Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/163113
Title: Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination.
Author: Sala Llonch, Roser
Smith, Stephen M.
Woolrich, Mark
Duff, Eugene P.
Keywords: Cervell
Percepció visual
Processament d'imatges
Brain
Visual perception
Image processing
Issue Date: 1-Feb-2019
Publisher: Wiley
Abstract: The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled dataset of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p<0.05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high-dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA-based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC.
Note: Versió postprint del document publicat a: https://doi.org/10.1002/hbm.24381
It is part of: Human Brain Mapping, 2019, vol. 40, num. 2, p. 407-419
URI: http://hdl.handle.net/2445/163113
Related resource: https://doi.org/10.1002/hbm.24381
ISSN: 1065-9471
Appears in Collections:Articles publicats en revistes (Biomedicina)

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