All code for this document is located at here.
Although the neurohcp package was built specifically for the the Human Connectome Project (HCP) data, it provides the worker functions for accessing an Amazon S3 bucket and downloading data. We have adapted these functions to work with the Functional Connectomes Project S3 Bucket (fcp-indi) from the INDI initiative. Although the code is the same, but the bucket is changed, we also must specify we do not want to sign the request as fcp-indi is an open bucket and the authentication we used for signing fails if we add keys to the data when unneccesary.
The data is freelly available;.
We will install the neurohcp package using the Neuroconductor installer:
source("http://neuroconductor.org/neurocLite.R")
neuro_install("neurohcp", release = "stable")Once these are set, the functions of neurohcp are ready to use. To test that the API keys are set correctly, one can run bucketlist:
neurohcp::bucketlist(sign = FALSE) Bucket CreationDate
1 hcp-openaccess 2018-08-13T15:10:17.000Z
2 hcp-openaccess-logfiles 2018-07-25T21:19:33.000Z
3 hcp-openaccess-logs-temp 2018-04-20T15:38:14.000Z
4 hcp-openaccess-logstorage-temp 2018-06-08T15:51:53.000Z
5 hcp-openaccess-test 2018-06-29T13:17:02.000Z
6 hcp-openaccess-trail-temp 2018-04-20T15:43:24.000ZWe see that fcp-indi is a bucket that we have access to, and therefore have access to the data.
In the neurohcp package, there is a data set indicating the scans read for each subject, named hcp_900_scanning_info. We can subset those subjects that have diffusion tensor imaging:
ids_with_dwi = hcp_900_scanning_info %>%
filter(scan_type %in% "dMRI") %>%
select(id) %>%
unique
head(ids_with_dwi)# A tibble: 6 x 1
id
<chr>
1 100307
2 100408
3 101006
4 101107
5 101309
6 101410Let us download the complete directory of diffusion data using download_hcp_dir:
r = download_hcp_dir("HCP/100307/T1w/Diffusion", verbose = FALSE)
print(basename(r$output_files))[1] "bvals" "bvecs"
[3] "data.nii.gz" "grad_dev.nii.gz"
[5] "nodif_brain_mask.nii.gz"This diffusion data is the data that can be used to create summaries such as fractional anisotropy and mean diffusivity.
If we create a new column with all the directories, we can iterate over these to download all the diffusion data for these subjects from the HCP database.
ids_with_dwi = ids_with_dwi %>%
mutate(id_dir = paste0("HCP/", id, "/T1w/Diffusion"))We can also download a single file using download_hcp_file. Here we will simply download the bvals file:
ret = download_hcp_file("HCP/100307/T1w/Diffusion/bvals", verbose = FALSE)devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.2 (2020-06-22)
os macOS Catalina 10.15.6
system x86_64, darwin17.0
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2020-10-02
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib
assertthat 0.2.1 2019-03-21 [2]
aws.s3 0.3.21 2020-04-07 [1]
aws.signature 0.6.0 2020-06-01 [2]
backports 1.1.10 2020-09-15 [1]
base64enc 0.1-3 2015-07-28 [2]
callr 3.4.4 2020-09-07 [1]
cli 2.0.2 2020-02-28 [2]
colorout * 1.2-2 2020-06-01 [2]
crayon 1.3.4 2017-09-16 [2]
curl 4.3 2019-12-02 [2]
desc 1.2.0 2020-06-01 [2]
devtools 2.3.1.9000 2020-08-25 [2]
digest 0.6.25 2020-02-23 [2]
dplyr * 1.0.2 2020-08-18 [2]
ellipsis 0.3.1 2020-05-15 [2]
evaluate 0.14 2019-05-28 [2]
fansi 0.4.1 2020-01-08 [2]
fs 1.5.0 2020-07-31 [2]
generics 0.0.2 2018-11-29 [2]
glue 1.4.2 2020-08-27 [1]
htmltools 0.5.0 2020-06-16 [2]
httr 1.4.2 2020-07-20 [2]
knitr 1.30 2020-09-22 [1]
lifecycle 0.2.0 2020-03-06 [2]
magrittr 1.5 2014-11-22 [2]
memoise 1.1.0 2017-04-21 [2]
neurohcp * 0.9.0 2020-10-01 [1]
pillar 1.4.6 2020-07-10 [2]
pkgbuild 1.1.0 2020-07-13 [2]
pkgconfig 2.0.3 2019-09-22 [2]
pkgload 1.1.0 2020-05-29 [2]
prettyunits 1.1.1 2020-01-24 [2]
processx 3.4.4 2020-09-03 [1]
ps 1.3.4 2020-08-11 [2]
purrr 0.3.4 2020-04-17 [2]
R6 2.4.1 2019-11-12 [2]
remotes 2.2.0 2020-07-21 [2]
rlang 0.4.7.9000 2020-09-09 [1]
rmarkdown 2.3 2020-06-18 [2]
rprojroot 1.3-2 2018-01-03 [2]
rstudioapi 0.11 2020-02-07 [2]
sessioninfo 1.1.1 2018-11-05 [2]
stringi 1.5.3 2020-09-09 [1]
stringr 1.4.0 2019-02-10 [2]
testthat 2.99.0.9000 2020-09-17 [1]
tibble 3.0.3 2020-07-10 [2]
tidyselect 1.1.0 2020-05-11 [2]
usethis 1.6.1.9001 2020-08-25 [2]
utf8 1.1.4 2018-05-24 [2]
vctrs 0.3.4 2020-08-29 [1]
withr 2.3.0 2020-09-22 [1]
xfun 0.18 2020-09-29 [1]
xml2 1.3.2 2020-04-23 [2]
yaml 2.2.1 2020-02-01 [2]
source
CRAN (R 4.0.0)
CRAN (R 4.0.2)
Github (cloudyr/aws.signature@5689733)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
Github (jalvesaq/colorout@726d681)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
Github (muschellij2/desc@b0c374f)
Github (r-lib/devtools@df619ce)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
local
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
Github (r-lib/rlang@60c0151)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
Github (r-lib/testthat@fbbd667)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
Github (r-lib/usethis@860c1ea)
CRAN (R 4.0.0)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.0)
CRAN (R 4.0.0)
[1] /Users/johnmuschelli/Library/R/4.0/library
[2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library