A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI

Abstract

Many cognitive and clinical neuroscience research studies seek to determine how contextual factors modulate cognitive processes. In fMRI, hypotheses about how context modulates distributed patterns of information processing are often tested by comparing functional connectivity between neural regions A and B as a function of task condition X and Y, which is termed context-modulated functional connectivity (FC). There exist two exploratory statistical approaches to testing context-modulated FC: the beta-series method and psychophysiological interaction (PPI) analysis methods. While these approaches are commonly used, their relative power for detecting context-modulated FC is unknown, especially with respect to real-world experimental parameters (e.g., number of stimulus repetitions, inter-trial-interval, stimulus duration, etc). Here, we use simulations to compare power for detecting context-modulated FC between the standard PPI formulation (sPPI), generalized PPI formulation (gPPI), and beta series method. Simulation results demonstrate that gPPI and beta series method are generally more powerful than sPPI. Whether gPPI or beta series method performed more powerfully depended on experiment parameters: block designs favor the gPPI, whereas the beta series method was more powerful for designs with more trial repetitions and it also retained more power under conditions of hemodynamic response function variability. On a real data set of adolescent girls, the PPI methods appeared to have greater sensitivity in detecting task-modulated FC when using a block design and the beta series method appeared to have greater sensitivity when using an event-related design with many trial repetitions. Implications of these performance results are discussed.

Breakdown of the brain’s functional network modularity with awareness

“…This method aims to account for task-activation effects and non-task-specific correlations separately from task-induced connectivity differences (38). gPPI is considered to be more powerful than a standard PPI analysis (61) and has been shown to be a better estimate of task-based functional connectivity than a cross-correlation coefficient (62) (see SI Results for similar results obtained with the Pearson correlations); it uses the general linear model (GLM) with three regressor types: condition-specific task regressors (analogous to those used in standard fMRI GLMs), a "seed" time-course regressor, and condition-specific interaction regressors. The seed time-course regressor is included to capture signal variance in regions that show correlations with the seed region outside of task periods of interest.…”

Mechanisms of interactive specialization and emergence of functional brain circuits supporting cognitive development in children

“…In a previous study, we found that, in typically developing children, short-term training over a course of 8 weeks on relatively simple problems, such as those used here, changes in brain activation were minimal and entirely restricted to the hippocampus. 63 In contrast, children with dyscalculia showed marked reductions in brain responses in multiple prefrontal, parietal and VTOC regions, resulting in normalization of brain activity to levels similar to those seen in typically developing children. Thus, it appears that long-term repeated co-activation of relevant circuitry or training on more complex problems would be required to observe the patterns of normative changes observed here.…”

“…Compared with standard PPI implementation in SPM, gPPI is more powerful, as evidenced by both simulation and empirical studies. It is also well suited for functional connectivity analysis of block design experiments, 63 such as those featured in the current investigation. 69 Similar to the activation step, voxel-wise effect sizes from the contrast of the Arithmetic condition with the Number identification condition were generated for each participant.…”

Vasopressin increases human risky cooperative behavior

“…We performed functional connectivity analyses using the method by Rissman (47) to analyze how AVP affects the interaction between the left dlPFC and other brain regions during Stag and Rabbit choices. We used the Rissman method because a recent investigation (48) has concluded that the method is more sensitive for the case of event-related designs with more trial repetitions (as the case in our experiment) and retains more power under conditions of hemodynamic response function variability.…”