The project started with the development of a software package, lavaPenalty (
https://github.com/bozenne/lavaPenalty(odnośnik otworzy się w nowym oknie)) implementing penalized LVMs. Compared to traditional LVMs, penalized LVMs use a penalty term to perform model selection and handle a possibly large number of variables (e.g. images). Available penalization terms are lasso penalty, ridge penalty, and nuclear norm penalty (to deal with 2D images). The main drawback of penalization is that it greatly complicates the quantification of the uncertainty in the estimated effects, making the results of the statistical analysis difficult to communicate.
The project was then focused on statistical inference in LVMs. First, the behavior of traditional statistical tests, namely Wald tests, was assessed in small samples and found to be unsatisfactory (inflated Type I error). A correction for Wald tests was derived [A4], validated in simulation studies, and implemented in a software package called lavaSearch2 (
https://github.com/bozenne/lavaSearch2(odnośnik otworzy się w nowym oknie)). Secondly, an existing method to efficiently handle multiple testing was extended to LVMs [A7]. It showed satisfactory statistical properties (i.e. good control of the Type I error) while being more powerful than the traditional Bonferroni correction. As a by-product, it can be modified to improve model-selection procedures in LVMs or combined with the correction to perform multiple testing in small samples. These developments are also implemented in the software package lavaSearch2. The project now targets the development of post selection inference tools for regularized LVMs.
The proposed developments have found direct application in neuroscience research projects. For instance, the work on multiple testing has helped to define a method for assessing predictive performances when the raw data can be processed using different technics [A1]. The software lavaSearch2 has been used to quantify the consequence of a concussion in term of neuroinflammation [A2]. Several collaborations were also established, e.g. to develop estimators robust to model misspecification [A5] and to study the brain’s serotonin system [A3,A6].
Manuscripts:
[A1, Published] Preprocessing, prediction and significance: Framework and application to brain imaging by Martin Nørgaard, Brice Ozenne, Claus Svarer, Stephen C. Strother, Vibe G. Frokjaer, Gitte M. Knudsen, and Melanie Ganz. Medical Image Computing and Computer Assisted Intervention conference 2019
[A2, Published] Molecular imaging of neuroinflammation in patients after mild traumatic brain injury by Sebastian Ebert, Per Jensen, Brice Ozenne, Armand S, Svarer C, Stenbaek DS, Moeller K, Dyssegaard A, Thomsen G, Steinmetz J, Forchhammer BH, Knudsen GM, Pinborg LH. European Journal of Neurology, 2019, DOI: 10.1111/ene.13971.
[A3, Published] Psychedelic effects of psilocybin correlate with serotonin 2A receptor occupancy and plasma psilocin levels by Martin K. Madsen, Patrick M. Fisher, Daniel Burmester, Agnete Dyssegaard, Dea S. Stenbæk, Sara Kristiansen, Sys S. Johansen, Sczabolz Lehel, Kristian Linnet, Claus Svarer, David Erritzoe, Brice Ozenne, Gitte M. Knudsen. Neuropsychopharmacology, 2019.
[A4, In revision] Small sample corrections for Wald tests in Latent Variable Models by Brice Ozenne, Patrick Fisher, and Esben Budtz-Jørgensen. Journal of the Royal Statistical Society, Series C.
[A5, In revision] On the estimation of average treatment effects with right censored time to event outcome and competing risks by Brice Ozenne, Thomas Harder Scheike, Laila Stærk, and Thomas Alexander Gerds. Biometrical journal
[A6, In revision] The Structure of the Serotonin System: a PET Imaging Study by Vincent Beliveau, Brice Ozenne, Stephen Strother, Douglas N Greve, Claus Svarer, Gitte M Knudsen. Neuroimage.
[A7, Submitted] Controlling the familywise error rate when performing multiple comparisons in a Linear Latent Variable Model by Brice Ozenne, Sebastian Elgaard Ebert, and Esben Budtz-Jørgensen. Psychometri
