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. Methodological improvements have significantly enhanced the accuracy of physiological (cardiac and respiratory) noise correction within brainstem imaging studies. These technological and methodological advancements have allowed for in-depth analyses of the brainstem's anatomy, including quantitative assessments and examinations of structural connectivity within both gray and white matter. Furthermore, functional studies, including assessments of activation patterns and functional connectivity, have revealed the brainstem's roles in both specialized functions and broader neural integration. Notably, these investigations have identified alterations in brainstem structure and function associated with various neurological disorders. SUMMARY: The aforementioned developments have allowed for a greater appreciation of the importance of the brainstem in the wider context of neuroscience and clinical neurology.

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. RECENT FINDINGS: Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth". SUMMARY: While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.

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. Sixteen PD patients and 18 healthy, age-matched controls completed a battery of movement tasks that included stationary or moving targets eliciting reactive or deliberate eye movements: pro-saccades, anti-saccades, visually guided pursuit, and rapid go/no-go manual interception. Compared with controls, patients demonstrated systematic impairments in tasks with stationary targets: pro-saccades were hypometric and anti-saccades were incorrectly initiated toward the cued target in ∼35% of trials compared with 14% errors in controls. In patients, task errors were linked to short latency saccades, indicating abnormalities in inhibitory control. However, patients' eye movements in response to dynamic targets were relatively preserved. PD patients were able to track and predict a disappearing moving target and make quick go/no-go decisions as accurately as controls. Patients' interceptive hand movements were slower on average but initiated earlier, indicating adaptive processes to compensate for motor slowing. We conclude that PD patients demonstrate stimulus and task dependency of oculomotor impairments, and we propose that preservation of eye and hand movement function in PD is linked to a separate functional pathway through the superior colliculus-brainstem loop that bypasses the fronto-basal ganglia network. Our results demonstrate that studying oculomotor and hand movement function in PD can support disease diagnosis and further our understanding of disease progression and dynamics.SIGNIFICANCE STATEMENT Eye movements are a promising clinical tool to aid in the diagnosis of movement disorders and to monitor disease progression. Although Parkinson's disease (PD) patients show some oculomotor abnormalities, it is not clear whether previously described eye movement impairments are task-specific. We assessed eye movements in PD under different visual (stationary vs moving targets) and movement (reactive vs deliberate) conditions. We demonstrate that PD patients are able to accurately track moving objects but make inaccurate eye movements toward stationary targets. The preservation of eye movements toward dynamic stimuli might enable patients to accurately act on the predicted motion path of the moving target. These results can inform the development of tools for the rehabilitation or maintenance of functional performance.

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. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.

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., under occlusions). In contrast, we propose a deep-learning framework, PA-Tran, that jointly estimates the keypoints status and 3D hand pose from a single RGB image with two dependent branches. The regression branch consists of a Transformer encoder which is trained to predict a set of target keypoints, given an input set of status, position, and visual features embedding from a convolutional neural network (CNN); the classification branch adopts a CNN for estimating the keypoints status. One key idea of PA-Tran is a selective mask training (SMT) objective that uses a binary encoding scheme to represent the status of the keypoints as observed or unobserved during training. In addition, by explicitly encoding the label status (observed/unobserved), the proposed PA-Tran can efficiently handle the condition when only partial annotation is available. Investigating the annotation percentage ranging from 50-100%, we show that training with partial annotation is more efficient (e.g., achieving the best 6.0 PA-MPJPE when using about 85% annotations). Moreover, we provide two new datasets. APDM-Hand, is for synthetic hands with APDM sensor accessories, which is designed for a specific hand task. PD-APDM-Hand, is a real hand dataset collected from Parkinson's Disease (PD) patients with partial annotation. The proposed PA-Tran can achieve higher estimation accuracy when evaluated on both proposed datasets and a more general hand dataset.

A Multi-sequence MRI Study in Parkinson's Disease: Association Between Rigidity and Myelin.

