scholarly journals Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

2022 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Xuan Cao ◽  
Fang Yang ◽  
Jingyi Zheng ◽  
Xiao Wang ◽  
Qingling Huang
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Likelihood Estimation ◽  
Imaging Data ◽  
Mri Imaging ◽  
New Family ◽  
Tensor Regression ◽  
Nonmotor Feature ◽  
Cortical Regions ◽  
The Individual

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

scholarly journals Mapping brain structural differences and neuroreceptor correlates in Parkinson’s disease visual hallucinations: a mega-analysis

2021 ◽  
Author(s):  
Miriam Vignando ◽  
Dominic ffytche ◽  
Simon Lewis ◽  
Phil Hyu Lee ◽  
Seok Chung ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Empirical Bayes ◽  
Large Scale ◽  
Visual Hallucinations ◽  
Imaging Data ◽  
Control Networks ◽  
Density Maps ◽  
Cortical Regions ◽  
Cortical Involvement

Abstract Parkinson’s psychosis (PDP) describes a spectrum of symptoms that may arise in Parkinson’s disease (PD) including visual hallucinations (VH). Imaging studies investigating the neural correlates of PDP have been inconsistent in their findings, due to differences in study design and limitations of scale. Here we use empirical Bayes harmonisation to pool together structural imaging data from multiple research groups into a large-scale mega-analysis, allowing us to apply new methodological approaches to identify cortical regions and networks involved in VH and their relation to receptor binding. Differences of cortical thickness and surface area show a wider cortical involvement underlying VH than previously recognised, including primary visual cortex and its surrounds, and the hippocampus, independent of its role in cognitive decline. Structural covariance analyses point to a strong involvement of the attentional control networks in PD-VH, while associations with receptor density maps suggest neurotransmitter loss may drive the cortical changes.

scholarly journals Mapping brain structural differences and neuroreceptor correlates in Parkinson's disease visual hallucinations: a mega-analysis

2021 ◽  
Author(s):  
Miriam Vignando ◽  
Dominic ffytche ◽  
Simon Lewis ◽  
Phil Hyu Lee ◽  
Seok Jong Chung ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Empirical Bayes ◽  
Large Scale ◽  
Visual Hallucinations ◽  
Imaging Data ◽  
Control Networks ◽  
Density Maps ◽  
Cortical Regions ◽  
Cortical Involvement

Parkinson's psychosis (PDP) describes a spectrum of symptoms that may arise in Parkinson's disease (PD) including visual hallucinations (VH). Imaging studies investigating the neural correlates of PDP have been inconsistent in their findings, due to differences in study design and limitations of scale. Here we use empirical Bayes harmonisation to pool together structural imaging data from multiple research groups into a large-scale mega-analysis, allowing us to apply new methodological approaches to identify cortical regions and networks involved in VH and their relation to receptor binding. Differences of cortical thickness and surface area show a wider cortical involvement underlying VH than previously recognised, including primary visual cortex and its surrounds, and the hippocampus, independent of its role in cognitive decline. Structural covariance analyses point to a strong involvement of the attentional control networks in PD-VH, while associations with receptor density maps suggest neurotransmitter loss may drive the cortical changes.

scholarly journals Resting-state functional reorganization in Parkinson's disease: An activation likelihood estimation meta-analysis

Cortex ◽  
2017 ◽  
Vol 92 ◽  
pp. 119-138 ◽  
Author(s):  
Masoud Tahmasian ◽  
Simon B. Eickhoff ◽  
Kathrin Giehl ◽  
Frank Schwartz ◽  
Damian M. Herz ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Resting State ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Activation Likelihood Estimation ◽  
Functional Reorganization

scholarly journals Levodopa improves handwriting and instrumental tasks in previously treated patients with Parkinson’s disease

2020 ◽  
Vol 127 (10) ◽  
pp. 1369-1376
Author(s):  
Thomas Müller ◽  
Ali Harati
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Behavior ◽  
Fine Motor ◽  
Motor Symptoms ◽  
Execution Speed ◽  
Previously Treated Patients ◽  
Previously Treated ◽  
And Performance ◽  
The Individual

