Research

Last updated on March 27th, 2021. For full publication list, see google scholar. Links to our software are available here & github.

In-vivo delineation of human brain networks and areas

Information processing occurs via the transformation of neural signals across brain networks. Resting-fMRI is a powerful tool allowing the non-invasive, simultaneous, interrogation of multiple brain networks in living individuals. We have utilized resting-fMRI to generate canonical parcellations of the cerebral cortex, cerebellum and striatum into distributed large-scale networks. Our parcellations are widely used as references to study human brain organization and disorders. We have extended our network-level parcellations to areal-level parcellations, which comprises hundreds of regions approximating classical cortical areas with distinct function, connectivity, architectonics and topography. Moving beyond population-level parcellations, we have developed algorithms to delineate individual-specific cerebral cortical and subcortical networks. These high-quality networks will provide the basis for studying individual differences in behavior and might facilitate precision medicine, such as individualized brain stimulation.

  1. The organization of the human cerebral cortex revealed by intrinsic functional connectivity. Yeo BTT*, Krienen FM*, Sepulcre J, Sabuncu MR, Lashkari L, Hollinshead M, Roffman JL, Smoller JW, Zöllei L, Polimeni JM, Fischl B, Liu H, Buckner RL. Journal of Neurophysiology, 106(3):1125–1165, 2011 [pdf]

  2. The organization of the human cerebellum revealed by intrinsic functional connectivity. Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BTT. Journal of Neurophysiology, 106:2322-2345, 2011 [pdf]

  3. The organization of the human striatum revealed by intrinsic functional connectivity. Choi EY, Yeo BTT, Buckner RL. Journal of Neurophysiology, 108(8):2242-2263, 2012 [pdf]

  4. Opportunities and limitations of functional connectivity MRI. Buckner RL, Krienen FM, Yeo BTT. Nature Neuroscience, 16:832-837, 2013 [pdf]

  5. Local-Global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Schaefer AL, Kong Ru, Gordon EM, Laumann TO, Zuo XN, Holmes AL, Eickhoff SB, Yeo BTT. Cerebral Cortex, 29:3095-3114, 2018 [pdf]

  6. Imaging-based parcellations of the human brain. Eickhoff SB, Yeo BTT, Genon S. Nature Reviews Neuroscience, 19:672-686, 2018 [pdf]

  7. Spatial topography of individual-specific cortical networks predicts human cognition, personality and emotion. Kong R, Li J, Sun N, Sabuncu MR, Schaefer A, Zuo XN, Holmes A, Eickhoff SB, Yeo BTT. Cerebral Cortex, 29:2533-2551, 2019 [pdf]

  8. Towards a Universal Taxonomy of Macro‑scale Functional Human Brain Networks. Uddin LQ, Yeo BTT, Spreng RN. Brain Topography, 32:926-942, 2019, doi:10.1007/s10548-019-00744-6 [pdf]

  9. The detailed organization of the human cerebellum estimated by intrinsic functional connectivity within the individual. Xue A, Kong R, Yang Q, Eldaief MC, Angeli P, DiNicola LM, Braga RM, Buckner RL, Yeo BTT. Journal of Neurophysiology. 125(2):358-384. doi: 10.1152/jn.00561.2020, 2021. [free download]

  10. Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. Kong R, Yang Q, Gordon E, Xue A, Yan X, Orban C, Zuo XN, Spreng N, Ge T, Holmes A, Eickhoff SB, Yeo BTT. Cerebral Cortex, 31, 4477–4500 doi: 10.1093/cercor/bhab101, 2021. [pdf]

Functional architecture of the human brain

Comprehensive delineation of brain parcellations (previous contribution) provides the foundations for new insights into human brain organization. For example, using resting-fMRI (from 1000 participants) and task-activation (from 10,449 experiments), we showed that sensory-motor regions were functionally specialized, i.e., they participated in one resting-state network or cognitive process. By contrast, the association cortex was characterized by complex zones ranging from highly specialized to highly flexible. Functionally flexible regions participated in multiple cognitive processes to different degrees. This heterogeneous selectivity was predicted by the connectivity between flexible and specialized regions. Functionally flexible regions might support binding or integrating specialized brain networks that, in turn, contribute to the ability to execute multiple and varied tasks. As another example, we recently demonstrated that dynamic functional connectivity explains task performance (but not self-reported measures) better than static functional connectivity. This suggests that resting brain dynamics at different timescales capture distinct aspects of human behavior. Finally, we demonstrated considerable heritability in both the size and spatial organization of individual-specific network topography across cortex.

