Group differences
- Everyone (n=97)
- Only valid SNT participants (n=73)
SNT relationships with hippocampal volume
- Normalized volume (percent)
- Asymmetry
JUNK - ignore

Group differences

Everyone (n=97)

# long dataset
df_long <-
  df %>% select(
    record_id,
    group.use,
    Sex,
    Age,
    tot_ctq,
    tot_tleq_nonW,
    exposures_count,
    ptsd,
    (contains("_pc") |
       contains("_Asym") |
       contains("_cm3")) &
      !contains("scid")
  ) #scid pcp shouldn't be included

df_long <- df_long %>%
  tidyr::pivot_longer(
    cols = -c(
      record_id,
      group.use,
      Sex,
      Age,
      tot_ctq,
      tot_tleq_nonW,
      exposures_count,
      ptsd,
      ICV_cm3
    ),
    names_to = "region",
    values_to = "value"
  )
df_long[12:14] <-
  str_split_fixed(df_long$region, "_", 3) # split column into 3 columns, by underscore
df_long <-
  df_long %>% select(!region) %>% rename(region = V1,
                                         lat = V2,
                                         measure = V3) # which subfield, which hemisphere, which metric (cm3, percent, asymmetry index)
df_long1 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "cm3" &
                       !region == "Hippocampus") # don't include total hippocampus
df_long2 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "pc" & !region == "Hippocampus") %>% group_by(region) %>%
  mutate(z_value = scale(value))
df_long3 <-
  df_long %>% filter(lat == "asymmetry" & !region == "Hippocampus")

MODEL 1: Volume in cm^3 controlling for total ICV

For pairwise comparisons: P value threshold, corrected for multiple comparisons = .05/(5 regions x 2 hemispheres) = .005

# MODEL 1: Volume in cm^3 controlling for total ICV
m1 <-
  aov_ez(
    id = "record_id",
    dv = "value",
    df_long1,
    between = c("group.use"),
    within = c("region", "lat"),
    covariate = c("ICV_cm3"),
    factorize = FALSE
  )

Warning: Numerical variables NOT centered on 0 (matters if variable in interaction):
   ICV_cm3
Contrasts set to contr.sum for the following variables: group.use

nice(m1)

Anova Table (Type 3 tests)

Response: value
                 Effect           df  MSE         F   ges p.value
1             group.use        2, 93 0.03      0.73  .007    .486
2               ICV_cm3        1, 93 0.03 38.45 ***  .163   <.001
3                region 2.33, 217.00 0.01    3.88 *  .016    .017
4      group.use:region 4.67, 217.00 0.01      0.39  .003    .843
5        ICV_cm3:region 2.33, 217.00 0.01  8.67 ***  .036   <.001
6                   lat        1, 93 0.00      0.01 <.001    .924
7         group.use:lat        2, 93 0.00    2.37 +  .002    .100
8           ICV_cm3:lat        1, 93 0.00      0.01 <.001    .918
9            region:lat 2.98, 277.30 0.00      0.82 <.001    .483
10 group.use:region:lat 5.96, 277.30 0.00    2.26 *  .004    .039
11   ICV_cm3:region:lat 2.98, 277.30 0.00      1.24  .001    .295
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG

emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region)

$emmeans
region = CA1:
 lat   emmean      SE df lower.CL upper.CL
 right  0.884 0.01102 93    0.862    0.906
 left   0.868 0.01088 93    0.847    0.890

region = CA2.CA3:
 lat   emmean      SE df lower.CL upper.CL
 right  0.205 0.00372 93    0.198    0.213
 left   0.175 0.00361 93    0.168    0.182

region = CA4.DG:
 lat   emmean      SE df lower.CL upper.CL
 right  0.696 0.00900 93    0.678    0.714
 left   0.669 0.00984 93    0.650    0.689

region = SR.SL.SM:
 lat   emmean      SE df lower.CL upper.CL
 right  0.525 0.00726 93    0.510    0.539
 left   0.531 0.00822 93    0.514    0.547

region = Subiculum:
 lat   emmean      SE df lower.CL upper.CL
 right  0.277 0.00430 93    0.269    0.286
 left   0.307 0.00518 93    0.297    0.317

Results are averaged over the levels of: group.use 
Confidence level used: 0.95 

$contrasts
region = CA1:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.01531 0.00836 93   1.831  0.0702

region = CA2.CA3:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03002 0.00327 93   9.189  <.0001

region = CA4.DG:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02693 0.00622 93   4.332  <.0001

region = SR.SL.SM:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.00606 0.00451 93  -1.343  0.1827

region = Subiculum:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.02973 0.00434 93  -6.850  <.0001

Results are averaged over the levels of: group.use

emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region | group.use)

$emmeans
region = CA1, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.896 0.01866 93    0.859    0.933
 left   0.880 0.01843 93    0.843    0.916

region = CA2.CA3, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.204 0.00629 93    0.191    0.216
 left   0.176 0.00612 93    0.164    0.188

region = CA4.DG, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.703 0.01524 93    0.673    0.733
 left   0.683 0.01666 93    0.650    0.717

region = SR.SL.SM, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.539 0.01228 93    0.515    0.564
 left   0.541 0.01391 93    0.514    0.569

region = Subiculum, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.277 0.00729 93    0.263    0.292
 left   0.310 0.00877 93    0.293    0.328

region = CA1, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.862 0.01948 93    0.823    0.901
 left   0.870 0.01924 93    0.831    0.908

region = CA2.CA3, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.207 0.00657 93    0.193    0.220
 left   0.169 0.00639 93    0.156    0.181

region = CA4.DG, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.681 0.01591 93    0.650    0.713
 left   0.655 0.01740 93    0.621    0.690

region = SR.SL.SM, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.501 0.01283 93    0.476    0.526
 left   0.531 0.01452 93    0.502    0.560

region = Subiculum, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.275 0.00761 93    0.260    0.290
 left   0.302 0.00916 93    0.284    0.320

region = CA1, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.893 0.01925 93    0.854    0.931
 left   0.856 0.01901 93    0.818    0.894

region = CA2.CA3, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.206 0.00649 93    0.193    0.219
 left   0.182 0.00631 93    0.169    0.194

region = CA4.DG, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.704 0.01572 93    0.673    0.735
 left   0.669 0.01719 93    0.635    0.703

region = SR.SL.SM, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.534 0.01267 93    0.509    0.559
 left   0.520 0.01435 93    0.491    0.548

region = Subiculum, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.280 0.00752 93    0.265    0.295
 left   0.309 0.00905 93    0.291    0.326

Confidence level used: 0.95 

$contrasts
region = CA1, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.01683 0.01415 93   1.189  0.2375

region = CA2.CA3, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02802 0.00553 93   5.066  <.0001

region = CA4.DG, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.01972 0.01052 93   1.874  0.0641

region = SR.SL.SM, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.00212 0.00764 93  -0.277  0.7824

region = Subiculum, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.03341 0.00735 93  -4.546  <.0001

region = CA1, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.00766 0.01478 93  -0.518  0.6057

region = CA2.CA3, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03801 0.00578 93   6.582  <.0001

region = CA4.DG, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02597 0.01099 93   2.364  0.0201

region = SR.SL.SM, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.03013 0.00798 93  -3.776  0.0003

region = Subiculum, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.02691 0.00767 93  -3.508  0.0007

region = CA1, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03676 0.01460 93   2.518  0.0135

region = CA2.CA3, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02403 0.00571 93   4.212  0.0001

region = CA4.DG, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03508 0.01085 93   3.232  0.0017

region = SR.SL.SM, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.01407 0.00788 93   1.784  0.0776

region = Subiculum, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.02887 0.00758 93  -3.809  0.0003

ggplot(df_long1, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Absolute volume (cm^3)")

MODEL 2: Percent (aka normalized volume)

For pairwise comparisons: P value threshold, corrected for multiple comparisons = .05/(5 regions x 2 hemispheres) = .005

# MODEL 2: Percent (aka normalized volume)
m1 <-
  aov_car(
    value ~  group.use * region * lat + Error(record_id / region / lat),
    df_long2,
    factorize = FALSE,
    type = 3
  )

Contrasts set to contr.sum for the following variables: group.use

nice(m1)

Anova Table (Type 3 tests)

Response: value
                Effect           df  MSE           F  ges p.value
1            group.use        2, 94 0.00        0.48 .005    .620
2               region 2.36, 221.47 0.00 2502.41 *** .915   <.001
3     group.use:region 4.71, 221.47 0.00        0.32 .003    .890
4                  lat        1, 94 0.00      5.75 * .002    .018
5        group.use:lat        2, 94 0.00      2.45 + .002    .091
6           region:lat 3.01, 283.14 0.00   22.15 *** .021   <.001
7 group.use:region:lat 6.02, 283.14 0.00      2.24 * .004    .040
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG

emmeans(m1, ~ group.use |
          region | lat, model = "multivariate", contr = "pairwise")

