Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns

Abstract

Individual learning performance of cognitive function is related to functional connections within ‘task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80–90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R2 = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.

Prediction of long-term memory scores in MCI based on resting-state fMRI

“…In particular, discrepant hyper- and hypo-activation of the MTL upon episodic memory tasks was reported in the literature (Terry et al, 2015), and its dependency on the severity of the MCI pathology was proposed (Dickerson and Sperling, 2008). A relationship between off-task intrinsic FC alterations and memory performance was already found in elderly controls (Andrews-Hanna et al, 2007, He et al, 2012, Mevel et al, 2013, Onoda et al, 2012, Touroutoglou et al, 2015, Wang et al, 2010a, Wang et al, 2010b, Yamashita et al, 2015). Applying a similar analysis to MCI would effectively reveal the brain networks that are altered in this condition.…”

Activation‐based association profiles differentiate network roles across cognitive loads

“…The deactivation of the DMN in WM task execution has been examined extensively (Koshino, Minamoto, Yaoi, Osaka, & Osaka, ; Mayer, Roebroeck, Maurer, & Linden, ), and our results indicated that the DMN was actively engaged in the actions of task‐positive networks (Spreng, Sepulcre, Turner, Stevens, & Schacter, ; Spreng, Stevens, Chamberlain, Gilmore, & Schacter, ). The engagement of the DMN in WM tasks has also been revealed by functional connectivity and brain network modeling (Liang, Zou, He, & Yang, ; Vatansever et al, ; Yamashita, Kawato, & Imamizu, ). Our results further showed that under a higher WM cognitive load (2‐back vs. 0‐back), greater deactivation of the DMN signified better WM performance (accuracy in 2‐back) (see Figure a where DMN is blue‐coded), consistent with the existing “less is more” perspective (Anticevic, Repovs, Shulman, & Barch, ).…”

Cortical surface architecture endophenotype and correlates of clinical diagnosis of autism spectrum disorder

“…We used a binomial test to examine the statistical significance of the selection counts. 42,44 At each fold, the classifier chose 8.93 AE 0.63 (mean AE SD) out of 68 ROI (see Results). Therefore, a binomial distribution Bi (n, p) was assumed.…”