Dear Freesurfer Mailing List,
I wish to run a GLM in freesurfer with 3 categorical variables (haplotype-2levels, gender-2levels, group-2levels), and 1 continuous variables (age). Unfortunately, the MRI images I am using come from five different testing sites, so I also need to account for the potentially confounding effects of using different scanners. So my fsgd file like this:
GroupDescriptorFile 1 Title GLM_SiteGender Class siteBrisbane-numRiskNoRisk-genderMale-groupCase Class siteBrisbane-numRiskNoRisk-genderFemale-groupCase Class siteBrisbane-numRiskNoRisk-genderMale-groupControl Class siteBrisbane-numRiskNoRisk-genderFemale-groupControl Class siteBrisbane-numRiskanyRisk-genderMale-groupCase Class siteBrisbane-numRiskanyRisk-genderFemale-groupCase Class siteBrisbane-numRiskanyRisk-genderMale-groupControl Class siteBrisbane-numRiskanyRisk-genderFemale-groupControl Class siteMelbourne-numRiskNoRisk-genderMale-groupCase Class siteMelbourne-numRiskNoRisk-genderFemale-groupCase Class siteMelbourne-numRiskNoRisk-genderMale-groupControl Class siteMelbourne-numRiskNoRisk-genderFemale-groupControl Class siteMelbourne-numRiskanyRisk-genderMale-groupCase Class siteMelbourne-numRiskanyRisk-genderFemale-groupCase Class siteMelbourne-numRiskanyRisk-genderMale-groupControl Class siteMelbourne-numRiskanyRisk-genderFemale-groupControl Class siteNewcastle-numRiskNoRisk-genderMale-groupCase Class siteNewcastle-numRiskNoRisk-genderFemale-groupCase Class siteNewcastle-numRiskNoRisk-genderMale-groupControl Class siteNewcastle-numRiskNoRisk-genderFemale-groupControl Class siteNewcastle-numRiskanyRisk-genderMale-groupCase Class siteNewcastle-numRiskanyRisk-genderFemale-groupCase Class siteNewcastle-numRiskanyRisk-genderMale-groupControl Class siteNewcastle-numRiskanyRisk-genderFemale-groupControl Class sitePerth-numRiskNoRisk-genderMale-groupCase Class sitePerth-numRiskNoRisk-genderFemale-groupCase Class sitePerth-numRiskNoRisk-genderMale-groupControl Class sitePerth-numRiskNoRisk-genderFemale-groupControl Class sitePerth-numRiskanyRisk-genderMale-groupCase Class sitePerth-numRiskanyRisk-genderFemale-groupCase Class sitePerth-numRiskanyRisk-genderMale-groupControl Class sitePerth-numRiskanyRisk-genderFemale-groupControl Class siteSydney-numRiskNoRisk-genderMale-groupCase Class siteSydney-numRiskNoRisk-genderFemale-groupCase Class siteSydney-numRiskNoRisk-genderMale-groupControl Class siteSydney-numRiskNoRisk-genderFemale-groupControl Class siteSydney-numRiskanyRisk-genderMale-groupCase Class siteSydney-numRiskanyRisk-genderFemale-groupCase Class siteSydney-numRiskanyRisk-genderMale-groupControl Class siteSydney-numRiskanyRisk-genderFemale-groupControl Variables age Input 100105SA sitePerth-anyRiskAnyRisk-genderMale-groupCase 23 ...
As the number of categorical variables really complicates the specification of contrast vectors, I was wondering if you could confirm whether these vectors are correct:
Main effect for haplotype (Risk vs No Risk): 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Main effect for gender (Male vs Female): 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Main effect for group (Case vs Control): 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between gender & group: 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between gender & haplotype: 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between group & haplotype: 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between gender, group, & haplotype:
0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Kind regards,
Bronwyn Overs Research Assistant
Neuroscience Research Australia
Neuroscience Research Australia Margarete Ainsworth Building Barker Street Randwick Sydney NSW 2031 Australia *M* 0411 308 769 *T* +61 2 9399 1883 *F* +61 2 9399 1265
neura.edu.au http://neura.edu.au
Follow @neuraustralia on twitter https://twitter.com/neuraustraliaFollow NeuRA on facebook https://www.facebook.com/NeuroscienceResearchAustraliaSubscribe to the NeuRA Magazine http://www.neura.edu.au/help-research/subscribe
As far as I can tell they look right to me. Good job!
