Possible sex differences in the anatomical characteristics of the Subcallosal Gyrus

The following is what I already have written. Please include or transform the following in the masterthesis paper because I already used this information to get my Proposal approved.

Possible sex differences in the anatomical characteristics of the SCG

Introduction

(Introductory sentences / social relevance)

Major depressive disorder (MDD) is a chronic disorder associated to reduced well-being and functioning (Hays, Wells, Sherbourne, Rogers, & Spritzer, 1995; Wells et al., 1989), with an average lifetime prevalence of 14.6% in high-income countries and 11.1% in low- to middle income countries (Bromet et al., 2011). According to the World Health Organization (2017) MDD is the leading cause of disability worldwide.

(Problem area – previous research)

A large group of individuals with MDD have Treatment Resistant Depression (TRD), although there is no consensus on the definition of TRD, there is an consensus on the failure to react to at least two dissimilar antidepressants (McIntyre et al., 2014). Recent methods of treatment offer prospect to treating TRD (Dandekar, Fenoy, Carvalho, Soares, & Quevedo, 2018). One of the most promising advances is diminishing overactivity in the Subcallosal Cingulate Gyrus (SCG) including Brodmann area 25 (BA25) with chronic high-frequency Deep Brain Stimulation (DBS) (Mayberg et al., 2005) which is associated to TRD. Despite these findings more recent research (Blumberger, Mulsant, & Daskalakis, 2013; Holtzheimer et al., 2017) shows inconsistent conclusions.

(Problem area – Relevant theory)

An solution to more optimal DBS treatment of TRD could lie in the localization of electrode contacts in SCG DBS. Hamani et al. (2009) compared electrode contacts of patients who responded to DBS and suggested a standardized method to targeting the SCG. Nevertheless the exact location and shape of the SCG is as unique as a fingerprint and therefore a challenging area for parcellating the white matter (WM) of the SCG (McCormick et al., 2006). Despite the crucial role of the anatomical characteristics of the SCG for targeting and therefore adequate DBS treatment of TRD. The literature on this topic is minuscule. Nevertheless, research on the neuroanatomy of the brain suggests sexual dimorphism by demonstrating greater concentration of white matter (WM) in men compared to women (Lemaître et al., 2005). Experiments like this arise questions as, should atlas-based maps (Cabezas, Oliver, Lladó, Freixenet, & Bach Cuadra, 2011) used to localize brain structures as the SCG in DBS treatment for MDD be adjusted to gender? Another argument is that targeting approaches for the SCG should differ between men and women because of the well-studied gender differences of MDD (Marcus et al., 2005). Furthermore, research showed volumetric reduction of the SCG in women with early-onset MDD (Botteron, Raichle, Drevets, Heath, & Todd, 2002). Although the above described findings altogether suggest the possibility in gender differences of the SCG in MDD and TRD patients

(Explanation of terms)

To do: When the thesis is finished. Check if the necessary terminology is already introduced and please add terminology I did not add yet.

(Scientific relevance (justification of the question))

To date no research has been done on gender differences of the SCG in the context of MDD. Due to the importance of the anatomical characteristics of the SCG in targeting with DBS, novel research to explore possible differences in WM for the SCG between female and male is suggested.

(Questioning)

What are the gender differences in the anatomical characteristics of the SCG in the context of MDD?

(Structure of the literature / announcement subsections)

To do: announce the subsections when thesis is finished.

Method

(introductory paragraph (Construct level))

To do: Write a brief introduction paragraph on construct level.

(Description of the research with preliminary of context (Construct level))

To do: please write an preliminary of the context. Include in this the following key questions and hypotheses.

