A neuromarker of sustained attention from whole-brain functional connectivity
Monica D Rosenberg, Emily S Finn, Dustin Scheinost, Xenophon Papademetris, Xilin Shen, R Todd Constable, Marvin M Chun, Monica D Rosenberg, Emily S Finn, Dustin Scheinost, Xenophon Papademetris, Xilin Shen, R Todd Constable, Marvin M Chun
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
References
- Cattell RB. Intelligence: its structure, growth and action. Advances in psychology. 1987;35
- Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ. Improving fluid intelligence with training on working memory. Proc Natl Acad Sci. 2008 doi: 10.1073/pnas.0801268105.
- Unsworth N, Fukuda K, Awh E, Vogel EK. Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cogn Psychol. 2014;71:1–26.
- Kyllonen PC, Christal RE. Reasoning ability is (little more than) working-memory capacity?! Intelligence. 1990;14:389–433.
- Engle RW, Kane MJ, Tuholski SW. Models of working memory: Mechanisms of active maintenance and executive control. 1999:102–134. doi: 10.1037/a0021324.
- Luck SJ, Vogel EK. Visual working memory capacity: From psychophysics and neurobiology to individual differences. Trends in Cognitive Sciences. 2013;17:391–400.
- Chun MM, Golomb JD, Turk-Browne NB. A Taxonomy of External and Internal Attention. Annu Rev Psychol. 2010;62:73–101.
- Rosenberg MD, Finn ES, Todd Constable R, Chun MM. Predicting moment-to-moment attentional state. Neuroimage. 2015 doi: 10.1016/j.neuroimage.2015.03.032.
- Warm JS, Parasuraman R, Matthews G. Vigilance requires hard mental work and is stressful. Hum Factors. 2008;50:433–441.
- Desimone R, Duncan J. Neural mechanisms of selective visual attention. Annu Rev Neurosci. 1995;18:193–222.
- Kastner S, Ungerleider LG. The neural basis of biased competition in human visual cortex. Neuropsychologia. 2001;39:1263–1276.
- Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3:201–215.
- Posner MI, Rothbart MK. Research on Attention Networks as a Model for the Integration of Psychological Science. Annu Rev Psychol. 2006;58:1–23.
- deBettencourt MT, Cohen JD, Lee RF, Norman KA, Turk-Browne NB. Closed-loop training of attention with real-time brain imaging. Nat Neurosci. 2015;18:470–475.
- Rosvold HE, Mirsky AF, Sarason I, Bransome ED, Beck LH. A continuous performance test of brain damage. J Consult Psychol. 1956;20:343–350.
- Riccio C, Reynolds C, Lowe P. Clinical applications of continuous performance tests: Measuring attention and impulsive responding in children and adults. Arch Clin Neuropsychol. 2001;20:559–560.
- Esterman M, Noonan SK, Rosenberg M, Degutis J. In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention. Cereb Cortex. 2013;23:2712–2723.
- Rosenberg M, Noonan S, DeGutis J, Esterman M. Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task. Atten Percept Psychophys. 2013;75:426–439.
- Fortenbaugh FC, et al. Sustained Attention Across the Life Span in a Sample of 10,000 Dissociating Ability and Strategy. Psychol Sci. 2015 0956797615594896.
- Barkley RA. Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol Bull. 1997;121:65–94.
- Shen X, Papademetris X, Constable RT. Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data. Neuroimage. 2010;50:1027–1035.
- Shen X, Tokoglu F, Papademetris X, Constable RT. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage. 2013;82:403–415.
- Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage. 2010;52:1059–1069.
- Steiger JH. Tests for comparing elements of a correlation matrix. Psychological Bulletin. 1980;87:245–251.
- Consortium T A.-200. The ADHD-200 Consortium. A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience. Front Syst Neurosci. 2012;6:62.
- DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale-IV: Checklists, norms, and clinical interpretation. Guilford Press; New York: 1998. p. 25.
- Dan L, Yu J, Vandenberg SG, Yuemei Z, CAIHONG T. Report on Shanghai norms for the Chinese translation of the Wechsler Intelligence Scale for Children-Revised. Psychol Rep. 1990;67:531–541.
- Finn ES, et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18:1664–1671.
- Stoodley CJ. The cerebellum and cognition: evidence from functional imaging studies. Cerebellum. 2012;11:352–65.
- Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013;80:807–815.
- Castellanos FX, Proal E. Large-scale brain systems in ADHD: Beyond the prefrontal-striatal model. Trends in Cognitive Sciences. 2012;16:17–26.
- Krain AL, Castellanos FX. Brain development and ADHD. Clin Psychol Rev. 2006;26:433–444.
- Huang L, Mo L, Li Y. Measuring the interrelations among multiple paradigms of visual attention: An individual differences approach. Journal of Experimental Psychology: Human Perception and Performance. 2012;38:414–428.
- Baldassarre A, et al. From the Cover: Individual variability in functional connectivity predicts performance of a perceptual task. Proceedings of the National Academy of Sciences. 2012;109:3516–3521.
- Smith SM, et al. Functional connectomics from resting-state fMRI. Trends in Cognitive Sciences. 2013;17:666–682.
- Gabrieli JDE, Ghosh SS, Whitfield-Gabrieli S. Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience. Neuron. 2015;85:11–26.
- Whelan R, et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014;512:185–189.
- Rosenberg MD, Finn ES, Constable RT, Chun MM. Predicting moment-to-moment attentional state
- Langner R, Eickhoff SB. Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. Psychol Bull. 2013;139:870–900.
- Turk-Browne NB. Functional Interactions as Big Data in the Human Brain. Science (80- ) 2013;342:580–584.
- Cao Q, et al. Abnormal neural activity in children with attention deficit hyperactivity disorder: a resting-state functional magnetic resonance imaging study. Neuroreport. 2006;17:1033–1036.
- Tian L, et al. Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. Neurosci Lett. 2006;400:39–43.
- Uddin LQ, et al. Network homogeneity reveals decreased integrity of default-mode network in ADHD. J Neurosci Methods. 2008;169:249–54.
- Wang L, et al. Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Hum Brain Mapp. 2009;30:638–649.
- Fair DA, et al. Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder. Biol Psychiatry. 2010;68:1084–91.
- Qiu M, et al. Changes of Brain Structure and Function in ADHD Children. Brain Topogr. 2011;24:243–252.
- Tomasi D, Volkow ND. Abnormal functional connectivity in children with attention-deficit/hyperactivity disorder. Biol Psychiatry. 2012;71:443–50.
- Cocchi L, et al. Altered Functional Brain Connectivity in a Non-Clinical Sample of Young Adults with Attention-Deficit/Hyperactivity Disorder. J Neurosci. 2012;32:17753–17761.
- Joshi A, et al. Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms. Neuroinformatics. 2011;9:69–84.
- Kaufman J, et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36:980–988.
- Friedman L, Glover GH The FBIRN Consortium. Reducing interscanner variability of activation in a multicenter fMRI study: Controlling for signal-to-fluctuation-noise-ratio (SFNR) differences. Neuroimage. 2006;33:471–481.
- Scheinost D, Papademetris X, Constable RT. The impact of image smoothness on intrinsic functional connectivity and head motion confounds. Neuroimage. 2014;95:13–21.
Source: PubMed