# BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

Dorian Manouvriez, Gregory Kuchcinski, Vincent Roca, A. R. Sillaire, Maxime Bertoux, Xavier Delbeuck, Jean-Pierre Pruvo, Simon Lecerf, Florence Pasquier, Thibaud Lebouvier, et al.

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## To cite this version:

Dorian Manouvriez, Gregory Kuchcinski, Vincent Roca, A. R. Sillaire, Maxime Bertoux, et al. BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease. Journal de Neuroradiologie / Journal of Neuroradiology, 2025, J Neuroradiol, 52 (5), pp.101365. 10.1016/j.neurad.2025.101365. hal-05303010

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Original Article

# BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease

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Dorian Manouvriez<sup>a,b,*</sup>, Grégory Kuchcinski<sup>a,c,d</sup>, Vincent Roca<sup>a</sup>, Adeline Rollin Sillaire<sup>e,f</sup>, Maxime Bertoux<sup>c,e</sup>, Xavier Delbeuck<sup>a,c,d</sup>, Jean-Pierre Pruvo<sup>a,c,d</sup>, Simon Lecerf<sup>c,e,f</sup>, Florence Pasquier<sup>c,e,f</sup>, Thibaud Lebouvier<sup>c,e,f</sup>, Renaud Lopes<sup>a,c</sup>

ARTICLE INFO

Keywords:

Early-onset Alzheimer's disease

Brain age gap estimation

Clustering

Magnetic resonance imaging

ABSTRACT

**Introduction:** Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving therapeutic strategies. This study aims to differentiate EOAD patients with varying rates of progression using Brain Age Gap Estimation (BrainAGE)-based clustering algorithm applied to structural magnetic resonance images (MRI).

**Methods:** A retrospective analysis of a longitudinal cohort consisting of 142 participants who met the criteria for early-onset probable Alzheimer's disease was conducted. Participants were assessed clinically, neuropsychologically and with structural MRI at baseline and annually for 6 years. A Brain Age Gap Estimation (BrainAGE) deep learning model pre-trained on 3,227 3D T1-weighted MRI of healthy subjects was used to extract encoded MRI representations at baseline. Then, k-means clustering was performed on these encoded representations to stratify the population. The resulting clusters were then analyzed for disease severity, cognitive phenotype and brain volumes at baseline and longitudinally.

**Results:** The optimal number of clusters was determined to be 2. Clusters differed significantly in BrainAGE scores (5.44 [± 8] years vs 15.25 [± 5 years], *p* < 0.001). The high BrainAGE cluster was associated with older age (*p* = 0.001) and higher proportion of female patients (*p* = 0.005), as well as greater disease severity based on Mini Mental State Examination (MMSE) scores (19.32 [±4.62] vs 14.14 [±6.93], *p* < 0.001) and gray matter volume (0.35 [±0.03] vs 0.32 [±0.02], *p* < 0.001). Longitudinal analyses revealed significant differences in disease progression (MMSE decline of -2.35 [±0.15] pts/year vs -3.02 [±0.25] pts/year, *p* = 0.02; CDR 1.58 [±0.10] pts/year vs 1.99 [±0.16] pts/year, *p* = 0.03).

**Conclusion:** K-means clustering of BrainAGE encoded representations stratified EOAD patients based on varying rates of disease progression. These findings underscore the potential of using BrainAGE as a biomarker for better understanding and managing EOAD.

Introduction

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disorder, characterized by significant inter-individual variability in its onset, progression and clinical manifestations. This

heterogeneity is evident in the wide range of symptoms, rates of cognitive decline, and neuropathological features observed among patients. Understanding this heterogeneity is crucial for improving the accuracy of diagnostic tools in AD and developing more personalized therapeutic approaches.¹

**Abbreviations:** Alzheimer's disease, AD; artificial intelligence, AI; brain reserve, BR; brain age gap estimation, BrainAGE; clinical dementia rating scale, CDR; clinical dementia rating scale sum of boxes, CDR-SoB; cortical total volume, CTV; cerebrospinal fluid, CSF; early-onset Alzheimer's disease, EOAD; hippocampal sparing, HpSp; intracranial volume, ICV; late-onset Alzheimer's disease, LOAD; limbic predominant, LP; mean absolute error, MAE; mini mental state examination, MMSE; magnetic resonance imaging, MRI; predicted age difference, PAD; stochastic gradient descent, SGD; typical Alzheimer's disease, tAD; visual association test, VAT

ARTICLE INFO

<footnote><sup>a</sup> UAR 2014-US 41-PLBS-Plateformes lilloises en Biologie & Santé, University of Lille, Lille, France</footnote>

<footnote><sup>b</sup> SIEMENS HEALTHCARE SAS, Courbevoie, France</footnote>

<footnote><sup>c</sup> INSERM, U1172-LilNCog-Lille Neuroscience & Cognition, University of Lille, Lille, France</footnote>

<footnote><sup>d</sup> Department of Neuroradiology, CHU Lille, F-59000 Lille, France</footnote>

