Neurocognitive Pattern Analysis Reveals Classificatory Hierarchy of Attention Deficits in Schizophrenia

Attention deficits, among other cognitive deficits, are frequently observed in schizophrenia. Although valid and reliable neurocognitive tasks have been established to assess attention deficits in schizophrenia, the hierarchical value of those tests as diagnostic discriminants on a single-subject le...

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Bibliographic Details
Published in:Schizophrenia bulletin 2014-07, Vol.40 (4), p.878-885
Main Authors: SHEN, Christina, POPESCU, Florin C, HAHN, Eric, TA, Tam T. M, DETTLING, Michael, NEUHAUS, Andres H
Format: Article
Language:English
Subjects:
Adolescent
Adult
Adult and adolescent clinical studies
Aged
Artificial Intelligence
Attention - physiology
Biological and medical sciences
Case-Control Studies
Cognition Disorders - physiopathology
Cognition Disorders - psychology
Female
Humans
Male
Medical sciences
Middle Aged
Neuropsychological Tests
Pattern Recognition, Automated
Psychology. Psychoanalysis. Psychiatry
Psychopathology. Psychiatry
Psychoses
Regular
Schizophrenia
Schizophrenia - physiopathology
Schizophrenic Psychology
Young Adult
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Summary:Attention deficits, among other cognitive deficits, are frequently observed in schizophrenia. Although valid and reliable neurocognitive tasks have been established to assess attention deficits in schizophrenia, the hierarchical value of those tests as diagnostic discriminants on a single-subject level remains unclear. Thus, much research is devoted to attention deficits that are unlikely to be translated into clinical practice. On the other hand, a clear hierarchy of attention deficits in schizophrenia could considerably aid diagnostic decisions and may prove beneficial for longitudinal monitoring of therapeutic advances. To propose a diagnostic hierarchy of attention deficits in schizophrenia, we investigated several facets of attention in 86 schizophrenia patients and 86 healthy controls using a set of established attention tests. We applied state-of-the-art machine learning algorithms to determine attentive test variables that enable an automated differentiation between schizophrenia patients and healthy controls. After feature preranking, hypothesis building, and hypothesis validation, the polynomial support vector machine classifier achieved a classification accuracy of 90.70% ± 2.9% using psychomotor speed and 3 different attention parameters derived from sustained and divided attention tasks. Our study proposes, to the best of our knowledge, the first hierarchy of attention deficits in schizophrenia by identifying the most discriminative attention parameters among a variety of attention deficits found in schizophrenia patients. Our results offer a starting point for hierarchy building of schizophrenia-associated attention deficits and contribute to translating these concepts into diagnostic and therapeutic practice on a single-subject level.
ISSN:0586-7614
1745-1701
DOI:10.1093/schbul/sbt107