34 research outputs found
Meta-Analysis of Differentially Expressed Genes in the Substantia Nigra in Parkinson's Disease Supports Phenotype-Specific Transcriptome Changes
Background: Studies regarding differentially expressed genes (DEGs) in Parkinson's disease (PD) have focused on common upstream regulators or dysregulated pathways or ontologies; however, the relationships between DEGs and disease-related or cell type-enriched genes have not been systematically studied. Meta-analysis of DEGs (meta-DEGs) are expected to overcome the limitations, such as replication failure and small sample size of previous studies.
Purpose: Meta-DEGs were performed to investigate dysregulated genes enriched with neurodegenerative disorder causative or risk genes in a phenotype-specific manner.
Methods: Six microarray datasets from PD patients and controls, for which substantia nigra sample transcriptome data were available, were downloaded from the NINDS data repository. Meta-DEGs were performed using two methods, combining p-values and combing effect size, and common DEGs were used for secondary analyses. Gene sets of cell type-enriched or disease-related genes for PD, Alzheimer's disease (AD), and hereditary progressive ataxia were constructed by curation of public databases and/or published literatures.
Results: Our meta-analyses revealed 449 downregulated and 137 upregulated genes. Overrepresentation analyses with cell type-enriched genes were significant in neuron-enriched genes but not in astrocyte- or microglia-enriched genes. Meta-DEGs were significantly enriched in causative genes for hereditary disorders accompanying parkinsonism but not in genes associated with AD or hereditary progressive ataxia. Enrichment of PD-related genes was highly significant in downregulated DEGs but insignificant in upregulated genes.
Conclusion: Downregulated meta-DEGs were associated with PD-related genes, but not with other neurodegenerative disorder genes. These results highlight disease phenotype-specific changes in dysregulated genes in PD.ope
PhenGenVar: A User-Friendly Genetic Variant Detection and Visualization Tool for Precision Medicine
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The First Korean Family With Boucher-Neuhauser Syndrome Carrying a Novel Mutation in PNPLA6
ope
The Protein-Protein Interaction Network of Hereditary Parkinsonism Genes Is a Hierarchical Scale-Free Network
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Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment
Background: Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer's disease.
Methods: We used the Open Access Series of Imaging Studies (OASIS) dataset to investigate changes in the CC. The individuals were divided into three groups using the Clinical Dementia Rating (CDR); 94 normal controls (NC) were not demented (NC group, CDR = 0), 56 individuals had very mild dementia (VMD group, CDR = 0.5), and 17 individuals were defined as having mild and moderate dementia (MD group, CDR = 1 or 2). Deep learning technology using a convolutional neural network organized in a U-net architecture was used to segment the CC in the midsagittal plane. Total CC length and regional magnetic resonance imaging (MRI) measurements of the CC were made.
Results: The total CC length was negatively associated with cognitive function. (beta = -0.139, p = 0.022) Among MRI measurements of the CC, the height of the anterior third (beta = 0.038, p <0.0001) and width of the body (beta = 0.077, p = 0.001) and the height (beta = 0.065, p = 0.001) and area of the splenium (beta = 0.059, p = 0.027) were associated with cognitive function. To distinguish MD from NC and VMD, the receiver operating characteristic analyses of these MRI measurements showed areas under the curves of 0.65-0.74. (total CC length = 0.705, height of the anterior third = 0.735, width of the body = 0.714, height of the splenium = 0.703, area of the splenium = 0.649).
