Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination

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.

Optimising network modelling methods for fMRI

“…In the literature, parcels can also be referred as nodes, modes, networks or region of interests, but here for simplicity and continuity, we will only refer to them as parcels. We only consider rfMRI-driven parcellations, as there is plenty of existing evidence that these outperform parcellations derived from pre-defined structural atlases [38,39,40].…”

Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information

“…For example, we bandpass filtered the timecourses with a high-end cutoff frequency equal to 0.18Hz. The rationale here was to avoid the influence of the periodic motor response during the math task (responses required every 5sec); yet future work should compare dFC efficacy for broader bandwidths or in a bandwidth-specific manner (Sala-Llonch et al, 2018. Also, it would be interesting to test if any other linear and non-linear dimensionality reduction methods (e.g., independent component analysis and kernel PCA) would further increase the efficacy.…”

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

“…In line with our observations, we argue that inclusion of the subcortical structures as network nodes can enhance the between-subject variability and stabilize the within-subject variability by providing a more comprehensive measurements on the entirety of the brain connectivity. Larger between-subject variability implies that the associated measurements are more recognizable between different subjects, leading to improved subject discrimination, a finding that has been demonstrated (59,60).…”