**2.5. Facial stimulation recordings**

experimental data acquisition, and they were told that, there was no threat of any sanctions regardless of the outcome. Pre-experimental test runs in a different but selected group of participants provided insights into how participants understood the instructions and handled the stimulus used in the present study [22, 27]. The latter was used to modify and simplify the task design for easy comprehension and strict adherence. Baseline vital signs were recorded

Measurements in dark condition were considered as light absent condition, as well as includ‐ ing background effects of scotopic vision [20, 51]. It is known that black may evoke a color experience [51]. Recording of a continuous train of velocity waveform envelopes was per‐ formed at rest with the participant mute, still, and attention-focused, in a dark visual field within a dark enclosed space with no mental or manual tasks to undertake. Although this had a similar effect to eye closure, but did not require eye muscle contractions and eye ball movements that could elicit motion artifacts. First, dark recording was obtained prior to stimuli administration for 60 seconds, and was used as reference for light stimulation conditions. An observer monitored the subject for movement artifacts, which were marked and removed from

The tasks were designed in our laboratory and the detailed rationale described elsewhere [22]. We have applied these tasks to show consistent and reliable recordings with TCD ultrasonog‐ raphy in prior studies [17, 22]. Briefly, specially adapted 3D-viewing device (Viewmaster, Portland, OR) was painted inside with black paint. The aperture on the right side of the device was closed to light, but the left side aperture was open, to be backlit from white light reflected from a remotely placed light source. In other words, there was left eye monocular vision, with light path from the left visual hemifield reaching the right side of the left eye retina, and crossing at chiasm to project contralaterally to the right visual cortex, while the light path from the right visual hemifield reaching the left side of the left eye retina, project ipsilaterally to the left visual cortex. The rationale for this design, relates to the fact that, in primates including humans, there is virtually total binocular vision; the left half of each retina project to the left visual cortex and the right half of each retina project to the right visual cortex. This means that the right visual cortex receives all its input from the left visual field and the left visual cortex receives all its inputs from the right visual field [52]. Color processing cells, receiving inputs from only one eye are grouped together within the same area of the striate cortex, extending from the upper to the lower cortical layers, and are referred to as ocular dominance columns (blobs) [15, 53, 54]. While those receiving inputs from both eyes are called hypercolumn [53, 55]. During binocular vision, there is binocular interaction due to stereopsis, the perception of depth [15]. Therefore, it is inappropriate to mix the inputs, from both retinas in a single neuron, before the information of color vision has been extracted [15]. The reflection from a light source was used, and projected onto a white surface flat screen, placed 125 cm from the lamp. The screen was positioned 80 cm from the nose ridge of the subject. The light source was a tungsten

in full consciousness under normal resting conditions.

**2.3. Recordings under dark condition**

144 Advancements and Breakthroughs in Ultrasound Imaging

**2.4. Color stimulation recordings**

recordings.

Figure 2 shows that object and facial paradigms used in the present study.

**Figure 2.** Shows object (Paradigm 1) and facial stimuli (Paradigms 2-5). (Source: Njemanze PC. Aviat Space Environ Med, 2004, 75:800-805).

Paradigm 1: *Object perception* - Checkerboard square paradigm. The paradigm comprised black and white chequered square of alternating black and white square dots. This was a nonverbal passive viewing task of an object foveally presented from a slide projector onto a screen placed in front of the participant, which was inclined at 30 degrees from the horizontal plane at a distance of 80 cm from the nasal ridge. During the presentation, a continuous train of velocity waveform envelopes was recorded with the participant mute, still, with fixed gaze, and attention focused on the object. There were no mental or manual tasks to perform while viewing the object. MFV measurements were made for 60 seconds.

Paradigm 2: *Face encoding task* - whole neutral face. A novel whole male face expressing a neutral emotion was presented. The participant was instructed to commit the face to memory and told that their memory would be tested later. During the presentation, MFV was recorded for 60 seconds while the participant viewed the face.