  1. The organization of local and distant functional connectivity in the human brain. Sepulcre J, Liu H, Talukdar T, Martinocorena I, Yeo BTT, Buckner RL. PLoS Computational Biology, 6(6): e1000808. doi:10.1371, 2010 [pdf]

  2. Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex. Yeo BTT, Krienen FM, Chee MWL, Buckner RL. Neuroimage, 88:212-227, 2014 [pdf]

  3. Reconfigurable state-dependent functional coupling modes cluster around a core functional architecture. Krienen FM, Yeo BTT, Buckner RL. Philosophical Transactions of the Royal Society B, 369:20130526, 2014 [pdf]

  4. Borders, map clusters, and supra-areal organization of the visual cortex. Buckner RL, Yeo BTT. Neuroimage, 93:293-297, 2014 [pdf]

  5. Functional specialization and flexibility in human association cortex. Yeo BTT, Krienen FM, Eickhoff SB, Yaakub SN, Fox PT, Buckner RL, Asplund CL, Chee MWL. Cerebral Cortex, 25:3654-3672, 2015 [pdf]

  6. The modular and integrative functional architecture of the human brain. Bertolero MA, Yeo BTT, D'Esposito M. Proceedings of the National Academy of Sciences USA, 112:E6798-E6807, 2015 [pdf]

  7. Data-driven extraction of a nested model of human brain function. Bolt T, Nomi JS, Yeo BTT, Uddin LQ. Journal of Neuroscience, 37:7263–7277, 2017 [pdf]

  8. The diverse club. Bertolero MA, Yeo BTT, D'Esposito M. Nature Communications 8:1277, 2017 [pdf]

  9. Topographic organization of the cerebral cortex and brain cartography. Eickhoff SB, Constable RT, Yeo BTT. NeuroImage, 170:332–347, 2018 [pdf]

  10. A mechanistic model of connector hubs, modularity and cognition. Bertolero MA, Yeo BTT, Bassett DS, D'Esposito M. Nature Human Behavior, 112: E6798, 2018 [pdf]

  11. Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants. Kernbach JM, Yeo BTT, Smallwood J, Margulies DS, Thiebaut de Schotten M. Walter H, Sabuncu MR, Holmes AJ, Gramfort A, Varoquaux G, Thirion B, Bzdok D. Proceedings of the National Academy of Sciences USA, 115:12295-12300, 2018 [pdf]

  12. Beyond consensus: embracing heterogeneity in curated neuroimaging meta-analysis. Ngo GN, Eickhoff SB, Fox PT, Spreng RN, Yeo BTT. NeuroImage, 200:142-158, 2019 [pdf]

  13. Topography and behavioral relevance of the global signal in the human brain. Li J*, Bolt T*, Bzdok B, Nomi JS, Yeo BTT, Spreng RN, Uddin LQ. Scientific Reports, 9:14286, doi:10.1038/s41598-019-50750-8, 2019 [free download]

  14. Resting brain dynamics at different timescales capture distinct aspects of human behavior. Liégeois R, Li J, R Kong, Orban C, Van De Ville D, Ge T, Sabuncu MR, Yeo BTT. Nature Communications, 10:2317, 2019 [free download]

  15. Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity. Orban C, Kong R, Li J, Chee MWL, Yeo BTT. PLoS Biology, 18(2): e3000602. doi: 10.1371/journal.pbio.3000602 , 2020 [free download] [science daily] [the week]

