work in magdeburg
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Work in Magdeburg. Wenjing Li 2012-11-23. Outline. Age and gender effects Graph analysis on MDD patients Cortical thickness – MRS correlation. Outline. Age and gender effects Graph analysis on MDD patients Cortical thickness – MRS correlation. Age and gender effects. Aim - PowerPoint PPT PresentationTRANSCRIPT
Work in Magdeburg
Wenjing Li2012-11-23
Outline
Age and gender effects
Graph analysis on MDD patients
Cortical thickness – MRS correlation
Outline
Age and gender effects
Graph analysis on MDD patients
Cortical thickness – MRS correlation
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.
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
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.
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
First version of this paper
Data: 78 subjects, including 38 males and 38 females, age range: 19~70 years
First version of this paper
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.
Revision
Correction for multiple comparisons? Combine the left and right subcortical
structures. Adjusted Bonforroni correction
Correction for TBV or ICV? We redid the analysis using TBV as
covariates. Small sample size
Rebuttal from the statistics and results.
Second version Gender Age Age*Gender
r p r p F p
Absolute Volumes
Caudate -.358 .002 -.278 .015 1.287 .260
Putamen -.438 <.001 -.430 <.001 .009 .925
Pallidum -.491 <.001 -.335 .003 .055 .815
Thalamus -.464 <.001 -.339 .003 1.786 .186
Hippocampus -.246 .032* -.181 .118 7.167 .009
Amygdala -.599 <.001 -.130 .262 .382 .539
Relative Volumes
Caudate .069 .551 -.175 .130 6.019 .017*
Putamen .098 .399 -.364 .001 2.464 .121
Pallidum -.037 .753 -.271 .018* 3.613 .061
Thalamus .089 .444 -.272 .018* .006 .937
Hippocampus .459 <.001 .052 .655 3.381 .070
Amygdala -.132 .257 .065 .578 .720 .399
Females Males
r square r p r square r p
Absolute Volumes
Caudate .218 -.467 .003 .029 -.171 .305
Putamen .309 -.556 <.001 .233 -.483 .002
Pallidum .248 -.498 .001 .132 -.363 .025*
Thalamus .115 -.340 .037* .276 -.525 <.001
Hippocampus .003 .057 .736 .205 -.453 .004
Amygdala .041 -.203 .223 .064 -.252 .127
Relative Volume
Caudate .169 -.411 .010* .021 .145 .384
Putamen .257 -.507 .001 .026 -.161 .333
Pallidum .243 -.493 .002 .001 -.029 .864
Thalamus .106 -.326 .046* .047 -.217 .191
Hippocampus .083 .289 .078 .020 -.140 .402
Amygdala .001 -.031 .855 .029 .169 .309
Females Males
Model r square p Model r square p
Absolute Volumes
Caudate Linear .218 .003 - - -
Putamen Linear .309 <.001 Quadratic .333 .001
Pallidum Linear .248 .001 Quadratic .347 .009
Thalamus Linear .115 .037* Quadratic .373 <.001
Hippocampus Quadratic .148 .060 Quadratic .321 <.001
Amygdala Quadratic .137 .075 Quadratic .235 .009
Relative Volume
Caudate Linear .169 .010* - - -
Putamen Linear .257 .001 - - -
Pallidum Linear .243 .002 - - -
Thalamus Linear .106 .046* - - -
Hippocampus Linear .083 .078 - - -
Amygdala - - - - - -
Second version
Outline
Age and gender effects
Graph analysis on MDD patients
Cortical thickness – MRS correlation
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
Global graph metrics
Local graph metrics
Index Regions
Rank Wilcoxon All regions rank
HC MDD P HC MDD
Betweenness centrality
SupraMarginal_R 49.82±20.26 69.52±15.96 0.0021 55 84
Supp_Motor_Area_R 45.73±21.39 26.62±15.77 0.0029 43 14
Degree Ant_Insula_L 19.64±15.58 39.10±21.54 0.0018 4 34
Strength Ant_Insula_L 14.23±14.96 35.67±22.79 <0.001 1 27
Outline
Age and gender effects
Graph analysis on MDD patients
Cortical thickness – MRS correlation
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
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.
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
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
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
THANKS!