Paradigm 3: *Facial elements sorting task* – of disarranged face. This facial task comprised sorting elements of a disarranged face. Participants were instructed to sort the elements of the face and arrange them into a whole face, one element at a time, for 60 seconds. The task required a sophisticated perceptual mechanism capable of extraction of components of a face; analysis of their width and height, distances between these elements, angles, contours, illumination, expression, hairline, hair style and so on; and constantly spatially fitting the puzzle by matching each element with that stored in memory and then proceeding to form the picture of the whole face. The task implies that, far more iterations were required to accomplish the recognition task. The rationale is based on the fact that, facial processing comprises several stages [57, 58]. However, on presentation of a face, this multi-stage processing occurs almost simultaneously [59]. The task design was an attempt to break down the processes into several iterative steps, and to exclude verbalizable features that may cause extraneous compounding effects [27]. The MFV was recorded for 60 seconds during the performance of the task.

respectively. The resulting values were used for further computations of laterality index (LI').

A New Functional Transcranial Doppler Spectroscopy (fTCDS) Study of Cerebral Asymmetry During..

10-s 10-s

The relative value of lateralization (LI), for each 10-s segment for each color, was calculated as the difference between LI' values measured during the 10-s segment of the color and the corresponding 10-s segment of baseline (onset of baseline corresponds with onset of color

In general, positive LI values suggest right lateralization, while negative LI values suggest left lateralization. Zero LI values showed no lateralization from baseline, or possible bilateral response. LI values calculated for each 10-s segment of the MFV envelope, were used for

In the design of the model for three dimensional color space, it could be presumed that opponency was accomplished across two orthogonal coordinates of blue and yellow, respec‐ tively [11]. In men, wavelength-differencing activity was captured by changes in the RMCA MFV for Yellow plotted on the Y-axis, and the RMCA MFV for Blue plotted on the X-axis [11]. In women, frequency-differencing [12] was captured by changes in the LMCA MFV for Yellow plotted on the Y-axis, and the LMCA MFV for Blue plotted on the X-axis. The luminance effect on the LMCA MFV in response to White light with the highest luminous flux, was plotted on the (Z - axis), in both men and women [11]. The reconstruction of the 3D-color space plots was performed using the quadratic function [11], fitted by the procedure that has the general form:

The relationships between observed variables were estimated in 3D-surface plot which offered a flexible tool for approximation. Moreover, the quadratic surface plot does not flex to accommodate local variations in data. When the overall pattern of changes in MFV dataset follows some segment of the quadratic surface, then a good fit could be achieved. The raw data spikes were displayed on the surface, to examine goodness of fit and assess the influence of outliers. The MFV gradient produced color scale sequence ranges, from minimum (green) to maximum (red) of *Z*-values of luminance effect. The color sequence ranges were used to adjudge the level of luminance effect required for wavelength-differencing activity in men,

and frequency-differencing activity, in women, respectively.

RMCA MFV plus LMCA MFV \* (5)

http://dx.doi.org/10.5772/54877

147

10-s 10-s LI = LI'color minus LI'baseline . (6)

2 2 Z= a + a X+ a Y + a X +a Y + a XY o1 2 3 4 5 (7)

( ) ( ) 10-s 10-s

RMCA MFV minus LMCA MFV LI' = 100.

Cerebral lateralization was assessed using LI' expressed as:

within the 60-s segment):

further analysis.

**2.7. Three dimensional color space**

Paradigm 4. *Facial recognition task:* This facial recognition task comprised disarranged facial elements with a part of the face left in place as a clue. Subjects were asked to recognize the face. The clues left in place were intended to introduce some measure of "automaticity" in the recognition process. In other words, the clue reduced the number of iterations required to accomplish the task.

Paradigm 5. *Facial recognition task:* This facial recognition task comprised a degraded face with missing elements of a greater level of complexity than that of Paradigm 4, but the contour and some elements were preserved in place to aid the subject to recognize the face using a 'fill-in effect' of the missing parts.