BACKGROUND: The pathophysiology of rigidity in Parkinson's disease (PD) is poorly understood. Multi-sequence functional and structural brain MRI may further clarify the origin of this clinical characteristic. PURPOSE: To examine both joint and unique relationships of MRI-based functional and structural imaging modalities to rigidity and other clinical features of PD. STUDY TYPE: Retrospective cross-sectional study. POPULATION: 31 PD subjects (aged 68.0 ± 5.9 years, 21 males) with average disease duration 9.3 ± 5.4 years. FIELD STRENGTH/SEQUENCE: Multi-echo GRASE, diffusion-weighted echo planar imaging (EPI), and blood oxygen level dependent contrast EPI T2*-weighted sequences on a 3T scanner. ASSESSMENT: Myelin water fraction (MWF) and fractional anisotropy (FA) of 20 white-matter regions of interest (ROIs), and functional connectivity derived from resting-state fMRI among 56 ROIs were assessed. The Unified Parkinson's Disease Rating Scale-Part III, Montreal Cognitive Assessment, Beck Depression Index, and Apathy Rating Scales were used to assess motor and non-motor symptoms. STATISTICAL TESTS: Multiset canonical correlation analysis (MCCA) and canonical correlation analysis (CCA) were utilized to examine the joint and unique relationships of multiple imaging measures with clinical symptoms of PD. A permutation test was used to determine statistical significance (P < 0.05). RESULTS: MCCA revealed a single significant component jointly linking MWF, FA, and functional connectivity to age, bradykinesia, and leg agility, non-motor symptoms of cognition, depression, and apathy, but not rigidity (P = 0.77), tremor (P = 0.50 and 0.67 on the left and right side), or sex (P = 0.54). After controlling for this joint component, CCA found a unique significant association between MWF and rigidity, but no other associations were detected, including with FA (P = 0.87). DATA CONCLUSION: MWF, FA, and functional connectivity can serve as multi-sequence imaging markers to characterize many PD symptoms. However, rigidity in PD is additionally associated with widespread myelin changes. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

Serotonergic System Impacts Levodopa Response in Early Parkinson's and Future Risk of Dyskinesia.

BACKGROUND: The serotonergic system is known to contribute to levodopa-derived dopamine release in advanced Parkinson's disease. OBJECTIVE: We investigated the role of the serotonergic system in determining response to treatment in early disease and risk for complications concurrently with dopaminergic alterations. METHODS: Eighteen patients with early and stable Parkinson's disease underwent multitracer positron emission tomography using [11 C]dihydrotetrabenazine (vesicular monoamine transporter 2 marker), [11 C]methylphenidate (dopamine transporter marker), [11 C]-3-amino-4-(2-dimethylaminomethylphenylsulfanyl)-benzonitrile (DASB, serotonin transporter marker), and [11 C]raclopride (D2 marker) to investigate relationships between striatal dopaminergic and serotonergic alterations and levodopa-induced dopamine release, related to motor response to treatment and risk for dyskinesias, using a novel joint pattern analysis. RESULTS: The joint pattern analysis revealed correlated spatial patterns conceptually related to abnormal dopamine turnover in the putamen (higher dopamine release associated with dopaminergic and serotonergic denervation); response to treatment significantly inversely correlated with turnover-related dopamine release (P < 10-5 ). Patterns identified without inclusion of the DASB data showed no correlation with clinical data, indicating an important contribution from the serotonergic system to a clinically relevant abnormal dopamine release in early disease. Subjects who experienced dyskinesia 3 years after baseline scans showed higher turnover-related dopamine release compared with subjects who remained stable (P < 0.01). CONCLUSIONS: Joint analysis of dopaminergic and serotonergic data identified a turnover-related dopamine release component, strongly related to motor response to levodopa in early disease and contributing to higher risk for dyskinesia. These findings suggest that the contribution of the serotonergic system to dopamine release not only increases the risk for motor complications but also fails to provide sustained therapeutic advantage in early disease. © 2020 International Parkinson and Movement Disorder Society.

