculature. with larger changes in IS originating from some of the local blood vessels. (Bl In contrast. IS collected from the same cortex (I s of baseline activity followed by I s of whisker C I stimulation at 5 Hz; average of 64 stimulation trials) exhibited the following stereotypical characteristics: (i) the temporal profile of the stimulus-dependent cortical 1S contained both a rising and a falling phase prior to the end of the trial interval; and (ii) thr: stimulus-dependent cortical IS clustered independently of local blood \es
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tially over each 460 p,rn X 460 p,rn cortical area and superimposing the average on the appropriate location.

3. Results 3.1. Intratrial analysis of visualization: commonmethod

B

6-1

center of stimulus-dependent IS region - - 1.5 to 4 mm away from center

010 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Time (s) Fig. 3. Average time course of IS across rats. For each rat (N = 17). IS was collected for 4.5 s in 500 ms frames, with 1 s of IS collected prior to the onset of stimulation of a single whisker (duration of 1 s at 5 Hz). Thus, the 1 s time point contains IS integrated during the first 0.5 s of whisker stimulation. (A) Example of the sampling procedure. Five cortical regions were assessed (filled circles). The size of each sampled area was discrete (0.05 mm*) so as to avoid cortical regions with large surface blood vessels. With the guidance of temporal profile arrays, the location of the sampled areas were determined in the following manner: (i) the region of stimulus-dependent cortical IS was determined and its geometric center was sampled, denoted with the letter c; (ii) four additional areas, one for each quadrant, were sampled 1.5-4 mm away from the center of the stimulus-dependent IS region. Horizontal scale bar = 1 mm. (B) A repeated measures ANOVA (N = 17 rats) found that the variable ‘location of activity’ (center of stimulus-dependent IS region versus 1.5-4 rmn away from center) was significant, F(1,16) = 12.53, p < 0.01, as well as the variable ‘timepoint of activity’ (relative to first frame), F(7,llZ) = 10.36, p < 0.01. Furthermore, a significant interaction was obtained between these two variables, F(7,112) = 13.56, p < 0.01. In contrast to cortical areas located 1.5-4 mm away from the center of the stimulus-dependent IS region, there was a significant increase in IS 0.5-2 s after stimulus ‘onset (* ). Symbols indicate those mean values which were found to be significantly different as determined with post-hoc paired t-test comparisons (alpha level set at 0.01). Error bars correspond to standard error. * Significantly different from prestimulus level. + Significantly different from center ‘of stimulus-dependent cortical IS.

Unlike the spontaneous IS within barrel cortex (Fig. 2A), IS in response to whisker stimulation exhibited consistent spatiotemporal characteristics (Fig. 2B). Relative to IS from surrounding cortical areas, the stimulus-dependent cortical IS increased significantly and remained elevated within 0.5-2.0 s after stimulus onset (Fig. 3) which is in agreement with previous findings (Grinvald et al., 1986; Masino et al., 1993). The common method of the intratrial analysis (Table 1) allowed the visualization of stimulus-dependent activity by exploiting IS collected during the rising phase (0.5-2.0 s after stimulus onset; see Fig. 3). Our use of the intratrial analysis required two steps: division followed by a mapping function. IS collected from stimulation trials was translated into ratio values; the average level of IS collected during the rising phase was expressed relatively to the average prestimulus IS (Table 1). As with the temporal profile arrays, the division calculations were performed at each pixel level to correct for uneven illumination (see Section 2.2). Following the division, we subtracted one from each ratio value to emphasize the varying component of the ratio values, which was in the order of N 10e4. Because stimulus-dependent IS corresponds to a decrease in light reflectance, the most negative ratio values corresponded to the largest increases in IS within 0.5-2 s after stimulus onset. An alternative method of intratrial analysis mentioned hereafter (Table 1) also includes this subtraction process and ‘ratio value’ refers to the varying component obtained after the subtraction process. A mapping function was then applied to the ratio values as described in Ts’o et al. (1990). For each data session, the distribution of the values was obtained and the median value was assigned to a constant middle grayscale value. The remaining ratio values were distributed among 255 grayscales. Typically, we clip the upper and lower 5% of the ratio value distribution to enhance the visualization, with the most negative values assigned to the black grayscale. Each pixel then received its appropriate grayscale value and the resultant image provided a visualization of the stimulus-dependent cortical activity as a dense cluster of dark grayscales. 3.2. Visualization of blood vessel representations with the common method

While the common method of intratrial analysis has been employed successfully for the visualization of single-whisker functional representations (Masino et al.,

