Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints
Open accessPublished version of a preprint
Abstract
Abstract Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently acquired data sets with repeated scans (total N=213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns. The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time, whereas this change was not observed in the diffusivity measures. Moreover, the local connectome fingerprint can be used as a phenotypic marker, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings. This novel approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome. Author Summary The local organization of white matter architecture is highly unique to individuals, making it a tangible metric of connectomic differences. The variability in local white matter architecture is found to be partially determined by genetic factors, but largely plastic across time. This approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Centralising or releasing data poses regulatory, ethical and legal challenges related to privacy and data protection, as well as technical ones; anonymising, controlling access and safely transferring healthcare data is non-trivial, and patient reidentification or information leakage cannot be ruled out unless anonymisation destroys data fidelity.
“Centralising or releasing data, however, poses not only regulatory, ethical and legal challenges, related to privacy and data protection, but also technical ones.”
From The future of digital health with federated learning (2020) · cites this paper as [38]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,748 · see citing works
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · see citing works
(1966) · Medical Entomology and Zoology · Cited by 10,293 · see citing works
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173 · see citing works
Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986) · Radiology · doi:10.1148/radiology.161.2.3763909 · Cited by 3,912 · see citing works
Principles of Nuclear Magnetic Resonance Microscopy
Callaghan, P. T. (1991) · doi:10.1093/oso/9780198539445.001.0001 · Cited by 3,625 · see citing works
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260 · see citing works
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747 · see citing works
The Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,605 · see citing works
Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water
Song, S.-K., Sun, S.-W., Ramsbottom, M. J., Chang, C., Russell, J., & Cross, A. H. (2002) · NeuroImage · doi:10.1006/nimg.2002.1267 · Cited by 2,582 · see citing works
Toward a quantitative assessment of diffusion anisotropy
Pierpaoli, C. M., & Basser, P. J. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910360612 · Cited by 2,392 · see citing works
Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template
Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A. V., Mahmood, A., Woods, R. P., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.12.035 · Cited by 1,832 · see citing works
Demyelination increases radial diffusivity in corpus callosum of mouse brain
Song, S.-K., Yoshino, J., Le, T. Q., Lin, S.-J., Sun, S.-W., Cross, A. H., & Armstrong, R. C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.01.028 · Cited by 1,658 · see citing works
The frequency distribution of the annual maximum (or minimum) values of meteorological elements
Jenkinson, A. F. (1955) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49708134804 · Cited by 1,573 · see citing works
Deterministic diffusion fiber tracking improved by quantitative anisotropy
Yeh, F.-C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C., & Tseng, W.-Y. I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080713 · Cited by 1,121 · see citing works
Self-citationPrinciples of Multivariate Analysis, A User's Perspective.
Kocherlakota, K., Kocherlakota, S., & Krzanowski, W. J. (1989) · Biometrics · doi:10.2307/2531791 · Cited by 1,093 · see citing works
Generalized ${ q}$-Sampling Imaging