Conclusions: Among MRI measurements, total CC length, the height of the anterior third and width of the body, and the height and area of the splenium were associated with cognitive decline. They had fair diagnostic validity in distinguishing MD from NC and VMD.ope
Mendelian Randomisation Study of Smoking, Alcohol, and Coffee Drinking in Relation to Parkinson's Disease
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Graphene plasmonics for actively tunable integrated photonic devices
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2017. 8. ๋ฐ๋จ๊ท.์ก๋ฐฉ ๊ฒฉ์ ๊ตฌ์กฐ ํํ๋ก ๊ฒฐํฉ๋์ด ์๋ ํ์ ์์๋ค์ ๋จ์ผ ๊ฒฉ์ ์ธต์ผ๋ก ์ด๋ฃจ์ด์ง ๊ทธ๋ํ์ ์ด์ฐจ์ (2D) ๊ตฌ์กฐ ํน์ฑ, ์ ํ์ ๋ถ์ฐ ํน์ฑ (linear dispersion with the massless Dirac point)์ ๋ฐ๋ฅธ ๋งค์ฐ ๋์ ์ ๊ธฐ ์ ๋์ฑ ๋ฑ์ ๋ง์ ์ฅ์ ์ ๊ฐ์ง๊ณ ์๊ธฐ ๋๋ฌธ์ ๊ทนํ ํ์ฅ ํ๊ณ ์ดํ ์์ญ์ ํ๋ผ์ฆ๋ชจ๋์ค๋ฅผ ์ํ ์ฐจ์ธ๋ ๊ตฌ์ฑ ๋ฌผ์ง๋ก ๊ฐ๊ด์ ๋ฐ๊ณ ์๋ค. ํนํ, ๊ทธ๋ํ ์์ฒด์ ์ ์ฐจ์ ๊ตฌ์กฐ ํน์ฑ์ ๊ดํ ์์์ ์ง์ ๋๋ฅผ ํฅ์์ํฌ ์ ์๊ณ , ๋ํ ๋ ๋ฒจ ์กฐ์ ์ ๋ฐ๋ฅธ ์ ๊ธฐ ์ ๋๋์ ๋ณํ ํน์ฑ์ ๊ทธ๋ํ์ ๊ดํ์ ํน์ฑ์ ๋ํ ๋ณ์กฐ ๊ฐ๋ฅ์ฑ๊ณผ ์ง๊ฒฐ๋๊ธฐ ๋๋ฌธ์ ๊ด ํก์์ฒด, ๊ด ๋ณ์กฐ๊ธฐ, ๋ฉํ ๋ฌผ์ง ๋ฑ๊ณผ ๊ฐ์ ๋ค์ํ ๊ทธ๋ํ ๊ธฐ๋ฐ์ ๋ฅ๋ ๋ณ์กฐ ํ๋ผ์ฆ๋ชจ๋ ์์์์ ํํ์ ๋ํ์ด๋ ์ง๋ฅ ์ ๊ธฐ์ฅ ๋ฐ์ด์ด์ค์ ์ธ๊ฐ๋ฅผ ํตํด ๊ทธ๋ํ์ ์ ๊ธฐ ์ ๋๋๋ฅผ ์กฐ์ ํจ์ผ๋ก์จ ๋น์ ํ๋ฆ์ ์ ์ดํ๊ธฐ ์ํ ์ฐ๊ตฌ๊ฐ ํ๋ฐํ ์งํ๋๊ณ ์๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ์ ํ์ํ ํน์ฑ๋ค์ ํ์ฉํ์ฌ ๋ฅ๋ ๋ณ์กฐ ์ง์ ๊ด ์์๋ฅผ ๊ตฌํํ๊ณ ๋น์ ํ๋ฆ์ ๋ณด๋ค ํจ์จ์ ์ผ๋ก ์ ์ดํ๊ธฐ ์ํ ๋ฐฉ์์ ์ ์ํ๋ ๋ฐ์ ์ด์ ์ ๋๊ณ ์์ผ๋ฉฐ, ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ ๋ชจ๋์ ๋ํ ์ ์ด ๋ฐ ๋ณ์กฐ ํจ์จ ์ฆ๋๋ฅผ ์ํด light-graphene ์ํธ์์ฉ ๋ฐ ๋ชจ๋ ๊ตญ์ํ ํน์ฑ์ ํฅ์์ํฌ ์ ์๋ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชจ๋ ์์คํ
๋ค์ ์ ์ํ๊ณ ์ ํ๋ค.
๋ฐฐ๊ฒฝ ์ด๋ก ์์๋ ๊ทธ๋ํ์ ๊ดํ์ ์ ๊ธฐ ์ ๋๋์ ๋ํ ์ด๋ก ์ ํด์ ๋ชจ๋ธ์ธ magneto-optical ์ ๊ธฐ ์ ๋๋ ๋ชจ๋ธ์ ๋ํ ๊ณ ์ฐฐ์ ๋ฐํ์ผ๋ก ๊ทธ๋ํ์ ๊ดํ์ ํก์ ์ ์ด ํน์ฑ๊ณผ ๋ณต์ ํ๋ฉด ์ ๊ธฐ ์ ๋๋ ์ฌ์ด์ ๊ด๊ณ๋ฅผ ๊ท๋ช
ํ๊ณ , ์ฃผํ์ ์๋ต ํน์ฑ ๋ฐ ๋ํ ๋ ๋ฒจ์ ๋ฐ๋ฅธ ๋ณ์กฐ ํน์ฑ์ ํ์ธํ์๋ค. ๋ํ, ์ด๋ฌํ magneto-optical ์ ๊ธฐ ์ ๋๋๋ฅผ ๊ฐ๋ ๊ทธ๋ํ ๋จ์ธต ๋ฐ๋ง์ ํ๋ฉด์์ ๋ฐ์ํ๋ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ ๋ชจ๋๋ค์ ๋ํ ๊ธฐ์ด ์ด๋ก ๊ณผ ์์น ํด์ ์ ์ฐ ๋ชจ์ฌ์์ ๊ทธ๋ํ ๋จ์ธต ๋ฐ๋ง์ ๋ค๋ฃจ๋ ๋ํ์ ์ธ ๋ฐฉ์์ ์ค๋ช
ํ์๋ค.
