Publications by authors named "J Daniel Ragland"

214 Publications

Reliability and Replicability of Implicit and Explicit Reinforcement Learning Paradigms in People With Psychotic Disorders.

Schizophr Bull 2021 Apr;47(3):731-739

Department of Psychology, University of Minnesota, Minneapolis, MN.

Background: Motivational deficits in people with psychosis may be a result of impairments in reinforcement learning (RL). Therefore, behavioral paradigms that can accurately measure these impairments and their change over time are essential.

Methods: We examined the reliability and replicability of 2 RL paradigms (1 implicit and 1 explicit, each with positive and negative reinforcement components) given at 2 time points to healthy controls (n = 75), and people with bipolar disorder (n = 62), schizoaffective disorder (n = 60), and schizophrenia (n = 68).

Results: Internal consistency was acceptable (mean α = 0.78 ± 0.15), but test-retest reliability was fair to low (mean intraclass correlation = 0.33 ± 0.25) for both implicit and explicit RL. There were no clear effects of practice for these tasks. Largely, performance on these tasks shows intact implicit and impaired explicit RL in psychosis. Symptom presentation did not relate to performance in any robust way.

Conclusions: Our findings replicate previous literature showing spared implicit RL and impaired explicit reinforcement in psychosis. This suggests typical basal ganglia dopamine release, but atypical recruitment of the orbitofrontal and dorsolateral prefrontal cortices. However, we found that these tasks have only fair to low test-retest reliability and thus may not be useful for assessing change over time in clinical trials.
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http://dx.doi.org/10.1093/schbul/sbaa165DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8084427PMC
April 2021

Using Computational Modelling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Apr 17. Epub 2021 Apr 17.

Background: Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g. psychosis in bipolar disorder vs schizophrenia). Computational modelling makes it possible to identify and mathematically differentiate behaviorally-unobservable, specific reinforcement-learning (RL) differences in schizophrenia (SZ) patients versus other disorders, likely due to a higher reliance on prediction-error(PE)-driven learning associated with basal ganglia, and under-reliance on explicit value representations associated with OFC.

Methods: We use a well-established probabilistic-RL task to replicate those findings in individuals with schizophrenia both on (N=120) and off (N=44) anti-psychotic medications, and include a patient comparison group of bipolar patients with psychosis (N=60) and healthy controls (n=72).

Results: Using accuracy, there was a main effect of group (F(3,279)=7.87, p<0.001, such that all patients groups were less accurate than controls. Using computationally derived parameters, both medicated and unmediated individuals with SZ, but not bipolar patients, demonstrated a reduced "mixing" parameter (F(3,295)=13.91,p<0.001), indicating less dependence on learning explicit value representations, as well as greater learning decay between training and test (F(1,289)=12.81, p<0.001). Unmedicated SZ also showed greater decision noise (F(3,295)=2.67, p=0.04).

Conclusions: Both medicated and unmedicated patients show overreliance on PE-driven learning, as well as significantly higher noise and value-related memory decay, compared to the healthy controls and the bipolar patients. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.
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http://dx.doi.org/10.1016/j.bpsc.2021.03.017DOI Listing
April 2021

Disrupted Modulation of Alpha and Low Beta Oscillations Mediates Temporal Sequence Memory Deficits in People with Schizophrenia.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Apr 13. Epub 2021 Apr 13.

Department of Psychiatry and Behavioral Sciences, University of California at Davis, Davis, CA, USA. Electronic address:

Background: People with schizophrenia (SZ) exhibit impaired episodic memory when relating objects to each other in time and space. Empirical studies and computational models suggest that low-frequency neural oscillations may be a mechanism by which the brain keeps track of temporal relationships during encoding and retrieval, with modulation of oscillatory power as sequences are learned. It is unclear whether sequence memory deficits in SZ are associated with altered neural oscillations.

Methods: Using electroencephalography, this study examined neural oscillations in 51 healthy controls and 37 people with SZ during a temporal sequence learning task. Multiple five-object picture sequences were presented across 4 study-test blocks in either fixed or random order. Participants answered semantic questions for each object (e.g., living/non-living), and sequence memory was operationalized as faster responses for fixed versus random sequences. Differences in oscillatory power between fixed versus random sequences provided a neural index of temporal sequence memory.

