Attention is central to many aspects of cognition, but there is no singular neural measure of a person’s overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual’s task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.
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Raw task and rest fMRI data used in the primary analyses (n = 92) are available at https://doi.org/10.15154/1520622.
Scripts for the predictive model (the general attention model, C2C model and CPM) construction are available for download at https://github.com/rayksyoo/General_Attention. Scripts for the other (statistical) analyses are available from the corresponding author upon request.
Chun, M. M., Golomb, J. D. & Turk-Browne, N. B. A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101 (2011).
Weissman, D. H., Roberts, K. C., Visscher, K. M. & Woldorff, M. G. The neural bases of momentary lapses in attention. Nat. Neurosci. 9, 971–978 (2006).
Heinrichs, R. W. & Zakzanis, K. K. Neurocognitive deficit in schizophrenia: a quantitative review of the evidence. Neuropsychology 12, 426–445 (1998).
Biederman, J., Newcorn, J. & Sprich, S. Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. Am. J. Psychiatry 148, 564–577 (1991).
Levin, H. S. et al. Neurobehavioral outcome following minor head injury: a three-center study. J. Neurosurg. 66, 234–243 (1987).
Rosenberg, M. D. et al. Functional connectivity predicts changes in attention observed across minutes, days, and months. Proc. Natl Acad. Sci. U. S. A. 117, 3797–3807 (2020).
Kucyi, A. et al. Prediction of stimulus-independent and task-unrelated thought from functional brain networks. Nat. Commun. 12, 1793 (2021).
Deary, I. J., Penke, L. & Johnson, W. The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 11, 201–211 (2010).
Miyake, A. et al. The unity and diversity of executive functions and their contributions to complex ‘frontal lobe’ tasks: a latent variable analysis. Cogn. Psychol. 41, 49–100 (2000).
Huang, L., Mo, L. & Li, Y. Measuring the interrelations among multiple paradigms of visual attention: an individual differences approach. J. Exp. Psychol. Hum. Percept. Perform. 38, 414–428 (2012).
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 215–229 (2002).
Kanwisher, N. & Wojciulik, E. Visual attention: insights from brain imaging. Nat. Rev. Neurosci. 1, 91–100 (2000).
Rosenberg, M. D., Finn, E. S., Scheinost, D., Constable, R. T. & Chun, M. M. Characterizing attention with predictive network models. Trends Cogn. Sci. 21, 290–302 (2017).
Rosenberg, M. D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).
Wu, E. X. W. et al. Overlapping attentional networks yield divergent behavioral predictions across tasks: neuromarkers for diffuse and focused attention? Neuroimage 209, 116535 (2020).
Kucyi, A., Hove, M. J., Esterman, M., Hutchison, R. M. & Valera, E. M. Dynamic brain network correlates of spontaneous fluctuations in attention. Cereb. Cortex 27, 1831–1840 (2017).
Shen, X. et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat. Protoc. 12, 506–518 (2017).
Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).
Woo, C. W., Chang, L. J., Lindquist, M. A. & Wager, T. D. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20, 365–377 (2017).
Gratton, C. et al. Defining individual-specific functional neuroanatomy for precision psychiatry. Biol. Psychiatry 88, 28–39 (2020).
Cohen, J. R. & D’Esposito, M. The segregation and integration of distinct brain networks and their relationship to cognition. J. Neurosci. 36, 12083–12094 (2016).
Yoo, K. et al. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. Neuroimage 197, 212–223 (2019).
Rosenberg, M. D. et al. Methylphenidate modulates functional network connectivity to enhance attention. J. Neurosci. 36, 9547–9557 (2016).
Rosenberg, M. D., Hsu, W.-T., Scheinost, D., Todd Constable, R. & Chun, M. M. Connectome-based models predict separable components of attention in novel individuals. J. Cogn. Neurosci. 30, 160–173 (2018).
Yoo, K. et al. Connectome-based predictive modeling of attention: comparing different functional connectivity features and prediction methods across datasets. Neuroimage 167, 11–22 (2018).
Lin, Q. et al. Resting-state functional connectivity predicts cognitive impairment related to Alzheimer’s disease. Front. Aging Neurosci. 10, 94 (2018).
Avery, E. W. et al. Distributed patterns of functional connectivity predict working memory performance in novel healthy and memory-impaired individuals. J. Cogn. Neurosci. 32, 241–255 (2019).
Zhang, H. et al. Do intrinsic brain functional networks predict working memory from childhood to adulthood? Hum. Brain Mapp. https://doi.org/10.1002/hbm.25143 (2020).
Tomasi, D. & Volkow, N. D. Network connectivity predicts language processing in healthy adults. Hum. Brain Mapp. 41, 3696–3708 (2020).
