Parkinsonism-mimic, 1-methyl-4-phenylpyridinium (MPP+) Induces Rapid Parkin Translocation to Damaged Mitochondria in Dopaminergic Neurons

Mentor: Karen O\u27Malley From the Washington University Undergraduate Research Digest: WUURD, Volume 8, Issue 2, Spring 2013. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor

Biomarkers Investigation for Parkinson Disease: Positron Emission Tomography (PET) Imaging-based Radiotracers

Mentor: Joel S. Perlmutter From the Washington University Undergraduate Research Digest: WUURD, Volume 7, Issue 1, Fall 2011. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor

Multi-Scale Network Regression for Brain-Phenotype Associations

International audienceBrain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships

Linked dimensions of psychopathology and connectivity in functional brain networks

Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated (r = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry