% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Eickhoff:894631,
author = {Eickhoff, Claudia R and Hoffstaedter, Felix and Caspers,
Julian and Reetz, Kathrin and Mathys, Christian and Dogan,
Imis and Amunts, Katrin and Schnitzler, Alfons and Eickhoff,
Simon B},
title = {{A}dvanced brain aging in {P}arkinson’s disease is
related to disease duration and individual impairment},
journal = {Brain communications},
volume = {3},
number = {3},
issn = {2632-1297},
address = {[Großbritannien]},
publisher = {Guarantors of Brain},
reportid = {FZJ-2021-03327},
pages = {fcab191},
year = {2021},
abstract = {Machine-learning can reliably predict individual age from
MRI data, revealing that patients with neurodegenerative
disorders show an elevated biological age. A surprising gap
in the literature, however, pertains to Parkinson’s
disease. Here we evaluate brain age in two cohorts of
Parkinson’s patients and investigated the relationship
between individual brain age and clinical characteristicsWe
assessed 372 patients with idiopathic Parkinson’s disease,
newly diagnosed cases from the Parkinson’s Progression
Marker Initiative database and a more chronic local sample,
as well as age- and sex-matched healthy controls. Following
morphometric preprocessing and atlas-based compression,
individual brain age was predicted using a multivariate
machine-learning model trained on an independent, multi-site
reference sample.Across cohorts, healthy controls were well
predicted with a mean error of 4.4 years. In turn,
Parkinson’s patients showed a significant (controlling for
age, gender $\&$ site) increase in brain age of
∼3 years. While this effect was already present in the
newly diagnosed PPMI sample, advanced biological age was
significantly related to disease duration as well as worse
cognitive and motor impairment.While biological age is
increased in patients with Parkinson’s disease, the effect
is at the lower end of what is found for other neurological
and psychiatric disorders. We argue that this may reflect a
heterochronicity between forebrain atrophy and small but
behaviourally salient midbrain pathology. Finally, we point
to the need to disentangle physiological aging trajectories,
lifestyle effects and core pathological changes.},
cin = {INM-1 / INM-7 / INM-11},
ddc = {610},
cid = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-7-20090406 /
I:(DE-Juel1)INM-11-20170113},
pnm = {5252 - Brain Dysfunction and Plasticity (POF4-525) / HBP
SGA3 - Human Brain Project Specific Grant Agreement 3
(945539) / JL SMHB - Joint Lab Supercomputing and Modeling
for the Human Brain (JL SMHB-2021-2027)},
pid = {G:(DE-HGF)POF4-5252 / G:(EU-Grant)945539 / G:(DE-Juel1)JL
SMHB-2021-2027},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:34541531},
UT = {WOS:000734327400060},
doi = {10.1093/braincomms/fcab191},
url = {https://juser.fz-juelich.de/record/894631},
}