Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients

Kuchcinski, Grégory LU ; Rumetshofer, Theodor LU orcid ; Zervides, Kristoffer A. LU orcid ; Lopes, Renaud ; Gautherot, Morgan ; Pruvo, Jean Pierre ; Bengtsson, Anders A. LU ; Hansson, Oskar LU orcid ; Jönsen, Andreas LU and Sundgren, Pia C.Maly LU orcid (2023) In Frontiers in Aging Neuroscience 15.
Abstract

Introduction: Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model. Methods: Seventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a... (More)

Introduction: Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model. Methods: Seventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a neuropsychological evaluation and a measurement of neurofilament light protein in plasma (NfL). A BrainAGE model with a 3D convolutional neural network architecture, pre-trained on the 3D-T1 images of 1,295 healthy female subjects to predict their chronological age, was applied on the images of SLE patients and controls in order to compute the BrainAGE. SLE patients were divided into 2 groups according to the BrainAGE distribution (high vs. low BrainAGE). Results: BrainAGE z-score was significantly higher in SLE patients than in controls (+0.6 [±1.1] vs. 0 [±1.0], p = 0.02). In SLE patients, high BrainAGE was associated with longer reaction times (p = 0.02), lower psychomotor speed (p = 0.001) and cognitive flexibility (p = 0.04), as well as with higher NfL after adjusting for age (p = 0.001). Conclusion: Using a deep-learning BrainAGE model, we provide evidence of increased brain aging in SLE patients, which reflected neuronal damage and cognitive impairment.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
aging, brain, deep learning, magnetic resonance imaging, systemic lupus erythematosus
in
Frontiers in Aging Neuroscience
volume
15
article number
1274061
publisher
Frontiers Media S. A.
external identifiers
  • pmid:37927336
  • scopus:85175690634
ISSN
1663-4365
DOI
10.3389/fnagi.2023.1274061
language
English
LU publication?
yes
additional info
Funding Information: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study has received funding by the French Society of Neuroradiology (SFNR), GK; the French Society of Radiology (SFR), GK; Collège des Enseignants en Radiologie de France (CERF), GK; Lille University, GK; Lille University Hospital, GK; Anna-Greta Crafoord Foundation, AJ; Greta and Johan Kock Foundation, AJ; Lund University, AJ; Stiftelsen Konung Gustaf V:80-årsfond, PS; Alfred Österlund Foundation, PS; Swedish Rheumatic Association, PS; Swedish Research Council (2016–00906), OH; the Knut and Alice Wallenberg foundation (2017–0383), OH; the Marianne and Marcus Wallenberg foundation (2015.0125), OH; the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, OH; the Swedish Alzheimer Foundation (AF-939932), OH; the Swedish Brain Foundation (FO2021-0293), OH; The Parkinson foundation of Sweden (1280/20), OH; the Cure Alzheimer’s fund, OH; the Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse, OH; the Skåne University Hospital Foundation (2020-O000028), OH; Regionalt Forskningsstöd (2020–0314), OH; and the Swedish federal government under the ALF agreement (2018-Projekt0279), OH. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Publisher Copyright: Copyright © 2023 Kuchcinski, Rumetshofer, Zervides, Lopes, Gautherot, Pruvo, Bengtsson, Hansson, Jönsen and Sundgren.
id
1618800b-13d4-4ecd-9cfc-d8b1ce3965eb
date added to LUP
2023-11-30 12:00:35
date last changed
2024-04-13 13:14:05
@article{1618800b-13d4-4ecd-9cfc-d8b1ce3965eb,
  abstract     = {{<p>Introduction: Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model. Methods: Seventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a neuropsychological evaluation and a measurement of neurofilament light protein in plasma (NfL). A BrainAGE model with a 3D convolutional neural network architecture, pre-trained on the 3D-T1 images of 1,295 healthy female subjects to predict their chronological age, was applied on the images of SLE patients and controls in order to compute the BrainAGE. SLE patients were divided into 2 groups according to the BrainAGE distribution (high vs. low BrainAGE). Results: BrainAGE z-score was significantly higher in SLE patients than in controls (+0.6 [±1.1] vs. 0 [±1.0], p = 0.02). In SLE patients, high BrainAGE was associated with longer reaction times (p = 0.02), lower psychomotor speed (p = 0.001) and cognitive flexibility (p = 0.04), as well as with higher NfL after adjusting for age (p = 0.001). Conclusion: Using a deep-learning BrainAGE model, we provide evidence of increased brain aging in SLE patients, which reflected neuronal damage and cognitive impairment.</p>}},
  author       = {{Kuchcinski, Grégory and Rumetshofer, Theodor and Zervides, Kristoffer A. and Lopes, Renaud and Gautherot, Morgan and Pruvo, Jean Pierre and Bengtsson, Anders A. and Hansson, Oskar and Jönsen, Andreas and Sundgren, Pia C.Maly}},
  issn         = {{1663-4365}},
  keywords     = {{aging; brain; deep learning; magnetic resonance imaging; systemic lupus erythematosus}},
  language     = {{eng}},
  month        = {{08}},
  publisher    = {{Frontiers Media S. A.}},
  series       = {{Frontiers in Aging Neuroscience}},
  title        = {{MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients}},
  url          = {{http://dx.doi.org/10.3389/fnagi.2023.1274061}},
  doi          = {{10.3389/fnagi.2023.1274061}},
  volume       = {{15}},
  year         = {{2023}},
}