Diffusion tensor metrics, motor and non-motor symptoms in de novo Parkinson's disease.

INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neurons' degeneration of the substantia nigra, presenting with motor and non-motor symptoms. We hypothesized that altered diffusion metrics are associated with clinical symptoms in de novo PD patients. METHODS: Fractional Anisotropy (FA) and Mean (MD), Axial (AD), and Radial Diffusivity (RD) were assessed in 55 de novo PD patients (58.62 ± 9.85 years, 37 men) and 55 age-matched healthy controls (59.92 ± 11.25 years, 34 men). Diffusion-weighted images and clinical variables were collected from the Parkinson's Progression Markers Initiative study. Tract-based spatial statistics were used to identify white matter (WM) changes, and fiber tracts were localized using the JHU-WM tractography atlas. Motor and non-motor symptoms were evaluated in patients. RESULTS: We observed higher FA values and lower RD values in patients than controls in various fiber tracts (p-TFCE < 0.05). No significant MD or AD difference was observed between groups. Diffusion metrics of several regions significantly correlated with non-motor (state and trait anxiety and daytime sleepiness) and axial motor symptoms in the de novo PD group. No correlations were observed between diffusion metrics and other clinical symptoms evaluated. CONCLUSION: Our findings suggest microstructural changes in de novo PD fiber tracts; however, limited associations with clinical symptoms reveal the complexity of PD pathology. They may contribute to understanding the neurobiological changes underlying PD and have implications for developing targeted interventions. However, further longitudinal research with larger cohorts and consideration of confounding factors are necessary to elucidate the underlying mechanisms of these diffusion alterations in de novo PD.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Social and non-social working memory in neurodegeneration.

Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both working memory and social deficits. We assessed working memory domain-specificity across behavioral, electrophysiological, and neuroimaging dimensions in 245 participants. A novel working memory task involving social and non-social stimuli with three load levels was assessed across controls and different neurodegenerative conditions with recognized impairments in: working memory and social cognition (behavioral-variant frontotemporal dementia); general cognition (Alzheimer's disease); and unspecific patterns (Parkinson's disease). We also examined resting-state theta oscillations and functional connectivity correlates of working memory domain-specificity. Results in controls and all groups together evidenced increased working memory demands for social stimuli associated with frontocinguloparietal theta oscillations and salience network connectivity. Canonical frontal theta oscillations and executive-default mode network anticorrelation indexed non-social stimuli. Behavioral-variant frontotemporal dementia presented generalized working memory deficits related to posterior theta oscillations, with social stimuli linked to salience network connectivity. In Alzheimer's disease, generalized working memory impairments were related to temporoparietal theta oscillations, with non-social stimuli linked to the executive network. Parkinson's disease showed spared working memory performance and canonical brain correlates. Findings support a social-specific working memory and related disease-selective pathophysiological mechanisms.

Comparison between a handheld ultrasound device and a traditional ultrasound for performing transcranial sonography in patients with Parkinson's disease.

OBJECTIVE: The aim of this study is to compare a portable ultrasound (US) device and a traditional US for performing transcranial ultrasonography (CCT) in patients with Parkinson's disease (PD). METHODS: This is a cross-sectional, observational, and analytical study. The study recruited a total of 129 individuals from two public hospitals in the city of Rio de Janeiro in a prospective and non-randomized manner between September 2019 and July 2021 as follows: group A with 31 patients with PD, group B with 65 patients with PD, and group C with 64 healthy individuals. Group A was used to collect data to establish the agreement analysis of the TCS measurements between the two devices. Groups B and C provided data for constructing the receiver operating characteristic curve for the handheld US. The subjects underwent the assessment of the transtemporal bone window (TW) quality, the mesencephalon area, the size of the third ventricle, and the substantia nigra (SN) hyperechogenicity area. RESULTS: There was a good agreement between the methods regarding the quality of the TW-Kappa concordance coefficient of 100% for the right TW and 83% for the left, the midbrain area-intraclass correlation coefficient (ICC) of 69%, the SN area ICC = 90% for the right SN and 93% for the left and the size of the third ventricle ICC = 96%. The cutoff point for the SN echogenic area in the handheld US was 0.20 cm2 . CONCLUSIONS: The handheld US is a viable imaging method for performing TCS because it shows good agreement with the measurements performed with traditional equipment, and the measurement of SN echogenic area for PD diagnosis presents good sensitivity and specificity.

White matter hyperintensity presence, quantity, and location exhibits no association with motor and non-motor manifestations of PD.

