library(dplyr)
library(fs)
library(readr)
library(stringr)Appendix A — Exclude Scans with “Red” Quality
For each MRI product, all pipeline derivatives have been included. This means that products were included regardless of their quality, and so some products are known to have poor quality–rated “red” or incomparable. This kit walks through one way to incorporate those outputs for FreeSurfer products. For details on how the ratings were generated, see Section 5.2.1.
First, start by loading the table of FreeSurfer outputs. In this example, the cortical parcellations will be used.
aparc <- read_tsv("data/pre-surgery/mris/derivatives/freesurfer/aparc.tsv")
aparc# A tibble: 399,992 × 14
StructName NumVert SurfArea GrayVol ThickAvg ThickStd MeanCurv GausCurv
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bankssts 1360 909 2055 2.52 0.397 0.112 0.02
2 caudalanteriorc… 852 604 1626 2.25 0.887 0.131 0.024
3 caudalmiddlefro… 2870 1857 4018 2.16 0.485 0.104 0.018
4 cuneus 1595 1090 2019 1.74 0.515 0.141 0.027
5 entorhinal 480 306 1366 3.03 0.816 0.105 0.018
6 fusiform 3434 2414 8326 2.91 0.603 0.123 0.023
7 inferiorparietal 4984 3387 7491 2.06 0.508 0.122 0.022
8 inferiortemporal 3816 2614 8405 2.79 0.552 0.118 0.021
9 isthmuscingulate 1167 782 1820 2.14 0.832 0.117 0.021
10 lateraloccipital 6159 4023 8578 2.02 0.536 0.132 0.027
# ℹ 399,982 more rows
# ℹ 6 more variables: FoldInd <dbl>, CurvInd <dbl>, sub <dbl>, ses <chr>,
# hemisphere <chr>, parc <chr>The overall quality ratings (red/yellow/green) for the rawdata are included in the *scans.tsv files. For example
$ cat sub-10003/ses-V1/sub-10003_ses-V1_scans.tsv
filename rating
func/sub-10003_ses-V1_task-cuff_run-01_bold.nii.gz green
dwi/sub-10003_ses-V1_dwi.nii.gz green
func/sub-10003_ses-V1_task-rest_run-01_bold.nii.gz green
func/sub-10003_ses-V1_task-rest_run-02_bold.nii.gz green
anat/sub-10003_ses-V1_T1w.nii.gz green
fmap/sub-10003_ses-V1_acq-fmrib0_dir-AP_epi.nii.gz n/a
fmap/sub-10003_ses-V1_acq-fmrib0_dir-PA_epi.nii.gz n/a
fmap/sub-10003_ses-V1_acq-dwib0_dir-AP_epi.nii.gz n/a
fmap/sub-10003_ses-V1_acq-dwib0_dir-PA_epi.nii.gz n/aTo load the files, first list them all with fs::dir_ls.
scan_files <- dir_ls(
"data/pre-surgery/mris/bids/",
glob = "*scans.tsv",
recurse = TRUE
)
head(scan_files)data/pre-surgery/mris/bids/sub-10005/ses-V1/sub-10005_ses-V1_scans.tsv
data/pre-surgery/mris/bids/sub-10008/ses-V1/sub-10008_ses-V1_scans.tsv
data/pre-surgery/mris/bids/sub-10010/ses-V1/sub-10010_ses-V1_scans.tsv
data/pre-surgery/mris/bids/sub-10011/ses-V1/sub-10011_ses-V1_scans.tsv
data/pre-surgery/mris/bids/sub-10015/ses-V1/sub-10015_ses-V1_scans.tsv
data/pre-surgery/mris/bids/sub-10017/ses-V1/sub-10017_ses-V1_scans.tsvThey can be read and bound together using readr::read_tsv. Note that the BIDS way to represent null values is with 'n/a'.
