BACKGROUND: The significant proportion of schizophrenia patients refractory to treatment, primarily directed at the dopamine system, suggests that multiple mechanisms may underlie psychotic symptoms. Reinforcement learning tasks have been employed in schizophrenia to assess dopaminergic functioning and reward processing, but these have not directly compared groups of treatment-refractory and non-refractory patients. METHODS: In the current functional magnetic resonance imaging study, 21 patients with treatment-resistant schizophrenia (TRS), 21 patients with non-treatment-resistant schizophrenia (NTR), and 24 healthy controls (HC) performed a probabilistic reinforcement learning task, utilizing emotionally valenced face stimuli which elicit a social bias toward happy faces. Behavior was characterized with a reinforcement learning model. Trial-wise reward prediction error (RPE)-related neural activation and the differential impact of emotional bias on these reward signals were compared between groups. RESULTS: Patients showed impaired reinforcement learning relative to controls, while all groups demonstrated an emotional bias favoring happy faces. The pattern of RPE signaling was similar in the HC and TRS groups, whereas NTR patients showed significant attenuation of RPE-related activation in striatal, thalamic, precentral, parietal, and cerebellar regions. TRS patients, but not NTR patients, showed a positive relationship between emotional bias and RPE signal during negative feedback in bilateral thalamus and caudate. CONCLUSION: TRS can be dissociated from NTR on the basis of a different neural mechanism underlying reinforcement learning. The data support the hypothesis that a favorable response to antipsychotic treatment is contingent on dopaminergic dysfunction, characterized by aberrant RPE signaling, whereas treatment resistance may be characterized by an abnormality of a non-dopaminergic mechanism - a glutamatergic mechanism would be a possible candidate.
BACKGROUND: The significant proportion of schizophrenia patients refractory to treatment, primarily directed at the dopamine system, suggests that multiple mechanisms may underlie psychotic symptoms. Reinforcement learning tasks have been employed in schizophrenia to assess dopaminergic functioning and reward processing, but these have not directly compared groups of treatment-refractory and non-refractory patients. METHODS: In the current functional magnetic resonance imaging study, 21 patients with treatment-resistant schizophrenia (TRS), 21 patients with non-treatment-resistant schizophrenia (NTR), and 24 healthy controls (HC) performed a probabilistic reinforcement learning task, utilizing emotionally valenced face stimuli which elicit a social bias toward happy faces. Behavior was characterized with a reinforcement learning model. Trial-wise reward prediction error (RPE)-related neural activation and the differential impact of emotional bias on these reward signals were compared between groups. RESULTS: Patients showed impaired reinforcement learning relative to controls, while all groups demonstrated an emotional bias favoring happy faces. The pattern of RPE signaling was similar in the HC and TRS groups, whereas NTR patients showed significant attenuation of RPE-related activation in striatal, thalamic, precentral, parietal, and cerebellar regions. TRS patients, but not NTR patients, showed a positive relationship between emotional bias and RPE signal during negative feedback in bilateral thalamus and caudate. CONCLUSION: TRS can be dissociated from NTR on the basis of a different neural mechanism underlying reinforcement learning. The data support the hypothesis that a favorable response to antipsychotic treatment is contingent on dopaminergic dysfunction, characterized by aberrant RPE signaling, whereas treatment resistance may be characterized by an abnormality of a non-dopaminergic mechanism - a glutamatergic mechanism would be a possible candidate.
Authors: F E Scheepers; C C de Wied; H E Hulshoff Pol; W van de Flier; J A van der Linden; R S Kahn Journal: Neuropsychopharmacology Date: 2001-01 Impact factor: 7.853
Authors: Keith M Shafritz; Toshikazu Ikuta; Allison Greene; Delbert G Robinson; Juan Gallego; Todd Lencz; Pamela DeRosse; Peter B Kingsley; Philip R Szeszko Journal: Brain Imaging Behav Date: 2019-04 Impact factor: 3.978
Authors: Charlotte M Horne; Angad Sahni; Sze W Pang; Lucy D Vanes; Timea Szentgyorgyi; Bruno Averbeck; Rosalyn J Moran; Sukhwinder S Shergill Journal: Neuroimage Clin Date: 2022-04-06 Impact factor: 4.891
Authors: Evangelos Papanastasiou; Elias Mouchlianitis; Dan W Joyce; Philip McGuire; Tobias Banaschewski; Arun L W Bokde; Uli Bromberg; Christian Büchel; Erin Burke Quinlan; Sylvane Desrivières; Herta Flor; Vincent Frouin; Hugh Garavan; Philip Spechler; Penny Gowland; Andreas Heinz; Bernd Ittermann; Jean-Luc Martinot; Marie-Laure Paillère Martinot; Eric Artiges; Frauke Nees; Dimitri Papadopoulos Orfanos; Luise Poustka; Sabina Millenet; Juliane H Fröhner; Michael N Smolka; Henrik Walter; Robert Whelan; Gunter Schumann; Sukhwinder Shergill Journal: JAMA Psychiatry Date: 2018-10-01 Impact factor: 21.596