Title :

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients.

Author(s) :

Kuchcinski, Gregory [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172
Rumetshofer, Theodor [Auteur]
Zervides, Kristoffer A. [Auteur]
Lopes, Renaud [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Gautherot, Morgan [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Pruvo, Jean-Pierre [Auteur]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Bengtsson, Anders A. [Auteur]
Hansson, Oskar [Auteur]
Jönsen, Andreas [Auteur]
Sundgren, Pia C. Maly [Auteur]

Journal title :

Frontiers in Aging Neuroscience

Volume number :

15

Publication date :

2023-10-20

ISSN :

1663-4365

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

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 ...
Show 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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
Inserm
CHU Lille

Collections :

• Lille Neurosciences & Cognition (LilNCog) - U 1172

Submission date :

2024-01-15T22:10:49Z
2024-12-13T09:27:03Z