$emmeans
region = CA1, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0594 0.001239 94   0.0569   0.0618
 Lower WTC-exposed 0.0575 0.001297 94   0.0549   0.0601
 PTSD              0.0596 0.001277 94   0.0570   0.0621

region = CA2.CA3, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0135 0.000418 94   0.0127   0.0143
 Lower WTC-exposed 0.0138 0.000438 94   0.0129   0.0146
 PTSD              0.0137 0.000431 94   0.0129   0.0146

region = CA4.DG, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0466 0.001048 94   0.0445   0.0487
 Lower WTC-exposed 0.0454 0.001097 94   0.0433   0.0476
 PTSD              0.0471 0.001080 94   0.0449   0.0492

region = SR.SL.SM, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0358 0.000818 94   0.0341   0.0374
 Lower WTC-exposed 0.0334 0.000857 94   0.0317   0.0351
 PTSD              0.0355 0.000843 94   0.0338   0.0372

region = Subiculum, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0184 0.000478 94   0.0174   0.0193
 Lower WTC-exposed 0.0184 0.000501 94   0.0174   0.0194
 PTSD              0.0186 0.000493 94   0.0176   0.0196

region = CA1, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0583 0.001236 94   0.0558   0.0607
 Lower WTC-exposed 0.0580 0.001294 94   0.0554   0.0606
 PTSD              0.0572 0.001274 94   0.0547   0.0597

region = CA2.CA3, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0116 0.000397 94   0.0108   0.0124
 Lower WTC-exposed 0.0113 0.000415 94   0.0104   0.0121
 PTSD              0.0122 0.000409 94   0.0114   0.0130

region = CA4.DG, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0452 0.001140 94   0.0430   0.0475
 Lower WTC-exposed 0.0438 0.001194 94   0.0415   0.0462
 PTSD              0.0448 0.001175 94   0.0424   0.0471

region = SR.SL.SM, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0359 0.000920 94   0.0341   0.0378
 Lower WTC-exposed 0.0354 0.000963 94   0.0334   0.0373
 PTSD              0.0344 0.000948 94   0.0326   0.0363

region = Subiculum, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0206 0.000583 94   0.0195   0.0218
 Lower WTC-exposed 0.0201 0.000611 94   0.0189   0.0213
 PTSD              0.0205 0.000601 94   0.0193   0.0217

Confidence level used: 0.95 

$contrasts
region = CA1, lat = right:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0018854 0.001794 94   1.051  0.5467
 Highly resilient - PTSD                -0.0001742 0.001779 94  -0.098  0.9947
 (Lower WTC-exposed) - PTSD             -0.0020596 0.001820 94  -1.132  0.4972

region = CA2.CA3, lat = right:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed) -0.0002801 0.000605 94  -0.463  0.8888
 Highly resilient - PTSD                -0.0002551 0.000600 94  -0.425  0.9054
 (Lower WTC-exposed) - PTSD              0.0000250 0.000614 94   0.041  0.9991

region = CA4.DG, lat = right:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0011820 0.001517 94   0.779  0.7168
 Highly resilient - PTSD                -0.0004455 0.001505 94  -0.296  0.9528
 (Lower WTC-exposed) - PTSD             -0.0016276 0.001540 94  -1.057  0.5430

region = SR.SL.SM, lat = right:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0024019 0.001184 94   2.028  0.1111
 Highly resilient - PTSD                 0.0002592 0.001175 94   0.221  0.9735
 (Lower WTC-exposed) - PTSD             -0.0021427 0.001202 94  -1.783  0.1809

region = Subiculum, lat = right:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0000123 0.000693 94   0.018  0.9998
 Highly resilient - PTSD                -0.0001993 0.000687 94  -0.290  0.9547
 (Lower WTC-exposed) - PTSD             -0.0002115 0.000703 94  -0.301  0.9513

region = CA1, lat = left:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0002591 0.001789 94   0.145  0.9885
 Highly resilient - PTSD                 0.0010726 0.001774 94   0.604  0.8180
 (Lower WTC-exposed) - PTSD              0.0008135 0.001816 94   0.448  0.8954

region = CA2.CA3, lat = left:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0003441 0.000574 94   0.599  0.8209
 Highly resilient - PTSD                -0.0005655 0.000570 94  -0.993  0.5832
 (Lower WTC-exposed) - PTSD             -0.0009097 0.000583 94  -1.561  0.2678

region = CA4.DG, lat = left:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0013864 0.001651 94   0.840  0.6792
 Highly resilient - PTSD                 0.0004685 0.001637 94   0.286  0.9559
 (Lower WTC-exposed) - PTSD             -0.0009178 0.001675 94  -0.548  0.8478

region = SR.SL.SM, lat = left:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0005777 0.001332 94   0.434  0.9016
 Highly resilient - PTSD                 0.0014879 0.001321 94   1.127  0.5001
 (Lower WTC-exposed) - PTSD              0.0009102 0.001351 94   0.674  0.7794

region = Subiculum, lat = left:
 contrast                                 estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.0005128 0.000845 94   0.607  0.8167
 Highly resilient - PTSD                 0.0001441 0.000838 94   0.172  0.9838
 (Lower WTC-exposed) - PTSD             -0.0003687 0.000857 94  -0.430  0.9032

P value adjustment: tukey method for comparing a family of 3 estimates

emmeans(m1, ~ lat |
          region | group.use, model = "multivariate", contr = "pairwise")

$emmeans
region = CA1, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0594 0.001239 94   0.0569   0.0618
 left  0.0583 0.001236 94   0.0558   0.0607

region = CA2.CA3, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0135 0.000418 94   0.0127   0.0143
 left  0.0116 0.000397 94   0.0108   0.0124

region = CA4.DG, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0466 0.001048 94   0.0445   0.0487
 left  0.0452 0.001140 94   0.0430   0.0475

region = SR.SL.SM, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0358 0.000818 94   0.0341   0.0374
 left  0.0359 0.000920 94   0.0341   0.0378

region = Subiculum, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0184 0.000478 94   0.0174   0.0193
 left  0.0206 0.000583 94   0.0195   0.0218

region = CA1, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0575 0.001297 94   0.0549   0.0601
 left  0.0580 0.001294 94   0.0554   0.0606

region = CA2.CA3, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0138 0.000438 94   0.0129   0.0146
 left  0.0113 0.000415 94   0.0104   0.0121

region = CA4.DG, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0454 0.001097 94   0.0433   0.0476
 left  0.0438 0.001194 94   0.0415   0.0462

region = SR.SL.SM, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0334 0.000857 94   0.0317   0.0351
 left  0.0354 0.000963 94   0.0334   0.0373

region = Subiculum, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0184 0.000501 94   0.0174   0.0194
 left  0.0201 0.000611 94   0.0189   0.0213

region = CA1, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0596 0.001277 94   0.0570   0.0621
 left  0.0572 0.001274 94   0.0547   0.0597

region = CA2.CA3, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0137 0.000431 94   0.0129   0.0146
 left  0.0122 0.000409 94   0.0114   0.0130

region = CA4.DG, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0471 0.001080 94   0.0449   0.0492
 left  0.0448 0.001175 94   0.0424   0.0471

region = SR.SL.SM, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0355 0.000843 94   0.0338   0.0372
 left  0.0344 0.000948 94   0.0326   0.0363

region = Subiculum, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0186 0.000493 94   0.0176   0.0196
 left  0.0205 0.000601 94   0.0193   0.0217

Confidence level used: 0.95 

$contrasts
region = CA1, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001115 0.000931 94   1.198  0.2340

region = CA2.CA3, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001898 0.000363 94   5.230  <.0001

region = CA4.DG, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001404 0.000705 94   1.992  0.0493

region = SR.SL.SM, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.000161 0.000513 94  -0.315  0.7538

region = Subiculum, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.002243 0.000490 94  -4.577  <.0001

region = CA1, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.000511 0.000975 94  -0.524  0.6013

region = CA2.CA3, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.002522 0.000380 94   6.637  <.0001

region = CA4.DG, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001608 0.000738 94   2.179  0.0318

region = SR.SL.SM, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.001985 0.000537 94  -3.697  0.0004

region = Subiculum, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.001743 0.000513 94  -3.395  0.0010

region = CA1, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.002362 0.000960 94   2.461  0.0157

region = CA2.CA3, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001587 0.000374 94   4.244  0.0001

region = CA4.DG, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.002318 0.000726 94   3.191  0.0019

region = SR.SL.SM, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001067 0.000529 94   2.019  0.0463

region = Subiculum, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.001900 0.000505 94  -3.761  0.0003

emmeans(m1, ~ lat |
          group.use, model = "multivariate", contr = "pairwise")

$emmeans
group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0347 0.000616 94   0.0335   0.0360
 left  0.0343 0.000649 94   0.0330   0.0356

group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0337 0.000645 94   0.0324   0.0350
 left  0.0337 0.000680 94   0.0324   0.0351

group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0349 0.000635 94   0.0336   0.0362
 left  0.0338 0.000669 94   0.0325   0.0351

Results are averaged over the levels of: region 
Confidence level used: 0.95 

$contrasts
group.use = Highly resilient:
 contrast       estimate       SE df t.ratio p.value
 right - left  0.0004024 0.000344 94   1.168  0.2456

group.use = Lower WTC-exposed:
 contrast       estimate       SE df t.ratio p.value
 right - left -0.0000219 0.000361 94  -0.061  0.9518

group.use = PTSD:
 contrast       estimate       SE df t.ratio p.value
 right - left  0.0010869 0.000355 94   3.062  0.0029

Results are averaged over the levels of: region

ggplot(df_long2, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Normalized volume (%)")

MODEL 3: Asymmetry index

For pairwise comparisons: P value threshold, corrected for multiple comparisons = .05/(5 regions) = .01

# MODEL 3: Asymmetry index
m1 <-
  aov_car(
    value ~  group.use * region + Error(record_id / region),
    df_long3,
    factorize = TRUE,
    type = 3
  )