On 10/24/2014 01:23 AM, Bronwyn Overs wrote:
Dear Freesurfer Mailing List,
I wish to run a GLM in freesurfer with 3 categorical variables (haplotype-2levels, gender-2levels, group-2levels), and 1 continuous variables (age). Unfortunately, the MRI images I am using come from five different testing sites, so I also need to account for the potentially confounding effects of using different scanners. So my fsgd file like this:
GroupDescriptorFile 1 Title GLM_SiteGender Class siteBrisbane-numRiskNoRisk-genderMale-groupCase Class siteBrisbane-numRiskNoRisk-genderFemale-groupCase Class siteBrisbane-numRiskNoRisk-genderMale-groupControl Class siteBrisbane-numRiskNoRisk-genderFemale-groupControl Class siteBrisbane-numRiskanyRisk-genderMale-groupCase Class siteBrisbane-numRiskanyRisk-genderFemale-groupCase Class siteBrisbane-numRiskanyRisk-genderMale-groupControl Class siteBrisbane-numRiskanyRisk-genderFemale-groupControl Class siteMelbourne-numRiskNoRisk-genderMale-groupCase Class siteMelbourne-numRiskNoRisk-genderFemale-groupCase Class siteMelbourne-numRiskNoRisk-genderMale-groupControl Class siteMelbourne-numRiskNoRisk-genderFemale-groupControl Class siteMelbourne-numRiskanyRisk-genderMale-groupCase Class siteMelbourne-numRiskanyRisk-genderFemale-groupCase Class siteMelbourne-numRiskanyRisk-genderMale-groupControl Class siteMelbourne-numRiskanyRisk-genderFemale-groupControl Class siteNewcastle-numRiskNoRisk-genderMale-groupCase Class siteNewcastle-numRiskNoRisk-genderFemale-groupCase Class siteNewcastle-numRiskNoRisk-genderMale-groupControl Class siteNewcastle-numRiskNoRisk-genderFemale-groupControl Class siteNewcastle-numRiskanyRisk-genderMale-groupCase Class siteNewcastle-numRiskanyRisk-genderFemale-groupCase Class siteNewcastle-numRiskanyRisk-genderMale-groupControl Class siteNewcastle-numRiskanyRisk-genderFemale-groupControl Class sitePerth-numRiskNoRisk-genderMale-groupCase Class sitePerth-numRiskNoRisk-genderFemale-groupCase Class sitePerth-numRiskNoRisk-genderMale-groupControl Class sitePerth-numRiskNoRisk-genderFemale-groupControl Class sitePerth-numRiskanyRisk-genderMale-groupCase Class sitePerth-numRiskanyRisk-genderFemale-groupCase Class sitePerth-numRiskanyRisk-genderMale-groupControl Class sitePerth-numRiskanyRisk-genderFemale-groupControl Class siteSydney-numRiskNoRisk-genderMale-groupCase Class siteSydney-numRiskNoRisk-genderFemale-groupCase Class siteSydney-numRiskNoRisk-genderMale-groupControl Class siteSydney-numRiskNoRisk-genderFemale-groupControl Class siteSydney-numRiskanyRisk-genderMale-groupCase Class siteSydney-numRiskanyRisk-genderFemale-groupCase Class siteSydney-numRiskanyRisk-genderMale-groupControl Class siteSydney-numRiskanyRisk-genderFemale-groupControl Variables age Input 100105SA sitePerth-anyRiskAnyRisk-genderMale-groupCase 23 ...
As the number of categorical variables really complicates the specification of contrast vectors, I was wondering if you could confirm whether these vectors are correct:
Main effect for haplotype (Risk vs No Risk): 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Main effect for gender (Male vs Female): 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Main effect for group (Case vs Control): 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between gender & group: 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0.5 -0.5 -0.5 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between gender & haplotype: 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between group & haplotype: 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 0.5 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Interaction between gender, group, & haplotype:
0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Kind regards,
Bronwyn Overs Research Assistant
Neuroscience Research Australia
Neuroscience Research Australia Margarete Ainsworth Building Barker Street Randwick Sydney NSW 2031 Australia *M* 0411 308 769 *T* +61 2 9399 1883 *F* +61 2 9399 1265
neura.edu.au http://neura.edu.au
Follow @neuraustralia on twitter https://twitter.com/neuraustraliaFollow NeuRA on facebook https://www.facebook.com/NeuroscienceResearchAustraliaSubscribe to the NeuRA Magazine http://www.neura.edu.au/help-research/subscribe
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Bronwyn Overs -
Douglas N Greve