Principal component analysis to identify the major contributors to task-activated neurovascular responses.
Alzheimer's disease
Cerebrovascular response
Dementia
Neurovascular coupling
Journal
Cerebral circulation - cognition and behavior
ISSN: 2666-2450
Titre abrégé: Cereb Circ Cogn Behav
Pays: Netherlands
ID NLM: 101774849
Informations de publication
Date de publication:
2022
2022
Historique:
received:
30
10
2021
revised:
09
01
2022
accepted:
10
01
2022
entrez:
3
11
2022
pubmed:
4
11
2022
medline:
4
11
2022
Statut:
epublish
Résumé
Consensus on the optimal metrics for neurovascular coupling (NVC) is lacking. The aim of this study was to use principal component analysis (PCA) to determine the most significant contributors to NVC responses in healthy adults (HC), Alzheimer's disease (AD), and mild cognitive impairment (MCI). PCA was applied to three datasets: 1) 69 HC, 2) 30 older HC, 34 AD, and 22 MCI, 3) 1&2 combined. Data were extracted on peak percentage change in cerebral blood flow velocity (CBFv), variance ratio (VR), cross-correlation function peak (CCF), and blood pressure, for five cognitive tasks. An equamax rotation was applied and factors were significant where the eignevalue was ≥1. Rotated factor loadings ≥0.4 determined significant NVC variables. PCA identified 12 significant factors accounting for 78% of variance (all datasets). Contributing variables loaded differently on the factors across the datasets. In datasets 1&2, peak percentage change in CBFv contributed to factors explaining the most variance (45-58%), whereas cognitive test scores, fluency and memory domains contributed the least (15-37%). In the combined dataset, CBFv, CCF and fluency domain contributed the majority (33-43%), whereas VR and attention the least (6-24%). Peak percentage change in CBFv and the visuospatial task consistently accounted for a large proportion of the variance, suggesting these are robust NVC markers for future studies.
Sections du résumé
Background
UNASSIGNED
Consensus on the optimal metrics for neurovascular coupling (NVC) is lacking. The aim of this study was to use principal component analysis (PCA) to determine the most significant contributors to NVC responses in healthy adults (HC), Alzheimer's disease (AD), and mild cognitive impairment (MCI).
New method
UNASSIGNED
PCA was applied to three datasets: 1) 69 HC, 2) 30 older HC, 34 AD, and 22 MCI, 3) 1&2 combined. Data were extracted on peak percentage change in cerebral blood flow velocity (CBFv), variance ratio (VR), cross-correlation function peak (CCF), and blood pressure, for five cognitive tasks. An equamax rotation was applied and factors were significant where the eignevalue was ≥1. Rotated factor loadings ≥0.4 determined significant NVC variables.
Results
UNASSIGNED
PCA identified 12 significant factors accounting for 78% of variance (all datasets). Contributing variables loaded differently on the factors across the datasets. In datasets 1&2, peak percentage change in CBFv contributed to factors explaining the most variance (45-58%), whereas cognitive test scores, fluency and memory domains contributed the least (15-37%). In the combined dataset, CBFv, CCF and fluency domain contributed the majority (33-43%), whereas VR and attention the least (6-24%).
Conclusions
UNASSIGNED
Peak percentage change in CBFv and the visuospatial task consistently accounted for a large proportion of the variance, suggesting these are robust NVC markers for future studies.
Identifiants
pubmed: 36324414
doi: 10.1016/j.cccb.2022.100039
pii: S2666-2450(22)00004-6
pmc: PMC9616234
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100039Informations de copyright
© 2022 The Authors. Published by Elsevier B.V.
Références
Am J Physiol Regul Integr Comp Physiol. 2005 Jun;288(6):R1581-8
pubmed: 15677522
Nat Rev Neurosci. 2017 Jul;18(7):419-434
pubmed: 28515434
Biochim Biophys Acta. 2016 May;1862(5):887-900
pubmed: 26705676
Dement Geriatr Cogn Dis Extra. 2012 Jan;2(1):258-70
pubmed: 22822408
Neuropsychol Rev. 2000 Dec;10(4):213-31
pubmed: 11132101
Nat Rev Neurosci. 2011 Nov 03;12(12):723-38
pubmed: 22048062
Trends Neurosci. 2002 Dec;25(12):621-5
pubmed: 12446129
J Neurophysiol. 2018 Mar 1;119(3):1084-1094
pubmed: 29187557
J Ultrasound Med. 2005 Aug;24(8):1065-70
pubmed: 16040820
Physiol Meas. 2021 Jul 28;42(7):
pubmed: 34229305
BMC Neurosci. 2018 Oct 17;19(1):62
pubmed: 30333009
J Neurosci Methods. 2017 Jun 01;284:57-62
pubmed: 28455103
Exp Gerontol. 2017 Aug;94:52-58
pubmed: 27845201
Neuron. 2017 Sep 27;96(1):17-42
pubmed: 28957666
J Neurosci Methods. 2020 Jul 15;341:108779
pubmed: 32417533
J Cereb Blood Flow Metab. 2016 Apr;36(4):647-64
pubmed: 26661243
Biomedicines. 2021 Mar 26;9(4):
pubmed: 33810484
J Cereb Blood Flow Metab. 2020 Mar;40(3):656-666
pubmed: 30841780
J Alzheimers Dis Rep. 2018 Sep 28;2(1):153-164
pubmed: 30480258
J Alzheimers Dis. 2010;22(2):415-21
pubmed: 20847429
Neuropsychologia. 1971 Mar;9(1):97-113
pubmed: 5146491
J Appl Physiol (1985). 2005 Dec;99(6):2352-62
pubmed: 16099892
Cell Mol Neurobiol. 2016 Mar;36(2):167-79
pubmed: 26898552
Neuron. 2013 Nov 20;80(4):844-66
pubmed: 24267647