Fig. 4. Temporal profile of the stimulus-dependent blood vessel activity as assessed with the frame visualization analysis. The above images arc visualizations of activity after whisker stimulation (duration of 1 s at 5 Hz) relative to activity collected immediately prior to stimulus onset (frame I ). Top. middle. and bottom images correspond respectively to activity collected during: 0.5 s (integrated from 0.5 s to 0.99 5). I .S s (integrated from I .S s to I .99 5). and 2.5 s (integrated from 2.5 s to 2.99 s) after stimulus onset. Dark areas correspond to activity that has increased relative to frame 1; hence, stimulus-dependent cortical activity is visualized as a coherent black patch while stimulus-dependent vessel activity is visuaiiaed as thin and elongated vessel representations. (A) Example from the left cortex of a 300 g male rat with whisker D2 stimulation showing stimulus-dependent cortical activity with mimmal changes in activity from local blood vessels. (B) Example from the right cortex of the same rat (whisker E3 stimulation) illustrates that. in addition to the stimulus-dependent cortical activity, typically there is also elevated activity from blood vessels (both arterie< and vcrns) m response to \%,hisker stimulation. Note that this activity is slower to rise and fall than the stimulus-dependent cortical activity.

1993; Narayan et al.. 1994; Peterson and Goldreich, 19941, we found that the images obtained with this method typitally contained blood vessel representations in addition to whisker representations (Fig. 4B, Fig. 5C). By visualizing activity over time in consecutive 500 ms frames (frame visualization analysis), we were able to distinguish two types of stimulus-dependent activity: increased activity

originating primarily from cortical tissue versus increased activity originating primarily from local blood vessels (compare Fig. 4A and B). It appears that the common method allows the visualization of single-whisker functional representations with minimal blood vessel representations when vessel activity does not increase in response to whisker stimulation (Fig. 4A). However. vessel activity

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Table 1 Two methods Prestimulus Representative

for the intratrial

analysis

IS level example

et al. /Journal

of Neuroscience

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of visualization

IS level during rising phase for the common

IS level during

falling

phase

Division

formula

method (3+4+5+6)/4

Frames O-l

Frames

3-6

NA (0 +

Several examples Frame 1 Frame

1

for the alternative

1)/2

method Frames 3-6

Frame 8

(3+4+5+6)/4

Frames

Frame

Cl+ 8)/z (3+4+5+6)/4

3-6

7

(1 + 7)/2 (3+4+5)/3 Frame Frame

1 1

Frames Frames

3-5 3-5

Frame Frame

7 6

cl+ 7)/2 (3+4+5)/3

Cl+ 6)/2

The above are examples which apply to data sessions with IS collected in 500 ms frames (first frame = frame 0) and 1 s whisker stimulation frame 2. Hence, frames O-l contain 1 s of activity collected immediately prior to stimulus onset, frames 3-6 = 2 s of activity collected stimulus onset, frame 8 = 0.5 s of activity collected 3 s after stimulus onset, and so forth.

typically increased in amplitude shortly after stimulus onset (Fig. 4B) and therefore appeared as blood vessel representations on the images created with the common method (Fig. 5B,C). Th u s, an alternative method for the intratrial analysis was needed in order to overcome the contributions from the stimulus-dependent vessel activity. 3.3. Minimization of blood uessel representations: Altematiue method of intratrial analysis Although blood vessel representations need not hinder the localization of the stimulus-dependent cortical activity (Fig. 5B,C), their presence can impede attempts by a computer algorithm to quantify its areal extent (Fig. 6C). We thus employed an alternative method for the intratrial analysis which exploited the differences in temporal characteristics between the stimulus-dependent cortical activity

beginning at 0.5-2 s after

and the stimulus-dependent vessel activity (Table 1). Within 2-3 s after stimulus onset, the stimulus-dependent cortical activity exhibited a rapid decline towards baseline subsequent to its peak increase (Fig. 4A). In contrast, the stimulus-dependent vessel activity rarely exhibited such a decrease during this time period (Fig. 4B), which is similar to previous findings of arterial (Grinvald et al., 1986) and venous (Narayan et al., 1994) IS in response to stimulation. Thus, the alternative method of the intratrial analysis exploited data collected 2-3 s after stimulus onset in order to take advantage of the slower time course of the stimulus-dependent vessel activity. Table 1 summarizes the common and alternative methods which can be used for the intratrial analysis of visualization. The alternative method shared similar calculation procedures with the common method with one general exception; unlike the common method, the denominator of

