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Curtis Tatsuoka

Publications and source records attributed to Curtis Tatsuoka.

2 recordsLinked to original sources

Associating functional recovery with neurocognitive profiles identified using partially ordered classification models.

Neurocognitive deficits are a core feature of schizophrenia and a significant cause of functional disability. However, targeting these deficits with new treatment approaches will only yield functional improvements if those cognitive operations that are responsible for different dimensions of functional recovery can be identified. A major challenge is that conventional neuropsychological tests, the most practical tools for broadly sampling cognitive functions in treatment trials, are polyfactorial, so that task performance is influenced by multiple cognitive operations. Hence, it is difficult to pinpoint exactly for which cognitive operations a low scoring subject may have poor functionality. We have previously applied in a neuropsychological test battery administered to 220 patients having schizophrenia or schizoaffective disorder, Bayesian statistical methods (yielding partially ordered sets, or posets) designed to mimic the expert analysis of a neuropsychologist by classifying patients into discrete groupings or "states" each having a unique cognitive profile. Here, we report on the association of attributes describing these states (viz. working memory, capacity for divergent thinking, cognitive flexibility and psychomotor speed) with two domains of functional outcome (work/education and residential functioning) rated up to 18 months later. After multiplicity correction, only working memory was associated with work/education outcome. While working memory was not associated with residential outcome, the remaining three attributes were. These findings suggest that different neurocognitive operations may be responsible for different outcome domains. Findings support the use of the poset methodology for clarifying patterns of relationships between discrete neurocognitive attributes and domains of functional outcome.

Adolescent↗

Distinguishing neurocognitive functions in schizophrenia using partially ordered classification models.

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.

Adult↗