Principal component analysis of synaptic density measured with [
Alzheimer’s disease
Positron emission tomography
Principal component analysis
SV2A
Synaptic density
[(11)C]UCB-J
Journal
NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070
Informations de publication
Date de publication:
2023
2023
Historique:
received:
20
09
2022
revised:
01
05
2023
accepted:
19
06
2023
medline:
18
9
2023
pubmed:
10
7
2023
entrez:
9
7
2023
Statut:
ppublish
Résumé
Synaptic loss is considered an early pathological event and major structural correlate of cognitive impairment in Alzheimer's disease (AD). We used principal component analysis (PCA) to identify regional patterns of covariance in synaptic density using [ [ Parallel analysis determined three significant PCs explaining 70.2% of the total variance. PC1 was characterized by positive loadings with similar contributions across the majority of ROIs. PC2 was characterized by positive and negative loadings with strongest contributions from subcortical and parietooccipital cortical regions, respectively, while PC3 was characterized by positive and negative loadings with strongest contributions from rostral and caudal cortical regions, respectively. Within the AD group, PC1 subject scores were positively correlated with performance across all cognitive domains (Pearson r = 0.24-0.40, P = 0.06-0.006), PC2 subject scores were inversely correlated with age (Pearson r = -0.45, P = 0.002) and PC3 subject scores were significantly correlated with CDR-sb (Pearson r = 0.46, P = 0.04). No significant correlations were observed between cognitive performance and PC subject scores in CN participants. This data-driven approach defined specific spatial patterns of synaptic density correlated with unique participant characteristics within the AD group. Our findings reinforce synaptic density as a robust biomarker of disease presence and severity in the early stages of AD.
Sections du résumé
BACKGROUND
Synaptic loss is considered an early pathological event and major structural correlate of cognitive impairment in Alzheimer's disease (AD). We used principal component analysis (PCA) to identify regional patterns of covariance in synaptic density using [
METHODS
[
RESULTS
Parallel analysis determined three significant PCs explaining 70.2% of the total variance. PC1 was characterized by positive loadings with similar contributions across the majority of ROIs. PC2 was characterized by positive and negative loadings with strongest contributions from subcortical and parietooccipital cortical regions, respectively, while PC3 was characterized by positive and negative loadings with strongest contributions from rostral and caudal cortical regions, respectively. Within the AD group, PC1 subject scores were positively correlated with performance across all cognitive domains (Pearson r = 0.24-0.40, P = 0.06-0.006), PC2 subject scores were inversely correlated with age (Pearson r = -0.45, P = 0.002) and PC3 subject scores were significantly correlated with CDR-sb (Pearson r = 0.46, P = 0.04). No significant correlations were observed between cognitive performance and PC subject scores in CN participants.
CONCLUSIONS
This data-driven approach defined specific spatial patterns of synaptic density correlated with unique participant characteristics within the AD group. Our findings reinforce synaptic density as a robust biomarker of disease presence and severity in the early stages of AD.
Identifiants
pubmed: 37422964
pii: S2213-1582(23)00146-8
doi: 10.1016/j.nicl.2023.103457
pmc: PMC10338149
pii:
doi:
Substances chimiques
Amyloid
0
Amyloidogenic Proteins
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
103457Subventions
Organisme : NIA NIH HHS
ID : P30 AG066508
Pays : United States
Organisme : NIA NIH HHS
ID : P50 AG047270
Pays : United States
Organisme : NIA NIH HHS
ID : K23 AG057794
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG052560
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG062276
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG057553
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG021342
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG057912
Pays : United States
Organisme : NIMH NIH HHS
ID : T32 MH019961
Pays : United States
Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: APM, REC, and CHvD report grants from National Institutes of Health for the conduct of the study. APM reports grants for clinical trials from Genentech, Eli Lilly, and Janssen Pharmaceuticals outside the submitted work. MKC reports research support from the Dana Foundation and Eli Lilly and clinical trials from Merck outside the submitted work. YH reports research grants from the UCB and Eli Lilly outside the submitted work. YH, NBN, and REC have a patent for a newer version of the tracer. REC is a consultant for Rodin Therapeutics and has received research funding from UCB. REC reports having received grants from AstraZeneca, Astellas, Eli Lilly, Pfizer, Taisho, and UCB, outside the submitted work. CHvD reports consulting fees from Kyowa Kirin, Roche, Merck, Eli Lilly, and Janssen and grants for clinical trials from Biogen, Novartis, Eli Lilly, Merck, Eisai, Janssen, Roche, Genentech, Toyama, and Biohaven, outside the submitted work. No other disclosures are reported. AHC has licensed technology to Elysium Health, Inc. and hasreceived consulting fees from FOXO Technologies, Inc., bothfor work unrelated to the present manuscript.