Schizophrenia Bulletin Advance Access originally published online on January 19, 2006
Schizophrenia Bulletin 2006 32(4):679-691; doi:10.1093/schbul/sbj038
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Distinguishing Neurocognitive Functions in Schizophrenia Using Partially Ordered Classification Models
2 Center for Neuropsychiatric Outcome and Rehabilitation Research, The Zucker Hillside Hospital, North Shore Long Island Jewish Health System, Glen Oaks, New York
3 Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, New York
4 Department of Human Development, Teachers College, Columbia University, New York, New York
5 University of California, Los Angeles
1To whom correspondence should be addressed; Center for Neuropsychiatric Rehabilitation Research, Zucker Hillside Hospital, North Shore Long Island Jewish Hospital, 75-59 263rd St., Glen Oaks, NY 11004; tel.: 718-470-8342, fax: 718-962-2742. Email: jaeger{at}lij.edu.
Current methods for statistical analysis of neuropsychological test data in schizophrenia are inherently insufficient for revealing valid cognitive impairment profiles. While neuropsychological tests aim to selectively sample discrete cognitive domains, test performance often requires several cognitive operations or "attributes." Conventional statistical approaches assign each neuropsychological score of interest to a single attribute or "domain" (e.g., attention, executive, etc.), and scores are calculated for each. This can yield misleading information about underlying cognitive impairments. We report findings applying a new method for examining neuropsychological test data in schizophrenia, based on finite partially ordered sets (posets) as classification models.
A total of 220 schizophrenia outpatients were administered the Positive and Negative Symptom Scale (PANSS) and a neuropsychological test battery. Selected tests were submitted to cognitive attribute analysis a priori by two neuropsychologists. Applying Bayesian classification methods (posets), each patient was classified with respect to proficiency on the underlying attributes, based upon his or her individual test performance pattern.
Twelve cognitive "classes" are described in the sample. Resulting classification models provided detailed "diagnoses" into "attribute-based" profiles of cognitive strength/weakness, mimicking expert clinician judgment. Classification was efficient, requiring few measures to achieve accurate classification. Attributes were associated with PANSS factors in the expected manner (only the negative and cognition factors were associated with the attributes), and a double dissociation was observed in which divergent thinking was selectively associated with negative symptoms, possibly reflecting a manifestation of Kraepelin's hypothesis regarding the impact of volitional disturbances on thought.
Using posets for extracting more precise cognitive information from neuropsychological data may reveal more valid cognitive endophenotypes, while dramatically reducing the amount of testing required.
Keywords: schizophrenia / neurocognitive deficits / neuropsychological test domains / neuropsychological test data reduction / clustering techniques / Bayesian methods
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