<footnote><sup>e</sup> Memory Center, DISTALZ, Lille, France</footnote>

<footnote><sup>f</sup> Neurology Department, Lille University Medical Center, Lille, France</footnote>

<footnote>*Corresponding author at: rue Emile Laine, 59000, Lille, France.</footnote>

<footnote>E-mail address: dorian.manouvriez@gmail.com (D. Manouvriez).</footnote>
Among the factors contributing to this heterogeneity, age at onset plays a critical role. Patients with early-onset AD (EOAD) are more likely to experience a faster progression² and often present with atypical clinical features, commonly in the form of non-amnestic symptoms.³ However, stratifying patients with EOAD according to their disease progression remains challenging, largely due to the individual resilience to neuropathological development (brain reserve) and the ability to compensate for cognitive decline (cognitive reserve).

Therefore, establishing clinically available biomarkers that can identify individuals with similar rates of progression is of paramount importance for clinical and precision medicine. Magnetic resonance imaging (MRI) could serve as a powerful tool for identifying neuroanatomic atrophy closely associated with the distribution of tau neuropathology.⁴,⁵ Global cortical atrophy is more pronounced in EOAD compared to late-onset AD (LOAD).⁶,⁷ But focal atrophy patterns have been associated with different biological subtypes of AD.⁸ A thorough assessment of brain structure is thus required, and this can be achieved through the application of artificial intelligence (AI) techniques. AI, including machine learning and deep learning, has the capacity to detect complex and subtle patterns within MRI images.

Recently, several AI models, collectively referred as BrainAGE (Brain Age Gap Estimation), have been developed to assess deviations in brain structure from the normal aging trajectory in cognitively healthy individuals,⁹,¹⁰ These methods involve training an algorithm to predict the age of a healthy individual based on their brain's MR images. The BrainAGE score, defined as the difference between estimated brain age and chronological age, can reflect brain resilience or vulnerability. A younger score may indicate greater resilience to cognitive decline and disease progression, while an older score could suggest reduced resilience and a higher risk of cognitive impairment, linking the model directly to both brain resilience and cognitive health. The BrainAGE score, learned from deep learning models, encoded a series of hierarchical feature maps through convolution layers. These feature maps capture and condense essential information from MR images to estimate the subject's age. The resulting condensed maps form an encoded representation of the MR images. Due to its high dimension, this encoded representation may also contain additional information for individual-level brain phenotyping beyond the age estimate.¹¹

In a previous study, we demonstrated that the BrainAGE score was associated with the severity of cognitive impairment in patients with EOAD, and to a lesser extent, with their neuropsychological phenotypes.¹² The goal of this study was to develop an AI framework based on the BrainAGE model and structural MRI to perform a stratification of patients with EOAD. We hypothesize that the encoded representations learned by the BrainAGE model serve as a biomarker of brain resilience to disease progression, and that applying a clustering algorithm to these representations will help identifying patient groups with distinct rates of disease progression, irrespective of their phenotype.

## Materials and methods

### EOAD population

In the present study, prospectively acquired data were analyzed retrospectively. Patients with EOAD were recruited and examined at the Memory Resources and research Center of the University Hospital of Lille, France. These participants were part of the COMAJ cohort.¹³ The inclusion criteria were as follows: onset of symptoms at 60 years old or younger, and fulfillment of the NIA-AA criteria.¹⁴ These evaluations encompassed clinical history, neurological and neuropsychological examinations, genotyping, magnetic resonance imaging (MRI) and ¹⁸FDG-PET imaging, disease progression and cerebrospinal fluid (CSF) biomarker assays. Patients were followed-up annually for a period of 6 years, involving clinical, neuropsychological, and MRI assessments. For the purpose of the present study, participants with genetic mutation or familial form were excluded. The study received approval from the

local investigational review board (CPP Ile-de-France VI Groupe Hospitalier Pitié-Salpetrière; reference 110-05). Written informed consent was obtained from all participants.

### MRI acquisition and preprocessing

Three-dimensional T1-weighted (3D T1) gradient-echo images were acquired on the 3T MRI scanner (Achieva, Philips, Best, The Netherlands), using an 8-channel phased-array head coil and whole-body coil transmission (field of view = 256×256×160 mm³, isotropic voxel size 1 × 1 × 1 mm³, TR = 9.9 ms, TE = 4.6 ms, and flip angle = 8°) for initial evaluation and follow-up. Images were corrected for magnetic field inhomogeneity effects and skull stripped using VolBrain software.¹⁵ Then, preprocessed 3D-T1 images were linearly registered into MNI space and resampled to 1 mm³ using SPM software (https://www.fil.ion.ucl.ac.uk/spm/software/). Intensity normalization was performed using min-max normalization.

### BrainAGE model

We employed the pre-trained BrainAGE model described in¹² as a feature extractor (Fig. 1), which was developed using 3,227 T1w-MRI from 2,065 healthy participants. Ethical approval was obtained for all data collection sites, and informed consent was secured from every participant. All individuals were confirmed to be free from neurological and psychiatric disease based on the local study protocols. Details of the database used to train the BrainAGE model are provided in Table 1. Further details between EOAD population and training database are given in Figure S2 and Table S1.

