Biology subjects

Hernaus, D.

Publications and source records attributed to Hernaus, D..

4 recordsLinked to original sources

bioRxiv2018

Impaired expected value computations in schizophrenia are associated with a reduced ability to integrate reward probability and magnitude of recent outcomes.

ABSTRACTO_ST_ABSBackgroundC_ST_ABSMotivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on \"value-less\" stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits.\n\nMethodsHealthy volunteers (n= 38) and PSZ (n=49) completed a reinforcement learning paradigm consisting of four stimulus pairs. Reward magnitude (3/2/1/0 points) and probability (90%/80%/20%/10%) together determined each stimulus EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value.\n\nResultsPSZ with high motivational deficits made increasingly less optimal choices as the difference in reward value (probability*magnitude) between two competing stimuli increased. Using a previously-validated computational hybrid model, PSZ relied less on EV (\"Q-learning\") and more on stimulus-response learning (\"actor-critic\"), which correlated with SANS motivational deficit severity. PSZ specifically failed to represent reward magnitude, consistent with model demonstrations showing that response tendencies in the actor-critic were preferentially driven by reward probability.\n\nConclusionsProbability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on value-less stimulus-response associations. Consistent with previous work and confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work reconfirms the importance of decreased Q-learning/increased actor-critic-type learning as an explanatory framework for a range of EV deficits in PSZ.

neuroscience↗

bioRxiv2017

Impaired expected value computations coupled with overreliance on prediction error learning in schizophrenia

BackgroundWhile many have emphasized impaired reward prediction error (RPE) signaling in schizophrenia, multiple studies suggest that some decision-making deficits may arise from overreliance on RPE systems together with a compromised ability to represent expected value. Guided by computational frameworks, we formulated and tested two scenarios in which maladaptive representation of expected value should be most evident, thereby delineating conditions that may evoke decision-making impairments in schizophrenia.\n\nMethodsIn a modified reinforcement learning paradigm, 42 medicated people with schizophrenia (PSZ) and 36 healthy volunteers learned to select the most frequently rewarded option in a 75-25 pair: once when presented with more deterministic (90-10) and once when presented with more probabilistic (60-40) pairs. Novel and old combinations of choice options were presented in a subsequent transfer phase. Computational modeling was employed to elucidate contributions from RPE systems (\"actor-critic\") and expected value (\"Q-leaming\").\n\nResultsPSZ showed robust performance impairments with increasing value difference between two competing options, which strongly correlated with decreased contributions from expected value-based (\"Q-leaming\") learning. Moreover, a subtle yet consistent contextual choice bias for the \"probabilistic\" 75 option was present in PSZ, which could be accounted for by a context-dependent RPE in the \"actor-critic\".\n\nConclusionsWe provide evidence that decision-making impairments in schizophrenia increase monotonically with demands placed on expected value computations. A contextual choice bias is consistent with overreliance on RPE-based learning, which may signify a deficit secondary to the maladaptive representation of expected value. These results shed new light on conditions under which decisionmaking impairments may arise.

neuroscience↗

bioRxiv2017

A proof-of-principle study of the effect of combined haloperidol and levodopa administration on working memory-related brain activation in humans

Background and PurposeCognitive deficits including impaired working memory are a hallmark feature of schizophrenia. Changes in prefrontal cortex function modulated by dopamine D1 receptors, play a potentially important role in the pathology underlying such deficits. However, pharmacological interventions that selectively engage the D1 receptor are severely restricted for research in humans. The present study is a proof-of-principle for enhancing cognitive performance and associated brain activation via indirect D1 stimulation. Here, we combine the non-selective dopamine agonist L-dopa with the D2-antagonist haloperidol, theoretically producing increased stimulation at the D1 receptor.\n\nExperimental ApproachFourteen healthy volunteers received placebo or combined carbidopa (125 mg, 100mg L-dopa) plus haloperidol (2 mg) orally on two separate occasions according to a within-subjects cross-over design. Drug-induced differences in brain activity were assessed during an N-back working memory task in a 3T magnetic resonance imaging environment.\n\nKey ResultsDrug treatment was associated with a reduction in activity in a large number of brain areas, most prominently occipital/temporal brain areas during 2-back performance, which may be due to the effects of haloperidol specifically. Drug treatment was also associated with greater functional connectivity within parts of the salience network during all N-back trials.\n\nConclusion and ImplicationsThis preliminary study provides initial evidence for combined L-dopa/haloperidol modulation in cognition-related brain areas and networks, which is relevant for the treatment of cognitive impairments in mental illness.

neuroscience↗

bioRxiv2017

Catecholaminergic Manipulation Alters Dynamic Network Topology Across Behavioral States

The human brain is able to flexibly adapt its information processing capacity to meet a variety of cognitive challenges. Recent evidence suggests that this flexibility is reflected in the dynamic reorganization of the functional connectome. The ascending catecholaminergic arousal systems of the brain are a plausible candidate mechanism for driving alterations in network architecture, enabling efficient deployment of cognitive resources when the environment demands them. We tested this hypothesis by analyzing both task-free and task-based fMRI data following the administration of atomoxetine, a noradrenaline reuptake inhibitor, compared to placebo, in two separate human fMRI studies. Our results demonstrate that the manipulation of central catecholamine levels leads to a reorganization of the functional connectome in a manner that is sensitive to ongoing cognitive demands.

neuroscience↗