Fig. 5. Examples of identical cortical areas analyzed with the common versus the alternative method of visualization. For a comparison of the two methods see text and Table 1. (A) IS is collected from the left cortex of a 350 g male rat in 250 ms frames. Whisker stimulation (1 s at 5 Hz) begins 1.25 s after the start of data collection. Vertical double-headed arrow indicates the difference between the numerator value (level of upper horizontal bar) and the denominator value (level of lower horizontal bar) as determined with the intratrial analysis (solid = stimulus-dependent cortical IS, dashed = stimulusdependent vessel IS). The numerator value is the average IS collected during 0.25-2 s after stimulus onset (time points 1.5, 1.75, 2, and 2.25 s) and applies to both the common and the alternative method. The only difference between the two methods is how the value of the denominator is calculated: the common method incorporates the average of two data points collected during O-O.5 s prior to stimulus onset (time points 0.5 and 0.75 s) while the alternative method incorporates the average of two data points collected during O-O.25 s prior to stimulus onset (time point 0.75 s) and 2-2.25 s after stimulus onset (time point 3.25 s). Note that with the common method the height of the vertical double-headed arrow is similar for both the cortical IS and the vessel IS. In contrast, the height of the vertical arrow is relatively small for the vessel IS as compared to the cortical IS when using the alternative method. y-axis = magnitude of IS in fractional change units; x-axis = time in seconds. (B) Visualization of stimulus-dependent cortical activity from the same data file as in (A). Because of the presence of blood vessel representations, it is more difficult to visualize whisker C2 representation (white arrow) when using the common method. Horizontal scale bar = 1 mm. (C) Examples from two additional rats which further illustrate the minimization of blood vessel representations when using the alternative method. Top row contains images of whisker C2 representation from the right cortex of a 420 g male rat; bottom row contains images of whisker E3 representation from the left cortex of a 545 g male rat (same data file as in Fig. 4B). Details of the common and alternative methods provided in (A) also apply to this panel with the following exceptions. Top right example is obtained by dividing with prestimulus activity and activity collected during 2.5-3 s after stimulus onset. Bottom right example is obtained by dividing with prestimulus activity and activity collected during 3-3.5 s after stimulus onset. Data collected in 500 ms frames with 1 s of activity collected prior to single whisker stimulation of 1 s duration at 5 Hz. Horizontal scale bar = 1 mm.

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the division formula for the alternative method contained data collected during the falling phase of the stimulus-dependent cortical activity (2-3 s after stimulus onset) in addition to data collected prior to stimulus onset.

c

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The advantages of the alternative method over the common method are illustrated in Fig. 5. For the common method as well as the alternative method, the numerator value utilized in the division calculation is an average of

N METHOD

7, 6

stimulus

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 lime (s)

stimulus

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 lime(s)

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data collected during the rising phase of both the stimulus-dependent cortical IS and the stimulus-dependent vessel IS. When using the common method, only averaged IS collected prior to stimulus onset is utilized in the denominator for the division calculation. An example of the difference between the numerator and the denominator as calculated with the common method is indicated by the vertical arrows in Fig. 5A (solid vertical arrow = stimulusdependent cortical IS; dashed vertical arrow = stimulusdependent vessel IS). In this case, the amplitudes of both vertical arrows are similar. Generally, the common method provides a clear visualization of stimulus-dependent cortical activity with minimal blood vessel representations when the amplitude of the vessel IS is smaller than the cortical IS. In contrast, the alternative method allows visualization of cortical activity with minimal blood vessel representations in most cases (Fig. 5B,C), even when the amplitude of the stimulus-dependent vessel IS is larger than for the stimulus-dependent cortical IS (Fig. 5A). Although its numerator value is the same as for the common method, the alternative method is designed such that the denominator contains prestimulus data as well as data collected during the falling phase of the stimulus-dependent IS (Table 1). Since the latency to the falling phase is longer for the stimulus-dependent vessel IS, the denominator value is larger for the stimulus-dependent vessel IS than for the stimulus-dependent cortical IS (see level of lower horizontal bar below vertical arrows in Fig. 5A; solid = stimulus-dependent cortical IS; dashed = stimulusdependent vessel IS). This results in a smaller ratio value for the stimulus-dependent vessel IS than for the stimulusdependent cortical IS. The differences between the numerator and the denominator values as determined with the alternative method - thus, the new ratio values - are shown in Fig. 5A. By using a division calculation which maximizes the ratio value for the stimulus-dependent cortical IS, and simultaneously minimizes the ratio value for the stimulus-dependent vessel IS, the alternative method provides a clearer visualization as compared to the common method by minimizing or eliminating blood vessel representations (Fig. 5B,C). Interestingly, dividing only by IS collected during the falling phase (2-3 s after stimulus onset) without averaging with IS collected prior to stimulus onset is not as effective in providing a clear visualization of stimulus-dependent cortical activity (data not shown). Data collected during 2-3 s after stimulus onset may still include stimulus-dependent cortical IS and thus, the effect of dividing by stimulus-dependent cortical IS which may not have returned to baseline levels is probably minimized by averaging with IS collected prior to stimulus onset. In a minority ,of cases, neither the common nor the alternative method provides a clear visualization of stimulus-dependent cortical activity. Instead, only blood vessel representations are visualized. These cases contain stimu-