๋จผ์ , ์ด๋ฌํ ์ด๋ก ์ ๋ฐฐ๊ฒฝ์ ๋ฐํ์ผ๋ก ๊ทธ๋ํ ๊ธฐ๋ฐ์ ์ ์ฐจ์ metal-gap-dielectric (MGD) ๋ํ๋ก ์์คํ
์ ์ ์ํ์๋ค. ์ ์ฐจ์ MGD ๋ํ๋ก ์์คํ
์ ๋ ์ผ์ฐจ์ ๊ฒฝ๊ณ ์กฐ๊ฑด์ ์ํ์ฌ, gap ๊ทธ๋ํ ์ธต์ ๊ฐํ๊ฒ ๊ตญ์ํ๋์ด ์ ํํ๋ ์ ์ฐจ์ ํ์ด๋ธ๋ฆฌ๋ ๊ทธ๋ํ ๊ฐญ ํ๋ผ์ฆ๋ชฌ (H-GGP) ๋ชจ๋๋ฅผ ๋ฐ์์ํค๋ฉฐ ์ด๋ฌํ H-GGP ๋ชจ๋์ ๋
ํนํ ์ ๊ธฐ์ฅ ๋ถํฌ ํจํด์ ๊ธฐ์ธํ์ฌ ๋งค์ฐ ํฅ์๋ light-graphene ๊ฒน์นจ ์์น๋ฅผ ๊ฐ๋๋ค. ๋ํ ์ด์ ๊ฐ์ด ์ฆ๊ฐํ light-graphene ๊ฒน์นจ ์์น์ ์ํฅ์ผ๋ก, H-GGP ๋ชจ๋๋ ๊ทธ๋ํ์ ๋ํ ๋ ๋ฒจ ๋ณ์กฐ์ ๋ํ์ฌ mode profile์ ์์ ์ ์ผ๋ก ์ ์งํ๋ฉด์ ์ ํ ์์์ ๋ณ์กฐ ๋ฏผ๊ฐ๋ ํฅ์๊ณผ ์ ํ ๋ณ์กฐ๊ฐ ๊ฐ๋ฅํ ํน์ฑ์ ๋ณด์ธ๋ค.
ํํธ, ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ์ lateral ๋ฐฉํฅ ๋ชจ๋ ๊ตญ์ํ๋ฅผ ์ํ ์ ์ ์ฒด ๋ถํ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ (DLGSP) ๋ํ๋ก ๊ตฌ์กฐ๋ฅผ ์ ์ํ์๋๋ฐ, ์ด๋ ๊ทธ๋ํ์ ์ฌ๋จํ๊ฑฐ๋ ํ๋ฉด ์ ๊ธฐ ์ ๋๋๋ฅผ ๊ณต๊ฐ์ ์ผ๋ก ๋ณ์กฐํ ํ์๊ฐ ์๊ธฐ ๋๋ฌธ์ ๋๋
ธ ๊ณต์ ๊ธฐ์ ์ ์ด์ฉํ ์ ์์ด ์ฉ์ดํ๊ณ ๋ค์ํ ๊ตฌ์กฐ๋ก ์์ฉ์ด ๊ฐ๋ฅํ ์ฅ์ ์ ๊ฐ๋๋ค. ๋ํ DLGSP ๋ชจ๋๋ ๋์ ์ ํจ ๋ชจ๋ ๊ตด์ ๋ฅ ํน์ฑ์ ๋ณด์ด๊ณ ๊ทธ๋ํ์ ํ๋ฉด ์ ๊ธฐ ์ ๋๋ ๋ณํ์ ๋ํด ๋งค์ฐ ๋ฏผ๊ฐํ ๋ณ์กฐ ํน์ฑ์ ๊ฐ๊ธฐ ๋๋ฌธ์ ์ด๋ฌํ ํน์ฑ๋ค์ ๊ธฐ๋ฐ์ผ๋ก ์ค๊ณํ ๊ฒฐํฉ DLGSP ๋ํ๋ก ์์คํ
์์๋ ์ฃผ๊ธฐ๊ฐ ๋งค์ฐ ์งง์ ๋ธ๋กํ ์ง๋ ํ์์ ๊ตฌํ์ด ๊ฐ๋ฅํ๋ฉฐ ๊ทธ๋ํ์ ๋ํ ๋ ๋ฒจ ๋ณ์กฐ๋ฅผ ํตํด ๋ธ๋กํ ์ง๋์ ์ ํ ํน์ฑ (์ฃผ๊ธฐ ๋ฐ ์ง๋)์ ํ์ฅ ํ๊ณ ์์ญ์์ ํจ์จ์ ์ผ๋ก ์ ์ดํ ์ ์๋ค.