Contrasts set to contr.sum for the following variables: group.use

nice(m1)

Anova Table (Type 3 tests)

Response: value
            Effect           df    MSE         F  ges p.value
1        group.use        2, 94 217.89      0.66 .004    .520
2           region 2.55, 239.69 224.64 59.69 *** .315   <.001
3 group.use:region 5.10, 239.69 224.64    2.29 * .034    .046
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG

emmeans(m1, ~ group.use |
          region, model = "multivariate", contr = "pairwise")

$emmeans
region = CA1:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient    1.777 1.71 94  -1.6191     5.17
 Lower WTC-exposed  -0.938 1.79 94  -4.4940     2.62
 PTSD                4.362 1.76 94   0.8618     7.86

region = CA2.CA3:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient   15.413 3.10 94   9.2521    21.57
 Lower WTC-exposed  20.357 3.25 94  13.9044    26.81
 PTSD               12.758 3.20 94   6.4071    19.11

region = CA4.DG:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient    3.327 1.64 94   0.0613     6.59
 Lower WTC-exposed   3.779 1.72 94   0.3589     7.20
 PTSD                5.340 1.70 94   1.9742     8.71

region = SR.SL.SM:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient   -0.384 1.55 94  -3.4610     2.69
 Lower WTC-exposed  -5.554 1.62 94  -8.7770    -2.33
 PTSD                3.549 1.60 94   0.3769     6.72

region = Subiculum:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient  -11.543 2.37 94 -16.2396    -6.85
 Lower WTC-exposed  -8.816 2.48 94 -13.7343    -3.90
 PTSD               -9.133 2.44 94 -13.9737    -4.29

Confidence level used: 0.95 

$contrasts
region = CA1:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)    2.714 2.48 94   1.096  0.5189
 Highly resilient - PTSD                  -2.585 2.46 94  -1.053  0.5458
 (Lower WTC-exposed) - PTSD               -5.300 2.51 94  -2.109  0.0935

region = CA2.CA3:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)   -4.944 4.49 94  -1.100  0.5163
 Highly resilient - PTSD                   2.655 4.46 94   0.596  0.8227
 (Lower WTC-exposed) - PTSD                7.599 4.56 94   1.666  0.2235

region = CA4.DG:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)   -0.452 2.38 94  -0.190  0.9803
 Highly resilient - PTSD                  -2.013 2.36 94  -0.852  0.6714
 (Lower WTC-exposed) - PTSD               -1.562 2.42 94  -0.646  0.7950

region = SR.SL.SM:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)    5.171 2.24 94   2.304  0.0601
 Highly resilient - PTSD                  -3.933 2.23 94  -1.767  0.1864
 (Lower WTC-exposed) - PTSD               -9.103 2.28 94  -3.997  0.0004

region = Subiculum:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)   -2.727 3.42 94  -0.796  0.7062
 Highly resilient - PTSD                  -2.410 3.40 94  -0.710  0.7584
 (Lower WTC-exposed) - PTSD                0.317 3.48 94   0.091  0.9954

P value adjustment: tukey method for comparing a family of 3 estimates

emmeans(m1, ~ group.use, model = "multivariate", contr = "pairwise")

$emmeans
 group.use         emmean   SE df lower.CL upper.CL
 Highly resilient    1.72 1.13 94   -0.530     3.97
 Lower WTC-exposed   1.77 1.19 94   -0.589     4.12
 PTSD                3.38 1.17 94    1.058     5.69

Results are averaged over the levels of: region 
Confidence level used: 0.95 

$contrasts
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  -0.0476 1.64 94  -0.029  0.9995
 Highly resilient - PTSD                 -1.6573 1.63 94  -1.019  0.5666
 (Lower WTC-exposed) - PTSD              -1.6097 1.66 94  -0.968  0.5991

Results are averaged over the levels of: region 
P value adjustment: tukey method for comparing a family of 3 estimates

ggplot(df_long3, aes(group.use, value, fill = region)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ lat * region, scales = "fixed") + labs(title =
                                                                                                            "Asymmetry index")

Only valid SNT participants (n=73)

below_50_df <-
  readRDS(
    "/Users/sarenseeley/Dropbox/Postdoc/mentoring/Isabella Fonseca/SNT/isabella_r_scripts/below_50_df.rds"
  ) %>% rename(record_id = sub_id)
drop_ids <-
  c(below_50_df$record_id, "NWTC-034") #excludes NWTC-034 [lots of missing trials]
# long dataset
df_long <-
  df %>% select(
    record_id,
    group.use,
    Sex,
    Age,
    tot_ctq,
    tot_tleq_nonW,
    exposures_count,
    ptsd,
    (contains("_pc") |
       contains("_Asym") |
       contains("_cm3")) &
      !contains("scid")
  ) %>% filter(!record_id %in% drop_ids) #scid pcp shouldn't be included

df_long <- df_long %>%
  tidyr::pivot_longer(
    cols = -c(
      record_id,
      group.use,
      Sex,
      Age,
      tot_ctq,
      tot_tleq_nonW,
      exposures_count,
      ptsd,
      ICV_cm3
    ),
    names_to = "region",
    values_to = "value"
  )
df_long[12:14] <-
  str_split_fixed(df_long$region, "_", 3) # split column into 3 columns, by underscore
df_long <-
  df_long %>% select(!region) %>% rename(region = V1,
                                         lat = V2,
                                         measure = V3) # which subfield, which hemisphere, which metric (cm3, percent, asymmetry index)
df_long1 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "cm3" &
                       !region == "Hippocampus") # don't include total hippocampus
df_long2 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "pc" & !region == "Hippocampus") %>% group_by(region) %>%
  mutate(z_value = scale(value))
df_long3 <-
  df_long %>% filter(lat == "asymmetry" & !region == "Hippocampus")

MODEL 1: Volume in cm^3 controlling for total ICV

# MODEL 1: Volume in cm^3 controlling for total ICV
m1 <-
  aov_ez(
    id = "record_id",
    dv = "value",
    df_long1,
    between = c("group.use"),
    within = c("region", "lat"),
    covariate = c("ICV_cm3"),
    factorize = FALSE
  )

Warning: Numerical variables NOT centered on 0 (matters if variable in interaction):
   ICV_cm3
Contrasts set to contr.sum for the following variables: group.use

nice(m1)

Anova Table (Type 3 tests)

Response: value
                 Effect           df  MSE         F   ges p.value
1             group.use        2, 88 0.03      0.97  .010    .383
2               ICV_cm3        1, 88 0.03 33.82 ***  .149   <.001
3                region 2.40, 210.79 0.01    3.25 *  .015    .032
4      group.use:region 4.79, 210.79 0.01      0.49  .004    .780
5        ICV_cm3:region 2.40, 210.79 0.01  8.54 ***  .038   <.001
6                   lat        1, 88 0.00      0.00 <.001    .993
7         group.use:lat        2, 88 0.00      1.92  .002    .153
8           ICV_cm3:lat        1, 88 0.00      0.03 <.001    .873
9            region:lat 2.93, 257.80 0.00      0.83 <.001    .478
10 group.use:region:lat 5.86, 257.80 0.00    2.05 +  .004    .061
11   ICV_cm3:region:lat 2.93, 257.80 0.00      1.32  .001    .268
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG

emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region)

$emmeans
region = CA1:
 lat   emmean      SE df lower.CL upper.CL
 right  0.888 0.01107 88    0.866    0.910
 left   0.872 0.01113 88    0.850    0.894

region = CA2.CA3:
 lat   emmean      SE df lower.CL upper.CL
 right  0.207 0.00379 88    0.199    0.214
 left   0.177 0.00364 88    0.169    0.184

region = CA4.DG:
 lat   emmean      SE df lower.CL upper.CL
 right  0.699 0.00879 88    0.682    0.717
 left   0.673 0.00986 88    0.653    0.692

region = SR.SL.SM:
 lat   emmean      SE df lower.CL upper.CL
 right  0.525 0.00741 88    0.511    0.540
 left   0.534 0.00833 88    0.517    0.550

region = Subiculum:
 lat   emmean      SE df lower.CL upper.CL
 right  0.277 0.00441 88    0.269    0.286
 left   0.308 0.00536 88    0.297    0.318

Results are averaged over the levels of: group.use 
Confidence level used: 0.95 

$contrasts
region = CA1:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.01553 0.00872 88   1.782  0.0782

region = CA2.CA3:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03036 0.00338 88   8.975  <.0001

region = CA4.DG:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02632 0.00646 88   4.077  0.0001

region = SR.SL.SM:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.00834 0.00450 88  -1.852  0.0674

region = Subiculum:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.03019 0.00457 88  -6.606  <.0001

Results are averaged over the levels of: group.use

emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region | group.use)

