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Parcellating cortical functional networks in individuals

Abstract

The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.

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Figure 1: Parcellating the functional networks in an individual subject's brain using an iterative adjusting approach.
Figure 2: Iterative brain parcellation is highly reproducible within subjects and captures differences across subjects.
Figure 3: Quantitative analyses of intra-subject reliability and inter-subject variability based on the HCP subjects.
Figure 4: Brain lateralization is reflected in the network parcellation.
Figure 5: Sensorimotor networks identified by individual brain parcellation correspond to functional regions localized by invasive cortical stimulation.

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Acknowledgements

The authors thank X. Peng and M. Li for technical assistance. Data set II was provided by the Human Connectome Project, WU-Minn Consortium (principal investigators, D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Data set III was provided by the Brain Genomics Superstruct Project of Harvard University and the Massachusetts General Hospital (principal investigators, R.L.B., J. Roffman and J. Smoller), with support from the Center for Brain Science Neuroinformatics Research group, the Athinoula A. Martinos Center for Biomedical Imaging, and the Center for Human Genetic Research. Twenty investigators at Harvard and MGH contributed data to the overall project. This work was supported by NIH grants K25NS069805 (H.L.), R01NS091604 (H.L.), P50MH106435 (R.L.B. and H.L.), K01MH099232 (A.J.H.), R01HD067312 (G.L.), P41EB015902 (G.L.), OeNB Nr. 15929 (G.L.), Medical Imaging Cluster of the Medical University of Vienna (G.L.), National Basic Research Program of China Grant 2011CB504100 (X.W.), National Science Foundation of China Grant 61473169 (B.H.) and National Program on Key Basic Research Projects of China Grant 2011CB933204 (B.H.).

Author information

Authors and Affiliations

Authors

Contributions

D.W., R.L.B. and H.L. conceived the study; D.W. and H.L. designed the algorithm and performed the analyses in healthy subjects with support from S.S., G.L. and R.P.; H.L., B.H., M.D.F. and T.Q. designed and performed the analyses in patients; D.J.H., A.J.H., K.L., J.T.B., S.M.S., K.W. and X.W. provided support and guidance with data interpretation. H.L., D.W. and R.L.B. wrote the manuscript with contribution from M.D.F. and D.J.H. All authors commented on the manuscript.

Corresponding authors

Correspondence to Bo Hong or Hesheng Liu.

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Competing interests

H.L., D.W., R.L.B. and M.D.F. are listed as inventors on patent applications related to mapping functional brain organization using fMRI. M.D.F. is listed as inventor on patent applications or patents on guiding noninvasive brain stimulation with fMRI.

Integrated supplementary information

Supplementary Figure 1 Functional cortical parcellation is gradually refined In the iterative procedure.

The maps show the results of an individual subject for 12 iterations. The functional map was gradually modified as the iteration proceeded and then reached a stable solution. The vertices in the primary visual and sensorimotor regions showed relative stable assignment over the iterations. However, vertices in the association cortices showed greater adjustment over the iterations. For example, in the lateral frontal lobe, the red network shrank after several iterations and the yellow network started to appear in the superior frontal lobe after the fourth iteration, expanding as the iteration proceeded.

Supplementary Figure 2 Individual parcellation captures differences across subjects and achieves high reproducibility within subjects.

The maps display the results in both hemispheres for three subjects from Dataset I that showed the highest, median, and lowest reproducibility across five sessions (mean Dice Coefficients are 92%, 77%, and 65%, respectively).

Supplementary Figure 3 Intra-subject reliability and inter-subject variability of network parcellation.

The maps demonstrate the spatial distribution of reliability and variability after each iteration. As the iterative search progressed, reliability decreased while variability increased. Inter-subject variability was most prominent in the association areas.

Supplementary Figure 4 Parcellation results based on the task fMRI data.

The maps demonstrate the parcellation results of a subject based on the concatenated task data and the data of single tasks.

Supplementary Figure 5 Test-retest reliability of the network lateralization.

The intra-subject test-retest reliability of the LI was computed using the two resting-state sessions of each subject. The relation of the LIs derived from the two scanning sessions was plotted for 100 HCP subjects. Each circle in the scatterplots represents a subject. For the most left-lateralized network, the lateralization indices estimated in the two sessions showed a correlation coefficient of 0.57 (p<10-9). For the most right-lateralized network, the correlation between the two sessions was 0.42 (p<10-4).

Supplementary Figure 6 Tongue and hand sensorimotor areas localized by ECS, traditional task activation fMRI, direct projection of the population-based atlas to the individual, and iterative cortical parcellation.

Each row represents the results of one patient. The mapping results were projected to each individual’s cortical surface reconstructed from the MRI T1 images. The four columns on the left illustrate the tongue regions, while the four columns on the right show the hand regions. The red dots on the ECS maps indicate negative electrodes (no symptoms related to the sensorimotor cortex were reported when stimulated), while the yellow dots indicate positive electrodes. Compared to task fMRI activation maps, the high-confidence target regions identified by the iterative parcellation approach were more consistent with the results of ECS. The iterative parcellation also outperformed the direct projection of the population-based atlas to the individual subject’s cortical surface.

Supplementary Figure 7 Iterative parcellation with the number of networks flexibly determined in each individual.

The iterative parcellation procedure was initiated from a population-based atlas consisting of 25 networks. The number of networks was gradually adjusted by merging the networks with similar time courses (e.g., r >0.5). Once a merger occurred, the iterative parcellation was restarted with the reduced number of networks. The parcellation results of a single subject using this strategy were displayed. The parcellation started at 25 networks and converged into 19 networks. The parcellation maps showed high reproducibility across two different days. The color scheme of the networks was arbitrarily selected for each map.

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Wang, D., Buckner, R., Fox, M. et al. Parcellating cortical functional networks in individuals. Nat Neurosci 18, 1853–1860 (2015). https://doi.org/10.1038/nn.4164

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