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

Imaging of impulse control disorders in Parkinson's disease

Impulse control disorders (ICD) are frequent and cumbersome behavioral disorders in patients with Parkinson's disease (PD). Understanding their pathophysiological underpinnings is crucial. Molecular imaging using positron emission tomography (PET) and single-photon emission computed tomography (SPECT) clearly indicates preexisting vulnerability and abnormal sensitization of the pre- and postsynaptic dopaminergic system. Functional magnetic resonance imaging (fMRI) studies reveal abnormal connectivity within the reward system involving the ventral striatum and orbitofrontal cortex. These alterations pinpoint the dysfunction of reinforcement learning in ICD, which is biased toward the overvaluation of reward and underestimation of risk, and the deficit in inhibitory control mechanisms related to abnormal connectivity within and between the limbic and the associative and motor networks.

Dopaminergic therapy disrupts decision-making in impulsive-compulsive Parkinsonian patients

Impulsive-compulsive behaviors (ICB) are common non-motor symptoms of Parkinson's disease (PD) often associated with dopaminergic drugs (DD) therapy. We investigated the acute effects of DD on decision-making in PD patients with ICB (ICB+) and without it (ICB-), and in healthy controls (HC). Participants performed a risk-based decision-making task twice, with PD patients tested before (DD OFF) and after (DD ON) DD intake. In DD OFF, all groups developed a risk-averting strategy. In DD ON, ICB+ patients (but not ICB- nor HC) reverted to riskier choices. We conclude that DD has a specific strong acute effect on ICB+ patients' decision-making.

Clinical management model for impulse control disorders in Parkinson's disease

Over the last decade, we have gained a better understanding of impulse control disorder in Parkinson's disease (PD-ICD), a medication complication in PD. Researchers were aware of its complexity and took efforts to learn more about its diagnostic and treatment possibilities. Nevertheless, clinical management for it is currently neglected. We conducted a narrative overview of literature published from 2012 to October 2023 on various aspects of clinical management for PD-ICD. A potential "susceptibility-catalytic-stress" model in the development of PD-ICD was proposed and a profile encoding predictors for PD-ICD was created. Based on these predictors, some methods for prediction were recently developed for better prediction, such as the polymorphic dopamine genetic risk score and the clinic-genetic ICD-risk score. A variety of treatment options, including dose reduction of dopamine receptor agonists (DAs), DAs removal, DAs switch, and add-on therapy, are investigated with inconsistent reports. Based on current findings, we developed a clinical management model prototype centered on prevention, consisting of prediction, prevention, follow-up and monitoring, therapy, and recurrence prevention, for clinical reference, and further proposed 4 key clinical management principles, including standardization, prediction centered, persistence, and whole course.

Behavioral disorders in Parkinson disease: current view

Patients with Parkinson disease (PD) frequently experience several behavioral symptoms, such as anxiety, apathy, irritability, agitation, impulsive control and obsessive-compulsive or REM sleep behavior disorders, which can cause severe psychosocial problems and impair quality of life. Occurring in 30-70% of PD patients, these symptoms can manifest at early stages of the disease, sometimes even before the appearance of classic motor symptoms, while others can develop later. Behavioral changes in PD show distinct patterns of brain atrophy, dopaminergic and serotonergic deterioration, altered neuronal connectivity in frontostriatal, corticolimbic, default mode and other networks due to a cascade linking molecular pathologies and deficits in multiple behavior domains. The changes suggest a multi-system neurodegenerative process in the context of a specific α-synucleinopathy inducing a variety of biochemical and functional changes, the neurobiological basis and clinical relevance of which await further elucidation. This paper is intended to review the recent literature with focus on the main behavioral disturbances in PD patients, their epidemiology, clinical features, risk factors, animal models, neuroimaging findings, pathophysiological backgrounds, and treatment options of these deleterious lesions.

Neural Reinforcement Learning Signals Predict Recovery From Impulse Control Disorder Symptoms in Parkinson's Disease

BACKGROUND

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

METHODS

We adopted a computational psychiatry approach and leveraged the longitudinal, prospective Personalized Parkinson Project (136 people with Parkinson's disease, within 5 years of diagnosis) to combine dopaminergic learning theory-informed functional magnetic resonance imaging with machine learning (at baseline) to predict ICD symptom recovery after 2 years of follow-up. We focused on change in Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease Rating Scale scores in the entire sample 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 and medial prefrontal cortex, and the behavioral accuracy score measured while on medication were associated with greater recovery from impulse control symptoms 2 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. We showed that reinforcement learning modeling parameters predicted recovery from ICD symptoms in Parkinson's disease.