INTRODUCTION: Nonspecific areas of brain white matter hyperintensity (WMH) are commonly found in the elderly. Some studies have shown that the presence, quantity, and location of WMHs may be associated with the development of cognitive and motor decline in patients with Parkinson's disease (PD), but the results remain controversial. This study aimed to evaluate the relationship of WMH to motor and non-motor symptoms, including dysautonomia and rapid eye movement sleep behavior disorder (RBD), in patients with PD. METHODS: Brain magnetic resonance images were acquired from 120 patients diagnosed with PD and analyzed for WMH classification and quantification. Motor symptoms were quantified using sub-scores of the Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)-III. Dysautonomia was evaluated by autonomic reactivity tests, and polysomnography was used for the diagnosis of RBD. RESULTS: Age, total value of the MDS-UPDRS-III tremor sub-score, and the presence of dysautonomia were found to be linearly positively associated. Specifically, the duration of PD was positively associated with rigidity, bradykinesia, axial symptoms, prevalence of dysautonomia, and RBD sub-scores. However, in the multivariate analysis adjusted for variables of interest, no statistical significance was found for any of the models. CONCLUSION: The presence, quantity, and location of WMH were not associated with the analyzed motor and non-motor manifestations of PD.

Iron- and Neuromelanin-Weighted Neuroimaging to Study Mitochondrial Dysfunction in Patients with Parkinson's Disease.

The underlying causes of Parkinson's disease are complex, and besides recent advances in elucidating relevant disease mechanisms, no disease-modifying treatments are currently available. One proposed pathophysiological hallmark is mitochondrial dysfunction, and a plethora of evidence points toward the interconnected nature of mitochondria in neuronal homeostasis. This also extends to iron and neuromelanin metabolism, two biochemical processes highly relevant to individual disease manifestation and progression. Modern neuroimaging methods help to gain in vivo insights into these intertwined pathways and may pave the road to individualized medicine in this debilitating disorder. In this narrative review, we will highlight the biological rationale for studying these pathways, how distinct neuroimaging methods can be applied in patients, their respective limitations, and which challenges need to be overcome for successful implementation in clinical studies.

Magnetic resonance and dopamine transporter imaging for the diagnosis of Parkinson´s disease: a narrative review.

BACKGROUND: the diagnosis of Parkinson's disease (PD) can be challenging, especially in the early stages, albeit its updated and validated clinical criteria. Recent developments on neuroimaging in PD, altogether with its consolidated role of excluding secondary and other neurodegenerative causes of parkinsonism, provide more confidence in the diagnosis across the different stages of the disease. This review highlights current knowledge and major recent advances in magnetic resonance and dopamine transporter imaging in aiding PD diagnosis. OBJECTIVE: This study aims to review current knowledge about the role of magnetic resonance imaging and neuroimaging of the dopamine transporter in diagnosing Parkinson's disease. METHODS: We performed a non-systematic literature review through the PubMed database, using the keywords "Parkinson", "magnetic resonance imaging", "diffusion tensor", "diffusion-weighted", "neuromelanin", "nigrosome-1", "single-photon emission computed tomography", "dopamine transporter imaging". The search was restricted to articles written in English, published between January 2010 and February 2022. RESULTS: The diagnosis of Parkinson's disease remains a clinical diagnosis. However, new neuroimaging biomarkers hold promise for increased diagnostic accuracy, especially in earlier stages of the disease. CONCLUSION: Future validation of new imaging biomarkers bring the expectation of an increased neuroimaging role in the diagnosis of PD in the following years.

Prelemniscal Radiations as a Target for the Treatment of Parkinson Disease - Individual Variations in the Stereotactic Location of Fiber Components: A Probabilistic Tractography Study.

