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 data set 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 < .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.
Highlights
The mapping and analysis of correlated brain activity patterns present in functional magnetic resonance imaging recordings has widespread applications including the investigation of the organization of cognitive processing, the decoding of brain states, and the development of biophysical models and clinical biomarkers [Barkhof et al, 2014; Castellanos et al, 2013]
We examined the performance of a symmetrized version of the Human Connectome Project (HCP) atlas consisting of averaging the time series within the same region across hemispheres, the full atlas containing 360 regions and a combined version including subcortical regions defined from the Harvard-Oxford subcortical atlas [Smith et al, 2004]
2.7 Summary scores and differences between methods Final classification performance scores were computed as the percentage of correct classification across tasks and subjects
Summary
The mapping and analysis of correlated brain activity patterns present in functional magnetic resonance imaging (fMRI) recordings has widespread applications including the investigation of the organization of cognitive processing, the decoding of brain states, and the development of biophysical models and clinical biomarkers [Barkhof et al, 2014; Castellanos et al, 2013]. One of the major products of FC analyses of FMRI has been the identification of resting state networks (RSNs) [Damoiseaux et al, 2006]. RSNs are correlated patterns of brain activity that are consistently found during rest, and reflect the major functionally specialized brain networks related to cognition [Smith et al, 2009]. FC approaches can inform other methods in estimating effective connectivity (EC), the underlying functional and structural relationships producing correlated brain activity [Friston, 1994, [Woolrich and Stephan, 2013].
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