Connectivity

The Connectivity library (CONNECTIVITYLIB) computes functional connectivity metrics, stores the resulting networks, and provides graph-theoretic utilities. It is implemented in pure C++ with real-time capability in mind and imposes no modality-specific constraints — the same API works for MEG, EEG, and source-level data.

Architecture

The library is organized into three groups of classes:

Group	Key Classes	Role
API	Connectivity, ConnectivitySettings	User-facing entry point — configure inputs, select metrics, launch computation
Metrics	One class per metric (e.g., ConnectivityPLI)	Implement the spectral/statistical estimator for each measure
Network containers	Network, NetworkNode, NetworkEdge	Store and query the resulting connectivity graph

Data Flow

1. The user populates a ConnectivitySettings object with trial data, node positions, desired metrics, and sampling frequency.
2. Connectivity::calculate() dispatches each requested metric to its dedicated class.
3. Results are returned as one Network per metric, containing NetworkNode and NetworkEdge objects.

NetworkEdge instances are stored as smart pointers, which keeps memory usage manageable for large, fully connected graphs. Network also exposes functions for distance-matrix computation, basic graph measures, and weight-based thresholding.

Trial-Based Computation

Connectivity is computed across trials, not across time. This design targets evoked-response experiments where data are segmented into stimulus-locked epochs. For resting-state recordings (no stimulus), the continuous data can be split into equally sized blocks and treated as pseudo-trials — a standard approach in resting-state connectivity studies.

note

Time-resolved (sample-by-sample) connectivity is not currently supported. Use the trial-based approach for spontaneous data.

Usage

The example below computes all-to-all Phase Lag Index (PLI) and Imaginary Coherence from MEG gradiometer epochs:

// Prepare input data
FiffRawData raw("sample_audvis_raw.fif");
RowVectorXi picks = raw.info.pick_types("grad");
Eigen::MatrixXi events;
MNE::read_events("sample_audvis_raw-eve.fif", events);

// Read epochs: -100 ms to 400 ms relative to event type 3
MNEEpochDataList data;
data = MNEEpochDataList::readEpochs(raw, events, -0.1, 0.4, 3, picks);

// Configure connectivity
ConnectivitySettings settings;
settings.setNodePositions(raw.info, picks);
settings.setConnectivityMethods(QStringList() << "pli" << "imagcohy");
settings.setSamplingFrequency(raw.info.sfreq);
for (MNEEpochData::SPtr pItem : data)
    settings.append(pItem->epoch);

// Compute — returns one Network per metric
QList<Network> networks = Connectivity::calculate(settings);

Source-level connectivity works identically — pass source-localized signals instead of sensor-level data.

Supported Metrics

Metric	Keyword	Domain
Correlation	cor	Time
Cross-Correlation	xcor	Time
Coherence	coh	Frequency
Imaginary Coherence	imagcohy	Frequency
Phase Locking Value	plv	Phase
Phase Lag Index	pli	Phase
Weighted Phase Lag Index	wpli	Phase
Unbiased Squared Phase Lag Index	uspli	Phase
Debiased Squared Weighted Phase Lag Index	dswpli	Phase