Background: The aim of this systematic review was (1) to identify the brain regions involved in anxiety in Parkinson's disease (PD) based on neuroimaging studies and (2) to interpret the findings against the background of dysfunction of the fear circuit and limbic cortico‐striato‐thalamocortical circuit.Methods: Studies assessing anxiety symptoms in PD patients and studies using magnetic resonance imaging, positron emission tomography, or single‐photon emission computed tomography were included.Results: The severity of anxiety was associated with changes in the fear circuit and the cortico‐striato‐thalamocortical limbic circuit. In the fear circuit, a reduced gray‐matter volume of the amygdala and the anterior cingulate cortex (ACC); an increased functional connectivity (FC) between the amygdala and orbitofrontal cortex (OFC) and hippocampus and between the striatum and the medial prefrontal cortex (PFC), temporal cortex, and insula; and a reduced FC between the lateral PFC and the OFC, hippocampus, and amygdala were reported. In the cortico‐striato‐thalamocortical limbic circuit, a reduced FC between the striatum and ACC; a reduced dopaminergic and noradrenergic activity in striatum, thalamus, and locus coeruleus; and a reduced serotoninergic activity in the thalamus were reported.Conclusion: To conclude, anxiety is associated with structural and functional changes in both the hypothesized fear and the limbic cortico‐striato‐thalamocortical circuits. These circuits overlap and may well constitute parts of a more extensive pathway, of which different parts play different roles in anxiety. The neuropathology of PD may affect these circuits in different ways, explaining the high prevalence of anxiety in PD and also the associated cognitive, motor, and psychiatric symptoms. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Anxiety is a severe problem for at least one-third of people living with Parkinson’s disease (PD). Anxiety appears to have a greater adverse impact on quality of life than motor impairment. Despite its high prevalence and impact on daily life, anxiety is often undiagnosed and untreated. To better address anxiety in PD, future research must improve knowledge about the mechanism of anxiety in PD and address the lack of empirical evidence from clinical trials. In response to these challenges, the Parkinson’s Foundation sponsored an expert meeting on anxiety on June 13th and 14th 2018. This paper summarizes the findings from that meeting informed by a review of the existing literature and discussions among patients, caregivers, and an international, clinician-scientist, expert panel working group. The goal is to provide recommendations to improve our understanding and treatment of anxiety in PD.

Besides the hallmark motor symptoms (rest tremor, hypokinesia, rigidity, and postural instability), patients with Parkinson’s disease (PD) have non-motor symptoms, namely neuropsychiatric disorders. They are frequent and may influence the other symptoms of the disease. They have also a negative impact on the quality of life of patients and their caregivers. In this article, we will describe the clinical manifestations of the main PD-related behavioral disorders (depression, anxiety disorders, apathy, psychosis, and impulse control disorders). We will also provide an overview of the clinical and preclinical literature regarding the underlying mechanisms with a focus on the role of the dopaminergic and non-dopaminergic systems.

The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose‐driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose‐driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Gait is often considered as an automatic movement but cortical control seems necessary to adapt gait pattern with environmental constraints. In order to study cortical activity during real locomotion, electroencephalography (EEG) appears to be particularly appropriate. It is now possible to record changes in cortical neural synchronization/desynchronization during gait. Studying gait initiation is also of particular interest because it implies motor and cognitive cortical control to adequately perform a step. Time-frequency analysis enables to study induced changes in EEG activity in different frequency bands. Such analysis reflects cortical activity implied in stabilized gait control but also in more challenging tasks (obstacle crossing, changes in speed, dual tasks…). These spectral patterns are directly influenced by the walking context but, when analyzing gait with a more demanding attentional task, cortical areas other than the sensorimotor cortex (prefrontal, posterior parietal cortex, etc.) seem specifically implied. While the muscular activity of legs and cortical activity are coupled, the precise role of the motor cortex to control the level of muscular contraction according to the gait task remains debated. The decoding of this brain activity is a necessary step to build valid brain–computer interfaces able to generate gait artificially.