A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

By André Moraes Bastos, Jan-Mathijs Schoffelen. Frontiers in Systems Neuroscience, 2016

1,280 citations recorded by the source corpus.

Oscillatory neuronal activity may provide a mechanism for dynamic network coordination. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own advantages and disadvantages. This tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. First, we review metrics for functional connectivity, including coherence, phase synchronization, phase-slope index, and Granger causality, with the specific aim to provide an intuition for how these metrics work, as well as their quantitative definition. Next, we highlight a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis, including the common reference problem, the signal to noise ratio problem, the volume conduction problem, the common input problem, and the sample size bias problem. These pitfalls will be illustrated by presenting a set of MATLAB-scripts, which can be executed by the reader to simulate each of these potential problems. We discuss how these issues can be addressed using current methods.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e3347c54f6d2ae092b4cc6d936f92881d654ddd4

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Citations in the corpus

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, neutral): “However, contrary to the model-based methods, it is more difficult to interpret this measure in functional connectivity analysis due to its generality (Bastos and Schoffelen, 2016).”
Communication dynamics in complex brain networks (2017, methodology): “Alternative statistical methods used to infer the directionality of information flow, such as Granger causality 71 , transfer entropy 72,73 and others 74 , show that dominant patterns of information flow are associated with specific frequencies in neuro physiological recordings 75,76 .”
Cortical-limbic circuit dynamics of approach-avoidance conflict in humans (2026, unknown): “We calculated the imaginary part of coherence, which removes the interactions due to potentially spurious volume conduction effects 38 , between theta activity in pairs of electrodes from different regions.”
Temporal order of activations and interactions during arithmetic calculations measured by intracranial electrophysiological recordings in the human brain (2026, unknown): “FC results, assessed here through PLV, should be interpreted with great caution 65 .”
Intracranial EEG signals disentangle multi-areal neural dynamics of vicarious pain perception (2024, methodology): “…same referencemay bringthe ﬂ uctuations of this reference to the referenced signals, thereby yield-ingspuriouscorrelationsbetweentwore-referencedsignals 80 ;thus,we further repeated the analyses with iEEG signals referencing to the nearest white-matter neighbor, yielding a bipolar montage 31,35 .”
An intracranial dissection of human escape circuits (2024, unknown): “This method improved the spatial speci ﬁ city of the recorded effects and effectively attenuated noise 33,101,102 .”
Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, background): “Various brain connectivities based on the corresponding mathematical principles have been proposed to identify the significant evidence of functional (unidirectional) or effective (bidirectional) connectivity in the brain using graph-theory-based network analysis [26,27].”
Resting-state EEG connectome topology and social norm processing in healthy young adults (2026, unknown): “Moreover, although source reconstruction partially reduces the influ - ence of volume conduction and spatial leakage, these effects cannot be eliminated and may still influence functional con - nectivity estimates and derived graph-theoretical metrics (Bastos and Schoffelen 2016 ).”
Linking the multiple-demand cognitive control system to human electrophysiological activity (2025, unknown): “In electrophysiological studies using MEG or EEG, FC is often derived from phase synchrony within specific frequency bands or through cross-frequency coupling, reflecting oscillation-based connectivity (Bastos and Schoffelen, 2016).”