Major depressive disorder (MDD) is a critical disorder according to Hays, Wells, Sherbourne, Rogers, & Spritzer, 1995; Wells et al. (1989) that is characterized by the deterioration of the functionality and well-being of an individual. MDD is a heterogeneous condition that has vastly defied significant subtyping(Dunlop et al., 2017).The treatments for the disorder include psychotherapy that includes behavioral therapy and antidepressant treatments. It is essential to note that both forms of medication have equal efficiency based on the patients that receive either intervention. A combination process for treatment with antidepressants and psychotherapy improves the rates of remission among patients. However, various barriers that include time, the preference of the patient, time, and cost preclude patients from making the appropriate choice.
Most importantly, various patients that are not able to respond to a particular singular intervention could respond to a different treatment. As a result, this aforementioned observation suggests that psychological or biological variationscould be identified and hence, the field of medicine could improve treatment efficiency in the process of selecting the treatment method. Bromet et al. (2011) discuss that the person affected with MDD could have a lifetime that is up to 14.6% in first-world nations that generate high income and 11.1% in middle and low-income nations. Moreover, the WHO (2017) has proven that MDD negatively affects society by causing disability, globally.
Problem Statement
McIntyre et al. (2014) elaborate that a significant number of people that are affected with MDD experience “treatment-resistant depression (TRD.” Albeit, there is no overall consensus on the meaning of TRD. However, researchers agree that some patients fail to respond to particular antidepressants and various treatment intervention. In this connection, Dandekar, Fenoy, Carvalho, Soares, & Quevedo (2018) have identified interventions that medical practitioners can use to treat TRD.
According to Mayberg et al. (2005), one key advance in the medical field in the process of seeking the best interventions of MDD is the diminishing reactions in the SCG that includes the “Boardman area 25 (BA25)” in addition to “Deep Brain Stimulation (DBS)” of high frequency. Also, Mayberg et al. (2005) identified that there is a significant relationship between DBS and TRD. Moreover, Blumberger, Mulsant, & Daskalakis, 2013; Holtzheimer et al., 2017 found inconsistency in the research of the relationship between MDD and the SCG that has prompted the initiation of this research.
Justification and Gap
A solution that could be incorporated for the process of treating TRD with DBS interventions includes electrode localization contacts in SCG via the DBS. By comparing the contacts for the electrodes on various patients that responded to DBS, Hamani et al. (2009) suggested that a standardized approach for targeting the SCG should be incorporated. Nonetheless, McCormick et al. (2006) argue that the particular shape and location of the SCG is fingerprint unique and hence, there exists a challenge for the process of parcellation of the white matter.
Anatomical features play a key role that aids medical professionals to identify the SCG in the process of providing DBS. Research studies on the brain’s neuroanatomy suggest there exist sexual-based dimorphism in the brain structures of individuals. Lemaître et al.(2005) argue that the variation in the dimorphism identified above is achieved by demonstrating the existence of an increased concentration of white matter (WM) among males in comparison to women. This previously mentioned experiment posits various queries on the use of atlas maps of the brain that is incorporated to localize various structures of the brain in the process of the DBS interventions(Cabezas, Oliver, Lladó, Freixenet, & Bach Cuadra, 2011).
Moreover, the existence of the variation between men and women’s target of the SCG is evident in research by Marcus et al. (2005) hence, the argument of gender should be studied further to understand the association of MDD and SCG. Furthermore, studies by Botteron, Raichle, Drevets, Heath, & Todd (2002) illustrate the reduction of the volumetric size of women’s SCG in the initial stages of being affected by MDD. As a result, there is a need for further research on the gender variation that influences the SCG differences in TRD and MDD patients.
Currently, no research has been undertaken on the variation in gender in the context of the intervention of SCG treatment for MDD. It is important to identify the anatomical variation of the SCG while incorporating DBS for MDD. As a result, more research should be conducted to explore the differences in WM within the SCG that differs between men and women.
Purpose
Research Questions
- What are the gender variations in the anatomical characteristics of the SCG in the context of MDD?
- What are the gender differences in the volumes of the white matter of the SCG?
Hypothesis
- There are no variations in the white matter characteristics of the SCG.
