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Morinan G, Dushin Y, Sarapata G, Rupprechter S, Peng Y, Girges C, Salazar M, Milabo C, Sibley K, Foltynie T, Cociasu I, Ricciardi L, Baig F, Morgante F, Leyland LA, Weil RS, Gilron R, O’Keeffe J. Computer vision quantification of whole-body Parkinsonian bradykinesia using a large multi-site population. NPJ Parkinsons Dis 2023; 9:10. [PMID: 36707523 PMCID: PMC9883391 DOI: 10.1038/s41531-023-00454-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 01/13/2023] [Indexed: 01/28/2023] Open
Abstract
Parkinson's disease (PD) is a common neurological disorder, with bradykinesia being one of its cardinal features. Objective quantification of bradykinesia using computer vision has the potential to standardise decision-making, for patient treatment and clinical trials, while facilitating remote assessment. We utilised a dataset of part-3 MDS-UPDRS motor assessments, collected at four independent clinical and one research sites on two continents, to build computer-vision-based models capable of inferring the correct severity rating robustly and consistently across all identifiable subgroups of patients. These results contrast with previous work limited by small sample sizes and small numbers of sites. Our bradykinesia estimation corresponded well with clinician ratings (interclass correlation 0.74). This agreement was consistent across four clinical sites. This result demonstrates how such technology can be successfully deployed into existing clinical workflows, with consumer-grade smartphone or tablet devices, adding minimal equipment cost and time.
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Affiliation(s)
- Gareth Morinan
- Machine Medicine Technologies Ltd., The Leather Market Unit 1.1.1 11/13 Weston Street, London, SE1 3ER UK
| | - Yuriy Dushin
- Machine Medicine Technologies Ltd., The Leather Market Unit 1.1.1 11/13 Weston Street, London, SE1 3ER, UK.
| | - Grzegorz Sarapata
- Machine Medicine Technologies Ltd., The Leather Market Unit 1.1.1 11/13 Weston Street, London, SE1 3ER UK
| | - Samuel Rupprechter
- Machine Medicine Technologies Ltd., The Leather Market Unit 1.1.1 11/13 Weston Street, London, SE1 3ER UK
| | - Yuwei Peng
- Machine Medicine Technologies Ltd., The Leather Market Unit 1.1.1 11/13 Weston Street, London, SE1 3ER UK
| | - Christine Girges
- grid.436283.80000 0004 0612 2631Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG UK
| | - Maricel Salazar
- grid.436283.80000 0004 0612 2631Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG UK
| | - Catherine Milabo
- grid.436283.80000 0004 0612 2631Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG UK
| | - Krista Sibley
- grid.436283.80000 0004 0612 2631Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG UK
| | - Thomas Foltynie
- grid.436283.80000 0004 0612 2631Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG UK
| | - Ioana Cociasu
- grid.264200.20000 0000 8546 682XNeuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George’s, University of London, Cranmer Terrace, London, SW17 0RE UK
| | - Lucia Ricciardi
- grid.264200.20000 0000 8546 682XNeuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George’s, University of London, Cranmer Terrace, London, SW17 0RE UK
| | - Fahd Baig
- grid.264200.20000 0000 8546 682XNeuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George’s, University of London, Cranmer Terrace, London, SW17 0RE UK
| | - Francesca Morgante
- grid.264200.20000 0000 8546 682XNeuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George’s, University of London, Cranmer Terrace, London, SW17 0RE UK ,grid.10438.3e0000 0001 2178 8421Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy, Via Consolare Valeria, 98165 Messina, Italy
| | - Louise-Ann Leyland
- grid.436283.80000 0004 0612 2631Dementia Research Center, Institute of Neurology, University College London, Queen Square, London, WC1N 3AR UK
| | - Rimona S. Weil
- grid.436283.80000 0004 0612 2631Dementia Research Center, Institute of Neurology, University College London, Queen Square, London, WC1N 3AR UK
| | - Ro’ee Gilron
- grid.266102.10000 0001 2297 6811The Starr Lab, University of California San Francisco, 513 Parnassus Ave, HSE-823, San Francisco, CA 94143 USA
| | - Jonathan O’Keeffe
- Machine Medicine Technologies Ltd., The Leather Market Unit 1.1.1 11/13 Weston Street, London, SE1 3ER UK
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Abstract
