The MyConnectome Project

Studying a single human brain in detail

Project maintained by poldrack Hosted on GitHub Pages — Theme by mattgraham

People change. And when they do, their brains change too.

The MyConnectome project characterized how the brain of one person changed over the course of more than one year. When completed, it was the most ambitious study to date of a single living person’s brain ever attempted. The data have provided new insights into the dynamics of brain activity and their relationship to bodily metabolism and psychological function. The project is also openly sharing a large amount of biological data for future reuse.

See media coverage of the project at the Dana Foundation, Priceonomics, MIT Tech Review, Discover Magazine, and KUT.

The data from this study have already resulted in a number of academic papers:

  1. Poldrack RA, Laumann TO, Koyejo O, Gregory B, Hover A, Chen MY, Luci J, Joo SJ, Handwerker D, Liang J, Boyd R, Hunicke- Smith S, Simpson ZB, Caven T, Sochat V, Shine JM, Gordon E, Snyder AZ, Adeyemo B, Petersen SE, Glahn D, McKay DR, Curran JE, Göring HHH, Carless MA, Blangero J, Frick L, Marcotte E, Mumford JA (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications
  2. Laumann T, Gordon E, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, Mumford JA, Poldrack RA, Petersen SE (2015). Functional network and areal organization of a densely-sampled individual human brain. Neuron, 87, 657-70.
  3. Qin Y, Yao J, Wu DC, Nottingham RM, Mohr S, Hunicke-Smith S, Lambowitz AM (2015).  High-throughput sequencing of human plasma RNA by using thermostable group II intron reverse transcriptase. RNA,  22: 111-128.  
  4. Shine JM, Koyejo O, Poldrack RA (2016). Temporal meta-states are associated with di erential patterns of dynamic connectivity, network topology and attention. Proceedings of the National Academy of Sciences. 113(35):9888-91.
  5. Betzel RF, Satterthwaite TD, Gold JI, Bassett DS (2017).  Positive affect, surprise, and fatigue are correlates of network flexibility.  Scientific Reports, 31;7(1):520.
  6. Power JD (2017). A simple but useful way to assess fMRI scan qualities. Neuroimage, 154:150-158.
  7. Tong Y, Yao JF, Chen JJ, Frederick BD. (2018).  The resting-state fMRI arterial signal predicts differential blood transit time through the brain.  Journal of Cerebral Blood Flow & Metabolism.
  8. Rasero J, Pellicoro M, Angelini L, Cortes JM, Marinazzo D, Stramaglia S. (2018).  Consensus clustering approach to group brain connectivity matrices. Network Neuroscience.
  9. Almgren H, Van de Steen, F, Kühn S,  Razi A,  Friston K,  Marinazzo D (2018).  Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study. Neuroimage.  
  10. Yang HS, Liang Z, Yao JF, Shen X, Frederick BD, Tong Y (2019) Vascular effects of caffeine found in BOLD fMRI.. J Neurosci Res
  11. Aslan S, Hocke L, Schwarz N, Frederick B (2019). Extraction of the cardiac waveform from simultaneous multislice fMRI data using slice sorted averaging and a deep learning reconstruction filter. Neuroimage. 
  12. Seitzman BA, Gratton C, Laumann TO, Gordon EM, Adeyemo B, Dworetsky A, Kraus BT, Gilmore AW, Berg JJ, Ortega M, Nguyen A, Greene DJ, McDermott KB, Nelson SM, Lessov-Schlaggar CN, Schlaggar BL, Dosenbach NUF, Petersen SE. (2019). Trait-like variants in human functional brain networks. Proc Natl Acad Sci U S A. 
  13. Jiang L, Qiao K, Sui D, Zhang Z, Dong HM (2019). Functional criticality in the human brain: Physiological, behavioral and neurodevelopmental correlates. PLoS One
  14. Yao JF, Wang JH, Yang HS, Liang Z, Cohen-Gadol AA, Rayz VL, Tong Y. (2019). Cerebral circulation time derived from fMRI signals in large blood vessels. J Magn Reson Imaging.
  15. Power JD, Lynch CJ, Silver BM, Dubin MJ, Martin A, Jones RM. (2019). Distinctions among real and apparent respiratory motions in human fMRI data. Neuroimage.
  16. Almgren H, Van de Steen F, Razi A, Friston K, Marinazzo D. (2020). The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI. Neuroimage.
  17. Watanabe T, Rees G (2018). Comparing the temporal relationship of structural and functional connectivity changes in different adult human brain networks: a single-case study. Wellcome Open Research.
  18. Ver Steeg G, Harutyunyan H, Moyer D, Galstyan A (2019).  Fast structure learning with modular regularization.   Advances in Neural Information Processing Systems 32 (NIPS 2019)
  19. Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, Wagstyl K, Kirkpatrick Reardon P, Clasen L, Liu S, Messinger A, Leopold DA, Fonagy P, Dolan RJ, Jones PB, Goodyer IM; NSPN Consortium, Raznahan A, Bullmore ET. (2018). Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron.
  20. Gu X., Eklund A., Knutsson H. (2017) Repeated Tractography of a Single Subject: How High Is the Variance?. In: Schultz T., Özarslan E., Hotz I. (eds) Modeling, Analysis, and Visualization of Anisotropy. Mathematics and Visualization.
