A neuromarker of sustained attention from whole-brain functional connectivity

Abstract

Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

Figures

Fig. 1

Functional connectivity models predict sustained attention performance. Scatter plots show correlations between observed gradCPT d′ values and predictions by positive and negative networks and general linear models (GLM) that take into account positive and negative network strength. Network models were iteratively trained on task data from n − 1 subjects in the gradCPT data set and tested on task data (top row) and resting-state data (bottom row) from the left-out individual.

Fig. 2

Functional connections predicting gradCPT performance and ADHD-RS scores. (A) The 757 edges in the high-attention network (predicting higher d′ values in the gradCPT sample and lower ADHD-RS scores in the ADHD-200 sample) are visualized in orange. The 630 edges in the low-attention network (predicting lower d′ values in the gradCPT sample and higher ADHD-RS scores in the ADHD-200 sample) are visualized in blue. Edges that appear in both the gradCPT and ADHD networks appear in bold. Macroscale regions include prefrontal cortex (PFC), motor cortex (Mot), insula (Ins), parietal (Par), temporal (Tem), occipital (Occ), limbic (including the cingulate cortex, amygdala and hippocampus; Lim), cerebellum (Cer), subcortical (thalamus and striatum; Sub), brainstem (Bsm). (B) Differences in the number of edges between each pair of macroscale regions, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. (C) Differences in the number of edges between each pair of canonical networks, calculated by subtracting the number of edges in the low-attention network from the number in the high-attention network. Canonical networks include the subcortical-cerebellum (SubC), motor (MT), medial frontal (MF), visual I (VI), visual II (VII), visual association (VA), default mode (DM), and frontoparietal (FP).

Fig. 3

Sustained Attention Network (SAN) models, defined with gradCPT subjects, significantly predict scores on the ADHD-Rating Scale (ADHD-RS) in an independent sample of children and adolescents from the ADHD-200 dataset. Predictions are negatively correlated with ADHD-RS scores because models were trained to predict d′; thus, higher predictions correspond to better attentional abilities and lower ADHD-RS scores. These individuals were diagnosed with ADHD (solid dots) or as typically developing controls (TDC, hollow dots).

Fig. 4

Connectivity models defined on ADHD-200 data predict gradCPT performance in an independent group of participants. Scatter plots show predictions of models defined using edges negatively (orange) and positively (blue) related to ADHD-RS scores in ADHD-200 resting state data. Predictions of a GLM, which incorporates low- and high-ADHD network strength, are shown in black. These models were applied to gradCPT task (top) and resting-state data (bottom).