๋ํ, ๊ทธ๋ํ์ ์ ์ฐจ์ ๊ตฌ์กฐ ํน์ฑ์ ์ํ ๊ด ํก์ ํจ์จ ๊ฐ์ ๋ฌธ์ ๋ฅผ ๊ทน๋ณตํ๊ณ ๊ด ํก์ ํน์ฑ์ ๊ทน๋ํํ๊ธฐ ์ํด ๊ทธ๋ํ-์ ์ ์ฒด ๋ค์ธต ๋ฐ๋ง ๊ตฌ์กฐ ๊ธฐ๋ฐ์ ๊ด๋์ญ ์์ ๊ด ํก์์ฒด๋ฅผ ์ ์ํ์๋ค. ์ ์ํ ํฑ๋ ํํ์ ๊ด ํก์ ๊ตฌ์กฐ์ฒด๋ ์๊ณก ๋ฉํ๋ฌผ์ง ๋ํ๋ก์ ๋๋ฆฐ ๋น ๋ชจ๋๋ฅผ ํ์ฉํจ์ผ๋ก์จ ๊ทธ๋ํ์ ์ํ ์์ ๊ด ํก์ ํน์ฑ์ด ๋์ ์ฃผํ์ ๋์ญ์์ ์ ์งํ ๋ฟ๋ง ์๋๋ผ ๊ทธ๋ํ์ ๋ํ ๋ ๋ฒจ ์กฐ์ ์ ํตํด ์์ ๊ด ํก์ ๋์ญ์ ๋ฅ๋ ๋ณ์กฐ๊ฐ ๊ฐ๋ฅํ ์ฅ์ ์ ๊ฐ๋๋ค.
์ด๋ฌํ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ค์ ๊ทธ๋ํ ์์ฒด์ ์ ์ฐจ์ ๊ตฌ์กฐ ํน์ฑ์ผ๋ก ์ธํด ๊ดํ ๋ชจ๋์ ๊ทธ๋ํ์ ์ํธ ์์ฉ์ด ๊ฐ์ํ๋ ๊ทผ๋ณธ์ ์ธ ๋ฌธ์ ์ ์ ๋ณด์ํ๊ณ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ ๋ชจ๋์ ๋ํ ์ ์ด ๋ฐ ๋ณ์กฐ, ๊ด ํก์ ํจ์จ์ ๊ทน๋ํํ๋ ๊ธฐ์ ์ ํ์ฉํ ์ ์๊ธฐ ๋๋ฌธ์ ๋ฅ๋ ๋ณ์กฐ ์ง์ ๊ด ์์์ ๊ฐ๋ฐ ๋ฐ ์ฑ๋ฅ ํฅ์์ ์ํ ๋ค์ํ ์์ฉ ๊ฐ๋ฅ์ฑ๊ณผ ์์ ๋๋ฅผ ์ ์ํ ์ ์๋ค.์ 1 ์ฅ ์ ๋ก 1
1.1 ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ 1
1.1.1 ๊ทธ๋ํ ๊ธฐ๋ฐ์ ๊ด์ ์ ๋ฐ ๊ดํ ์์ ์ฐ๊ตฌ 3
1.1.2 ๊ทธ๋ํ ํ๋ผ์ฆ๋ชจ๋์ค์ ๋ฉํ ๋ฌผ์ง ์ฐ๊ตฌ 8
1.1.3 ๊ทธ๋ํ/๊ธ์ ํผ์ฑ ๊ตฌ์กฐ๋ฅผ ์ด์ฉํ ํ๋ผ์ฆ๋ชจ๋์ค ์ฐ๊ตฌ 12
1.2 ์ฐ๊ตฌ์ ๋ชฉ์ 15
1.3 ๋
ผ๋ฌธ์ ๊ตฌ์ฑ 17
์ 2 ์ฅ ๋ฐฐ๊ฒฝ ์ด๋ก 18
2.1 ๊ทธ๋ํ์ magneto-optical ์ ๊ธฐ ์ ๋๋ 19
2.2 ๊ทธ๋ํ์ ํ๋ฉด ํ๋ผ์ฆ๋ชฌ ํ ๋ถ์ 28
2.2.1 ํ๋ฉด ์ ๊ธฐ ์ ๋๋ (surface conductivity) ๋ชจ๋ธ 28
2.2.2 ๋ฑ๊ฐ ์ ์ ์จ (equivalent permittivity) ๋ชจ๋ธ 34
2.3 ๊ทธ๋ํ ๊ธฐ๋ฐ์ ํ๋ผ์ฆ๋ชจ๋ ๋ํ๋ก ๊ธฐ์ 40
์ 3 ์ฅ Light-graphene ์ํธ์์ฉ์ ํฅ์์ ์ํ ์ ์ฐจ์ hybrid graphene gap plasmon 45
3.1 ์ ์ฐจ์ metal-gap-dielectric (MGD) ๋ํ๋ก ์์คํ
48
3.2 ์ ์ฐจ์ H-GGP ๋ชจ๋์ ์ ํ ํน์ฑ ๋ถ์ 52
3.2.1 H-GGP ๋ชจ๋์ ์ ๊ธฐ์ฅ ๋ถํฌ ํน์ฑ 53
3.2.2 Gap ๊ทธ๋ํ ์์ญ์ ๋๋น ์กฐ์ ์ ๋ฐ๋ฅธ H-GGP ๋ชจ๋์ ์ฃผ์ ๋ชจ๋ ํน์ฑ ๋ณํ 57
3.2.3 H-GGP ๋ชจ๋์ ์ ๊ธฐ์ฅ ์ง์ ํน์ฑ ๋ฐ light-graphene ๊ฒน์นจ ์์น 60
3.2.4 ์์จ (T=300K)์์์ H-GGP ๋ชจ๋ ํน์ฑ 64
3.3 ๋ํ ๋ ๋ฒจ ๋ณํ์ ๋ฐ๋ฅธ H-GGP ๋ชจ๋์ ๋ณ์กฐ ํน์ฑ 67
3.4 ๊ฒฐ๋ก 71
์ 4 ์ฅ ์ ์ ์ฒด ๋ถํ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ ๋ํ๋ก ์์คํ
์ ๋ธ๋กํ ์ง๋ ๋ถ์ 72
4.1 ์ ์ ์ฒด ๊ธฐํ์ ์ํ ๊ทธ๋ํ ํ๋ฉด ํ๋ผ์ฆ๋ชฌ ๋ชจ๋์ ์ ํ ํน์ฑ ๋ณํ 75
4.2 ์ ์ ์ฒด ๋ถํ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ ๋ํ๋ก 77