BACKGROUND Experience of emotion is closely linked to valuation. Mood can be viewed as a bias to experience positive or negative emotions and abnormally biased subjective reward valuation and cognitions are core characteristics of major depression. METHODS Thirty-four unmedicated subjects with major depressive disorder and controls estimated the probability that fractal stimuli were associated with reward, based on passive observations, so they could subsequently choose the higher of either their estimated fractal value or an explicitly presented reward probability. Using model-based functional magnetic resonance imaging, we estimated each subject's internal value estimation, with psychophysiological interaction analysis used to examine event-related connectivity, testing hypotheses of abnormal reward valuation and cingulate connectivity in depression. RESULTS Reward value encoding in the hippocampus and rostral anterior cingulate was abnormal in depression. In addition, abnormal decision-making in depression was associated with increased anterior mid-cingulate activity and a signal in this region encoded the difference between the values of the two options. This localised decision-making and its impairment to the anterior mid-cingulate cortex (aMCC) consistent with theories of cognitive control. Notably, subjects with depression had significantly decreased event-related connectivity between the aMCC and rostral cingulate regions during decision-making, implying impaired communication between the neural substrates of expected value estimation and decision-making in depression. CONCLUSIONS Our findings support the theory that abnormal neural reward valuation plays a central role in major depressive disorder (MDD). To the extent that emotion reflects valuation, abnormal valuation could explain abnormal emotional experience in MDD, reflect a core pathophysiological process and be a target of treatment.
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Affiliation(s)
- S Rupprechter
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
| | - A Stankevicius
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
| | - Q J M Huys
- Max Planck Centre for Computational Psychiatry and Ageing Research, UCL, London, UK
- Camden and Islington NHS Foundation Trust, London, UK
| | - P Series
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
| | - J D Steele
- Division of Imaging Science and Technology, Medical School, University of Dundee, Dundee, UK
- Department of Neurology, Ninewells Hospital, NHS Tayside, Dundee, UK
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Rupprechter S, Romaniuk L, Series P, Hirose Y, Hawkins E, Sandu AL, Waiter GD, McNeil CJ, Shen X, Harris MA, Campbell A, Porteous D, Macfarlane JA, Lawrie SM, Murray AD, Delgado MR, McIntosh AM, Whalley HC, Steele JD. Blunted medial prefrontal cortico-limbic reward-related effective connectivity and depression. Brain 2020; 143:1946-1956. [PMID: 32385498 PMCID: PMC7296844 DOI: 10.1093/brain/awaa106] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 01/14/2020] [Accepted: 02/06/2020] [Indexed: 12/12/2022] Open
Abstract
Major depressive disorder is a leading cause of disability and significant mortality, yet mechanistic understanding remains limited. Over the past decade evidence has accumulated from case-control studies that depressive illness is associated with blunted reward activation in the basal ganglia and other regions such as the medial prefrontal cortex. However it is unclear whether this finding can be replicated in a large number of subjects. The functional anatomy of the medial prefrontal cortex and basal ganglia has been extensively studied and the former has excitatory glutamatergic projections to the latter. Reduced effect of glutamatergic projections from the prefrontal cortex to the nucleus accumbens has been argued to underlie motivational disorders such as depression, and many prominent theories of major depressive disorder propose a role for abnormal cortico-limbic connectivity. However, it is unclear whether there is abnormal reward-linked effective connectivity between the medial prefrontal cortex and basal ganglia related to depression. While resting state connectivity abnormalities have been frequently reported in depression, it has not been possible to directly link these findings to reward-learning studies. Here, we tested two main hypotheses. First, mood symptoms are associated with blunted striatal reward prediction error signals in a large community-based sample of recovered and currently ill patients, similar to reports from a number of studies. Second, event-related directed medial prefrontal cortex to basal ganglia effective connectivity is abnormally increased or decreased related to the severity of mood symptoms. Using a Research Domain Criteria approach, data were acquired from a large community-based sample of subjects who participated in a probabilistic reward learning task during event-related functional MRI. Computational modelling of behaviour, model-free and model-based functional MRI, and effective connectivity dynamic causal modelling analyses were used to test hypotheses. Increased depressive symptom severity was related to decreased reward signals in areas which included the nucleus accumbens in 475 participants. Decreased reward-related effective connectivity from the medial prefrontal cortex to striatum was associated with increased depressive symptom severity in 165 participants. Decreased striatal activity may have been due to decreased cortical to striatal connectivity consistent with glutamatergic and cortical-limbic related theories of depression and resulted in reduced direct pathway basal ganglia output. Further study of basal ganglia pathophysiology is required to better understand these abnormalities in patients with depressive symptoms and syndromes.