  21. Laumann TO, Snyder AZ, Mitra A, Gordon EM, Gratton C, Adeyemo B, Gilmore AW, Nelson SM, Berg JJ, Greene DJ, McCarthy JE, Tagliazucchi E, Laufs H, Schlaggar BL, Dosenbach NUF, Petersen SE. (2017). On the Stability of BOLD fMRI Correlations.  Cereb Cortex
  22. Hinrich, J. L., Nielsen, S. F. V., Riis, N. A. B., Eriksen, C., Frøsig, J., Kristensen, M. D. F., Schmidt, M. N., Madsen, K. H., & Mørup, M. (2017). Scalable group level probabilistic sparse factor analysis. In Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 6314-6318). IEEE. International Conference on Acoustics, Speech and Signal Processing. Proceedings 
  23. St-Jean S, De Luca A, Tax CMW, Viergever MA, Leemans A. (2020). Automated characterization of noise distributions in diffusion MRI data. Med Image Anal.
  24. Nielsen SFV, Schmidt MN, Madsen KH, Mørup M. (2018). Predictive assessment of models for dynamic functional connectivity. Neuroimage.
  25. Ghassami AE et al. (2020). Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. ICML.
  26. Ghassami A, Kiyavash N, Huang B, Zhang K. (2018). Multi-domain Causal Structure Learning in Linear Systems.. Adv Neural Inf Process Syst.
  27. Huang B, Zhang K, Xie P,  Gong M,  Xing E, Glymour C (2019). Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering.  NIPS2019.
  28. Aslan S, Hocke L, Schwarz N, Frederick B (2019). Extraction of the cardiac waveform from simultaneous multislice fMRI data using slice sorted averaging and a deep learning reconstruction filter. Neuroimage,  198: p. 303-316
  29. Kai J, Khan AR. Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults. Front Neuroinform. 2022 Feb 17;16:777853. doi: 10.3389/fninf.2022.777853. PMID: 35250526; PMCID: PMC8891507.
  30. Yusuf Osmanlıoğlu et al 2020 Connectomic consistency: a systematic stability analysis of structural and functional connectivity. J. Neural Eng. 17 045004
  31. Lebedev, A.V., Abé, C., Acar, K. et al. Large-scale societal dynamics are reflected in human mood and brain. Sci Rep 12, 4646 (2022).
  32. Ryan V Raut, Anish Mitra, Scott Marek, Mario Ortega, Abraham Z Snyder, Aaron Tanenbaum, Timothy O Laumann, Nico U F Dosenbach, Marcus E Raichle, Organization of Propagated Intrinsic Brain Activity in Individual Humans, Cerebral Cortex, Volume 30, Issue 3, March 2020, Pages 1716–1734
  33. Di, X., Woelfer, M., Kühn, S., Zhang, Z., & Biswal, B. B. (2022). Estimations of the weather effects on brain functions using functional MRI: A cautionary note. Human Brain Mapping, 43( 11), 3346– 3356.
  34. Selena I. Huisman, Arthur T.J. van der Boog, Fia Cialdella, Joost J.C. Verhoeff, Szabolcs David. Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning, Radiotherapy and Oncology, Volume 175, 2022, Pages 18-25,
  35. Richard F. Betzel, Sarah A. Cutts, Sarah Greenwell, Joshua Faskowitz, Olaf Sporns, Individualized event structure drives individual differences in whole-brain functional connectivity, NeuroImage, Volume 252, 2022, 118993,
  36. Charles J. Lynch, Jonathan D. Power, Matthew A. Scult, Marc Dubin, Faith M. Gunning, Conor Liston. Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI, Cell Reports, Volume 33, Issue 12, 2020, 108540,
  37. Monti, R.P., Zhang, K. & Hyvärinen, A.. (2020). Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, in Proceedings of Machine Learning Research 115:186-195.
  38. Zhang, R., Shokri-Kojori, E. & Volkow, N.D. Seasonal effect—an overlooked factor in neuroimaging research. Transl Psychiatry 13, 238 (2023).
  39. Dimitriadis, S.I. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher’s Choice Paths. Neuroinform 21, 71–88 (2023).
  40. Shammi More, Georgios Antonopoulos, Felix Hoffstaedter, Julian Caspers, Simon B. Eickhoff, Kaustubh R. Patil (2023). Brain-age prediction: A systematic comparison of machine learning workflows. NeuroImage, Volume 270, 119947.
  41. Li, A., Liu, H., Lei, X. et al. Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness. Nat Commun 14, 3238 (2023).
  42. Ashishi Puri, Snehlata Shakya, Sanjeev Kumar, (2023). A fractional order-based mixture of central Wishart (FMoCW) model for reconstructing white matter fibers from diffusion MRI. Psychiatry Research: Neuroimaging, Volume 333, 111673..
  43. Ashishi Puri , Sanjeev Kumar, A generalized order mixture model for tracing connectivity of white matter fascicles complexity in brain from diffusion MRI, Mathematical Medicine and Biology: A Journal of the IMA, 2023;, dqad002
  44. Arnone, E., Negri, L., Panzica, F., & Sangalli, L. M. (2023). Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis. Biometrics.
  45. Puri, A., Kumar, S. (2023) An iterative algorithm for computing gradient directions for white matter fascicles detection in brain MRI. Phys Eng Sci Med 46, 165–178
  46. Richard F. Betzel, Sarah A. Cutts, Jacob Tanner, Sarah A. Greenwell, Thomas Varley, Joshua Faskowitz, Olaf Sporns; Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI. Network Neuroscience 2023

About the project

Learn more about the MyConnectome project

FAQ

Frequently asked questions about the project

Data Sharing

How to access data

A project of the Poldrack Lab at  Stanford University