A neuromarker of sustained attention from whole-brain functional connectivity

By Monica D. Rosenberg, Emily S. Finn, Dustin Scheinost, X. Papademetris, X. Shen, R. Todd Constable, Marvin M. Chun. Nature Neuroscience, 2015

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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.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/678d8484589ff1505b3f4154b1aaceb7fab65232

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Citations in the corpus

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, background): “127 functional brain architecture revealed by RSFC (Fox et al., 2012; Mueller et al., 2013; Smith et al., 2015; Finn et al., 2015; Rosenberg et al., 2016; Dubois and Adolphs, 2016; Bertolero et al., 2018; Kong et al., 2018), here we explored the utilitarian question of whether GSR strengthens or wea”
Using connectome-based predictive modeling to predict individual behavior from brain connectivity (2017, methodology): “For an example of this experimental design, see Rosenberg et al. 5 .”
Efficacy of Mindfulness-Based Interventions for Attention and Executive Function in Children and Adolescents—a Systematic Review (2017, background): “2006) and, in recent studies, the ability to obtain functional brain networks of attentional performance (Rosenberg et al. 2015).”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, methodology): “Our final predictive model was only composed of edges that appeared in every round of cross-validation (cf. Rosenberg et al. 2015).”
A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, background): “Rosenberg and colleagues recently presented a neuromarker for sustained attention from whole-brain functional connectivity (Rosenberg, Finn, Scheinost, Papademetris, Shen, Constable & Chun 2016).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “…Driesen, Skudlarski, Gore, & Constable, 2006) and clinical conditions (e.g., psychiatric and neurological disorders; Fox & Greicius, 2010; M. Greicius, 2008), and can be used to predict behavioral performance (e.g., M. D. Rosenberg et al., 2016) and individual identity (e.g., Finn et al., 2015).”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, background): “Second, our results suggest that individual-specific cortical network topography might serve as a fingerprint of human behavior, which might complement the usage of functional connectivity strength in the vast majority of previous literature (Hampson et al., 2006; Finn et al., 2015; Rosenberg et al.”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, positive): “The most frequently used algorithms include ordinary least squares (OLS) regression (Rosenberg et al., 2015; Shen et al., 2017), least absolute shrinkage and selection operator (LASSO) regression (Wager et al.”