Brain network topology predicts participant adherence to mental training programs

Abstract

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity.

Keywords: Graph theory; Machine learning; Meditation; Mental training programs; Resting-state fMRI.

Figure 1.

Overview of study design and the pipeline for graph theoretical analysis of resting-state fMRI time series. Healthy participants (N = 51, 35 women, age = mean 26) were (A) scanned for structural (T1) and functional MRI resting-state data and (B) randomized to attend a meditation (n = 29) or a creative writing (n = 22) training program. (C) A set of nonoverlapping brain regions were obtained according to a prior parcellation scheme (optimized Harvard-Oxford) from resting-state functional MRI, (D) averaged time series within regions of interest were extracted, and (E) a weighted interregional correlation matrix was obtained from BOLD time series and (F) was thresholded over a range of thresholds for each participant. (G) The correlation matrix was calculated to assess various graph metrics, and statistical analysis was performed to predict adherence.

Figure 2.

Predicting adherence based on regional connectivity. (A) Spatial pattern of the brain connection for all participants pooled together, creative writing and meditation groups plotted during resting-state fMRI acquisition based on relationship between graph properties and homework. Glass brain images are showing regions (circles) in which graph metrics significantly predicted adherence. The red lines represent edges (connections) between the significant nodes at threshold (T = 0.35). The top red-colored glass brains represent clustering connections; the middle ones are based on local efficiency; and the blue-colored glass brains are based on spatial distribution and connectivity pattern of homework adherence and hubness as degree centrality. (B) The complete parcellation scheme consisted of 131 regions that mapped to five resting-state networks listed in the legend. The circles represent the regions of the brain that were predictive for each group. Overlapped circles indicate that the region was significant for both groups. (C) The bar graph represents the sum of nodes available for all participants, creative writing, and meditation groups based on the five known resting-state brain networks.

Figure 3.

Scatterplots of the relationships between graph metrics and adherence criteria (for the correlation observed at threshold, T = 0.35) corrected at p value ≤ 0.05. (A) Based on total homework, for results pooled from all participants together and separately for the meditation group. (B) Based on attendance, for results pooled from all participants and separately for creative writing group. Higher clustering and local efficiency and less integration (global efficiency) and high system segregation (not shown; see Table 3) in brain connectivity in resting-state fMRI predicted adherence. The shaded area shows confidence interval.

Figure 4.

Predicting adherence based on machine learning perspective. (A) Effective features based on the feature selection method. (B) Regional connectivity measures of the brain selected by the backward elimination method for predicting adherence. Blue and green points on this figure represent low and high classes, respectively. (C) ROC curve for four different classifiers for predicting adherence to mental training programs. As we can see, decision tree shows higher area under curve (AUC = 0.77) compared with other classifiers. (D) Comparison of the score of different classifiers on predicting adherence to behavioral training course (prediction based on nodal measures of resting-state fMRI).