Pipelines
Every analysis step you run in the app maps to one of these pipelines. This reference lists what each one takes as input and produces as output.
Connectivity and graph theory
| Pipeline | Input | Outputs |
|---|---|---|
graph_metrics | Connectivity matrix (CSV, NPZ, MAT) | Global BCT metrics, per-node metrics, module assignments |
graph_sensitivity | Connectivity matrix | Density-sweep robustness report: BCT metrics across thresholds + per-metric coefficient of variation / stability flag |
Key parameters: threshold (edge weight cutoff), n_rand_smallworld (null model count).
EEG
| Pipeline | Input | Outputs |
|---|---|---|
eeg_preprocess | EDF, BrainVision, EEGLab | Filtered recording, bad channels, optional ICA summary |
eeg_features | EEG preprocess run | Band power, spectral entropy, PLV/coherence/wPLI connectivity |
eeg_artifacts | Raw EEG (EDF, BrainVision, EEGLab, FIF) | Per-channel QC metrics, ICA component maps/timecourses/spectra, auto EOG/muscle flags, raw preview |
eeg_connectivity | EEG preprocess run or raw EEG | Multi-estimator connectivity for a band (coherence, imaginary coherence, PLV, PLI, wPLI, debiased wPLI, PPC, AEC) + estimator-agreement matrix |
ieeg_analysis | Intracranial EEG/ECoG (EDF, FIF, BrainVision) | Shaft-aware bipolar referencing, band power incl. high gamma, peri-event high-gamma response |
eeg_apply_cleaning | Raw EEG + reviewed ICA | Cleaned recording with confirmed bad-channel interpolation and selected ICA components removed; before/after variance |
microstates | EEG preprocess run | State maps, coverage, duration, transitions, GEV |
time_frequency | EEG preprocess run | Spectrogram stats, band-power dynamics |
source_localize | EEG preprocess run | Simplified source-space summary |
fMRI
| Pipeline | Input | Outputs |
|---|---|---|
bids_validate | BIDS ZIP archive | Validation report, missing-field warnings |
fmri_qc | 4D fMRI NIfTI (+ optional confounds/motion file) | Framewise displacement, DVARS, outlier scrubbing, tSNR, subject-exclusion flag |
task_glm | 4D fMRI NIfTI + BIDS events (onset, duration, trial_type) | First-level ROI GLM: HRF design matrix, per-condition beta/z, contrast effect/t/z, event-timing QC |
functional_connectivity | Preprocessed NIfTI | ROI timeseries, Pearson correlation matrix, Fisher z |
dynamic_fc | FC matrix or NIfTI | Sliding-window states, state timecourses |
Key FC parameters: atlas_name (aal, schaefer_100/200/400), confound_strategy, bandpass cutoffs.
Cohort
| Pipeline | Input | Outputs |
|---|---|---|
graph_metrics_batch | Multiple matrix datasets with group labels | Per-subject metrics table |
cohort_nbs | Group-labeled connectivity matrices | NBS components, edge statistics, heatmap data |
cohort_metrics | Batch graph metrics results | Group comparison table with effect sizes |
cohort_predict | Group-labeled or target-valued connectivity matrices | Cross-validated classification (ROC AUC, balanced accuracy, sensitivity/specificity) or regression (R², MAE, Pearson r), permutation p-value, edge-stability map, subject-level predictions |
cluster_stats | Group-labeled EEG features runs | Sensor × band cluster permutation test (cluster mass, max-statistic FWE p-values), t-map, significant clusters |
multiplex | ≥ 2 same-node connectivity matrices (layers) | Multiplex metrics: per-layer density/strength, overlapping strength, participation coefficient, layer-similarity matrix |
Interpretation
| Pipeline | Input | Outputs |
|---|---|---|
interpret | Completed metrics / NBS / prediction / microstate run | Narrative JSON with metric citations |
Workflow templates
Reusable protocol presets chain the pipelines above so you can run a multi-step protocol from one panel instead of launching each pipeline individually. The Run protocol panel executes the chain one step at a time, carrying each step's output into the next.
| Template | Modality | Steps |
|---|---|---|
| Resting EEG → microstates | EEG | eeg_preprocess → eeg_features → microstates |
| EEG connectivity | EEG | eeg_preprocess → eeg_features |
| Resting-state fMRI → dynamic FC | fMRI | functional_connectivity → dynamic_fc |
Job lifecycle
Every run moves through the same states, shown as a status badge on the dataset and cohort pages:
queued → running → complete | failed
Versioning
Every run records a pipeline version. Include run IDs and versions in methods sections for reproducibility.
Implementation sources
Graph metrics are ported from Brain Connectivity Toolbox (BCT) reference algorithms. EEG preprocessing uses MNE-Python. Functional connectivity uses nilearn atlas parcellation.