Model-based dynamical analysis of functional disconnection in schizophrenia
Open accessAbstract
Schizophrenia is shown to be associated with impaired interactions in functional macro-networks of the brain. The focus of our study was if there is an impairment of cognitive control of learning during schizophrenia. To investigate this question, we collected fMRI data from a group of stable schizophrenia patients and controls performing an object-location associative learning task in which the learning performance of the patient group was significantly worse. We applied Dynamic Causal Modeling to analyze the fMRI data. A set of causal models of BOLD signal generation was defined to evaluate connections between five regions material to the task (Primary Visual Cortex, Superior Parietal and Inferior Temporal Cortex, Hippocampus and Dorsal Prefrontal Cortex). Bayesian model selection was used to investigate hypotheses on differences in model architecture across groups, and indicated fundamental differences in model architecture in patients compared to controls. Models lacking connections related to cognitive control were more probable in the patient group. Hypotheses on differences in effective connectivity between groups were tested by comparing estimates of neural coupling parameters in winning model structures. This analysis indicated reduced fronto-hippocampal and hippocampo-inferior temporal coupling in patients, and reduced excitatory modulation of these pathways by learning. These findings may account for the documented reductions in learning performance of schizophrenia patients.
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DCM studies in schizophrenia demonstrated differences in functional network architecture and effective connectivity compared to healthy controls across various tasks.
“These studies demonstrated differences in functional network architecture and effective connectivity compared to healthy controls across various tasks.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [133]
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Computational approaches to psychiatry
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Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
Haker, H., Schneebeli, M., & Stephan, K. E. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00107 · Cited by 130 · see citing works
- Authors
- Bányai, M., Diwadkar, V. A., & Érdi, P.
- Affiliations
- Institute for Particle and Nuclear Physics (HU)
- Wayne State University (US)
- Corresponding author
- Diwadkar, Vaibhav A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-10
- Volume
- 58
- Issue
- 3
- Pages
- 870-877
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3221737
- OpenAlex
- W2101237528
- Cited by
- 40
- References
- 60
- Citations by year
- 2025: 2 · 2023: 3 · 2022: 1 · 2021: 8 · 2020: 1 · 2019: 2 · 2018: 1 · 2017: 3 · 2016: 4 · 2015: 2 · 2014: 4 · 2013: 4 · 2012: 4
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Mental Health (MH68680)
- Children's Research Center of Michigan
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- schizophrenia, functional disconnection, associative learning, effective connectivity, hippocampus, prefrontal cortex, inferior temporal cortex, superior parietal cortex, primary visual cortex, dynamic causal modeling, Bayesian model selection
- MeSH terms
- Bayes Theorem, Brain, Humans, Image Interpretation, Computer-Assisted, Learning, Magnetic Resonance Imaging, Models, Neurological, Neural Pathways, Schizophrenia
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