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Timothy O. Laumann on Muck Rack

Timothy O. Laumann

As seen in: Nature, Wiley Online Library, PNAS, BioRxiv, The Lancet, Cell Press, The World Bank Research Observer
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Articles

Commonality and variability in functional networks in children under 5 years old

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By Jiaxin Cindy Tu, Chenyan Lu, Trevor Day, Robert J.M. Hermosillo, Lucille Moore, Anxu Wang, Xintian Tina Wang Verified, Donna L. Dierker, Aidan Latham, Jeanette K Kenley, Damien A. Fair, Jed T. Elison, Chad M. Sylvester, Barbara B Warner, Joan Luby, Cynthia E. Rogers, Deanna Barch, Christopher D. Smyser, Timothy O. Laumann, Evan M. Gordon, Adam T. Eggebrecht, Muriah D Wheelock
| Nature Verified
Abstract Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices.
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Objective quality assessment for precision functional MRI data

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By Charles Lynch, Megan Chang, Immanuel G. Elbau, Evan M. Gordon, Timothy O. Laumann, Jingnan Du, Zach Ladwig, Diana Perez, Diana Pérez, Megan Johnson Verified, Parsa Nilchian, Nili Solomonov, Maximilian Lueckel, Indira Summerville, Jolene Chou, Claire Ho, Nicola Manfredi
| Cell Press Verified
Lynch et al. introduce the network similarity index (NSI), a tool for assessing whether individual fMRI datasets contain enough coherent brain network structure for precision mapping applications. This work provides an objective framework for evaluating data quality, guiding additional data collection, and improving confidence in individualized brain maps.
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Longer scans boost prediction and cut costs in brain-wide association studies - Nature

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By Shaoshi Zhang, Thomas Nichols, Ru Kong, Timothy O. Laumann, Evan M. Gordon, Christopher Chen, Lijun An, Sebastian N. Roemer-Cassiano, Qingyu Hu, Jianxun Ren, Hesheng Liu, Juan Zhou, Danilo Bzdok
| Nature Verified
Abstract A pervasive dilemma in brain-wide association studies1 (BWAS) is whether to prioritize functional magnetic resonance imaging (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant).
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