What question did this study set out to answer?

The primary aim is to evaluate the FLightcase toolbox for federated learning in predicting cognitive status using brain imaging.

May 7, 2026Open Access

Real-world federated learning for brain imaging scientists.

Key Points

The primary aim is to evaluate the FLightcase toolbox for federated learning in predicting cognitive status using brain imaging.
Trained a DenseNet neural network to predict age from T1-weighted MRI across three datasets: IXI, SALD, CamCAN.
Employed federated training in a network across three centers: Brussels, Greifswald, Prague.
Compared shallow and deep transfer learning for fine-tuning model performance on the SDMT test.
Federated training achieved a mean absolute error (MAE) of 6.08 for age prediction, outperforming the centralized model (MAE = 7.02).
Pearson correlations for age prediction reached up to 0.93 across various datasets (p < .001).
Deep transfer learning resulted in lower MAE (10.71, 9.67, and 8.98) for SDMT predictions compared to shallow transfer learning (11.05).

Abstract

Background Federated learning (FL) has the potential to boost deep learning in neuroimaging but is rarely deployed in real-world scenarios, where its true potential lies. We propose FLightcase, a new FL toolbox tailored for brain research, and evaluate it on a real-world FL network to predict the cognitive status in patients with multiple sclerosis (MS) from brain magnetic resonance imaging (MRI). Methods We first trained a DenseNet neural network to predict age from T1-weighted brain MRI on three open-source datasets: IXI (586 images), SALD (491 images), and CamCAN (653 images). These were distributed across the three centres in our FL network: Brussels (BE), Greifswald (DE), and Prague (CZ). We benchmarked this federated model with a centralised version. The best-performing brain age model was then fine-tuned to predict performance on the symbol digit modalities test (SDMT) of patients with MS (Brussels: 96 images, Greifswald: 756 images, Prague: 2,424 images). Shallow transfer learning (TL) was compared with deep transfer learning, in which weights were updated either in the last layer or across the entire network, respectively. Results Federated training outperformed centralised training, predicting age with a mean absolute error (MAE) of 6.08 versus 7.02. Federated training yielded Pearson correlations (all p < .001) between true and predicted age of0.88 (IXI, Brussels), 0.91 (SALD, Greifswald), and 0.93 (CamCAN, Prague). Fine-tuning of the centralised model to SDMT was most successful with a deep TL paradigm (MAE = 9.19) compared to shallow TL (MAE = 11.05). Across Brussels, Greifswald, and Prague, deep TL predicted SDMT with MAEs of 10.71, 9.67, and 8.98, respectively, and yielded Pearson correlations between true and predicted SDMT of.25 (p = 0.282), 0.40 (p < 0.001), and 0.50 (p < 0.001). Conclusion Real-world federated learning using FLightcase is feasible for neuroimaging research in MS, enabling access to large MS imaging databases without sharing data. The federated SDMT-decoding model is promising and could be improved in the future by adopting FL algorithms that address the non-IID data issue and consider other imaging modalities. We hope our detailed real-world experiments and open-source distribution of FLightcase will prompt researchers to move beyond simulated FL environments.

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Cite This Study

Denissen et al. (Thu,) studied this question.

synapsesocial.com/papers/69fc2c718b49bacb8b34803b https://doi.org/https://doi.org/10.48620/97434

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