- There are gender differences in the white matter characteristics of the SCG.
- There is no significant effect for gender in the volume of the SCG
- There is a significant effect for gender in the volume of the SCG
Literature Review
DBS
MDD has can be characterized to have an overall prevalence of between 15 and 20% (Puigdemont et al., 2011). Moreover, researchers have identified that MDD is a major global cause of disability in addition to prevalence in treatment resistance. An average 33% of affected patients are not able to attain the remission criteria after the first treatment sequence the leaves clinicians with limited options for alleviating hopelessness, sadness, suicide thoughts, and absence of pleasure in patients going through MDD.
“Electroconvulsive therapy (ECT) is a medical strategy that has been used for (TRD). Conversely, a significant number of patients that undergo the intervention do not respond. Moreover, such individuals experience constant relapses and are not able to tolerate the effects of ECT intervention. The effects include the individual experiencing adverse disturbances of their memory. Furthermore, other approaches such as the “transcranial magnetic stimulation (TMS)” have triggered research for the treatment of depression that resists drugs. However, (Puigdemont et al., 2011) argues that this aforementioned intervention exhibitsheterogeneous results.
Also, DBS is an approach that is currently vastly tested as a therapy for various patients that are affected with TRD. The process of DBS includes high-frequency stimulation of various electrodes that are implanted stereotaxically in particular brain segments. The sections of the brain that are incorporated in the procedure include the subthalamic nucleus for interventions that resist “Parkinson’s disease.” Moreover, (Puigdemont et al., 2011) argues that according to numerous medical research, DBS has illustrated advanced results in TRD intervention and should be adopted as a therapy method.
DBS can modulate nerve-based transmissions in adjustable and reversible manners (Puigdemont et al., 2011). Various target regions have been researched for the DBS approach to modulate circuits in the cortico-limbic region that includes the anterior limb, ventral capsule, and the SCG. Research shows that an average 60% of patients that underwent treatment for 6 years had a response when subjected to the “subgenual cingulate (Cg25).”
As a result, Cg25 could be a crucial element that can be incorporated in the control of cortico-limbic circuitsdue to theintegration with various structures of the brain that influence disorders. The brain structures that are influenced in this case include “the anterior cingulate cortex, thalamus, and caudate nucleus.” The probability of modulating dysfunctional events on the SCG from the circuit should issue an opportunity to deal with issues of depression.
Methodology
Participants
Existing literature and data that consists of imaging from a 7T MRI will be incorporated in the research. The MRI has a feature for high definition and will be used for imaging. The data for this research was obtained from 2016 to 2018. Moreover, the data were obtained from 107 subjects. The ages of the subjects were at a mean of 42.30. Also, the minimum bar of age for the subjects was 19 while the maximum was at 80. Moreover, the women and men involved shares 56.1% and 43.9% respectively.
Materials
The rater undertook manual parcellations on only 93 individuals on their SCG. The rater was required to load the WBMR scans to the FSleyes to determine the contrast and brightness values for the most effective subjective visibility for every individual scan. This aforementioned process will be undertaken using a high definition monitor. The rater then progressively moves their crosshair towards the midsagittal point and identify the significantly rostral section of the corpus callosum. This section is incorporated to locate the anterior ACC that is the first plane. Moreover, the other plane is identified by the initial coronal slice where the putamen is visible in the basal ganglia. Afterward, the rater is required to parcellate and track the white matter of the SCG.
Design
The identification of the variations in the manual parcellations of 93 subjects among males and females shall comprise of an analysis of the right and left hemisphere. In addition, the researcher found that 14 participants of the study had already experienced a manual parcellations procedure.
Procedure
The data for the study was initially collected since this research paper is an extension of a much comprehensive study.The volumes of white matter in the subcallosal gyrus will be used as the continuous variable in this research. Moreover, the variation between the volumes of WM in women and men will be assessed in this study. Also, the question of the variation in volumes of the RH and LH in the SCG prompts the need for a t-test.