The BrainAGE model was based on a 3D convolutional neural network (3D-CNN) architecture.¹²,¹⁶ The CNN architecture consisted of five repeated blocks followed by a flatten layer and a fully connected layer to predict brain age. Each block included 3D convolution, ReLU activation, 3D convolution, 3D batch normalization, ReLU activation and max-pooling operations.

The input was preprocessed 3D T1w-MRI with dimensions 182×218×182 voxels. Model training was optimized using Stochastic Gradient Descent (SGD) algorithm with a learning rate of 0.001, momentum of 0.1 and learning rate decay of 5e-05.¹⁷ The loss function was mean absolute error (MAE), and training was performed over 150 epochs with a batch size of 8. Early stopping was applied based on validation MAE to prevent overfitting. Data augmentation techniques, including shifting and rotation, were applied on-the-fly to prevent overfitting as they empirically found to improve performance.¹⁸

Brain age estimated by the BrainAGE model was corrected from the regression toward the mean phenomenon.¹⁹ The phenomenon of regression toward the mean can lead to systematic bias –in our case, the overestimation of younger participants' age and the underestimation of older participants' age. To address this issue, we adjusted the predicted age using the following correction model:²⁰

$$ \text{regressed age predicted} = \text{intercept} + \alpha \times \text{chronological age} + \text{error} \quad (1) $$

Since our analysis employed the BrainAGE model developed by¹², we employed their parameterization. α was selected by k-fold cross validation on their hold-out test dataset, which was used to fine-tune the BrainAGE model's hyperparameters, and was estimated at α = 0.13.

### Clustering algorithm

Fig. 1 illustrates the clustering pipeline. Encoded MRI representations of the EOAD population were extracted using the BrainAGE model pre-trained on a healthy cohort. These representations were then used as input for a k-means clustering algorithm.²¹ K-means algorithm was executed using the scikit-learn python library²² with k-means++
<figure><img src="image_6.png" /><figcaption>**Fig. 1.** Training and clustering BrainAGE pipeline.</figcaption></figure>

Up: BrainAGE model is trained over a healthy population to generate an MRI encoded representation that can predict age.

Bottom: BrainAGE model is used to extract MRI encoded representation of each patient in EOAD population which is then clustered to identify distinct EOAD groups.

initialisation.²³ To overcome the high dimension of the encoded representation (19,200 features), k-means was performed 150 times and we kept the clustering which had the lower within-cluster sum of squares distance. The optimal number of clusters was determined using four metrics: the Gap Statistic, Silhouette score, Calinski-Harabasz, and Davies-Bouldin values.²⁴

## Neuropsychological assessment

In order to consider disease severity, cognitive decline and clinical phenotypes of participants within the clusters were collected. The Clinical Dementia Rating Scale Sum of Boxed (CDR-SoB) score was used as a marker of the disease severity.²⁵ If CDR-SoB was

unavailable but the CDR had been assessed, CDR-SoB value was assigned based on the corresponding CDR score, following the guidelines outlined by O'Bryant et al.²⁵ The Mini-Mental State Examination (MMSE) score was also used as a marker of the disease severity.²⁶ If the MMSE score was greater than 10, a detailed neuropsychological evaluation was conducted, covering four major cognitive domains: episodic memory (assessed using the Visual Association Test (VAT)²⁷), language (using the DO80 confrontation naming test with 80 images), visuospatial function (using the Beery-Buknetica Developmental test of Visual Motor Integration (Beery VMI test)²⁸) and executive functions (assessed via categorical verbal fluency (animal) and letter fluency (letter P)). Regarding clinical phenotypes, multidisciplinary retrospective meetings were per-

<table><caption>**Table 1**
Subject information for the BrainAGE model training dataset.</caption><thead><tr><td>Study</td><td>N</td><td>Age, years</td><td>Age range</td><td>Female, n (%)</td><td>Manufacturer</td><td>Website</td></tr></thead><tbody><tr><td>IXI</td><td>181</td><td>46.87 ± 16.73</td><td>20–81</td><td>94 (51 %)</td><td>PHILIPS</td><td>https://brain-development.org/</td></tr><tr><td>HCP</td><td>1783</td><td>28.76 ± 3.7</td><td>22–37</td><td>968 (54 %)</td><td>SIEMENS</td><td>https://www.humanconnectome.org/</td></tr><tr><td>COBRE</td><td>238</td><td>38.18 ± 11.53</td><td>18–65</td><td>64 (6 %)</td><td>SIEMENS</td><td>https://www.mrn.org/common/cobre-phase-3</td></tr><tr><td>MCIC</td><td>264</td><td>33.60 ± 12.21</td><td>18–60</td><td>80 (30 %)</td><td>SIEMENS</td><td>https://www.nitrc.org/projects/mcic/</td></tr><tr><td>NmorphCH</td><td>141</td><td>31.37 ± 8.42</td><td>20–46</td><td>66 (46 %)</td><td>SIEMENS</td><td>http://schizconnect.org/</td></tr><tr><td>NKIRS</td><td>620</td><td>42.62 ± 18.27</td><td>18–85</td><td>406 (65 %)</td><td>SIEMENS</td><td>http://fcon_1000/projects.nitrc.org/indi/enhanced/</td></tr></tbody></table>

