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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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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.
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Figure 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.

Mentions: Demonstrating that functional connectivity can be used to predict attentional performance in novel individuals, observed and predicted d′ values were significantly correlated (positive network: r = 0.86, p = 3.4e−8; negative network: r = 0.87, p = 1.6e−8; Fig. 1). A general linear model (GLM) constructed using strength in both networks also generated significant d′ predictions (r = 0.84, p = 1.3e−7). However, GLM predictions were not more accurate than the positive (Steiger’s z = 0.51, p = 0.61) and negative (Steiger’s z = 1.78, p = 0.08) networks’ predictions, suggesting that these two tails provide some degree of redundant information. Positive and negative networks did not differ in their predictive power (Steiger’s z = 0.45, p = 0.65).


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)

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.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 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.
Mentions: Demonstrating that functional connectivity can be used to predict attentional performance in novel individuals, observed and predicted d′ values were significantly correlated (positive network: r = 0.86, p = 3.4e−8; negative network: r = 0.87, p = 1.6e−8; Fig. 1). A general linear model (GLM) constructed using strength in both networks also generated significant d′ predictions (r = 0.84, p = 1.3e−7). However, GLM predictions were not more accurate than the positive (Steiger’s z = 0.51, p = 0.61) and negative (Steiger’s z = 1.78, p = 0.08) networks’ predictions, suggesting that these two tails provide some degree of redundant information. Positive and negative networks did not differ in their predictive power (Steiger’s z = 0.45, p = 0.65).

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