# Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

Vincent Roca, Marc Tommasi, Paul Andrey, Aurélien Bellet, Markus Schirmer, Hilde Henon, Laurent Puy, Julien Ramon, Grégory Kuchcinski, Martin Bretzner, et al.

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Vincent Roca, Marc Tommasi, Paul Andrey, Aurélien Bellet, Markus Schirmer, et al. Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction. 2025. hal-05244644

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# Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

Vincent Roca<sup>a,*</sup>, Marc Tommasi<sup>b</sup>, Paul Andrey<sup>b</sup>, Aurélien Bellet<sup>c</sup>, Markus D. Schirmer<sup>d</sup>, Hilde Henon<sup>e</sup>, Laurent Puy<sup>e,f</sup>, Julien Ramon<sup>g</sup>, Grégory Kuchcinski<sup>a,f,g</sup>, Martin Bretzner<sup>a,f,g</sup>, Renaud Lopes<sup>a,f,h</sup>, on behalf of the ARIANES study group<sup>1</sup>

<sup>a</sup>Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, F-59000 Lille, France

<sup>b</sup>Univ. Lille, Inria, F-59000 Lille, France

<sup>c</sup>Univ. Montpellier, Inria, F-34090 Montpellier, France

<sup>d</sup>Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA

<sup>e</sup>CHU Lille, Département de Neurologie vasculaire, F-59037 Lille, France

<sup>f</sup>Univ. Lille, Inserm, CHU Lille, U1172 - Lille Neurosciences & Cognition, F-59037 Lille, France

<sup>g</sup>CHU Lille, Département de Neuroradiologie, F-59037 Lille, France

<sup>h</sup>CHU Lille, Département de Médecine Nucléaire, F-59037 Lille, France

## Abstract

**Objective:** Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health, with potential implications for post-stroke recovery. However, training robust BrainAGE models requires large, diverse datasets, often restricted by privacy and regulatory concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcomes.

**Methods:** We used pre-treatment FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcomes at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for these outcomes, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalisation outcome.

**Results:** While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcomes, and multivariate predictions of these outcomes showed the significance of the association between BrainAGE and post-stroke recovery.

**Conclusion:** FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for personalized prognostic modeling in stroke care.

**Keywords:** brain MRI, brain age, federated learning, ischemic stroke, functional outcomes

## 1. Introduction

Stroke is a leading cause of mortality and long-term disability. Ischemic strokes account for approximately 87% of all cases [1], making them the primary focus of therapeutic advancements. Advances in acute ischemic

<footnote>*Corresponding author at: Lille, 59037 France. E-mail address: vincentroca9@outlook.fr</footnote>

<footnote n="1">¹Members of the ARIANES study group: Jean-Pierre Pruvo, Mathieu Masy, Denis Berteloot, Cyril Bruge, Sebastien Verclytte, Emmanuel Michelin, Thierry Stekelorom, and Wael Yacoub.</footnote>
stroke treatment, such as thrombolysis and mechanical thrombectomy (MT), have significantly improved survival and functional outcomes rates. However, these therapies alone do not ensure complete recovery, and many patients require long-term rehabilitation to regain functional abilities. Identifying patients who might need dedicated care is the first step towards personalized medicine and underscores the importance of accurately assessing and predicting post-stroke functional outcomes to guide patient management.

Several factors influence post-stroke outcomes, including severity and location of stroke, pre-existing comorbidities, and timely access to specialized care. Among these, age is a critical determinant, as older patients often experience worse recovery trajectories due to reduced neuroplasticity and higher rates of comorbid conditions [2]. However, the effects of aging are highly dependent on past medical history, lifestyle, and genetics and are variable among the subjects. Some will experience healthy, disease-free aging, whereas others will suffer more from the consequences of age-related diseases such as ischemic stroke [3].

In recent years, brain age gap estimation (BrainAGE) has emerged as a more personalized measure of biological brain health and resilience, offering insights into recovery potential that chronological age alone cannot predict [4]. BrainAGE quantifies the difference between an individual's chronological age and their brain-predicted age, as estimated by machine learning (ML) algorithms trained on neuroimaging data. A lower BrainAGE may reflect greater resilience to functional disabilities and disease severity, while a higher BrainAGE could indicate reduced resilience and an elevated risk of post-stroke disability—linking the metric to brain health and resilience.

Traditionally, BrainAGE has been trained on T1-weighted (T1w) MRI scans to assess the risk of aging-related diseases, such as Alzheimer's disease [5]. Its application to stroke care remains underexplored, despite its potential to offer an innovative approach for predicting patient outcomes and guiding personalized treatment strategies. A lower BrainAGE has been associated with a reduced risk of post-stroke cognitive impairment up to 36 months after stroke, even in patients without previous cognitive impairments [6]. Bretzner et al. [7] used fluid-attenuated inversion recovery (FLAIR) sequences, which are more systematically acquired in the acute phase than T1w images, to train a BrainAGE algorithm. They showed that patients with a higher BrainAGE on FLAIR images had an unfavorable vascular profile and worse prognosis after stroke. However, despite being based on a large international multicenter cohort of 4500 patients, these results do not account for modern stroke management practices, such as MT. This underscores the need for further validation efforts to adapt BrainAGE models to contemporary treatment strategies.

To fully harness the potential of BrainAGE for post-stroke outcome prediction, it is essential to train ML algorithms on large, diverse, and representative datasets. However, privacy concerns and regulatory constraints often limit data sharing across institutions, making federated learning (FL) an innovative and viable solution to address these challenges [8]. Unlike traditional centralized learning, where data from multiple centers is pooled into a single repository, FL allows models to be trained locally at each site, sharing only model updates rather than raw data. This decentralized paradigm is particularly advantageous for neuroimaging research [9].

Previous studies have demonstrated that ML models can achieve comparable performances in age prediction under centralized and federated settings [10, 11, 12, 13, 14, 15, 16]. However, these applications have been restricted to healthy populations, preventing the evaluation of BrainAGE as a biomarker for brain diseases. Moreover, these prior studies did not leverage truly multicentric datasets but rather simulated data distributions across centers, bypassing site-specific variability, which can significantly affect age prediction performance [17].

This study aimed to evaluate the performance of FL for training BrainAGE models in ischemic stroke patients who underwent MT, and to assess BrainAGE as a biomarker for post-stroke outcomes. To this end, we implemented BrainAGE estimation using centralized, federated, and single-site training strategies. To account for varying computational capacities across centers, we tested these three approaches using base ML models of different complexities, simulating scenarios where centers had limited or extensive computational resources. Leveraging a multicenter dataset of FLAIR images from 16 hospital centers acquired prior to any treatment, we systematically assessed age prediction accuracy, explored associations between BrainAGE and vascular phenotypes, and evaluated its predictive value for post-stroke functional outcomes.
## 2. Materials and Methods

### 2.1. Ethics

The ethical committee (Comité de protection des personnes Nord-Ouest IV) classified the study as observational on March 9, 2010, and the committee protecting personal information of the patient approved the study by December 21, 2010 (n° 10.677). The DPO department of the Lille University Hospital attests to the declaration of the implementation methods of this project, in accordance with the applicable personal data protection regulation, in particular the General Data Protection Regulation (UE) 2016/679.

### 2.2. Participants and clinical variables

We included all consecutive patients admitted to our comprehensive stroke center for MT due to acute ischemic stroke between January 1, 2015, and December 31, 2021. All patients were treated according to the latest international guidelines [18]. FLAIR brain imaging acquired prior to any treatment was obtained for each participant in one of 18 stroke centers in our stroke network in the Hauts-de-France region. Clinical data were prospectively collected by trained vascular neurologists, including demographic characteristics (age, sex assigned at birth) and major vascular risk factors: hypertension (HTN), diabetes mellitus (DM), atrial fibrillation (AF), smoking status (SMK), and hypercholesterolemia (HCL). Stroke severity upon admission was assessed using the National Institutes of Health Stroke Scale (NIHSS). Additional variables included the time interval in minutes between the acquisition of brain MRI and the arterial puncture (P2P), intravenous thrombolysis (IVT), and successful recanalisation (RECA, defined as mTICI ≥ 2b on final angiography). Functional outcomes were assessed using the modified Rankin Scale (mRS) at 3 months post-stroke.

