Neural Circuitry of Salience and Reward Processing in Psychosis

Kesby, James P.; Murray, Graham; Knolle, Franziska

doi:10.1016/j.bpsgos.2021.12.003

Cited by 25 publications

(36 citation statements)

References 155 publications

(231 reference statements)

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“…Cognitive impairments are particularly interesting due to the prodromal onset and the impact on functional outcome 14,30,[71][72][73] . Using ROC analysis, we were able to show that the specific expression of modelling parameters (Figure 4A) as well as the individual performances measures (Figure 4B), allowed a good classification of FEP patients, with significant differentiation from controls and ARMS, indicating that differences in the neuropsychological processes underlying the performance in the Go/NoGo task are relevant for the psychopathology of psychosis 9,13,19 . Importantly, however, when combining modelling parameters and performance measures in the ROC analysis, the identification of all group ACTION SELECTION ACROSS EARLY STAGES OF PSYCHOSIS 20 associations improved by at least 8%.…”

Section: Discussionmentioning

confidence: 99%

“…In psychosis, optimal action selection seems to be impaired as alterations in value-based action selection 4,5 (e.g., reinforcement learning) and in action-outcome (reward/punishment) learning can be observed [6][7][8][9][10][11][12] . These behavioural and cognitive alterations often precede disease onset 13 ; and, therefore, are a robust sign of the pathophysiology of the disorder 14 .…”

Section: Introductionmentioning

confidence: 99%

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Action selection in early stages of psychosis: an active inference approach

Knolle

Sterner

Moutoussis³

et al. 2022

Preprint

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Background and HypothesisIn order to interact successfully with our environment, we need to build a model, to make sense of noisy and ambiguous inputs. An inaccurate model, as suggested to be the case in psychosis, disturbs optimal action selection. Recent computational models, such as active inference (AI), have emphasized the importance of action selection, treating it as a key part of the inferential process. Based on an AI-framework, we examined prior knowledge and belief precision in an action-based task, given that alterations in these parameters have been linked to the development of psychotic symptoms. We further sought to determine whether task performance and modelling parameters would be suitable for classification of patients and controls.Study Design23 at-risk-mental-state individuals, 26 first-episode psychosis patients and 31 controls completed a probabilistic Go/NoGo task in which action choice (Go/ NoGo) was dissociated from outcome valence (gain/ loss). We examined group differences in performance and AI-model parameters, and then performed receiver operating characteristic (ROC) analyses to assess group-classification.Study ResultsWe found reduced overall performance in patients. AI-modelling revealed that patients showed increased forgetting, reduced confidence in policy selection and less optimal general choice behavior, with poorer action-state associations. Importantly, ROC-analysis revealed fair-to-good classification performances of all groups, when combining modelling parameters and performance measures.ConclusionFindings show that AI-modelling of this task not only provides further explanation for dysfunctional mechanisms underlying decision making in psychosis, but may also be highly relevant for future research on the development of biomarkers for early identification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Action selection in early stages of psychosis: an active inference approach

Knolle

Sterner

Moutoussis³

et al. 2022

Preprint

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“…Those regions are highly relevant for the psychopathology in psychosis, and structural alterations are well described in these areas (de Moura et al, 2018; Glahn et al, 2008; Meda et al, 2008; Tanskanen et al, 2010; van Erp et al, 2016; van Haren et al, 2016; Veijola et al, 2014; Vos et al, 2015; Wood et al, 2001). Also functional alterations have been detected in those areas, with regard to functional connectivity in general and in the default and salience networks specifically (Alloza et al, 2020; Avram et al, 2020; Du et al, 2018; Jukuri et al, 2013; Littow et al, 2015; Manoliu et al, 2014; Palaniyappan and Liddle, 2012), as well as cognitive, reward and salience processing (Boehme et al, 2015; Borgan et al, 2021; Ermakova et al, 2018; Haarsma et al, 2020; Katthagen et al, 2020; Kesby et al, 2021; Knolle et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Grey matter morphometric biomarkers for classifying early schizophrenia and PD psychosis: a multicentre study

Knolle

Arumugham

Barker

et al. 2022

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BackgroundPsychotic symptoms occur in a majority of schizophrenia patients, and in approximately 50% of all Parkinson’s disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little, however, is known about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as schizophrenia and PD.MethodsThe present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms.ResultsSCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD as well as PDN and Con-PD, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC∼0.80) of FEP and Con-Psy, and fair performance (AUC∼0.72) when differentiating PDP from Con-PD. Importantly, best performance was found in partly overlapping networks including the precuneus. Finally, reduced GM volume in SCN with increased variability was linked to increased psychotic symptoms in both FEP and PDP.ConclusionAlterations within selected SCNs seem to be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide first evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

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“…Executive functions, and specifically planning, have been consistently shown to be impaired in schizophrenia. 53 , 54 Evidence suggests that impaired executive functioning is associated with reduced activation in the left dorsolateral prefrontal cortex, rostral/dorsal anterior cingulate cortex, left thalamus, and inferior/posterior cortical areas 55 and that planning deficits in individuals with schizophrenia are not task specific, but rather affect central cognitive processes critical for planned behaviour. 56 It is, therefore, likely that motivation for PA in people with schizophrenia is impacted by a deregulation of planning and decision-making.…”

Section: Barriers and Strategies To Adopt And Maintain Physical Activ...mentioning

confidence: 99%

Biopsychology of Physical Activity in People with Schizophrenia: An Integrative Perspective on Barriers and Intervention Strategies

Arnautovska¹,

Kesby²,

Korman³

et al. 2022

NDT

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People with severe mental illness such as schizophrenia experience high physical comorbidity, leading to a 15–20-year mortality gap compared with the general population. Lifestyle behaviours such as physical activity (PA) play important roles in the quest to bridge this gap. Interventions to increase PA engagement in this population have potential to be efficacious; however, their effectiveness can be hindered by low participant engagement, including low adherence and high drop-out, and by implementation of interventions that are not designed to compensate for the cognitive and motivational impairments characteristic for this group. Moreover, and importantly, the negative symptoms of schizophrenia are associated with neurobiological changes in the brain, which—based on principles of biopsychology—can contribute to poor motivation and impaired decision-making processes and behavioural maintenance. To increase PA levels in people with schizophrenia, better understanding of these neurological changes that impact PA engagement is needed. This has the potential to inform the design of interventions that, through enhancement of motivation, could effectively increase PA levels in this specific population. Incorporating strategies that address the dopamine dysregulation associated with schizophrenia, such as boosting the role of reward and self-determined motivation, may improve long-term PA maintenance, leading to habitual PA. Consideration of motivation and behavioural maintenance is also needed to impart health benefits such as prevention of chronic disease, which is associated with currently low PA levels in this high metabolic risk population. Taking a biopsychological perspective, we outline the neural pathways involved in motivation that are impacted by schizophrenia and propose strategies for promoting motivation for and PA engagement from adoption to habit formation.

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Neural Circuitry of Salience and Reward Processing in Psychosis

Cited by 25 publications

References 155 publications

Action selection in early stages of psychosis: an active inference approach

Action selection in early stages of psychosis: an active inference approach

Grey matter morphometric biomarkers for classifying early schizophrenia and PD psychosis: a multicentre study

Biopsychology of Physical Activity in People with Schizophrenia: An Integrative Perspective on Barriers and Intervention Strategies

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