The largest group sample for the research consisted of 107 subjects. In this case, manual parcellations were conducted on the SCG of the individuals’ right and left hemispheres by two different raters.These raters were identified as A and B. Moreover, the raters divided the sample into two where the first rater undertook parcellations for 93 individuals while the second-rater undertook the same procedure for 14. Also, the procedures were not conducted simultaneously by both raters. Each subject was handles by one rater in the entire procedure of this first sample group. As a result, manual parcellations were categorized into two variables. The variables were labeled ‘SCG_volume_right and SCG_volume_left.
In addition, a second sample group was considered to control and identify the quality of the parcellations that were undertaken in the first sample group above. In this case, the manual parcellations were undertaken by 3 raters. However, each parcellation in the control was undertaken by two raters, ‘Ref and Pro.’ For the ProVolume set of subjects, the process of the rating was undertaken by either rater A or B that were more experienced in manual parcellations. The manual parcellations that were conducted in the left hemisphere of the brain were identified as the ProVolume_L and consisted of 52 procedures. Moreover, 58 similar parcellations were undertaken in the right hemisphere and labeled ProVolume_R. In addition, the research included another sample conducted by the Ref that consisted of the third rater, C and B. C did not have as much experience as rater B. The Ref group included 54 parcellations of the left hemisphere and was labeled RefVolume_L in addition to 58 parcellations labeled RefVolume_R.
Moving forward, the researchers made a comparison of the Ref and Pro to the Dice_R and Dice_L variables. The dice overlap for both the SCGs of both the right and left hemispheres. The Dice analysis is essential since it identifies the inter-rater correlation and specifies how good or bad it is. The level of overlap determines the presentations of A v. B or B v. C that I used to identify the agreements of the raters.
Analysis
The researcher was 95% confident while conducting all the tests. Descriptive statistics and bivariate correlations of the above-identified variables of the process of manual parcellations are identified in the tables X and Y below.
| Descriptive Statistics | |||||
| N | Minimum | Maximum | Mean | Std. Deviation | |
| Age | 107 | 19 | 80 | 42,30 | 19,255 |
| SCG_Volume_Left | 105 | 14,956066 | 302,860350 | 108,29178480 | 54,592048349 |
| SCG_Volume_right | 106 | 18,119850 | 329,033450 | 118,95933924 | 54,131181726 |
| ProVolume_L | 52 | 26,748 | 433,726 | 138,19428 | 68,398257 |
| RefVolume_L | 54 | 14,956 | 280,426 | 122,90287 | 57,381463 |
| ProVolume_R | 58 | 52,921 | 329,033 | 148,28127 | 65,533787 |
| RefVolume_R | 58 | 23,009 | 312,352 | 137,05926 | 59,411658 |
| Voldiff_R | 58 | ,00 | 3,81 | ,4503 | ,64360 |
| Dice_R | 58 | ,23 | ,87 | ,5835 | ,14421 |
| AVG_R | 58 | ,29 | 4,03 | 1,3187 | ,73099 |
| Voldiff_L | 52 | ,00 | 6,46 | ,7223 | 1,14518 |
| Dice_L | 52 | ,02 | ,78 | ,5163 | ,17336 |
| AVG_L | 52 | ,48 | 5,95 | 1,6952 | 1,13759 |
| Valid N (listwise) | 50 | ||||
Table 1
| Correlations | |||||||
| SCG_Volume_L | SCG_Volume_R | ProVolume_L | ProVolume_R | RefVolume_L | RefVolume_R | ||
| SCG_Volume_L | Pearson Correlation | 1 | ,616** | ,612** | ,319* | ,739** | ,471** |
| Sig. (2-tailed) | ,000 | ,000 | ,037 | ,000 | ,001 | ||
