Impulse control disorder in Parkinson's disease is associated with abnormal frontal value signalling.

Dopaminergic medication is well established to boost reward- versus punishment-based learning in Parkinson's disease. However, there is tremendous variability in dopaminergic medication effects across different individuals, with some patients exhibiting much greater cognitive sensitivity to medication than others. We aimed to unravel the mechanisms underlying this individual variability in a large heterogeneous sample of early-stage patients with Parkinson's disease as a function of comorbid neuropsychiatric symptomatology, in particular impulse control disorders and depression. One hundred and ninety-nine patients with Parkinson's disease (138 ON medication and 61 OFF medication) and 59 healthy controls were scanned with functional MRI while they performed an established probabilistic instrumental learning task. Reinforcement learning model-based analyses revealed medication group differences in learning from gains versus losses, but only in patients with impulse control disorders. Furthermore, expected-value related brain signalling in the ventromedial prefrontal cortex was increased in patients with impulse control disorders ON medication compared with those OFF medication, while striatal reward prediction error signalling remained unaltered. These data substantiate the hypothesis that dopamine's effects on reinforcement learning in Parkinson's disease vary with individual differences in comorbid impulse control disorder and suggest they reflect deficient computation of value in medial frontal cortex, rather than deficient reward prediction error signalling in striatum. See Michael Browning (https://doi.org/10.1093/brain/awad248) for a scientific commentary on this article.

Role of dopamine and clinical heterogeneity in cognitive dysfunction in Parkinson's disease.

Parkinson's disease (PD) is commonly treated with dopaminergic medication, which enhances some, while impairing other cognitive functions. It can even contribute to impulse control disorder and addiction. We describe the history of research supporting the dopamine overdose hypothesis, which accounts for the large within-patient variability in dopaminergic medication effects across different tasks by referring to the spatially non-uniform pattern of dopamine depletion in dorsal versus ventral striatum. However, there is tremendous variability in dopaminergic medication effects not just within patients across distinct tasks, but also across different patients. In the second part of this chapter we review recent studies addressing the large individual variability in the negative side effects of dopaminergic medication on functions that implicate dopamine, such as value-based learning and choice. These studies begin to unravel the mechanisms of dopamine overdosing, thus revising the strict version of the overdose hypothesis. For example, the work shows that the canonical boosting of reward-versus punishment-based choice by medication is greater in patients with depression and a non-tremor phenotype, which both implicate, among other pathology, more rather than less severe dysregulation of the mesolimbic dopamine system. Future longitudinal cohort studies are needed to identify how to optimally combine different clinical, personality, cognitive, neural, genetic and molecular predictors of detrimental medication effects in order to account for as much of the relevant variability as possible. This will provide a useful tool for precision neurology, allowing individual and contextual tailoring of (the dose of) dopaminergic medication in order to maximize its cognitive benefits, yet minimize its side effects.

Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.

PURPOSE: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence. DESIGN: Retrospective study. PARTICIPANTS: VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses. METHODS: Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC). MAIN OUTCOME MEASURES: The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results. RESULTS: From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time. CONCLUSIONS: We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.

Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.

Importance: Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss. Objective: To provide a quantitative characterization and classification of central VF loss in end-stage glaucoma. Design, Setting, and Participants: This retrospective cohort study collected data from 5 US glaucoma services from June 1, 1999, through October 1, 2014. A total of 2912 reliable 10-2 VFs of 1103 eyes from 1010 patients measured after end-stage 24-2 VFs with a mean deviation (MD) of -22 dB or less were included in the analysis. Data were analyzed from March 28, 2018, through May 23, 2019. Main Outcomes and Measures: Central VF patterns were determined by an artificial intelligence algorithm termed archetypal analysis. Longitudinal analyses were performed to investigate whether the development of central VF defect mostly affects specific vulnerability zones. Results: Among the 1103 patients with the most recent VFs, mean (SD) age was 70.4 (14.3) years; mean (SD) 10-2 MD, -21.5 (5.6) dB. Fourteen central VF patterns were determined, including the most common temporal sparing patterns (304 [27.5%]), followed by mostly nasal loss (280 [25.4%]), hemifield loss (169 [15.3%]), central island (120 [10.9%]), total loss (91 [8.3%]), nearly intact field (56 [5.1%]), inferonasal quadrant sparing (42 [3.8%]), and nearly total loss (41 [3.7%]). Location-specific median total deviation analyses partitioned the central VF into a more vulnerable superonasal zone and a less vulnerable inferotemporal zone. At 1-year and 2-year follow-up, new defects mostly occurred in the more vulnerable zone. Initial encroachments on an intact central VF at follow-up were more likely to be from nasal loss (11 [18.4%]; P < .001). One of the nasal loss patterns had a substantial chance at 2-year follow-up (8 [11.0%]; P = .004) to shift to total loss, whereas others did not. Conclusions and Relevance: In this study, central VF loss in end-stage glaucoma was found to exhibit characteristic patterns that might be associated with different subtypes. Initial central VF loss is likely to be nasal loss, and 1 specific type of nasal loss is likely to develop into total loss.

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Purpose: To detect visual field (VF) progression by analyzing spatial pattern changes. Methods: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. Results: In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). Conclusions: The archetype method can inform clinicians of VF progression patterns.