• What are the gender differences in the anatomical characteristics of the SCG in the context of MDD?(theoretical, you could also do this in the introduction and skip it in the method section)
• What are the gender differences in the white matter volumes of the SCG? (operational)
• H0. There are no gender differences in the white matter characteristics of the SCG.
• H1. There are gender differences in the white matter characteristics of the SCG.
• H0. There is no significant effect for gender in the volume of the SCG
• H1. There is a significant effect for gender in the volume of the SCG

Participants

An existing pool of data consisting of ultra-high resolution in-vivo anatomical imaging from 7T MRI will be used. The data were collected between 2016 and 2018 and stored by Alkemade et al., (unpublished data). This pool of data consists out of 107 healthy subjects with a mean age of 42.30 (SD=19.26). The minimum age is 19 and the maximum is 80. Of the participants, 56.1% is female and 43.9% is male.

Materials

The rater had to manually parcellate the SCG on the selected 93 subjects. After the WB MR scans are loaded in to FSLeyes. The rater had to determine the brightness and contrast values for the best subjective visibility separate for each scan. This will be done on a Sony KD-55XD8505 ultra high definition screen with a resolution of 3840×2160 pixels. Afterwards, the rater has to move his crosshair to the midsagittal position to locate the most rostral part of the corpus callosum. This part is used to landmark the first vertical plane (the anterior ACC). The second plane is determined by the first coronal slice at which the putamen is seen within the basal ganglia. From here on the rater has to track and parcellate the WM SCG. For an extensive description of the parcellation method of the SCG see McCormick et al. (2006).

Design

To asses if there are gender differences for the SCG manual parcellations according to McCormick et al. (2006) will be done for the left and right hemisphere for 93 subjects. The remaining 14 subjects were excluded for parcellations because the SCG of these participants was already parcellated by others.

In the single datafile you can recognize the 93 subjects done by myself by my initials YTT in the name of the datafile: example sub-001_ses-1_acq-wb_mod-t1map_mask-scg_hem-l_rat-ytt_calc-bin_out.nii.gz

The remaining 14 subjects were excluded for my part of parcellations but can be included for a second analysis by adding the 14 single others, recognizable by the initials of my supervisor AAX:

example sub-002_ses-1_acq-wb_mod-t1map_mask-scg_hem-l_rat-aax_calc-bin_out.nii.gz

I already had did some analyses concerning the outliers but lost the files unfortunately and I can already report that if I wrote it down correctly, in the single datafile for the Left Hemisphere the volumes for scanid-066 and scanid-095 are outliers and should be removed cause they are human errors made by me (did not save these files correctly). For the right hemisphere sub-038 information is missing so this side should also be excluded. Furthermore, for the right hemisphere the following participants scanid-065, scanid-086 & scanid-014 should also be outliers but can be included in the analysis because they are not human errors, their subcallosal gyrus just happens to be big. But please do the analyses again to see if you get the same outliers or different ones? Let me know ASAP if there are any other outliers so I check the files immediately in FSLeyes.

Very important! These are the analyses I want to have done. Please with tables.

1. Are there gender difference in the volumes of the SCG in the 93 subjects done by me YTT (in the filename). So, every scanid in the single file except the following 002, 007, 012, 017, 028, 031, 035, 043, 044, 047, 055, 059, 065, 092 if I’m correct.
2. Are there gender differences in the volumes of the SCG in the 107 subjects done by me (YTT) and AAX).

The above analyses are quite simple and you can find the data in the file named ‘single’. The following analyses are conjunctions. You can find them in the datafiles named ‘conjunctions’.

• Is there a gender difference if we make an mean for every hemisphere for every subject (Segvolume+Refvolume = outcome, outcome divided by 2 for every participant/hemisphere). So, we can say we made the sample more trustworthy by adding multiple raters in to the analysis. Afterwards please analyse if there is an effect for gender.