$emmeans
region = CA1, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.902 0.01909 88    0.864    0.940
 left   0.886 0.01919 88    0.848    0.924

region = CA2.CA3, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.205 0.00653 88    0.192    0.218
 left   0.177 0.00628 88    0.164    0.189

region = CA4.DG, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.709 0.01516 88    0.679    0.739
 left   0.689 0.01700 88    0.655    0.722

region = SR.SL.SM, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.539 0.01279 88    0.513    0.564
 left   0.544 0.01437 88    0.516    0.573

region = Subiculum, group.use = Highly resilient:
 lat   emmean      SE df lower.CL upper.CL
 right  0.276 0.00761 88    0.261    0.291
 left   0.310 0.00925 88    0.292    0.329

region = CA1, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.863 0.01907 88    0.825    0.901
 left   0.871 0.01916 88    0.833    0.909

region = CA2.CA3, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.207 0.00652 88    0.194    0.220
 left   0.169 0.00627 88    0.156    0.181

region = CA4.DG, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.682 0.01515 88    0.652    0.712
 left   0.656 0.01698 88    0.622    0.690

region = SR.SL.SM, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.502 0.01277 88    0.477    0.527
 left   0.532 0.01436 88    0.504    0.561

region = Subiculum, group.use = Lower WTC-exposed:
 lat   emmean      SE df lower.CL upper.CL
 right  0.276 0.00760 88    0.260    0.291
 left   0.303 0.00924 88    0.284    0.321

region = CA1, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.898 0.01940 88    0.859    0.936
 left   0.860 0.01950 88    0.821    0.899

region = CA2.CA3, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.209 0.00664 88    0.195    0.222
 left   0.184 0.00638 88    0.172    0.197

region = CA4.DG, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.706 0.01541 88    0.675    0.736
 left   0.673 0.01728 88    0.639    0.708

region = SR.SL.SM, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.535 0.01299 88    0.510    0.561
 left   0.525 0.01461 88    0.496    0.554

region = Subiculum, group.use = PTSD:
 lat   emmean      SE df lower.CL upper.CL
 right  0.280 0.00773 88    0.265    0.296
 left   0.310 0.00940 88    0.291    0.328

Confidence level used: 0.95 

$contrasts
region = CA1, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.01651 0.01503 88   1.098  0.2751

region = CA2.CA3, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02862 0.00583 88   4.906  <.0001

region = CA4.DG, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02054 0.01113 88   1.845  0.0684

region = SR.SL.SM, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.00542 0.00776 88  -0.698  0.4870

region = Subiculum, group.use = Highly resilient:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.03417 0.00788 88  -4.335  <.0001

region = CA1, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.00749 0.01501 88  -0.499  0.6193

region = CA2.CA3, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03803 0.00583 88   6.526  <.0001

region = CA4.DG, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02611 0.01112 88   2.347  0.0212

region = SR.SL.SM, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.03031 0.00775 88  -3.909  0.0002

region = Subiculum, group.use = Lower WTC-exposed:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.02700 0.00787 88  -3.430  0.0009

region = CA1, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03757 0.01527 88   2.459  0.0159

region = CA2.CA3, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.02444 0.00593 88   4.122  0.0001

region = CA4.DG, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.03232 0.01132 88   2.857  0.0053

region = SR.SL.SM, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left  0.01071 0.00789 88   1.358  0.1779

region = Subiculum, group.use = PTSD:
 contrast     estimate      SE df t.ratio p.value
 right - left -0.02941 0.00801 88  -3.672  0.0004

ggplot(df_long1, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Absolute volume (cm^3)")

MODEL 2: Percent (aka normalized volume)

# MODEL 2: Percent (aka normalized volume)
m1 <-
  aov_car(
    value ~  group.use * region * lat + Error(record_id / region / lat),
    df_long2,
    factorize = FALSE,
    type = 3
  )

Contrasts set to contr.sum for the following variables: group.use

nice(m1)

Anova Table (Type 3 tests)

Response: value
                Effect           df  MSE           F  ges p.value
1            group.use        2, 89 0.00        0.68 .007    .509
2               region 2.44, 217.06 0.00 2454.13 *** .919   <.001
3     group.use:region 4.88, 217.06 0.00        0.37 .003    .861
4                  lat        1, 89 0.00      4.39 * .002    .039
5        group.use:lat        2, 89 0.00        1.90 .002    .156
6           region:lat 2.96, 263.80 0.00   21.44 *** .022   <.001
7 group.use:region:lat 5.93, 263.80 0.00      2.04 + .004    .062
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG

emmeans(m1, ~ group.use |
          region | lat, model = "multivariate", contr = "pairwise")

$emmeans
region = CA1, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0597 0.001266 89   0.0572   0.0622
 Lower WTC-exposed 0.0575 0.001266 89   0.0550   0.0600
 PTSD              0.0597 0.001287 89   0.0572   0.0623

region = CA2.CA3, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0136 0.000436 89   0.0127   0.0145
 Lower WTC-exposed 0.0138 0.000436 89   0.0129   0.0146
 PTSD              0.0139 0.000444 89   0.0130   0.0148

region = CA4.DG, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0470 0.001022 89   0.0450   0.0490
 Lower WTC-exposed 0.0454 0.001022 89   0.0434   0.0475
 PTSD              0.0469 0.001039 89   0.0449   0.0490

region = SR.SL.SM, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0357 0.000852 89   0.0340   0.0374
 Lower WTC-exposed 0.0334 0.000852 89   0.0317   0.0351
 PTSD              0.0355 0.000866 89   0.0338   0.0372

region = Subiculum, lat = right:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0183 0.000504 89   0.0173   0.0193
 Lower WTC-exposed 0.0184 0.000504 89   0.0174   0.0194
 PTSD              0.0186 0.000512 89   0.0176   0.0196

region = CA1, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0586 0.001280 89   0.0561   0.0612
 Lower WTC-exposed 0.0580 0.001280 89   0.0555   0.0606
 PTSD              0.0572 0.001301 89   0.0547   0.0598

region = CA2.CA3, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0116 0.000412 89   0.0108   0.0125
 Lower WTC-exposed 0.0113 0.000412 89   0.0104   0.0121
 PTSD              0.0123 0.000418 89   0.0115   0.0132

region = CA4.DG, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0455 0.001160 89   0.0432   0.0478
 Lower WTC-exposed 0.0438 0.001160 89   0.0415   0.0461
 PTSD              0.0448 0.001179 89   0.0425   0.0472

region = SR.SL.SM, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0360 0.000943 89   0.0342   0.0379
 Lower WTC-exposed 0.0354 0.000943 89   0.0335   0.0372
 PTSD              0.0347 0.000958 89   0.0328   0.0367

region = Subiculum, lat = left:
 group.use         emmean       SE df lower.CL upper.CL
 Highly resilient  0.0206 0.000618 89   0.0194   0.0218
 Lower WTC-exposed 0.0201 0.000618 89   0.0189   0.0213
 PTSD              0.0205 0.000628 89   0.0193   0.0218

Confidence level used: 0.95 

$contrasts
region = CA1, lat = right:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00219900 0.001791 89   1.228  0.4399
 Highly resilient - PTSD                -0.00000793 0.001805 89  -0.004  1.0000
 (Lower WTC-exposed) - PTSD             -0.00220693 0.001805 89  -1.222  0.4432

region = CA2.CA3, lat = right:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed) -0.00018866 0.000617 89  -0.306  0.9498
 Highly resilient - PTSD                -0.00033408 0.000622 89  -0.537  0.8534
 (Lower WTC-exposed) - PTSD             -0.00014542 0.000622 89  -0.234  0.9703

region = CA4.DG, lat = right:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00154697 0.001445 89   1.070  0.5349
 Highly resilient - PTSD                 0.00007029 0.001457 89   0.048  0.9987
 (Lower WTC-exposed) - PTSD             -0.00147668 0.001457 89  -1.013  0.5705

region = SR.SL.SM, lat = right:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00232723 0.001205 89   1.932  0.1357
 Highly resilient - PTSD                 0.00018283 0.001215 89   0.151  0.9876
 (Lower WTC-exposed) - PTSD             -0.00214440 0.001215 89  -1.766  0.1871

region = Subiculum, lat = right:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed) -0.00006371 0.000712 89  -0.089  0.9956
 Highly resilient - PTSD                -0.00028684 0.000718 89  -0.400  0.9159
 (Lower WTC-exposed) - PTSD             -0.00022313 0.000718 89  -0.311  0.9482

region = CA1, lat = left:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00061497 0.001810 89   0.340  0.9384
 Highly resilient - PTSD                 0.00137730 0.001825 89   0.755  0.7315
 (Lower WTC-exposed) - PTSD              0.00076233 0.001825 89   0.418  0.9084

region = CA2.CA3, lat = left:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00039314 0.000582 89   0.675  0.7783
 Highly resilient - PTSD                -0.00067465 0.000587 89  -1.150  0.4864
 (Lower WTC-exposed) - PTSD             -0.00106778 0.000587 89  -1.820  0.1691

region = CA4.DG, lat = left:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00169910 0.001640 89   1.036  0.5563
 Highly resilient - PTSD                 0.00069449 0.001654 89   0.420  0.9075
 (Lower WTC-exposed) - PTSD             -0.00100461 0.001654 89  -0.607  0.8164

region = SR.SL.SM, lat = left:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00066997 0.001333 89   0.503  0.8702
 Highly resilient - PTSD                 0.00127682 0.001344 89   0.950  0.6103
 (Lower WTC-exposed) - PTSD              0.00060685 0.001344 89   0.452  0.8939

region = Subiculum, lat = left:
 contrast                                  estimate       SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)  0.00047742 0.000874 89   0.546  0.8486
 Highly resilient - PTSD                 0.00005249 0.000881 89   0.060  0.9980
 (Lower WTC-exposed) - PTSD             -0.00042493 0.000881 89  -0.482  0.8799

