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Lifespan Informatics & Neuroimaging Center

Innovation in data science and translational neuroscience to understand brain development and mental illness

RESEARCH

  Our research uses advanced analytics to integrate complex brain images and rich behavioral data.   Ultimately, we seek to map normal brain development and understand how alterations in brain maturation increase risk of psychiatric illness.

Research
RecentPubs

RECENT PUBLICATIONS

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Steven L. Meisler

Neuron

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Highly replicable multi-site patterns of white matter development

The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S. effort to track brain development in adolescence. Diffusion MRI (dMRI) offers a powerful window into white matter, but large, multi-site datasets are difficult to process and analyze. We address this with the ABCD-BIDS Community Collection (ABCC, release 3.1.0): an open resource of 24,000+ fully processed dMRI scans. ABCC includes analysis-ready data, detailed quality metrics, advanced microstructural measures, and individualized white matter tractography. Using these data, we show that newer dMRI metrics outperform traditional measures in detecting developmental change—and are more robust to differences in image quality. We also demonstrate that harmonizing data across scanners improves the consistency of findings across sites. ABCC lowers barriers to large-scale neuroimaging and provides a powerful, open foundation for studying adolescent brain development.

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Brooke Sevchik and Parker Singleton

Neuropsychopharmacology

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MDMA for PTSD: A living review

Here, we present a living systematic review and open-data meta-analytic resource on MDMA treatment for PTSD. In this initial release, 6 randomized controlled trials comprising 286 participants are included in the database. All six studies included in the review to date are sponsored by the Multidisciplinary Association for Psychedelic Studies (MAPS). All code, data, and a meta-analytic dashboard are available via sypres.io

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Steven L. Meisler and Taylor Salo

bioRxiv

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MIRROR Dataset Data Descriptor

We introduce the Myelin Imaging Reliability and ReprOducibility Resource (MIRROR), an open multimodal test–retest MRI dataset for benchmarking scalable approaches to myelin-sensitive imaging. Across 27 measures derived from diffusion MRI, ihMT, MP2RAGE, QSM, T1w/T2w imaging, and relaxometry, we show that metrics differ substantially in tissue contrast, intermetric correspondence, and test–retest reliability. We openly release the imaging data, derived maps, and processing code to support method development, acquisition selection, and future validation against histological and other independent references.

Ted
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ted satterthwaite

Ted is the McLure II Professor of Psychiatry & Behavioral Research at the University of Pennsylvania Perelman School of Medicine. His research uses multi-modal neuroimaging to describe both normal and abnormal patterns of brain development, in order to better understand the origins of mental illnesses.

Lifespan Informatics and Neuroimaging Center

Richards Research Labs, 5th Floor

3700 Hamilton Walk

Philadelphia, PA 19104

Email: sattertt@pennmedicine.upenn.edu

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