Sex Differences in the Subcallosal Cingulate Gyrus (SCG)

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.


Research Questions

  1. What are the gender variations in the anatomical characteristics of the SCG in the context of MDD?
  2. What are the gender differences in the volumes of the white matter of the SCG?


  1. There are no variations in the white matter characteristics of the SCG.
  • There are gender differences in the white matter characteristics of the SCG.
  1. 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


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.



            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.


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.


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.


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.


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

  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.


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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Computer science is a tough subject. Fortunately, our computer science experts are up to the match. No need to stress and have sleepless nights. Our academic writers will tackle all your computer science assignments and deliver them on time. Let us handle all your python, java, ruby, JavaScript, php , C+ assignments!


While psychology may be an interesting subject, you may lack sufficient time to handle your assignments. Don’t despair; by using our academic writing service, you can be assured of perfect grades. Moreover, your grades will be consistent.


Engineering is quite a demanding subject. Students face a lot of pressure and barely have enough time to do what they love to do. Our academic writing service got you covered! Our engineering specialists follow the paper instructions and ensure timely delivery of the paper.


In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.


Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.


We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!


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Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.

What discipline/subjects do you deal in?

We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.

Are your writers competent enough to handle my paper?

Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

What if I don’t like the paper?

There is a very low likelihood that you won’t like the paper.

Reasons being:

  • When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
  • We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

In the event that you don’t like your paper:

  • The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
  • We will have a different writer write the paper from scratch.
  • Last resort, if the above does not work, we will refund your money.

Will the professor find out I didn’t write the paper myself?

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

What if the paper is plagiarized?

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

When will I get my paper?

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment  Help Service Works

1.      Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2.      Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3.      Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4.      Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

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