The exclusion in the task design of performance ratings by any observer and any competitive indices was done to minimize any role of anxiety. Furthermore, positively and negatively valenced pictures, culturally familiar faces, as well as female faces, were also not used in the present study. Pretest runs suggested that, the latter factors could cause emotional activation both subliminally and supraliminally, with compounding effects on autonomic responses [60]. The design rationale for this pedigree of paradigms have been described in detail elsewhere [27]. Participants were later debriefed on the sequence of task execution and the climax attained. In post-test debriefings, we focused on ''what participants were thinking'' at each stage of the task, difficulties, distractions, and any confounding experiences or thoughts. The participants described in detail the sequence and strategy used for each task execution, and how they resolved internal conflicts that arose during task performance. Their self-rating of performance on a 4-point scale (from poor to best performance) was also assessed relative to self-attained target performance for the same task during pretest runs. An observer monitored motion artifacts such as eye movements and voluntary and involuntary movements, who documented time of occurrence on the MFV train for use in later analysis.

#### **2.6. Calculations**

Artifacts were marked and removed prior to data analysis. Data averaging comprised 10 second segments of the train of velocity waveform envelopes for the dark task and each of the paradigms, respectively. For baseline and each stimulus, 60 seconds of recording resulted in six MFV values for dark and each task, respectively. These values were used for further calculations. In other words, velocity waveform envelopes for the relevant 60-s intervals were first averaged in 10-s segments, to produce six values for black and each color condition respectively. The resulting values were used for further computations of laterality index (LI'). Cerebral lateralization was assessed using LI' expressed as:

$$\text{LL'} = \frac{\left(\text{RMCA MVV}\_{10\text{-s}} \text{ minus LMCA MFV}\_{10\text{-s}}\right)}{\left(\text{RMCA MFV}\_{10\text{-s}} \text{plus LMCA MFV}\_{10\text{-s}}\right)} \text{ \*100.} \tag{5}$$

The relative value of lateralization (LI), for each 10-s segment for each color, was calculated as the difference between LI' values measured during the 10-s segment of the color and the corresponding 10-s segment of baseline (onset of baseline corresponds with onset of color within the 60-s segment):

$$\text{LI} = \text{LI'color}\_{10\text{-}s} \text{ minus } \text{LI'baseline}\_{10\text{-}s}. \tag{6}$$

In general, positive LI values suggest right lateralization, while negative LI values suggest left lateralization. Zero LI values showed no lateralization from baseline, or possible bilateral response. LI values calculated for each 10-s segment of the MFV envelope, were used for further analysis.

#### **2.7. Three dimensional color space**

of their width and height, distances between these elements, angles, contours, illumination, expression, hairline, hair style and so on; and constantly spatially fitting the puzzle by matching each element with that stored in memory and then proceeding to form the picture of the whole face. The task implies that, far more iterations were required to accomplish the recognition task. The rationale is based on the fact that, facial processing comprises several stages [57, 58]. However, on presentation of a face, this multi-stage processing occurs almost simultaneously [59]. The task design was an attempt to break down the processes into several iterative steps, and to exclude verbalizable features that may cause extraneous compounding effects [27]. The MFV was recorded for 60 seconds during the performance of the task.

Paradigm 4. *Facial recognition task:* This facial recognition task comprised disarranged facial elements with a part of the face left in place as a clue. Subjects were asked to recognize the face. The clues left in place were intended to introduce some measure of "automaticity" in the recognition process. In other words, the clue reduced the number of iterations required to

Paradigm 5. *Facial recognition task:* This facial recognition task comprised a degraded face with missing elements of a greater level of complexity than that of Paradigm 4, but the contour and some elements were preserved in place to aid the subject to recognize the face using a 'fill-in

