Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships

Abstract

Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.

Keywords: Cognitive demand; Functional connectivity; Individualized prediction; Reading comprehension; Task state.

Fig. 1.. Prediction results of two reading comprehension abilities (PVT and ORRT).

All 8 fMRI conditions yielded significant correlations between observed and predicted (a) PVT or (b) ORRT scores. Specifically, for PVT, the WM task yielded the best-performing model (c), and the language task yielded the second-best model (d). For ORRT, the language task achieved the highest prediction accuracy (e), and the WM task achieved the second-highest prediction accuracy (f). Combining FC features from all 8 fMRI conditions generated improved prediction performance (red bar in a, b) than using any single condition alone. An optimal combination of 6 cognitive conditions achieved the best predictions for both (g) PVT and (h) ORRT (r[PVT] = 0.503 ± 0.009, r[ORRT] = 0.498 ± 0.012; pink bar in a, b). Values in the x-axis and y-axis were normalized for visualization. (i) Given equal scan durations, all task-based models except emotion again achieved higher prediction accuracies than rest-based models. Abbreviation: Emo, emotion; Gam, gambling; Lang, language; Mot, motor; Rel, relational; Soc, social; WM, working memory; PVT, picture vocabulary test; ORRT, oral reading recognition test.

Fig. 2.. Prediction results of fluid intelligence, cognitive flexibility and working memory capacity.

(a) For the prediction of fluid intelligence, the WM task achieved the highest prediction accuracy (r[WM] = 0.378 ± 0.0139, p = 2.0 × 10⁻⁴), and the emotion task achieved the lowest prediction accuracy (r[Emotion] = 0.222 ± 0.0164, p < 1.0 × 10⁻³). (b) Importantly, integrating 8-condition connectivity data achieved improved prediction performance than using any single state alone (r = 0.409 ± 0.0116, p = 2.0 × 10⁻⁴). (c) For cognitive flexibility, the WM task yielded the best-performing model (r[WM] = 0.311 ± 0.0114, p = 2.0 × 10⁻⁴), and the emotion task yielded the worst-performing model (r[Emo] = 0.099 ± 0.0198, p > 0.05). (d) Combining all 8-condition connectivity data also achieved improved prediction performance than using any single state alone (r = 0.330 ± 0.0150, p = 2.0 × 10⁻⁴). (e) For the prediction of working memory capacity, the WM task yielded the best-performing model (r[WM] = 0.302 ± 0.0154, p = 2.0 × 10⁻⁴), and the rest yielded the worst-performing model (r[Rest] = 0.083 ± 0.0191, p > 0.05). (f) Moreover, combining all 8-state connectivity data also achieved improved prediction performance than using any single state alone (r = 0.339 ± 0.0151, p = 2.0 × 10⁻⁴).

Fig. 3.

(a) Similarity of FC or node weights within (main diagonal) and between-conditions (off-diagonal). The similarity was quantified by the correlations of whole-brain FC or node weights between each condition pairs. The weight distributions demonstrated higher within-condition similarities (r [FC weights] = 0.654–0.757, r[node weights] = 0.622–0.734) than between-condition similarities (r [FC weights] = 0.185–0.289, r[node weights] = 0.224–0.338). (b) Edge overlap within (main diagonal) and between (off-diagonal) each pair of fMRI conditions. Cells in the matrix plots are plotted as number of shared edges within and between each pair of cognitive conditions. Values in the row or column names represent the number of edges identified by permutation test under a threshold of p < 0.05. The PVT and ORRT models are represented in the lower and upper triangles respectively.

Fig. 4.. The FCs that contributed to reading comprehension prediction and the overrepresented networks.

The most predictive FCs determined by permutation test were demonstrated in the circle plot for cognitive conditions of (a) rest, (b) language and (c) working memory. As shown in the circle plot, functional edges for PVT and ORRT are visualized in orange and blue, respectively. Cells in the matrix plots are plotted as the fraction of the most significantly predictive FCs in each pair of canonical networks, normalized by the fraction of total edges belonging to that pair. A value > 1 indicated overrepresentation of that network pair to the prediction model. Abbreviation: VIS, visual; MOT: somatomotor; DAN: dorsal attention network; VAN: ventral attention network; LIM: limbic; FPN: frontoparietal network; DMN: default mode network; SUB: subcortical.

Fig. 5.

The number of significantly predictive edges between each macroscale brain region pair determined by permutation tests under the threshold of p < 0.05. As shown in the circle and matrix plot, the 246 FC nodes are grouped into 24 macroscale brain regions that are anatomically defined by the Brainnetome atlas. Cells in the matrix plots are plotted as number of edges within and between each pair of brain regions.

Fig. 6.

The identified reading comprehension predictive models demonstrated a robust generalizability across fMRI conditions. Namely, the prediction models built on one fMRI state could be applied to FC data from other different conditions to predict (a) PVT and (b) ORRT with appreciable accuracy.