https://www.brainnetworkslab.com/people daily 0.75 2025-08-14 https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536773734173-S5QN4YQEZPRTUOZXZK83/IMG_0563.jpg People https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/0c77c918-8c9f-439b-bdce-eca23ac1bea8/14560561-1BFE-411F-B6B9-BAD71D2BB3C2.jpg People - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/b789a727-64fa-4f2a-9796-da44c3384df2/IMG_9404-1.jpg People - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/c1059693-d2e5-4bbf-8345-60111bec0c05/network.png People Co-authorship network. https://www.brainnetworkslab.com/contact-us daily 0.75 2025-12-11 https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1543503190752-74YKO17ZS06RUDFDP3LT/website%2Blogo.png Contact us https://www.brainnetworkslab.com/research daily 0.75 2023-08-31 https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1603977220601-3KI73Z7UYAR1IJQ5Y6OW/NIH_NIBIB_Vertical_Logo_2Color.jpg Research https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1603977243569-IMNUBLMQXCK9B4SDZ1D9/NSF_logo.png Research https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1571145371745-M3GBRB6RKBX0DCGFB7DH/edgecentricconnectomes.png Research Brain networks are typically modeled as interactions between neural elements. We have developed a framework for generating networks based on the interactivity of edges. This enables us to investigate circuit-level interactions with exquisite temporal resolution. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536845380966-3OZK5DYLL5OXR2PXA4V8/image-asset.png Research Structural connectivity represents the brain’s physical wires — axonal projections or white-matter tracts — whose network organization shapes the correlation structure of neural activity, i.e. functional connectivity. Our work in this area seeks to understand this relationship in greater detail and takes advantage of both in silico simulations and empirical observations. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536845350521-L3EEAUBOO5HA2Q77GTN9/image-asset.png Research What routing policy does the brain use to transmit signals and information from one brain area to another? Can these policies be passive and decentralized or do they require top-down, centralized control? We simulate different routing policies, which allows us to explore their advantages and disadvantages, identify tradeoffs, and to develop theories of large-scale inter-areal communication. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536845364244-ZY3THGD40RY4TT47CJEG/image-asset.png Research Network controllability is a theoretical framework for investigating how time-varying input signals influence the trajectory of a networked dynamical system as it moves through a high-dimensional state space. We apply this framework to biological neural networks to gain insight into the topological properties that support transitions between distinct network states and the energetic cost of driving the network along a desired trajectory towards a target state. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536845312069-H78ZGZPFGUGSCJGSPE8L/generative.png Research Brain networks grow and evolve over time. This developmental process is the result of a multitude of factors that combine to generate a mature network with functionally adaptive topological properties. We aim to invert this process and uncover the wiring rules and organizational principles that guide brain network development. To this end, we build generative models that realize simple mechanisms of brain network growth and compare the synthetic networks generated by models with empirical observations. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536845392557-V5Q1AQE5N8K34ANO23RT/image-asset.png Research The correlation structure of brain activity fluctuates over short timescales. We use multi-layer network models and time series analysis to investigate the origins of these fluctuations and to assess their relationship with cognitive and psychological processes. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1537478646454-UGMO3L3BHDPF3I5MCFT9/distance.png Research Brain networks are spatially-embedded and the formation, maintenance, and usage of their connections requires material and energy — i.e. costs. In general, longer connections are most costly than short. As a result, brain networks favor forming short-range connections. However, long-distance connections enhance network diversity and promote efficient information transfer. Therefore, brain networks must tradeoff between the formation of costly features that enhance functionality and a brain-wide drive to reduce wiring cost. