Selective Corticostriatal Dysfunction in Schizophrenia: Examination of Motor and Cognitive Skill Learning

Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA.
Neuropsychology (Impact Factor: 3.27). 12/2007; 22(1):100-109. DOI: 10.1037/0894-4105.22.1.100
Source: PubMed


[Correction Notice: An erratum for this article was reported in Vol 22(3) of
Neuropsychology (see record
2008-05020-015). Table 1 on page 102 should have included the BPRS Depression-Anxiety subscale score 9.00 (3.99) under the column heading Schiz pts. Table displays means with standard deviations in parentheses.] [Correction Notice: An erratum for this article was reported in Vol 22(2) of
Neuropsychology (see record
2008-02526-002). The DOI for the supplemental materials was printed incorrectly. The correct DOI is as follows:] It has been suggested that patients with schizophrenia have corticostriatal circuit dysfunction (Carlsson & Carlsson, 1990). Skill learning is thought to rely on corticostriatal circuitry and different types of skill learning may be related to separable corticostriatal loops (Grafton, Hazeltine, & Ivry, 1995; Poldrack, Prabhakaran, Seger, & Gabrieli, 1999). The authors examined motor (Serial Reaction Time task, SRT) and cognitive (Probabilistic Classification task, PCT) skill learning in patients with schizophrenia and normal controls. Development of automaticity was examined, using a dual task paradigm, across three training sessions. Patients with schizophrenia were impaired at learning on the PCT compared to controls. Performance gains of controls occurred within the first session, whereas patients only improved gradually and never reached the performance level of controls. In contrast, patients were not impaired at learning on the SRT relative to controls, suggesting that patients with schizophrenia may have dysfunction in a specific corticostriatal subcircuit. (PsycINFO Database Record (c) 2012 APA, all rights reserved)

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    • "choices ) , and to categorize the stimuli through feedback - based associative learning . Thus , since learning mechanisms differ , the under - lying neural networks are also likely to be different . Recent clinical studies also showed dissociations between preserved motor and impaired cognitive learning in adults with neuropsychiatric disorders ( Foerde et al . , 2008 ; Marsh et al . , 2005 ) . Conversely , spared cognitive learning but deficient motor learning was observed in two children with progressive idiopathic dystonia ( Mayor - Dubois et al . , 2010 ) . All these data are in favor of parallel and partially independent networks supporting different types of procedural learning ( Alexander , De"
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    ABSTRACT: In this study, we investigated motor and cognitive procedural learning in typically developing children aged 8–12 years with a serial reaction time (SRT) task and a probabilistic classification learning (PCL) task. The aims were to replicate and extend the results of previous SRT studies, to investigate PCL in school-aged children, to explore the contribution of declarative knowledge to SRT and PCL performance, to explore the strategies used by children in the PCL task via a mathematical model, and to see whether performances obtained in motor and cognitive tasks correlated. The results showed similar learning effects in the three age groups in the SRT and in the first half of the PCL tasks. Participants did not develop explicit knowledge in the SRT task whereas declarative knowledge of the cue–outcome associations correlated with the performances in the second half of the PCL task, suggesting a participation of explicit knowledge after some time of exposure in PCL. An increasing proportion of the optimal strategy use with increasing age was observed in the PCL task. Finally, no correlation appeared between cognitive and motor performance. In conclusion, we extended the hypothesis of age invariance from motor to cognitive procedural learning, which had not been done previously. The ability to adopt more efficient learning strategies with age may rely on the maturation of the fronto-striatal loops. The lack of correlation between performance in the SRT task and the first part of the PCL task suggests dissociable developmental trajectories within the procedural memory system.
    Child Neuropsychology 07/2015; DOI:10.1080/09297049.2015.1058347 · 2.42 Impact Factor
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    • "Probabilistic association learning (requiring gradual learning of probabilistic-based cue–outcome associations) elicits frontal–parietal–striatal activity in healthy people (Poldrack et al. 1999; Fera et al. 2005) and is related to reduced activity in a neural network that includes the dorsolateral prefrontal cortex (DLPFC), parietal cortex and caudate nucleus in schizophrenia (Weickert et al. 2009). Probabilistic association learning is impaired in people with schizophrenia (Weickert et al. 2002; Foerde et al. 2008; Horan et al. 2008), the offspring of people with schizophrenia who are at high risk of developing schizophrenia (Wagshal et al. 2012, 2013) and in unaffected siblings of people with schizophrenia (Weickert et al. 2010; Wagshal et al. 2014), with deficits in overall performance and learning rate. "
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    Psychological Medicine 08/2014; 45(04). DOI:10.1017/S0033291714001925 · 5.94 Impact Factor
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    • "The relationship between BDNF and probabilistic association learning is of interest as peripheral blood BDNF levels correlate with brain activity during probabilistic association learning in healthy adults [14] and the BDNF val66met polymorphism also influences prefrontal cortex function in healthy people and those at high genetic risk for schizophrenia [15] [16] [17] [18]. Probabilistic association learning is impaired in schizophrenia [19] [20] [21] [22] and is associated with abnormal fronto-striatal activity [23]. Siblings of people with schizophrenia who may share risk genes are also more likely to show probabilistic association learning deficits [21]. "
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    ABSTRACT: The brain derived neurotrophic factor (BDNF) val66met polymorphism rs6265 influences learning and may represent a risk factor for schizophrenia. Healthy people with high schizotypal personality traits display cognitive deficits that are similar to but not as severe as those observed in schizophrenia and they can be studied without confounds of antipsychotics or chronic illness. How genetic variation in BDNF may impact learning in individuals falling along the schizophrenia spectrum is unknown. We predicted that schizotypal personality traits would influence learning and that schizotypal personality-based differences in learning would vary depending on the BDNF val66met genotype. Eighty-nine healthy adults completed the Schizotypal Personality Questionnaire (SPQ) and a probabilistic association learning test. Blood samples were genotyped for the BDNF val66met polymorphism. An ANOVA was performed with BDNF genotype (val homozygotes and met-carriers) and SPQ score (high/low) as grouping variables and probabilistic association learning as the dependent variable. Participants with low SPQ scores (fewer schizotypal personality traits) showed significantly better learning than those with high SPQ scores. BDNF met-carriers displaying few schizotypal personality traits performed best, whereas BDNF met-carriers displaying high schizotypal personality traits performed worst. Thus, the BDNF val66met polymorphism appears to influence probabilistic association learning differently depending on the extent of schizotypal personality traits displayed.
    Behavioural Brain Research 08/2014; 174:137-142. DOI:10.1016/j.bbr.2014.07.041 · 3.03 Impact Factor
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