All FLAIR images were visually inspected, and those with major artifacts (e.g., folding, motion, metal, or severe bias-field inhomogeneity) were excluded. Participants with missing clinical data and those with basilar artery occlusion—due to their distinct and typically poor prognosis [19]—were also excluded.

### 2.3. Age prediction

#### 2.3.1. Centralized, federated and single-site models

We studied different ML algorithms for age estimation (section 2.3.3), each implemented under three distinct training configurations:

* centralized: The images from all hospital centers were aggregated onto a single machine for model training.

* federated: The images were kept separate in each center, centralizing only the training gradients after each local epoch (section 2.3.2).

* single-site: Only the images from Center 1—the most represented center in the dataset (section 3.1)—were used for training.

We followed different strategies to obtain age prediction for each image of our test set—which consisted of all the images acquired outside Center 1—with each implemented model. For the single-site models, training was performed solely on the Center 1 images, and inference was applied on the remaining images. For the centralized and federated models, we employed five-fold cross-validation on the test set, preserving the distribution of centers across folds. In each fold, the training set included the Center 1 images and the training portion of the fold, while inference was performed on the corresponding test portion.

Comparing the centralized and federated approaches allowed for an evaluation of the replicability of results obtained in a centralized setting—which is supposed to be optimal in terms of prediction performances—with FL. The single-site approach served as an ablated version to assess the added value of multicenter training data.
### 2.3.2. Implementation of federated learning

We implemented the FedAvg algorithm [20] for FL. In FedAvg, each training round consists of (i) transmitting the current global model weights to each client (i.e., hospital center), (ii) each client performing local training steps, and (iii) updating the global model by aggregating the clients' updated weights using a weighted average.

Given the small number of clients in our dataset (section 3.1), we included all clients in every round, with each client communicating with the server after a single local epoch, as communication costs were a minor concern compared to convergence.

We used Declearn<fnref n="2" />, a Python package designed to facilitate FL by coordinating distributed models through a central server, to implement FedAvg.

### 2.3.3. Machine learning approaches

Using the previously defined training configurations (section 2.3.1), we implemented different regression models for age prediction in order to compare the approaches at different levels of complexity.

#### 2.3.3.1 Volume-based approach

We applied SynthSeg [21] to our FLAIR images to extract 32 brain volumes for each image, which we then normalized with the estimated total intracranial volume. These volumes served as input features for linear regression trained using stochastic gradient descent, optimizing the mean absolute error with an L2 regularization term.

We implemented two versions of this model. The first, **VolSimpleLR**, directly used the 32 volumes as input features. To increase model complexity and capture potential nonlinear patterns, we developed **VolAugmentedLR**, which expanded the input space by generating all polynomial combinations of the original features up to degree 2. This included each original feature, its square, and all pairwise products, resulting in a total of 560 features.

For the centralized and single-site settings, models were trained for 1000 epochs using inverse scaling learning rate decay, starting at 0.5 for VolSimpleLR and 0.07 for VolAugmentedLR. In the federated configuration, training proceeded for 1000 rounds with linear learning rate decay (found empirically to be more stable than inverse scaling in this setting), ranging from 0.1 to 0.01 for VolSimpleLR and from 0.02 to 0.002 for VolAugmentedLR. To accelerate convergence, the intercept was initialized to the average age in the training set for the centralized model and to the average age in Center 1 for the federated and single-site models.

A five-fold cross-validation procedure was used in the centralized and single-site models to tune the L2 penalty weight by minimizing mean absolute error. The FL models adopted the L2 penalty weights identified in the single-site training.

#### 2.3.3.2 Radiomic-based approach

We set up a more complex model, **RadiomicsLR**, based on a feature set consisting of white-matter (WM) radiomics. To minimize technical variability, we first preprocessed the FLAIR images as follows: (i) skull-stripping with HD-BET [22], (ii) bias correction with N4ITK [23], (iii) linear registration with FSL-FLIRT [24] (six degrees of freedom), and (iv) z-score normalization of intensities based on the mean and standard deviations computed within the registered brain mask. For registration, we selected a reference image with 0.6 x 0.6 mm of pixel resolution in axial plane and 5.5 mm of slice thickness—an average resolution within our dataset (section 3.1)—since spatial resampling near the original voxel size enhances the reproducibility of radiomics [25].

Next, we extracted WM masks from the registered SynthSeg segmentations and computed 1560 WM radiomics for each image using Pyradiomics [26]. The extraction parameters were based on those used by Bretzner et al. [7] (available online, see section 2.5).

<footnote n="2">²https://gitlab.inria.fr/magnet/declearn/declearn2/-/tree/v2.6.0?ref_type=tags</footnote>
The min-max normalized radiomics served as inputs for linear models configured as in section 2.3.3.1, with reduced learning rates: inverse scaling starting at 0.004 for the centralized and single-site models, and linear decay from 0.01 to 0.001 for the federated model.

### 2.3.3.3 Voxel-based approach

Finally, we implemented a deep learning model, VoxelsCNN, based on the 3D convolutional neural network architecture described by Cole et al. [27]. Preprocessing was the same as before radiomics extraction (section 2.3.3.2). We modified the max-pooling layers in Cole et al. [27]'s convolutional blocks to not downsample along the head-foot axis, which is weakly resolved in our images (section 3.1). In the federated model, we replaced Batch Normalization with Layer Normalization [28], as the latter has been found to perform better in FL [29]. To accelerate convergence, we initialized the intercept of the output dense layer to the average age in the training set for the centralized model and to the average age in Center 1 for the federated and single-site models.

All models used a batch size of 8. Centralized and single-site models were trained for 1000 epochs using the Adam optimizer [30], with linear learning rate decay from 0.001 to 0.0001. For the federated model, we used plain stochastic gradient descent—found empirically to yield more stable training in this context—and limited training to 500 epochs, using a learning rate decay from 0.0005 to 0.00005 to reduce computational overhead. To save GPU memory and speed up computations, a mixed precision policy was adopted [31].

## 2.4. Experiments

### 2.4.1. Prediction errors

We assessed each model's prediction accuracy by computing the absolute difference between the actual and predicted ages for each MR image in the test set. Within each of the four ML approaches outlined in section 2.3.3, we compared the prediction errors between the training configurations (section 2.3.1) using two-tailed Wilcoxon signed-rank tests.

### 2.4.2. Association between BrainAGE and clinical outcomes

To explore BrainAGE's clinical relevance in our population, we investigated its associations with various clinical variables related to cardiovascular phenotypes, stroke severity, and post-stroke functional outcomes.

#### 2.4.2.1 BrainAGE computation

We defined BrainAGE following a standard approach [32]. We first computed the predicted age difference (PAD) by subtracting the actual age from the predicted age for each test image. Next, PAD was linearly regressed on the real age to mitigate regression towards the mean. Finally, we derived BrainAGE by subtracting the regression-estimated PAD from the actual PAD. To approximate real-world application where BrainAGE must be estimated for a single new image, linear regression and PAD correction were performed using ten-fold cross-validation on the test set.

#### 2.4.2.2 Clinical determinants of accelerated brain aging

To explore correlations between clinical outcomes and brain aging, we compared BrainAGE across the following binary variables: sex, HTN, DM, AF, SMK, and HCL. Differences were assessed for statistical significance using two-tailed Mann-Whitney U tests.

#### 2.4.2.3 Impact of accelerated brain aging on post-stroke functional outcome

We used a standard mRS cutoff [7, 33] to dichotomize post-stroke functional outcomes into good (mRS at 3 months ≤ 2) or poor (mRS at 3 months > 2). Using this categorization, we compared the BrainAGEs between participants with good and poor outcomes using two-tailed Mann-Whitney U tests.