| N | 91 | 90 | 38 | 43 | 38 | 43 | |
| SCG_Volume_R | Pearson Correlation | ,616** | 1 | ,411* | ,628** | ,591** | ,684** |
| Sig. (2-tailed) | ,000 | ,011 | ,000 | ,000 | ,000 | ||
| N | 90 | 92 | 37 | 44 | 39 | 44 | |
| ProVolume_L | Pearson Correlation | ,612** | ,411* | 1 | ,647** | ,412* | ,311 |
| Sig. (2-tailed) | ,000 | ,011 | ,000 | ,010 | ,065 | ||
| N | 38 | 37 | 38 | 36 | 38 | 36 | |
| ProVolume_R | Pearson Correlation | ,319* | ,628** | ,647** | 1 | ,343* | ,499** |
| Sig. (2-tailed) | ,037 | ,000 | ,000 | ,038 | ,001 | ||
| N | 43 | 44 | 36 | 44 | 37 | 44 | |
| RefVolume_L | Pearson Correlation | ,739** | ,591** | ,412* | ,343* | 1 | ,736** |
| Sig. (2-tailed) | ,000 | ,000 | ,010 | ,038 | ,000 | ||
| N | 38 | 39 | 38 | 37 | 40 | 37 | |
| RefVolume_R | Pearson Correlation | ,471** | ,684** | ,311 | ,499** | ,736** | 1 |
| Sig. (2-tailed) | ,001 | ,000 | ,065 | ,001 | ,000 | ||
| N | 43 | 44 | 36 | 44 | 37 | 44 | |
| **. Correlation is significant at the 0.01 level (2-tailed). | |||||||
| *. Correlation is significant at the 0.05 level (2-tailed). | |||||||
Table 2
The Right and Left Hempishere’s Comparison
A comparison of the right and left hemisphere SCG volumes of the Ref and Pro variables was undertaken. The volumes on the left hemisphere were at (119.45, sd=54.52) that was a significantly bigger amount than the volume in the left that was at (108.20, sd=54.84) as shown in table 3 below. Moreover, the results of the based on the t-test conducted by the researcher were (t(103)=2.29, p = 0.024) as illustrated in table 4. Also, no variation was identified between the mean Ref and Pro volumes in the left and right hemispheres.
| Paired Samples Statistics | |||||
| Mean | N | Std. Deviation | Std. Error Mean | ||
| Pair 1 | SCG_Volume_Left | 108,20747513 | 104 | 54,849548909 | 5,378440773 |
| SCG_Volume_right | 119,45218028 | 104 | 54,524814010 | 5,346597896 | |
| Pair 2 | ProVolume_L | 137,63033 | 50 | 69,355372 | 9,808331 |
| ProVolume_R | 145,13137 | 50 | 63,996312 | 9,050445 | |
| Pair 3 | RefVolume_L | 121,20505 | 51 | 53,749028 | 7,526369 |
| RefVolume_R | 132,94658 | 51 | 57,313788 | 8,025536 | |
Table 3
| Paired Samples Test | |||||||||
| Paired Differences | t | df | Sig. (2-tailed) | ||||||
| Mean | Std. Deviation | Std. Error Mean | 95% Confidence Interval of the Difference | ||||||
| Lower | Upper | ||||||||
| Pair 1 | SCG_Volume_Left – SCG_Volume_right | -11,2447 | 50,121320 | 4,91479 | -20,99205 | -1,4973 | -2,288 | 103 | ,024 |
| Pair 2 | ProVolume_L – ProVolume_R | -7,501041 | 63,283374 | 8,949621 | -25,4859 | 10,483895 | -,838 | 49 | ,406 |
| Pair 3 | RefVolume_L – RefVolume_R | -11,7415 | 50,919742 | 7,130190 | -26,062 | 2,579880 | -1,647 | 50 | ,106 |
Table 4
Differences of Sex in the Volumes of SCG
To conduct a test on the variation in sex of the volumes of the SCG in the right and left hemisphere, a general linear multivariate model was incorporated with SCG sizes of the right and left hemispheres as the dependent variables of the research. Moreover, age and sex were identified as the predictors of age. Also, age was used as a control variable to identify the effect of gender on the volume and size of the SCG. The multivariate impact for both sex and age was characterized to achieve a statistics significance at a confidence level of 95%. The effect was derived based on the Wilks Lambda. Moreover, at the confidence level of 90%, sex was characterized to have a statistics-based significance of (F (2,100)=2.85, p = 0.062) as shown in table 5 below.
