Data-driven measures of functional connectivity

Tianhu Lei; John Dell; Raphy Magee; Timothy P. L. Roberts

doi:10.1117/12.812286

27 February 2009 Data-driven measures of functional connectivity

Tianhu Lei, John Dell, Raphy Magee, Timothy P. L. Roberts

Author Affiliations +

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 72621Z (2009) https://doi.org/10.1117/12.812286
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Studying interactions within the brain leads to an emerging field: functional connectivity. Functional connectivity between two brain units (neuron columns, recording sites, regions) can be defined as the temporal correlation between their time courses. Correlation between time courses of brain units are measured in different ways, e.g., Data-driven and/or Model-based approaches. This paper focuses on the former. The commonly used measures in Data-driven approach include, but are not limited to Coherence, Synchronization, Mutual Information, Nonlinear correlation coefficient, and Phase-Locking Values. We first describe the underlying reasons why these measures originated from distinctive fields of science and engineering can be applied to assess functional connectivity; then give the quantitative evaluation to each measure and indicate what are the limitations and conditions when they are applied, finally demonstrate the relations between these measures that may provide a basis for consistent assessments and interpretations on functional connectivity under investigation.

Citation Download Citation

Tianhu Lei, John Dell, Raphy Magee, and Timothy P. L. Roberts "Data-driven measures of functional connectivity", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 72621Z (27 February 2009); https://doi.org/10.1117/12.812286

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KEYWORDS

Brain

Mode locking

Nonlinear dynamics

Magnetoencephalography

Information theory

Data modeling

Electroencephalography

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