P value adjustment: tukey method for comparing a family of 3 estimates

emmeans(m1, ~ lat |
          region | group.use, model = "multivariate", contr = "pairwise")

$emmeans
region = CA1, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0597 0.001266 89   0.0572   0.0622
 left  0.0586 0.001280 89   0.0561   0.0612

region = CA2.CA3, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0136 0.000436 89   0.0127   0.0145
 left  0.0116 0.000412 89   0.0108   0.0125

region = CA4.DG, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0470 0.001022 89   0.0450   0.0490
 left  0.0455 0.001160 89   0.0432   0.0478

region = SR.SL.SM, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0357 0.000852 89   0.0340   0.0374
 left  0.0360 0.000943 89   0.0342   0.0379

region = Subiculum, group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0183 0.000504 89   0.0173   0.0193
 left  0.0206 0.000618 89   0.0194   0.0218

region = CA1, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0575 0.001266 89   0.0550   0.0600
 left  0.0580 0.001280 89   0.0555   0.0606

region = CA2.CA3, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0138 0.000436 89   0.0129   0.0146
 left  0.0113 0.000412 89   0.0104   0.0121

region = CA4.DG, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0454 0.001022 89   0.0434   0.0475
 left  0.0438 0.001160 89   0.0415   0.0461

region = SR.SL.SM, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0334 0.000852 89   0.0317   0.0351
 left  0.0354 0.000943 89   0.0335   0.0372

region = Subiculum, group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0184 0.000504 89   0.0174   0.0194
 left  0.0201 0.000618 89   0.0189   0.0213

region = CA1, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0597 0.001287 89   0.0572   0.0623
 left  0.0572 0.001301 89   0.0547   0.0598

region = CA2.CA3, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0139 0.000444 89   0.0130   0.0148
 left  0.0123 0.000418 89   0.0115   0.0132

region = CA4.DG, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0469 0.001039 89   0.0449   0.0490
 left  0.0448 0.001179 89   0.0425   0.0472

region = SR.SL.SM, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0355 0.000866 89   0.0338   0.0372
 left  0.0347 0.000958 89   0.0328   0.0367

region = Subiculum, group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0186 0.000512 89   0.0176   0.0196
 left  0.0205 0.000628 89   0.0193   0.0218

Confidence level used: 0.95 

$contrasts
region = CA1, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001073 0.000990 89   1.084  0.2815

region = CA2.CA3, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001940 0.000383 89   5.064  <.0001

region = CA4.DG, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001456 0.000746 89   1.951  0.0542

region = SR.SL.SM, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.000328 0.000522 89  -0.629  0.5313

region = Subiculum, group.use = Highly resilient:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.002284 0.000527 89  -4.338  <.0001

region = CA1, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.000511 0.000990 89  -0.516  0.6069

region = CA2.CA3, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.002522 0.000383 89   6.582  <.0001

region = CA4.DG, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001608 0.000746 89   2.155  0.0339

region = SR.SL.SM, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.001985 0.000522 89  -3.802  0.0003

region = Subiculum, group.use = Lower WTC-exposed:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.001743 0.000527 89  -3.310  0.0013

region = CA1, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.002458 0.001007 89   2.442  0.0166

region = CA2.CA3, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.001600 0.000390 89   4.107  0.0001

region = CA4.DG, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.002080 0.000759 89   2.742  0.0074

region = SR.SL.SM, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left  0.000766 0.000531 89   1.443  0.1527

region = Subiculum, group.use = PTSD:
 contrast      estimate       SE df t.ratio p.value
 right - left -0.001945 0.000535 89  -3.633  0.0005

emmeans(m1, ~ lat |
          group.use, model = "multivariate", contr = "pairwise")

$emmeans
group.use = Highly resilient:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0349 0.000619 89   0.0336   0.0361
 left  0.0345 0.000658 89   0.0332   0.0358

group.use = Lower WTC-exposed:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0337 0.000619 89   0.0325   0.0349
 left  0.0337 0.000658 89   0.0324   0.0350

group.use = PTSD:
 lat   emmean       SE df lower.CL upper.CL
 right 0.0349 0.000629 89   0.0337   0.0362
 left  0.0339 0.000669 89   0.0326   0.0353

Results are averaged over the levels of: region 
Confidence level used: 0.95 

$contrasts
group.use = Highly resilient:
 contrast       estimate       SE df t.ratio p.value
 right - left  0.0003714 0.000368 89   1.011  0.3150

group.use = Lower WTC-exposed:
 contrast       estimate       SE df t.ratio p.value
 right - left -0.0000219 0.000368 89  -0.060  0.9527

group.use = PTSD:
 contrast       estimate       SE df t.ratio p.value
 right - left  0.0009918 0.000374 89   2.655  0.0094

Results are averaged over the levels of: region

ggplot(df_long2, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1)) + geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Normalized volume (%)")

MODEL 3: Asymmetry index

# MODEL 3: Asymmetry index
m1 <-
  aov_car(
    value ~  group.use * region + Error(record_id / region),
    df_long3,
    factorize = TRUE,
    type = 3
  )

Contrasts set to contr.sum for the following variables: group.use

nice(m1)

Anova Table (Type 3 tests)

Response: value
            Effect           df    MSE         F  ges p.value
1        group.use        2, 89 226.90      0.43 .003    .653
2           region 2.49, 221.80 230.88 57.69 *** .317   <.001
3 group.use:region 4.98, 221.80 230.88    2.02 + .031    .077
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘+’ 0.1 ‘ ’ 1

Sphericity correction method: GG

emmeans(m1, ~ group.use | region, contr = "pairwise")

$emmeans
region = CA1:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient    1.767 1.82 89  -1.8442     5.38
 Lower WTC-exposed  -0.938 1.82 89  -4.5489     2.67
 PTSD                4.522 1.85 89   0.8509     8.19

region = CA2.CA3:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient   15.491 3.27 89   9.0009    21.98
 Lower WTC-exposed  20.357 3.27 89  13.8668    26.85
 PTSD               12.855 3.32 89   6.2579    19.45

region = CA4.DG:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient    3.466 1.75 89  -0.0204     6.95
 Lower WTC-exposed   3.779 1.75 89   0.2922     7.26
 PTSD                4.890 1.78 89   1.3460     8.43

region = SR.SL.SM:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient   -0.831 1.54 89  -3.8938     2.23
 Lower WTC-exposed  -5.554 1.54 89  -8.6169    -2.49
 PTSD                2.449 1.57 89  -0.6644     5.56

region = Subiculum:
 group.use          emmean   SE df lower.CL upper.CL
 Highly resilient  -11.756 2.54 89 -16.8041    -6.71
 Lower WTC-exposed  -8.816 2.54 89 -13.8638    -3.77
 PTSD               -9.330 2.58 89 -14.4616    -4.20

Confidence level used: 0.95 

$contrasts
region = CA1:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)    2.705 2.57 89   1.052  0.5461
 Highly resilient - PTSD                  -2.755 2.59 89  -1.063  0.5395
 (Lower WTC-exposed) - PTSD               -5.459 2.59 89  -2.107  0.0942

region = CA2.CA3:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)   -4.866 4.62 89  -1.053  0.5454
 Highly resilient - PTSD                   2.636 4.66 89   0.566  0.8386
 (Lower WTC-exposed) - PTSD                7.502 4.66 89   1.611  0.2465

region = CA4.DG:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)   -0.313 2.48 89  -0.126  0.9913
 Highly resilient - PTSD                  -1.424 2.50 89  -0.569  0.8368
 (Lower WTC-exposed) - PTSD               -1.111 2.50 89  -0.444  0.8971

region = SR.SL.SM:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)    4.723 2.18 89   2.167  0.0826
 Highly resilient - PTSD                  -3.280 2.20 89  -1.492  0.2994
 (Lower WTC-exposed) - PTSD               -8.003 2.20 89  -3.641  0.0013

region = Subiculum:
 contrast                               estimate   SE df t.ratio p.value
 Highly resilient - (Lower WTC-exposed)   -2.940 3.59 89  -0.818  0.6927
 Highly resilient - PTSD                  -2.426 3.62 89  -0.670  0.7817
 (Lower WTC-exposed) - PTSD                0.514 3.62 89   0.142  0.9889

P value adjustment: tukey method for comparing a family of 3 estimates

ggplot(df_long3, aes(group.use, value, fill = region)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ lat * region, scales = "fixed") + labs(title =
                                                                                                            "Asymmetry index")

SNT relationships with hippocampal volume

Normalized volume (percent)

library(corrplot)
cordf <-
  df%>% filter(!record_id %in% drop_ids & !is.na(affil_mean_mean)) %>% select(contains("mean_mean"), ends_with("pc") &
                  !contains("scid")) 
cor_result <- Hmisc::rcorr(as.matrix(cordf))
cor_matrix <- cor_result$r  # Correlation matrix
p_matrix <- cor_result$P    # P-values matrix

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.05)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .5,
  title = "p <.05",
  tl.pos  = "ld",
  type = "lower"
)


significant_cor_matrix <- cor_matrix * (p_matrix <= 0.01)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .5,
  title = "p <.01",
  tl.pos  = "ld",
  type = "lower"
)



ggplot(cordf, aes(x=`CA2-CA3_right_pc`, y=pov_2d_dist_mean_mean)) +
    geom_point(size = 1) + theme_clean() + geom_smooth(method = "lm")