Abstract Motor symptoms in patients with Parkinson’s disease may be determined with instrumental tests and rating procedures. Their outcomes reflect the functioning and the impairment of the individual patient when patients are tested off and on dopamine substituting drugs. Objectives were to investigate whether the execution speed of a handwriting task, instrumentally assessed fine motor behavior, and rating scores improve after soluble levodopa application. 38 right-handed patients were taken off their regular drug therapy for at least 12 h before scoring, handwriting, and performance of instrumental devices before and 1 h after 100 mg levodopa intake. The outcomes of all performed procedures improved. The easy-to-perform handwriting task and the instrumental tests demand for fast and precise execution of movement sequences with considerable cognitive load in the domains' attention and concentration. These investigations may serve as additional tools for the testing of the dopaminergic response.

scholarly journals Neuroimaging in Parkinson’s disease dementia: connecting the dots

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Rimona S Weil ◽  
Joey K Hsu ◽  
Ryan R Darby ◽  
Louis Soussand ◽  
Michael D Fox
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Brain Network ◽  
Activation Likelihood Estimation ◽  
Stage Specificity ◽  
Network Mapping

Abstract Dementia is a common and devastating symptom of Parkinson’s disease but the anatomical substrate remains unclear. Some evidence points towards hippocampal involvement but neuroimaging abnormalities have been reported throughout the brain and are largely inconsistent across studies. Here, we test whether these disparate neuroimaging findings for Parkinson’s disease dementia localize to a common brain network. We used a literature search to identify studies reporting neuroimaging correlates of Parkinson’s dementia (11 studies, 385 patients). We restricted our search to studies of brain atrophy and hypometabolism that compared Parkinson’s patients with dementia to those without cognitive involvement. We used a standard coordinate-based activation likelihood estimation meta-analysis to assess for consistency in the neuroimaging findings. We then used a new approach, coordinate-based network mapping, to test whether neuroimaging findings localized to a common brain network. This approach uses resting-state functional connectivity from a large cohort of normative subjects (n = 1000) to identify the network of regions connected to a reported neuroimaging coordinate. Activation likelihood estimation meta-analysis failed to identify any brain regions consistently associated with Parkinson’s dementia, showing major heterogeneity across studies. In contrast, coordinate-based network mapping found that these heterogeneous neuroimaging findings localized to a specific brain network centred on the hippocampus. Next, we tested whether this network showed symptom specificity and stage specificity by performing two further analyses. We tested symptom specificity by examining studies of Parkinson’s hallucinations (9 studies, 402 patients) that are frequently co-morbid with Parkinson’s dementia. We tested for stage specificity by using studies of mild cognitive impairment in Parkinson’s disease (15 studies, 844 patients). Coordinate-based network mapping revealed that correlates of visual hallucinations fell within a network centred on bilateral lateral geniculate nucleus and correlates of mild cognitive impairment in Parkinson’s disease fell within a network centred on posterior default mode network. In both cases, the identified networks were distinct from the hippocampal network of Parkinson’s dementia. Our results link heterogeneous neuroimaging findings in Parkinson’s dementia to a common network centred on the hippocampus. This finding was symptom and stage-specific, with implications for understanding Parkinson’s dementia and heterogeneity of neuroimaging findings in general.

scholarly journals Erythrocyte Protoporphyrin Levels in Patients with Friedreich's and other Ataxias

1979 ◽  
Vol 6 (2) ◽  
pp. 227-232 ◽  
Author(s):  
R.O. Morgan ◽  
G. Naglie ◽  
D.F. Horrobin ◽  
A. Barbeau
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Friedreich’S Ataxia ◽  
Friedreich's Ataxia ◽  
Normal Range ◽  
Free Erythrocyte Protoporphyrin ◽  
Erythrocyte Protoporphyrin ◽  
The Individual

SummaryOf 13 patients with Fried-reich's ataxia (Type la) and 17 with type Ila recessive ataxias, all were found to have levels of “free erythrocyte protoporphyrin “ (FEP) above the normal range. The rise in FEP in Friedreich's ataxia correlated well with the age of the individual and thus appears to be related to the course of the disease. Subjects with olivo-ponto-cerebel-lar atrophy, Charlevoix syndrome, Duchenne muscular dystrophy, and Parkinson's disease were also found to have significantly elevated FEP, although the distribution overlapped with the normal range.The finding of elevated FEP may indicate a relative heme deficiency in ataxia due to inhibition offerrochelatase leading to a state of ineffective, persistent erythropoiesis. The possibility of a prosta-glandin abnormality being related to this defect and to the pathogenesis of ataxia is considered.

