Package: sihnpy
sihnpy:Study ofInter-individualHeterogeneity ofNeuroimaging inPythonHi! Welcome tosihnpy!A critical ongoing issue in science is the lack of diversity in our study populations: some groups of individuals are often under-represented, if represented at all. This becomes particularly critical when considering health research.How can we know that a drug tested in men will work in women? How can we know that symptoms Alzheimer's disease will present in the same way despite everyone's different life trajectories?That's not all. Even when we take a group of individuals that are traditionally considered to be quite homogenous (let's say young adults), we are realizing thattheir brains are actually quite different from one another.[^Mueller_2013],[^Finn_2015] This goes hand-in-hand with the new wave of personalized medicine: if everyone has different circumstances and backgrounds, shouldn't we personalize how we conduct research and deliver healthcare? But this is also difficult to pursue as most of the traditional statistical analyses we have are made tocompare groupsor toobserve a variable within a single group.This is wheresihnpycomes in.My goal was to provide researchers with an easy-to-use, one-stop-shop for simple methods aimed at studying inter-individual differences. During my PhD, I focused on using methods that could help us better understand these differences, or at the very least, offer more diversity in the way we can study a "traditionally" homogenous group.Neuroscientist by training, most of the tools I develop insihnpyare neuroimaging-focused. That said, with a few exceptions, the tools should also be useable in other domains. Also, I am not a computer scientist, but I really love coding and I am eagerly learning as I developsihnpy. If something is not working right, feel free toopen an issueto let me know or to discuss how I can improvesihnpy.AuthorsFrédéric St-Onge - MSc - PhD candidate at McGill UniversityGabriel St-Onge - MScIf you usesinhpyin your academic work (or well, any other work where you would need such a package), please consider citing the paper detailing the development of the first few modules of this package and/or citing the package (example below in APA form):St-Onge F, Javanray M, Pichet Binette A, Strikwerda-Brown C, Remz J, Spreng RN, Shafiei G, Misic B, Vachon-Presseau E, Villeneuve S. (In press). Functional connectome fingerprinting across the lifespan. Network Neuroscience. doi:10.1162/netn_a_00320St-Onge F & St-Onge G (2023). sihnpy: Study of Inter-individual Heterogeneity of Neuroimaging in Python. v[VERSION HERE], URL:https://github.com/stong3/sihnpyInstallationYou can install the most recent version ofsinhpyusing pip:$pipinstallsihnpyHead to our Github page to take a look at the code more closely:https://github.com/stong3/sihnpyUsageThe package is separated in distinct modules with their own rationale, explanation, use cases and limitations. Please visit the documentation for specific information on each module. Below is a quick summary of each available module:Datasets: Contains a subset of the PREVENT-AD Open data which can be used to test functionalities of the packageFingerprinting: Computes individual-specific signatures of variables and the related metricsSpatial extent: Computes individual-specific measures of how "abnormal" regions across the brain are compared to pre-computed thresholdsSliding-window: Sample selection tool to create overlapping subgroups of individuals within a sampleImbalance mapping: Individual-specific measures of group deviation on region-to-region covarianceWhile I try my best to simplify the concepts as much as possible, note thatan understanding of Python's basics is probably necessaryto run most of the modules. TheSoftware carpentryhas a great free (most important!!) module to learn Python. You can find ithere.ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensesihnpywas created by Frédéric St-Onge. It is licensed under the terms of the MIT license.Note thatsihnpyis shipped with a small subset of thePREVENT-AD dataset. These data are provided in open access through the generosity and hard work of the SToP-AD center at the Douglas Mental Health University Institute affiliated with McGill University, Montreal, Canada. Using these data to test the functionalities of the software is encouraged. However, users using thedatasetsmodule agree to the terms of use of the PREVENT-AD dataset (seeLicensefor more information).CreditsThe package was developed in part from the input of members from theVilleneuveLab. Come see the awesome work we do on aging and Alzheimer's disease!sihnpywas created withcookiecutterand thepy-pkgs-cookiecuttertemplate. This package was developped alongside my reading of the bookPython packages, an open-source book written by Tomas Beuzen & Tiffany Timbers, availableheredetailing how to create a Python package from A to Z.The logo of the package used acopyright-free image from Pixabaycreated by the user StarGlade, which was modified to createsihnpy's logo.We thank thePREVENT-AD cohortcoordinating team from theSToP-Alzheimer Centerat theDouglas Mental Health University Instituteaffiliated withMcGill Universityas they are responsible for the data collection. Special thanks to Jennifer Tremblay-Mercier, coordinator at the SToP-Alzheimer center, and Dr. Sylvia Villeneuve, director of the SToP-Alzheimer center, for allowing the sharing of the data. Importantly, we would also like to thank the participants of the PREVENT-AD cohort, without whom we would not be able to have these data.The download of the data was made possible by the support from theCanadian Open Neuroscience Platform (CONP), which is currently storing the data for open access. The CONP is funded in part byBrain Canada, in partnership withHealth Canada.We also acknowledge the support received by Calcul Québec and the Digital Research Alliance of Canada (DRAC) in the development ofsihnpy. Many of the functionalities insihnpywere tested and developped on a high performance computing environment such asBeluga. The preprocessing and storage of the Prevent-AD data available insihnpywas also executed on Beluga.I was also financially supported by theFonds de Recherche du Québec - Santé, theHealthy Brains for Healthy Lives initiativeandMitacsat different point during my studies and the development of this software.The funding organizations mentioned above did not participate in the decision making process involved in the development of the software.References[^Mueller_2013]: Mueller et al. (2013). Neuron.10.1016/j.neuron.2012.12.028[^Finn_2015]: Finn et al. (2015). Nat Neuro.10.1038/nn.4135