Age is reported as mean ± standard deviation. Age range is reported as min-max. Categorical variables are reported as the number over the total number (percentage).
formed to determine the patients' initial and most prominent cogni-
tive impairment based on (i) the caregiver's report collected at pre-
sentation and (ii) the neuropsychological profile at presentation.
Consistent with the modified NIA-AA criteria for probable AD¹⁴ the
initial and most prominent cognitive impairments (after amnesia)
affected language, visuospatial or executive functioning. Conse-
quently, in this study, non-amnestic phenotypes were categorized as
either presenting impairments in language, visuospatial or executive
functions which corresponded to the logopenic variant of primary
progressive aphasia,²⁹ posterior cortical atrophy³⁰ and behavioral/
dysexecutive variant of AD,³¹ respectively.

Volumetric and morphometric analyses

In order to assess the neuroimaging factors learned by our BrainAGE
model-based clustering, several brain volumetric and morphometric anal-
yses were performed. Brain volumes (gray matter, hippocampus, white
matter and CSF) were estimated using Volbrain software¹⁵ and normal-
ized by intracranial volume. Similar to cognitive phenotypes (amnestic
and non-amnestic), we defined brain atrophy phenotypes based on the
hippocampal-to-cortical volume ratio (HV:CTV). Limbic-predominant
(LP) referred an HV:CTV below the 25th percentile, hippocampal sparing
(HpSp) to an HV:CTV above the 75th percentile and typical AD (tAD) to
an HV:CTV between the 25th and 75th percentiles.³²,³³ The cortical AD
signature,³⁴,³⁵ and the cognitive resilience signature³⁶ were assessed by aver-
aging the cortical thickness of specific regions of interest from the Des-
trieux atlas³⁷ (Fig. 2). Cortical thickness was estimated using Freesurfer
software (v.7.1.0, https://surfer.nmr.mgh.harvard.edu/).

Statistical analyses

Statistical analyses on demographic, biological and clinical data were
conducted to differentiate patient characteristics within each cluster.
These analyses were performed using R software (v.4.2.2, https://www.
r-project.org/). The threshold for statistical significance was set at
p < 0.05, corrected for multiple comparisons using false discovery rate
(FDR).

Baseline analysis

To understand the biological basis of BrainAGE clusters, we assessed
baseline intergroup differences in demographic and biological data
between the clusters using linear regression for continuous variables
and logistic regression for categorical variables. The significant variables
were included as covariates in a linear regression model to compare
between-group differences in neuropsychological scores and MRI volu-
metric measures.

Longitudinal analysis

To estimate the prognostic value of BrainAGE clusters, we conducted
a longitudinal analysis of disease severity markers adjusted on baseline
severity. Neuropsychological scores and MRI volumetric measures, dif-
ferences between clusters were examined using univariate linear mixed-
effects (LME) models. This approach offers flexibility and power for ana-
lyzing longitudinal data, accommodating varying rates of missing data,
non-uniform timing and including participants with only a single time

<figure><img src="image_7.png" /><figcaption>**Fig. 2.** Regions of interest for cortical AD and cognitive signatures</figcaption></figure>

Cortical AD signature: superior and inferior frontal gyrus - supramarginal gyrus - superior parietal gyrus - precuneus gyrus - parahippocampal gyrus - inferior temporal

Cognitive resilience signature: anterior cingulate gyrus - angular gyrus - middle temporal gyrus

Overlap between AD and Resilience signature: temporal pole.
point to characterize population-level, region specific differences. LME models were fitted with (i) fixed effects, including chronological age at baseline, sex, cluster group, group-by-follow-up time interaction and (ii) random effects, including only the intercept, as the focus was on studying cluster effects across groups rather than individual patients.

The predictive value of the encoded representation of BrainAGE was compared with that of predicted age difference (PAD) and brain reserve (BR). Longitudinal analyses were repeated with PAD and BR groups. PAD was calculated as the difference between chronological age and corrected predicted brain age.<sup>11,12</sup> BR was estimated with intracranial volume (ICV).<sup>38,39</sup> Groups were created based on the median of each biomarker. We constructed a handcrafted feature vector for each subject to provide another comparative baseline—beyond simply stratifying the population by PAD or BR—for our AI method. This vector included normalized volumes of gray matter, white matter, CSF, the hippocampus, and the subject’s age. We then applied the k-means clustering algorithm to group the subjects into two distinct clusters based on these features.

# Results

## Participants

From 2009 to 2017, a total of 217 participants who met the criteria for probable AD were included in this study. Based on the availability and quality of MRI data, 142 participants without genetic mutation or familial form were retained at baseline (mean age 59.33 ± 4.00 years, mean educational level 9.88 ± 2.94 years; 57%).