| Multivariate Tests | |||||||
| Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
| Intercept | Pillai’s Trace | ,540 | 58,791b | 2,000 | 100,000 | ,000 | ,540 |
| Wilks’ Lambda | ,460 | 58,791b | 2,000 | 100,000 | ,000 | ,540 | |
| Hotelling’s Trace | 1,176 | 58,791b | 2,000 | 100,000 | ,000 | ,540 | |
| Roy’s Largest Root | 1,176 | 58,791b | 2,000 | 100,000 | ,000 | ,540 | |
| Age | Pillai’s Trace | ,005 | ,260b | 2,000 | 100,000 | ,771 | ,005 |
| Wilks’ Lambda | ,995 | ,260b | 2,000 | 100,000 | ,771 | ,005 | |
| Hotelling’s Trace | ,005 | ,260b | 2,000 | 100,000 | ,771 | ,005 | |
| Roy’s Largest Root | ,005 | ,260b | 2,000 | 100,000 | ,771 | ,005 | |
| Sex | Pillai’s Trace | ,054 | 2,853b | 2,000 | 100,000 | ,062 | ,054 |
| Wilks’ Lambda | ,946 | 2,853b | 2,000 | 100,000 | ,062 | ,054 | |
| Hotelling’s Trace | ,057 | 2,853b | 2,000 | 100,000 | ,062 | ,054 | |
| Roy’s Largest Root | ,057 | 2,853b | 2,000 | 100,000 | ,062 | ,054 | |
| a. Design: Intercept + Age + Sex | |||||||
| b. Exact statistic | |||||||
Table 5
Taking a univariate approach, the impact of sex on the volume of the SCG on the right side was found to be significant at (F(1,101)=5.07, p = 0.027) that illustrates there was anincrease of the mean in volume by 24.00 among men shown in table 6. Further, the sex effect on the left was only found to be significant when the procedure was at a confidence level of 90%, (F(1,101)=3.82, p = 0.054), that shows there was an increase of 21.07 volume among males.
| Tests of Between-Subjects Effects | |||||||
| Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared |
| Corrected Model | SCG_Volume_Left | 11964,822a | 2 | 5982,411 | 2,028 | ,137 | ,039 |
| SCG_Volume_right | 15165,490b | 2 | 7582,745 | 2,631 | ,077 | ,050 | |
| Intercept | SCG_Volume_Left | 244027,830 | 1 | 244027,830 | 82,733 | ,000 | ,450 |
| SCG_Volume_right | 292321,736 | 1 | 292321,736 | 101,442 | ,000 | ,501 | |
| Age | SCG_Volume_Left | 1254,372 | 1 | 1254,372 | ,425 | ,516 | ,004 |
| SCG_Volume_right | 1136,558 | 1 | 1136,558 | ,394 | ,531 | ,004 | |
| Sex | SCG_Volume_Left | 11252,673 | 1 | 11252,673 | 3,815 | ,054 | ,036 |
| SCG_Volume_right | 14601,709 | 1 | 14601,709 | 5,067 | ,027 | ,048 | |
| Error | SCG_Volume_Left | 297907,898 | 101 | 2949,583 | |||
| SCG_Volume_right | 291048,911 | 101 | 2881,672 | ||||
| Total | SCG_Volume_Left | 1527593,919 | 104 | ||||
| SCG_Volume_right | 1790172,031 | 104 | |||||
| Corrected Total | SCG_Volume_Left | 309872,721 | 103 | ||||
| SCG_Volume_right | 306214,400 | 103 | |||||
| a. R Squared = ,039 (Adjusted R Squared = ,020) | |||||||
| b. R Squared = ,050 (Adjusted R Squared = ,031) | |||||||
Table 6
It is important to note that normality and homogeneity assumptions were examined using the “Kolmogorov-Smirnov and Levene tests. For the right hemisphere of the brain, Levene’s test identified evidence of variances for heterogeneity, (F(1,102)=4.46, p = 0.037). However, a visual-based inspection of the plots of the box illustrated that there was a slight deviation as shown in table 7 and Figure 1. In addition, the K-S test identified above did not illustrate any evidence for normality deviation evidenced by table 8 below.