Results: Although both groups showed reaction time (RT) differences in late blocks (blocks 3 and 4), this evidence of sequence memory was reduced in people with SZ relative to healthy controls. Decreases in globally-distributed pre-stimulus alpha (8-12 Hz) and beta1 (13-20 Hz) power for fixed versus random sequences in late blocks were also attenuated in people with SZ relative to healthy controls. Moreover, changes in oscillatory power predicted individual RT differences, and fully mediated the relationship between group and sequence memory.

Conclusions: Disrupted modulation of alpha and beta1 EEG oscillations is a candidate mechanism of temporal sequence memory deficits in people with SZ.
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http://dx.doi.org/10.1016/j.bpsc.2021.04.002DOI Listing
April 2021

Schizophrenia and bipolar disorder are associated with opposite brain reward anticipation-associated response.

Neuropsychopharmacology 2021 05 15;46(6):1152-1160. Epub 2021 Jan 15.

Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA.

Blunted and exaggerated neuronal response to rewards are hypothesized to be core features of schizophrenia spectrum disorders (SZ) and bipolar disorder (BD), respectively. Nonetheless, direct tests of this hypothesis, in which response between SZ and BD is compared in the same study, are lacking. Here we examined the functional correlates of reward processing during the Incentivized Control Engagement Task (ICE-T) using 3T fMRI. Reward-associated activation was examined in 49 healthy controls (HCs), 52 recent-onset individuals with SZ, and 22 recent-onset individuals with Type I BD using anterior cingulate (ACC), anterior insula, and ventral striatal regions of interest. Significant group X reward condition (neutral vs. reward) interactions were observed during reward anticipation in the dorsal ACC (F(2,120) = 4.21, P = 0.017) and right insula (F(2,120) = 4.77, P = 0.010). The ACC interaction was driven by relatively higher activation in the BD group vs. HCs (P = 0.007) and vs. individuals with SZ (P = 0.010). The insula interaction was driven by reduced activation in the SZ group relative to HCs (P = 0.018) and vs. people with BD (P = 0.008). A composite of reward anticipation-associated response across all associated ROIs also differed significantly by diagnosis (F(1,120) = 5.59, P = 0.02), BD > HC > SZ. No effects of group or group X reward interactions were observed during reward feedback. These results suggest that people with SZ and BD have opposite patterns of activation associated with reward anticipation but not reward receipt. Implications of these findings in regard to Research Domain Criteria-based classification of illness and the neurobiology of reward in psychosis are discussed.
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http://dx.doi.org/10.1038/s41386-020-00940-0DOI Listing
May 2021

Both unmedicated and medicated individuals with schizophrenia show impairments across a wide array of cognitive and reinforcement learning tasks.

Psychol Med 2020 Aug 17:1-11. Epub 2020 Aug 17.

Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.

Background: Schizophrenia is a disorder characterized by pervasive deficits in cognitive functioning. However, few well-powered studies have examined the degree to which cognitive performance is impaired even among individuals with schizophrenia not currently on antipsychotic medications using a wide range of cognitive and reinforcement learning measures derived from cognitive neuroscience. Such research is particularly needed in the domain of reinforcement learning, given the central role of dopamine in reinforcement learning, and the potential impact of antipsychotic medications on dopamine function.

Methods: The present study sought to fill this gap by examining healthy controls (N = 75), unmedicated (N = 48) and medicated (N = 148) individuals with schizophrenia. Participants were recruited across five sites as part of the CNTRaCS Consortium to complete tasks assessing processing speed, cognitive control, working memory, verbal learning, relational encoding and retrieval, visual integration and reinforcement learning.

Results: Individuals with schizophrenia who were not taking antipsychotic medications, as well as those taking antipsychotic medications, showed pervasive deficits across cognitive domains including reinforcement learning, processing speed, cognitive control, working memory, verbal learning and relational encoding and retrieval. Further, we found that chlorpromazine equivalency rates were significantly related to processing speed and working memory, while there were no significant relationships between anticholinergic load and performance on other tasks.

Conclusions: These findings add to a body of literature suggesting that cognitive deficits are an enduring aspect of schizophrenia, present in those off antipsychotic medications as well as those taking antipsychotic medications.
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http://dx.doi.org/10.1017/S003329172000286XDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095353PMC
August 2020