At baseline, all participants had a recorded MMSE score. Other neuropsychological assessments varied in coverage, with data available for 136 participants for the CDR-SoB and 97 participants for the Beery test. Additionally, 124 participants had CSF biomarker assays and 132 had APOE ε4 genotyping. Overall participants had a moderately severe disease and cognitive impairment (median CDR-SoB score 6.00; mean MMSE 16.45 ± 6.52) with an age at onset of 54.28 ± 3.55 years and a disease duration of 5.11 ± 2.78 years. Among participants, 50% had non-amnestic phenotype.

Participant number declined over time, reaching 5 participants by year 6 (Fig. 3).

## Clustering

Clustering was carried out solely at baseline, and patients maintained their cluster assignment throughout the longitudinal analysis. The optimal solution based on our metrics varied between two and three clusters (Figure S1). However, the 3-cluster solution resulted in one cluster containing only three patients. Therefore, we opted for the two-cluster solution.

To ensure the relevance of the clusters and the encoded representations by the model, we compared the BrainAGE scores of patients in each cluster (Fig. 4). Cluster #1 consisted of 63 subjects with a mean predicted brain age of 63.44 ± 7.21 years, while cluster #2 comprised 79 subjects with a mean predicted brain age of 75.66 ± 4.38 years. BrainAGE scores were different between clusters (5.44 ± 8.13 years for cluster #1 and 15.25 ± 5.11 years for cluster #2, *p* < 0.001); based on these results, we designated cluster #1 as BrainAGE- and cluster #2 as BrainAGE+.

## Demographic, biological and clinical differences at baseline

The demographic, biological and clinical differences between clusters are presented in Table 2. The clusters only exhibited differences in age and sex which were consequently included as covariates in the subsequent statistical analyses. Patients from BrainAGE+ were older and were more likely to be male.

## Neuropsychological and brain volume differences at baseline

Neuropsychological, volume and cortical thickness differences are presented in Table 3. The BrainAGE+ cluster had lower MMSE scores (FDR corrected *p*-value < 0.001) and higher CDR-SoB scores (FDR-corrected *p*-value < 0.001) compared to the BrainAGE- cluster. Concerning detailed neuropsychological tests, the BrainAGE+

<figure><img src="image_8.png" /><figcaption>Fig. 3. Flowchart of the study population.</figcaption></figure>
BrainAGE score

<figure><img src="image_9.png" /><figcaption>Fig. 4. BrainAGE distribution between clusters</figcaption></figure>

x-axis corresponds to the corrected brain age distribution between clusters. y-axis corresponds to the BrainAGE score (corrected brain age - chronological age). p-value < 0.001.

cluster demonstrated a lower executive (categorical fluency test, FDR-corrected p-value = 0.004) and language performance (DO80 test, FDR-corrected p-value = 0.01). Additionally, the BrainAGE + cluster had lower gray matter and larger CSF volumes. The type of clusters was not associated with any cognitive phenotypes (amnestic vs non-amnestic) or atrophy pattern (typical AD vs hippocampal sparing vs limbic predominant). However, a lower cortical thickness in cortical AD signature and cognitive resilience signature areas was observed in BrainAGE +.

<table><caption>**Table 2**
Demographic and biological differences at baselines.</caption><thead><tr><th>Variable</th><th>N</th><th>BrainAGE-</th><th>BrainAGE +</th><th>FDR corrected p-value</th><th>f<sup>2</sup> effect</th></tr></thead><tbody><tr><td><strong>Demographic</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age, years</td><td>142</td><td>57.99 ± 4.33</td><td>60.42 ± 3.36</td><td><strong>0.001</strong></td><td>0.10</td></tr><tr><td>Education level, years</td><td>129</td><td>9.98 ± 3.12<br>(# of missing, n = 7)</td><td>9.79 ± 2.81<br>(# of missing, n = 6)</td><td>0.72</td><td>0.001</td></tr><tr><td>Disease duration, years</td><td>139</td><td>4.63 ± 2.81<br>(# of missing, n = 2)</td><td>5.55 ± 2.73<br>(# of missing, n = 1)</td><td>0.07</td><td>0.03</td></tr><tr><td>Female, n</td><td>142</td><td>45/63 (71 %)</td><td>37/79 (46 %)</td><td><strong>0.007</strong></td><td>2.83</td></tr><tr><td><strong>Biologic</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total TAU, pg/mL</td><td>125</td><td>799.08 ± 451.67<br>(# of missing, n = 6)</td><td>724.71 ± 387.32<br>(# of missing, n = 11)</td><td>0.32</td><td>0.008</td></tr><tr><td>p-TAU / Aβ42</td><td>126</td><td>0.23 ± 0.14<br>(# of missing, n = 7)</td><td>0.21 ± 0.14<br>(# of missing, n = 9)</td><td>0.64</td><td>0.003</td></tr><tr><td>APOE ε4</td><td>138</td><td>34/60 (57 %)<br>(# of missing, n = 3)</td><td>39/78 (50 %)<br>(# of missing, n = 1)</td><td>0.49</td><td>0.77</td></tr></tbody></table>

Continuous variables are reported as mean ± standard deviation. Categorical variables are reported as the number over the total number (percentage). APOE ε4, at least one APOE ε4 allele. Number of missing values are presented as # of missing. Significant FDR corrected p-values are in bold. p-values are corrected among demographic and biologic separately.