The exclusion in the task design of performance ratings by any observer and any competitive indices was done to minimize any role of anxiety. Furthermore, positively and negatively valenced pictures, culturally familiar faces, as well as female faces, were also not used in the present study. Pretest runs suggested that, the latter factors could cause emotional activation both subliminally and supraliminally, with compounding effects on autonomic responses [60]. The design rationale for this pedigree of paradigms have been described in detail elsewhere [27]. Participants were later debriefed on the sequence of task execution and the climax attained. In post-test debriefings, we focused on ''what participants were thinking'' at each stage of the task, difficulties, distractions, and any confounding experiences or thoughts. The participants described in detail the sequence and strategy used for each task execution, and how they resolved internal conflicts that arose during task performance. Their self-rating of performance on a 4-point scale (from poor to best performance) was also assessed relative to self-attained target performance for the same task during pretest runs. An observer monitored motion artifacts such as eye movements and voluntary and involuntary movements, who

Artifacts were marked and removed prior to data analysis. Data averaging comprised 10 second segments of the train of velocity waveform envelopes for the dark task and each of the paradigms, respectively. For baseline and each stimulus, 60 seconds of recording resulted in six MFV values for dark and each task, respectively. These values were used for further calculations. In other words, velocity waveform envelopes for the relevant 60-s intervals were first averaged in 10-s segments, to produce six values for black and each color condition

documented time of occurrence on the MFV train for use in later analysis.

accomplish the task.

**2.6. Calculations**

effect' of the missing parts.

146 Advancements and Breakthroughs in Ultrasound Imaging

In the design of the model for three dimensional color space, it could be presumed that opponency was accomplished across two orthogonal coordinates of blue and yellow, respec‐ tively [11]. In men, wavelength-differencing activity was captured by changes in the RMCA MFV for Yellow plotted on the Y-axis, and the RMCA MFV for Blue plotted on the X-axis [11]. In women, frequency-differencing [12] was captured by changes in the LMCA MFV for Yellow plotted on the Y-axis, and the LMCA MFV for Blue plotted on the X-axis. The luminance effect on the LMCA MFV in response to White light with the highest luminous flux, was plotted on the (Z - axis), in both men and women [11]. The reconstruction of the 3D-color space plots was performed using the quadratic function [11], fitted by the procedure that has the general form:

$$\mathbf{Z} = \mathbf{a}\_o + \mathbf{a}\_1 \mathbf{X} + \mathbf{a}\_2 \mathbf{Y} + \mathbf{a}\_3 \mathbf{X}^2 + \mathbf{a}\_4 \mathbf{Y}^2 + \mathbf{a}\_5 \mathbf{X} \mathbf{Y} \tag{7}$$

The relationships between observed variables were estimated in 3D-surface plot which offered a flexible tool for approximation. Moreover, the quadratic surface plot does not flex to accommodate local variations in data. When the overall pattern of changes in MFV dataset follows some segment of the quadratic surface, then a good fit could be achieved. The raw data spikes were displayed on the surface, to examine goodness of fit and assess the influence of outliers. The MFV gradient produced color scale sequence ranges, from minimum (green) to maximum (red) of *Z*-values of luminance effect. The color sequence ranges were used to adjudge the level of luminance effect required for wavelength-differencing activity in men, and frequency-differencing activity, in women, respectively.

### **2.8. Exponential function model**

The 3D-graph was examined to uncover the relationship between luminance effects (*Z*-axis) and wavelength-differencing on the RMCA MFV for Yellow plotted on the *Y*-axis in men. The observation of an exponential growth suggested that, the data could be fitted to an exponential function model. The LMCA MFV for White light (*Y*-axis) and RMCA MFV for Yellow (X-axis) was fitted to an exponential function of a 2D graph, given the following:

The exponential function with positive base *b* > 1 is the function:

$$y = b^{\chi} \tag{8}$$

) *<sup>x</sup>*

( ) and ( ) *<sup>x</sup>*

and ( ) ( ) *<sup>x</sup>*

where *c* is the speed of light in a vacuum and remains constant at 3.00 × 108

and the frequency regions with the highest estimates were marked as peaks.

l

l

frequency (*f*), respectively, that is given by: λ = c /*f*.

to produce *x*.

