Evolution of impulsive-compulsive behaviors and cognition in Parkinson's disease

Neural reinforcement learning signals predict recovery from impulse control disorder symptoms in Parkinson's disease

BACKGROUND

Impulse control disorders (ICD) in Parkinson's disease (PD) are associated with a heavy burden on patients and caretakers. While recovery can occur, ICD persists in many patients despite optimal management. The basis for this inter-individual variability in recovery is unclear and poses a major challenge to personalized health care.

METHODS

We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis.

RESULTS

Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.

CONCLUSIONS

Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD.

Impulse control behaviors and apathy commonly co-occur in de novo Parkinson's disease and predict the incidence of levodopa-induced dyskinesia

OBJECTIVE

Impulse control behaviors (ICBs) and apathy are believed to represent opposite motivational expressions of the same behavioral spectrum involving hypo- and hyperdopaminergic status, but this has been recently debated. Our study aims to estimate the co-occurrence of ICBs and apathy in early Parkinson's disease (PD) and to determine whether this complex neuropsychiatric condition is an important marker of PD prognoses.

METHODS

Neuropsychiatric symptoms, clinical data, neuroimaging results, and demographic data from de novo PD patients were obtained from the Parkinson's Progression Markers Initiative, a prospective, multicenter, observational cohort. The clinical characteristics of ICBs co-occurring with apathy and their prevalence were analyzed. We compared the prognoses of the different groups during the 8-year follow-up. Multivariate Cox regression analysis was conducted to predict the development of levodopa-induced dyskinesia (LID) using baseline neuropsychiatric symptoms.

RESULTS

A total of 422 PD patients and 195 healthy controls (HCs) were included. In brief, 87 (20.6 %) de novo PD patients and 37 (19.0 %) HCs had ICBs at baseline. Among them, 23 (26.4 %) de novo PD patients and 3 (8.1 %) HCs had clinical symptoms of both ICBs and apathy. The ICBs and apathy group had more severe non-motor symptoms than the isolated ICBs group. Cox regression analysis demonstrated that the co-occurrence of ICBs and apathy was a risk factor for LID development (HR 2.229, 95 % CI 1.209 to 4.110, p = 0.010).

CONCLUSIONS

Co-occurrence of ICBs and apathy is common in patients with early PD and may help to identify the risk of LID development.

The impact of psychiatric comorbidity on Parkinson's disease outcomes: a systematic review and meta-analysis

Background

The burden of psychiatric symptoms in Parkinson's disease includes depression, anxiety, apathy, psychosis, and impulse control disorders. However, the relationship between psychiatric comorbidities and subsequent prognosis and neurological outcomes is not yet well understood. In this systematic review and meta-analysis, in individuals with Parkinson's disease, we aimed to characterise the association between specific psychiatric comorbidities and subsequent prognosis and neurological outcomes: cognitive impairment, death, disability, disease progression, falls or fractures and care home admission.

Methods

We searched MEDLINE, Embase, PsycINFO and AMED up to 13th November 2023 for longitudinal observational studies which measured disease outcomes in people with Parkinson's disease, with and without specific psychiatric comorbidities, and a minimum of two authors extracted summary data. Studies of individuals with other parkinsonian conditions and those with outcome measures that had high overlap with psychiatric symptoms were excluded to ensure face validity. For each exposure-outcome pair, a random-effects meta-analysis was conducted based on standardised mean difference, using adjusted effect sizes-where available-in preference to unadjusted effect sizes. Study quality was assessed using the Newcastle-Ottawa Scale. Between-study heterogeneity was assessed using the I2 statistic and publication bias was assessed using funnel plots. PROSPERO Study registration number: CRD42022373072.

Findings

There were 55 eligible studies for inclusion in meta-analysis (n = 165,828). Data on participants' sex was available for 164,514, of whom 99,182 (60.3%) were male and 65,460 (39.7%) female. Study quality was mostly high (84%). Significant positive associations were found between psychosis and cognitive impairment (standardised mean difference [SMD] 0.44, [95% confidence interval [CI] 0.23-0.66], I2 30.9), psychosis and disease progression (SMD 0.46, [95% CI 0.12-0.80], I2 70.3%), depression and cognitive impairment (SMD 0.37 [95% CI 0.10-0.65], I2 27.1%), depression and disease progression (SMD 0.46 [95% CI 0.18-0.74], I2 52.2), depression and disability (SMD 0.42 [95% CI 0.25-0.60], I2 7.9%), and apathy and cognitive impairment (SMD 0.60 [95% CI 0.02-1.19], I2 27.9%). Between-study heterogeneity was moderately high.

Interpretation

Psychosis, depression, and apathy in Parkinson's disease are all associated with at least one adverse outcome, including cognitive impairment, disease progression and disability. Whether this relationship is causal is not clear, but the mechanisms underlying these associations require exploration. Clinicians should consider these psychiatric comorbidities to be markers of a poorer prognosis in people with Parkinson's disease. Future studies should investigate the underlying mechanisms and which treatments for these comorbidities may affect Parkinson's disease outcomes.

Funding

Wellcome Trust, UK National Institute for Health Research (NIHR), National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust and King's College London, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at University College London Hospitals NHS Foundation Trust, National Brain Appeal.