## Cluster differences in longitudinal analysis

Longitudinal analysis was conducted on all cognitive scores and brain volumes adjusted by sex and age at baseline. Subjects in the BrainAGE + cluster exhibited a faster cognitive decline on MMSE and a faster progression on CDR-SoB (Fig. 5). Volume of CSF increased faster in BrainAGE + cluster, hippocampus volume decreased faster in BrainAGE-cluster but other brain volumes did not have different rates of progression (Table 4).

Unlike the BrainAGE clusters, the PAD and BR groups or handcrafted vector clusters did not show differences in MMSE or CDR-SoB variation (Table S2, S3 and S4).

## Discussion

In this study, we applied a clustering approach to the encoded representation of BrainAGE in a population with EOAD. Two clusters were identified and characterized by significant differences in BrainAGE. Patients classified as BrainAGE + were older and more frequently male. Neuropsychological assessments revealed lower general cognitive efficiency, higher disease severity, and reduced performance in both naming test and verbal fluency. MRI analysis showed decreased hippocampus volumes in these patients. Longitudinal analyses demonstrated a steeper decline in the BrainAGE + cluster.

Previous studies applying BrainAGE in late-onset Alzheimer's disease (LOAD) have consistently shown an increased predicted brain age compared to healthy controls.⁴⁰ In patients with mild cognitive impairment (MCI), BrainAGE has proven to be an accurate predictor of conversion to AD, with each additional BrainAGE year correlating with a 10 % increased risk of developing AD over a 36-month follow-up period.⁴¹ Longitudinal analyses have also demonstrated accelerated BrainAGE in patients with MCI and converter profiles as well as in patients with AD.⁴² So far, analyses of BrainAGE in patients with early-onset remain limited. In a previous study, we observed increased BrainAGE in EOAD and its significant rise during longitudinal follow-up.¹² Its correlation with longitudinal cognitive decline had not been previously evaluated.

The novelty of the present study lies in leveraging the encoded representation of BrainAGE, specifically the output from the model's last flat-ten layer. Consistent with previous research highlighting the utility of internal representations in deep learning BrainAGE model for
<table><caption>**Table 3** Neuropsychological, brain volumes, atrophy and clinical phenotypes differences at baseline.</caption><thead><tr><th>Variable</th><th>BrainAGE-</th><th>BrainAGE +</th><th>FDR corrected p-value</th><th>f<sup>2</sup> effect</th></tr></thead><tbody><tr><td><strong>Cognitive scores</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>MMSE</td><td>19.32 ± 4.62</td><td>14.14 ± 6.93</td><td><strong>&lt; 0.001</strong></td><td>0.16</td></tr><tr><td>CDR-SoB (median -IQR)</td><td>5 - [3.5 - 6.62]</td><td>7 - [5 - 11]</td><td><strong>0.002</strong></td><td>0.09</td></tr><tr><td>VAT</td><td>3.52 ± 3.78</td><td>3.96 ± 3.84</td><td>0.35</td><td>0.01</td></tr><tr><td>DO80</td><td>71.02 ± 9.50</td><td>64.71 ± 14.66</td><td><strong>0.01</strong></td><td>0.08</td></tr><tr><td>Beery</td><td>19.06 ± 4.01</td><td>18.08 ± 4.20</td><td>0.27</td><td>0.03</td></tr><tr><td>Letter fluency</td><td>11.52 ± 7.92</td><td>8.23 ± 6.28</td><td>0.07</td><td>0.05</td></tr><tr><td>Categorical fluency</td><td>14.77 ± 6.45</td><td>11.28 ± 7.09</td><td><strong>0.004</strong></td><td>0.10</td></tr><tr><td><strong>Brain volumes (volume percentage)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Gray matter</td><td>0.35 ± 0.03</td><td>0.32 ± 0.02</td><td><strong>&lt; 0.001</strong></td><td>0.36</td></tr><tr><td>White matter</td><td>0.28 ± 0.02</td><td>0.27 ± 0.03</td><td>0.96</td><td>0.004</td></tr><tr><td>Hippocampus</td><td>4.77e-03 ± 6.65e-04</td><td>4.40e-03 ± 7.44e-04</td><td>0.40</td><td>0.01</td></tr><tr><td>CSF</td><td>0.26 ± 0.03</td><td>0.30 ± 0.04</td><td><strong>&lt; 0.001</strong></td><td>0.21</td></tr><tr><td><strong>Cognitive phenotype</strong></td><td></td><td></td><td></td><td><strong>Odd Ratio</strong></td></tr><tr><td>Amnestic</td><td>33/60 (55 %)</td><td>38/77 (49 %)</td><td>0.51</td><td>0.80</td></tr><tr><td><strong>Atrophy phenotype</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Typical AD, n</td><td>35/63 (55 %)</td><td>35/79 (46 %)</td><td></td><td>REF</td></tr><tr><td>Hippocampal sparing, n</td><td>14/63 (22 %)</td><td>22/79 (27 %)</td><td>0.76</td><td>1.95</td></tr><tr><td>Limbic predominant, n</td><td>14/63 (22 %)</td><td>22/79 (27 %)</td><td>0.95</td><td>0.95</td></tr><tr><td><strong>Total area cortical thickness (mm)</strong></td><td></td><td></td><td></td><td><strong>f<sup>2</sup> effect</strong></td></tr><tr><td>cortical AD signature</td><td>2.31 ± 0.11</td><td>2.20 ± 0.12</td><td><strong>&lt; 0.001</strong></td><td>0.20</td></tr><tr><td>cortical resilience signature</td><td>2.43 ± 0.13</td><td>2.29 ± 0.12</td><td><strong>&lt; 0.001</strong></td><td>0.23</td></tr></tbody></table>