Next, we evaluated the predictive value of BrainAGE for functional outcomes through logistic regressions, with the dichotomized functional outcomes (good vs. poor) as the dependent variable. Independent variables included BrainAGE, age, sex, HTN, DM, AF, SMK, HCL, NIHSS, P2P, IVT, and RECA. Each variable's contribution was assessed using adjusted odds ratios with corresponding 95% Wald confidence intervals. Statistical significance was determined using the Wald test on the logistic regression coefficients.
<figure><img src="image_2.png" /><figcaption>Figure 1: Flowchart of data and analysis.</figcaption></figure>

## 2.5. Data and code availability

The FLAIR images used in this study are not publicly available due to the sensitive nature of the data, which could compromise participant privacy. However, parameters for radiomics extraction and code for training and applying the ML models with the different training configurations are accessible in a public repository: https://github.com/RocaVincent/federated_brain_age.

# 3. Results

## 3.1. Population

1674 participants were included in this study (Figure 1). Table 1 summarizes the demographic and MRI acquisition characteristics for each of the 16 medical centers. Center 1 contributed the largest portion of the dataset, accounting for 39% of the cohort. Age distributions across centers were similar, as confirmed by a Kruskal-Wallis H-test on the respective age groups (p = 0.6824). FLAIR images had high in-plane (axial) resolution and typical clinical high slice thickness, with notable heterogeneity observed across centers. Among the clinical variables, NIHSS, P2P and IVT were significantly associated with the acquisition center (Appendix A).

Table 2 summarizes the demographic and clinical characteristics of the test set participants.

## 3.2. Examples of BrainAGE estimation

Figure 2a depicts the brain of a patient admitted for a left middle cerebral artery stroke. The image reveals substantial cortical and subcortical atrophy, with enlarged sulci and ventricles. Confluent periventricular white matter hyperintensities and a prior stroke lesion in the right occipital lobe are also visible. The corresponding BrainAGE was positive across all methods.

On the contrary, Figure 2b shows the brain of a patient who, notwithstanding a right deep middle artery stroke, exhibits a good brain health with a maintained parenchymal trophicity, collapsed sulci, thin ventricles, and minimal WM hyperintensities. The corresponding BrainAGE was negative for all methods.

## 3.3. Prediction errors

Figure 3 shows the absolute age prediction errors. The comparison of the four ML approaches reveals that greater model complexity led to more precise age estimations. When comparing the three training methods, the centralized approach achieved the lowest errors, with statistically significant differences except when compared to the federated VolSimpleLR and VolAugmentedLR models. The federated models
<table><caption>Table 1: Demographic and MRI acquisition characteristics in the study population.</caption><thead><tr><th>medical center</th><th>nb of participants</th><th>age, years<sup>#</sup></th><th>manufacturer<sup>†</sup></th><th>field strength, Tesla<sup>†</sup></th><th>in-plane pixel size, mm<sup>2</sup></th><th>slice thickness, mm<sup>#</sup></th></tr></thead><tbody><tr><td>Center 1</td><td>651</td><td>69.90 ± 15.37</td><td>Philips (650); Siemens (1)</td><td>1.5 (644); 3.0 (7)</td><td>0.89 ± 0.06</td><td>5.17 ± 0.44</td></tr><tr><td>Center 2</td><td>184</td><td>70.64 ± 12.78</td><td>GE (183); Siemens (1)</td><td>1.5 (182); 3.0 (2)</td><td>0.50 ± 0.04</td><td>5.95 ± 0.48</td></tr><tr><td>Center 3</td><td>135</td><td>69.55 ± 14.99</td><td>GE (79); Siemens (56)</td><td>1.5 (122); 3.0 (13)</td><td>0.59 ± 0.16</td><td>5.91 ± 0.21</td></tr><tr><td>Center 4</td><td>84</td><td>73.52 ± 14.02</td><td>GE (66); Siemens (18)</td><td>3.0 (75); 1.5 (9)</td><td>0.58 ± 0.16</td><td>5.38 ± 0.40</td></tr><tr><td>Center 5</td><td>83</td><td>70.54 ± 15.52</td><td>GE (40); Siemens (43)</td><td>3.0 (46); 1.5 (37)</td><td>0.45 ± 0.06</td><td>5.53 ± 0.28</td></tr><tr><td>Center 6</td><td>82</td><td>79.70 ± 14.10</td><td>Siemens (76); GE (6)</td><td>1.5 (66); 3.0 (16)</td><td>0.51 ± 0.21</td><td>5.60 ± 0.46</td></tr><tr><td>Center 7</td><td>79</td><td>70.97 ± 13.36</td><td>GE</td><td>1.5</td><td>0.50 ± 0.00</td><td>6.16 ± 0.36</td></tr><tr><td>Center 8</td><td>66</td><td>70.18 ± 16.73</td><td>GE</td><td>1.5</td><td>0.58 ± 0.17</td><td>6.00 ± 0.00</td></tr><tr><td>Center 9</td><td>63</td><td>69.37 ± 13.94</td><td>Philips (62); GE (1)</td><td>1.5</td><td>0.62 ± 0.07</td><td>5.54 ± 0.18</td></tr><tr><td>Center 10</td><td>62</td><td>68.85 ± 13.50</td><td>Siemens</td><td>1.5</td><td>0.57 ± 0.23</td><td>5.78 ± 0.44</td></tr><tr><td>Center 11</td><td>46</td><td>69.22 ± 12.70</td><td>Siemens</td><td>1.5 (37); 3.0 (9)</td><td>0.78 ± 0.04</td><td>4.97 ± 0.18</td></tr><tr><td>Center 12</td><td>44</td><td>70.07 ± 15.12</td><td>Philips</td><td>3.0 (41); 1.5 (3)</td><td>0.57 ± 0.13</td><td>5.00 ± 0.98</td></tr><tr><td>Center 13</td><td>32</td><td>67.28 ± 12.05</td><td>Siemens (17); GE (15)</td><td>1.5</td><td>0.70 ± 0.21</td><td>5.45 ± 0.53</td></tr><tr><td>Center 14</td><td>30</td><td>69.53 ± 16.13</td><td>Siemens</td><td>1.5 (26); 3.0 (4)</td><td>0.79 ± 0.13</td><td>4.91 ± 0.71</td></tr><tr><td>Center 15</td><td>23</td><td>64.91 ± 17.76</td><td>Siemens (16); Philips (7)</td><td>1.5</td><td>0.80 ± 0.10</td><td>4.93 ± 0.35</td></tr><tr><td>Center 16</td><td>10</td><td>70.70 ± 16.40</td><td>Philips</td><td>1.5</td><td>0.63 ± 0.11</td><td>4.93 ± 0.35</td></tr><tr><td>Total</td><td>1674</td><td>70.03 ± 14.71</td><td>Philips (773); GE (535); Siemens (366)</td><td>1.5 (1461); 3.0 (213)</td><td>0.69 ± 0.20</td><td>5.47 ± 0.57</td></tr></tbody></table>

<footnote># Values are expressed as mean ± standard deviation.</footnote>

<footnote>† The number of participants is indicated in brackets if there are several options.</footnote>
<table><caption>Table 2: Demographic and clinical characteristics in the test set.</caption><tr><td>sample size, nb of participants</td><td>1023</td></tr><tr><td>age, years, mean ± standard deviation</td><td>70.10 ± 14.27</td></tr><tr><td>sex, % of male</td><td>46</td></tr><tr><td>hypertension, %</td><td>68</td></tr><tr><td>diabetes mellitus, %</td><td>21</td></tr><tr><td>atrial fibrillation, %</td><td>37</td></tr><tr><td>smoking, %</td><td>17</td></tr><tr><td>hypercholesterolemia, %</td><td>41</td></tr><tr><td>NIH Stroke Scale upon admission, median (interquartile range)</td><td>16 (8)</td></tr><tr><td>time interval between MRI and the arterial puncture, minutes, mean ± standard deviation</td><td>148.18 ± 40.27</td></tr><tr><td>previous intravenous thrombolysis, %</td><td>62</td></tr><tr><td>successful recanalisation, %</td><td>83</td></tr><tr><td>Modified Rankin Scale at three months, median (% of good outcome (≤ 2))</td><td>3 (38)</td></tr></table>

<figure><img src="image_3.png" /><figcaption>(a) Participant aged 62 with positive BrainAGE for all methods.

