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Neuromelanin and T2*-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.
Afiliação
  • Ben Bashat D; Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. dafnab@tlvmc.gov.il.
  • Thaler A; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. dafnab@tlvmc.gov.il.
  • Lerman Shacham H; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. dafnab@tlvmc.gov.il.
  • Even-Sapir E; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Hutchison M; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
  • Evans KC; Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Orr-Urterger A; Department of Nuclear Medicine, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Cedarbaum JM; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Droby A; Department of Nuclear Medicine, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Giladi N; Biogen Inc., Cambridge, MA, USA.
  • Mirelman A; Biogen Inc., Cambridge, MA, USA.
  • Artzi M; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
NPJ Parkinsons Dis ; 8(1): 139, 2022 Oct 21.
Article em En | MEDLINE | ID: mdl-36271084
ABSTRACT
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 T2* 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 T2* 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) [15LRRK2-PD, 16GBA-PD, and 15idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24LRRK2-NMC and 23GBA-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 T2* 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 T2*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article