Personalized Instrinsic Network Topography (PINT) is a techinique for using fMRI data to find "personalized" regions of interest for sampling intrinsic connectivity presented in this paper:
Dickie EW, Ameis SH, Shahab S, Calarco N, Smith DE, Miranda D, et al. Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder. Biol Psychiatry [Internet]. 2018 Mar 17; Preprint availble here: https://www.biorxiv.org/content/early/2017/07/11/161893
For each participant in a dataset, the PINT algorithm shifts the locations of so-called template regions of interest (ROIs) to a nearby cortical location, or “personalized” ROI, that maximizes the correlation of the ROI with the rest of the ROIs from its network.
The "tempate" regions of interest are defined by an input text file. The input regions used for the Dickie et al (2018) paper are available for download here
Personalized Instrinsic Network Topography is run using the ciftify_PINT_vertices function.
Usage:
ciftify_PINT_vertices [options] <func.dtseries.nii> <left-surface.gii> <right-surface.gii> <input-vertices.csv> <outputprefix>
Arguments:
<func.dtseries.nii> Paths to directory source image
<left-surface.gii> Path to template for the ROIs of network regions
<right-surface.gii> Surface file .surf.gii to read coordinates from
<input-vertices.csv> Table of template vertices from which to Start
<outputprefix> Output csv file
Options:
--outputall Output vertices from each iteration.
--pre-smooth FWHM Add smoothing [default: 0] for PINT iterations. See details.
--sampling-radius MM Radius [default: 6] in mm of sampling rois
--search-radius MM Radius [default: 6] in mm of search rois
--padding-radius MM Radius [default: 12] in mm for min distance between roi centers
--pcorr Use maximize partial correlation within network (instead of pearson).
--corr Use full correlation instead of partial (default debehviour is --pcorr)
-v,--verbose Verbose logging
--debug Debug logging in Erin's very verbose style
-h,--help Print help
Note before running PINT, you first need preprossed fMRI data in cifti format that has been mapped to the LR_32k surface. For more on doing that, check out the rest of the ciftify package.
For the example, we will continue from the outputs generated in this example tutorial.
Note: we will take the clean functional file
ciftify_PINT_vertices is run for sub-50004 like follows.
Note: we will take the "cleanned" functional rest file as our input
Note: for simplicity (but recommended), we placed the Yeo7_2011_80verts.csv file, downloaded from here. Inside the our output directory - PINT_out.
export CIFTIFY_WORKDIR=/path/to/derviatives/ciftify
# note I like to output the files into a separate directory for each input
mkdir PINT_out/sub-50004
ciftify_PINT_vertices --pcorr \
${CIFTIFY_WORKDIR}/sub-50004/MNINonLinear/clean.dtseries.nii \
${CIFTIFY_WORKDIR}/sub-50004/MNINonLinear/fsaverage_LR32k/sub-50004.L.midthickness.32k_fs_LR.surf.gii \
${CIFTIFY_WORKDIR}/sub-50004/MNINonLinear/fsaverage_LR32k/sub-50004.R.midthickness.32k_fs_LR.surf.gii \
/PINT_out/Yeo7_2011_80verts.csv \
/PINT_out/sub-50004/sub-50004_task-rest
After PINT runs, we should see four output files in /PINT_out/sub-50004
/PINT_out/sub-50004/
├── sub-50004_task-rest_pint.log
├── sub-50004_task-rest_summary.csv
├── sub-50004_task-rest_tvertex_meants.csv
└── sub-50004_task-rest_pvertex_meants.csv
| output file | description |
|---|---|
_pint.log |
log file containing the settings and number of iterations |
_summary.csv |
summary file of vertices, the pvertex column contains the new "personalized" vertex locations |
_tvertex_meants.csv |
The fMRI timecourses extracted from the "template" (i.e. before) PINT ROIs |
_pvertex_meants.csv |
The fMRI timecourses extracted from the "personalized" (i.e. after) PINT ROIs |
After running PINT. We strongly recommend that you generate QC visualizations using cifti_vis_PINT
Usage:
cifti_vis_PINT snaps [options] <func.dtseries.nii> <subject> <PINT_summary.csv>
cifti_vis_PINT subject [options] <func.dtseries.nii> <subject> <PINT_summary.csv>
cifti_vis_PINT index [options]
Arguments:
<func.dtseries.nii> A dtseries file to feed into