VolSimpleLR +12.60 +12.54 +13.48
VolAugmentedLR +12.72 +14.39 +14.86
RadiomicsLR +11.94 +11.98 +9.94
VoxelsCNN +9.78 +6.33 +7.53</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b) Participant aged 68 with negative BrainAGE for all methods.

VolSimpleLR -8.69 -9.39 -9.49
VolAugmentedLR -9.69 -9.92 -8.01
RadiomicsLR -15.91 -10.61 -9.11
VoxelsCNN -1.65 -13.14 -10.90</figcaption></figure>

**Figure 2:** BrainAGE estimated on two FLAIR images of the test set. The middle axial slice after skull-stripping, inhomogeneity correction and linear registration is shown for each one, along with BrainAGE for each implemented model.
<figure><img src="image_5.png" /><figcaption>**Figure 3: Absolute age prediction errors in the test set.** Boxplots with significant paired comparisons of errors (*: p < 0.05; **: p < 0.01; ***: p < 0.001) and mean ± standard deviation of the errors are shown.</figcaption></figure>

<table><thead><tr><th></th><th>centralized</th><th>federated</th><th>single-site</th></tr></thead><tbody><tr><td>VolSimpleLR</td><td>8.03 ± 6.22</td><td>8.13 ± 6.43</td><td>8.49 ± 6.91</td></tr><tr><td>VolAugmentedLR</td><td>7.44 ± 5.87</td><td>7.56 ± 5.85</td><td>7.65 ± 6.07</td></tr><tr><td>RadiomicsLR</td><td>6.85 ± 5.57</td><td>7.35 ± 5.97</td><td>8.60 ± 8.65</td></tr><tr><td>VoxelsCNN</td><td>5.53 ± 4.50</td><td>6.46 ± 4.99</td><td>7.04 ± 5.94</td></tr></tbody></table>

exhibited higher errors than the centralized models but significantly outperformed the single-site versions in pairwise comparisons. Notably, *RadiomicsLR* outperformed the two volume-based approaches in both the centralized and federated configurations, but demonstrated more variability and higher errors in the single-site configuration.

### 3.4. *Clinical phenotype and brain aging*

Several observations emerge from the BrainAGE comparisons by clinical phenotype (Figure 4). Patients with DM exhibited significantly higher BrainAGE across all models. For HTN and AF, higher median BrainAGEs were observed across all models, but statistical significance was only reached for HTN with some models. This tendency was also observed with SMK and HCL—with statistical significance for HCL with the single-site *VolAugmentedLR* model—, but with some exceptions (e.g., lower median BrainAGE obtained for SMK with the centralized RadiomicsLR model). Regarding sex, males exhibited significantly higher BrainAGE in the *VolSimpleLR* and *VolAugmented* models. In contrast, *RadiomicsLR* and *VoxelsCNN* models yielded higher median BrainAGEs for females, with statistical significance for the centralized and single-site *RadiomicsLR* models.

### 3.5. *Post-stroke functional outcomes and brain aging*

All implemented models exhibited significantly higher BrainAGE in the patients with poor functional outcomes, regardless of whether centralized, federated, or single-site training was used (Figure 5).

The contributions of BrainAGE in logistic regression of good functional outcomes are summarized in Table 3. Across all models, the odds ratio for BrainAGE was below 1, indicating that higher BrainAGE was associated with poorer post-stroke outcomes. The corresponding regression coefficients were significantly different from 0. Furthermore, BrainAGE had a lower odds ratio with the *RadiomicsLR* and *VoxelsCNN* models.

Appendix B presents the contributions of the other independent variables. Age, NIHSS, P2P, IVT and RECA were consistently significant predictors of the functional outcomes. Sex was also significant with certain models.
<figure><img src="image_6.png" /><figcaption>**Figure 4: Comparison of BrainAGE for each clinical phenotype in the test set.** For the sex variable, the presence or absence of the phenotype corresponds to male and female, respectively. Asterisks indicate significant unpaired comparisons of BrainAGE (*: p < 0.05; **: p < 0.01; ***: p < 0.001). Abbreviations: HTN: hypertension; DM: diabetes mellitus; AF: atrial fibrillation; SMK: smoking; HCL: hypercholesterolemia.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 5: Comparison of BrainAGE between good and poor functional outcomes in the test set. Asterisks indicate significant unpaired comparisons of BrainAGE (*: p < 0.05; **: p < 0.01; ***: p < 0.001).</figcaption></figure>

<table><caption>Table 3: Odds ratios of BrainAGE in multiple logistic regression of good functional outcomes in the test set.</caption><thead><tr><th></th><th>centralized</th><th>federated</th><th>single-site</th></tr></thead><tbody><tr><td>VolSimpleLR</td><td>0.973**<br>[0.954, 0.993]</td><td>0.978*<br>[0.960, 0.996]</td><td>0.981*<br>[0.963, 0.998]</td></tr><tr><td>VolAugmentedLR</td><td>0.973**<br>[0.954, 0.992]</td><td>0.975*<br>[0.956, 0.994]</td><td>0.973**<br>[0.955, 0.992]</td></tr><tr><td>RadiomicsLR</td><td>0.962***<br>[0.940, 0.984]</td><td>0.964**<br>[0.943, 0.986]</td><td>0.966**<br>[0.944, 0.988]</td></tr><tr><td>VoxelsCNN</td><td>0.960**<br>[0.936, 0.984]</td><td>0.972**<br>[0.952, 0.993]</td><td>0.955***<br>[0.931, 0.980]</td></tr></tbody></table>

Odds ratios with the corresponding 95% confidence interval are reported. Asterisks indicate that the BrainAGE regression coefficient was significantly different from 0 (*: p < 0.05; **: p < 0.01; ***: p < 0.001). Age, sex, hypertension, diabetes mellitus, atrial fibrillation, smoking, hypercholesterolemia, NIH Stroke Scale upon admission, time interval in minutes between MRI and the arterial puncture, previous intravenous thrombolysis, and successful recanalisation were the other independent variables.
# 4. Discussion

By leveraging a multicenter ischemic stroke clinical imaging cohort, we evaluated the effectiveness of FL in training FLAIR-based BrainAGE models of varying complexities. While centralized models achieved slightly more accurate predictions, FL significantly outperformed single-site models. Analyses of BrainAGE in relation to clinical phenotypes and its role as a predictor of post-stroke functional outcomes revealed comparable findings between the different implemented methods.

## 4.1. Age prediction performance

For the four implemented ML models, lower prediction errors were observed with centralized learning, as expected. The absence of statistical significance in the comparison with FL for the two volume-based ML approaches is likely attributable to the model's simplicity (32 input volumes).

Although centralized models performed best, FL consistently outperformed single-site learning, under-scoring its value when data centralization is not feasible. FL performances could potentially improve with techniques designed to address inter-client heterogeneity [34, 35, 36]. However, these methods outperform FedAvg only in some cases, particularly when label distributions vary across centers [37], whereas age distributions were similar in our study.

Prediction errors also indicate that more complex models tend to provide more precise predictions, which is consistent with previous findings [38]. However, under single-site learning, *RadiomicsLR* performed worse than *VolSimpleLR* and *VolAugmentedLR*, despite its greater complexity (1560 vs. 32 input features). This can be attributed to the low inter-scanner reproducibility of radiomics computed on FLAIR brain images [39], emphasizing the advantages of multicenter training datasets.

## 4.2. Clinical training data

Even when BrainAGE is applied to pathological subjects, the widely adopted practice is to train the predictive models on a healthy population [5, 6, 40, 41, 42]. As an exception, Bretzner et al. [7] trained their model with MR images from stroke patients. In addition to the demonstrated clinical relevance, the authors suggested that their BrainAGE could better translate to routine clinical care. In our study, based on a clinical cohort, we argue that our goal was not to use BrainAGE for distinguishing between healthy individuals and stroke patients, but rather to investigate its correlation with vascular risk factors and post-stroke functional outcomes within a stroke cohort. Moreover, using a training set that is representative of the application data may help mitigate unexpected behaviors during inference, such as potential confounding by stroke lesions.

