Towards biologically constrained attractor models of schizophrenia

Area of Science:

• Computational neuroscience
• Neurobiology of schizophrenia
• Cognitive deficits

Background:

• Dominant theories of schizophrenia implicate dopamine and glutamate in working memory (WM) deficits.
• Biophysical network models can replicate WM impairments in people with schizophrenia (PSZ).
• Multiple, even opposing, circuit alterations can produce similar network behaviors.

Purpose of the Study:

• Critically review literature linking NMDAR hypofunction to WM precision loss in PSZ.
• Evaluate if current experimental data can differentiate competing biophysical models of WM deficits.
• Identify key experimental and computational questions to advance understanding.

Main Methods:

• Network simulations exploring WM deficits.
• Critical revision of computational and experimental literature.
• Analysis of NMDAR hypofunction effects on network dynamics.

Main Results:

• Current experimental evidence is insufficient to distinguish between competing biophysical models of WM deficits in PSZ.
• Network simulations show that various alterations can lead to similar behavioral outcomes.
• Unresolved issues include the impact of E/I ratio changes and short-term plasticity.

Conclusions:

• Further integrated experimental and computational research is needed to understand the neurobiology of cognitive deficits in schizophrenia.
• Clarifying the role of NMDARs, E/I balance, and synaptic plasticity is crucial.
• Distinguishing between network models will improve therapeutic strategies for PSZ.