scans <- read_tsv(scan_files, na = "n/a")
scans# A tibble: 8,247 × 2
filename rating
<chr> <chr>
1 anat/sub-10005_ses-V1_T1w.nii.gz yellow
2 anat/sub-10008_ses-V1_T1w.nii.gz green
3 dwi/sub-10008_ses-V1_dwi.nii.gz green
4 fmap/sub-10008_ses-V1_acq-fmrib0_dir-AP_epi.nii.gz <NA>
5 fmap/sub-10008_ses-V1_acq-fmrib0_dir-PA_epi.nii.gz <NA>
6 func/sub-10008_ses-V1_task-rest_run-01_bold.nii.gz green
7 func/sub-10008_ses-V1_task-cuff_run-01_bold.nii.gz green
8 func/sub-10008_ses-V1_task-rest_run-02_bold.nii.gz green
9 fmap/sub-10008_ses-V1_acq-dwib0_dir-AP_epi.nii.gz <NA>
10 fmap/sub-10008_ses-V1_acq-dwib0_dir-PA_epi.nii.gz <NA>
# ℹ 8,237 more rowsRatings will be used to exclude some FreeSurfer products, and so we only need the ratings for the T1w scans.
t1w_ratings <- scans |> filter(str_detect(filename, "T1w"))In this example, all scans with a rating “red” will be excluded, so filter for those scans.
red_t1w <- t1w_ratings |> filter(rating == "red")
red_t1w# A tibble: 14 × 2
filename rating
<chr> <chr>
1 anat/sub-10144_ses-V1_T1w.nii.gz red
2 anat/sub-10216_ses-V1_T1w.nii.gz red
3 anat/sub-10258_ses-V1_T1w.nii.gz red
4 anat/sub-10267_ses-V1_T1w.nii.gz red
5 anat/sub-10553_ses-V1_T1w.nii.gz red
6 anat/sub-10559_ses-V1_T1w.nii.gz red
7 anat/sub-10667_ses-V1_T1w.nii.gz red
8 anat/sub-10700_ses-V1_T1w.nii.gz red
9 anat/sub-10720_ses-V1_T1w.nii.gz red
10 anat/sub-10796_ses-V1_T1w.nii.gz red
11 anat/sub-10811_ses-V1_T1w.nii.gz red
12 anat/sub-20040_ses-V1_T1w.nii.gz red
13 anat/sub-20224_ses-V1_T1w.nii.gz red
14 anat/sub-25188_ses-V1_T1w.nii.gz red The FreeSurfer rows are defined by the entities sub and ses, so extract these from the filename column.
red_t1w_w_entities <- red_t1w |>
mutate(
sub = str_extract(filename, "(?<=sub-)[[:digit:]]{5}") |> as.integer(),
ses = str_extract(filename, "V[13]")
)Finally, this table with red ratings can be used to exclude participants with a low quality T1w scan.
aparc_wo_red <- aparc |> anti_join(red_t1w_w_entities, by = join_by(sub, ses))
aparc_wo_red# A tibble: 394,766 × 14
StructName NumVert SurfArea GrayVol ThickAvg ThickStd MeanCurv GausCurv
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bankssts 1360 909 2055 2.52 0.397 0.112 0.02
2 caudalanteriorc… 852 604 1626 2.25 0.887 0.131 0.024
3 caudalmiddlefro… 2870 1857 4018 2.16 0.485 0.104 0.018
4 cuneus 1595 1090 2019 1.74 0.515 0.141 0.027
5 entorhinal 480 306 1366 3.03 0.816 0.105 0.018
6 fusiform 3434 2414 8326 2.91 0.603 0.123 0.023
7 inferiorparietal 4984 3387 7491 2.06 0.508 0.122 0.022
8 inferiortemporal 3816 2614 8405 2.79 0.552 0.118 0.021
9 isthmuscingulate 1167 782 1820 2.14 0.832 0.117 0.021
10 lateraloccipital 6159 4023 8578 2.02 0.536 0.132 0.027
# ℹ 394,756 more rows
# ℹ 6 more variables: FoldInd <dbl>, CurvInd <dbl>, sub <dbl>, ses <chr>,
# hemisphere <chr>, parc <chr>