`geom_smooth()` using formula = 'y ~ x'

summary(lm(`CA2-CA3_right_pc` ~ pov_2d_dist_mean_mean,cordf))


Call:
lm(formula = `CA2-CA3_right_pc` ~ pov_2d_dist_mean_mean, data = cordf)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0048955 -0.0013655 -0.0002003  0.0017543  0.0051987 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)   
(Intercept)           0.0077333  0.0027812   2.781  0.00694 **
pov_2d_dist_mean_mean 0.0008596  0.0003928   2.189  0.03192 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.002411 on 71 degrees of freedom
Multiple R-squared:  0.0632,    Adjusted R-squared:  0.05001 
F-statistic:  4.79 on 1 and 71 DF,  p-value: 0.03192

ggplot(cordf, aes(x=`CA2-CA3_right_pc`, y=pov_3d_dist_mean_mean)) +
    geom_point(size = 1) + theme_clean() + geom_smooth(method = "lm")

`geom_smooth()` using formula = 'y ~ x'

summary(lm(`CA2-CA3_right_pc` ~ pov_3d_dist_mean_mean,cordf))


Call:
lm(formula = `CA2-CA3_right_pc` ~ pov_3d_dist_mean_mean, data = cordf)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.004932 -0.001439 -0.000150  0.001804  0.005235 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)  
(Intercept)           0.0007289  0.0058604   0.124   0.9014  
pov_3d_dist_mean_mean 0.0013150  0.0005894   2.231   0.0288 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.002408 on 71 degrees of freedom
Multiple R-squared:  0.06552,   Adjusted R-squared:  0.05235 
F-statistic: 4.978 on 1 and 71 DF,  p-value: 0.02883

ggplot(cordf, aes(x=`CA2-CA3_right_pc`, y=neu_3d_angle_mean_mean)) +
    geom_point(size = 1) + theme_clean() + geom_smooth(method = "lm")

`geom_smooth()` using formula = 'y ~ x'

summary(lm(`CA2-CA3_right_pc` ~ neu_3d_angle_mean_mean,cordf))


Call:
lm(formula = `CA2-CA3_right_pc` ~ neu_3d_angle_mean_mean, data = cordf)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0049395 -0.0013923 -0.0000898  0.0017558  0.0050299 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)  
(Intercept)            -0.01936    0.01506  -1.285    0.203  
neu_3d_angle_mean_mean  0.02080    0.00945   2.201    0.031 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.00241 on 71 degrees of freedom
Multiple R-squared:  0.06389,   Adjusted R-squared:  0.05071 
F-statistic: 4.846 on 1 and 71 DF,  p-value: 0.03096

Asymmetry

library(corrplot)
cordf <-
  df %>% filter(!record_id %in% drop_ids & !is.na(affil_mean_mean))%>% select(contains("mean_mean"), ends_with("asymmetry"))
cor_result <- Hmisc::rcorr(as.matrix(cordf))
cor_matrix <- cor_result$r  # Correlation matrix
p_matrix <- cor_result$P    # P-values matrix

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.05)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .7,
  title = "p <.05",
  tl.pos  = "ld",
  type = "lower"
)



significant_cor_matrix <- cor_matrix * (p_matrix <= 0.01)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .7,
  title = "p <.01",
  tl.pos  = "ld",
  type = "lower"
)

NA
NA

Regressions also work with cm^3 controlling for ICV:

cordf <-
    df%>% filter(!record_id %in% drop_ids & !is.na(affil_mean_mean)) %>% select(contains("mean_mean"), ends_with("cm3"))
summary(lm(`CA2-CA3_right_cm3` ~ pov_2d_dist_mean_mean,cordf))


Call:
lm(formula = `CA2-CA3_right_cm3` ~ pov_2d_dist_mean_mean, data = cordf)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.07641 -0.02559  0.00238  0.02491  0.10251 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)   
(Intercept)           0.115938   0.042755   2.712  0.00839 **
pov_2d_dist_mean_mean 0.012943   0.006038   2.144  0.03550 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03706 on 71 degrees of freedom
Multiple R-squared:  0.06078,   Adjusted R-squared:  0.04755 
F-statistic: 4.595 on 1 and 71 DF,  p-value: 0.0355

summary(lm(`CA2-CA3_right_cm3` ~ pov_3d_dist_mean_mean,cordf))


Call:
lm(formula = `CA2-CA3_right_cm3` ~ pov_3d_dist_mean_mean, data = cordf)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.07600 -0.02598  0.00240  0.02479  0.10224 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)  
(Intercept)           0.017293   0.090302   0.191   0.8487  
pov_3d_dist_mean_mean 0.019113   0.009082   2.104   0.0389 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0371 on 71 degrees of freedom
Multiple R-squared:  0.05872,   Adjusted R-squared:  0.04546 
F-statistic: 4.429 on 1 and 71 DF,  p-value: 0.03888

summary(lm(`CA2-CA3_right_cm3` ~ neu_2d_angle_mean_mean,cordf))


Call:
lm(formula = `CA2-CA3_right_cm3` ~ neu_2d_angle_mean_mean, data = cordf)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.082499 -0.026207 -0.000673  0.023983  0.096770 

Coefficients:
                       Estimate Std. Error t value            Pr(>|t|)    
(Intercept)            0.195934   0.013921  14.075 <0.0000000000000002 ***
neu_2d_angle_mean_mean 0.005612   0.006622   0.847                 0.4    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03805 on 71 degrees of freedom
Multiple R-squared:  0.01001,   Adjusted R-squared:  -0.003931 
F-statistic: 0.7181 on 1 and 71 DF,  p-value: 0.3996

JUNK - ignore

---
title: "Hippocampal volume"
author: "Saren H. Seeley"
date: ''
output:
  html_notebook:
    toc: yes
  pdf_document:
    toc: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(warn = FALSE)
knitr::opts_chunk$set(include = TRUE)
knitr::opts_knit$set(root.dir = "/Users/sarenseeley/Dropbox/Postdoc/nwtc_study/")
options(scipen=999)

rm(list = ls())

# load packages
library(emmeans)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(ggthemes)
library(afex)

filter <- dplyr::filter
select <- dplyr::select
rename <- dplyr::rename
```

```{r, include=FALSE}
library(dplyr)
library(readr)
library(purrr)
library(readxl)
library(stringr)
# Define the path to the parent folder containing the subfolders
parent_folder_path <-
  "/Users/sarenseeley/Dropbox/Postdoc/nwtc_study/data/volBrain/HIPS"

# List all CSV files in the parent folder and its subfolders
csv_files <- NULL
csv_files <-
  list.files(
    path = parent_folder_path,
    pattern = glob2rx("report*.csv"),
    recursive = TRUE,
    full.names = TRUE
  )

# Read each CSV file and store its data as a separate data frame in a list
data_list <-
  map(csv_files,
      ~ read.csv(
        .x,
        sep = ";",
        stringsAsFactors = FALSE,
        check.names = FALSE
      ))

# Combine all the data frames into a single data frame, stacking them vertically
volBrain_Combined_df <- bind_rows(data_list)

all_subject_ids <-
  read.csv(
    "/Users/sarenseeley/Dropbox/Postdoc/nwtc_study/data/volBrain/HIPS/_volBrain HIPS log.csv",
    stringsAsFactors = FALSE
  )

# Extract the two columns you want to add, but only for the rows matching volBrain_Combined_df
record_id <- all_subject_ids[[5]]#[1:nrow(volBrain_Combined_df)]

# Add the new columns to the volBrain_Combined_df data frame as the first two columns
volBrain_Combined_df <- cbind(record_id, volBrain_Combined_df)

snt_data <-
  read_excel(
    "/Users/sarenseeley/Dropbox/Postdoc/mentoring/Isabella Fonseca/SNT/data/SNT_data/SNT-behavior_n80.xlsx"
  ) %>% rename(record_id = sub_id)
snt_data$record_id <-
  str_replace(snt_data$record_id, "nwtc", "NWTC-")

df <- full_join(volBrain_Combined_df, snt_data, by = "record_id")
df <-
  df %>% filter(!record_id == "NWTC-1172" & !record_id == "NWTC-034")
colnames(df) <- gsub(" ", "_", colnames(df))
colnames(df) <- gsub("%", "pc", colnames(df))


other <- readRDS("data/_cleaned/clean_v2_dataset_2024.rds")
grp <- read.csv("notes/_nwtc_bids_key.csv")[1:97, c(1:3)]
df <- left_join(df, other, by = "record_id")
df <- left_join(df, grp, by = "record_id")
df <-
  df %>% mutate(
    group.use = factor(
      group.use,
      levels = c("Resilient", "Low-exposed", "PTSD"),
      labels = c("Highly resilient", "Lower WTC-exposed", "PTSD"),
      ordered = FALSE
    ),
    health_meds_psych.cns_bin = if_else(health_meds_psych.cns == 0, 0, 1),
    ptsd = if_else(group.use == "PTSD", 1, 0)
  ) %>% rename(meds_allPsych = health_meds_psych.cns,
               meds_allPsych_bin = health_meds_psych.cns_bin)
df$meds_allPsych_bin[df$bids_id == "sub-084"] <-
  1 # zolpidem night before 4pm scan

```


# Group differences

## Everyone (n=97)