Continuous variables are reported as mean ± standard deviation. Categorical variables are reported as the number over the total number (percentage). Significant p-values are in bold.

IQR: Inter-Quartile Range.

classification tasks,¹¹ our findings demonstrate that clustering based on the BrainAGE encoded representation is associated with longitudinal cognitive decline. Compared to alternative approaches—such as PAD, absolute representations or handcrafted features—these BrainAGE derived representations more effectively capture the spatial heterogeneity of brain atrophy patterns.

Our findings provide additional evidence supporting the biological relevance of BrainAGE methods. Compared to LOAD, EOAD is marked by a heterogeneous phenotype with a higher prevalence of atypical presentations. According to⁴³, the AD spectrum can be described along two main axes: one related to typicality and the other related to severity.⁴³ The BrainAGE encoded representation in our study was associated with multimodal biomarkers of severity, including cortical grey matter

atrophy and global cognitive impairment. However, BrainAGE-driven clusters did not differ significantly in terms of clinical presentation (amnestic vs non-amnestic) or MRI patterns of atrophy (hippocampal sparing vs limbic predominant vs typical AD).

Interestingly, the clusters not only differed in baseline characteristics but also in their predictive value for cognitive decline over a six-year follow-up period. For instance, MMSE decline and CDR-SoB increase were 0.7 points/year and 0.4 pts/year faster, respectively, in the BrainAGE + cluster. On the contrary, longitudinal increase in CSF volume was faster in BrainAGE-. This apparent discrepancy may be explained by a ceiling effect in BrainAGE + patients with already severe brain atrophy. Cognitive decline in AD is predominantly influenced by amyloid and tau burden.⁴⁴,⁴⁵ For a given amount of neuropathological lesions, clinical

<figure><img src="image_10.png" /><figcaption>**Fig. 5.** Cognitive rate of progression per cluster

BrainAGE + cluster is represented in red. BrainAGE- cluster is in blue. p-values for slope differences are respectively 0.02 (MMSE) and 0.03 (CDR-SoB).</figcaption></figure>
<table><caption>**Table 4**
Rate of decline between clusters on severity and brain volumes.</caption><thead><tr><th></th><th>BrainAGE-</th><th>BrainAGE +</th><th>FDR corrected p-value</th></tr></thead><tbody><tr><td><strong>Severity scores</strong></td><td></td><td></td><td></td></tr><tr><td>MMSE, pts./years</td><td>-2.35 ± 0.15</td><td>-3.02 ± 0.25</td><td><strong>0.02</strong></td></tr><tr><td>CDR-SoB, pts./years</td><td>1.58 ± 0.10</td><td>1.99 ± 0.16</td><td><strong>0.03</strong></td></tr><tr><td><strong>Brain volumes</strong></td><td></td><td></td><td></td></tr><tr><td>Gray matter, %/years</td><td>-8.37e-03 ± 5.68e-04</td><td>-6.52e-03 ± 9.33e-04</td><td>0.14</td></tr><tr><td>White matter, %/years</td><td>-7.88e-03 ± 9.89e-04</td><td>-4.56e-03 ± 1.61e-03</td><td>0.16</td></tr><tr><td>Hippocampus, %/years</td><td>-1.76e-04 ± 8.18e-06</td><td>-1.42e-04 ± 1.35e-05</td><td><strong>0.03</strong></td></tr><tr><td>CSF, %/years</td><td>1.72e-02 ± 1.28e-03</td><td>1.16e-02 ± 2.09e-03</td><td><strong>0.02</strong></td></tr></tbody></table>

severity is modulated by cognitive and brain reserve. In this study, we did not find any association between education level, a proxy for cognitive reserve and BrainAGE encoded representation. Taken together, our results suggest that BrainAGE encoded representation may serve as a biomarker of brain reserve. Brain reserve refers to the brain's capacity to tolerate neuropathological damage while maintaining brain structure.⁴⁶ It is influenced by various factors, including genetics, education, social, cognitive and physical activity, and lifestyle factors. Consistent with the enhanced differentiation model proposed by⁴⁷, the baseline differences in cognitive performances between clusters are further exacerbated over time. This protective effect in BrainAGE may be explained by two mechanisms. First, baseline differences in BrainAGE may reflect a combination of genetic and developmental factors contributing to a more resilient brain structure. Second, the additional protective effect on the rate of cognitive decline during longitudinal follow-up may be driven by more active compensatory mechanisms. These mechanisms could include increased recruitment of alternative neural networks, upregulation of neurotrophic factors, or more efficient synaptic remodeling.⁴⁸