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536852235748-O9RQI3VQ42U0A628JLKT/mesoscale.png Research Most real-world networks exhibit meso-scale structure meaning that their nodes and edges can be clustered into sub-networks called “communities”. Communities interact to form different motifs, e.g. assortatively or as cores and peripheries, each suited to realize a different network function. We study meso-scale structure in biological neural networks to gain insight into their relationship with brain function. We also develop new methods for detected and characterizing meso-scale structure. https://www.brainnetworkslab.com/coderesources daily 0.75 2024-03-03 https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1553530651801-IWHV4CWV35DOF9I6G0Z8/modelContruction.png Code/Resources Matlab code for generating spatially-constrained synthetic time series using a phase-randomization procedure [Link to code]. If you use this code, please cite: Zamani Esfahlani, F., Bertolero, M.A., Bassett, D.S., Betzel, R.F. (2019). Space-independent community and hub structure of functional brain networks. [Link to paper] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1553515580317-GRHNW7Y0L85CO1XHFETD/exampleNet.png Code/Resources Matlab code for generating visually appealing force-directed (and community-labeled) networks. [Link to code] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1553631560555-E9FERWBIHTKK0743X54S/D2l7LqHW0AIUE_S.jpg Code/Resources Matlab code for generating distance-dependent consistency matrices [Link to code]. If you use this code, please cite: Betzel, R. F., Griffa, A., Hagmann, P., & Mišić, B. (2018). Distance-dependent consensus thresholds for generating group-representative structural brain networks. Network Neuroscience, 1-22. [Link to paper] Note: the code provided here bins connections by distance and seems to avoid overfitting issues that we sometimes observed with the previous version. The original code can be found here [Link to code]. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1553690830117-BLLDEVF0MTGJL9N3CDYY/generativeModelEnergyLandscape.png Code/Resources Matlab code for generative modeling of structural connectivity matrices [Link to code]. If you use this code, please cite: Betzel, R. F., Avena-Koenigsberger, A., Goñi, J., He, Y., De Reus, M. A., Griffa, A., ... & Van Den Heuvel, M. (2016). Generative models of the human connectome. Neuroimage, 124, 1054-1064. [Link to paper] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1554999113988-OWPYRP6CAK9ZEE9337WF/Schematic.png Code/Resources Matlab code for fitting weighted stochastic blockmodel. [Link to code] If you use the code, please cite: Betzel, R. F., Medaglia, J. D., & Bassett, D. S. (2018). Diversity of meso-scale architecture in human and non-human connectomes. Nature communications, 9(1), 346. [Link to paper] Betzel, R. F., Bertolero, M. A., & Bassett, D. S. (2018). Non-assortative community structure in resting and task-evoked functional brain networks. bioRxiv, 355016. [Link to paper] Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221-248. [Link to paper, Link to toolbox] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1556626527574-S6DMYXVEJB1KKNDBYR4R/Screen%2BShot%2B2019-04-30%2Bat%2B8.13.22%2BAM.jpg Code/Resources Matlab code for generating surrogate networks that preserve degree distribution (exactly), distribution of physical connection lengths (approximately), and the weight-length relationship (approximately). [Link to code]. If you use this code, please cite: Betzel, R. F., & Bassett, D. S. (2018). Specificity and robustness of long-distance connections in weighted, interareal connectomes. Proceedings of the National Academy of Sciences, 115(21), E4880-E4889. [Link to paper] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1561731616091-VAWPQ82HVAMS1TRJNR92/summarystatistics.png Code/Resources Matlab code for sampling multi-layer, multi-resolution, multi-subject communities using modularity maximization [Link to code]. If you use this code, please cite: Betzel, R. F., Bertolero, M. A., Gordon, E. M., Gratton, C., Dosenbach, N. U., & Bassett, D. S. (2018). The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time. bioRxiv, 413278. [Link to paper] Note: This code requires the GenLouvain toolbox. Please download [here] and cite both: Jutla, I. S., Jeub, L. G., & Mucha, P. J. (2011). A generalized Louvain method for community detection implemented in MATLAB. URL http://netwiki. amath. unc. edu/GenLouvain. Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J. P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. science, 328(5980), 876-878. [Link to paper] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1580842708030-KZT7PLW8WQVDA2KKA7BY/network.png Code/Resources Matlab code for generating ego-centric co-authorship networks. [Link to code]. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1589392094456-LL8T53DY00VVH6RILUF1/Screen+Shot+2020-05-13+at+1.47.53+PM.png Code/Resources Matlab code for generating and clustering edge time series [Link to code]. If you use this code, please cite: Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience. 23, 1644–1654. [Link to preprint] Jo, Youngheun, et al. "The diversity and multiplexity of edge communities within and between brain systems." bioRxiv (2020). [Link to paper] Zamani Esfahlani, F., Jo, Y., Faskowitz, J., Byrge, L., Kennedy, D., Sporns, O., Betzel, R.F. (2020). High-amplitude co-fluctuations in cortical activity drive functional connectivity. PNAS, 117(45), 28393–28401. [Link to paper] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1608563335947-NDFINCG1ASKDBPA4N5I7/image-asset.png Code/Resources Matlab code for generating and analyzing bipartitions of edge time series [Link to code] [Edge time series video]. If you use this code, please cite: Sporns, O., Faskowitz, J., Teixeira, A.S., Cutts, S. Betzel, R.F. (2020). Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series. (to appear Network Neuroscience). [Link to paper] Zamani Esfahlani, F., Jo, Y., Faskowitz, J., Byrge, L., Kennedy, D., Sporns, O., Betzel, R.F. (2020). High-amplitude co-fluctuations in cortical activity drive functional connectivity. PNAS, 117(45), 28393–28401. [Link to paper] https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/a20fc770-99ec-4e90-9d52-27b0c91ad629/GHeHrUmWsAActF-.jpeg Code/Resources - Make it stand out Matlab code for performing optimal control using spatially diffuse inputs [Link to code]. If you use this code, please cite: Betzel, R. F., Puxeddu, M. G., Seguin, C., Bazinet, V., Luppi, A., Podschun, A., ... & Parkes, L. (2024). Controlling the human connectome with spatially diffuse input signals. bioRxiv, 2024-02. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1599251260082-33OX0TVUZLBAABCM8G15/Screen%2BShot%2B2020-09-04%2Bat%2B4.26.35%2BPM.jpg Code/Resources Matlab code for analyzing high-amplitude frames in edge time series [Link to code]. If you use this code, please cite: Zamani Esfahlani, F., Jo, Y., Faskowitz, J., Byrge, L., Kennedy, D., Sporns, O., Betzel, R.F. (2020). High-amplitude co-fluctuations in cortical activity drive functional connectivity. PNAS, 117(45), 28393–28401. [Link to paper] https://www.brainnetworkslab.com/home daily 1.0 2024-11-19 https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1732031612871-5ZSV2BUM7Q9WSRMZYYG8/CEHD_W22_Feat2_MIDB_Ext-1024x683.jpg Home https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536876574817-XCZ4AV2WHOXHPPMY8Y8K/cajal_stellate_neurons_in_the_cerebellum.jpg Home - the structure and function of biological neural networks Nervous systems are comprised of structurally and functionally connected neural elements. These elements form vast networks that help shape brain function and cognitive processes. In our lab we use methods from network science to study the organization and behavior of biological neural networks so that we can better understand their role in health and disease. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536841696978-CRKDOAJRTT69PO4F8XQL/overviewFigureNew.png Home - Research Our work involves analysis of network data at different spatial, temporal, and topological scales. Our goal is to understand the underlying principles that shape the organization and function of biological neural networks. https://images.squarespace-cdn.com/content/v1/5b9901a6cc8fed4aea6ad307/1536840830773-4D567SMNXFB35SLBVGUU/475253_actual.jpg Home - JOIN US Interested in working in the lab? We always welcome applications from motivated individuals. https://www.brainnetworkslab.com/publications daily 0.75 2026-02-24