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person's home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.

Omission of temporal nuisance regressors from dual regression can improve accuracy of fMRI functional connectivity maps.

Functional connectivity (FC) maps from brain fMRI data can be derived with dual regression, a proposed alternative to traditional seed-based FC (SFC) methods that detect temporal correlation between a predefined region (seed) and other regions in the brain. As with SFC, incorporating nuisance regressors (NR) into the dual regression must be done carefully, to prevent potential bias and insensitivity of FC estimates. Here, we explore the potentially untoward effects on dual regression that may occur when NR correlate highly with the signal of interest, using both synthetic and real fMRI data to elucidate mechanisms responsible for loss of accuracy in FC maps. Our tests suggest significantly improved accuracy in FC maps derived with dual regression when highly correlated temporal NR were omitted. Single-map dual regression, a simplified form of dual regression that uses neither spatial nor temporal NR, offers a viable alternative whose FC maps may be more easily interpreted, and in some cases be more accurate than those derived with standard dual regression.

Exercise increases caudate dopamine release and ventral striatal activation in Parkinson's disease.

BACKGROUND: The objective of this study was to examine the effects of aerobic exercise on evoked dopamine release and activity of the ventral striatum using positron emission tomography and functional magnetic resonance imaging in Parkinson's disease (PD). METHODS: Thirty-five participants were randomly allocated to a 36-session aerobic exercise or control intervention. Each participant underwent an functional magnetic resonance imaging scan while playing a reward task before and after the intervention to determine the effect of exercise on the activity of the ventral striatum in anticipation of reward. A subset of participants (n = 25) completed [11 C] raclopride positron emission tomography scans to determine the effect of aerobic exercise on repetitive transcranial magnetic stimulation-evoked release of endogenous dopamine in the dorsal striatum. All participants completed motor (MDS-UPDRS part III, finger tapping, Timed-up-and-go) and nonmotor assessments (Starkstein Apathy Scale, Beck Depression Inventory, reaction time, Positive and Negative Affect Schedule, Trail Making Test [A and B], and Montreal Cognitive Assessment) before and after the interventions. RESULTS: The aerobic group exhibited increased activity in the ventral striatum during functional magnetic resonance imaging in anticipation of 75% probability of reward (P = 0.01). The aerobic group also demonstrated increased repetitive transcranial magnetic stimulation-evoked dopamine release in the caudate nucleus (P = 0.04) and increased baseline nondisplaceable binding potential in the posterior putamen of the less affected repetitive transcranial magnetic stimulation-stimulated hemisphere measured by position emission tomography (P = 0.03). CONCLUSIONS: Aerobic exercise alters the responsivity of the ventral striatum, likely related to changes to the mesolimbic dopaminergic pathway, and increases evoked dopamine release in the caudate nucleus. This suggests that the therapeutic benefits of exercise are in part related to corticostriatal plasticity and enhanced dopamine release. © 2019 International Parkinson and Movement Disorder Society.

White matter myelin profiles linked to clinical subtypes of Parkinson's disease.