  16. Structure-function coupling in the human connectome: A machine learning approach. Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A. Neuroimage. 226:117609. doi: 10.1016/j.neuroimage.2020.117609, 2021. [free download]

  17. Deep learning identifies partially overlapping subnetworks in the human social brain. Kiesow H, Spreng RN, Holmes AJ, Chakravarty MM, Marquand AF, Yeo BTT, Bzdok D. Communications Biology. 4(1):65, doi: 10.1038/s42003-020-01559-z, 2021 [free download]

  18. Structure-function coupling in the human connectome: A machine learning approach. Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A. Neuroimage. 226:117609. doi: 10.1016/j.neuroimage.2020.117609, 2021. [free download]

  19. Heritability of individualized cortical network topography. Anderson KM, Ge T, Kong R, Patrick LM, Spreng RN, Sabuncu RM, Yeo BTT, Holmes AJ. Proceedings of the National Academy of Sciences USA. 118 (9): e2016271118 . doi: 10.1073/pnas.2016271118, 2021 [free download]

  20. A Parsimonious Description of Global Functional Brain Organization in Three Spatiotemporal Patterns. Bolt T, Nomi JS, Bzdok D, Chang C, Yeo BTT, Uddin LQ, Keilholz SD. Nature Neuroscience, 25, 1093-1103. doi: 10.1038/s41593-022-01118-1, 2022 [pdf]

Individual differences in behavior and disorder

Machine learning is critical for precision medicine. For example, current mental disorder categories are based on symptom checklists and not carving nature by its joints. Subtypes within disorders and overlaps across disorders suggest the existence of shared neurobiological factors across disorders. We have utilized unsupervised machine learning to discover latent factors in late onset Alzheimer’s Disease (AD) dementia. The factors were associated with distinct atrophy patterns, as well as distinct memory and executive function decline trajectories among dementia patients and at-risk nondemented participants. There is significant ongoing work in the lab on discovering factors (or subtypes) underlying heterogeneity in neurological and psychiatric disorders, such as autism spectrum disorder, schizophrenia, etc. While unsupervised machine learning is useful for discovering new insights into mental disorders, supervised machine learning is useful when we know what we want to predict, e.g., behavioral traits, disease progression and treatment responses. For example, we have utilized deep recurrent neural networks to predict AD progression up to five years into the future. As of March 27th 2021, our algorithm is ranked third out of more than fifty entries in the TADPOLE challenge.

  1. Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder. Baker JT, Holmes AJ, Masters GA, Yeo BTT, Krienen FM, Buckner RL, Öngür D. JAMA Psychiatry, 71:109-118, 2014 [pdf]

  2. Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. Yeo BTT, Tandi J, Chee MWL. Neuroimage 111:147-158, 2015 [pdf]

  3. Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease. Zhang XM, Mormino EC, Sun N, Sperling RA, Sabuncu MR, Yeo BTT. Proceedings of the National Academy of Sciences USA, 113:E6535–E6544, 2016 [free download]

  4. Inference in the age of big data: future perspectives on neuroscience. Bzdok D, Yeo BTT. NeuroImage, 155:549-564, 2017 [pdf]

  5. The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Reinen JM, Chen O, Hutchison OY, Yeo BTT, Anderson KM, Sabuncu MR, Öngür D, Roffman JL, Smoller JW, Baker JT, Holmes AJ. Nature Communications 9:1157, 2018 [pdf]

  6. Modeling Alzheimer’s disease progression using deep recurrent neural networks. Nguyen M, Alexander DC, Feng J, Yeo BTT. International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2018 [pdf]

  7. Is deep learning better than kernel regression for functional connectivity prediction of fluid intelligence? He T, Kong R, Holmes AJ, Sabuncu MR, Eickhoff SB, Bzdok D, Feng J, Yeo BTT. International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2018 [pdf] Runner-Up, PRNI Best Paper Award

  8. Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Typical Late-Onset Alzheimer's Disease. Sun N, Mormino EC, Chen J, Sabuncu MR, Yeo BTT. NeuroImage, 201, 116043. doi: 10.1016/j.neuroimage.2019.116043, 2019 [pdf] [code]