| Levene’s Test of Equality of Error Variances | ||||
| F | df1 | df2 | Sig. | |
| SCG_Volume_Left | ,080 | 1 | 102 | ,778 |
| SCG_Volume_right | 4,461 | 1 | 102 | ,037 |
| Tests the null hypothesis that the error variance of the dependent variable is equal across groups. | ||||
| a. Design: Intercept + Age + Sex | ||||
Table 7
Figure 1
| One-Sample Kolmogorov-Smirnov Test | |||
| Studentized Residual for SCG_Volume_Left | Studentized Residual for SCG_Volume_right | ||
| N | 104 | 104 | |
| Normal Parameters,b | Mean | -,0005 | -,0006 |
| Std. Deviation | 1,00521 | 1,00505 | |
| Most Extreme Differences | Absolute | ,081 | ,079 |
| Positive | ,081 | ,079 | |
| Negative | -,038 | -,038 | |
| Test Statistic | ,081 | ,079 | |
| Asymp. Sig. (2-tailed) | ,086c | ,105c | |
| a. Test distribution is Normal. | |||
| b. Calculated from data. | |||
| c. Lilliefors Significance Correction. | |||
Table 8
Sex-based variations in Ref Volumes
To test the variation in sex in the volumes of the Ref in the right and left hemisphere, a general linear multivariate model was incorporated with Ref right and left volumes used as dependent variables illustrated in table 9. Moreover, age and sex were included as predictors in the research. Age was identified as a control for showing the impact of the sex of Ref-based volume. In addition, the multivariate impact for both sex and age achieved a statistics based significance of a confidence level of 95%. Also, sex attained a statistics significance at 90%, that is, (F(2,47)=3.11, p = 0.054).
Furthermore, univariately, the sex effect on the Ref volume of the right was found to be significant, (F(1,48)=5.57, p = 0.022), that shows an increase of 37.59 among men shown in table 10. Also, there was no sex-based statistical significance on the left. Also, the researchers undertook a Levene’s and K-S test and found that there was no heterogeneity evidence for the variances shown in tables 11 and 12. Importantly, the K-S test illustrates that no normality deviation was seen in the right hemisphere. However, within the left hemisphere, there was evidence for normality deviation (KS=0.13, p=0.034).
| Multivariate Tests | |||||||
| Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
| Intercept | Pillai’s Trace | ,567 | 30,824b | 2,000 | 47,000 | ,000 | ,567 |
| Wilks’ Lambda | ,433 | 30,824b | 2,000 | 47,000 | ,000 | ,567 | |
| Hotelling’s Trace | 1,312 | 30,824b | 2,000 | 47,000 | ,000 | ,567 | |
| Roy’s Largest Root | 1,312 | 30,824b | 2,000 | 47,000 | ,000 | ,567 | |
| Age | Pillai’s Trace | ,004 | ,090b | 2,000 | 47,000 | ,914 | ,004 |
| Wilks’ Lambda | ,996 | ,090b | 2,000 | 47,000 | ,914 | ,004 | |
| Hotelling’s Trace | ,004 | ,090b | 2,000 | 47,000 | ,914 | ,004 | |
| Roy’s Largest Root | ,004 | ,090b | 2,000 | 47,000 | ,914 | ,004 | |
| Sex | Pillai’s Trace | ,117 | 3,109b | 2,000 | 47,000 | ,054 | ,117 |
| Wilks’ Lambda | ,883 | 3,109b | 2,000 | 47,000 | ,054 | ,117 | |
| Hotelling’s Trace | ,132 | 3,109b | 2,000 | 47,000 | ,054 | ,117 | |
| Roy’s Largest Root | ,132 | 3,109b | 2,000 | 47,000 | ,054 | ,117 | |