4.3 ์ ์ ์ฒด ๋ถํ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ ๋ชจ๋๋ฅผ ์ด์ฉํ ๊ฒฐํฉ ๋ํ๋ก ์์คํ
์์์ ๋ธ๋กํ ์ง๋ ํน์ฑ ๋ถ์ 83
4.3.1 ์ ์ ์ฒด ๋ถํ ๊ทธ๋ํ ํ๋ผ์ฆ๋ชฌ ๋ชจ๋ ๊ธฐ๋ฐ์ ๊ฒฐํฉ ๋ํ๋ก ์์คํ
83
4.3.2 ๊ฒฐํฉ ๋ชจ๋ ์ด๋ก ์ ์ด์ฉํ ๋ธ๋กํ ์ง๋ ํ์ ๋ถ์ 86
4.3.3 ๋ํ ๋ ๋ฒจ ๋ณํ์ ๋ฐ๋ฅธ ๋ธ๋กํ ์ง๋์ ๋ณ์กฐ ํน์ฑ 91
4.4 ๊ฒฐ๋ก 95
์ 5 ์ฅ ์๊ณก ๋ฉํ๋ฌผ์ง ๋ํ๋ก ์ด๋ก ์ ํ์ฉํ ๊ทธ๋ํ-์ ์ ์ฒด ๋ค์ธต ๋ฐ๋ง ๊ธฐ๋ฐ์ ๊ด๋์ญ ์์ ๊ด ํก์ ๊ตฌ์กฐ์ฒด 96
5.1 ์๊ณก ๋ฉํ๋ฌผ์ง ๋ํ๋ก์ ๋ชจ๋ ํน์ฑ 97
5.2 ๊ทธ๋ํ-์ ์ ์ฒด ๋ค์ธต ๋ฐ๋ง ๊ตฌ์กฐ ๊ธฐ๋ฐ์ ๊ด๋์ญ ์์ ๊ด ํก์์ฒด 101
5.2.1 ๊ทธ๋ํ-์ ์ ์ฒด ๋ค์ธต ๋ฐ๋ง ํฑ๋ ๊ตฌ์กฐ์ ๊ด ํก์ ํน์ฑ 101
5.2.2 ๊ทธ๋ํ-์ ์ ์ฒด ๋ค์ธต ๋ฐ๋ง ํฑ๋ ๊ตฌ์กฐ์ ํก์ ๋์ญ ๋ณ์กฐ ํน์ฑ 104
5.3 ๊ฒฐ๋ก 106
์ 6 ์ฅ ๊ฒฐ๋ก 107
์ฐธ๊ณ ๋ฌธํ 109
Abstract 116Docto
Accuracy of Machine Learning Using the Montreal Cognitive Assessment for the Diagnosis of Cognitive Impairment in Parkinson's Disease
Objective: The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson's disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI.
Methods: In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson's Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method.
Results: Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ยฑ 0.03) and machine learning (0.78 ยฑ 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ยฑ 0.05), but not that of machine learning (0.74 ยฑ 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87-0.89).
Conclusion: Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.ope
Genome-wide Association and Meta-analysis of Age at Onset in Parkinson Disease: Evidence From the COURAGE-PD Consortium
Background and objectives: Considerable heterogeneity exists in the literature concerning genetic determinants of the age at onset (AAO) of Parkinson disease (PD), which could be attributed to a lack of well-powered replication cohorts. The previous largest genome-wide association studies (GWAS) identified SNCA and TMEM175 loci on chromosome (Chr) 4 with a significant influence on the AAO of PD; these have not been independently replicated. This study aims to conduct a meta-analysis of GWAS of PD AAO and validate previously observed findings in worldwide populations.