```{r}
# long dataset
df_long <-
  df %>% select(
    record_id,
    group.use,
    Sex,
    Age,
    tot_ctq,
    tot_tleq_nonW,
    exposures_count,
    ptsd,
    (contains("_pc") |
       contains("_Asym") |
       contains("_cm3")) &
      !contains("scid")
  ) #scid pcp shouldn't be included

df_long <- df_long %>%
  tidyr::pivot_longer(
    cols = -c(
      record_id,
      group.use,
      Sex,
      Age,
      tot_ctq,
      tot_tleq_nonW,
      exposures_count,
      ptsd,
      ICV_cm3
    ),
    names_to = "region",
    values_to = "value"
  )
df_long[12:14] <-
  str_split_fixed(df_long$region, "_", 3) # split column into 3 columns, by underscore
df_long <-
  df_long %>% select(!region) %>% rename(region = V1,
                                         lat = V2,
                                         measure = V3) # which subfield, which hemisphere, which metric (cm3, percent, asymmetry index)
df_long1 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "cm3" &
                       !region == "Hippocampus") # don't include total hippocampus
df_long2 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "pc" & !region == "Hippocampus") %>% group_by(region) %>%
  mutate(z_value = scale(value))
df_long3 <-
  df_long %>% filter(lat == "asymmetry" & !region == "Hippocampus")
```


### MODEL 1: Volume in cm^3 controlling for total ICV

_For pairwise comparisons: P value threshold, corrected for multiple comparisons = .05/(5 regions x 2 hemispheres) = .005_

```{r}
# MODEL 1: Volume in cm^3 controlling for total ICV
m1 <-
  aov_ez(
    id = "record_id",
    dv = "value",
    df_long1,
    between = c("group.use"),
    within = c("region", "lat"),
    covariate = c("ICV_cm3"),
    factorize = FALSE
  )
nice(m1)
emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region)
emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region | group.use)

ggplot(df_long1, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Absolute volume (cm^3)")
```

### MODEL 2: Percent (aka normalized volume)

_For pairwise comparisons: P value threshold, corrected for multiple comparisons = .05/(5 regions x 2 hemispheres) = .005_

```{r}
# MODEL 2: Percent (aka normalized volume)
m1 <-
  aov_car(
    value ~  group.use * region * lat + Error(record_id / region / lat),
    df_long2,
    factorize = FALSE,
    type = 3
  )
nice(m1)
emmeans(m1, ~ group.use |
          region | lat, model = "multivariate", contr = "pairwise")
emmeans(m1, ~ lat |
          region | group.use, model = "multivariate", contr = "pairwise")
emmeans(m1, ~ lat |
          group.use, model = "multivariate", contr = "pairwise")

ggplot(df_long2, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Normalized volume (%)")
```
### MODEL 3: Asymmetry index

_For pairwise comparisons: P value threshold, corrected for multiple comparisons = .05/(5 regions) = .01_


```{r}
# MODEL 3: Asymmetry index
m1 <-
  aov_car(
    value ~  group.use * region + Error(record_id / region),
    df_long3,
    factorize = TRUE,
    type = 3
  )
nice(m1)
emmeans(m1, ~ group.use |
          region, model = "multivariate", contr = "pairwise")
emmeans(m1, ~ group.use, model = "multivariate", contr = "pairwise")


ggplot(df_long3, aes(group.use, value, fill = region)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ lat * region, scales = "fixed") + labs(title =
                                                                                                            "Asymmetry index")
```





## Only valid SNT participants (n=73)

```{r}
below_50_df <-
  readRDS(
    "/Users/sarenseeley/Dropbox/Postdoc/mentoring/Isabella Fonseca/SNT/isabella_r_scripts/below_50_df.rds"
  ) %>% rename(record_id = sub_id)
drop_ids <-
  c(below_50_df$record_id, "NWTC-034") #excludes NWTC-034 [lots of missing trials]
# long dataset
df_long <-
  df %>% select(
    record_id,
    group.use,
    Sex,
    Age,
    tot_ctq,
    tot_tleq_nonW,
    exposures_count,
    ptsd,
    (contains("_pc") |
       contains("_Asym") |
       contains("_cm3")) &
      !contains("scid")
  ) %>% filter(!record_id %in% drop_ids) #scid pcp shouldn't be included

df_long <- df_long %>%
  tidyr::pivot_longer(
    cols = -c(
      record_id,
      group.use,
      Sex,
      Age,
      tot_ctq,
      tot_tleq_nonW,
      exposures_count,
      ptsd,
      ICV_cm3
    ),
    names_to = "region",
    values_to = "value"
  )
df_long[12:14] <-
  str_split_fixed(df_long$region, "_", 3) # split column into 3 columns, by underscore
df_long <-
  df_long %>% select(!region) %>% rename(region = V1,
                                         lat = V2,
                                         measure = V3) # which subfield, which hemisphere, which metric (cm3, percent, asymmetry index)
df_long1 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "cm3" &
                       !region == "Hippocampus") # don't include total hippocampus
df_long2 <-
  df_long %>% filter(!measure == "" &
                       !lat == "total" &
                       measure == "pc" & !region == "Hippocampus") %>% group_by(region) %>%
  mutate(z_value = scale(value))
df_long3 <-
  df_long %>% filter(lat == "asymmetry" & !region == "Hippocampus")
```


### MODEL 1: Volume in cm^3 controlling for total ICV

```{r}
# MODEL 1: Volume in cm^3 controlling for total ICV
m1 <-
  aov_ez(
    id = "record_id",
    dv = "value",
    df_long1,
    between = c("group.use"),
    within = c("region", "lat"),
    covariate = c("ICV_cm3"),
    factorize = FALSE
  )
nice(m1)
emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region)
emmeans(m1, model = "multivariate", contr = "pairwise", ~ lat |
          region | group.use)

ggplot(df_long1, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Absolute volume (cm^3)")
```

### MODEL 2: Percent (aka normalized volume)


```{r}
# MODEL 2: Percent (aka normalized volume)
m1 <-
  aov_car(
    value ~  group.use * region * lat + Error(record_id / region / lat),
    df_long2,
    factorize = FALSE,
    type = 3
  )
nice(m1)
emmeans(m1, ~ group.use |
          region | lat, model = "multivariate", contr = "pairwise")
emmeans(m1, ~ lat |
          region | group.use, model = "multivariate", contr = "pairwise")
emmeans(m1, ~ lat |
          group.use, model = "multivariate", contr = "pairwise")

ggplot(df_long2, aes(group.use, value, fill = lat)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1)) + geom_hline(yintercept = 0) + facet_wrap( ~ region, scales = "free") + labs(title =
                                                                                                     "Normalized volume (%)")
```


### MODEL 3: Asymmetry index

```{r}
# MODEL 3: Asymmetry index
m1 <-
  aov_car(
    value ~  group.use * region + Error(record_id / region),
    df_long3,
    factorize = TRUE,
    type = 3
  )
nice(m1)
emmeans(m1, ~ group.use | region, contr = "pairwise")

ggplot(df_long3, aes(group.use, value, fill = region)) +
  stat_summary(
    geom = "col",
    fun = mean,
    position = "dodge",
    color = "black"
  ) +
  stat_summary(
    geom = "errorbar",
    fun.data = mean_se,
    position = position_dodge(.9),
    width = 0.25,
    color = "black"
  )   + theme_clean() + theme(axis.text.x=element_text(angle=45, hjust=1))+ geom_hline(yintercept = 0) + facet_wrap( ~ lat * region, scales = "fixed") + labs(title =
                                                                                                            "Asymmetry index")
```





# SNT relationships with hippocampal volume

## Normalized volume (percent)

```{r}
library(corrplot)
cordf <-
  df%>% filter(!record_id %in% drop_ids & !is.na(affil_mean_mean)) %>% select(contains("mean_mean"), ends_with("pc") &
                  !contains("scid")) 
cor_result <- Hmisc::rcorr(as.matrix(cordf))
cor_matrix <- cor_result$r  # Correlation matrix
p_matrix <- cor_result$P    # P-values matrix

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.05)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .5,
  title = "p <.05",
  tl.pos  = "ld",
  type = "lower"
)

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.01)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .5,
  title = "p <.01",
  tl.pos  = "ld",
  type = "lower"
)


ggplot(cordf, aes(x=`CA2-CA3_right_pc`, y=pov_2d_dist_mean_mean)) +
    geom_point(size = 1) + theme_clean() + geom_smooth(method = "lm")
summary(lm(`CA2-CA3_right_pc` ~ pov_2d_dist_mean_mean,cordf))


ggplot(cordf, aes(x=`CA2-CA3_right_pc`, y=pov_3d_dist_mean_mean)) +
    geom_point(size = 1) + theme_clean() + geom_smooth(method = "lm")
summary(lm(`CA2-CA3_right_pc` ~ pov_3d_dist_mean_mean,cordf))


ggplot(cordf, aes(x=`CA2-CA3_right_pc`, y=neu_3d_angle_mean_mean)) +
    geom_point(size = 1) + theme_clean() + geom_smooth(method = "lm")
summary(lm(`CA2-CA3_right_pc` ~ neu_3d_angle_mean_mean,cordf))


```
## Asymmetry

```{r}
library(corrplot)
cordf <-
  df %>% filter(!record_id %in% drop_ids & !is.na(affil_mean_mean))%>% select(contains("mean_mean"), ends_with("asymmetry"))
cor_result <- Hmisc::rcorr(as.matrix(cordf))
cor_matrix <- cor_result$r  # Correlation matrix
p_matrix <- cor_result$P    # P-values matrix