Yeh, F.-C. F., Wedeen, V. J., & Tseng, W. I. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2045126 · Cited by 1,030 · see citing works
Self-citationCognition through the lifespan: mechanisms of change
Craik, F. I. M., & Bialystok, E. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.01.007 · Cited by 845 · see citing works
Mapping Human Whole-Brain Structural Networks with Diffusion MRI
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J.-P. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000597 · Cited by 788 · see citing works
Contributions and challenges for network models in cognitive neuroscience
Sporns, O. (2014) · Nature Neuroscience · doi:10.1038/nn.3690 · Cited by 743 · see citing works
Thomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725 · see citing works
White matter maturation reshapes structural connectivity in the late developing human brain
Hagmann, P., Sporns, O., Madan, N., Cammoun, L., Pienaar, R., Wedeen, V. J., Meuli, R., Thiran, J.-P., & Grant, P. E. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1009073107 · Cited by 700 · see citing works
NTU-90: A high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction
Yeh, F.-C. F., & Tseng, W. I. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.021 · Cited by 509 · see citing works
Self-citationDiffusion MRI at 25: exploring brain tissue structure and function
Le Bihan, D., & Johansen-Berg, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.006 · Cited by 498 · see citing works
Neurobiology of intelligence: science and ethics
Gray, J. R., & Thompson, P. M. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1405 · Cited by 479 · see citing works
Sun, S., Liang, H., Trinkaus, K., Cross, A. H., Armstrong, R. C., & Song, S. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20774 · Cited by 461 · see citing works
Simmonds, D. J., Hallquist, M. N., Asato, M. R., & Luna, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.044 · Cited by 443 · see citing works
Reveley, C., Seth, A. K., Pierpaoli, C., Silva, A. C., Yu, D., Saunders, R. C., Leopold, D. A., & Ye, F. Q. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418198112 · Cited by 417 · see citing works
Genetics of brain fiber architecture and intellectual performance
Chiang, M.-C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., Klunder, A. D., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., et al. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.4184-08.2009 · Cited by 406 · see citing works
Toward accurate diagnosis of white matter pathology using diffusion tensor imaging
Budde, M. D., Kim, J. H., Liang, H., Schmidt, R. E., Russell, J. H., Cross, A. H., & Song, S. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21200 · Cited by 399 · see citing works
Measuring macroscopic brain connections in vivo
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015) · Nature Neuroscience · doi:10.1038/nn.4134 · Cited by 334 · see citing works
Connectome: how the brain's wiring makes us who we are
(2012) · Choice Reviews Online · doi:10.5860/choice.50-1457 · Cited by 319 · see citing works
Connectometry: A statistical approach harnessing the analytical potential of the local connectome
Yeh, F.-C. F., Badre, D., & Verstynen, T. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.053 · Cited by 249 · see citing works
Self-citationMGH–USC Human Connectome Project datasets with ultra-high b-value diffusion MRI
Fan, Q., Witzel, T., Nummenmaa, A. R., Dijk, K. R. A. V., Horn, J. D. V., Drews, M. K., Somerville, L. H., Sheridan, M. A., Santillana, R. M., Snyder, J., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.075 · Cited by 244 · see citing works
Kochunov, P., Jahanshad, N., Marcus, D., Winkler, A., Sprooten, E., Nichols, T. E., Wright, S. N., Hong, L. E., Patel, B., Behrens, T., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.050 · Cited by 215 · see citing works
Estimation of fiber orientation and spin density distribution by diffusion deconvolution
Yeh, F.-C. F., Wedeen, V. J., & Tseng, W. I. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.087 · Cited by 167 · see citing works
