PPI: Data Wrangling Report

Data Wrangling
Parameters
- MRI
- PPI
- HRF
PSY Variables
PHYS Variable
- Input
- Detrend
- Upsample
- Convolve
PPI Variable
Design Matrix

Data Wrangling

Hmisc::list.tree(params$data_wrangling, depth = 3)

##  params$data_wrangling = list 5 (230616 bytes)
## .  params = list 3
## . .  mri = list 2
## . .  ppi = list 3
## . .  hrf = list 1( data.frame )
## .  psy_var = list 7
## . .  events = list 3( data.frame )
## . .  trial_type_by_volume = list 2( data.frame )
## . .  contrast_table = list 3( data.frame )
## . .  contrast = list 3( data.frame )
## . .  upsample = list 3( data.frame )
## . .  convolve = list 3( data.frame )
## . .  downsample = list 3( data.frame )
## .  phys_var = list 4
## . .  input = list 1( data.frame )
## . .  detrend = list 1( data.frame )
## . .  upsample = list 1( data.frame )
## . .  deconvolve = list 1( data.frame )
## .  ppi_var = list 3
## . .  interaction = list 3( data.frame )
## . .  convolve = list 3( data.frame )
## . .  downsample = list 3( data.frame )
## .  design_matrix = list 7( data.frame )
## . .  psy_stimulus_vs_response = double 105= 0 0.10078 1.5873 ...
## . .  psy_music_vs_tones = double 105= 0 -0.26874 -4.2329 ...
## . .  psy_positive_music_vs_negative_music = double 105= 0 0 0 0 0 0 0 0 ...
## . .  phys = double 105= 3.0961 6.6414 9.1888 ...
## . .  ppi_stimulus_vs_response = double 105= 0 0.13324 1.1015 ...
## . .  ppi_music_vs_tones = double 105= 0 -0.35531 -2.9373 ...
## . .  ppi_positive_music_vs_negative_music = double 105= 0 0 0 0 0 0 0 0 ...
## . A  row.names = integer 105= 1 2 3 4 5 6 7 8 ...

Parameters

MRI

params_mri <- params$data_wrangling$params$mri %>%
  as.data.frame() %>%
  t() %>%
  as.data.frame() %>%
  rownames_to_column("parameter")
colnames(params_mri)[2] <- "value"
  kable(params_mri) %>%
  kable_styling(., bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left")

parameter	value
tr	3
n_volumes	105

PPI

params_ppi <- params$data_wrangling$params$ppi %>%
  as.data.frame() %>%
  t() %>%
  as.data.frame() %>%
  rownames_to_column("parameter")
colnames(params_ppi)[2] <- "value"
  kable(params_ppi) %>%
  kable_styling(., bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left")

parameter	value
upsample_factor	16
detrend_factor	2
deconvolve	1

HRF

visualize_time_series(params$data_wrangling$params$hrf)

PSY Variables

Events

params$data_wrangling$psy_var$events %>% 
  kable() %>%
  kable_styling(., bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left")

onset	duration	trial_type
0.0	33.0	tones
33.0	3.0	response
36.0	31.5	negative_music
67.5	3.0	response
70.5	31.5	tones
102.0	3.0	response
105.0	31.5	positive_music
136.5	3.0	response
139.5	31.5	tones
171.0	3.0	response
174.0	31.5	negative_music
205.5	3.0	response
208.5	31.5	tones
240.0	3.0	response
243.0	31.5	positive_music
274.5	3.0	response
277.5	31.5	tones
309.0	3.0	response

Trial Type by Volume

# params$data_wrangling$psy_var$trial_type_by_volume %>%
#   kable() %>%
#   kable_styling(., bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left") 

params$data_wrangling$psy_var$trial_type_by_volume %>%
  mutate(value = 0) %>%
  ggplot(., aes(volume, value, color = trial_type)) +
  geom_point() +
  theme_minimal() +
  labs(y = NULL) +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        legend.position="bottom") +
  scale_x_continuous(breaks = seq(0, max(params$data_wrangling$psy_var$trial_type_by_volume$volume), 10))

Contrast Table

params$data_wrangling$psy_var$contrast_table %>%
  kable() %>%
  kable_styling(., bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F, position = "left")

stimulus_vs_response	music_vs_tones	positive_music_vs_negative_music
0.25	0.3333333	-0.5
0.25	0.3333333	0.5
-0.75	0.0000000	0.0
0.25	-0.6666667	0.0

Contrast

visualize_time_series(params$data_wrangling$psy_var$contrast, ncol = 1)

Upsample

params$data_wrangling$psy_var$upsample %>% visualize_time_series(., ncol = 1, scales = "free_y")

Convolve

params$data_wrangling$psy_var$convolve %>% visualize_time_series(., ncol = 1, scales = "free_y")

Downsample

params$data_wrangling$psy_var$downsample %>% visualize_time_series(., ncol = 1, scales = "free_y")

PHYS Variable

Input

params$data_wrangling$phys_var$input %>% visualize_time_series()

Detrend

params$data_wrangling$phys_var$detrend %>% visualize_time_series()

Upsample

params$data_wrangling$phys_var$upsample %>% visualize_time_series()

Convolve

params$data_wrangling$phys_var$deconvolve %>% visualize_time_series()

PPI Variable

Interaction

params$data_wrangling$ppi_var$interaction %>% visualize_time_series(., ncol = 1, scales = "free_y")

Convolve

params$data_wrangling$ppi_var$convolve %>% visualize_time_series(., ncol = 1, scales = "free_y")

Downsample

params$data_wrangling$ppi_var$downsample %>% visualize_time_series(., ncol = 1, scales = "free_y")

Design Matrix

params$data_wrangling$design_matrix %>% visualize_time_series_heatmap(., transpose = T, scale_data = "min-max", palette = "Greys", variable_axis_label = NULL, caption = "Note: Variables are scaled to min-max for visualization purposes")