Schizophrenia is characterized by alterations of language and inferential processes. In spite of extensive research, core mechanisms of these disturbances remain uncertain. The overall objective of this RO1 proposal is to use DISCERN, a neural network simulation of natural language processing (Miikkulainen & Dyer 1991; Miikkulainen 1993, 1998), to investigate the mechanism(s) of language-based disturbances in schizophrenia. DISCERN learns stories, utilizes inferential processes, replies to questions, and produces coherent, multi-sentence narrative paraphrases of episodic memories. To enhance applicability of DISCERN as a model of human narrative language production, a larger corpus of stories will be learned that incorporates emotion-coding and self-reference. Simulations will be conducted to determine if disrupted function in different neural modules of DISCERN can produce three core language-based illness manifestations of schizophrenia -- (I) positive thought disorder (such as derailment and illogicality), (II) negative thought disorder (reduced language outputs), and (III) delusions of the idee fixe type. DISCERN will be used to compare and contrast effects of excessive noise versus reduced network connectivity when applied to semantic and working memory modules. Both types of """"""""lesions"""""""" have been postulated to play an important role in the pathophysiology of schizophrenia. Noise-induced lesions are predicted to produce word selection errors and curtail language output -- but not to produce positive thought disorder or delusions. In contrast, connectivity loss, when applied to story processing modules, is predicted to simulate all three disturbances, i.e., derailment and curtailment of language outputs as well as production of """"""""fixed"""""""" narratives that simulate delusions. A parallel, pilot study of normal subjects and patients with schizophrenia will assess narrative recall of episodic memory. These behavioral data will be used to test and refine models of normal and schizophrenic language production. These findings will significantly advance our understanding of illness mechanisms in schizophrenia and direct future research aimed at developing more selective treatments that reverse these abnormalities.
| Hoffman, Ralph E; Grasemann, Uli; Gueorguieva, Ralitza et al. (2011) Using computational patients to evaluate illness mechanisms in schizophrenia. Biol Psychiatry 69:997-1005 |