I will try to explain what this file consists of but I don’t know the exact details to be honest cause my supervisor made this rapport for me:

1. Segvolume: This is the ‘primary’ volume. I don’t how to explain this and don’t know how we should explain this in this masterthesis but hopefully you can figure it out. The parameter for Segvolume is very simple. If rater AAX did the segmentation include this segmentation in Segvolume if AAX did not do the segmentation, use the segmentation of YTT. The thought behind this is that AAX (my supervisor) has experience in parcellations of the SCG then me, therefore here parcellation is probably of higher quality and if possible, should always be included if hers is not available I am so to speak the second preferable rater cause it’s my research.
2. Refvolume: After the segmented volumes were determined we need another segmentation of the SCG to make conjunctions. For this variable the parameter is also simple. The code first checked if there is a segmentation available from me as a rater (YTT) if this was not available a third rater (also a student as me) came in to play with the initials VHX. So when Segvolume was done by AAX the chosen Refvolume was done by YTT. But if YTT already was chosen for the afore mentioned Segvolume different rater should be choosed and this was a third rater (VHX) his segmentations were used as Refvolume to make conjuctions with. This was done to calculate the following variables.
3. Volume difference. I really don’t understand this variable and don’t know which analyse she used herefore. For example: Sub-002 has the following Segvolumes and Refvolumes.

SegVolume,sub-002_ses-1_acq-wb_mod-t1map_mask-scg_hem-r_rat-aax_calc-bin_out.nii.gz,-,-,165.9548

RefVolume,-,sub-002_ses-1_acq-wb_mod-t1map_mask-scg_hem-r_rat-vhx_calc-bin_out.nii.gz,-,181.77373

=

Volume_difference,sub-002_ses-1_acq-wb_mod-t1map_mask-scg_hem-r_rat-aax_calc-bin_out.nii.gz,sub-002_ses-1_acq-wb_mod-t1map_mask-scg_hem-r_rat-vhx_calc-bin_out.nii.gz,-,0.087025344

I don’t know which analyse type she used here to come to the following volume difference. Maybe for now we should ignore this part unless you seem to know which analyse type she used? Also I don’t know how this should give any information about my key questions?

• Dice overlap. This one is very important! This analyse tells how good inter-rater agreement is. How higher the overlap how better the segmentations of AAX VS. YTT or YTT VS. VHX how better the inter-rater agreement. If you want more info about the dice score check:

It’s important that you analyse the Mean and SD of the dice score and write something about it. For example see this artice (full pdf) https://www.ncbi.nlm.nih.gov/pubmed/24650599 they write the following: The mean Dice coefficient ranged between 0.72 and 0.89, and the ICC ranged between 0.72 and 0.87 across structures, indicating that all probabilistic maps have excellent inter-rater agreement and are of similar quality compared to other segmentation protocols (Aljabar et al., 2009; Babalola et al., 2009; Fleiss, 1981;see Table 1).  I only investigated one small part named the subcallosal gyrus, see if you can find an article of manual segmentations of brain structures that are also small such as the subcallosal gyrus and what the overlap is there so we can cite this article and compare it to our dice score and use it as argumentation that our dice overlap is good, because my supervisor already told me that we scored good on our dice overlap because it’s a smaller brain structure.

• Average surface distance

What I found about this:

What I understand of this is that the average surface distance is the Mean Error between de distance of the contours of the mask (segmentation of the SCG) and the voxels. So how lower the value of this error how better the inter-rater agreement. Please also do an analyse on the mean of this and find an useful article of this and cite this while telling something about the mean of this error.

6.Possible outcomes could also be explained by another factor, namely age. Please also check this for me.

Procedure

The data for this research are already collected because they are part of a bigger research. In this bigger research, 107 whole brain magnetic resonance images where collected in ultra-high 7T MRI equipment. To access these data, the rater was granted access from the University of Amsterdam to the protected Tux11 server via X2go client software (X2go, 2019). After randomization and selection of 93 subjects, the rater had to manually parcellate the SCG for these subjects as described by McCormick et al. (2006). Manual segmentation will be performed using FSLeyes version: 0.27.3 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes/).

Instructions for analyses:

WM volumes of the SCG will be the continues variable. Due to potential differences between the WM volumes of men and women, the effect of gender will be assessed.  