Beaty, R. E. et al. Robust prediction of individual creative ability from brain functional connectivity. Proc. Natl Acad. Sci. U. S. A. 115, 1087–1092 (2018).
Hsu, W.-T., Rosenberg, M. D., Scheinost, D., Constable, R. T. & Chun, M. M. Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Soc. Cogn. Affect. Neurosci. 13, 224–232 (2018).
Jiang, R. et al. Connectome-based individualized prediction of temperament trait scores. Neuroimage 183, 366–374 (2018).
Cai, H., Chen, J., Liu, S., Zhu, J. & Yu, Y. Brain functional connectome-based prediction of individual decision impulsivity. Cortex 125, 288–298 (2020).
Esterman, M., Noonan, S. K., Rosenberg, M. & Degutis, J. In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention. Cereb. Cortex 23, 2712–2723 (2013).
Fan, J., McCandliss, B. D., Fossella, J., Flombaum, J. I. & Posner, M. I. The activation of attentional networks. Neuroimage 26, 471–479 (2005).
Kardan, O. et al. Adult neuromarkers of sustained attention and working memory predict inter- and intra-individual differences in these processes in youth. Preprint at bioRxiv https://doi.org/10.1101/2021.08.01.454530 (2021).
Engle, R. W. Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23 (2002).
Yoo, K. et al. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome. Preprint at bioRxiv https://doi.org/10.1101/2020.12.23.424176 (2020).
Noble, S. et al. Influences on the test-retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cereb. Cortex 27, 5415–5429 (2017).
Varoquaux, G. Cross-validation failure: small sample sizes lead to large error bars. NeuroImage 180, 68–77 (2018).
Jangraw, D. C. et al. A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task. Neuroimage 166, 99–109 (2018).
Fountain-Zaragoza, S., Samimy, S., Rosenberg, M. D. & Prakash, R. S. Connectome-based models predict attentional control in aging adults. Neuroimage 186, 1–13 (2019).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).
DuPaul, G. J., Power, T. J., Anastopoulos, A. D. & Reid, R. ADHD Rating Scale—IV: Checklists, Norms, and Clinical Interpretation (Guilford, 1998).
Consortium, T. A.-200. The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012).
Satterthwaite, T. D. et al. Neuroimaging of the Philadelphia neurodevelopmental cohort. NeuroImage 86, 544–553 (2014).
Casey, B. J. et al. The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).
Wojciulik, E. & Kanwisher, N. The generality of parietal involvement in visual attention. Neuron 23, 747–764 (1999).
Duncan, J. & Owen, A. M. Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci. 23, 475–483 (2000).
Ramnani, N. & Owen, A. M. Anterior prefrontal cortex: insights into function from anatomy and neuroimaging. Nat. Rev. Neurosci. 5, 184–194 (2004).
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).
Pardo, J. V., Fox, P. T. & Raichle, M. E. Localization of a human system for sustained attention by positron emission tomography. Nature 349, 61–64 (1991).
Corbetta, M., Shulman, G. L., Miezin, F. M. & Petersen, S. E. Superior parietal cortex activation during spatial attention shifts and visual feature conjunction. Sci. (80-.) 270, 802–805 (1995).
Hopfinger, J. B., Buonocore, M. H. & Mangun, G. R. The neural mechanisms of top-down attentional control. Nat. Neurosci. 3, 284–291 (2000).
Sprague, T. C. & Serences, J. T. Attention modulates spatial priority maps in the human occipital, parietal and frontal cortices. Nat. Neurosci. 16, 1879–1887 (2013).
Wimmer, R. D. et al. Thalamic control of sensory selection in divided attention. Nature 526, 705–709 (2015).
Heinze, H. J. et al. Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature 372, 543–546 (1994).
Coull, J. T., Vidal, F., Nazarian, B. & Macar, F. Functional anatomy of the attentional modulation of time estimation. Sci. (80-.) 303, 1506–1508 (2004).
Gao, J. H. et al. Cerebellum implicated in sensory acquisition and discrimination rather than motor control. Sci. (80-.). 272, 545–547 (1996).
Leiner, H. C., Leiner, A. L. & Dow, R. S. Does the cerebellum contribute to mental skills? Behav. Neurosci. 100, 443–454 (1986).
Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M. & Raichle, M. E. Positron emission tomographic studies of the processing of single words. J. Cogn. Neurosci. 1, 153–170 (1989).
Stoodley, C. J. The cerebellum and cognition: evidence from functional imaging studies. Cerebellum 11, 352–365 (2012).
Strick, P. L., Dum, R. P. & Fiez, J. A. Cerebellum and nonmotor function. Annu. Rev. Neurosci. 32, 413–434 (2009).
Buckner, R. L. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron 80, 807–815 (2013).