Methods: A meta-analysis was performed on PD AAO GWAS of 30 populations of predominantly European ancestry from the Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease (COURAGE-PD) Consortium. This was followed by combining our study with the largest publicly available European ancestry dataset compiled by the International Parkinson Disease Genomics Consortium (IPDGC).
Results: The COURAGE-PD Consortium included a cohort of 8,535 patients with PD (91.9%: Europeans and 9.1%: East Asians). The average AAO in the COURAGE-PD dataset was 58.9 years (SD = 11.6), with an underrepresentation of females (40.2%). The heritability estimate for AAO in COURAGE-PD was 0.083 (SE = 0.057). None of the loci reached genome-wide significance (p < 5 ร 10-8). Nevertheless, the COURAGE-PD dataset confirmed the role of the previously published TMEM175 variant as a genetic determinant of the AAO of PD with Bonferroni-corrected nominal levels of significance (p < 0.025): (rs34311866: ฮฒ(SE)COURAGE = 0.477(0.203), p COURAGE = 0.0185). The subsequent meta-analysis of COURAGE-PD and IPDGC datasets (Ntotal = 25,950) led to the identification of 2 genome-wide significant association signals on Chr 4, including the previously reported SNCA locus (rs983361: ฮฒ(SE)COURAGE+IPDGC = 0.720(0.122), p COURAGE+IPDGC = 3.13 ร 10-9) and a novel BST1 locus (rs4698412: ฮฒ(SE)COURAGE+IPDGC = -0.526(0.096), p COURAGE+IPDGC = 4.41 ร 10-8).
Discussion: Our study further refines the genetic architecture of Chr 4 underlying the AAO of the PD phenotype through the identification of BST1 as a novel AAO PD locus. These findings open a new direction for the development of treatments to delay the onset of PD..ope