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.
Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson's disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, ...commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson's disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician-for example, during remote home assessments. Videos (n=729) were collected as part of routine MDS-UPDRS gait assessments of Parkinson's patients, and a deep learning library was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician. Six features were calculated from time-series signals of the extracted key-points. These features characterized key aspects of the movement including speed (step frequency, estimated using a novel Gamma-Poisson Bayesian model), arm swing, postural control and smoothness (or roughness) of movement. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation. Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 606 video clips showing patients walking towards or away from the camera (Pearson's r=0.80, p<0.001). Our classifier achieved a balanced accuracy of 50% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians' ratings in 95% of cases. There was a significant correlation between clinician labels and model estimates (Spearman's ρ=0.52, p<0.001). We show how the interpretability of the feature values could be used by clinicians to support their decision-making and provide insight into the model's objective UPDRS rating estimation. The severity of gait impairment in Parkinson's disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e., videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective rating (or second opinion), and has the potential to be used for remote home assessments, which would allow for more frequent monitoring.
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.
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.
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.
The ability to arise from a sitting to a standing position is often impaired in Parkinson's disease (PD). This impairment is associated with an increased risk of falling, and higher risk of dementia. ...We propose a novel approach to estimate Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) ratings for “item 3.9” (arising from chair) using a computer vision-based method, whereby we use clinically informed reasoning to engineer a small number of informative features from high dimensional markerless pose estimation data.
We analysed 447 videos collected via the KELVIN-PD™ platform, recorded in clinical settings at multiple sites, using commercially available mobile smart devices. Each video showed an examination for item 3.9 of the MDS-UPDRS and had an associated severity rating from a trained clinician on the 5-point scale (0, 1, 2, 3 or 4).
The deep learning library OpenPose was used to extract pose estimation key points from each frame of the videos, resulting in time-series signals for each key point. From these signals, features were extracted which capture relevant characteristics of the movement; velocity variation, smoothness, whether the patient used their hands to push themselves up, how stooped the patient was while sitting and how upright the patient was when fully standing. These features were used to train an ordinal classification system (with one class for each of the possible ratings on the UPDRS), based on a series of random forest classifiers.
The UPDRS ratings estimated by this system, using leave-one-out cross validation, corresponded exactly to the ratings made by clinicians in 79% of videos, and were within one of those made by clinicians in 100% of cases. The system was able to distinguish normal from Parkinsonian movement with a sensitivity of 62.8% and a specificity of 90.3%. Analysis of misclassified examples highlighted the potential of the system to detect potentially mislabelled data.
We show that our computer-vision based method can accurately quantify PD patients’ ability to perform the arising from chair action. As far as we are aware this is the first study estimating scores for item 3.9 of the MDS-UPDRS from singular monocular video. This approach can help prevent human error by identifying unusual clinician ratings, and provides promise for such a system being used routinely for clinical assessments, either locally or remotely, with potential for use as stratification and outcome measures in clinical trials.
•The ability to arise from a chair, often impaired in Parkinson’s disease, is commonly assessed subjectively by clinicians.•A novel approach, using 2D pose estimation and machine learning, can robustly estimate disease severity using only videos.•The system is a good candidate for routine in office use or for remote home assessment of patients.
Even though chord roots constitute a fundamental concept in music theory, existing models do not explain and determine them to full satisfaction. We present a new method which takes sequential ...context into account to resolve ambiguities and detect nonharmonic tones. We extract features from chord pairs and use a decision tree to determine chord roots. This leads to a quantitative improvement in correctness of the predicted roots in comparison to other models. All this raises the question how much harmonic and nonharmonic tones actually contribute to the perception of chord roots.
Acute decompensation (AD) of cirrhosis is defined as the acute development of ascites, gastrointestinal hemorrhage, hepatic encephalopathy, infection or any combination thereof, requiring ...hospitalization. The presence of organ failure(s) in patients with AD defines acute-on-chronic liver failure (ACLF). The PREDICT study is a European, prospective, observational study, designed to characterize the clinical course of AD and to identify predictors of ACLF.
A total of 1,071 patients with AD were enrolled. We collected detailed pre-specified information on the 3-month period prior to enrollment, and clinical and laboratory data at enrollment. Patients were then closely followed up for 3 months. Outcomes (liver transplantation and death) at 1 year were also recorded.
