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A neuromarker of sustained attention from whole-brain functional connectivity.

Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, Chun MM - Nat. Neurosci. (2015)

Bottom Line: 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.These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Yale University, New Haven, Connecticut, USA.

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.

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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).
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Figure 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).

Mentions: To identify edges that consistently predicted attentional function across datasets, we defined high- and low-ADHD networks in the full Peking University sample. These networks were constructed using the analysis pipeline described in the Network definition section above, except that ADHD-RS score was used as the measure of attention instead of gradCPT d′. In addition, 236 nodes of the original 268 were used due to a lack of whole-brain coverage in some individuals (see online Methods for more information). Strength in the resulting high-ADHD network, containing 595 edges, was correlated with more severe symptoms scores (r = 0.75, p = 2.04e−21); and strength in the low-ADHD network, 477 edges, was correlated with less severe symptoms (r = −0.76, p = 1.20e−22). Note that this analysis is not cross-validated within the Peking University sample; rather, it validates network strength as a summary statistic in this dataset. Demonstrating that ADHD networks generalize to unseen subjects, models based on strength in the high- and low-ADHD networks during task and at rest predicted d′ in the gradCPT sample (Fig. 4); this is the reverse of the analysis described in the External validation: ADHD symptom prediction section above, indicating that this method achieves significant predictive power even after exchanging the roles of training and testing datasets.


A neuromarker of sustained attention from whole-brain functional connectivity.

Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Constable RT, Chun MM - Nat. Neurosci. (2015)

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).
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4696892&req=5

Figure 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).
Mentions: To identify edges that consistently predicted attentional function across datasets, we defined high- and low-ADHD networks in the full Peking University sample. These networks were constructed using the analysis pipeline described in the Network definition section above, except that ADHD-RS score was used as the measure of attention instead of gradCPT d′. In addition, 236 nodes of the original 268 were used due to a lack of whole-brain coverage in some individuals (see online Methods for more information). Strength in the resulting high-ADHD network, containing 595 edges, was correlated with more severe symptoms scores (r = 0.75, p = 2.04e−21); and strength in the low-ADHD network, 477 edges, was correlated with less severe symptoms (r = −0.76, p = 1.20e−22). Note that this analysis is not cross-validated within the Peking University sample; rather, it validates network strength as a summary statistic in this dataset. Demonstrating that ADHD networks generalize to unseen subjects, models based on strength in the high- and low-ADHD networks during task and at rest predicted d′ in the gradCPT sample (Fig. 4); this is the reverse of the analysis described in the External validation: ADHD symptom prediction section above, indicating that this method achieves significant predictive power even after exchanging the roles of training and testing datasets.

Bottom Line: 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.These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Yale University, New Haven, Connecticut, USA.

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.

Show MeSH
Related in: MedlinePlus