## 4.3. BrainAGE and clinical phenotypes

The comparison of BrainAGE between patients with and without each clinical phenotype revealed a significant difference for DM, regardless of the BrainAGE model used. This aligns with the findings of Bretzner et al. [7]. However, unlike Bretzner et al., we did not observe significant BrainAGE differences for HTN, AF, or SMK, except with certain models (e.g., HTN with the VolAugmentedLR models). The discrepancy in results could be attributed to the difference in sample sizes (n = 1023 vs. 4163), which affects statistical power. Another explanation is the age disparity observed for the non-significant phenotypes (including HCL), whereas age distributions were similar between DM and non-DM patients (Appendix C). Indeed, through the subtraction of chronological age from the predicted age and subsequent PAD correction (section 2.4.2.1), BrainAGE is designed to be independent of chronological age. As a result, if a biological difference is strongly associated with an age difference, it is unlikely to correspond with a BrainAGE difference. Besides, among the clinical phenotypes included, DM was the most predictive of post-stroke functional outcomes in our multivariate analyses (Appendix B). This supports the hypothesis that biological differences unrelated to age were only discriminant between DM and non-DM patients, explaining why BrainAGE comparisons were significant only for this phenotype.

The significant BrainAGE differences we observed between male and female patients with certain models did not align with previous literature [7, 43]. However, we did not investigate the causes of these differences
as (i) there were significant age differences between males and females in our dataset (Appendix C), making relevant comparisons difficult, and (ii) the direction of the difference mainly depended of the ML model and not the training configuration (higher BrainAGE for males with the *VolSimpleLR* and *VolAugmentedLR* models vs. higher BrainAGE for females with the *RadiomicsLR* and *VoxelsCNN* models).

### 4.4. BrainAGE and post-stroke functional outcomes

Patients with good functional outcomes exhibited significantly lower BrainAGE across all models. Moreover, BrainAGE remained significantly associated with functional outcomes in logistic regression analyses, even after adjusting for vascular risk factors and post-stroke treatment variables. Nonetheless, BrainAGE estimates from the *RadiomicsLR* and *VoxelsCNN* models showed stronger associations with functional outcomes than those from the *VolSimpleLR* and *VolAugmentedLR* models. Examining age prediction errors alongside these results suggests that more accurate age predictions tend to produce BrainAGE measures with greater clinical relevance.

However, this conclusion is nuanced by the observation that the single-site *RadiomicsLR* and *VoxelsCNN* models, despite yielding higher prediction errors, still produced BrainAGE values that were strongly associated with outcomes. Similarly, Bashyam et al. [40], who investigated BrainAGE in a large cohort of patients with Alzheimer's disease, schizophrenia, and depression, reported that the best age prediction did not lead to the most clinically relevant BrainAGE. Nevertheless, clinical studies involving smaller datasets (e.g., rare diseases with limited number of patients per site) may benefit from using FL to leverage multicenter data and potentially reduce prediction error.

### 4.5. Limitations and perspectives

In section 4.2, we emphasized the potential benefits of using a clinical dataset for BrainAGE model training. Nonetheless, the standard practice of training and evaluating age prediction models on healthy subjects may produce more accurate estimates and allow for more reliable comparisons across training configurations, given the typically lower and less variable prediction errors.

Additionally, using BrainAGE algorithms in stroke patients involves considering stroke lesions. However, rapid lesion changes are commonly observed in MR images during the acute phase [44], and the time interval between symptom onset and imaging completion is often unknown [45], making it particularly variable in a multicenter dataset. Although we accounted for the time from imaging completion to treatment initiation, the variable delay between symptom onset and imaging could affect the consistency of BrainAGE estimations. Moreover, brain lesions can hinder automatic tools used to preprocess MR images [46]. Future studies could explore segmentation tools to mask out lesions, allowing BrainAGE to be estimated independently of lesion-related effects.

We compared models trained using FL with those trained on a single site to assess the value of gathering data from multiple centers. One might argue that our comparisons were not entirely fair, as federated models were trained on a larger dataset than their single-site counterparts. However, as Stripelis et al. [16] suggest, since the purpose of FL is to aggregate data from multiple centers, it is appropriate to compare federated models with centralized models trained on smaller datasets.

In our dataset, age distributions were similar between the centers. Future studies may evaluate FL-based BrainAGE on stroke cohorts with greater heterogeneity in age distributions. Such variability would make age prediction more challenging under FL [16].

## 5. Conclusion

To build on previous studies demonstrating the utility of BrainAGE as a biomarker for brain disorders, we trained BrainAGE models on FLAIR images from a clinical stroke cohort using FL. FL allows the development of machine learning models on multicenter datasets without requiring data centralization, making it a promising solution to address data-sharing regulations. In our experiments, FL consistently outperformed single-site training. Moreover, the observed associations between BrainAGE and vascular risk factors, along with its predictive value for post-stroke functional outcomes, indicate that FL-derived BrainAGE may be as clinically informative as those obtained through centralized training.
<table><caption>Table A.1: Clinical characteristics by center in the study population.</caption><thead><tr><th>medical center</th><th>sex, %#</th><th>HTN, %</th><th>DM, %</th><th>AF, %</th><th>SMK, %</th><th>HCL, %</th><th>NIHSS, median *</th><th>P2P, median ***</th><th>IVT, % ***</th><th>RECA, %</th><th>mRS_m3, median</th></tr></thead><tbody><tr><td>Center 1</td><td>45</td><td>66</td><td>21</td><td>41</td><td>19</td><td>42</td><td>15</td><td>46</td><td>45</td><td>80</td><td>3</td></tr><tr><td>Center 2</td><td>43</td><td>64</td><td>29</td><td>37</td><td>12</td><td>46</td><td>17</td><td>130.5</td><td>55</td><td>81</td><td>3</td></tr><tr><td>Center 3</td><td>45</td><td>67</td><td>18</td><td>38</td><td>19</td><td>45</td><td>16</td><td>134</td><td>50</td><td>81</td><td>3</td></tr><tr><td>Center 4</td><td>49</td><td>79</td><td>13</td><td>45</td><td>13</td><td>35</td><td>19</td><td>110.5</td><td>57</td><td>83</td><td>3</td></tr><tr><td>Center 5</td><td>47</td><td>67</td><td>24</td><td>40</td><td>13</td><td>34</td><td>16</td><td>135</td><td>70</td><td>87</td><td>3</td></tr><tr><td>Center 6</td><td>48</td><td>65</td><td>22</td><td>40</td><td>21</td><td>34</td><td>15</td><td>183</td><td>66</td><td>89</td><td>3</td></tr><tr><td>Center 7</td><td>44</td><td>65</td><td>19</td><td>37</td><td>19</td><td>44</td><td>16</td><td>157</td><td>70</td><td>86</td><td>3</td></tr><tr><td>Center 8</td><td>42</td><td>68</td><td>11</td><td>35</td><td>15</td><td>41</td><td>16</td><td>141</td><td>68</td><td>73</td><td>4</td></tr><tr><td>Center 9</td><td>57</td><td>73</td><td>19</td><td>30</td><td>24</td><td>48</td><td>14</td><td>140</td><td>68</td><td>81</td><td>3</td></tr><tr><td>Center 10</td><td>44</td><td>68</td><td>29</td><td>26</td><td>21</td><td>44</td><td>17</td><td>151.5</td><td>61</td><td>90</td><td>3</td></tr><tr><td>Center 11</td><td>37</td><td>61</td><td>22</td><td>37</td><td>22</td><td>39</td><td>16</td><td>188.5</td><td>70</td><td>80</td><td>3.5</td></tr><tr><td>Center 12</td><td>52</td><td>75</td><td>18</td><td>45</td><td>11</td><td>39</td><td>16</td><td>184</td><td>66</td><td>91</td><td>3</td></tr><tr><td>Center 13</td><td>47</td><td>69</td><td>16</td><td>41</td><td>16</td><td>37</td><td>19</td><td>121</td><td>69</td><td>87</td><td>2.5</td></tr><tr><td>Center 14</td><td>40</td><td>80</td><td>20</td><td>30</td><td>17</td><td>43</td><td>15</td><td>173.5</td><td>63</td><td>80</td><td>4</td></tr><tr><td>Center 15</td><td>43</td><td>65</td><td>39</td><td>39</td><td>9</td><td>30</td><td>15</td><td>167</td><td>74</td><td>83</td><td>4</td></tr><tr><td>Center 16</td><td>60</td><td>60</td><td>10</td><td>30</td><td>30</td><td>30</td><td>13</td><td>191.5</td><td>70</td><td>70</td><td>3</td></tr></tbody></table>

Asterisks indicate significant dependence between each variable and hospital centers (*: p < 0.05; **: p < 0.01; ***: p < 0.001).