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Affiliation(s)
| | - Liana Romaniuk
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Peggy Series
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Yoriko Hirose
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Emma Hawkins
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | | | - Gordon D Waiter
- Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK
| | | | - Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Mathew A Harris
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - David Porteous
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Jennifer A Macfarlane
- Division of Imaging Science and Technology, Medical School, University of Dundee, Dundee, UK
| | | | - Alison D Murray
- Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK
| | | | | | | | - J Douglas Steele
- Division of Imaging Science and Technology, Medical School, University of Dundee, Dundee, UK
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Rupprechter S, Stankevicius A, Huys QJM, Steele JD, Seriès P. Major Depression Impairs the Use of Reward Values for Decision-Making. Sci Rep 2018; 8:13798. [PMID: 30218084 PMCID: PMC6138642 DOI: 10.1038/s41598-018-31730-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 08/16/2018] [Indexed: 11/27/2022] Open
Abstract
Depression is a debilitating condition with a high prevalence. Depressed patients have been shown to be diminished in their ability to integrate their reinforcement history to adjust future behaviour during instrumental reward learning tasks. Here, we tested whether such impairments could also be observed in a Pavlovian conditioning task. We recruited and analysed 32 subjects, 15 with depression and 17 healthy controls, to study behavioural group differences in learning and decision-making. Participants had to estimate the probability of some fractal stimuli to be associated with a binary reward, based on a few passive observations. They then had to make a choice between one of the observed fractals and another target for which the reward probability was explicitly given. Computational modelling was used to succinctly describe participants' behaviour. Patients performed worse than controls at the task. Computational modelling revealed that this was caused by behavioural impairments during both learning and decision phases. Depressed subjects showed lower memory of observed rewards and had an impaired ability to use internal value estimations to guide decision-making in our task.
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Affiliation(s)
- Samuel Rupprechter
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom
| | - Aistis Stankevicius
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom
| | - Quentin J M Huys
- Centre for Addictive Disorders, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
- Translational Neuromodeling Unit, Institute of Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - J Douglas Steele
- Division of Imaging Science and Technology, Medical School, University of Dundee, Dundee, United Kingdom
| | - Peggy Seriès
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom.
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Cohen Hoffing R, Karvelis P, Rupprechter S, Seriès P, Seitz AR. The Influence of Feedback on Task-Switching Performance: A Drift Diffusion Modeling Account. Front Integr Neurosci 2018; 12:1. [PMID: 29456494 PMCID: PMC5801306 DOI: 10.3389/fnint.2018.00001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 01/11/2018] [Indexed: 11/28/2022] Open
Abstract
Task-switching is an important cognitive skill that facilitates our ability to choose appropriate behavior in a varied and changing environment. Task-switching training studies have sought to improve this ability by practicing switching between multiple tasks. However, an efficacious training paradigm has been difficult to develop in part due to findings that small differences in task parameters influence switching behavior in a non-trivial manner. Here, for the first time we employ the Drift Diffusion Model (DDM) to understand the influence of feedback on task-switching and investigate how drift diffusion parameters change over the course of task switch training. We trained 316 participants on a simple task where they alternated sorting stimuli by color or by shape. Feedback differed in six different ways between subjects groups, ranging from No Feedback (NFB) to a variety of manipulations addressing trial-wise vs. Block Feedback (BFB), rewards vs. punishments, payment bonuses and different payouts depending upon the trial type (switch/non-switch). While overall performance was found to be affected by feedback, no effect of feedback was found on task-switching learning. Drift Diffusion Modeling revealed that the reductions in reaction time (RT) switch cost over the course of training were driven by a continually decreasing decision boundary. Furthermore, feedback effects on RT switch cost were also driven by differences in decision boundary, but not in drift rate. These results reveal that participants systematically modified their task-switching performance without yielding an overall gain in performance.
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Affiliation(s)
- Russell Cohen Hoffing
- UCR Brain Game Center, Department of Cognitive Psychology, University of California, Riverside, Riverside, CA, United States
| | - Povilas Karvelis
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Samuel Rupprechter
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Peggy Seriès
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Aaron R Seitz
- UCR Brain Game Center, Department of Cognitive Psychology, University of California, Riverside, Riverside, CA, United States
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