Self-citationGenetics of microstructure of cerebral white matter using diffusion tensor imaging
Kochunov, P., Glahn, D. C., Lancaster, J. L., Winkler, A. M., Smith, S. M., Thompson, P. M., Almasy, L. A., Duggirala, R., Fox, P. T., & Blangero, J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.078 · Cited by 158 · see citing works
Advanced MRI techniques to improve our understanding of experience-induced neuroplasticity
Tardif, C. L., Gauthier, C. J., Steele, C. J., Bazin, P.-L., Schäfer, A., Schaefer, A., Turner, R., & Villringer, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.047 · Cited by 127 · see citing works
Heritability of the network architecture of intrinsic brain functional connectivity
Sinclair, B., Hansell, N. K., Blokland, G. A. M., Martin, N. G., Thompson, P. M., Breakspear, M. J., de Zubicaray, G. I., Wright, M. J., & McMahon, K. L. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.048 · Cited by 75 · see citing works
Gene Network Effects on Brain Microstructure and Intellectual Performance Identified in 472 Twins
Chiang, M., Marina Barysheva, A., McMahon, K. L., de Zubicaray, G. I., Johnson, K., Montgomery, G. W., Martin, N. G., Toga, A. W., Wright, M. J., Shapshak, P., et al. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5993-11.2012 · Cited by 55 · see citing works
Investigating brain connectivity heritability in a twin study using diffusion imaging data
Shen, K., Rose, S., Fripp, J., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Thompson, P. M., Wright, M. J., & Salvado, O. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.041 · Cited by 47 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works
Current clinical applications of diffusion-tensor imaging in neurological disorders
Tae, W.-S., Ham, B.-J., Pyun, S.-B., Kang, S.-H., & Kim, B.-J. (2018) · Journal of Clinical Neurology · doi:10.3988/jcn.2018.14.2.129 · Cited by 302 · see citing works
Betzel, R. F., Griffa, A., Hagmann, P., & Mišić, B. (2018) · Network Neuroscience · doi:10.1162/netn_a_00075 · Cited by 165 · see citing works
Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation
Hollunder, B., Ostrem, J. L., Sahin, I. A., Rajamani, N., Oxenford, S., Butenko, K., Neudorfer, C., Reinhardt, P., Zvarova, P., Polosan, M., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01570-1 · Cited by 187 · see citing works
Self-citationDifferential tractography as a track-based biomarker for neuronal injury
Yeh, F.-C. F., Zaydan, I., Suski, V. R., Lacomis, D., Richardson, R. M., Maroon, J. C., & Barrios‐Martinez, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116131 · Cited by 142 · see citing works
Self-citationGarcia, J. O., Ashourvan, A., Muldoon, S. F., Vettel, J. M., & Bassett, D. S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2786710 · Cited by 128 · see citing works
Self-citationHigh-resolution connectomic fingerprints: Mapping neural identity and behavior
Sina Mansour, L., Tian, Y., Yeo, B. T. T., Cropley, V., & Zalesky, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117695 · Cited by 110 · see citing works
Griffa, A., Amico, E., Liégeois, R., Ville, D. V. D., & Preti, M. G. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118970 · Cited by 102 · see citing works
Zimmermann, J., Perry, A., Breakspear, M. J., Schirner, M., Sachdev, P. S., Wen, W., Kochan, N. A., Mapstone, M., Ritter, P., McIntosh, A. R., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.04.017 · Cited by 97 · see citing works
Individual structural features constrain the mouse functional connectome
Melozzi, F., Bergmann, E., Harris, J. A., Kahn, I., Jirsa, V., & Bernard, C. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906694116 · Cited by 89 · see citing works
Personalized brain network models for assessing structure–function relationships
Bansal, K., Nakuci, J., & Muldoon, S. F. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.014 · Cited by 84 · see citing works
Federated Learning for Diabetic Retinopathy Detection Using Vision Transformers
Chetoui, M., & Akhloufi, M. A. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3040058 · Cited by 56 · see citing works