OBJECTIVE: Prelemniscal radiation (Raprl) lesions and deep brain stimulation effectively control motor symptoms of Parkinson disease, but individual variations in the stereotactic location of its fiber components constitute a significant concern. The objective of this study was to determine individual variations in the stereotactic location of fiber tracts composing Raprl. METHODS: Raprl fiber composition was determined in a group of 10 Parkinson patients and 10 matched controls using 3T magnetic resonance imaging, brain imaging processed for diffusion-weighted images, tract density imaging, and constrained spherical deconvolution. The stereotactic position of the point of maximal proximity (PMP), which is the point where the most significant number of fibers is concentrated in the smallest volume in the tractography, was evaluated in the right and left hemispheres of the same person, between individuals and between patients and controls for each tract in coordinates "x," "y," and "z." The stereotactic coordinates at which PMP of all tracts meet were statistically determined, representing the recommended aim for this target. RESULTS: Stereotactic coordinates of the 3 fiber tracts composing Raprl, cerebellar-thalamic-cortical, globus pallidus-peduncle-pontine nucleus, and mesencephalic-orbital frontal cortex, did not vary between right and left hemispheres in the same person and between patients and controls. In contrast, PMP variability between individuals was significant, mainly for the mesencephalic-orbitofrontal tract. Therefore, probabilistic tractography can better determine individual variations to plan electrode trajectories. CONCLUSIONS: Individual PMP variations for fiber tracts in Raprl, identified by probabilistic tractography, provide a platform for planning the stereotactic approach to conform volumes for deep brain stimulation and lesions.

Comparative analysis of striatal [18 F]FDOPA uptake in a partial lesion model of Parkinson's disease in rats: Ratio method versus graphical model.

Animal models of Parkinson's disease are useful to evaluate new treatments and to elucidate the etiology of the disease. Hence, it is necessary to have methods that allow quantification of their effectiveness. [18 F]FDOPA-PET (FDOPA-PET) imaging is outstanding for this purpose because of its capacity to measure changes in the dopaminergic pathway noninvasively and in vivo. Nevertheless, PET acquisition and quantification is time-consuming making it necessary to find faster ways to quantify FDOPA-PET data. This study evaluated Male Wistar rats by FDOPA, before and after being partially injured with 6-OHDA unilaterally. MicroPET scans with a duration of 120 min were acquired and Patlak reference plots were created to estimate the influx constant Kc in the striatum using the full dynamic scan data. Additionally, simple striatal-to-cerebral ratios (SCR) of short static acquisitions were computed and compared with the Kc values. Good correlation (r > 0.70) was obtained between Kc and SCR, acquired between 80-120 min after FDOPA administration with frames of 10 or 20 min and both methods were able to separate the FDOPA-uptake of healthy controls from that of the PD model (SCR -28%, Kc -71%). The present study concludes that Kc and SCR can be trustfully used to discriminate partially lesioned rats from healthy controls.

A multimodal Parkinson quantification by fusing eye and gait motion patterns, using covariance descriptors, from non-invasive computer vision.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a motor neurodegenerative disease principally manifested by motor disabilities, such as postural instability, bradykinesia, tremor, and stiffness. In clinical practice, there exist several diagnostic rating scales that coarsely allow the measurement, characterization and classification of disease progression. These scales, however, are only based on strong changes in kinematic patterns, and the classification remains subjective, depending on the expertise of physicians. In addition, even for experts, disease analysis based on independent classical motor patterns lacks sufficient sensitivity to establish disease progression. Consequently, the disease diagnosis, stage, and progression could be affected by misinterpretations that lead to incorrect or inefficient treatment plans. This work introduces a multimodal non-invasive strategy based on video descriptors that integrate patterns from gait and eye fixation modalities to assist PD quantification and to support the diagnosis and follow-up of the patient. The multimodal representation is achieved from a compact covariance descriptor that characterizes postural and time changes of both information sources to improve disease classification. METHODS: A multimodal approach is introduced as a computational method to capture movement abnormalities associated with PD. Two modalities (gait and eye fixation) are recorded in markerless video sequences. Then, each modality sequence is represented, at each frame, by primitive features composed of (1) kinematic measures extracted from a dense optical flow, and (2) deep features extracted from a convolutional network. The spatial distributions of these characteristics are compactly coded in covariance matrices, making it possible to map each particular dynamic in a Riemannian manifold. The temporal mean covariance is then computed and submitted to a supervised Random Forest algorithm to obtain a disease prediction for a particular patient. The fusion of the covariance descriptors and eye movements integrating deep and kinematic features is evaluated to assess their contribution to disease quantification and prediction. In particular, in this study, the gait quantification is associated with typical patterns observed by the specialist, while ocular fixation, associated with early disease characterization, complements the analysis. RESULTS: In a study conducted with 13 control subjects and 13 PD patients, the fusion of gait and ocular fixation, integrating deep and kinematic features, achieved an average accuracy of 100% for early and late fusion. The classification probabilities show high confidence in the prediction diagnosis, the control subjects probabilities being lower than 0.27 with early fusion and 0.3 with late fusion, and those of the PD patients, being higher than 0.62 with early fusion and 0.51 with late fusion. Furthermore, it is observed that higher probability outputs are correlated with more advanced stages of the disease, according to the H&Y scale. CONCLUSIONS: A novel approach for fusing motion modalities captured in markerless video sequences was introduced. This multimodal integration had a remarkable discrimination performance in a study conducted with PD and control patients. The representation of compact covariance descriptors from kinematic and deep features suggests that the proposed strategy is a potential tool to support diagnosis and subsequent monitoring of the disease. During fusion it was observed that devoting major attention to eye fixational patterns may contribute to a better quantification of the disease, especially at stage 2.