BACKGROUND: White matter (WM) microstructural integrity is important for effective brain functioning and alterations have been shown in many neurodegenerative diseases. PURPOSE: To investigate WM myelin profiles and their relation to clinical features of Parkinson's disease (PD). STUDY TYPE: Retrospective cross-sectional. POPULATION: In all, 29 PD subjects and 15 healthy controls. FIELD STRENGTH/SEQUENCE: Multiecho GRASE with 10 msec echo spacing and echo planar imaging (EPI) diffusion-weighted (b-value = 700 with 32 gradient directions) on a 3T scanner. ASSESSMENT: Myelin water fraction (MWF) and fractional anisotropy (FA) across 20 WM regions of interest (ROIs) were compared between groups. Partial least squares (PLS) was used to associate MWF and FA with clinical and behavioral measures. STATISTICAL TESTS: Group comparisons were done using two-sample t-tests. PLS was assessed with permutation tests. Bootstrapping was used to investigate the robustness of imaging features. RESULTS: No group differences in myelin content could be detected with univariate tests. A three-component PLS model linked MWF profiles to clinical phenotypes but no FA profiles. The three components appeared to follow along broad motor/nonmotor subtypes of "akinetic-rigid," "tremor-predominant," and "depression/apathy" subtypes, respectively. The first component showed associations between overall motor scores (r = -0.43, P = 0.0196) and cognitive performance (r = 0.44, P = 0.0171) with interhemispheric and long-range association fibers. A second component linked overall motor scores (r = -0.58, P = 0.0009) and tremor scores (r = -0.48, P = 0.0091) to predominantly projection fibers. The last component related depression (r = -0.60, P = 0.0006) and apathy scores (r = -0.66, P = 0.0001 and r = -49, P = 0.0072) to a mixture of association and projection fibers. DATA CONCLUSION: MWF was robustly linked to distinct clinical subtypes of PD and may serve as an additional tool to characterize the disease. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:164-174.

Controlling a motorized orthosis to follow elbow volitional movement: tests with individuals with pathological tremor.

BACKGROUND: There is a need for alternative treatment options for tremor patients who do not respond well to medications or surgery, either due to side effects or poor efficacy, or that are excluded from surgery. The study aims to evaluate feasibility of a voluntary-driven, speed-controlled tremor rejection approach with individuals with pathological tremor. The suppression approach was investigated using a robotic orthosis for suppression of elbow tremor. Importantly, the study emphasizes the performance in relation to the voluntary motion. METHODS: Nine participants with either Essential Tremor (ET) or Parkinson's disease (PD) were recruited and tested off medication. The participants performed computerized pursuit tracking tasks following a sinusoid and a random target, both with and without the suppressive orthosis. The impact of the Tremor Suppression Orthosis (TSO) at the tremor and voluntary frequencies was determined by the relative power change calculated from the Power Spectral Density (PSD). Voluntary motion was, in addition, assessed by position and velocity tracking errors. RESULTS: The suppressive orthosis resulted in a 94.4% mean power reduction of the tremor (p < 0.001) - a substantial improvement over reports in the literature. As for the impact to the voluntary motion, paired difference tests revealed no statistical effect of the TSO on the relative power change (p = 0.346) and velocity tracking error (p = 0.283). A marginal effect was observed for the position tracking error (p = 0.05). The interaction torque with the robotic orthosis was small (0.62 Nm) when compared to the maximum voluntary torque that can be exerted by adult individuals at the elbow joint. CONCLUSIONS: Two key contributions of this work are first, a recently proposed approach is evaluated with individuals with tremor demonstrating high levels of tremor suppression; second, the impact of the approach to the voluntary motion is analyzed comprehensively, showing limited inhibition. This study also seeks to address a gap in studies with individuals with tremor where the impact of engineering solutions on voluntary motion is unreported. This study demonstrates feasibility of the wearable technology as an effective treatment that removes tremor with limited impediment to intentional motion. The goal for such wearable technology is to help individuals with pathological tremor regain independence in activities affected by the tremor condition. Further investigations are needed to validate the technology.

Education, and the balance between dynamic and stationary functional connectivity jointly support executive functions in relapsing-remitting multiple sclerosis.

