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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.

 
 

RECENT PUBLICATIONS

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Valerie Sydnor

bioRxiv

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Characterizing the Spatiotemporal Sequence of Cortical Plasticity

Leveraging intrinsic activity amplitude as a functional marker of plasticity, we find evidence for a cortical gradient of neurodevelopmental plasticity in youth. Declines in the amplitude of intrinsic activity were initiated heterochronously across regions, coupled to the maturation of a plasticity breaking factor, impacted by children’s developmental environments, and temporally staggered along a sensorimotor-association axis from ages 8 to 18.

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Sheila Shanmugan

PNAS

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Sex Differences in Association Network Topography

We identified normative developmental sex differences in the functional topography of personalized association networks including the ventral attention network and default mode network. Furthermore, chromosomal enrichment analyses revealed that sex differences in multivariate patterns of functional topography were spatially coupled to the expression of X- linked genes as well as astrocytic and excitatory neuronal cell-type signatures. These results highlight the role of sex as a biological variable in shaping functional brain development in youth.

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Chenying Zhao

bioRxiv

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ModelArray

ModelArray is a scalable R package for statistical analysis of fixel data derived from diffusion MRI. It supports linear and nonlinear modeling and is extensible to more models. Full documentation: https://pennlinc.github.io/ModelArray/

 
 
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ted satterthwaite

Ted is an Associate Professor in the Department of Psychiatry 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.