Then Rule *i*) is λ(*f*(*x*)) = *x*.

And Rule *ii*) is *f*(λ(*x*)) = *x*.

**2.11. Fourier analysis**

**2.12. Other statistics**

Now, let:

functions:

Rule *i*) embodies the definition of a logarithm: log*bx* is the *exponent* to which *b* must be raised

These rules satisfy the definition of a pair of inverse functions. Therefore for any base *b*, the

are inverses. This defines the well established relationship between wavelength (λ) and

The Fourier transform algorithm was applied using standard software (Time series and forecasting module, Statistica for Macintosh, StatSoft, OK, USA). The most efficient approach for Fourier algorithm requires that, the length of the input series is equal to a power of 2. If this is not the case, additional computations have to be performed. To obtain the required time series, the data were averaged in 10-second segments for one minute duration for each stimulus; yielding 6 data points for each participant and a total of 48 data points for all eight men and women, respectively. Smoothing the periodogram values was accomplished using a weighted moving average transformation. Hamming window was applied as a smoother [61, 62]. The spectral density estimates, derived from single series Fourier analysis, were plotted,

All analyses were performed using the software package Statistica (StatSoft, OK, USA). Results were given as mean±SD and plots represented as mean/SE/1.96\*SE where applicable. Analysis of LI was recomputed after excluding outliers. Analysis of variance (ANOVA) was applied to spectral density estimates between two minima including the peak (as maxima) to examine the effects of paradigms on cortical and subcortical responses. The stimulus effect was assessed

*b*

A New Functional Transcranial Doppler Spectroscopy (fTCDS) Study of Cerebral Asymmetry During..

*b*

*x b f x log x* = = (13)

m/s.

*x b f x log x* = = (12)

*<sup>b</sup> ii log b x* = (11)

http://dx.doi.org/10.5772/54877

149

It is defined for every real number *x* for any base *b*.

There are two important things to note:

The *y*-intercept is at (0, 1). For, *b*<sup>0</sup> = 1.

The negative *x*-axis is a horizontal asymptote. For, when *x* is a large negative number e.g. *b* −10,000 - then *y* is a very small positive number.

#### **2.9. Logarithmic function model**

In women, the 3D-graph was examined to uncover the relationship between luminance effects (*Z*-axis) and frequency-differencing on the LMCA MFV for Blue plotted on the *X*-axis. The observation of a logarithmic growth suggested that, the data could be fitted to a logarithmic function. The LMCA MFV for White light (*Y*-axis) and LMCA MFV for Blue (*X*-axis) was fitted to a logarithmic function of a 2D graph, given the following:

The logarithmic function with base *b* is the function:

$$y = \log\_b x \tag{9}$$

*b* is normally a number greater than 1 (although it need only be greater than 0 and not equal to 1). The function is defined for all *x* > 0. The negative *y*-axis is a vertical asymptote.

#### **2.10. Inverse relations of exponential and logarithmic functions**

The inverse of any exponential function is a logarithmic function. For, in any base *b*:

$$\begin{array}{c} i) \ b^{\log} b^{x} = \mathfrak{x}\_{\prime} \end{array} \tag{10}$$

and

A New Functional Transcranial Doppler Spectroscopy (fTCDS) Study of Cerebral Asymmetry During.. http://dx.doi.org/10.5772/54877 149

$$\begin{aligned} \text{(iii)} \,\log\_b b^{\text{x}} &= \text{x} \end{aligned} \tag{11}$$

Rule *i*) embodies the definition of a logarithm: log*bx* is the *exponent* to which *b* must be raised to produce *x*.