Graphical network characteristics and nonstationary functional connectivity features, both derived from resting-state functional magnetic resonance imaging (rsfMRI) data, have been associated with cognitive performance in healthy subjects. How these features jointly relate to cognition in diseased states has not been investigated. In this study, 46 relapsing-remitting multiple sclerosis subjects underwent rsfMRI scans and a focused cognitive battery. With a sliding window approach, we examined six dynamic network features that indicated how connectivity changed over time as well as six measures derived from graph theory to reflect static network characteristics. Multiset canonical correlation analysis (MCCA) was then carried out to investigate the relations between dynamic network features, stationary network characteristics, cognitive testing, demographic, disease severity, and mood. Multiple sclerosis (MS) subjects demonstrated weaker connectivity strength, decreased network density, reduced global changes, but increased changes in interhemispheric connectivity compared to controls. The MCCA model determined that executive functions and processing speed ability measured by Wechsler Adult Intelligence Scale IV (WAIS-IV) Working Memory Index, WAIS-IV Processing Speed Index, and the Verbal Fluency Test were positively correlated with education, dynamic connectivity, and static connectivity strength; while poor task switching was correlated with disease severity, psychiatric comorbidities such as depression, anxiety, and fatigue, and static network density. Taken together, our results suggest that better executive functioning in MS requires maintenance of a continued coordination between stationary and dynamic functional connectivity as well as the support of education, and dynamic functional connectivity may provide an additional cognitive biomarker of disease severity in the MS population.

Habitual exercisers versus sedentary subjects with Parkinson's Disease: Multimodal PET and fMRI study.

BACKGROUND: The benefits of exercise in PD have been linked to enhanced dopamine (DA) transmission in the striatum. OBJECTIVE: To examine differences in DA release, reward signaling, and clinical features between habitual exercisers and sedentary subjects with PD. METHODS: Eight habitual exercisers and 9 sedentary subjects completed [11 C]raclopride PET scans before and after stationary cycling to determine exercise-induced release of endogenous DA in the dorsal striatum. Additionally, functional MRI assessed ventral striatum activation during reward anticipation. All participants completed motor (UPDRS III; finger tapping; and timed-up-and-go) and nonmotor (Beck Depression Inventory; Starkstein Apathy Scale) assessments. RESULTS: [11 C]Raclopride analysis before and after stationary cycling demonstrated greater DA release in the caudate nuclei of habitual exercisers compared to sedentary subjects (P < 0.05). Habitual exercisers revealed greater activation of ventral striatum during the functional MRI reward task (P < 0.05) and lower apathy (P < 0.05) and bradykinesia (P < 0.05) scores versus sedentary subjects. CONCLUSIONS: Habitual exercise is associated with preservation of motor and nonmotor function, possibly mediated by increased DA release. This study formulates a foundation for prospective, randomized controlled studies. © 2018 International Parkinson and Movement Disorder Society.

The role of high-field magnetic resonance imaging in parkinsonian disorders: Pushing the boundaries forward.

Historically, magnetic resonance imaging (MRI) has contributed little to the study of Parkinson's disease (PD), but modern MRI approaches have unveiled several complementary markers that are useful for research and clinical applications. Iron- and neuromelanin-sensitive MRI detect qualitative changes in the substantia nigra. Quantitative MRI markers can be derived from diffusion weighted and iron-sensitive imaging or volumetry. Functional brain alterations at rest or during task performance have been captured with functional and arterial spin labeling perfusion MRI. These markers are useful for the diagnosis of PD and atypical parkinsonism, to track disease progression from the premotor stages of these diseases and to better understand the neurobiological basis of clinical deficits. A current research goal using MRI is to generate time-dependent models of the evolution of PD biomarkers that can help understand neurodegeneration and provide reliable markers for therapeutic trials. This article reviews recent advances in MRI biomarker research at high-field (3T) and ultra high field-imaging (7T) in PD and atypical parkinsonism. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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

ABSTRACT: 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. This review explores the advantages and challenges associated with using AI-driven video monitoring to care for elderly patients with movement disorders. The AI-based video monitoring systems offer improved efficiency and objectivity in remote patient monitoring, enabling real-time analysis of data, more uniform outcomes and augmented support for clinical trials. However, challenges, such as video quality, privacy compliance and noisy training labels, during development need to be addressed. Ultimately, the advancement of video monitoring for movement disorders is expected to evolve towards discreet, home-based evaluations during routine daily activities. This progression must incorporate data security, ethical considerations and adherence to regulatory standards.