Pursuing Multiple Biomarkers for Early Idiopathic Parkinson's Disease Diagnosis.

Parkinson's disease (PD) ranks first in the world as a neurodegenerative movement disorder and occurs most commonly in an idiopathic form. PD patients may have motor symptoms, non-motor symptoms, including cognitive and behavioral changes, and symptoms related to autonomic nervous system (ANS) failures, such as gastrointestinal, urinary, and cardiovascular symptoms. Unfortunately, the diagnostic accuracy of PD by general neurologists is relatively low. Currently, there is no objective molecular or biochemical test for PD; its diagnosis is based on clinical criteria, mainly by cardinal motor symptoms, which manifest when patients have lost about 60-80% of dopaminergic neurons. Therefore, it is urgent to establish a panel of biomarkers for the early and accurate diagnosis of PD. Once the disease is accurately diagnosed, it may be easier to unravel idiopathic PD's pathogenesis, and ultimately, finding a cure. This review discusses several biomarkers' potential to set a panel for early idiopathic PD diagnosis and future directions.

Global efficiency of the motor network is decreased in Parkinson's disease in comparison with essential tremor and healthy controls.

BACKGROUND: Graph theory (GT) is a mathematical field that analyses complex networks that can be applied to neuroimaging to quantify brain's functional systems in Parkinson's disease (PD) and essential tremor (ET). OBJECTIVES: To evaluate the functional connectivity (FC) measured by the global efficiency (GE) of the motor network in PD and compare it to ET and healthy controls (HC), and correlate it to clinical parameters. METHODS: 103 subjects (54PD, 18ET, 31HC) were submitted to structural and functional MRI. A network was designed with regions of interest (ROIs) involved in motor function, and GT was applied to determine its GE. Clinical parameters were analyzed as covariates to estimate the impact of disease severity and medication on GE. RESULTS: GE of the motor circuit was reduced in PD in comparison with HC (p .042). Areas that most contributed to it were left supplementary motor area (SMA) and bilateral postcentral gyrus. Tremor scores correlated positively with GE of the motor network in PD subgroups. For ET, there was an increase in the connectivity of the anterior cerebellar network to the other ROIs of the motor circuit in comparison with PD. CONCLUSIONS: FC measured by the GE of the motor network is diminished in PD in comparison with HC, especially due to decreased connectivity of left SMA and bilateral postcentral gyrus. This finding supports the theory that there is a global impairment of the motor network in PD, and it does not affect just the basal ganglia, but also areas associated with movement modulation. The ET group presented an increased connectivity of the anterior cerebellar network to the other ROIs of the motor circuit when compared to PD, which reinforces what it is known about its role in this pathology.

Aerobic Exercise Combined With Transcranial Direct Current Stimulation Over the Prefrontal Cortex in Parkinson Disease: Effects on Cortical Activity, Gait, and Cognition.

BACKGROUND: Since people with Parkinson disease (PD) rely on limited prefrontal executive resources for the control of gait, interventions targeting the prefrontal cortex (PFC) may help in managing PD-related gait impairments. Transcranial direct current stimulation (tDCS) can be used to modulate PFC excitability and improve prefrontal cognitive functions and gait. OBJECTIVE: We investigated the effects of adding anodal tDCS applied over the PFC to a session of aerobic exercise on gait, cognition, and PFC activity while walking in people with PD. METHODS: A total of 20 people with PD participated in this randomized, double-blinded, sham-controlled crossover study. Participants attended two 30-minute sessions of aerobic exercise (cycling at moderate intensity) combined with different tDCS conditions (active- or sham-tDCS), 1 week apart. The order of sessions was counterbalanced across the sample. Anodal tDCS (2 mA for 20 minutes [active-tDCS] or 10 s [sham-tDCS]) targeted the PFC in the most affected hemisphere. Spatiotemporal gait parameters, cognitive functions, and PFC activity while walking were assessed before and immediately after each session. RESULTS: Compared with the pre-assessment, participants decreased step time variability (effect size: -0.4), shortened simple and choice reaction times (effect sizes: -0.73 and -0.57, respectively), and increased PFC activity in the stimulated hemisphere while walking (effect size: 0.54) only after aerobic exercise + active-tDCS. CONCLUSION: The addition of anodal tDCS over the PFC to a session of aerobic exercise led to immediate positive effects on gait variability, processing speed, and executive control of walking in people with PD.