Impulse Control Disorders in Parkinson's Disease and Atypical Parkinsonian Syndromes-Is There a Difference?

BACKGROUND AND OBJECTIVES

Impulse control disorders (ICDs) are characterized by potentially harmful actions resulting from disturbances in the self-control of emotions and behavior. ICDs include disorders such as gambling, hypersexuality, binge eating, and compulsive buying. ICDs are known non-motor symptoms in Parkinson's disease (PD) and are associated primarily with the use of dopaminergic treatment (DRT) and especially dopamine agonists (DA). However, in atypical parkinsonism (APS), such as progressive supranuclear palsy (PSP) or multiple system atrophy (MSA), there are only single case reports of ICDs without attempts to determine the risk factors for their occurrence. Moreover, numerous reports in the literature indicate increased impulsivity in PSP. Our study aimed to determine the frequency of individual ICDs in APS compared to PD and identify potential factors for developing ICDs in APS.

MATERIALS AND METHODS

Our prospective study included 185 patients with PD and 35 with APS (27 patients with PSP and 9 with MSA) hospitalized between 2020 and 2023 at the Neurological Department of University Central Hospital in Katowice. Each patient was examined using the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease (QUIP) to assess ICDs. Additionally, other scales were used to assess the advancement of the disease, the severity of depression, and cognitive impairment. Information on age, gender, age of onset, disease duration, and treatment used were collected from medical records and patient interviews.

RESULTS

ICDs were detected in 23.39% of patients with PD (including binge eating in 11.54%, compulsive buying in 10.44%, hypersexuality in 8.79%, and pathological gambling in 4.40%), in one patient with MSA (hypersexuality and pathological gambling), and in 18.52% of patients with PSP (binge eating in 3.70%, compulsive buying in 7.41%, and hypersexuality in 11.11%). We found no differences in the frequency of ICDs between individual diseases (p = 0.4696). We confirmed that the use of higher doses of DA and L-dopa in patients with PD, as well as a longer disease duration and the presence of motor complications, were associated with a higher incidence of ICDs. However, we did not find any treatment effect on the incidence of ICDs in APS.

CONCLUSIONS

ICDs are common and occur with a similar frequency in PD and APS. Well-described risk factors for ICDs in PD, such as the use of DRT or longer disease duration, are not fully reflected in the risk factors for ICDs in APS. This applies especially to PSP, which, unlike PD and MSA, is a tauopathy in which, in addition to the use of DRT, other mechanisms related to the disease, such as disorders in neuronal loops and neurotransmitter deficits, may influence the development of ICDs. Further prospective multicenter studies recruiting larger groups of patients are needed to fully determine the risk factors and mechanisms of ICD development in APS.

Altered neural activity in the reward-related circuit associated with anhedonia in mild to moderate Major Depressive Disorder

BACKGROUND

Anhedonia is a significant predictor of disease progression and treatment outcomes in Major Depressive Disorder (MDD), linked to reward network dysfunctions. However, understanding of its underlying neural mechanisms remains limited. This study aimed to investigate the brain functional mechanisms underlying MDD with anhedonia using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

The Snaith-Hamilton Pleasure Scale (SHAPS) was used to evaluation MDD with anhedonia (anMDD) and non-anhedonia MDD (non-anMDD). Forty-eight patients with anMDD, Forty-four patients with non-anMDD, and Fifty healthy controls (HCs) were enrolled for the fMRI scans. A seed-based functional connectivity (FC) method was employed to explore reward network abnormalities.

RESULTS

anMDD patients exhibited lower FC values in Ventral Striatum (VS), right lateral Ventral Tegmental Area (VTA_R), left Thalamus (THA_L), and higher FC values in Ventromedial Prefrontal Cortex (vmPFC), left Anterior Insula (AI_L), and Presupplementary Motor Area (Pre-SMA) compared to HCs. Comparing anMDD to non-anMDD, significant differences were observed in FC values of VS, vmPFC, Pre-SMA, and THA_L regions. Correlation analysis revealed positive correlations between FC values of VS_R and NAc_R, as well as THA_L and Cerebellum_Crus1_L, with SHAPS scores. Negative correlations were observed between FC values of Pre-SMA and the right caudate, and between vmPFC and Frontal_Mid_Orb_L, and SHAPS scores.

CONCLUSION

Both anMDD and non-anMDD groups demonstrated abnormal FCs in the reward network. These findings indicate distinct roles of reward-related circuits in the two subtypes, contributing to a refined understanding of depression phenotypes and potential directions for targeted interventions.