# Sex was encoded as 1 for males and 0 for females.

Abbreviations: HTN: hypertension; DM: diabetes mellitus; AF: atrial fibrillation; SMK: smoking; HCL: hypercholesterolemia; NIHSS: NIH Stroke Scale upon admission; P2P: time interval in minutes between MRI and the arterial puncture; IVT: previous intravenous thrombolysis; RECA: successful recanalisation; mRS_m3: Modified Rankin Scale at three months after stroke.

## Declaration of competing interests

Markus D. Schirmer is supported by the Heitman Stroke Foundation and NIA R21AG083559.

## Funding

The Lille University Hospital, leading the StrokeAge project, receives financial support from the Hauts-de-France region as part of the call for projects "Recherche clinique dans les établissements de santé en région Hauts-de-France".

## Appendix A. Clinical characteristics by center in the study population

Table A.1 presents the distribution of clinical variables across centers in the study dataset. To assess the dependence between each variable and hospital centers, we conducted Kruskal-Wallis H tests for continuous variables (NIHSS, P2P and mRS at three months) and Pearson's chi-squared tests with Yates's correction for binary variables.

## Appendix B. Odds ratios and Wald test in logistic regression of functional outcomes

The contributions of each predictor in logistic regression are summarized in Table B.1.
<table><caption>Table B.1: Standardized odds ratio in multiple logistic regression of good functional outcomes in the test set.</caption><thead><tr><th></th><th></th><th>age</th><th>sex#</th><th>HTN</th><th>DM</th><th>AF</th><th>SMK</th><th>HCL</th><th>NIHSS</th><th>P2P</th><th>IVT</th><th>RECA</th><th>BrainAGE</th></tr></thead><tbody><tr><td rowspan="3">VolSimpleLR</td><td rowspan="2">centralized</td><td>0.535</td><td>1.189</td><td>1.023</td><td>0.885</td><td>1.034</td><td>0.907</td><td>1.064</td><td>0.535</td><td>0.809</td><td>1.462</td><td>2.099</td><td>0.811</td></tr><tr><td>***</td><td>*</td><td></td><td></td><td></td><td></td><td></td><td>***</td><td>**</td><td>***</td><td>***</td><td>**</td></tr><tr><td>federated</td><td>0.536</td><td>1.187</td><td>1.024</td><td>0.880</td><td>1.028</td><td>0.907</td><td>1.067</td><td>0.536</td><td>0.809</td><td>1.464</td><td>2.105</td><td>0.833</td></tr><tr><td rowspan="3">VolAugmentedLR</td><td rowspan="2">centralized</td><td>0.537</td><td>1.183</td><td>1.024</td><td>0.877</td><td>1.025</td><td>0.907</td><td>1.068</td><td>0.537</td><td>0.808</td><td>1.465</td><td>2.108</td><td>0.846</td></tr><tr><td>***</td><td>*</td><td></td><td></td><td></td><td></td><td></td><td>***</td><td>**</td><td>***</td><td>***</td><td>*</td></tr><tr><td>federated</td><td>0.535</td><td>1.193</td><td>1.033</td><td>0.882</td><td>1.026</td><td>0.905</td><td>1.068</td><td>0.537</td><td>0.810</td><td>1.463</td><td>2.103</td><td>0.822</td></tr><tr><td rowspan="3">VolAugmentedLR</td><td rowspan="2">centralized</td><td>0.534</td><td>1.196</td><td>1.037</td><td>0.883</td><td>1.027</td><td>0.907</td><td>1.069</td><td>0.536</td><td>0.809</td><td>1.462</td><td>2.105</td><td>0.806</td></tr><tr><td>***</td><td>*</td><td></td><td></td><td></td><td></td><td></td><td>***</td><td>**</td><td>***</td><td>***</td><td>**</td></tr><tr><td>federated</td><td>0.527</td><td>1.127</td><td>1.025</td><td>0.895</td><td>1.030</td><td>0.900</td><td>1.080</td><td>0.530</td><td>0.809</td><td>1.478</td><td>2.119</td><td>0.771</td></tr><tr><td rowspan="3">RadiomicsLR</td><td rowspan="2">centralized</td><td>0.526</td><td>1.131</td><td>1.024</td><td>0.891</td><td>1.026</td><td>0.898</td><td>1.076</td><td>0.531</td><td>0.822</td><td>1.469</td><td>2.109</td><td>0.774</td></tr><tr><td>***</td><td></td><td></td><td></td><td></td><td></td><td></td><td>***</td><td>*</td><td>***</td><td>***</td><td>**</td></tr><tr><td>federated</td><td>0.522</td><td>1.117</td><td>1.022</td><td>0.880</td><td>1.023</td><td>0.899</td><td>1.079</td><td>0.536</td><td>0.822</td><td>1.477</td><td>2.097</td><td>0.739</td></tr><tr><td rowspan="3">VoxelsCNN</td><td rowspan="2">centralized</td><td>0.531</td><td>1.137</td><td>1.029</td><td>0.888</td><td>1.019</td><td>0.900</td><td>1.069</td><td>0.531</td><td>0.807</td><td>1.473</td><td>2.129</td><td>0.783</td></tr><tr><td>***</td><td></td><td></td><td></td><td></td><td></td><td></td><td>***</td><td>**</td><td>***</td><td>***</td><td>**</td></tr><tr><td>federated</td><td>0.531</td><td>1.137</td><td>1.019</td><td>0.879</td><td>1.034</td><td>0.901</td><td>1.076</td><td>0.540</td><td>0.807</td><td>1.475</td><td>2.107</td><td>0.821</td></tr><tr><td rowspan="3">VoxelsCNN</td><td rowspan="2">centralized</td><td>0.525</td><td>1.136</td><td>1.023</td><td>0.883</td><td>1.033</td><td>0.895</td><td>1.074</td><td>0.533</td><td>0.807</td><td>1.478</td><td>2.109</td><td>0.764</td></tr><tr><td>***</td><td></td><td></td><td></td><td></td><td></td><td></td><td>***</td><td>**</td><td>***</td><td>***</td><td>***</td></tr></tbody></table>

<footnote># Sex was encoded as 1 for males and 0 for females.</footnote>

<footnote>Asterisks indicate that the BrainAGE regression coefficient was significantly different from 0 (*: p < 0.05; **: p < 0.01; ***: p < 0.001).</footnote>

<footnote>Abbreviations: HTN: hypertension; DM: diabetes mellitus; AF: atrial fibrillation; SMK: smoking; HCL: hypercholesterolemia; NIHSS: NIH Stroke Scale upon admission; P2P: time interval in minutes between MRI and the arterial puncture; IVT: previous intravenous thrombolysis; RECA: successful recanalisation.</footnote>
<figure><img src="image_8.png" /><figcaption>**Figure C.1: Comparison of ages for each clinical phenotype in the test set.** For the sex variable, the presence or absence of the phenotype corresponds to male and female, respectively. Asterisks indicate significant unpaired comparisons of ages (*: p < 0.05; **: p < 0.01; **: p < 0.001).</figcaption></figure>

## Appendix C. Age distribution per clinical phenotype

Figure C.1 presents age distributions for each clinical phenotype in the test set. Statistical significance of age differences was assessed using two-tailed Mann-Whitney U tests.