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.05)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .7,
  title = "p <.05",
  tl.pos  = "ld",
  type = "lower"
)


significant_cor_matrix <- cor_matrix * (p_matrix <= 0.01)

corrplot(
  significant_cor_matrix,
  method = "color",
  tl.col = "black",
  outline = TRUE,
  addrect = TRUE,
  tl.cex = .7,
  title = "p <.01",
  tl.pos  = "ld",
  type = "lower"
)


```

Regressions also work with cm^3 controlling for ICV:

```{r}
cordf <-
    df%>% filter(!record_id %in% drop_ids & !is.na(affil_mean_mean)) %>% select(contains("mean_mean"), ends_with("cm3"))
summary(lm(`CA2-CA3_right_cm3` ~ pov_2d_dist_mean_mean,cordf))
summary(lm(`CA2-CA3_right_cm3` ~ pov_3d_dist_mean_mean,cordf))
summary(lm(`CA2-CA3_right_cm3` ~ neu_2d_angle_mean_mean,cordf))
```

# JUNK - ignore

```{r, include=FALSE,echo=FALSE,eval=FALSE}
cordf<-df %>%
  #filter(group.use=="PTSD")%>% 
  select(contains("tot_tleq")|contains("tot_ctq")&!ends_with("cm3")&!ends_with("cat")&!contains("qles"),ends_with("pc") & !contains("scid"),ends_with("asymmetry"),exposures_count) 
cor_result <- Hmisc::rcorr(as.matrix(cordf))
cor_matrix <- cor_result$r  # Correlation matrix
p_matrix <- cor_result$P    # P-values matrix

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.05)

corrplot::corrplot(significant_cor_matrix, method = "color", tl.col = "black",outline = TRUE, addrect = TRUE,tl.cex = .7)

summary(lm(`Hippocampus_asymmetry`~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(`CA1_asymmetry`~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(`CA2-CA3_asymmetry`~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(`CA4-DG_asymmetry`~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(`SR-SL-SM_asymmetry`~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(`Subiculum_asymmetry`~ tot_ctq + tot_tleq_exclCT_nonW,df))

summary(lm(abs(`Hippocampus_asymmetry`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`CA1_asymmetry`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`CA2-CA3_asymmetry`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`CA4-DG_asymmetry`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`SR-SL-SM_asymmetry`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`Subiculum_asymmetry`)~ tot_ctq + tot_tleq_exclCT_nonW,df))

summary(lm(abs(`Hippocampus_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW ,df))
summary(lm(abs(`CA1_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`CA2-CA3_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`CA4-DG_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`SR-SL-SM_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`Subiculum_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))

summary(lm(abs(`Hippocampus_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW ,df))
summary(lm(abs(`CA1_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`CA2-CA3_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`CA4-DG_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`SR-SL-SM_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))
summary(lm(abs(`Subiculum_total_pc`)~ tot_ctq + tot_tleq_exclCT_nonW,df))

nice(aov_ez(id="record_id", dv="Hippocampus_left_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="Hippocampus_right_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA1_left_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA1_right_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA2-CA3_left_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA2-CA3_right_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA4-DG_left_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA4-DG_right_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="SR-SL-SM_left_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="SR-SL-SM_right_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="Subiculum_left_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="Subiculum_right_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))

nice(aov_ez(id="record_id", dv="Hippocampus_total_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA1_total_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA2-CA3_total_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA4-DG_total_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="SR-SL-SM_total_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))
nice(aov_ez(id="record_id", dv="Subiculum_total_cm3", data=df, between=c("clin_tot_caps5_pm"), covariate="ICV_cm3", factorize=FALSE))

df<-df %>% mutate(Hippocampus_asymmetry_abs=abs(Hippocampus_asymmetry),
                  CA1_asymmetry_abs=abs(CA1_asymmetry),
                  `CA2-CA3_asymmetry_abs`=abs(`CA2-CA3_asymmetry`),
                  `CA4-DG_asymmetry_abs`=abs(`CA4-DG_asymmetry`),
                  `SR-SL-SM_asymmetry_abs`=abs(`SR-SL-SM_asymmetry`),
                  `Subiculum_asymmetry_abs`=abs(`Subiculum_asymmetry`))

nice(aov_ez(id="record_id", dv="Hippocampus_asymmetry", data=df, between=c("ptsd"), covariate=c("tot_tleq_nonW"),factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA1_asymmetry", data=df, between=c("ptsd"), covariate=c("tot_tleq_nonW"),factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA2-CA3_asymmetry", data=df,between=c("ptsd"), covariate=c("tot_tleq_nonW"),factorize=FALSE))
nice(aov_ez(id="record_id", dv="CA4-DG_asymmetry", data=df,between=c("ptsd"), covariate=c("tot_tleq_nonW"),factorize=FALSE))
nice(aov_ez(id="record_id", dv="SR-SL-SM_asymmetry", data=df,between=c("group.use"),covariate=c("tot_tleq_nonW"),factorize=FALSE))
nice(aov_ez(id="record_id", dv="SR-SL-SM_asymmetry", data=df,between=c("group.use"),covariate=c("tot_tleq_nonW"),factorize=FALSE))
nice(aov_ez(id="record_id", dv="Subiculum_asymmetry",data=df, between=c("ptsd"), covariate=c("tot_tleq_nonW"),factorize=FALSE))


emmeans(aov_ez(id="record_id", dv="SR-SL-SM_asymmetry", data=df,between=c("group.use"),covariate=c("tot_tleq_nonW"),factorize=FALSE), ~group.use,contr="pairwise")


cordf<-df %>% select(starts_with("cog"),ends_with("pc") & !contains("scid")|ends_with("asymmetry"))

cor.mat<-cor_mat(cordf)
cor.mat %>% cor_plot(label = FALSE, insignificant = "blank",significant.level = .05) 


cor_result <- Hmisc::rcorr(as.matrix(cordf))
cor_matrix <- cor_result$r  # Correlation matrix
p_matrix <- cor_result$P    # P-values matrix

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.01)

corrplot(significant_cor_matrix, method = "color", tl.col = "black",outline = TRUE, addrect = TRUE,tl.cex = .7)

below_50_df <- readRDS("/Users/sarenseeley/Dropbox/Postdoc/mentoring/Isabella Fonseca/SNT/isabella_r_scripts/below_50_df.rds") %>% rename(record_id=sub_id)
df <- df %>% filter(!record_id %in% below_50_df$record_id & !missing_trials >30) #excludes NWTC-034 [lots of missing trials]
# SNT
cordf<-df %>% select(contains("mean_mean") & !contains("affil"),ends_with("total_pc") & !contains("scid") |ends_with("asymmetry"))
cordf<-df %>% select(contains("affil_mean") & !contains("power_"),ends_with("total_pc") & !contains("scid")|ends_with("asymmetry"))
cordf<-df %>% select(contains("dist_mean") & !contains("power_"),ends_with("total_pc") & !contains("scid")|ends_with("asymmetry"))
cor_result <- Hmisc::rcorr(as.matrix(cordf))
cor_matrix <- cor_result$r  # Correlation matrix
p_matrix <- cor_result$P    # P-values matrix

significant_cor_matrix <- cor_matrix * (p_matrix <= 0.05)

corrplot(significant_cor_matrix, method = "color", tl.col = "black",outline = TRUE, addrect = TRUE,tl.cex = .7)


cor.test(df$pov_2d_dist_mean_mean,df$`CA2-CA3_total_pc`)
cor.test(df$pov_3d_dist_mean_mean,df$`CA2-CA3_total_pc`)

cor.test(df$pov_2d_dist_mean_mean[df$group.use=="PTSD"],df$`CA2-CA3_total_pc`[df$group.use=="PTSD"])
cor.test(df$pov_2d_dist_mean_mean[df$group.use=="Lower WTC-exposed"],df$`CA2-CA3_total_pc`[df$group.use=="Lower WTC-exposed"])
cor.test(df$pov_2d_dist_mean_mean[df$group.use=="Highly resilient"],df$`CA2-CA3_total_pc`[df$group.use=="Highly resilient"])

cor.test(df$pov_3d_dist_mean_mean[df$group.use=="PTSD"],df$`CA2-CA3_total_pc`[df$group.use=="PTSD"])
cor.test(df$pov_3d_dist_mean_mean[df$group.use=="Lower WTC-exposed"],df$`CA2-CA3_total_pc`[df$group.use=="Lower WTC-exposed"])
cor.test(df$pov_3d_dist_mean_mean[df$group.use=="Highly resilient"],df$`CA2-CA3_total_pc`[df$group.use=="Highly resilient"])

plot(df$pov_2d_dist_mean_mean[df$group.use=="PTSD"],df$`CA2-CA3_total_pc`[df$group.use=="PTSD"])
plot(df$pov_3d_dist_mean_mean[df$group.use=="PTSD"],df$`CA2-CA3_total_pc`[df$group.use=="PTSD"])

```


Hippocampal volume

Saren H. Seeley

Group differences

Everyone (n=97)

MODEL 1: Volume in cm^3 controlling for total ICV

MODEL 2: Percent (aka normalized volume)

MODEL 3: Asymmetry index

Only valid SNT participants (n=73)

MODEL 1: Volume in cm^3 controlling for total ICV

MODEL 2: Percent (aka normalized volume)

MODEL 3: Asymmetry index

SNT relationships with hippocampal volume

Normalized volume (percent)

Asymmetry

JUNK - ignore