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. Thirteen Parkinson's disease patients with apathy and 13 Parkinson's disease patients without apathy as well as 12 healthy controls were instructed to squeeze a hand grip device to earn a monetary reward proportional to the grip force they used. Event-related spectral perturbations during the presentation of a reward cue and squeezing were analysed using multiset canonical correlation analysis to detect different orthogonal components of temporally consistent event-related spectral perturbations across trials and participants. The first component, predominantly located over parietal regions, demonstrated suppression of low-beta (12-20 Hz) power (i.e. beta desynchronization) during reward cue presentation that was significantly smaller in Parkinson's disease patients with apathy compared with healthy controls. Unlike traditional event-related spectral perturbation analysis, the beta desynchronization in this component was significantly correlated with clinical apathy scores. Higher monetary rewards resulted in larger beta desynchronization in healthy controls but not Parkinson's disease patients. The second component contained gamma and theta frequencies and demonstrated exaggerated theta (4-8 Hz) power in Parkinson's disease patients with apathy during the reward cue and squeezing compared with healthy controls (HCs), and this was positively correlated with Montreal Cognitive Assessment scores. The third component, over central regions, demonstrated significantly different beta power across groups, with apathetic groups having the lowest beta power. Our results emphasize that altered low-beta and low-theta oscillations are critical for reward processing and motor planning in Parkinson's disease patients with apathy and these may provide a target for non-invasive neuromodulation.

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 0.715 for severity assessment. A post-hoc explanation model is employed to assess the importance of features and tasks in diagnosis and severity evaluation. Notably, novel tasks that are not adapted from MDS-UPDRS Part III like the circle drawing, coordination test, and alternative tapping test are found to be highly important, suggesting digital assessments for PD can go beyond digitizing existing tests. The alternative tapping test emerges as the most significant task. Using its features alone achieves prediction accuracies comparable to the full task set, underscoring its potential as an independent screening tool. This study addresses a notable research gap by digitalizing a wide array of tests, including novel ones, and conducting a comparative analysis of their feature and task importance. These insights provide guidance for task selection and future development in PD mobile assessments, a field previously lacking such comparative studies.

Novel spatial analysis method for PET images using 3D moment invariants: applications to Parkinson's disease.

We present a novel analysis method for positron emission tomography (PET) data that uses the spatial characteristics of the radiotracer's distribution within anatomically-defined regions of interest (ROIs) to provide an independent feature that may aid in characterizing pathological and normal states. The analysis of PET data for research purposes traditionally involves kinetic modeling of the concentration of the radiotracer over time within a ROI to derive parameters related to the uptake/binding of the radiotracer in the body. Here we describe an analysis method to quantify the spatial changes present in PET images based on 3D shape descriptors that are invariant to translation, scaling, and rotation, called 3D moment invariants (3DMIs). An ROI can therefore be characterized not only by the radiotracer's uptake rate constant or binding potential within the ROI, but also the 3D spatial shape and distribution of the radioactivity throughout the ROI. This is particularly relevant in Parkinson's disease (PD), where both the kinetic and the spatial distribution of the tracer are known to change due to disease: the posterior parts of the striatum (in particular in the putamen) are affected before the anterior parts. Here we show that 3DMIs are able to quantify the spatial distribution of PET radiotracer images allowing for discrimination between healthy controls and PD subjects. More importantly, 3DMIs are found to be well correlated with subjects' scores on the United Parkinson's Disease Rating Scale (a clinical measure of disease severity) in all anatomical regions studied here (putamen, caudate and ventral striatum). On the other hand, kinetic parameters only show significant correlation to clinically-assessed PD severity in the putamen. We also find that 3DMI-characterized changes in spatial patterns of dopamine release in response to l-dopa medication are significantly correlated with PD severity. These findings suggest that quantitative studies of a radiotracer's spatial distribution may provide complementary information to kinetic modeling that is relatively robust to intersubject variability and may contribute novel information in PET neuroimaging studies.