Gradient Biotech

Quick start

This walkthrough covers a connectivity matrix from upload to graph metrics and visualization.

1. Create a study

Click Create study (or Studies → New study). Enter a name (for example Connectivity demo) and choose a modality. Connectivity matrix is the fastest path for a first run.

2. Upload a connectivity matrix

On the study page, drag-and-drop or choose a square symmetric matrix file:

  • CSV — n×n numeric matrix (comma-separated)
  • NPZ or NumPy .npy — array stored under key matrix or as the sole array
  • MATLAB .mat — variable named matrix, conn, or W

Don't have a matrix handy? See Sample data for a minimal example you can save as a CSV and upload directly.

After uploading, you are redirected to the dataset workspace.

3. Run graph metrics

On the dataset workspace, click Compute graph metrics. Wait for the job to complete — status updates on the page.

Review global metrics (clustering, path length, global efficiency, modularity, small-worldness) and per-node BCT measures in the table.

4. Explore visualizations

When the run completes:

  • Connectivity heatmap — matrix values with optional threshold
  • Network graph — force-directed layout colored by module
  • 3D brain view — when atlas coordinates are available for region labels

5. Cohort comparison (optional)

Upload additional matrices to the same study. Open Cohort analysis from the study page, assign group labels to each dataset, then run Batch graph metrics and Network-Based Statistics (NBS) when at least two groups exist.

6. Interpret (optional)

After graph metrics or NBS complete, use Interpret on the dataset or cohort page for a plain-language summary that cites your computed values.

If Interpret isn't available in your workspace, ask your administrator to enable AI interpretation.

Next steps