Neuroimaging advances in Parkinson's disease

Rispoli, Vittorio; Schreglmann, Sebastian R; Bhatia, Kailash P.

doi:10.1097/wco.0000000000000584

Cited by 31 publications

(25 citation statements)

References 88 publications

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“…Rs-fMRI is widely used in the study of PD (Gu et al, 2016 ; Amoroso et al, 2018 ; Hohenfeld et al, 2018 ; Rispoli et al, 2018 ), and ALFF is one of its commonly used metrics (Hu et al, 2015 ; Gu et al, 2016 ). In this study, the ROI-based method (Tang et al, 2017 ; Sun et al, 2018 ) was used to extract the histogram features of the ALFF images in specific brain areas using the AAL 116 atlas, and the machine learning method was used for data dimensionality reduction and modeling to explore the diagnostic value of the rs-fMRI-based machine learning in PD.…”

Section: Discussionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson’s disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

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Section: Discussionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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“…PD is typically characterized by levodopa responsive rigidity, bradykinesia, and tremor, whereas atypical Parkinsonian disorders present with several other motor systems and tend to be poorly responsive to levodopa. These disorders have been extensively studied using a multitude of structural neuroimaging sequences and modalities of analysis (Paviour et al, 2006;Heim et al, 2018;Rispoli et al, 2018); however, texture analysis (TA), which is a quantitative method of characterizing tissue types based on texture, has seldom been performed for Parkinsonian disorders.…”

Section: Introductionmentioning

confidence: 99%

A New Statistical Framework for Corpus Callosum Sub-Region Characterization Based on LBP Texture in Patients With Parkinsonian Disorders: A Pilot Study

et al. 2020

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The study is conducted to identify the best corpus callosum (CC) sub-region that corresponds to highest callosal tissue alteration occurred due to Parkinsonism. In this regard the efficacy of local binary pattern (LBP) based texture analysis (TA) of CC is performed to quantify the changes in topographical distribution of callosal fiber connected to different regions of cortex. The extent of highest texture alteration in CC is used for differential diagnosis. Materials and Methods: Study included subjects with Parkinson's disease (PD) (n = 20), and atypical Parkinsonian disorders -multiple system atrophy (MSA) (n = 20), Progressive supranuclear palsy (PSP) (n = 20), and healthy controls (n = 20).For each subject, we have automated the ROI extraction within mid-sagittal CC, followed by LBP TA. Two-class support vector machine (SVM) classification for each disorder as against HC is performed using extracted LBP features like energy and entropy. Correct classification ratio (CCR) is computed as the fraction of correctly classified ROIs at each of the CC sub-regions based on well-known Witelson and Hofer schemes. Based on CCR values, the "Scatter Index (SI)" is proposed to capture how localized (closer to 0) or scattered (closer to 1) the textural changes are among the CC sub-regions, across all subjects per class. The CCR values are further utilized to classify the disease groups.Results: Highest alteration of texture is observed in mid-body of CC. The consistency of this finding is quantified using SI for all subjects in a specific class that results more localized textural changes in PSP (15%) and MSA (25%), in comparison to PD (47%). Classification among disease groups results maximum classification accuracy of 90% in classifying PSP from PD-NC. Conclusion:Our result demonstrates the efficacy of proposed methodology in analyzing tissue alteration in MRI of Parkinsonian disorders and thus has potential to become valuable tool in computer aided differential diagnosis.

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“…NM is a protein polymer-similar to the skin pigment melanin-and a by-product of catecholamine synthesis, which is governed by the cytosolic dopamine content. It is predomi- nantly located in the SN pars compacta, ventral tegmental area, and locus coeruleus (42). Neurodegeneration in PD with dopaminergic cell loss causes reduced NM-pigmented neurons, which results in depigmentation (43).…”

Section: Nm Imagingmentioning

confidence: 99%

Imaging the Substantia Nigra in Parkinson Disease and Other Parkinsonian Syndromes

Bae¹,

Kim

Sohn

et al. 2021

Radiology

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which results in a trilaminar pattern (Fig 1). The use of 3-T SWI has yielded similar nigrosome 1 findings (Fig 2, A-D) (22)(23)(24)(25)(26)(27)(28). Across the literature, different terms, such as swallow tail sign, SN dorsolateral hyperintensity, or simply nigrosome 1, refer to nigrosome 1 appearance. Nigrosome Imaging in PDOn both 3-and 7-T MRI scans, patients with PD show nigral hyperintensity loss (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28). This loss of nigral hyperintensity reflects signal hypointensity due to iron deposition associated with dopaminergic neuronal loss. When using 7-T MRI, the diagnosis of PD based on nigral hyperintensity loss compared with the healthy population has shown high diagnostic performance, with sensitivity and specificity of up to 100% (21). The diagnostic performance for PD using nigral hyperintensity loss at 3-T SWI compared with healthy control subjects varies but is generally satisfactory (22-28). Moreover, the asymmetric nigrosome involvement correlates with asymmetry in clinical motor symptoms (24,28). This variability in diagnostic performance may be due to diversity in the imaging protocols and study populations (Table ). However, a meta-analysis of 3-T SWI studies on the diagnosis of PD reported sensitivity of 94.6% and specificity of 94.4%, which provide sufficient evidence for the high diagnostic performance of 3-T SWI (29). When comparing 3-and 7-T MRI of the SN, the sensitivity, specificity, and diagnostic accuracy for PD diagnosis were higher with 7-T MRI (93%, 100%, 96%, respectively) than with 3-T MRI (79%, 94%, 86%, respectively) (19).Rapid eye movement sleep behavior disorder (RBD) is a common sleep-related symptom in patients with PD. Especially idiopathic RBD is known as the premotor disease prior to the development of synucleinopathy, such as PD or dementia with Lewy bodies (DLB) (30,31). A high rate of loss of nigral hyperintensity was observed in patients with idiopathic RBD (30,31). Bae et al (31) showed that patients with idiopathic RBD with loss of nigral hyperintensity had lower dopamine transporter uptake at SPECT than those with intact nigral signal, progressing into overt PD or DLB, suggesting that nigrosome imaging could be a marker for synucleinopathy conversion risk in premotor disease. Further studies with larger and more diverse prodromal cohorts with longitudinal followup will provide greater insights.

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Neuroimaging advances in Parkinson's disease

Cited by 31 publications

References 88 publications

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

A New Statistical Framework for Corpus Callosum Sub-Region Characterization Based on LBP Texture in Patients With Parkinsonian Disorders: A Pilot Study

Imaging the Substantia Nigra in Parkinson Disease and Other Parkinsonian Syndromes

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