Now, let:

**2.8. Exponential function model**

148 Advancements and Breakthroughs in Ultrasound Imaging

The 3D-graph was examined to uncover the relationship between luminance effects (*Z*-axis) and wavelength-differencing on the RMCA MFV for Yellow plotted on the *Y*-axis in men. The observation of an exponential growth suggested that, the data could be fitted to an exponential function model. The LMCA MFV for White light (*Y*-axis) and RMCA MFV for Yellow (X-axis)

The negative *x*-axis is a horizontal asymptote. For, when *x* is a large negative number e.g. *b*

In women, the 3D-graph was examined to uncover the relationship between luminance effects (*Z*-axis) and frequency-differencing on the LMCA MFV for Blue plotted on the *X*-axis. The observation of a logarithmic growth suggested that, the data could be fitted to a logarithmic function. The LMCA MFV for White light (*Y*-axis) and LMCA MFV for Blue (*X*-axis) was fitted

*b* is normally a number greater than 1 (although it need only be greater than 0 and not equal

to 1). The function is defined for all *x* > 0. The negative *y*-axis is a vertical asymptote.

The inverse of any exponential function is a logarithmic function. For, in any base *b*:

*<sup>x</sup> y b* = (8)

*<sup>b</sup> y log x* = (9)

,) *log <sup>x</sup> ib b x* = (10)

was fitted to an exponential function of a 2D graph, given the following:

The exponential function with positive base *b* > 1 is the function:

= 1.

to a logarithmic function of a 2D graph, given the following:

**2.10. Inverse relations of exponential and logarithmic functions**

The logarithmic function with base *b* is the function:

It is defined for every real number *x* for any base *b*.

There are two important things to note:

−10,000 - then *y* is a very small positive number.

The *y*-intercept is at (0, 1). For, *b*<sup>0</sup>

**2.9. Logarithmic function model**

and

$$\mathcal{A}\begin{pmatrix}\mathbf{x}\end{pmatrix} = \mathbf{b}^{\mathbf{x}} \text{ and } \begin{pmatrix}\mathbf{x}\end{pmatrix} = \log\_b \mathbf{x} \tag{12}$$

Then Rule *i*) is λ(*f*(*x*)) = *x*.

And Rule *ii*) is *f*(λ(*x*)) = *x*.

These rules satisfy the definition of a pair of inverse functions. Therefore for any base *b*, the functions:

$$\mathcal{A}\begin{pmatrix}\mathbf{x}\end{pmatrix} = b^x \text{ and } f\begin{pmatrix}\mathbf{x}\end{pmatrix} = \log\_b \mathbf{x} \tag{13}$$

are inverses. This defines the well established relationship between wavelength (λ) and frequency (*f*), respectively, that is given by: λ = c /*f*.

where *c* is the speed of light in a vacuum and remains constant at 3.00 × 108 m/s.

#### **2.11. Fourier analysis**

The Fourier transform algorithm was applied using standard software (Time series and forecasting module, Statistica for Macintosh, StatSoft, OK, USA). The most efficient approach for Fourier algorithm requires that, the length of the input series is equal to a power of 2. If this is not the case, additional computations have to be performed. To obtain the required time series, the data were averaged in 10-second segments for one minute duration for each stimulus; yielding 6 data points for each participant and a total of 48 data points for all eight men and women, respectively. Smoothing the periodogram values was accomplished using a weighted moving average transformation. Hamming window was applied as a smoother [61, 62]. The spectral density estimates, derived from single series Fourier analysis, were plotted, and the frequency regions with the highest estimates were marked as peaks.