Cortical Activity Underlying Gait Improvements Achieved With Dopaminergic Medication During Usual Walking and Obstacle Avoidance in Parkinson Disease.

BACKGROUND: Dopaminergic medication improves gait in people with Parkinson disease (PD). However, it remains unclear if dopaminergic medication modulates cortical activity while walking. OBJECTIVE: We investigated the effects of dopaminergic medication on cortical activity during unobstructed walking and obstacle avoidance in people with PD. METHODS: A total of 23 individuals with PD, in both off (PDOFF) and on (PDON) medication states, and 30 healthy older adults (control group [CG]) performed unobstructed walking and obstacle avoidance conditions. Cortical activity was acquired through a combined functional near-infrared spectroscopy electroencephalography (EEG) system, along with gait parameters, through an electronic carpet. Prefrontal cortex (PFC) oxygenated hemoglobin (HbO2) and EEG absolute power from FCz, Cz, and CPz channels were calculated. RESULTS: HbO2 concentration reduced for people with PDOFF during obstacle avoidance compared with unobstructed walking. In contrast, both people with PDON and the CG had increased HbO2 concentration when avoiding obstacles compared with unobstructed walking. Dopaminergic medication increased step length, step velocity, and ß and γ power in the CPz channel, regardless of walking condition. Moreover, dopaminergic-related changes (ie, on-off) in FCz/CPz γ power were associated with dopaminergic-related changes in step length for both walking conditions. CONCLUSIONS: PD compromises the activation of the PFC during obstacle avoidance, and dopaminergic medication facilitates its recruitment. In addition, PD medication increases sensorimotor integration during walking by increasing posterior parietal cortex (CPz) activity. Increased γ power in the CPz and FCz channels is correlated with step length improvements achieved with dopaminergic medication during unobstructed walking and obstacle avoidance in PD.

A Real-World Study of Cerebral 99mTc-TRODAT-1 Single-Photon Emission Computed Tomography (SPECT) Imaging of the Dopamine Transporter in Patients with Parkinson Disease from a Tertiary Hospital in Brazil.

BACKGROUND Studies on the routine clinical use of dopamine transporter (DAT) imaging have largely been conducted in Europe and the United States. In this real-world study, we investigated the use of cerebral 99mTc-TRODAT-1 SPECT imaging of DAT in patients with Parkinson disease (PD) at a tertiary hospital in Brazil. MATERIAL AND METHODS We included 119 patients with suspected PD or clinically unclear parkinsonism who underwent brain scintigraphy with 99mTc-TRODAT-1 during a 3-year period. Additionally, a brief interview was conducted with the physician who requested the scan to determine the usefulness of the method in clinical decision-making. RESULTS Regarding the scan requests, most were intended to evaluate or confirm dopaminergic denervation (69%), distinguish PD from essential tremor (10%), or distinguish degenerative parkinsonism from drug-induced parkinsonism (6%). Data analysis showed that scintigraphy with 99mTc-TRODAT-1 was useful in 85% of cases, changing the management of 75% of the patients who underwent a scan. The majority of physicians who requested the scan were neurologists, and 54% were self-reported movement disorder specialists. An inappropriate use of DAT imaging was seen in 5% of cases. CONCLUSIONS This study demonstrated that brain scintigraphy with the DAT ligand 99mTc-TRODAT-1 may influence diagnostic or therapeutic interventions, meaning that Brazilian physicians who requested the exam have taken in vivo DAT results into account at the time of clinical decision-making.

Long-Term Improvement of Parkinson Disease Motor Symptoms Derived From Lesions of Prelemniscal Fiber Tract Components.

