General Intelligence Is Associated with Working Memory-Related Functional Connectivity Change: Evidence from a Large-Sample Study

Network and state specificity in connectivity-based predictions of individual behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Altered Functional Connectivity in Working Memory Network After Acute Sleep Deprivation

Acute sleep deprivation (SD) has a detrimental effect on working memory (WM). However, prior functional magnetic resonance imaging (fMRI) studies have failed to reach consistent results on brain functions underlying WM decline after acute SD. Thus, we aimed to identify convergent patterns of abnormal brain functions due to WM decline after acute SD. A coordinate-based activation likelihood estimation (ALE) meta-analysis of task-state fMRI studies testing the effects of acute SD on WM was performed to construct WM network. Then 26 healthy subjects with regular sleep performed the n-back task and underwent resting-state fMRI scanning before and after 24 h of SD. The functional connectivity (FC) among these brain regions and correlations with WM performance were calculated. The ALE results displayed that SD subjects performing WM-related tasks had consistent hypoactivation in the occipital lobe, left middle occipital gyrus, parietal lobe, precuneus, inferior parietal lobule, right sub-gyral, right cuneus, right limbic lobe, and right posterior cingulate. Consistent hyperactivation was showed in the left cerebrum, including the lingual gyrus, posterior lobe, cuneus, temporal lobe, and fusiform gyrus. These identified brain regions as the seeds to construct WM network. The increased FC between the left declive and right sub-gyral, left cuneus and left lingual gyrus, and left cuneus and right post cingulate were found. Furthermore, the impaired WM performance negatively correlated with increased FC. Taken together, our findings highlight that the altered FC in WM network may be the underlying mechanisms of WM decline after acute SD.

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Alpha and Low Gamma Embedded With White Noise Binaural Beats Modulating Working Memory among Malaysian Young Adult: A Preliminary fMRI Study

Introduction: Binaural beats (BB) provisions alpha and gamma have been suggested to modulate working memory (WM), while white noise (WN) acted as a control condition. Methods: The current study overlays WN on alpha and gamma tones to study its modulating role on WM performance. A block-design n-back task paradigm used to determine the effect of load on embedded BB on WM performance using functional magnetic resonance imaging. Results: Six young adults (3 males and 3 females) with mean age of 23.5 ± 0.84 within the Kota Bharu vicinity participated in the study. A repeated-measures ANOVA (p<0.05) on response accuracy indicate medium effect size on condition (η2 =0.420), and large effect sizes on groups (η2 = 0.388) and load (η2 = 0.487). The potential practical difference is more evident on low- (0-back) and high-load (3-back). GWN provision marginally excels, implying its entrainment may benefit WM processing. A repeated-measures ANOVA (p<0.05) on reaction time (RT) implied a large effect size on all variables (condition: η2=0.065, groups: η2=0.227 and load: η2=0.169). It was observed that BB exposure elicits a slow processing speed which worsens RT. The neural correlates suggest activated regions in GWN and AWN are associated with attentional mechanisms and WM processes. Conclusion: Preliminary findings indicate both embedded BB has a potential to improve WM performance with the cost of slower processing speed. GWN provision modulates attentional mechanisms benefiting WM performance and AWN may enhance performance in extreme ends of WM load.