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  "Title": "Automated Generation of Data-Informed GLM Models in Task-Based\nfMRI Data Analysis",
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  "Authors@R": "c(person(\"Jure\", \"Demšar\", , \"jure.demsar@fri.uni-lj.si\", c(\"cre\", \"aut\")),\nperson(\"Nina\", \"Purg\", , \"nina.purg@ff.uni-lj.si\", c(\"aut\")),\nperson(\"Grega\", \"Repovš\", , \"grega.repovs@psy.ff.uni-lj.si\", \"aut\"))",
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  "Description": "Analysis of task-related functional magnetic resonance\nimaging (fMRI) activity at the level of individual participants\nis commonly based on general linear modelling (GLM) that allows\nus to estimate to what extent the blood oxygenation level\ndependent (BOLD) signal can be explained by task response\npredictors specified in the GLM model. The predictors are\nconstructed by convolving the hypothesised timecourse of neural\nactivity with an assumed hemodynamic response function (HRF).\nTo get valid and precise estimates of task response, it is\nimportant to construct a model of neural activity that best\nmatches actual neuronal activity. The construction of models is\nmost often driven by predefined assumptions on the components\nof brain activity and their duration based on the task design\nand specific aims of the study. However, our assumptions about\nthe onset and duration of component processes might be wrong\nand can also differ across brain regions. This can result in\ninappropriate or suboptimal models, bad fitting of the model to\nthe actual data and invalid estimations of brain activity. Here\nwe present an approach in which theoretically driven models of\ntask response are used to define constraints based on which the\nfinal model is derived computationally using the actual data.\nSpecifically, we developed 'autohrf' — a package for the 'R'\nprogramming language that allows for data-driven estimation of\nHRF models. The package uses genetic algorithms to efficiently\nsearch for models that fit the underlying data well. The\npackage uses automated parameter search to find the onset and\nduration of task predictors which result in the highest fitness\nof the resulting GLM based on the fMRI signal under predefined\nrestrictions. We evaluate the usefulness of the 'autohrf'\npackage on publicly available datasets of task-related fMRI\nactivity. Our results suggest that by using 'autohrf' users can\nfind better task related brain activity models in a quick and\nefficient manner.",
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  "Repository": "https://demsarjure.r-universe.dev",
  "Date/Publication": "2024-01-16 13:41:21 UTC",
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