Assessing impulse control behaviors in early Parkinson's disease: a longitudinal study

Objective

Impulse control behaviors (ICBs) frequently coexist with Parkinson's disease (PD). However, the predictors of ICBs in PD remain unclear, and there is limited data on the biological correlates of ICBs in PD. In this study, we examined clinical, imaging, and biological variables to identify factors associated with longitudinal changes in ICBs in early-stage PD.

Methods

The data for this study were obtained from the Parkinson's Progression Markers Initiative, an international prospective cohort study that evaluates markers of disease progression in PD. We examined clinical, imaging, and biological variables to determine their associations with ICBs over a period of up to 5 years. Cox regression models were employed to investigate the predictors of ICBs in early-stage, untreated PD.

Results

The study enrolled 401 individuals with PD and 185 healthy controls (HC). At baseline, 83 PD subjects (20.7%) and 36 HC (19.5%) exhibited ICBs. Over the course of 5 years, the prevalence of ICBs increased in PD (from 20.7% to 27.3%, p < 0.001), while it decreased in HC (from 19.5% to 15.2%, p < 0.001). Longitudinally, the presence of ICBs in PD was associated with depression, anxiety, autonomic dysfunction, and excessive daytime sleepiness (EDS). However, there was no significant association observed with cognitive dysfunction or motor severity. Treatment with dopamine agonists was linked to ICBs at years 3 and 4. Conversely, there was no association found between ICBs and presynaptic dopaminergic dysfunction. Additionally, biofluid markers in baseline and the first year did not show a significant association with ICBs. A predictive index for ICBs was generated, incorporating three baseline characteristics: anxiety, rapid eye movement sleep behavior disorder (RBD), and p-tau levels in cerebrospinal fluid (CSF).

Conclusion

During the early stages of PD, there is a notable increase in ICBs over time. These ICBs are associated with depression, anxiety, autonomic dysfunction, EDS, and the use of dopaminergic medications, particularly dopamine agonists. Anxiety, RBD, and p-tau levels in CSF are identified as predictors for the incident development of ICBs in early PD. Further longitudinal analyses will provide a more comprehensive understanding of the associations between ICBs and imaging findings, as well as biomarkers. These analyses will help to better characterize the relationships and implications of these factors in the context of ICBs in early PD.

Machine learning-based prediction of impulse control disorders in Parkinson's disease from clinical and genetic data

Goal: Impulse control disorders (ICDs) are frequent non-motor symptoms occurring during the course of Parkinson’s disease (PD). The objective of this study was to estimate the predictability of the future occurrence of these disorders using longitudinal data, the first study using cross-validation and replication in an independent cohort. Methods: We used data from two longitudinal PD cohorts (training set: PPMI, Parkinson’s Progression Markers Initiative; test set: DIGPD, Drug Interaction With Genes in Parkinson’s Disease). We included 380 PD subjects from PPMI and 388 PD subjects from DIGPD, with at least two visits and with clinical and genetic data available, in our analyses. We trained three logistic regressions and a recurrent neural network to predict ICDs at the next visit using clinical risk factors and genetic variants previously associated with ICDs. We quantified performance using the area under the receiver operating characteristic curve (ROC AUC) and average precision. We compared these models to a trivial model predicting ICDs at the next visit with the status at the most recent visit. Results: The recurrent neural network (PPMI: 0.85 [0.80 – 0.90], DIGPD: 0.802 [0.78 – 0.83]) was the only model to be significantly better than the trivial model (PPMI: ROC AUC = 0.75 [0.69 – 0.81]; DIGPD: 0.78 [0.75 – 0.80]) on both cohorts. We showed that ICDs in PD can be predicted with better accuracy with a recurrent neural network model than a trivial model. The improvement in terms of ROC AUC was higher on PPMI than on DIGPD data, but not clinically relevant in both cohorts. Conclusions: Our results indicate that machine learning methods are potentially useful for predicting ICDs, but further works are required to reach clinical relevance.

Assessment of therapeutic strategies for management of impulse control disorder in Parkinson's disease

BACKGROUND

Impulse control disorders (ICD) occur frequently in individuals with Parkinson's disease. So far, prevention is the best treatment. Several strategies for its treatment have been suggested, but their frequency of use and benefit have scarcely been explored.

OBJECTIVE

To investigate which strategy is the most commonly used in a real-life setting and its rate of response.

METHODS

A longitudinal study was conducted. At the baseline evaluation, data on current treatment and ICD status according to QUIP-RS were collected. The treatment strategies were categorized as "no-change", dopamine agonist (DA) dose lowering, DA removal, DA switch or add-on therapy. At the six-month follow-up visit, the same tools were applied.

RESULTS

A total of 132 individuals (58.3% men) were included; 18.2% had at least one ICD at baseline. The therapeutic strategy most used in the ICD group was no-change (37.5%), followed by DA removal (16.7%), DA switch (12.5%) and DA lowering (8.3%). Unexpectedly, in 20.8% of the ICD subjects the DA dose was increased. Overall, nearly 80% of the subjects showed remission of their ICD at follow-up.

CONCLUSIONS

Regardless of the therapy used, most of the subjects presented remission of their ICD at follow-up Further research with a longer follow-up in a larger sample, with assessment of decision-making processes, is required in order to better understand the efficacy of strategies for ICD treatment.