Northwestern University — Neuroimaging Analytics Pipeline

Clinical research R scripts developed during my role as Clinical Research Coordinator at the Northwestern University Mesulam Center for Cognitive Neurology and Alzheimer's Disease. These scripts support multi-modal PET/MRI imaging data management, quality control, and automated reporting for the Imaging Core.

Note: All scripts connect to internal REDCap databases and institutional file systems. No data, credentials, or PHI are included in this repository. See Credential Setup below before running.

Repository Structure

professional-nu-reports/
├── amyloid_pet/
│   └── amyloid_reads.R          # Amyloid PET visual/SUVr discrepancy analysis
├── imaging_dashboard/
│   └── README.md                # In-development R Shiny dashboard (placeholder)
├── imaging_report/
│   ├── imaging_report.Rmd       # Automated HTML imaging report (R Markdown)
│   ├── run_imaging_report.R     # Script to render the report programmatically
│   └── setup_cron.R             # Schedules weekly report via cron job
└── tau_pet_metrics/
    └── Tau_PET_Metrics.Rmd      # Tau PET read discrepancy analysis & reporting

Scripts Overview

amyloid_pet/amyloid_reads.R

Pulls and cleans Amyloid PET data from two methods — IBC (Imaging Core) and GAAIN (UC Berkeley) — then identifies discrepancies between visual clinical reads and quantitative SUVr/Centiloid values. Produces four ggplot2 visualizations and exports discrepancy tables to Excel.

Key techniques: REDCap API, tidyverse, ggplot2, ggrepel, multi-source data merging, threshold-based QC flagging.

imaging_report/imaging_report.Rmd

Parameterized R Markdown report that generates an HTML dashboard of Imaging Core scan metrics across MRI, Amyloid PET, Tau PET, and FDG PET modalities. Includes longitudinal scan timelines, diagnosis distributions, and period-specific counts (budget year and RPPR reporting window).

Key techniques: REDCap API, tidyverse, ggplot2, gt tables, pivot_longer, automated narrative generation.

imaging_dashboard/ (In development)

An interactive R Shiny dashboard planned a companion to imaging_report.Rmd. Will provide real-time participant eligibility tracking and expanded scan metrics for day-to-day Imaging Core monitoring. See the dashboard README.md for full details on planned features and scope.

imaging_report/run_imaging_report.R

Wrapper script that renders imaging_report.Rmd programmatically using rmarkdown::render(), with error handling and logging.

imaging_report/setup_cron.R

Uses the cronR package to schedule run_imaging_report.R to run automatically every Monday at 9:00 AM, producing a fresh weekly report with no manual intervention.

tau_pet_metrics/Tau_PET_Metrics.Rmd

R Markdown report analyzing Tau PET read concordance across researcher and clinician raters for Imaging Core and PPA study scans. Identifies discrepant reads, flags individual raters, and produces summary tables and plots.

Key techniques: REDCap API, pivot_longer/wider, rowwise consensus logic, knitr::kable, ggplot2, scales.

Credential Setup

These scripts authenticate to REDCap via API tokens. Tokens are never stored in this repository.

Option A — Local credentials file (recommended for local use)

Create a file called redcap_api_info.R in your working directory (it is listed in .gitignore and will never be committed):

# redcap_api_info.R  ← DO NOT COMMIT THIS FILE
url            <- "https://redcap.yourInstitution.edu/api/"
imaging_token  <- "YOUR_IMAGING_REDCAP_TOKEN"
dr_token       <- "YOUR_DATA_REQUEST_REDCAP_TOKEN"
uds3_token     <- "YOUR_UDS3_REDCAP_TOKEN"
uds4_token     <- "YOUR_UDS4_REDCAP_TOKEN"

Then source it at the top of each script:

source("redcap_api_info.R")

Option B — Environment variables (recommended for servers/cron)

Add the following to your ~/.Renviron file:

REDCAP_URL=https://redcap.yourInstitution.edu/api/
REDCAP_IMAGING_TOKEN=your_token_here
REDCAP_DR_TOKEN=your_token_here

Then read them in R:

url           <- Sys.getenv("REDCAP_URL")
imaging_token <- Sys.getenv("REDCAP_IMAGING_TOKEN")

Required R Packages

install.packages(c(
  "tidyverse",   # Data wrangling and ggplot2
  "redcapAPI",   # REDCap API interface
  "httr",        # HTTP requests
  "readxl",      # Read Excel files
  "writexl",     # Write Excel files
  "openxlsx",    # Multi-sheet Excel output
  "janitor",     # Data cleaning utilities
  "gt",          # Publication-quality tables
  "knitr",       # R Markdown rendering
  "scales",      # Axis formatting
  "ggrepel",     # Non-overlapping plot labels
  "rmarkdown",   # Report rendering
  "cronR",       # Cron job scheduling (macOS/Linux)
  "here"         # Relative file paths
))

Output Files

All output files (CSVs, Excel files, PNGs, HTML reports) are excluded from version control via .gitignore. Scripts write to a local Output/ directory relative to the project root.