  9. Somatosensory-motor dysconnectivity spans multiple transdiagnostic dimensions of psychopathology. Kebets V, Holmes AJ, Orban C, Tang S, Li J, Sun N, Kong R, Poldrack R, Yeo BTT. Biological Psychiatry, 86:779-791, 2019 [pdf] [code]

  10. Intrinsic Functional Connectivity of the Brain in Adults with a Single Cerebral Hemisphere. Kliemann D, Adolphs R, Tyszka JM, Fischl B, Yeo BTT, Nair R, Dubois J, Paul LK. Cell Reports, 29:2398-2407, 2019, doi:10.1016/j.celrep.2019.10.067 [free download]

  11. Latent atrophy factors related to phenotypical variants of posterior cortical atrophy. Groot C, Yeo BTT, Vogel JW, Zhang X, Sun N, Mormino EC, Pijnenburg YA, Miller BL, Rosen HJ, La Joie R, Barkhof F, Scheltens P, van der Flier WM, Rabinovici GD, Ossenkoppele R. Neurology, 95 (12): 1672-1685. doi: 10.1212/WNL.0000000000010362, 2020 [pdf]

  12. Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. He T, Kong R, Holmes AJ, Nguyen M, Sabuncu MR, Eickhoff SB, Bzdok D, Feng J, Yeo BTT. NeuroImage, 206, 116276. doi: 10.1016/j.neuroimage.2019.116276, 2020 [free download] [code]

  13. Towards Neurosubtypes in Autism. Hong SJ, Vogelstein J , Bernhardt BC, Yeo BTT, Milham Michael, Di Martino A. Biological Psychiatry, 88:1071-1082. doi: 10.1016/j.biopsych.2020.03.022 , 2020 [pdf]

  14. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Schulz M, Yeo BTT, Vogelstein J, Mourao-Miranda J, Kather J, Kording K, Richards BA, Bzdok B. Nature Communications, 11, 4238. doi: 10.1038/s41467-020-18037-z, 2020 [free download]

  15. Predicting Alzheimer's disease progression using deep recurrent neural networks. Nguyen M, He T, An L, Alexander DC, Feng J, Yeo BTT. Neuroimage, 222, 117203, doi: 10.1016/j.neuroimage.2020.117203 , 2020. [free download] [code]

  16. Reconciling dimensional and categorical models of autism heterogeneity: a brain connectomics & behavioral study. Tang S*, Sun N*, Floris DL, Zhang X, Di Martino A, Yeo BTT. Biological Psychiatry, 87:1071-1082. doi: 10.1016/j.biopsych.2019.11.009, 2020 [free download]

  17. High-resolution connectomic fingerprints: Mapping neural identity and behavior. Mansour L S, Tian Y, Yeo BTT, Cropley V, Zalesky A. Neuroimage. 229:117695. doi: 10.1016/j.neuroimage.2020.117695, 2021. [free download]

  18. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Chen J*, Tam A*, Kebets V, Orban C, Ooi LQR, Marek S, Dosenbach N, Eickhoff SB, Bzdok D, Holmes AJ, Yeo BTT. Nature Communications, 13, 2217. doi:10.1038/s41467-022-29766-8, 2022 [free download]

  19. Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. He T, An L, Feng J, Bzdok D, Holmes AJ, Eickhoff SB, Yeo BTT. Nature Neuroscience. 25, 795–804. doi: 10.1038/s41593-022-01059-9, 2022 [pdf]

  20. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Li J, Bzdok D, Chen J, Tam A, Ooi LQR, Holmes AJ, Ge T, Patil KR, Jabbi M, Eickhoff SB, Yeo BTT*, Genon S*. Science Advances, 8(11), eabj1812. doi:10.1126/sciadv.abj1812, 2022 [free download]

  21. Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research. Chen J, Patil KR, Yeo BTT, Eickhoff SB. Biological Psychiatry, in press. doi: 10.1016/j.biopsych.2022.07.025, 2022 [pdf]

