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Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease

MRI was suggested as a promising method for the diagnosis and assessment of Parkinson’s Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T(2)* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with...

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Autores principales: Ben Bashat, Dafna, Thaler, Avner, Lerman Shacham, Hedva, Even-Sapir, Einat, Hutchison, Matthew, Evans, Karleyton C., Orr-Urterger, Avi, Cedarbaum, Jesse M., Droby, Amgad, Giladi, Nir, Mirelman, Anat, Artzi, Moran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586960/
https://www.ncbi.nlm.nih.gov/pubmed/36271084
http://dx.doi.org/10.1038/s41531-022-00405-9
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author Ben Bashat, Dafna
Thaler, Avner
Lerman Shacham, Hedva
Even-Sapir, Einat
Hutchison, Matthew
Evans, Karleyton C.
Orr-Urterger, Avi
Cedarbaum, Jesse M.
Droby, Amgad
Giladi, Nir
Mirelman, Anat
Artzi, Moran
author_facet Ben Bashat, Dafna
Thaler, Avner
Lerman Shacham, Hedva
Even-Sapir, Einat
Hutchison, Matthew
Evans, Karleyton C.
Orr-Urterger, Avi
Cedarbaum, Jesse M.
Droby, Amgad
Giladi, Nir
Mirelman, Anat
Artzi, Moran
author_sort Ben Bashat, Dafna
collection PubMed
description MRI was suggested as a promising method for the diagnosis and assessment of Parkinson’s Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T(2)* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T(2)* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T(2)* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can’t be performed. Combining neuromelanin and T(2)*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.
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spelling pubmed-95869602022-10-23 Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease Ben Bashat, Dafna Thaler, Avner Lerman Shacham, Hedva Even-Sapir, Einat Hutchison, Matthew Evans, Karleyton C. Orr-Urterger, Avi Cedarbaum, Jesse M. Droby, Amgad Giladi, Nir Mirelman, Anat Artzi, Moran NPJ Parkinsons Dis Article MRI was suggested as a promising method for the diagnosis and assessment of Parkinson’s Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T(2)* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T(2)* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T(2)* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can’t be performed. Combining neuromelanin and T(2)*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase. Nature Publishing Group UK 2022-10-21 /pmc/articles/PMC9586960/ /pubmed/36271084 http://dx.doi.org/10.1038/s41531-022-00405-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ben Bashat, Dafna
Thaler, Avner
Lerman Shacham, Hedva
Even-Sapir, Einat
Hutchison, Matthew
Evans, Karleyton C.
Orr-Urterger, Avi
Cedarbaum, Jesse M.
Droby, Amgad
Giladi, Nir
Mirelman, Anat
Artzi, Moran
Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease
title Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease
title_full Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease
title_fullStr Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease
title_full_unstemmed Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease
title_short Neuromelanin and T(2)*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease
title_sort neuromelanin and t(2)*-mri for the assessment of genetically at-risk, prodromal, and symptomatic parkinson’s disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586960/
https://www.ncbi.nlm.nih.gov/pubmed/36271084
http://dx.doi.org/10.1038/s41531-022-00405-9
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