Structural white matter connectometry of reading and dyslexia
Sihvonen, A. J., Virtala, P., Thiede, A., Laasonen, M. R., & Kujala, T. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118411 · Cited by 54 · see citing works
Federated Learning in Ocular Imaging: Current Progress and Future Direction
Nguyen, T. X., Ran, A. R., Hu, X., Yang, D., Jiang, M., Dou, Q., & Cheung, C. Y.-L. (2022) · Diagnostics · doi:10.3390/diagnostics12112835 · Cited by 52 · see citing works
Sánchez-Catasús, C. A., Bohnen, N. I., D’Cruz, N., & Müller, M. L. T. M. (2021) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.261939 · Cited by 50 · see citing works
Updates in deep learning research in ophthalmology
Ng, W. Y., Zhang, S., Wang, Z., Ong, C., Gunasekeran, D. V., Lim, G. Y. S., Zheng, F., Tan, S. C. Y., Tan, G. S. W., Rim, T. H., et al. (2021) · Clinical Science · doi:10.1042/cs20210207 · Cited by 49 · see citing works
Valizadeh, S. A., Riener, R., Elmer, S., & Jäncke, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.005 · Cited by 48 · see citing works
Data-driven brain network models differentiate variability across language tasks
Bansal, K., Medaglia, J. D., Bassett, D. S., Vettel, J. M., & Muldoon, S. F. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006487 · Cited by 42 · see citing works
Self-citationZhang, L., Wang, L., & Zhu, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_6 · Cited by 41 · see citing works
Transcription modulates chromatin dynamics and locus configuration sampling
Forte, G., Buckle, A., Boyle, S., Marenduzzo, D., Gilbert, N., & Brackley, C. A. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01059-8 · Cited by 40 · see citing works
Subject specificity of the correlation between large-scale structural and functional connectivity
Zimmermann, J., Griffiths, J. D., Schirner, M., Ritter, P., & McIntosh, A. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00055 · Cited by 40 · see citing works
White Matter Changes Correlates of Peripheral Neuroinflammation in Patients with Parkinson’s Disease
Haghshomar, M., Rahmani, F., Aarabi, M. H., Shahjouei, S., Sobhani, S., & Rahmani, M. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.10.050 · Cited by 38 · see citing works
Alpha Band Resting-State EEG Connectivity Is Associated With Non-verbal Intelligence
Zakharov, I., Олеговна, Т. А., Adamovich, T., Kovas, Y. V., & Malykh, S. B. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00010 · Cited by 37 · see citing works
Bajaj, S., Vanuk, J. R., Smith, R. S., Dailey, N. S., & Killgore, W. D. S. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00616 · Cited by 32 · see citing works
The developing brain structural and functional connectome fingerprint
Ciarrusta, J., Christiaens, D., Fitzgibbon, S., Dimitrova, R., Hutter, J., Hughes, E., Duff, E., Price, A., Cordero-Grande, L., Tournier, J.-D., et al. (2021) · bioRxiv · doi:10.1016/j.dcn.2022.101117 · Cited by 31 · see citing works
Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis
Kumar, K., Desrosiers, C., Kaleem Siddiqi, A., Colliot, O., & Toews, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.083 · Cited by 33 · see citing works
An, Y. Y., Lee, E., Lee, S. A., Choi, J. H., Park, J. M., Lee, T.-K., Kim, H., & Lee, J. D. (2023) · JAMA Otolaryngology–Head & Neck Surgery · doi:10.1001/jamaoto.2023.0824 · Cited by 32 · see citing works
Multi-modal brain fingerprinting: A manifold approximation based framework
Kumar, K., Toews, M., Chauvin, L., Colliot, O., & Desrosiers, C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.006 · Cited by 31 · see citing works
Sánchez-Catasús, C. A., Bohnen, N. I., Yeh, F.-C. F., D’Cruz, N., Kanel, P., & Müller, M. L. T. M. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.120.248500 · Cited by 31 · see citing works
Self-citationPresurgical temporal lobe epilepsy connectome fingerprint for seizure outcome prediction
Morgan, V. L., Sainburg, L. E., Johnson, G. W., Janson, A. P., Levine, K. K., Rogers, B. P., Chang, C. E., & Englot, D. J. (2022) · Brain Communications · doi:10.1093/braincomms/fcac128 · Cited by 31 · see citing works