ciftify_PINT_vertices.py map
<subject> Subject ID for HCP surfaces
<PINT_summary.csv> The output csv (*_summary.csv) from the PINT
analysis step
Options:
--qcdir PATH Full path to location of QC directory
--ciftify-work-dir PATH The directory for HCP subjects (overrides
CIFTIFY_WORKDIR/ HCP_DATA enivironment variables)
--subjects-filter STR A string that can be used to filter out subject
directories
--roi-radius MM Specify the radius [default: 6] of the plotted rois
(in mm)
--pvertex-col COLNAME The column [default: pvertex] to read the personlized vertices
--hcp-data-dir PATH DEPRECATED, use --ciftify-work-dir instead
-v,--verbose Verbose logging
--debug Debug logging in Erin's very verbose style
-n,--dry-run Dry run
--help Print help
Using the outputs we set above
export CIFTIFY_WORKDIR=/path/to/derviatives/ciftify
# cifti_vis_PINT subject <func.dtseries.nii> <subject> <PINT_summary.csv>
cifti_vis_PINT subject \
${CIFTIFY_WORKDIR}/sub-50004/MNINonLinear/clean.dtseries.nii \
sub-50004 \
PINT_out/sub-50004/sub-50004_task-rest_summary.csvThis will create a QC visualizations inside ${CIFTIFY_WORKDIR}/qc_PINT folder. Note, you can change the output directory for the qc visualizations using teh --qcdir /path/to/qcdir option.
Lastly, when all "subject" level qc pages have been created. cifti_vis_PINT should be called in index mode to create top level index html pages.
export CIFTIFY_WORKDIR=/path/to/derviatives/ciftify
# create the index pages
cifti_vis_PINT index
# view the QA page index
cd ${CIFTIFY_WORKDIR}/qc_PINT
firefox index.html
It's helpful for statistics to combine all the data all scans' _summary.csv files into one larger csv.
Moreover, this utility also recalcuates the distance between the "tvertex" and "pvertex" vertices on a standard surface from the HCP S1200 release. This output - the std_distance column in the concatenated file - was the "distance" metric where effects of age and ASD diagnosis were observed in Dickie et al (2018).
Usage:
ciftify_postPINT1_concat [options] <concatenated-pint> <PINT_summary.csv>...
Arguments:
<concatenated-pint> The concatenated PINT output to write
<PINT_summary.csv> The PINT summary files (repeatable)
Options:
--surfL SURFACE The left surface on to measure distances on (see details)
--surfR SURFACE The right surface to to measure distances on (see details)
--no-distance-calc Will not calculate the distance from the template vertex
--pvertex-col COLNAME The column [default: pvertex] to read the personlized vertices
--debug Debug logging in Erin's very verbose style
-n,--dry-run Dry run
--help Print help
cd PINT_out
# ciftify_postPINT1_concat [options] <concatenated-pint> <PINT_summary.csv>...
ciftify_postPINT1_concat all_PINT_summaries_concat.csv sub*/*_summary.csv
Note - again - the std_distance column in the all_PINT_summaries_concat.csv would be the recommended column for final analyses. It was the "distance" metric where effects of age and ASD diagnosis were observed in Dickie et al (2018).
This utility measures the distance between personalized vertices across subjects. It was important for the test-retest calculation reported in the Dickie et al 2018 paper.
ciftify_postPINT2_sub2sub [options] <concatenated-pint> <output_sub2sub.csv>
Arguments:
<concatenated-pint> The concatenated PINT outputs (csv file)
<output_sub2sub.csv> The outputfile name
Options:
--surfL SURFACE The left surface on to measure distances on (see details)
--surfR SURFACE The right surface to to measure distances on (see details)
--roiidx INT Measure distances for only this roi (default will loop over all ROIs)
--pvertex-col COLNAME The column [default: pvertex] to read the personlized vertices
--debug Debug logging in Erin's very verbose style
-n,--dry-run Dry run
--help Print help
If using reporting on PINT, please cite:
Dickie EW, Ameis SH, Shahab S, Calarco N, Smith DE, Miranda D, et al. Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder. Biol Psychiatry [Internet]. 2018 Mar 17; Available from: http://www.sciencedirect.com/science/article/pii/S0006322318313040 Preprint availble here: https://www.biorxiv.org/content/early/2017/07/11/161893