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A hierarchical model for integrating unsupervised generative embedding and empirical Bayes
- Source :
- Journal of Neuroscience Methods. 269:6-20
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- Background Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonly used for inferring effective connectivity from individual subject data. Recently introduced “generative embedding” approaches have used DCM-based connectivity parameters for supervised classification of individual patients or to find unknown subgroups in heterogeneous groups using unsupervised clustering methods. New method We present a novel framework which combines DCMs with finite mixture models into a single hierarchical model. This approach unifies the inference of connectivity parameters in individual subjects with inference on population structure, i.e. the existence of subgroups defined by model parameters, and allows for empirical Bayesian estimates of a subject’s connectivity based on subgroup-specific prior distributions. We introduce a Markov chain Monte Carlo sampling method for inversion of this hierarchical generative model. Results This paper formally introduces the idea behind our novel concept and demonstrates the face validity of the model in application to both simulated data as well as an empirical fMRI dataset from healthy controls and patients with schizophrenia. Comparison with existing method(s) The analysis of our empirical fMRI data demonstrates that our approach results in superior model evidence than the conventional non-hierarchical inversion of DCMs. Conclusions In this paper, we have presented a novel unified framework to jointly infer the effective connectivity parameters in DCMs for multiple subjects and, at the same time, discover connectivity-defined cluster structure of the whole population, using a mixture model approach.
- Subjects :
- Male
0301 basic medicine
MCMC
Computer science
Inference
computer.software_genre
Hierarchical database model
170 Ethics
0302 clinical medicine
Cluster Analysis
Mixture model
DCM
education.field_of_study
Psychiatric spectrum diseases
General Neuroscience
2800 General Neuroscience
Brain
Markov chain Monte Carlo sampling
Magnetic Resonance Imaging
Markov Chains
Generative model
symbols
Female
Monte Carlo Method
Adult
Models, Neurological
Bayesian probability
Population
610 Medicine & health
Neuroimaging
Machine learning
Clustering
03 medical and health sciences
symbols.namesake
Humans
10237 Institute of Biomedical Engineering
Computer Simulation
education
Cluster analysis
Models, Statistical
business.industry
Reproducibility of Results
Bayes Theorem
Markov chain Monte Carlo
030104 developmental biology
Schizophrenia
Artificial intelligence
Dynamic causal modelling
business
computer
Software
030217 neurology & neurosurgery
Unsupervised Machine Learning
Subjects
Details
- ISSN :
- 01650270
- Volume :
- 269
- Database :
- OpenAIRE
- Journal :
- Journal of Neuroscience Methods
- Accession number :
- edsair.doi.dedup.....9afc417740a4efb11a5815c2d00ce31d