Bell, K. L., Purcell, J. B., Harnett, N. G., Goodman, A. M., Mrug, S., Schuster, M. A., Elliott, M. N., Emery, S. T., & Knight, D. C. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.05.012 · Cited by 29 · see citing works
Gatto, R. G., Martin, P. R., Ali, F., Clark, H. M., Duffy, J. R., Utianski, R. L., Botha, H., Machulda, M. M., Dickson, D. W., Josephs, K. A., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103030 · Cited by 29 · see citing works
Walsh, M. J. M., Pagni, B. A., Monahan, L., Delaney, S., Smith, C. J., Baxter, L. C., & Braden, B. B. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac069 · Cited by 27 · see citing works
Brain tract structure predicts relapse to stimulant drug use
Tisdall, L., MacNiven, K. H., Padula, C. B., Leong, J. K., & Knutson, B. D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116703119 · Cited by 26 · see citing works
Churchill, N. W., Hutchison, M. G., Graham, S. J., & Schweizer, T. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-58073-9 · Cited by 25 · see citing works
Right hemispheric structural connectivity and poststroke language recovery
Sihvonen, A. J., Vadinova, V., Garden, K. L., Meinzer, M., Roxbury, T., O’Brien, K. M., Copland, D. A., McMahon, K. L., & Brownsett, S. L. E. (2023) · Human Brain Mapping · doi:10.1002/hbm.26252 · Cited by 25 · see citing works
Sherbaf, F. G., Rahmani, F., Jooyandeh, S. M., & Aarabi, M. H. (2018) · Acta Neurologica Belgica · doi:10.1007/s13760-018-0896-x · Cited by 25 · see citing works
Local connectome phenotypes predict social, health, and cognitive factors
Powell, M. A., Garcia, J. O., Yeh, F.-C. F., Vettel, J. M., & Verstynen, T. (2017) · Network Neuroscience · doi:10.1162/netn_a_00031 · Cited by 24 · see citing works
Self-citationSihvonen, A. J., Siponkoski, S., Martínez‐Molina, N., Laitinen, S., Holma, M., Ahlfors, M., Kuusela, L., Pekkola, J., Koskinen, S. K., & Särkämö, T. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11082184 · Cited by 23 · see citing works
Bajaj, S., Raikes, A. C., Razi, A., Miller, M. A., & Killgore, W. D. S. (2021) · Journal of Central Nervous System Disease · doi:10.1177/11795735211015076 · Cited by 23 · see citing works
Raikes, A. C., Hernandez, G. D., Matthews, D. C., Lukic, A. S., Law, M., Shi, Y., Schneider, L. S. S., & Brinton, R. D. (2022) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12258 · Cited by 21 · see citing works
Within-subject reproducibility varies in multi-modal, longitudinal brain networks
Nakuci, J., Wasylyshyn, N., Cieslak, M., Elliott, J. C., Bansal, K., Giesbrecht, B., Grafton, S. T., Vettel, J. M., Garcia, J. O., & Muldoon, S. F. (2023) · Scientific Reports · doi:10.1038/s41598-023-33441-3 · Cited by 22 · see citing works
Self-citationSherbaf, F. G., Abadi, Y. R., Zadeh, M. M., Ashraf‐Ganjouei, A., Moghaddam, H. S., & Aarabi, M. H. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00441 · Cited by 22 · see citing works
Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain
Krupnik, R., Yovel, Y., & Assaf, Y. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1074-21.2021 · Cited by 22 · see citing works
Sherbaf, F. G., Mohajer, B., Ashraf‐Ganjouei, A., Zadeh, M. M., Javinani, A., Moghaddam, H. S., Shandiz, M. S., & Aarabi, M. H. (2018) · Frontiers in Endocrinology · doi:10.3389/fendo.2018.00608 · Cited by 21 · see citing works
Oberlin, L. E., Victoria, L. W., Ilieva, I. P., Dunlop, K., Hoptman, M. J., Avari, J., Alexopoulos, G. S., & Gunning, F. M. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.24142 · Cited by 21 · see citing works
Li, M., Yeh, F.-C. F., Huang, S., Huang, C., Zhang, H., & Liu, J. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.751902 · Cited by 20 · see citing works
Self-citationFiber-specific structural properties relate to reading skills in children and adolescents
Meisler, S. L., & Gabrieli, J. D. E. (2022) · eLife · doi:10.7554/elife.82088 · Cited by 17 · see citing works