When the 93 manual parcellations for the subjects are done it is of importance to see if data are normally distributed and if there is homogeneity of variances. This will be analysed by a histogram and a residual plot. The results of the Kolmogorov Smirnov and the Shapiro-Wilk will be used to assess normality. If these assumptions hold, an independent samples t-test will be performed. If not, a non-parametric test such as the Mann-Whitney test will be executed or the data will be transformed, when appropriate. For example, with a log transformation depending on the skewness of the data.

In addition, it is of importance to asses if there are significant differences in the volumes of the SCG between the LH and RH. For this a paired t-test will be used. If there is no difference between the SCG of the LH and RH the data will be collapsed to form data of one individual.

Based on the results of the above described analyses, it will be determined how the dataset will be treated. If there are significant differences between de LH and RH, the datasets for each hemisphere will be analysed separately of each other. If the datasets of the LH and RH do not differ, the datasets can be treated as one dataset.

Furthermore, because of the possibility of differences in age between male and female this will also be assessed with an independents t-test. Afterwards, if the necessary assumptions hold, a regression model will be constructed containing age and gender as explanatory variables or a stratified analysis on age groups will be performed.

Results

To do: Please describe here the results on operational level.

You can read my intended results which I wrote for the proposal under this:

The results of the intended research will provide the first suggestions in possible differences in anatomical characteristics of the SCG between gender. If results suggest differences in the WM of the SCG, the approach in treating MDD and TRD by DBS should be reconsidered. Perhaps an altered approach in targeting the SCG should be considered, specifically and independently for males and females. If the results do not suggest differences in the WM of the SCG an alternated approach should not be suggested yet. Nevertheless, the results of the intended research are to date expected to be novel and therefore not conclusive. Therefore, there are no predictions made for the outcome of the results. This research hopes to show the first insights in sex differences in the SCG. Furthermore, the sample of subjects used in this research consists of healthy subjects, thus either way the results of this study will conclude, generalisation to MDD or TRD patients should be treated cautious. Further research on the anatomical characteristics of the SCG of subjects with MDD and TRD is suggested independent of the results of this explorative research

The analyses I talked about in the method section can be answered in this part.

lead-out context (Construct level)

To do: Write an lead-out

(Transition between study descriptions)

To do: write an transition between study descriptions

Conclusion section (Construct level)

Write a conclusion on construct level

Conclusions and Discussion

To do: Try to elaborate here the conclusions and discussions and also the limitations of this research.

Answer the question (conclusion)

To do: Answer the following question: What are the gender differences in the anatomical characteristics of the SCG in the context of MDD?

Substantiate the answer that is given above

To do: Elaborate the answer that is given.

Discussion points + Suggestions follow-up study

To do: Write discussion points.

Closing

To do: Write a closing

Abstract

To do: Please write an abstract when you are finished conforming the APA guidelines.

Used References

Blumberger, D. M., Mulsant, B. H., & Daskalakis, Z. J. (2013). What Is the Role of Brain Stimulation Therapies in the Treatment of Depression? Current Psychiatry Reports, 15(7), 368. https://doi.org/10.1007/s11920-013-0368-1

Botteron, K. N., Raichle, M. E., Drevets, W. C., Heath, A. C., & Todd, R. D. (2002). Volumetric reduction in left subgenual prefrontal cortex in early onset depression. Biological Psychiatry, 51(4), 342–344. https://doi.org/https://doi.org/10.1016/S0006-3223(01)01280-X

Bromet, E., Andrade, L. H., Hwang, I., Sampson, N. A., Alonso, J., de Girolamo, G., … Kessler, R. C. (2011). Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine. https://doi.org/10.1186/1741-7015-9-90

Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., & Bach Cuadra, M. (2011). A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 104(3), e158–e177. https://doi.org/10.1016/J.CMPB.2011.07.015

Dandekar, M. P., Fenoy, A. J., Carvalho, A. F., Soares, J. C., & Quevedo, J. (2018). Deep brain stimulation for treatment-resistant depression: an integrative review of preclinical and clinical findings and translational implications. Molecular Psychiatry, 23, 1094. Retrieved from https://doi.org/10.1038/mp.2018.2

Hamani, C., Mayberg, H., Snyder, B., Giacobbe, P., Kennedy, S., & Lozano, A. M. (2009). Deep brain stimulation of the subcallosal cingulate gyrus for depression: anatomical location of active contacts in clinical responders and a suggested guideline for targeting. Journal of Neurosurgery JNS, 111(6), 1209–1215. https://doi.org/10.3171/2008.10.JNS08763

Hays, R. D., Wells, K. B., Sherbourne, C. D., Rogers, W., & Spritzer, K. (1995). Functioning and Well-being Outcomes of Patients With Depression Compared With Chronic General Medical Illnesses. JAMA Psychiatry, 52(1), 11–19. https://doi.org/10.1001/archpsyc.1995.03950130011002

Holtzheimer, P. E., Husain, M. M., Lisanby, S. H., Taylor, S. F., Whitworth, L. A., McClintock, S., … Mayberg, H. S. (2017). Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. The Lancet Psychiatry, 4(11), 839–849. https://doi.org/10.1016/S2215-0366(17)30371-1

Lemaître, H., Crivello, F., Grassiot, B., Alpérovitch, A., Tzourio, C., & Mazoyer, B. (2005). Age- and sex-related effects on the neuroanatomy of healthy elderly. NeuroImage, 26(3), 900–911. https://doi.org/https://doi.org/10.1016/j.neuroimage.2005.02.042

Marcus, S. M., Young, E. A., Kerber, K. B., Kornstein, S., Farabaugh, A. H., Mitchell, J., … Rush, A. J. (2005). Gender differences in depression: Findings from the STAR*D study. Journal of Affective Disorders, 87(2), 141–150. https://doi.org/https://doi.org/10.1016/j.jad.2004.09.008

Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., … Kennedy, S. H. (2005). Deep Brain Stimulation for Treatment-Resistant Depression. Neuron, 45(5), 651–660. https://doi.org/10.1016/J.NEURON.2005.02.014

McCormick, L. M., Ziebell, S., Nopoulos, P., Cassell, M., Andreasen, N. C., & Brumm, M. (2006). Anterior cingulate cortex: An MRI-based parcellation method. NeuroImage, 32(3), 1167–1175. https://doi.org/10.1016/J.NEUROIMAGE.2006.04.227

McIntyre, R. S., Filteau, M.-J., Martin, L., Patry, S., Carvalho, A., Cha, D. S., … Miguelez, M. (2014). Treatment-resistant depression: Definitions, review of the evidence, and algorithmic approach. Journal of Affective Disorders, 156, 1–7. https://doi.org/10.1016/J.JAD.2013.10.043

Wells, K. B., Stewart, A., Hays, R. D., Burnam, M. A., Rogers, W., Daniels, M., … Ware, J. (1989). The functioning and well-being of depressed patients: results from the Medical Outcomes Study. Jama, 262(7), 914–919.

World Health Organization. (2017). Depression and Other Common Mental Disorders. Cc By-Nc-Sa 3.0 Igo. https://doi.org/CC BY-NC-SA 3.0 IGO

X2go (2019). about:start [X2Go – everywhere@home]. [online] Available at:

https://wiki.x2go.org/doku.php/about:start [Accessed 18 Jul. 2019].

The following here under is the evaluation form the supervisor is going to use

APPENDIX 4b

   Department of Psychology, University of Amsterdam   

Evaluation form Masterthesis

2nd assessor

Title Masterthesis: …………………………………..

Name student: …………………………………..

Student Number: …………………………………..

Name supervisor: …………………………………..

Name 2nd assessor: …………………………………..

Solution:

Looking for help with your homework?
Grab a 30% Discount and Get your paper done!

30% OFF

Turnitin Report

Formatting

Title Page

Citation

Place an Order