Modeling the fMRI signal via Hierarchical Clustered Hidden Process Models.
Year: 2007
Type: article
Abstract: Machine Learning techniques have been used quite widely for the task of predicting cognitive processes from fMRI data. However, these models do not describe well the fMRI signal when it is generated by multiple cognitive processes that are simultaneously active. In this paper we consider the problem of accurately modeling the fMRI signal of a human subject who is performing a task involving multiple concurrent cognitive processes. We present a Hierarchical Clustering extension of Hidden Process Models which, by taking advantage of automatically discovered similarities in the activation among neighboring voxels, achieves significantly better performance than standard generative models in terms of Average Log Likelihood.
Source: PubMed Central
Institution: Siemens Healthcare (United States)
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W1509686736