Browsing by Subject "Nonnegative matrix factorization"

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    Age‐dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury
    (Wiley, 2022) Bouchard, Heather C.; Sun, Delin; Dennis, Emily L.; Newsome, Mary R.; Disner, Seth G.; Elman, Jeremy; Silva, Annelise; Velez, Carmen; Irimia, Andrei; Davenport, Nicholas D.; Sponheim, Scott R.; Franz, Carol E.; Kremen, William S.; Coleman, Michael J.; Williams, M. Wright; Geuze, Elbert; Koerte, Inga K.; Shenton, Martha E.; Adamson, Maheen M.; Coimbra, Raul; Grant, Gerald; Shutter, Lori; George, Mark S.; Zafonte, Ross D.; McAllister, Thomas W.; Stein, Murray B.; Thompson, Paul M.; Wilde, Elisabeth A.; Tate, David F.; Sotiras, Aristeidis; Morey, Rajendra A.; Psychiatry, School of Medicine
    Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age‐related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non‐negative matrix factorization (NMF) is a data‐driven approach that detects covarying patterns (components) within high‐dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self‐reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n = 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta‐Analysis) Military Brain Injury working group. Regressions were used to examine TBI‐ and mTBI‐related associations in NMF‐derived components while adjusting for age, sex, post‐traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age‐dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q < 0.05), which are spatially unconstrained by traditionally defined WM tracts. One component, occupying the most peripheral location, exhibited significantly stronger age‐dependent differences in Veterans with mTBI. We found NMF to be powerful and effective in detecting covarying patterns of FA associated with mTBI by applying standard parametric regression modeling. Our results highlight patterns of WM alteration that are differentially affected by TBI and mTBI in younger compared to older military Veterans.
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    Point process modeling of drug overdoses with heterogeneous and missing data
    (Institute of Mathematical Statistics, 2021) Liu, Xueying; Carter, Jeremy; Ray, Brad; Mohler, George; Computer and Information Science, School of Science
    Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space–time, could help better focus limited social and health services. In this work we present a spatial-temporal point process model for drug overdose clustering. The data input into the model comes from two heterogeneous sources: (1) high volume emergency medical calls for service (EMS) records containing location and time but no information on the type of nonfatal overdose, and (2) fatal overdose toxicology reports from the coroner containing location and high-dimensional information from the toxicology screen on the drugs present at the time of death. We first use nonnegative matrix factorization to cluster toxicology reports into drug overdose categories, and we then develop an EM algorithm for integrating the two heterogeneous data sets, where the mark corresponding to overdose category is inferred for the EMS data and the high volume EMS data is used to more accurately predict drug overdose death hotspots. We apply the algorithm to drug overdose data from Indianapolis, showing that the point process defined on the integrated data out-performs point processes that use only coroner data (AUC improvement 0.81 to 0.85). We also investigate the extent to which overdoses are contagious, as a function of the type of overdose, while controlling for exogenous fluctuations in the background rate that might also contribute to clustering. We find that drug and opioid overdose deaths exhibit significant excitation with branching ratio ranging from 0.72 to 0.98.