1 / 33

Age and Gender Effects on Cortical Thickness in MDD Patients: A Graph Analysis

This study investigates the impact of age and gender on cortical thickness among Major Depressive Disorder (MDD) patients using graph analysis. We analyze 78 subjects (38 males, 38 females) aged 19-70 years. The aim is to understand subcortical structures' involvement in psychiatric disorders and their age trajectories. Our methodology includes data selection, processing, and extensive statistical analysis. Key findings relate to the correlation of cortical thickness and metabolite levels, enhancing knowledge of gender-specific aging effects in MDD.

wang-rosa
Télécharger la présentation

Age and Gender Effects on Cortical Thickness in MDD Patients: A Graph Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Work in Magdeburg Wenjing Li 2012-11-23

  2. Outline Age and gender effects Graph analysis on MDD patients Cortical thickness – MRS correlation

  3. Outline Age and gender effects Graph analysis on MDD patients Cortical thickness – MRS correlation

  4. Age and gender effects • Aim • To investigate the effects of age and gender on subcortical structures in healthy subjects • Why subcortical structures? • Previous studies have reported subcortical structures are involved in psychiatric disorders. • No studies had reported gender specificity of age effects on subcortical structures by then.

  5. How was this work done? • 2010.6 • Generation of the idea: gender differences of age trajectories on brain structures • 2010.7 – 2010.10 • Data selection • Data processing and analysis for age and gender effects on subcortical structures • 2010.11 – 2011.12 • First draft finished

  6. How was this work done? • 2011.1 – 2011.6 • Modifying and polishing • First submission to HBM in June • 2011.8 • Decision of the HBM: Reversible rejection • 2011.9 – 2012.4 • Reanalysis based on the reviews • Re-construct the manuscript • Resubmission to HBM in April.

  7. How this work was done? • 2012.5 • Decision of the HBM: major revision • 2012.5 – 2012.6 • Revision and resubmission • 2012.7 • Decision of the HBM: accepted

  8. First version of this paper Data: 78 subjects, including 38 males and 38 females, age range: 19~70 years

  9. First version of this paper

  10. Reviews for the first version • Reviewer 1: • Lots of tests – correction for multiple comparisons • Correction for TBV instead of ICV? • Small sample size • Reviewer 2: • Recommended to publish but with some minor problem.

  11. Revision • Correction for multiple comparisons? • Combine the left and right subcortical structures. • Adjusted Bonforronicorrection • Correction for TBV or ICV? • We redid the analysis using TBV as covariates. • Small sample size • Rebuttal from the statistics and results.

  12. Second version

  13. Second version

  14. Outline Age and gender effects Graph analysis on MDD patients Cortical thickness – MRS correlation

  15. Distance penalty Regions that are spatially close have higher correlation coefficients whereas more distinct regions correlate less strongly. CIJ = CIJ.*log(distmat).^2; % CIJ is modified by ln.^2 of the distance

  16. Global graph metrics

  17. Local graph metrics

  18. Outline Age and gender effects Graph analysis on MDD patients Cortical thickness – MRS correlation

  19. CTh – MRS correlation • Datasets: • 46 healthy controls • 20 MDD and 20 healthy controls • Processing: • Freesurfer • Measurement: • Cortical thickness • MRS: glx (glu+gln), naa and ins in pgACC, dACC and dlPFC

  20. Analysis • Local correlation: • Correlate cortical thickness in the MRS region itself with its corresponding MRS value. • Global correlation: • Correlate cortical thickness throughout the whole brain with the MRS values.

  21. Models (for 46 HC) • Raw model: • CTh ~ MRS • Corrected model by ICV • CTh ~ MRS + ICV • Corrected model by further adding age and gender • CTh ~ MRS + ICV + age + gender

  22. Models (for 20MDD&20HC) • Besides the models using for 46HC, we further add the “Group” to test for the group interaction. • Raw model: • CTh ~ Group + MRS • Corrected model by ICV • CTh ~ Group + MRS + ICV • Corrected model by further adding age and gender • CTh ~ Group + MRS + ICV + age + gender

  23. Other work Correlation between graph metrics and MRS values. Extract the mean fALFF values within the detected ROI, and then correlate it with MRS values. FFT analysis

  24. Thanks!

More Related