Sihvonen, A. J., Soinila, S. O., & Särkämö, T. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00661-6 · Cited by 18 · see citing works
Can hubs of the human connectome be identified consistently with diffusion MRI?
Gajwani, M., Oldham, S., Pang, J. C. S., Arnatkevičiūtė, A., Tiego, J., Bellgrove, M. A., & Fornito, A. (2023) · Network Neuroscience · doi:10.1162/netn_a_00324 · Cited by 18 · see citing works
Classification of schizophrenia by intersubject correlation in functional connectome
Ji, G., Chen, X., Bai, T., Wang, L., Wei, Q., Gao, Y., Tao, L., He, K., Li, D., Dong, Y., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24527 · Cited by 18 · see citing works
Sex-Specific Patterns of Body Mass Index Relationship with White Matter Connectivity
Rahmani, F., Wang, Q., McKay, N. S., Keefe, S., Hantler, N., Hornbeck, R., Wang, Y., Hassenstab, J. J., Schindler, S. E., Xiong, C., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215329 · Cited by 16 · see citing works
Huang, S., Li, M., Yeh, F.-C. F., Huang, C., Zhang, H., & Liu, J. (2023) · NMR in Biomedicine · doi:10.1002/nbm.4991 · Cited by 16 · see citing works
Self-citationHodological organization of spoken language production and singing in the human brain
Pitkäniemi, A., Särkämö, T., Siponkoski, S., Brownsett, S. L. E., Copland, D. A., Sairanen, V., & Sihvonen, A. J. (2023) · Communications Biology · doi:10.1038/s42003-023-05152-y · Cited by 15 · see citing works
Structural connectivity changes in focal epilepsy: Beyond the epileptogenic zone
Barrios‐Martinez, J., Almast, A., Lin, I., Youssef, A., Aung, T., Fernandes-Cabral, D. T., Yeh, F.-C. F., Chang, Y., Mettenburg, J., Modo, M., et al. (2024) · Epilepsia · doi:10.1111/epi.18175 · Cited by 15 · see citing works
Self-citationChauvin, L., Kumar, K., Wachinger, C., Vangel, M., de Guise, J. A., Desrosiers, C., Wells, W. M., & Toews, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116208 · Cited by 15 · see citing works
Brain Structural Connectivity Differences in Patients with Normal Cognition and Cognitive Impairment
Zdanovskis, N., Platkājis, A., Kostiks, A., Karelis, G., & Grigorjeva, O. (2021) · Brain Sciences · doi:10.3390/brainsci11070943 · Cited by 14 · see citing works
Sherbaf, F. G., Same, K., & Aarabi, M. H. (2018) · Acta Neurologica Belgica · doi:10.1007/s13760-018-0937-5 · Cited by 14 · see citing works
Ware, A. L., Biekman, B. D., Hachey, R., MacLeod, M. C., Bird, W., Pathak, S., Clarke‐Rubright, E. K., Borrasso, A. J., Puccio, A. M., Glavin, K., et al. (2018) · Journal of Neurotrauma · doi:10.1089/neu.2018.5725 · Cited by 15 · see citing works
Barrios‐Martinez, J., Fernandes-Cabral, D. T., Abhinav, K., Fernandez-Miranda, J. C., Chang, Y., Suski, V. R., Yeh, F.-C. F., & Friedlander, R. M. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103062 · Cited by 13 · see citing works
Self-citationStructural Neuroplasticity Effects of Singing in Chronic Aphasia
Sihvonen, A. J., Pitkäniemi, A., Siponkoski, S., Kuusela, L., Martínez‐Molina, N., Laitinen, S., Särkämö, E., Pekkola, J., Melkas, S., Schlaug, G., et al. (2024) · eNeuro · doi:10.1523/eneuro.0408-23.2024 · Cited by 15 · see citing works
Han, Y., Jing, Y., Li, X., Zhou, H.-W., & Deng, F. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120664 · Cited by 14 · see citing works
Liu, X., Gao, X., Zhang, L., Yuan, Z., Zhang, C., Lu, W., Cui, D., Zheng, F., Qiu, J., & Xie, J. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25949 · Cited by 13 · see citing works
Personalized connectome fingerprints: Their importance in cognition from childhood to adult years
Munsell, B. C., Gleichgerrcht, E. L., Hofesmann, E., Delgaizo, J., McDonald, C. R., Marebwa, B., Styner, M. A., Fridriksson, J., Rorden, C., Focke, N. K., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117122 · Cited by 13 · see citing works
Li, M., Yeh, F.-C. F., Zeng, Q., Wu, X., Wang, X., Zhu, Z., Liu, X.-H., Liang, J., Chen, G., Zhang, H., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25670 · Cited by 13 · see citing works
Self-citationSCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00019-5 · Cited by 13 · see citing works