Allen, G., Buxton, R. B., Wong, E. C. & Courchesne, E. Attentional activation of the cerebellum independent of motor involvement. Sci. (80-.) 275, 1940–1943 (1997).
Rees, G., Frackowiak, R. & Frith, C. Two modulatory effects of attention that mediate object categorization in human cortex. Sci. (80-.) 275, 835–838 (1997).
Gottwald, B., Mihajlovic, Z., Wilde, B. & Mehdorn, H. M. Does the cerebellum contribute to specific aspects of attention? Neuropsychologia 41, 1452–1460 (2003).
Greene, A. S., Gao, S., Scheinost, D. & Constable, R. T. Task-induced brain state manipulation improves prediction of individual traits. Nat. Commun. 9, 2807 (2018).
Jiang, R. et al. Task-induced brain connectivity promotes the detection of individual differences in brain–behavior relationships. Neuroimage 207, 116370 (2020).
Sui, J., Jiang, R., Bustillo, J. & Calhoun, V. Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises. Biol. Psychiatry 88, 818–828 (2020).
Gao, S., Greene, A. S., Constable, R. T. & Scheinost, D. Combining multiple connectomes improves predictive modeling of phenotypic measures. Neuroimage 201, 116038 (2019).
Pujol, J. et al. Clinical application of functional magnetic resonance imaging in presurgical identification of the central sulcus. J. Neurosurg. 88, 863–869 (1998).
Bullmore, E. The future of functional MRI in clinical medicine. Neuroimage 62, 1267–1271 (2012).
Vanderwal, T., Kelly, C., Eilbott, J., Mayes, L. C. & Castellanos, F. X. Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging. Neuroimage 122, 222–232 (2015).
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. 75, 426–439 (2013).
Pylyshyn, Z. W. & Storm, R. W. Tracking multiple independent targets: evidence for a parallel tracking mechanism. Spat. Vis. 3, 179–197 (1988).
Luck, S. J. & Vogel, E. K. The capacity of visual working memory for features and conjunctions. Nature 390, 279–284 (1997).
Pashler, H. Familiarity and visual change detection. Percept. Psychophys. 44, 369–378 (1988).
Rouder, J. N., Morey, R. D., Morey, C. C. & Cowan, N. How to measure working memory capacity in the change detection paradigm. Psychon. Bull. Rev. 18, 324–330 (2011).
Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
Shen, X., Tokoglu, F., Papademetris, X. & Constable, R. T. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).
Scheinost, D. et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 193, 35–45 (2019).
Nichols, T. E. & Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1–25 (2002).
Abbas, K. et al. GEFF: graph embedding for functional fingerprinting. Neuroimage 221, 117181 (2020).
Amico, E. & Goñi, J. The quest for identifiability in human functional connectomes. Sci. Rep. 8, 8254 (2018).
Barch, D. M. et al. NeuroImage function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).
Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. Neuroimage 80, 144–168 (2013).
This project was supported by National Institutes of Health grant MH108591 to M.M.C. and by National Science Foundation grant BCS1558497 to M.M.C.
The authors declare no competing interests.
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Extended Data Fig. 1 Predictive anatomy of three task-based CPMs.
a. The scale bar in gradCPT, MOT and VSTM represents the relative ratio of predictive functional connections to all possible number of functional connections between networks with a sign representing whether the connection is in a positive or negative network. The scale bar in overlap represents the actual number of predictive functional connections with a sign representing whether the connection is in a positive or negative network. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory. MF: medial-frontal network, FP: frontoparietal network, DM: default mode network, VI: visual I, VII: visual II, VAs: visual association, SA: salience network, Subc: subcortex, Cbl: cerebellum. b. The number of predictive connections of three task-based CPMs in positive and negative networks.
Extended Data Fig. 2 Cross-prediction results of five common attention factor CPMs.
a. Cross-prediction results when models were applied to predict the common attention factor from different fMRI data. Models’ prediction accuracies were assessed by prediction q2 and correlation r between observed and predicted common factor measures. P values of significance were obtained using 1,000 permutations and corrected for all 5×5 tests (***: p < 0.001, **: p < 0.01, *: p < 0.05, and ~: p < 0.1). Rows represent different fMRI data used to predict a common attention factor used in model construction, and columns represent the same but in model validation. b. Cross-prediction results, taking into account shared variance (the common factor) between task behaviors. Models’ prediction accuracies were assessed by partial correlation between observed and predicted behavior scores while controlling for the shared variance. P values of significance were obtained using 1,000 permutations and corrected for all 5×9 tests (***: p < 0.001, **: p < 0.01, *: p < 0.05, and ~: p < 0.1). Rows represent different fMRI data used to predict a common attention factor used in model construction, and columns represent combinations of fMRI data and behavior scores used in model validation. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory.
