Advances in functional and structural imaging of the brainstem: implications for disease.

Purpose Of Review: The brainstem's complex anatomy and relatively small size means that structural and functional assessment of this structure is done less frequently compared to other brain areas. However, recent years have seen substantial progress in brainstem imaging, enabling more detailed investigations into its structure and function, as well as its role in neuropathology.

Recent Findings: Advancements in ultrahigh field MRI technology have allowed for unprecedented spatial resolution in brainstem imaging, facilitating the new creation of detailed brainstem-specific atlases.

fMRI-based spatio-temporal parcellations of the human brain.

Purpose Of Review: Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research.

Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes.

Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising clinical rating scales, which are semi-subjective and time-consuming. To address these challenges, the utilisation of artificial intelligence (AI) has emerged.

Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests.

This study introduces PDMotion, a mobile application comprising 11 digital tests, including those adapted from the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III and novel assessments, for remote Parkinson's Disease (PD) motor symptoms evaluation. Employing machine learning techniques on data from 50 PD patients and 29 healthy controls, PDMotion achieves accuracies of 0.878 for PD status prediction and 0.

Apathy scores in Parkinson's disease relate to EEG components in an incentivized motor task.

Apathy is one of the most prevalent non-motor symptoms of Parkinson's disease and is characterized by decreased goal-directed behaviour due to a lack of motivation and/or impaired emotional reactivity. Despite its high prevalence, the neurophysiological mechanisms underlying apathy in Parkinson's disease, which may guide neuromodulation interventions, are poorly understood. Here, we investigated the neural oscillatory characteristics of apathy in Parkinson's disease using EEG data recorded during an incentivized motor task.

Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson's Disease.

The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson's Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson's Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability.

Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson's Disease.

Artificial intelligence (AI) has garnered tremendous attention in health care, and many hope that AI can enhance our health system's ability to care for people with chronic and degenerative conditions, including Parkinson's Disease (PD). This paper reports the themes and lessons derived from a qualitative study with people living with PD, family caregivers, and health care providers regarding the ethical dimensions of using AI to monitor, assess, and predict PD symptoms and progression. Thematic analysis identified ethical concerns at four intersecting levels: personal, interpersonal, professional/institutional, and societal levels.

Sex, myelin, and clinical characteristics of Parkinson's disease.

Objective: To determine if there are sex differences in myelin in Parkinson's disease, and whether these explain some of the previously-described sex differences in clinical presentation.

Methods: Thirty-three subjects (23 males, 10 females) with Parkinson's disease underwent myelin water fraction (MWF) imaging, an MRI scanning technique of myelin content. MWF of 20 white matter regions of interest (ROIs) were assessed.

Automatic labeling of Parkinson's Disease gait videos with weak supervision.

Motor dysfunction in Parkinson's Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-consuming, expensive, semi-subjective, and may potentially result in conflicting labels across different raters. To address this problem, we propose an automatic, objective, and weakly-supervised method for labeling PD patients' gait videos.

An Individualized Multi-Modal Approach for Detection of Medication "Off" Episodes in Parkinson's Disease via Wearable Sensors.

The primary treatment for Parkinson's disease (PD) is supplementation of levodopa (L-dopa). With disease progression, people may experience motor and non-motor fluctuations, whereby the PD symptoms return before the next dose of medication. Paradoxically, in order to prevent wearing-off, one must take the next dose while still feeling well, as the upcoming off episodes can be unpredictable.

PA-Tran: Learning to Estimate 3D Hand Pose with Partial Annotation.

This paper tackles a novel and challenging problem-3D hand pose estimation (HPE) from a single RGB image using partial annotation. Most HPE methods ignore the fact that the keypoints could be partially visible (e.g.

Spatiotemporal patterns of putaminal dopamine processing in Parkinson's disease: A multi-tracer positron emission tomography study.

Alterations in different aspects of dopamine processing may exhibit different progressive behaviours throughout the course of Parkinson's disease. We used a novel data-driven multivariate approach to quantify and compare spatiotemporal patterns related to different aspects of dopamine processing from cross-sectional Parkinson's subjects obtained with: 1) 69 [C]±dihydrotetrabenazine (DTBZ) scans, most closely related to dopaminergic denervation; 2) 73 [C]d-threo-methylphenidate (MP) scans, marker of dopamine transporter density; 3) 50 6-[F]fluoro-l-DOPA (FD) scans, marker of dopamine synthesis and storage. The anterior-posterior gradient in the putamen was identified as the most salient feature associated with disease progression, however the temporal progression of the spatial gradient was different for the three tracers.