BACKGROUND: Prelemniscal radiations (Raprl) are composed of different fiber tracts, connecting the brain stem and cerebellum with basal ganglia and cerebral cortex. In Parkinson disease (PD), lesions in Raprl induce improvement of tremor, rigidity, and bradykinesia in some patients, while others show improvement of only 1 or 2 symptoms, suggesting different fiber tracts mediate different symptoms. OBJECTIVE: To search for correlations between improvements of specific symptoms with surgical lesions of specific fiber tract components of Raprl in patients with PD. METHODS: A total of 10 patients were treated with unilateral radiofrequency lesions directed to Raprl. The improvement for tremor, rigidity, bradykinesia, posture, and gait was evaluated at 24 to 33 mo after operation through the Unified Parkinson's Disease Rating Scale (UPDRS) score, and the precise location and extension of lesions through structural magnetic resonance imaging and probabilistic tractography at 6 to 8 mo postsurgery. Correlation between percentage of fiber tract involvement and percentage of UPDRS-III score improvement was evaluated through Spearman's correlation coefficient. RESULTS: Group average improvement was 86% for tremor, 62% for rigidity, 56% for bradykinesia, and 45% for gait and posture. Improvement in global UPDRS score correlated with extent of lesions in fibers connecting with contralateral cerebellar cortex and improvement of posture and gait with fibers connecting with contralateral deep cerebellar nuclei. Lesion of fibers connecting the globus pallidum with pedunculopontine nucleus induced improvement of gait and posture over other symptoms. CONCLUSION: Partial lesion of Raprl fibers resulted in symptom improvement at 2-yr follow-up. Lesions of selective fiber components may result in selective improvement of specific symptoms.

Gray Matter Atrophy in Parkinson's Disease and the Parkinsonian Variant of Multiple System Atrophy: A Combined ROI- and Voxel-Based Morphometric Study.

OBJECTIVES: Parkinson's disease (PD) and the parkinsonian variant of multiple system atrophy (MSA-P) are distinct neurodegenerative disorders that share similar clinical features of parkinsonism. The morphological alterations of these diseases have yet to be understood. The purpose of this study was to evaluate gray matter atrophy in PD and MSA-P using regions of interest (ROI)-based measurements and voxel-based morphometry (VBM). METHODS: We studied 41 patients with PD, 20 patients with MSA-P, and 39 controls matched for age, sex, and handedness using an improved T1-weighted sequence that eased gray matter segmentation. The gray matter volumes were measured using ROI and VBM. RESULTS: ROI volumetric measurements showed significantly reduced bilateral putamen volumes in MSA-P patients compared with those in PD patients and controls (p<0.05), and the volumes of the bilateral caudate nucleus were significantly reduced in both MSA-P and PD patients compared with those in the controls (p<0.05). VBM analysis revealed multifocal cortical and subcortical atrophy in both MSA-P and PD patients, and the volumes of the cerebellum and temporal lobes were remarkably reduced in MSA-P patients compared with the volumes in PD patients (p<0.05). CONCLUSIONS: Both PD and MSA-P are associated with gray matter atrophy, which mainly involves the bilateral putamen, caudate nucleus, cerebellum, and temporal lobes. ROI and VBM can be used to identify these morphological alterations, and VBM is more sensitive and repeatable and less time-consuming, which may have potential diagnostic value.

Voxel-wise meta-analysis of structural changes in gray matter of Parkinson's disease patients with mild cognitive impairment.

Evidence from previous voxel-based morphometry (VBM) studies indicates that widespread brain regions are involved in Parkinson's disease with mild cognitive impairment (PD-MCI). However, the spatial localization reported for gray matter (GM) abnormalities is heterogeneous. The aim of the present study was to quantitatively integrate studies on GM abnormalities observed in PD-MCI in order to determine whether a pattern exists. Eligible whole-brain VBM studies were identified by a systematic search of articles in PubMed and EMBASE databases spanning from 1995 to January 1, 2019. A meta-analysis was performed to investigate regional GM abnormalities in PD-MCI. The anisotropic effect size version of seed-based d mapping (AES-SDM) meta-analysis was conducted to explore the GMV differences of PD-MCI compared with PD patients with normal cognitive function (PD-NC). A total of 12 studies comprising 243 PD-MCI patients and 326 PD-NC were included in the meta-analysis. PD-MCI patients showed a robust GM decrease in the left insula and left superior temporal gyrus. Moreover, meta-regression analysis demonstrated that age, PD duration and stage, and Unified Parkinson's Disease Rating Scale III and Mini-Mental State Examination scores might be partly correlated with the GM abnormalities observed in PD-MCI patients. The convergent findings of this quantitative meta-analysis revealed a characteristic neuroanatomical pattern in PD-MCI. The findings provide some evidence that MCI in PD may result in the breakdown of the insula and temporal gyrus, which may serve as specific regions of interest for further investigations.