Raikes, A. C., Hernandez, G. D., Mullins, V. A., Wang, Y., Lopez, C. M., Killgore, W. D. S., Chilton, F. H., & Brinton, R. D. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.891531 · Cited by 12 · see citing works
Connectometry evaluation in patients undergoing carotid endarterectomy: an exploratory study
Porcu, M., Craboledda, D., Garofalo, P., Columbano, G., Barberini, L., Sanfilippo, R., Zaccagna, F., Wintermark, M., Montisci, R., & Saba, L. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-0024-9 · Cited by 12 · see citing works
Choir singing is associated with enhanced structural connectivity across the adult lifespan
Moisseinen, N., Ahveninen, L. M., Martínez‐Molina, N., Sairanen, V., Melkas, S., Kleber, B., Sihvonen, A. J., & Särkämö, T. (2024) · Human Brain Mapping · doi:10.1002/hbm.26705 · Cited by 11 · see citing works
Huang, C., Kritikos, M., Serrano‐Sosa, M., Hagan, T., Domkan, A., Meliker, J. R., Pellecchia, A. C., Santiago‐Michels, S., Carr, M. A., Kotov, R., et al. (2022) · Molecular Neurobiology · doi:10.1007/s12035-022-03059-z · Cited by 11 · see citing works
Ashraf‐Ganjouei, A., Rahmani, F., Aarabi, M. H., Moghaddam, H. S., Nazem-Zadeh, M.-R., Davoodi‐Bojd, E., & Hamid Soltanian-zadeh, A. (2019) · Neurological Sciences · doi:10.1007/s10072-019-03818-2 · Cited by 11 · see citing works
Williamson, B. J., Barnes‐Davis, M. E., Vannest, J. J., Anixt, J. S., Heydarian, H. C., Kuan, L., Laue, C. S., Pratap, J. “., Schapiro, M. B., Tseng, S. Y., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-28634-9 · Cited by 10 · see citing works
Bajaj, S., & Killgore, W. D. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116123 · Cited by 11 · see citing works
Structural brain networks correlating with poststroke cognition
Brownsett, S. L. E., Carey, L. M., Copland, D. A., Walsh, A., & Sihvonen, A. J. (2024) · Human Brain Mapping · doi:10.1002/hbm.26665 · Cited by 9 · see citing works
de Sousa, M. V. P., Chacur, M., Martins, D. O., & Rondinoni, C. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815305-5.00002-6 · Cited by 10 · see citing works
de Bézenac, C. E., Leek, N., Adan, G., Mohanraj, R., Biswas, S., Marson, A. G., & Keller, S. S. (2024) · Human Brain Mapping · doi:10.1002/hbm.70069 · Cited by 10 · see citing works
Ohnishi, T., Toda, W., Itagaki, S., Sato, A., Matsumoto, J., Ito, H., Ishii, S., Miura, I., & Yabe, H. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1093522 · Cited by 8 · see citing works
Salisbury, D. F., Seebold, D., Longenecker, J., Coffman, B. A., & Yeh, F.-C. F. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.06.001 · Cited by 8 · see citing works
Self-citationMultiplexity of human brain oscillations as a personal brain signature
Dimitriadis, S. I., Routley, B., Linden, D. E. J., & Singh, K. D. (2023) · Human Brain Mapping · doi:10.1002/hbm.26466 · Cited by 8 · see citing works
Liang, M. Z., Chen, P., Tang, Y. C., Liang, Y. Y., Li, S., Hu, G. Y., Sun, Z., Yu, Y. L., Molassiotis, A., Knobf, M. T., et al. (2024) · Depression and Anxiety · doi:10.1155/2024/5595912 · Cited by 8 · see citing works
White matter pathways as both a target and mediator of health behaviors
Porter, A., Leckie, R. L., & Verstynen, T. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13708 · Cited by 8 · see citing works
Self-citationBukkieva, T., Pospelova, M. L., Efimtsev, A. Y., Fionik, O. V., Alekseeva, T. M., Samochernykh, K. A., Gorbunova, E., Krasnikova, V. V., Makhanova, A. M., Nikolaeva, A., et al. (2022) · Pathophysiology · doi:10.3390/pathophysiology29040046 · Cited by 8 · see citing works
White Matter Tractography and Diffusion‐Weighted Imaging
Vettel, J. M., Cooper, N., Garcia, J. O., Yeh, F.-C. F., & Verstynen, T. (2017) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0027162 · Cited by 9 · see citing works
Self-citationPark, B. S., Choi, B., Heo, C. M., Lee, Y. J., Park, S., Kim, Y. W., Ko, J., Lee, D. A., & Park, K. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-47533-7 · Cited by 7 · see citing works
Can structure predict function at individual level in the human connectome?