| a. Design: Intercept + Age + Sex | |||||||
| b. Exact statistic | |||||||
Table 9
| Tests of Between-Subjects Effects | ||||||||||||
| Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared | |||||
| Corrected Model | RefVolume_R | 17205,196a | 2 | 8602,598 | 2,808 | ,070 | ,105 | |||||
| RefVolume_L | 1332,512b | 2 | 666,256 | ,223 | ,801 | ,009 | ||||||
| Intercept | RefVolume_R | 162931,382 | 1 | 162931,382 | 53,188 | ,000 | ,526 | |||||
| RefVolume_L | 137767,934 | 1 | 137767,934 | 46,206 | ,000 | ,490 | ||||||
| Age | RefVolume_R | 532,780 | 1 | 532,780 | ,174 | ,679 | ,004 | |||||
| RefVolume_L | 75,706 | 1 | 75,706 | ,025 | ,874 | ,001 | ||||||
| Sex | RefVolume_R | 17054,541 | 1 | 17054,541 | 5,567 | ,022 | ,104 | |||||
| RefVolume_L | 1299,732 | 1 | 1299,732 | ,436 | ,512 | ,009 | ||||||
| Error | RefVolume_R | 147038,316 | 48 | 3063,298 | ||||||||
| RefVolume_L | 143115,391 | 48 | 2981,571 | |||||||||
| Total | RefVolume_R | 1065657,937 | 51 | |||||||||
| RefVolume_L | 893671,769 | 51 | ||||||||||
| Corrected Total | RefVolume_R | 164243,512 | 50 | |||||||||
| RefVolume_L | 144447,903 | 50 | ||||||||||
| a. R Squared = ,105 (Adjusted R Squared = ,067) | ||||||||||||
| b. R Squared = ,009 (Adjusted R Squared = -,032) | ||||||||||||
| Table 10
Levene’s Test of Equality of Error Variancesa |
||||||||||||
| F | df1 | df2 | Sig. | |||||||||
| RefVolume_R | ,536 | 1 | 49 | ,468 | ||||||||
| RefVolume_L | ,911 | 1 | 49 | ,345 | ||||||||
| Tests the null hypothesis that the error variance of the dependent variable is equal across groups. | ||||||||||||
| a. Design: Intercept + Age + Sex | ||||||||||||
Table 11
| One-Sample Kolmogorov-Smirnov Test | |||
| Studentized Residual for RefVolume_R | Studentized Residual for RefVolume_L | ||
| N | 51 | 51 | |
| Normal Parametersa,b | Mean | -,0011 | -,0010 |
| Std. Deviation | 1,01356 | 1,00939 | |
| Most Extreme Differences | Absolute | ,098 | ,129 |
| Positive | ,098 | ,129 | |
| Negative | -,056 | -,074 | |
| Test Statistic | ,098 | ,129 | |
| Asymp. Sig. (2-tailed) | ,200c,d | ,034c | |
| a. Test distribution is Normal. | |||
| b. Calculated from data. | |||
| c. Lilliefors Significance Correction. | |||
| d. This is a lower bound of the true significance. | |||
Table 12
Pro Volumes Sex Variations
Similarly, in the case of the Pro volumes, the multivariate used in Ref volume was incorporated. Also, age and sex were included as predictors while age was the control for the impact of sex differences in the Pro volume illustrated in table 13. Results, in this case, show that there was no sex-based effect at 95% confidence. On the contrary, at this aforementioned level, an age effect of (F(2,46)=4.34, p = 0.019) was found. Univariately, the age effect on the Pro volumes of the left was identified to be significant at F(1,47)=7.00, p = 0.011 while that of the right was at (F(1,47)=6.10, p = 0.017. These aforementioned results show a mean rise in the volume of 1.08 in the right hemisphere and 1.26 in the left shown in table 14.