## References

[1] C. Capirossi, A. Laiso, L. Renieri, F. Capasso, N. Limbucci, Epidemiology, organization, diagnosis and treatment of acute ischemic stroke, European Journal of Radiology Open 11 (2023) 100527. doi:10.1016/j.ejro.2023.100527.

[2] B. A. Drozdowska, S. Singh, T. J. Quinn, Thinking about the future: A review of prognostic scales used in acute stroke, Frontiers in Neurology 10 (2019). doi:10.3389/fneur.2019.00274.

[3] S. Horvath, K. Raj, Dna methylation-based biomarkers and the epigenetic clock theory of ageing, Nature Reviews Genetics 19 (2018) 371-384. doi:10.1038/s41576-018-0004-3.

[4] K. Franke, C. Gaser, Ten years of brainage as a neuroimaging biomarker of brain aging: What insights have we gained?, Frontiers in Neurology 10 (2019). doi:10.3389/fneur.2019.00789.

[5] M. Gautherot, G. Kuchcinski, C. Bordier, A. R. Sillaire, X. Delbeuck, M. Leroy, X. Leclerc, J.-P. Pruvo, F. Pasquier, R. Lopes, Longitudinal analysis of brain-predicted age in amnestic and non-amnestic sporadic early-onset alzheimer's disease, Frontiers in Aging Neuroscience 13 (2021). doi:10.3389/fnagi.2021.729635.

[6] E. B. Aamodt, D. Alnæs, A.-M. G. de Lange, S. Aam, T. Schellhorn, I. Saltvedt, M. K. Beyer, L. T. Westlye, Longitudinal brain age prediction and cognitive function after stroke, Neurobiology of Aging 122 (2023) 55-64. doi:10.1016/j.neurobiolaging.2022.10.007.

[7] M. Bretzner, A. K. Bonkhoff, M. D. Schirmer, S. Hong, A. Dalca, K. Donahue, A.-K. Giese, M. R. Etherton, P. M. Rist, M. Nardin, R. W. Regenhardt, X. Leclerc, R. Lopes, M. Gautherot, C. Wang, O. R. Benavente, J. W. Cole, A. Donatti, C. Griessenauer, L. Heitsch, L. Holmegaard, K. Jood, J. Jimenez-Conde, S. J. Kittner, R. Lemmens, C. R. Levi, P. F. McArdle, C. W. McDonough, J. F. Meschia, C.-L. Phuah, A. Rolfs, S. Ropele, J. Rosand, J. Roquer, T. Rundek, R. L. Sacco, R. Schmidt, P. Sharma, A. Slowik, A. Sousa, T. M. Stanne, D. Strbian, T. Tatlisumak, V. Thijs, A. Vagal, J. Wasselius, D. Woo, O. Wu, R. Zand, B. B. Worrall, J. Maguire, A. G. Lindgren, C. Jern, P. Golland, G. Kuchcinski, N. S. Rost, Radiomics-derived brain age predicts functional outcome after acute ischemic stroke, Neurology 100 (2023). doi:10.1212/wnl.00000000000201596.

[8] N. Rieke, J. Hancox, W. Li, F. Milletari, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R. M. Summers, A. Trask, D. Xu, M. Baust, M. J. Cardoso, The future of digital health with federated learning, npj Digital Medicine 3 (2020). doi:10.1038/s41746-020-00323-1.

[9] S. S. Sandhu, H. T. Gorji, P. Tavakolian, K. Tavakolian, A. Akhbardeh, Medical imaging applications of federated learning, Diagnostics 13 (2023) 3140. doi:10.3390/diagnostics13193140.

[10] S. Basodi, R. Raja, B. Ray, H. Gazula, J. Liu, E. Verner, V. D. Calhoun, Federation of brain age estimation in structural neuroimaging data, in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2021, p. 3854-3857. doi:10.1109/embc46164.2021.9629865.

[11] S. S. Cheshmi, A. Mahyar, A. Soroush, Z. Rezvani, B. Farahani, Brain age estimation using structural mri: A clustered federated learning approach, in: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), IEEE, 2023, p. 1-6. URL: http://dx.doi.org/10.1109/COINS57856.2023.10189329. doi:10.1109/coins57856.2023.10189329.
[12] R. Souza, P. Mouches, M. Wilms, A. Tuladhar, S. Langner, N. D. Forkert, An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction, Journal of the American Medical Informatics Association 30 (2022) 112–119. doi:10.1093/jamia/ocac204.

[13] D. Stripelis, H. Saleem, T. Ghai, N. J. Dhinagar, U. Gupta, C. Anastasiou, G. Ver Steeg, S. Ravi, M. Naveed, P. M. Thompson, J. L. Ambite, Secure neuroimaging analysis using federated learning with homomorphic encryption, in: A. Walker, L. Rittner, E. Romero Castro, N. Lepore, J. Brieva, M. G. Linguraru (Eds.), 17th International Symposium on Medical Information Processing and Analysis, SPIE, 2021, p. 44. doi:10.1117/12.2606256.

[14] D. Stripelis, J. L. Ambite, P. Lam, P. Thompson, Scaling neuroscience research using federated learning, in: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE, 2021, p. 1191–1195. doi:10.1109/isbi48211.2021.9433925.

[15] D. Stripelis, U. Gupta, N. Dhinagar, G. V. Steeg, P. M. Thompson, J. L. Ambite, Towards Sparsified Federated Neuroimaging Models via Weight Pruning, Springer Nature Switzerland, 2022, p. 141–151. doi:10.1007/978-3-031-18523-6_14.

[16] D. Stripelis, U. Gupta, H. Saleem, N. Dhinagar, T. Ghai, C. Anastasiou, R. Sánchez, G. V. Steeg, S. Ravi, M. Naveed, P. M. Thompson, J. L. Ambite, A federated learning architecture for secure and private neuroimaging analysis, Patterns 5 (2024) 101031. doi:10.1016/j.patter.2024.101031.

[17] V. Roca, G. Kuchcinski, J.-P. Pruvo, D. Manouvriez, X. Leclerc, R. Lopes, A three-dimensional deep learning model for inter-site harmonization of structural mr images of the brain: Extensive validation with a multicenter dataset, Heliyon 9 (2023) e22647. doi:10.1016/j.heliyon.2023.e22647.

[18] W. J. Powers, A. A. Rabinstein, T. Ackerson, O. M. Adeoye, N. C. Bambakidis, K. Becker, J. Biller, M. Brown, B. M. Demaerschalk, B. Hoh, E. C. Jauch, C. S. Kidwell, T. M. Leslie-Mazwi, B. Ovbiagele, P. A. Scott, K. N. Sheth, A. M. Southerland, D. V. Summers, D. L. Tirschwell, 2018 guidelines for the early management of patients with acute ischemic stroke: A guideline for healthcare professionals from the american heart association/american stroke association, Stroke 49 (2018). doi:10.1161/str.0000000000000158.

[19] S. Bagheri, R. Sivanandham, A. Barsoum, L. Terlecki, R. Steele, R. Jackson, Basilar artery occlusions – morbidity and mortality outcomes (p8-5.031), Neurology 102 (2024). doi:10.1212/wnl.0000000000204527.

[20] B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y. Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data, in: A. Singh, J. Zhu (Eds.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of *Proceedings of Machine Learning Research*, PMLR, 2017, pp. 1273–1282. URL: https://proceedings.mlr.press/v54/mcmahan17a.html.

[21] B. Billot, D. N. Greve, O. Puonti, A. Thielscher, K. Van Leemput, B. Fischl, A. V. Dalca, J. E. Iglesias, Synthseg: Segmentation of brain mri scans of any contrast and resolution without retraining, Medical Image Analysis 86 (2023) 102789. doi:10.1016/j.media.2023.102789.

[22] F. Isensee, M. Schell, I. Pflueger, G. Brugnara, D. Bonekamp, U. Neuberger, A. Wick, H. Schlemmer, S. Heiland, W. Wick, M. Bendszus, K. H. Maier-Hein, P. Kickingereder, Automated brain extraction of multisequence mri using artificial neural networks, Human Brain Mapping 40 (2019) 4952–4964. doi:10.1002/hbm.24750.