  22. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Ooi LQR, Chen J, Zhang S, Tam A, Li J, Dhamala E, Zhou JH, Holmes AJ, Yeo BTT. Neuroimage, 263, 119636. doi: 10.1016/j.neuroimage.2022.119636, 2022 [free download]

Multi-scale neuroscience

The human brain is a complex system spanning from micrometer (cellular) to centimeter (fMRI) scales. We have contributed to bridging the gap between microscale and macroscale brain organizations. For example, we found that genes enriched in the supragranular layers of the human cerebral cortex (relative to mouse) were expressed in a topography reflecting broad cortical classes (sensory/motor, paralimbic, associational) and associated network properties. Therefore, molecular innovations of upper cortical layers may be important for the evolution of long-range corticocortical projections. In addition to genetics, we have been exploiting machine learning algorithms to invert large-scale biophysical models. The estimated biophysical model parameters in turn provide insights into the large-scale and cellular organization of the human brain. For example, we recently demonstrated that a biophysically plausible mean field model best explained resting-state functional connectivity when local microscale properties mirrored the hierarchical axis of the human brain.

  1. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain. Krienen FM, Yeo BTT, Ge T, Buckner RL, Sherwood C. Proceedings of the National Academy of Sciences USA, 113:E469-E478, 2016 [pdf]

  2. A spotlight on bridging microscale and macroscale human brain architecture. van den Heuvel M, Yeo BTT. Neuron, 93:1248-1251, 2017 [pdf]

  3. Gene expression links functional networks across cortex and striatum. Anderson KM, Krienen FM, Choi EY, Reinen JM, Yeo BTT, Holmes AJ. Nature Communications 9:1428, 2018 [pdf]

  4. Inversion of a large-scale computational model reveals a cortical hierarchy in the dynamic resting human brain. Wang P, Kong R, Liegeois R, Deco G, van den Heuvel MP, Yeo BTT. Science Advances, 5:eaat7854, 2019 [free download]

  5. Personality and local brain structure: Their shared genetic basis and reproducibility. Valk SL, Hoffstaedter F, Camilleri J, Kochunov P, Yeo BTT, Eickhoff SB. Neuroimage, 220, 117067, doi: 10.1016/j.neuroimage.2020.117067, 2020. [free download]

  6. Shaping Brain Structure: Genetic and Phylogenetic Axes of Macro Scale Organization of Cortical Thickness. Valk SL, Xu T, Margulies DS, Masouleh SK, Paquola C, Goulas A, Kochunov P, Smallwood J, Yeo BTT, Bernhardt BC, Eickhoff SB. Science Advances, 6 (39): eabb3417. doi: 10.1126/sciadv.abb3417, 2020.

  7. Macroscale and microcircuit dissociation of focal and generalized human epilepsies. Weng Y, Larivière S, Caciagli L, Vos de Wael R, Rodríguez-Cruces R, Royer J, Xu Q , Bernasconi N, Bernasconi A, Yeo BTT, Lu G , Zhang Z, Bernhardt BC. Communcations Biology, 3, 244. doi: 10.1038/s42003-020-0958-5, 2020

  8. Convergent molecular, cellular, and neural signatures of major depressive disorder. Anderson KM, Collins MA, Kong R, Fang K, Li J, He T, Chekroud AM, Yeo BTT, Holmes AJ. Proceedings of the National Academy of Sciences USA. 117 (40): 25138 - 25149. doi: 10.1073/pnas.2008004117, 2020. [free download]

  9. Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism. Park B, Hong SJ, Valk S, Paquola C, Benkarim O, Bethlehem RAI, Di Martino A, Milham MP, Gozzi A, Yeo BTT, Smallwood J, Bernhardt BC. Nature Communications. 12:2225. doi:10.1038/s41467-021-21732-0, 2021 [free download]

  10. Sensory-Motor Cortices Shape Functional Connectivity Dynamics in the Human Brain. Kong X, Kong R, Orban C, Wang P, Zhang S, Anderson K, Holmes A, Murray JD, Deco G, van den Heuvel M, Yeo BTT. Nature Communications, 12, 6373. doi:10.1038/s41467-021-26704-y, 2021 [free download]