This hypothesis is further supported by baseline associations with demographic factors (younger age, female sex) and neuroimaging findings (cortical “brain resilience” signature) that have been previously linked to brain reserve.⁴⁹ The protective effect of female sex may involve various mechanisms, including attenuated alterations in age-related gene expression related to energy production, an upregulation of the immune system in women compared to men, as well as sex-specific inflammatory responses to neuropathological lesions due to sex steroid hormone deficiencies.⁵⁰ Younger age has also been associated with greater brain resilience, which could be attributed to a lower prevalence of comorbid brain disorders such as cerebrovascular disease and more efficient neuronal repair mechanisms.⁵¹

These findings have implications for the clinical application of BrainAGE. If BrainAGE can reliably reflect brain reserve, it could serve as a valuable tool for patient stratification in clinical trials, as well as personalized treatment and follow up planning. Early identification of patients with lower brain reserve could enable more target interventions, potentially slowing cognitive decline. This is particularly critical in EOAD, where many patients are still professionally active, emphasizing the need for proactive and effective management strategies.

The BrainAGE MRI encoded representation offers distinct advantages that address key limitations in current neuroimaging-based disease prediction. One major strength lies in its disease independence. While deep learning models have shown promise in predicting disease progression using MRI and PET data,⁵²⁻⁵⁴ they are typically trained for specific diagnostic categories. This disease-specific design limits their generalizability and requires extensive expert annotations to ensure high-quality labels—challenges that hinder their scalability and cross-disease applicability. In contrast, BrainAGE provides a disease-agnostic and transdiagnostic biomarker of brain health,⁴¹,⁵⁵,⁵⁶ making it suitable for heterogeneous clinical populations and large-scale studies without the need for condition-specific tuning. One of the major strengths of BrainAGE lies in its training simplicity, requiring only chronological age for

supervision. Combined with its interpretability and clinical relevance,⁵⁷ BrainAGE has emerged as a reliable and scalable biomarker. A second key strength of the BrainAGE model is its capacity to automatically extract relevant and robust features from MRI data. Unlike traditional approaches based on handcrafted features—which can miss subtle yet clinically meaningful patterns—BrainAGE leverages deep learning to capture complex, high-dimensional markers associated with brain aging and disease severity.

Our study has some limitations. First, being a monocentric study, the reproducibility of our results across different centers remains to be tested.⁵⁸ Conducting multicenter studies in the future would enhance the robustness of our findings. Second, our study population primarily comprised moderate-to-severe EOAD patients. Therefore, extending our method to early-stage EOAD patients is essential to assess its applicability for patient selection in clinical trials. Additionally, while one could argue that our clusters might represent variations in disease stage rather than brain reserve, our longitudinal analysis - adjusted for baseline differences - demonstrates that cluster attribution independently influences cognitive decline, irrespective of baseline severity. Finally, our BrainAGE deep learning model relies solely on T1-MRI, capturing structural information. Exploring a multimodal neuroimaging approach would be valuable, as different modalities could provide complementary insights.⁵⁹

## Conclusion

In this study, we showed that BrainAGE encoded representation can distinguish patient groups characterized by distinct rates of cognitive decline, regardless of their phenotype. These findings underscore the potential of using BrainAGE as a biomarker for better understanding and managing EOAD.

## Acknowledgments

Authors would thank all the members and participants of the COMAJ cohort.

## Human and animal rights

The authors declare that the work described has not involved experimentation on humans or animals.

## CRediT authorship contribution statement

**Dorian Manouvriez:** Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review & editing, Writing - original draft. **Grégory Kuchcinski:** Conceptualization, Methodology, Supervision, Writing - review & editing. **Vincent Roca:** Writing - review & editing. **Adeline Rollin Sillaire:** Resources, Writing - review & editing. **Maxime Bertoux:** Writing - review & editing. **Xavier Delbeuck:** Resources, Writing - review & editing. **Jean-Pierre Pruvo:** Writing - review & editing. **Simon Lecerf:** Resources,
Writing - review & editing. Florence Pasquier: Resources, Writing - review & editing. Thibaud Lebouvier: Resources, Writing - review & editing. Renaud Lopes: Conceptualization, Methodology, Project administration, Supervision, Writing - review & editing.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dorian Manouvriez reports financial support was provided by SIEMENS HEALTHCARE SAS. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplementary materials

Supplementary material associated with this article can be found in
the online version at doi:10.1016/j.neurad.2025.101365.

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