| Multivariate Tests | |||||||
| Effect | Value | F | Hypothesis df | Error df | Sig. | Partial Eta Squared | |
| Intercept | Pillai’s Trace | ,412 | 16,126b | 2,000 | 46,000 | ,000 | ,412 |
| Wilks’ Lambda | ,588 | 16,126b | 2,000 | 46,000 | ,000 | ,412 | |
| Hotelling’s Trace | ,701 | 16,126b | 2,000 | 46,000 | ,000 | ,412 | |
| Roy’s Largest Root | ,701 | 16,126b | 2,000 | 46,000 | ,000 | ,412 | |
| Age | Pillai’s Trace | ,159 | 4,341b | 2,000 | 46,000 | ,019 | ,159 |
| Wilks’ Lambda | ,841 | 4,341b | 2,000 | 46,000 | ,019 | ,159 | |
| Hotelling’s Trace | ,189 | 4,341b | 2,000 | 46,000 | ,019 | ,159 | |
| Roy’s Largest Root | ,189 | 4,341b | 2,000 | 46,000 | ,019 | ,159 | |
| Sex | Pillai’s Trace | ,069 | 1,699b | 2,000 | 46,000 | ,194 | ,069 |
| Wilks’ Lambda | ,931 | 1,699b | 2,000 | 46,000 | ,194 | ,069 | |
| Hotelling’s Trace | ,074 | 1,699b | 2,000 | 46,000 | ,194 | ,069 | |
| Roy’s Largest Root | ,074 | 1,699b | 2,000 | 46,000 | ,194 | ,069 | |
| a. Design: Intercept + Age + Sex | |||||||
| b. Exact statistic | |||||||
Table 13
| Tests of Between-Subjects Effects | |||||||
| Source | Dependent Variable | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared |
| Corrected Model | ProVolume_R | 31827,256a | 2 | 15913,628 | 4,430 | ,017 | ,159 |
| ProVolume_L | 33450,093b | 2 | 16725,046 | 3,887 | ,027 | ,142 | |
| Intercept | ProVolume_R | 106413,612 | 1 | 106413,612 | 29,620 | ,000 | ,387 |
| ProVolume_L | 76364,922 | 1 | 76364,922 | 17,746 | ,000 | ,274 | |
| Age | ProVolume_R | 21900,456 | 1 | 21900,456 | 6,096 | ,017 | ,115 |
| ProVolume_L | 30124,137 | 1 | 30124,137 | 7,000 | ,011 | ,130 | |
| Sex | ProVolume_R | 12291,173 | 1 | 12291,173 | 3,421 | ,071 | ,068 |
| ProVolume_L | 5044,484 | 1 | 5044,484 | 1,172 | ,284 | ,024 | |
| Error | ProVolume_R | 168853,613 | 47 | 3592,630 | |||
| ProVolume_L | 202248,123 | 47 | 4303,152 | ||||
| Total | ProVolume_R | 1253836,595 | 50 | ||||
| ProVolume_L | 1182803,590 | 50 | |||||
| Corrected Total | ProVolume_R | 200680,869 | 49 | ||||
| ProVolume_L | 235698,216 | 49 | |||||
| a. R Squared = ,159 (Adjusted R Squared = ,123) | |||||||
| b. R Squared = ,142 (Adjusted R Squared = ,105) | |||||||
Table 14
Moreover, further Levene and K-S tests showed that there was no variances’ heterogeneity nor normality deviation illustrated in tables 15 and 16 below.
| Levene’s Test of Equality of Error Variances | ||||
| F | df1 | df2 | Sig. | |
| ProVolume_R | 3,122 | 1 | 48 | ,084 |
| ProVolume_L | ,835 | 1 | 48 | ,365 |
| Tests the null hypothesis that the error variance of the dependent variable is equal across groups. | ||||
| a. Design: Intercept + Age + Sex | ||||
Table 15
| One-Sample Kolmogorov-Smirnov Test | |||
| Studentized Residual for ProVolume_R | Studentized Residual for ProVolume_L | ||
| N | 50 | 50 | |
| Normal Parametersa,b | Mean | -,0013 | -,0005 |
| Std. Deviation | 1,01484 | 1,01313 | |
| Most Extreme Differences | Absolute | ,079 | ,095 |
| Positive | ,079 | ,095 | |
| Negative | -,049 | -,092 | |
| Test Statistic | ,079 | ,095 | |
| Asymp. Sig. (2-tailed) | ,200c,d | ,200c,d | |
| a. Test distribution is Normal. | |||
| b. Calculated from data. | |||
| c. Lilliefors Significance Correction. | |||
| d. This is a lower bound of the true significance. | |||
Table 16.
Furthermore, similar results were identified when the 93 subjects of rater A were examined. The right volume of the SCG was found to be (F(1,87)=4.31, p = 0.041 among men and F(1,87)=3.58, p =0.062 among women at the significance level of 90%. The effect of sex was identified in the Ref volumes, (F(1,34)=5.08, p = 0.031), in the right hemisphere. Moreover, both males and females Pro volumes were equal.
Conclusion
There is a relatively small variation in the volume of the SCG based on differences in sex. The p-value of the results identified in the analyses section above is rarely below 0.05. This aforementioned fact shows that there is evidence of the gender effect of the SCG volume.
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