Validation of the cognitively normal range and below normal range subtypes in chronically hospitalized patients with schizophrenia

Social cognition and apathy between two cognitive subtypes of schizophrenia: Are there the same or different profiles?

Objective

Cognitive impairment is an essential feature of schizophrenia, and it involves a broad array of nonsocial and social cognitive domains. This study aimed to examine whether there are the same or different social cognition profiles between two cognitive subtypes of schizophrenia.

Method

There were one hundred and two chronic and institutionalized patients with schizophrenia from two referral tracks. One group is "Cognitively Normal Range" (CNR) (N = 52), and another group is "Below Normal Range" (BNR) (N = 50). We assessed or collected their apathy, emotional perception judgment, facial expression judgment, and empathy by the Apathy Evaluation Scale, the International Affective Picture System, the Japanese and Caucasian Facial Expression of Emotion, and the Interpersonal Reactivity Index, respectively.

Results

We found different impairment profiles depending on the cognitive subtypes of the patient with schizophrenia. Surprisingly, the CNR presented impairments in apathy, emotional perception judgment, facial expression judgment, and empathy and feature impairment in empathy and affective apathy. In contrast, even though the BNR had significant neurocognition impairments, they had almost intact empathy with significantly impaired cognitive apathy. Both groups' global deficit scores (GDSs) were comparable, and all reached at least a mild impairment level.

Conclusions

The CNR and the BNR had similar abilities in emotional perception judgment and facial emotion recognition. They also had differentiable deficits in apathy and empathy. Our findings provide important clinical implications for neuropsychological pathology and treatment in schizophrenia.

Disentangling Cognitive Heterogeneity in Psychotic Spectrum Disorders

Neuropsychological impairments represent a central feature of psychosis-spectrum disorders. It is characterized by a great both within- and between-subjects variability (i.e. cognitive heterogeneity), which needs to be better disentangled. The present study aimed to describe the distribution of performance on the Brief Assessment of Cognition in Schizophrenia (BACS) by using the Equivalent Scores, in order to balance statistical methodological problems. To do so, cognitive performance groups were branded, identifying the main factors contributing to cognitive heterogeneity. A sample of 583 patients with a diagnosis of Schizophrenia or Psychotic Disorder Not Otherwise Specified was enrolled and assessed for neurocognition and intellectual level. K-means cluster analysis was performed based on BACS Equivalent Scores. Differences among clusters were analyzed throughout Analysis of Variance and Discriminant Function Analysis in order to identify the most significant predictors of cluster membership. For each cognitive task, roughly 40% of patients displayed poor performance, while up to 63% displayed a symbol-coding deficit. K-means cluster analysis depicted three profiles characterized by "near-normal" cognition, widespread impairment, and "borderline" profile. Discriminant analysis selected Verbal IQ and diagnosis as predictors of cluster membership. Our findings support the usefulness of Equivalent Scores and cluster analysis to explain cognitive heterogeneity, and tailor better interventions.

From cognitive and clinical substrates to functional profiles: Disentangling heterogeneity in schizophrenia

The relationship between neurocognition and functioning among patients with schizophrenia is well documented. However, integrating neuropsychological, clinical and psychopathological data to better investigate functional outcome still constitutes a challenge. Artificial neural network-based modeling might help to better capture clinical heterogeneity by analyzing the non-linear relationships among multiple variables. Two hundred and fourteen clinically stabilized patients with schizophrenia were recruited and assessed for neurocognition, psychopathology and functioning. Artificial neural network analyses were conducted to yield significant predictors of functional outcome among clinical and cognitive variables and to build distinct functional Profiles, each characterized by a different medley of cognitive and clinical features. Twenty-two key predictors of daily functioning emerged, encompassing neurocognitive and clinical domains, with major roles for processing speed and attention. Four Profiles were constructed based on specific levels of functioning, each characterized by a distinct distribution of key clinical and neurocognitve measures. This study highlights the importance of a more in-depth investigation of cognitive and clinical heterogeneity. A better understanding of the building blocks of these Profiles would lead to more individualized rehabilitation treatments.