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A brain-based general measure of attention

Abstract

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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Fig. 1: Prediction accuracy of nine CPMs.
Fig. 2: Cross-prediction results of nine original CPMs across all cognitive states and attention tasks.
Fig. 3: Cross-prediction results of five CPMs trained to predict a common attention factor using different fMRI data.
Fig. 4: Cross-prediction results of CPMs trained to predict task-specific variance.
Fig. 5: Network contribution to CPMs’ prediction performance.
Fig. 6: Prediction of individual behaviours by applying the original CPMs trained using task fMRI to rest fMRI with a rest-to-task connectome transformation using C2C modelling.
Fig. 7: The general attention model in internal validation.
Fig. 8: The general attention model generalizes to predict different attentional measures in four independent datasets.

Data availability

Raw task and rest fMRI data used in the primary analyses (n = 92) are available at https://doi.org/10.15154/1520622.

Code availability

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.

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Acknowledgements

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.

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Authors

Contributions

K.Y., M.D.R. and M.M.C. designed the study. Y.H.K. and E.W.A. performed fMRI experiments. K.Y. and M.D.R. analysed behavioural data. K.Y. and Y.H.K. analysed fMRI data. K.Y. conducted modelling and visualization. K.Y., M.M.C., M.D.R., Q.L., D.S. and R.T.C. discussed the results and implications. M.M.C. and R.T.C. supervised the project. K.Y., Y.H.K. and M.M.C. wrote the original draft; K.Y., M.M.C., M.D.R., Q.L., E.W.A., D.S. and R.T.C. reviewed the original draft and contributed to the final version of the paper.

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Correspondence to Kwangsun Yoo or Marvin M. Chun.

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Nature Human Behaviour thanks Jing Sui, Francisco Castellanos and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

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.

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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

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