scholarly journals Brasilia Parkinson Cohort: assessing clinical, neuropsychological and imaging predictors of cognitive decline in Parkinson's disease

2019 ◽  
Author(s):  
Pedro Renato de Paula Brandão ◽  
Fernando Bisinoto Maluf ◽  
Talyta Grippe ◽  
Ingrid Faber ◽  
Danilo Assis Pereira ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diffusion Tensor ◽  
Motor Symptoms ◽  
Imaging Data ◽  
Diagnostic And Statistical Manual ◽  
Nested Case Control ◽  
Non Motor Symptoms

The following study protocol describes the rationale and methods of a cohort with a nested case-control study, which aims to identify risk factors and predictors of cognitive dysfunction in Parkinson's disease (PD). It is a study that will follow PD every 18 months with a comprehensive neuropsychological, clinical (motor and non-motor symptoms) and imaging (Magnetic Resonance Imaging) data collection. The criteria for diagnosing mild cognitive impairment (MCI) and dementia will respect the parameters previously published by the International Working Group on Mild Cognitive Impairment, and compared with those recommended by the Fifth edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association (DSM-5) and the International Parkinson's and Movement Disorders Society (MDS) criteria. We will also evaluate the neural substrate and underpinnings of PD non-motor symptoms, using advanced neuroimaging techniques, such as diffusion tensor imaging (DTI) and gray matter and white matter volumetric measurements.

scholarly journals Brasilia Parkinson Cohort: assessing clinical, neuropsychological and imaging predictors of cognitive decline in Parkinson's disease

2019 ◽  
Author(s):  
Pedro Renato de Paula Brandão ◽  
Fernando Bisinoto Maluf ◽  
Talyta Grippe ◽  
Ingrid Faber ◽  
Danilo Assis Pereira ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diffusion Tensor ◽  
Motor Symptoms ◽  
Imaging Data ◽  
Diagnostic And Statistical Manual ◽  
Nested Case Control ◽  
Non Motor Symptoms

The following study protocol describes the rationale and methods of a cohort with a nested case-control study, which aims to identify risk factors and predictors of cognitive dysfunction in Parkinson's disease (PD). It is a study that will follow PD every 18 months with a comprehensive neuropsychological, clinical (motor and non-motor symptoms) and imaging (Magnetic Resonance Imaging) data collection. The criteria for diagnosing mild cognitive impairment (MCI) and dementia will respect the parameters previously published by the International Working Group on Mild Cognitive Impairment, and compared with those recommended by the Fifth edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association (DSM-5) and the International Parkinson's and Movement Disorders Society (MDS) criteria. We will also evaluate the neural substrate and underpinnings of PD non-motor symptoms, using advanced neuroimaging techniques, such as diffusion tensor imaging (DTI) and gray matter and white matter volumetric measurements.

scholarly journals Network structure of brain atrophy in de novo Parkinson's disease

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Yashar Zeighami ◽  
Miguel Ulla ◽  
Yasser Iturria-Medina ◽  
Mahsa Dadar ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Medial Temporal Lobe ◽  
Healthy Aging ◽  
De Novo ◽  
Progression Markers ◽  
Magnetic Resonance Imaging Mri ◽  
Cortical Regions ◽  
Clinical Measures ◽  
Deformation Based Morphometry

We mapped the distribution of atrophy in Parkinson's disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinson's Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation.

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

2019 ◽  
Vol 29 (09) ◽  
pp. 1950010 ◽  
Author(s):  
Octavio Martinez Manzanera ◽  
Sanne K. Meles ◽  
Klaus L. Leenders ◽  
Remco J. Renken ◽  
Marco Pagani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Positron Emission Tomography ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Fdg Pet ◽  
Characteristic Curve ◽  
Emission Tomography ◽  
Imaging Data ◽  
Positron Emission

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

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