Computational Parametric Mapping: A Method For Mapping Cognitive Models Onto Neuroimaging Data

Simon Steinkamp, Iyadh Chaker, Félix Hubert, David Meder, Oliver Hulme


Elucidating the neural basis of cognition requires incorporating cognitive models into the modeling of neural data. A prevalent strategy in neuroimaging is to fit computational models of cognition to concurrent behavior and then regress the latent variables of those models onto fMRI data. Here we introduce computational parametric mapping (CPM) as a framework which generalizes population receptive field mapping to the cognitive domain. CPM offers three main advances for cognitive computational modeling. First, CPM allows model comparison statistics and model parameters of cognitive computational models to be topographically mapped onto anatomical structures. Second, by embedding cognitive models into generative models that can be fit directly to neuroimaging data, CPM relaxes the constraint of mapping only behaviorally relevant variables. Finally, despite the computationally intensive process of estimating generative models independently for each voxel, our procedures are now fast enough to make it feasible to map extensive neural systems over multiple models. We will illustrate this approach via synthetic and pilot data for a reward learning experiment. We propose that this method can play a role in discovering topographic principles underlying the neural coding of cognitive processes.
Publikationsdato26 aug. 2022
Antal sider3
StatusUdgivet - 26 aug. 2022
Begivenhed2022 Conference on Cognitive Computational Neuroscience - San Fransico, San Fransico, USA
Varighed: 25 aug. 202228 aug. 2022


Konference2022 Conference on Cognitive Computational Neuroscience
LokationSan Fransico
BySan Fransico


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