#### **2.12. Other statistics**

All analyses were performed using the software package Statistica (StatSoft, OK, USA). Results were given as mean±SD and plots represented as mean/SE/1.96\*SE where applicable. Analysis of LI was recomputed after excluding outliers. Analysis of variance (ANOVA) was applied to spectral density estimates between two minima including the peak (as maxima) to examine the effects of paradigms on cortical and subcortical responses. The stimulus effect was assessed by multivariant analyses of variance (MANOVA) with repeated measures applied to the MFV data set, followed by planned Scheffé contrast that compared stimulus response relative to stimulus-absent Dark condition. Analysis of covariance (ANCOVA) with repeated measures was performed to demonstrate that, the difference found during visual stimulation persists even when the differences in baseline condition were partialled out. When applicable, oneway ANOVA of paired groups was used to assess differences in spectral density estimates between two minima including the peak (as maxima), under different stimulation conditions, for the RMCA and LMCA, respectively. The determination of LUMINANCE effect was derived by comparison of Dark versus Light conditions, and the direction relative to chromatic axis was either opposite (orthogonal axis) or parallel axis. WAVELENGTH-encoding was assessed as present when the effects of longer wavelength color (Yellow) were accentuated over shorter wavelength color (Blue) [11]. Conversely, ENERGY-encoding was present when the effects of higher frequency color (Blue), was accentuated over lower frequency color (Yellow) [11]. WAVELENGTH-differencing implicated WAVELENGTH-encoding main effect at S-peaks, and at least a tendency for ENERGY-encoding at C-peaks [11]. The pre-condition for WAVELENGTH-differencing requires that, a chromatic contrast detector sub-serving one area of chromatic space, excite a chromatic detector of opposite type and/or inhibit a chromatic detector of the same type in neighboring areas of chromatic space [11, 15]. On the other hand, FREQUENCY-differencing involved ENERGY-encoding main effect at C-peaks, and at least a tendency at S-peaks. CLTP process accentuated C-peaks over S-peaks due to prevailing SLTD. Conversely, SLTP process accentuated S-peaks over C-peaks, due to prevailing CLTD. The latter was followed by planned contrasts to examine luminance effect (dark versus Paradigm 1), discrimination of face from non-face or category-specific face effect (Paradigm 1 versus Paradigm 2), and face-processing strategy effect (Paradigm 2 versus Paradigm 3). The Paradigms 4 and 5 were used only in the *f*TCD analysis but not in the further, *f*TCDS analysis. The level of significance was at p=0.05.

**Stimulations RMCA**

**Stimulations RMCA**

*A*

*B*

**(cm/s)**

**(cm/s)**

Dark 64±1.26 -63.9±1.8

**Scheffé** *P* **(cm/s)**

A New Functional Transcranial Doppler Spectroscopy (fTCDS) Study of Cerebral Asymmetry During..

**Scheffé** *P* **(cm/s)**

**Table 1.** A. Mean±SE and Planned Contrasts of MFV Changes during Visual Stimulations from Dark Baseline in Men; B.

**Figure 3.** Box and whiskers plots of mean/SE /1.96\*SE of MFV (in cm/s) during dark and color stimulation in men and

To assess the overall gender-related differences in MFV during color stimulation, a MANOVA test was applied to MFV data set to assess differences between measurements in the RMCA and LMCA in men and women, with a 2 × 5 × 2 design: two levels of GENDER (Men and Women), five levels of STIMULATIONS, (Dark, Light, Blue, Yellow, and Red), and two levels

women. (Source modified from: Njemanze PC. Exp Transl Stroke Med 2010, 2:21-27.).

Mean±SE and Planned Contrasts of MFV Changes during Visual Stimulations from Dark Baseline in Women

Dark 81.6±2 - 80.7±1.8 - Light 82.3±2 *NS* 81.26±1.7 *NS* Blue 83.8±1.9 <*0.0001* 81.6±1.77 <*0.01* Yellow 82.57±1.86 *NS* 81.3±1.6 *NS* Red 82.1±1.9 *NS* 80.7±1.68 *NS*

Light 65.7±1.2 *<0.05* 64.2±1.67 *NS* Blue 66.8±1.28 <*0.0001* 65.6±1.8 <*0.01* Yellow 65.6±1.26 <*0.05* 64.4±1.7 *NS* Red 65.8±1.27 <*0.01* 64.4±1.6 *NS*

**LMCA Scheffé P**

http://dx.doi.org/10.5772/54877

151

**LMCA Scheffé P**