Connectivity-Based Brain Parcellation for Parkinson's Disease.

Connectivity-based parcellation (CBP) studies for exploring cerebral topographic organization have emerged rapidly, likely due to the joint developments of non-invasive imaging technologies and advances in computing science. CBP studies have extended our understanding of human brain development and many brain-related disorders such as Parkinson's Disease (PD), and have provided promising approaches to guide electrode placement during the planning of deep brain stimulation (DBS) surgery. This work reviews prevalent CBP methods, summarizing the methodological advantages and limitations of each.

Atlas-guided parcellation: Individualized functionally-homogenous parcellation in cerebral cortex.

Resting-state Magnetic resonance imaging-based parcellation aims to group the voxels/vertices non-invasively based on their connectivity profiles, which has achieved great success in understanding the fundamental organizational principles of the human brain. Given the substantial inter-individual variability, the increasing number of studies focus on individual parcellation. However, current methods perform individual parcellations independently or are based on the group prior, requiring expensive computational costs, precise parcel alignment, and extra group information.

Walking exercise alters pedunculopontine nucleus connectivity in Parkinson's disease in a dose-dependent manner.

Background: Gait disturbances are critical motor symptoms in Parkinson's disease (PD). The mechanisms of gait impairment in PD are not entirely understood but likely involve changes in the Pedunculopontine Nucleus (PPN), a critical locomotion center, and its associated connections. Exercise is universally accepted as helpful in PD, but the extent and intensity of exercise required for plastic changes are unclear.

Home EEG sleep assessment shows reduced slow-wave sleep in mild-moderate Alzheimer's disease.

Introduction: Sleep disturbances are common in Alzheimer's disease (AD), with estimates of prevalence as high as 65%. Recent work suggests that specific sleep stages, such as slow-wave sleep (SWS) and rapid eye movement (REM), may directly impact AD pathophysiology. A major limitation to sleep staging is the requirement for clinical polysomnography (PSG), which is often not well tolerated in patients with dementia.

A Joint Constrained CCA Model for Network-Dependent Brain Subregion Parcellation.

Connectivity-based brain region parcellation from functional magnetic resonance imaging (fMRI) data is complicated by heterogeneity among aged and diseased subjects, particularly when the data are spatially transformed to a common space. Here, we propose a group-guided functional brain region parcellation model capable of obtaining subregions from a target region with consistent connectivity profiles across multiple subjects, even when the fMRI signals are kept in their native spaces. The model is based on a joint constrained canonical correlation analysis (JC-CCA) method that achieves group-guided parcellation while allowing the data dimension of the parcellated regions for each subject to vary.

Preservation of Eye Movements in Parkinson's Disease Is Stimulus- and Task-Specific.

Parkinson's disease (PD) is a neurodegenerative disease that includes motor impairments, such as tremor, bradykinesia, and postural instability. Although eye movement deficits are commonly found in saccade and pursuit tasks, preservation of oculomotor function has also been reported. Here we investigate specific task and stimulus conditions under which oculomotor function in PD is preserved.

Galvanic Vestibular Stimulation Effects on EEG Biomarkers of Motor Vigor in Parkinson's Disease.

Impaired motor vigor (MV) is a critical aspect of Parkinson's disease (PD) pathophysiology. While MV is predominantly encoded in the basal ganglia, deriving (cortical) EEG measures of MV may provide valuable targets for modulation via galvanic vestibular stimulation (GVS). To find EEG features predictive of MV and examine the effects of high-frequency GVS.

Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal.

Background: Functional connectivity (FC) maps from brain fMRI data are often derived with seed-based methods that estimate temporal correlations between the time course in a predefined region (seed) and other brain regions (SCA, seed-based correlation analysis). Standard dual regression, which uses a set of spatial regressor maps, can detect FC with entire brain "networks," such as the default mode network, but may not be feasible when detecting FC associated with a single small brain region alone (for example, the amygdala).

New Method: We explored seed-based dual regression (SDR) from theoretical and practical points of view.

Frequency-Specific Effects of Galvanic Vestibular Stimulation on Response-Time Performance in Parkinson's Disease.

Galvanic vestibular stimulation (GVS) is being increasingly explored as a non-invasive brain stimulation technique to treat symptoms in Parkinson's disease (PD). To date, behavioral GVS effects in PD have been explored with only two stimulus types, direct current and random noise (RN). The interaction between GVS effects and anti-parkinsonian medication is unknown.