Work in Magdeburg

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

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

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

13. 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.

14. 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

15. 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.

16. 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

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

18. First version of this paper

19. 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.

20. 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.

21. Second version

22. Second version

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

24. 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

25. Global graph metrics

26. Local graph metrics

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

28. 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

29. 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.

30. 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

31. 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

32. 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

33. Thanks!