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lus-dependent vessel activity that are in synchrony with, and similar and/or larger in magnitude to, the stimulus-dependent cortical activity. Thus, one inherent limitation of both methods, common or alternative, is a situation in which the temporal profile of the stimulus-dependent vessel activity is identical to the stimulus-dependent cortical activity. 3.4. Area1 extent quantification:

Two considerations

We were interested in identifying regions which exhibited a specified percentile threshold of the stimulus-dependent cortical activity. We encountered two considerations while establishing the parameters for the threshold criterion. The first consideration was related to the technical aspects of the visualization process. As described in Section 3.1, two distributions of numerical values were created during this process, the ratio values and the grayscale values. We chose to conduct the quantification process on the ratio values since the grayscale values are limited in their dynamic range (256 shades of gray) and are influenced by factors related to the creation of images (e.g., clipping). The second consideration was related to the large variability in the dynamic range of ratio values which exists across data sessions (data not shown) and is probably related to general changes in spontaneous IS activity (Fig. 1A); for instance, the median of the dynamic range from one data session can lie outside the 95th percentile of the dynamic range from another data session. Thus, the threshold process was normalized with respect to a reference point that is specific to each data session. The median ratio value of each data session was used as the relative reference point since it represented most faithfully the midpoint of the dynamic range of each data session. The median was chosen instead of the mean since it is less sensitive to outlier ratio values which can originate from random noise and/or stimulus-dependent vessel activity. 3.5. Normalized threshold analysis

The computer algorithm first located the local peak within the area of the stimulus-dependent cortical activity; this was important for the exclusion of other minimum values which can result from stimulus-dependent vessel activity or random noise. Since a decrease in light reflectance indicated an increase in IS (see Section 3.1), the local peak within the area of the stimulus-dependent cortical activity corresponded to the local minimum ratio value. The algorithm determined the difference between the local peak and the median and normalized it to 100% (normalized difference); the normalization should account for general changes unrelated to stimulus delivery (Fig. IA). The algorithm enclosed the cortical area surrounding the local peak which contains any predefined percentile of the normalized difference. Because of the high frequency noise

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our technique is rather large (Fig. 6C) as compared to the anatomical representation of a single whisker within layer IV of barrel cortex (0.1-0.2 mm’; see Land and Simons, 1985; Jensen and Killackey, 1987; Riddle et al., 1992; Riddle and Purves, 1995; Zheng and Purves, 1995). We have obtained similarly large single-whisker functional representations in other investigations (Frostig et al., 1994; Chen-Bee and Frostig, 1996; Masino and Frostig, 1996). Furthermore, other investigators have found that stimulation of a single whisker activates a large cortical area as determined with optical imaging of voltage-sensitive dyes (Orbach et al., 1985; Grinvald et al., 1986) and intrinsic signals (Grinvald et al., 1986) as well as with single unit recordings (Armstrong-James and Fox, 1987: ArmstrongJames et al., 1992). Fig. 6C illustrates the area1 extent obtained from thresholding at the 50th percentile of the normalized difference. This enclosed cortical region was termed the ‘area at half-height’ since its boundary contained ratio values which were at half the ‘height’ of the difference between the peak and the median value. The normalized threshold analysis was successful in enclosing the stimulus-dependent cortical activity across various data sessions. In addition, it was applicable when the data session was processed with either the common or the alternative method of the inn-atria1 analysis of visualization as long as blood vessel representations were minimal. Most importantly, the normalized threshold analysis provided an objective means for quantifying the areal extent of the stimulus-dependent cortical activity.

