Details

Autor(en) / Beteiligte

• Todd, Juanita
• Michie, Patricia T.
• Schall, Ulrich
• Ward, Philip B.
• Catts, Stanley V.

Titel

Mismatch negativity (MMN) reduction in schizophrenia—Impaired prediction-error generation, estimation or salience?

Ist Teil von

• International journal of psychophysiology, 2012-02, Vol.83 (2), p.222-231

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2012

Quelle

MEDLINE

Beschreibungen/Notizen

• The model of mismatch negativity (MMN) as a simple index of change detection has been superseded by a richer understanding of how this event-related potential (ERP) reflects the representation of the sound environment in the brain. Our conceptualization of why the MMN is altered in certain groups must also evolve along with a better understanding of the activities reflected by this component. The detection of change incorporates processes enabling an automatic registration of “sameness”, a memory for such regularities and the application of this recent acoustic context to interpreting the present and future state of the environment. It also includes “weighting” the importance of this change to an organism's behaviour. In this light, the MMN has been considered a prediction error signal that occurs when the brain detects that the present state of the world violates a context-driven expectation about the environment. In this paper we revisit the consistent observation of reduced MMN amplitude in patients with schizophrenia. We review existing data to address whether the apparent deficit might reflect problems in prediction error generation, estimation or salience. Possible interpretations of MMN studies in schizophrenia are linked to dominant theories about the neurobiology of the illness. ► We review 20 years of research on reduced mismatch negativity (MMN) in schizophrenia. ► What can the reduction in MMN can teach us about the pathophysiology of schizophrenia? ► We review the functional relevance of reduced MMN in schizophrenia. ► We note that MMN reflects the activity of a context-sensitive network. ► We require more knowledge about the role of network components and their modulators.