Three groups of patients were identified. Pre-ACLF patients (n = 218) developed ACLF and had 3-month and 1-year mortality rates of 53.7% and 67.4%, respectively. Unstable decompensated cirrhosis (UDC) patients (n = 233) required ≥1 readmission but did not develop ACLF and had mortality rates of 21.0% and 35.6%, respectively. Stable decompensated cirrhosis (SDC) patients (n = 620) were not readmitted, did not develop ACLF and had a 1-year mortality rate of only 9.5%. The 3 groups differed significantly regarding the grade and course of systemic inflammation (high-grade at enrollment with aggravation during follow-up in pre-ACLF; low-grade at enrollment with subsequent steady-course in UDC; and low-grade at enrollment with subsequent improvement in SDC) and the prevalence of surrogates of severe portal hypertension throughout the study (high in UDC vs. low in pre-ACLF and SDC).
Acute decompensation without ACLF is a heterogeneous condition with 3 different clinical courses and 2 major pathophysiological mechanisms: systemic inflammation and portal hypertension. Predicting the development of ACLF remains a major future challenge.
NCT03056612.
Herein, we describe, for the first time, 3 different clinical courses of acute decompensation (AD) of cirrhosis after hospital admission. The first clinical course includes patients who develop acute-on-chronic liver failure (ACLF) and have a high short-term risk of death – termed pre-ACLF. The second clinical course (unstable decompensated cirrhosis) includes patients requiring frequent hospitalizations unrelated to ACLF and is associated with a lower mortality risk than pre-ACLF. Finally, the third clinical course (stable decompensated cirrhosis), includes two-thirds of all patients admitted to hospital with AD – patients in this group rarely require hospital admission and have a much lower 1-year mortality risk.
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•Patients with acutely decompensated cirrhosis without ACLF develop 3 different clinical courses.•Patients with pre-ACLF develop ACLF within 90 days and have high systemic inflammation and mortality.•Patients with unstable decompensated cirrhosis suffer from complications of severe portal hypertension.•Patients with stable decompensated cirrhosis have less frequent complications and lower 1-year mortality risk.
Acute decompensation (AD) of cirrhosis may present without acute-on-chronic liver failure (ACLF) (AD-No ACLF), or with ACLF (AD-ACLF), defined by organ failure(s). Herein, we aimed to analyze and ...characterize the precipitants leading to both of these AD phenotypes.
The multicenter, prospective, observational PREDICT study (NCT03056612) included 1,273 non-electively hospitalized patients with AD (No ACLF = 1,071; ACLF = 202). Medical history, clinical data and laboratory data were collected at enrolment and during 90-day follow-up, with particular attention given to the following characteristics of precipitants: induction of organ dysfunction or failure, systemic inflammation, chronology, intensity, and relationship to outcome.
Among various clinical events, 4 distinct events were precipitants consistently related to AD: proven bacterial infections, severe alcoholic hepatitis, gastrointestinal bleeding with shock and toxic encephalopathy. Among patients with precipitants in the AD-No ACLF cohort and the AD-ACLF cohort (38% and 71%, respectively), almost all (96% and 97%, respectively) showed proven bacterial infection and severe alcoholic hepatitis, either alone or in combination with other events. Survival was similar in patients with proven bacterial infections or severe alcoholic hepatitis in both AD phenotypes. The number of precipitants was associated with significantly increased 90-day mortality and was paralleled by increasing levels of surrogates for systemic inflammation. Importantly, adequate first-line antibiotic treatment of proven bacterial infections was associated with a lower ACLF development rate and lower 90-day mortality.
This study identified precipitants that are significantly associated with a distinct clinical course and prognosis in patients with AD. Specific preventive and therapeutic strategies targeting these events may improve outcomes in patients with decompensated cirrhosis.
Acute decompensation (AD) of cirrhosis is characterized by a rapid deterioration in patient health. Herein, we aimed to analyze the precipitating events that cause AD in patients with cirrhosis. Proven bacterial infections and severe alcoholic hepatitis, either alone or in combination, accounted for almost all (96-97%) cases of AD and acute-on-chronic liver failure. Whilst the type of precipitant was not associated with mortality, the number of precipitant(s) was. This study identified precipitants that are significantly associated with a distinct clinical course and prognosis of patients with AD. Specific preventive and therapeutic strategies targeting these events may improve patient outcomes.
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•Bacterial infections and/or severe alcoholic hepatitis are the major precipitants of acute decompensation and ACLF.•The type of precipitating event had no association with survival.•The number of identifiable events was significantly associated with surrogates of systemic inflammation and increased 90-day mortality.•Adequate first-line antibiotic treatment of proven bacterial infections reduced ACLF development and improved 90-day survival.•Strategies to prevent or treat precipitating events may improve outcome in decompensated cirrhosis.
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