Extended Data Fig. 3 A similarity of individual behaviours between different tasks.
The similarity was assessed by Pearson’s correlation of individual performances between attention tasks. Individual behaviors were significantly correlated between every pair of tasks. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory.
Extended Data Fig. 4 Cross-prediction results of task-specific CPMs.
a. Cross-prediction results, taking into account shared variance between task behaviors. Models’ prediction accuracies were assessed by partial correlation between observed and predicted behavior scores while controlling for the shared variance. P value was obtained using 1,000 permutations and corrected for multiple tests (***: p < 0.001, **: p < 0.01, *: p < 0.05, and ~: p < 0.1). Rows represent combinations of fMRI data and behavior scores used in model construction, and columns represent combinations of fMRI data and behavior scores used in model validation. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory. b. Cross-prediction results when models were applied to predict the common attention factor from different fMRI data. Models’ prediction accuracies were assessed by correlation between observed and predicted common factor. P value was obtained using 1,000 permutations and corrected for all 9×5 tests (***: p < 0.001, **: p < 0.01, *: p < 0.05, and ~: p < 0.1). Rows represent combinations of fMRI data and behavior scores used in model construction, and columns represent different fMRI data used to predict a common attention factor used in model validation.
Extended Data Fig. 5 Cross-prediction using connectivity between the frontoparietal (FP, 2), visual II (VII, 6), salience (SA, 8), subcortical (Subc, 9), cerebellar (Cbl, 10) networks.
Prediction of a model using connectivity between the medial-frontal (1), default mode (3), motor (4), visual I (5), visual association (7) networks was also obtained as a control. A. Rows represent combinations of networks (indicated by numbers) used in each model. Models’ prediction accuracies were assessed by correlating model-predicted and observed behavioral scores. B. Prediction performance of each network obtained by averaging all models that used the network in A. C. The same result as A, but model accuracies were assessed by q2. D. Prediction performance of each network obtained by averaging all models that used the network in C. GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory.
Extended Data Fig. 6 Similarity between C2C model-generated task connectomes and empirical task connectomes.
Error bar represents standard deviation from 1,000 iterations. A and C represent a spatial similarity between two connectomes assessed by Pearson’s correlation. Darker bars represent the similarity between empirical task and generated task connectomes, and lighter bars represent the similarity between empirical task and empirical rest connectomes. The higher similarity of the generated connectome indicates that the C2C model accurately generates the target task connectome from the rest connectome. B and D represent root mean square (RMS) difference between two connectomes. The smaller difference of the generated connectome indicates that the C2C model accurately generates the target task connectome from the rest connectome. In a box-whisker plot, a box covers the first to third quartile (q1 and q3, respectively) of the data, and a center line represents the median. A red dot represents the mean. Whisker covers approximately 99.3% of data (±2.7*standrad deviation), extended to the most extreme point that is not an outlier. A data point is considered an outlier if it is greater than q3+1.5*(q3-q1) or less than q1-1.5*(q3-q1). GradCPT: gradual-onset continuous performance task, MOT: multiple object tracking, and VSTM: visual short-term memory. *: p < 0.001 from 1,000 permutations.
Extended Data Fig. 7 The general attention connectome lookup table.
Out of a total 30,135 edges, 10,885 (36.1%) edges were pulled from gradCPT, 12,542 (41.6%) edges were from MOT, and 6,708 (22.3%) were from VSTM. The Ratio map was obtained based on All map. In each within- or between-network element in Ratio, the number of edges in the element for each task was counted and normalized by the total number of edges of each task. A task with the highest normalized value was assigned.
Extended Data Fig. 8 Scatter plots of predicted and observed attention scores in four external datasets.
Three models, the general attention model and two single task models (model 1 and 4 in Table 1) were trained within the internal dataset and then applied to rest connectomes in the four datasets. If a fitted line closely passes the origin (0,0) with a positive slope (staying within white quadrants), the model could be considered successfully predicting actual attentional abilities. There was no constraint on intercepts in fitting a line. The general model best generalized to predict various attentional measures in four independent external datasets.
Extended Data Fig. 9 Prediction error, assessed by mean square error (MSE), of the general attention model in four independent datasets.
The general model significantly reduced prediction error (assessed by MSE) compared to null models in four datasets. In all datasets, the general attention model produced the lowest prediction error among all models tested. ***: p < 0.001, **: p < 0.01, *: p < 0.05, and ~: p < 0.1 from 1,000 permutations.
Supplementary methods, results, discussion, references, Tables 1–7 and Figs. 1–11.
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Yoo, K., Rosenberg, M.D., Kwon, Y.H. et al. A brain-based general measure of attention. Nat Hum Behav 6, 782–795 (2022). https://doi.org/10.1038/s41562-022-01301-1