The intelligent brain in conflict

The role of neural load effects in predicting individual differences in working memory function

Studies of working memory (WM) function have tended to adopt either a within-subject approach, focusing on effects of load manipulations, or a between-subjects approach, focusing on individual differences. This dichotomy extends to WM neuroimaging studies, with different neural correlates being identified for within- and between-subjects variation in WM. Here, we examined this issue in a systematic fashion, leveraging the large-sample Human Connectome Project dataset, to conduct a well-powered, whole-brain analysis of the N-back WM task. We first demonstrate the advantages of parcellation schemes for dimension reduction, in terms of load-related effect sizes. This parcel-based approach is then utilized to directly compare the relationship between load-related (within-subject) and behavioral individual differences (between-subject) effects through both correlational and predictive analyses. The results suggest a strong linkage of within-subject and between-subject variation, with larger load-effects linked to stronger brain-behavior correlations. In frontoparietal cortex no hemispheric biases were found towards one type of variation, but the Dorsal Attention Network did exhibit greater sensitivity to between over within-subjects variation, whereas in the Somatomotor network, the reverse pattern was observed. Cross-validated predictive modeling capitalizing on this tight relationship between the two effects indicated greater predictive power for load-activated than load-deactivated parcels, while also demonstrating that load-related effect size can serve as an effective guide to feature (i.e., parcel) selection, in maximizing predictive power while maintaining interpretability. Together, the findings demonstrate an important consistency across within- and between-subjects approaches to identifying the neural substrates of WM, which can be effectively harnessed to develop more powerful predictive models.

Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

Flexible cognitive control: Effects of individual differences and brief practice on a complex cognitive task

Brain activations underlying cognitive processes are subject to modulation as a result of increasing cognitive demands, individual differences, and practice. The present study investigated these modulatory effects in a cognitive control task which required inhibition of prepotent responses based on the contents of working memory (WM) and which enabled a novel dissociation of item-specific and task-skill effects resulting from brief practice. Distinct responses in areas underlying WM and inhibitory control in the absence of behavioral changes reflected different effects of item repetition and general task practice on tonic working memory and phasic inhibitory processes. Item repetition was associated with decreases in both unique and common areas subserving WM and inhibitory control. In contrast, general task practice was reflected in decreases in the level of tonic WM activity required to maintain a consistently high level of task performance but increased activity in a number of core inhibitory regions including dorsolateral and inferior PFC and inferior parietal cortex. Furthermore, both practice and individual differences in task performance were associated with the ability to modulate and maintain activity in frontostriatal areas mediating attentional control, suggesting that the areas that differ between individuals can be modulated by practice within an individual. These results raise the possibility that a fundamental human ability, reflexive cognitive control, is amenable to practice.