Dysconnection in Schizophrenia: From Abnormal Synaptic Plasticity to Failures of Self-monitoring

By Klaas E. Stephan, Karl J. Friston, ChrisD . Frith. Schizophrenia bulletin, 2009

1,205 citations recorded by the source corpus.

Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)–mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/8cfeb68228d86d29ef7a86331cbc4aefac1a2e9b

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Citations in the corpus

The Computational Anatomy of Psychosis (2013, background): “Stephan et al. (2009), Morrison (2012), Carlsson et al. (1999), Lisman et al. (2008), Simpson et al. (2010). other in numerous ways (Cepeda and Levine, 2006).”
Observing the Observer (II): Deciding When to Decide (2010, background): “This is because spectrum diseases in psychiatry, such as schizophrenia or depression, display profound heterogeneity with regard to the underlying pathophysiological mechanisms, requiring the development of models that can infer subject-specific mechanisms from neurophysiological and/or behavioural ”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, background): “First, NMDA receptors are important for synaptic plasticity; hence, abnormal NMDA signaling is detrimental to learning and memory (903).”
Human Neuroscience (neutral): “…differences in learning may result from inter-individual variability in basic physiological mechanisms, such as the neuromodulatory regulation of synaptic plasticity (Thiel et al., 1998), and such differences may explain the heterogeneous nature of psychiatric diseases (Stephan et al., 2009).”
Coherence a measure of the brain networks: past and present (2016, neutral): “histamine, serotonin and melatonin [24, 44, 48].”
Gamma-band synchronization in the neocortex: novel analysis methods and their application to sensory and motivational systems (2013, background): “Finally, consistent with theories regarding schizophrenia as a disorder of inter-areal connectivity (Stephan et al., 2009; Lynall et al., 2010), our data show that local NMDA hypofunction causes marked changes in spikefield phase-synchronization, which may result in global dysconnectivity between br”
Dynamic models of large-scale brain activity (2017, background): “The positioning of schizophrenia as a ‘dysconnection’ syndrome is a natural target for this approach 127 .”
An aberrant precision account of autism (2014, neutral): “Individual variability in basic physiological mechanisms, such as neuromodulatory function, may give rise to individual differences in learning and such differences may explain the heterogeneous nature of psychiatric diseases (Stephan et al., 2009).”