Smolders, L., Baene, W. D., Rutten, G.-J. M., van der Hofstad, R., & Florack, L. M. J. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02796-2 · Cited by 7 · see citing works
Karahan, E., Tait, L., Si, R., Özkan, A., Szul, M. J., & Zhang, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.01.438129 · Cited by 6 · see citing works
Correlation of diffusion tensor tractography with obstructive sleep apnea severity
Park, B. S., Lee, D. A., Lee, H., Kim, J., Ko, J., Lee, W. H., Yi, J., & Park, K. M. (2024) · Brain and Behavior · doi:10.1002/brb3.3541 · Cited by 7 · see citing works
Sherbaf, F. G., Zadeh, M. M., Haghshomar, M., & Aarabi, M. H. (2018) · Acta Neurologica Belgica · doi:10.1007/s13760-018-0910-3 · Cited by 6 · see citing works
Rivera, D., Roa-Sanchez, P., Bidó, P., Speckter, H., Oviedo, J., & Stoeter, P. (2022) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2022.03.017 · Cited by 6 · see citing works
Correlation of Diffusion Tensor Tractography with Restless Legs Syndrome Severity
Park, K. M., Kim, K. T., Lee, D. A., & Cho, Y. W. (2023) · Brain Sciences · doi:10.3390/brainsci13111560 · Cited by 5 · see citing works
Vandewouw, M. M., Norris‐Brilliant, A., Rahman, A., Assimopoulos, S., Morton, S. U., Kushki, A., Cunningham, S. D., King, E. C., Goldmuntz, E., Miller, T. A., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120721 · Cited by 5 · see citing works
Radiological identification of the globus pallidus motor subregion in Parkinson’s disease
Sammartino, F., Marsh, R., Yeh, F.-C., Sondergaard, A., Changizi, B. K., & Krishna, V. (2021) · Journal of neurosurgery · doi:10.3171/2021.7.jns21858 · Cited by 5 · see citing works
Self-citationGao, X. S., Zhang, X., Zhang, L., Xu, X., & Zhu, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_34 · Cited by 5 · see citing works
A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging
Radhakrishnan, H., Zhao, C.-Y., Sydnor, V., Baller, E. B., Cook, P. A., Fair, D., Giesbrecht, B., Larsen, B., Murtha, K., Roalf, D., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.26580 · Cited by 4 · see citing works
Self-citationMoshchin, M., Cheng, K. P., Osting, S., Laluzerne, M., Hurley, S. A., Singh, A. P., Trevathan, J. K., Brzeczkowski, A., Yu, J. J., Lake, W. B., et al. (2023) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner52421.2023.10123839 · Cited by 5 · see citing works
Self-citationZhang, H., Ding, L., He, L., Zhou, R., Lu, W., Xu, T., Wu, Y., & Peng, D. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103786 · Cited by 4 · see citing works
Panda, G., Mishra, N., Sharma, D., Kutum, R., Bhoyar, R. C., Jain, A. K., Imran, M., Senthilvel, V., Divakar, M. K., Mishra, A., et al. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.858345 · Cited by 4 · see citing works
Wang, D., Fan, Q., Xiao, X., He, H., Yang, Y., & Li, Y. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0628-23.2023 · Cited by 4 · see citing works
Serrano, L. E., Lange, F., Dauth, A., Krenzlin, H., Perez, A., Januschek, E., Schumann, S., Jussen, D., Czabanka, M., Ringel, F. A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-67013-w · Cited by 4 · see citing works
Clouston, S., Huang, C., Ying, J., Sekendiz, Z., Kritikos, M., Fontana, A., Bangiyev, L., & Luft, B. J. (2024) · Research Square · doi:10.21203/rs.3.rs-3760289/v1 · Cited by 4 · see citing works
- Authors
- Yeh, F.-C., Vettel, J. M., Singh, A., Poczos, B., Grafton, S. T., Erickson, K. I., Tseng, W.-Y. I., & Verstynen, T. D.
- Affiliations
- University of Pittsburgh (US)
- University of California, Santa Barbara (US)
- Carnegie Mellon University (US)
- National Taiwan University (TW)
- Center for the Neural Basis of Cognition (US)
- Corresponding author
- Yeh, Fang-Cheng
- Verstynen, Timothy D.
- ORCID
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-11-15
- Volume
- 12
- Issue
- 11
- Pages
- e1005203
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5112901
- OpenAlex
- W2952835181
- Cited by
- 151
- References
- 41
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 19 · 2023: 22 · 2022: 27 · 2021: 13 · 2020: 8 · 2019: 10 · 2018: 20 · 2017: 8
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS Computational Biology (publishedVersion, public-domain)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Army Research Laboratory (W911NF-10-2-0022)
- National Science Foundation (1247658)
- Ministry of Economic Affairs (101-EC-17-A-19-S1-175)
- National Institutes of Health (US) (DK095172)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- white matter architecture, structural connectome, neuroplasticity, monozygotic twins, dizygotic twins, phenotypic markers, diffusion MRI
- MeSH terms
- Adult, Algorithms, Brain, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Male, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Young Adult, Diffusion Tensor Imaging, Connectome, White Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