[23] N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, J. C. Gee, N4itk: Improved n3 bias correction, IEEE Transactions on Medical Imaging 29 (2010) 1310–1320. doi:10.1109/tmi.2010.2046908.

[24] M. Jenkinson, P. Bannister, M. Brady, S. Smith, Improved optimization for the robust and accurate linear registration and motion correction of brain images, NeuroImage 17 (2002) 825–841. doi:10.1006/nimg.2002.1132.

[25] B. D. Wichtmann, F. N. Harder, K. Weiss, S. O. Schönberg, U. I. Attenberger, H. Alkadhi, D. Pinto dos Santos, B. Baeßler, Influence of image processing on radiomic features from magnetic resonance imaging, Investigative Radiology (2022). doi:10.1097/rli.0000000000000921.

[26] J. J. van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, R. G. Beets-Tan, J.-C. Fillion-Robin, S. Pieper, H. J. Aerts, Computational radiomics system to decode the radiographic phenotype, Cancer Research 77 (2017) e104–e107. doi:10.1158/0008-5472.can-17-0339.

[27] J. H. Cole, R. P. Poudel, D. Tsagkrasoulis, M. W. Caan, C. Steves, T. D. Spector, G. Montana, Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker, NeuroImage 163 (2017) 115–124. doi:10.1016/j.neuroimage.2017.07.059.

[28] J. L. Ba, J. R. Kiros, G. E. Hinton, Layer normalization, 2016. doi:10.48550/ARXIV.1607.06450.

[29] B. Casella, R. Esposito, A. Sciarappa, C. Cavazzoni, M. Aldinucci, Experimenting with normalization layers in federated learning on non-iid scenarios, IEEE Access 12 (2024) 47961–47971. doi:10.1109/access.2024.3383783.
[30] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, 2014. doi:10.48550/ARXIV.1412.6980.

[31] P. Mickevicius, S. Narang, J. Alben, G. Diamos, E. Elsen, D. Garcia, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, H. Wu, Mixed precision training, 2017. doi:10.48550/ARXIV.1710.03740.

[32] I. Beheshti, S. Nugent, O. Potvin, S. Duchesne, Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme, NeuroImage: Clinical 24 (2019) 102063. doi:10.1016/j.nicl.2019.102063.

[33] S. Nair, J. Hurly, D. Saylor, Evaluating the strengths and limitations of structured modified rankin scale validation studies – a systematic review, Journal of Stroke and Cerebrovascular Diseases 34 (2025) 108242. doi:10.1016/j.jstrokecerebrovasdis.2025.108242.

[34] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, V. Smith, Federated optimization in heterogeneous networks, in: I. Dhillon, D. Papailiopoulos, V. Sze (Eds.), Proceedings of Machine Learning and Systems, volume 2, 2020, pp. 429–450. URL: https://proceedings.mlsys.org/paper_files/paper/2020/file/1f5fe83998a09396ebe6477d9475ba0c-Paper.pdf.

[35] S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, A. T. Suresh, SCAFFOLD: Stochastic controlled averaging for federated learning, in: H. D. III, A. Singh (Eds.), Proceedings of the 37th International Conference on Machine Learning,
volume 119 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 5132-5143. URL: https://proceedings.mlr.org/v119/karimireddy20a.html.

[36] J. Wang, Q. Liu, H. Liang, G. Joshi, H. V. Poor, Tackling the objective inconsistency problem in heterogeneous federated optimization, in: H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, H. Lin (Eds.), Advances in Neural Information Processing Systems, volume 33, Curran Associates, Inc., 2020, pp. 7611-7623. URL: https://proceedings.neurips.cc/paper_files/paper/2020/file/564127c03caab942e503ee6f810f54fd-Paper.pdf.

[37] Q. Li, Y. Diao, Q. Chen, B. He, Federated learning on non-iid data silos: An experimental study, in: 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, 2022, p. 965-978. doi:10.1109/icde53745.2022.00077.

[38] Y. Wu, H. Gao, C. Zhang, X. Ma, X. Zhu, S. Wu, L. Lin, Machine learning and deep learning approaches in lifespan brain age prediction: A comprehensive review, Tomography 10 (2024) 1238-1262. doi:10.3390/tomography10080093.

[39] U. Pandey, J. Saini, M. Kumar, R. Gupta, M. Ingalhalikar, Normative baseline for radiomics in brain scp: Evaluating the robustness, regional variations, and reproducibility on scp: images, Journal of Magnetic Resonance Imaging 53 (2020) 394-407. doi:10.1002/jmri.27349.

[40] V. M. Bashyam, G. Erus, J. Doshi, M. Habes, I. M. Nasrallah, M. Truelove-Hill, D. Srinivasan, L. Mamourian, R. Pomponio, Y. Fan, L. J. Launer, C. L. Masters, P. Maruff, C. Zhuo, H. Völzke, S. C. Johnson, J. Fripp, N. Koutsouleris, T. D. Satterthwaite, D. Wolf, R. E. Gur, R. C. Gur, J. Morris, M. S. Albert, H. J. Grabe, S. Resnick, R. N. Bryan, D. A. Wolk, H. Shou, C. Davatzikos, Mri signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide, Brain 143 (2020) 2312-2324. doi:10.1093/brain/awaa160.

[41] F. Biondo, A. Jewell, M. Pritchard, D. Aarsland, C. J. Steves, C. Mueller, J. H. Cole, Brain-age is associated with progression to dementia in memory clinic patients, NeuroImage: Clinical 36 (2022) 103175. doi:10.1016/j.nicl.2022.103175.

[42] G. Richard, K. Kolskär, K. M. Ulrichsen, T. Kaufmann, D. Alnæs, A.-M. Sanders, E. S. Dørum, J. Monereo Sánchez, A. Petersen, H. Ihle-Hansen, J. E. Nordvik, L. T. Westlye, Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training, NeuroImage: Clinical 25 (2020) 102159. doi:10.1016/j.nicl.2019.102159.

[43] N. Sanford, R. Ge, M. Antoniades, A. Modabbernia, S. S. Haas, H. C. Whalley, L. Galea, S. G. Popescu, J. H. Cole, S. Frangou, Sex differences in predictors and regional patterns of brain age gap estimates, Human Brain Mapping 43 (2022) 4689-4698. doi:10.1002/hbm.25983.

[44] C. Vert, C. Parra-Fariñas, A. Rovira, Mr imaging in hyperacute ischemic stroke, European Journal of Radiology 96 (2017) 125-132. doi:10.1016/j.ejrad.2017.06.013.

[45] G. Thomalla, F. Boutitie, H. Ma, M. Koga, P. Ringleb, L. H. Schwamm, O. Wu, M. Bendszus, C. F. Bladin, B. C. V. Campbell, B. Cheng, L. Churilov, M. Ebinger, M. Endres, J. B. Fiebach, M. Fukuda-Doi, M. Inoue, T. J. Kleinig, L. L. Latour, R. Lemmens, C. R. Levi, D. Leys, K. Miwa, C. A. Molina, K. W. Muir, N. Nighoghossian, M. W. Parsons, S. Pedraza, P. D. Schellinger, S. Schwab, C. Z. Simonsen, S. S. Song, V. Thijs, D. Toni, C. Y. Hsu, N. Wahlgren, H. Yamamoto, N. Yassi, S. Yoshimura, S. Warach, W. Hacke, K. Toyoda, G. A. Donnan, S. M. Davis, C. Gerloff, Intravenous alteplase for stroke with unknown time of onset guided by advanced imaging: systematic review and meta-analysis of individual patient data, The Lancet 396 (2020) 1574-1584. doi:10.1016/s0140-6736(20)32163-2.

[46] P. Bey, K. Dhindsa, A. Kashyap, M. Schirner, J. Feldheim, M. Bönstrup, R. Schulz, B. Cheng, G. Thomalla, C. Gerloff, P. Ritter, A lesion-aware automated processing framework for clinical stroke magnetic resonance imaging, Human Brain Mapping 45 (2024). doi:10.1002/hbm.26701.