Fig. 6. Areal extent quantification of a single-whisker functional representation as obtained with the normalized threshold analysis. The scale bar corresponds to 1 mm and applies to all three images. (A) First, the functional representation of whisker C2 from the left barrel cortex of a 380 g male rat is visualized as a coherent black area with the use of the alternative method (average activity collected 0.5-2 s after stimulus onset divided by average activity collected O-O.5 s prior to stimulus onset and 34.5 s after stimulus onset; see Table 1). (B) Next, prior to any quantification, a Gaussian filter (half-width 5) is applied to the data to reduce high frequency noise. (C) An algorithm tinds the local peak value within the functional representation (cross-mark). After the difference between the locat peak value and the median value is normalized to 100% (normalized difference), the algorithm can enclose the cortical area that exhibits any desired percentile of the normalized difference. The white borders, starting with the smallest border, are examples of area1 extents obtained from thresholding either at the 10th. 3Oth, SOth, or 70th percentile of the normalized difference. The area occupied by a single whisker functional representation was rather large as compared to the anatomical representation of a single whisker within layer IV of barrel cortex (see text for details).

that accompanies the images created from the intratrial analysis (Fig. 5B,C, Fig. 6A), the ratio values were processed with a linear filter (Fig. 6B) prior to the enclosement step (Fig. 60 Interestingly, the area1 extent of a single-whisker functional representation as quantified with

4. Discussion

We have described a means for visualizing and quantifying the area1 extent of functional representations within rat barrel cortex as assessed with ISI. It combines the normalized threshold analysis of area1 extent quantification with an alternative method for the intratrial analysis of visualization to overcome limitations commonly encountered during image analysis such as: (i> stimulus-dependent vessel activity which appears as blood vessel representations in the images and hinders the quantification of stimulus-dependent cortical activity; (ii> large variabiiity in the. dynamic range across data sessions; and (iii) imagespecific processing which can force the data into a restricted range of grayscale values. The successful application of the normalized threshold analysis is dependent upon visualizing single-whisker functional representations with minimal blood vessel representations. In response to their consistent presence when using the common method of the intratrial analysis of visualization (Fig. 5B,C), we have developed an alternative method in order to minimize vessel representations (for a different approach see Carmona et al., 1995). As the normalized threshold analysis is sensitive to high fre-

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quency noise, the determination of the area1 extent values is based on the processing of filtered ratio values. Since linear filtering influences the area1 extent values obtained with the normalized threshold analysis, a regression-based estimation of areal values from unfiltered data is presented elsewhere (Masino and Frostig, 1996). The intratrial analysis of visualization also has limitations. One inherent limitation, regardless of which method is employed, is its inability to visualize single-whisker functional representations without blood vessel representations when the stimulus-dependent vessel activity follows a temporal profile that is in synchrony with the stimulusdependent cortical activity. In addition, the intratrial analysis is also dependent on the temporal profile of the stimulus-dependent IS (Fig. 3B) as described in the present study and may be sensitive to factors which can influence this temporal profile. For example, it has been found that the spatiotemporal characteristics of IS depends on the wavelength of illumination that is employed during the imaging sessions, since they emphasize different sources of IS (Frostig et al., 1990). Another limitation is the lack of a universal method for the intratrial analysis of visualization, which is probably related to the variability in the temporal profile of the stimulus-dependent cortical activity and/or stimulus-dependent vessel activity. Since different methods may emphasize different temporal aspects of the stimulus-dependent cortical activity, and may therefore visualize differently this activity, one should consider employing the same method across all data sessions when the aim of the project is to compare area1 extent values. For instance, the alternative method contains a data point from the falling phase of the stimulus-dependent IS in the denominator of the division formula and therefore the area1 extent obtained with this method may differ from the area1 extent ,obtained with the common method. Finally, the ,temporal profile of the stimulus-dependent cortical activity -may vary across different species as well as across different sensory modalities. However, Bakin et al. (1996) have successfully employed the alternative method of the intratrial ahalysis to create images of stimulus-dependent cortical activity with minimal blood vessel representations in the primary auditory cortex of adult rats and guinea pigs. Thus far, the combination of the alternative method of the intratrial analysis with the normalized threshold analysis has been implemented successfully for the investigation of various biological questions related to the dynamics of cortical functional organization. Specifically, we have assessed the degree of variability within the same animal over time (Masino and Frostig, 1996) as well as the degree of interhemispheric asymmetry across animals (Chen-Bee and Frostig, 1996). With the successful quantitative assessment of brain organization, IS1 demonstrates the potential for allowing investigations which require high spatial resolution, quantitative assessment of cortical functional organization.

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Acknowledgements The authors wish to thank Y. Dory for expert software support and contribution to Fig. 1A and to Dr. J. Vargo for statistical advice. This work was supported by NIH NRSA MH14599 from the University of California Irvine (C.H.C., S.A.M.), the Regents Fellowship (M.C.K.), NIH Grant MH-50362 (R.D.F.), and NSF Grant IBN 9405146 (R.D.F.).

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