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Unsupervised clustering of dopamine transporter PET imaging discovers heterogeneity of parkinsonism.

ABSTRACT: Parkinsonism has heterogeneous nature, showing distinctive patterns of disease progression and prognosis. We aimed to find clusters of parkinsonism based on 18 F-fluoropropyl-carbomethoxyiodophenylnortropane (FP-CIT) PET as a data-driven approach to evaluate heterogenous dopaminergic neurodegeneration patterns. Two different cohorts of patients who received FP-CIT PET were collected. A labeled cohort (n = 94) included patients with parkinsonism who underwent a clinical follow-up of at least 3?years (mean 59.0?±?14.6?months). An unlabeled cohort (n = 813) included all FP-CIT PET data of a single-center. All PET data were clustered by a dimension reduction method followed by hierarchical clustering. Four distinct clusters were defined according to the imaging patterns. When the diagnosis of the labeled cohort of 94 patients was compared with the corresponding cluster, parkinsonism patients were mostly included in two clusters, cluster "0" and "2." Specifically, patients with progressive supranuclear palsy were significantly more included in cluster 0. The two distinct clusters showed significantly different clinical features. Furthermore, even in PD patients, two clusters showed a trend of different clinical features. We found distinctive clusters of parkinsonism based on FP-CIT PET-derived heterogeneous neurodegeneration patterns, which were associated with different clinical features. Our results support a biological underpinning for the heterogeneity of neurodegeneration in parkinsonism.

SUBMITTER: Suh M

PROVIDER: S-EPMC7555082 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Unsupervised clustering of dopamine transporter PET imaging discovers heterogeneity of parkinsonism.

Suh Minseok M Im Jin Hee JH Choi Hongyoon H Kim Han-Joon HJ Cheon Gi Jeong GJ Jeon Beomseok B

Human brain mapping 20200805 16

Parkinsonism has heterogeneous nature, showing distinctive patterns of disease progression and prognosis. We aimed to find clusters of parkinsonism based on <sup>18</sup> F-fluoropropyl-carbomethoxyiodophenylnortropane (FP-CIT) PET as a data-driven approach to evaluate heterogenous dopaminergic neurodegeneration patterns. Two different cohorts of patients who received FP-CIT PET were collected. A labeled cohort (n = 94) included patients with parkinsonism who underwent a clinical follow-up of at ...[more]

PMID: 32757250

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Project description:PurposeThis work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism.MethodsThis study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning.ResultsThe proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP.ConclusionThis study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis.

Project description:ObjectiveTo investigate dopaminergic function in a large cohort of patients with corticobasal syndrome (CBS) and describe its relationship with clinical features in comparison to Parkinson's disease and healthy control subjects. In addition, we assessed prevalence and features of individuals with CBS and in vivo evidence of preserved nigral neuronal density.BackgroundSubstantia nigra pars compacta (SNc) neuronal degeneration is a mandatory pathological criterion for definite corticobasal degeneration, though sporadic autopsy-proven cases with ante-mortem imaging evidence of preserved nigral terminals have been recently described.MethodsIn this multicenter study, we investigated presynaptic nigrostriatal function in 36 outpatients fulfilling clinical criteria for "probable corticobasal degeneration" (age 71±7.3 years; disease duration 3.9±1.6 years), 37 PD and 24 healthy control subjects using FP-CIT single photon emission computed tomography. Clinical, neuropsychological, and magnetic resonance imaging assessment was performed to characterize CBS patients. Linear discriminant analysis was used to categorize normal vs. pathological scans.ResultsFP-CIT binding reduction in patients with CBS was characterized by larger variability, more uniform reduction throughout the striatum and greater hemispheric asymmetry compared to PD. Moreover, there was no significant correlation between tracer uptake values and clinical features such as disease duration and severity. Despite all CBS subjects showed obvious bilateral extrapyramidal signs, FP-CIT uptake was found to be normal bilaterally in four CBS patients and only unilaterally in other four cases. Extensive clinical, neuropsychological and imaging assessment did not reveal remarkable differences between CBS subjects with normal vs. pathological FP-CIT uptake.ConclusionsOur findings support the hypothesis that extrapyramidal motor symptoms in CBS are not invariably associated with SNc neuronal degeneration and that supranigral factors may play a major role in several cases. CBS individuals with normal FP-CIT uptake do not show any clinical or cognitive feature suggesting a different pathology than CBD.

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Unsupervised clustering of dopamine transporter PET imaging discovers heterogeneity of parkinsonism.

Publications

Unsupervised clustering of dopamine transporter PET imaging discovers heterogeneity of parkinsonism.

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