I calculated the intra-network and inter-network signatures in order to look for differences between HIV+ well-controlled, HIV + uncontrolled, and HIV- individuals.

To cohort match, I pulled a few ADRC participants to help match ages better - the controls were originally way younger than the HIV+ individuals.

Here are the compositions of the signatures:

I did not correct resting state values for age/sex for this analysis, since I could figure out how to include them as covariates. Instead, I did a multinomial regression to see if network signature, age, and sex could predict which category a participant would fall in (HIV negative control, HIV + and well controlled, HIV + and poorly controlled.)

## # weights:  15 (8 variable)
## initial  value 898.664852 
## iter  10 value 798.073741
## final  value 797.238119 
## converged
## Call:
## multinom(formula = status ~ Inter_Signature + participant_age + 
##     gender_check, data = df.now)
## 
## Coefficients:
##           (Intercept) Inter_Signature participant_age gender_check1
## Pos_UnCon   0.2626102     0.005834297    -0.029658796    -0.7566817
## Pos_Con    -0.3762992     0.059197925     0.003726935    -0.1418383
## 
## Std. Errors:
##           (Intercept) Inter_Signature participant_age gender_check1
## Pos_UnCon   0.3198907      0.02926576     0.006769053     0.2674785
## Pos_Con     0.2652176      0.02184857     0.005037259     0.1763387
## 
## Residual Deviance: 1594.476 
## AIC: 1610.476

## [1] "Exponentiated Model Coefficients"
##           (Intercept) Inter_Signature participant_age gender_check1
## Pos_UnCon   1.3003197        1.005851       0.9707767     0.4692209
## Pos_Con     0.6863969        1.060985       1.0037339     0.8677615
## [1] "Model Significance Values (p)"
##           (Intercept) Inter_Signature participant_age gender_check1
## Pos_UnCon   0.4116820     0.841984523    1.178498e-05   0.004670195
## Pos_Con     0.1559481     0.006739203    4.593768e-01   0.421193826
## # weights:  15 (8 variable)
## initial  value 898.664852 
## iter  10 value 797.312276
## final  value 796.666341 
## converged
## Call:
## multinom(formula = status ~ Intra_Signature + participant_age + 
##     gender_check, data = df.now)
## 
## Coefficients:
##           (Intercept) Intra_Signature participant_age gender_check1
## Pos_UnCon   0.5188570       0.1018922    -0.034532448    -0.8575605
## Pos_Con    -0.1072598       0.1413068    -0.001519106    -0.2250300
## 
## Std. Errors:
##           (Intercept) Intra_Signature participant_age gender_check1
## Pos_UnCon   0.3608223      0.06753548     0.007537798     0.2747295
## Pos_Con     0.2920064      0.04827660     0.005559400     0.1831043
## 
## Residual Deviance: 1593.333 
## AIC: 1609.333

## [1] "Exponentiated Model Coefficients"
##           (Intercept) Intra_Signature participant_age gender_check1
## Pos_UnCon   1.6801061        1.107264        0.966057     0.4241957
## Pos_Con     0.8982923        1.151778        0.998482     0.7984922
## [1] "Model Significance Values (p)"
##           (Intercept) Intra_Signature participant_age gender_check1
## Pos_UnCon   0.1504383     0.131369989    4.622311e-06   0.001799495
## Pos_Con     0.7133804     0.003422212    7.846611e-01   0.219082335

Here are the demographics:

##                              Stratified by Group
##                               Control        UnControlled   
##   n                             391             117         
##   Group (%)                                                 
##      Control                    391 (100.0)       0 (  0.0) 
##      UnControlled                 0 (  0.0)     117 (100.0) 
##      WellControlled               0 (  0.0)       0 (  0.0) 
##   participant_age (mean (sd)) 46.54 (16.64)   39.80 (16.76) 
##   gender_check = 1 (%)          115 ( 29.4)      23 ( 19.7) 
##   nadir_cd4 (mean (sd))         NaN (NA)     245.58 (204.90)
##   MathSig (mean (sd))         87.88 (37.73)   97.89 (37.83) 
##                              Stratified by Group
##                               WellControlled  p      test
##   n                              310                     
##   Group (%)                                   <0.001     
##      Control                       0 (  0.0)             
##      UnControlled                  0 (  0.0)             
##      WellControlled              310 (100.0)             
##   participant_age (mean (sd))  48.03 (14.33)  <0.001     
##   gender_check = 1 (%)            85 ( 27.4)   0.115     
##   nadir_cd4 (mean (sd))       230.31 (206.76)  0.544     
##   MathSig (mean (sd))          92.05 (50.38)   0.073