A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task

Abstract

Sustaining attention to the task at hand is a crucial part of everyday life, from following a lecture at school to maintaining focus while driving. Lapses in sustained attention are frequent and often problematic, with conditions such as attention deficit hyperactivity disorder affecting millions of people worldwide. Recent work has had some success in finding signatures of sustained attention in whole-brain functional connectivity (FC) measures during basic tasks, but since FC can be dynamic and task-dependent, it remains unclear how fully these signatures would generalize to a more complex and naturalistic scenario. To this end, we used a previously defined whole-brain FC network - a marker of attention that was derived from a sustained attention task - to predict the ability of participants to recall material during a free-viewing reading task. Though the predictive network was trained on a different task and set of participants, the strength of FC in the sustained attention network predicted reading recall significantly better than permutation tests where behavior was scrambled to simulate chance performance. To test the generalization of the method used to derive the sustained attention network, we applied the same method to our reading task data to find a new FC network whose strength specifically predicts reading recall. Even though the sustained attention network provided significant prediction of recall, the reading network was more predictive of recall accuracy. The new reading network's spatial distribution indicates that reading recall is highest when temporal pole regions have higher FC with left occipital regions and lower FC with bilateral supramarginal gyrus. Right cerebellar to right frontal connectivity is also indicative of poor reading recall. We examine these and other differences between the two predictive FC networks, providing new insight into the task-dependent nature of FC-based performance metrics.

Individual differences in reading comprehension and off-task thought relate to perceptually-coupled and decoupled cognition

“…MTG responds more strongly to memory-based and meaning-based decisions, consistent with the location of this cluster at the anterior end of the ventral visual stream and yet also within the DMN (Murphy et al, 2018). In line with these studies, the anterior and middle temporal lobe have been identified as important for text comprehension (Ferstl, Neumann, Bogler, & Von Cramon, 2008;Jangraw et al, 2018;Kuperberg, Lakshmanan, Caplan, & Holcomb, 2006). Nevertheless, activation within MTG is insufficient for comprehensionour results suggest this region also needs to be strongly activated by visual inputs and to interact with other regions of DMN implicated in comprehension.…”

An information network flow approach for measuring functional connectivity and predicting behavior

“…Multiple algorithms have been developed and used to estimate TE for continuous data. Here, we use the Kraskov, Stogbauer, and Grassberger (KSG) () technique which estimates a probability density function for a time series using Kernel estimation and alters the kernel width to adjust to the data using a nearest neighbor calculation (Lizier, ). The KSG approach is often used as the “best of breed solution” for TE estimation (Lizier, ).…”

The individual functional connectome is unique and stable over months to years

“…One of the key goals of precision neuroscience is relating individual differences in brain data to differences in phenotypic/clinical data. Indeed, this was an important component of the original identification study (Finn et al, 2015) and much exciting work has since been conducted in this area using a variety of methods (Beaty et al, 2018;Drysdale et al, 2017;Greene et al, 2018;Hearne et al, 2016;Hsu et al, 2018;Jangraw et al, 2018;Kaufmann et al, 2017;Kong et al, 2018;Rosenberg et al, 2016;Shen et al, 2017). We were unable to add to this growing literature, because only the SLIM dataset included behavioral measures, but future work could determine if the stability of single-subject fMRI data allows predictions to be made regarding behavior at longer time frames, similar to how previous investigators have used functional connectivity measures to predict a behavioral or psychiatric score in cross-sectional studies.…”