  11. Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex. Valk SL, Xu T, Paquola C, Park B, Bethlehem RAI, Vos de Wael R, Royer J, Masouleh SK, Bayrak S, Kochunov P, Yeo BTT, Margulies D, Smallw J, Eickhoff SB, Bernhardt BC. Nature Communications, 13, 2341. doi: 10.1038/s41467-022-29886-1, 2022 [free download]

Image processing and statistics

We have contributed towards resolving brain imaging processing and statistical issues. For example, an in-depth analysis of statistical issues involved in studying fMRI dynamics revealed several surprising results, e.g., first order autoregressive models explain fMRI dynamics better than nonlinear biophysical models. As another example, global signal regression (GSR) is one of the most controversial preprocessing steps for resting-state functional MRI. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Instead we considered the utilitarian question of whether GSR strengthens or weakens brain-behavior relationships. Across two healthy young adult datasets (N > 2000), we showed that GSR strengthened the associations between resting-state functional connectivity and most cognitive, personality and emotion measures. While the previous examples are specific to neuroimaging, we have also developed general methods applicable beyond neuroimaging. For example, the cerebral cortex is often represented as a 2D sphere, motivating our interest in spherical image processing and registration. We extended a sampling theorem in Euclidean space to the 2-Sphere and utilized the theorem to construct overcomplete wavelets for spherical image processing. We have also exploited differential geometric techniques and Lie group of diffeomorphisms to develop fast algorithms for image registration.

  1. On the construction of invertible filter banks on the 2-sphere. Yeo BTT, Ou W, Golland P. IEEE Transactions on Image Processing, 17(3):283--300, 2008 [pdf]

  2. Effects of registration regularization and atlas sharpness on segmentation accuracy. Yeo BTT*, Sabuncu MR*, Desikan R, Fischl B, Golland P. Medical Image Analysis, 12(5):603--615, 2008 [pdf]

  3. DT-REFinD: diffusion tensor registration with exact finite-strain differential. Yeo BTT, Vercauteren T, Fillard P, Peyrat J-M, Pennec X, Golland P, Ayache N, Clatz O. IEEE Transactions on Medical Imaging, 28(12):1914--1928, 2009 [pdf]

  4. Spherical demons: fast diffeomorphic landmark-free surface registration. Yeo BTT*, Sabuncu MR*, Vercauteren T, Ayache N, Fischl B, Golland P. IEEE Transactions on Medical Imaging, 29(3):650--668, 2010 [pdf]

  5. Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex. Yeo BTT, Sabuncu MR, Vercauteren T, Holt D, Amunts K, Zilles K, Golland P, Fischl B. IEEE Transactions on Medical Imaging, 29(7):1424--1441, 2010 [pdf]

  6. A generative model for image segmentation based on label fusion. Sabuncu MR*, Yeo BTT*, Van Leemput K, Fischl B, Golland P. IEEE Transactions on Medical Imaging, 29(10):1714--1729, 2010 [pdf]

  7. Interpreting temporal fluctuations in resting-state functional connectivity MRI. Liegeois R, Laumann TO, Snyder AZ, Zhou HJ, Yeo BTT. Neuroimage 163:437–455, 2017 [pdf]

  8. Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Wu J, Ngo GH, Greve DN, Li J, He T, Fischl B, Eickhoff SB, Yeo BTT. Human Brain Mapping, 39:3793–3808, 2018 [pdf]​ [code]

  9. Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior. Li J*, Kong R*, Liegeois R, Orban C, Tan Y, Sun N, Holmes AJ, Sabuncu MR, Ge T, Yeo BTT. NeuroImage, 196:126-141, 2019 [pdf]

  10. Goal-specific brain MRI harmonization. An L, Chen J, Chen P, He T, Chen C, Zhou JH, Yeo BTT. Neuroimage, 263, 119570. doi: 10.1016/j.neuroimage.2022.119570, 2022 [free download]