Transfer Learning on Structural Brain Age Models to Decode Cognition in MS: A Federated Learning Approach.

Introduction. Classical deep learning research requires lots of centralised data. However, data sets are often stored at different clinical centers, and sharing sensitive patient data such as brain images is difficult. In this manuscript, we investigated the feasibility of federated learning, sending models to the data instead of the other way round, for research on brain magnetic resonant images of people with multiple sclerosis (MS). Methods. Using transfer learning on a previously published brain age model, we trained a model to decode performance on the symbol digit modalities test (SDMT) of patients with MS from structural T1 weighted MRI. Three international centers in Brussels, Greifswald and Prague participated in the project. In Brussels, one computer served as the server coordinating the FL project, while the other served as client for model training on local data (n=97). The other two clients were Greifswald (n=104) and Prague (n=100). Each FL round, the server sent a global model to the clients, where its fully connected layer was updated on the local data. After collecting the local models, the server applied a weighted average of two randomly picked clients, yielding a new global model. Results. After 22 federated learning rounds, the average validation loss across clients reached a minimum. The model appeared to have learned to assign SDMT values close to the mean with a mean absolute error of 9.04, 10.59 and 10.71 points between true and predicted SDMT on the test data sets of Brussels, Greifswald and Prague respectively. The overall test MAE across all clients was 10.13 points. Conclusion. Federated learning is feasible for machine learning research on brain MRI of persons with MS, setting the stage for larger transfer learning studies to investigate the utility of brain age latent representations in cognitive decoding tasks

Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

Objectives: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. Methods: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict ‘brain age’ and ‘brain predicted age difference’ (BPAD = brain age–chronological age) for every subject. Results: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. Conclusions: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health

Promoting gender equality across the sustainable development goals

Gender issues, and gender equality in particular, can be regarded as cross-cutting issues in the implementation of the Sustainable Development Goals (SDGs), even though it is unclear how they are taken into account. This study addresses this information gap by performing an assessment of the emphasis on gender issues across all the other 16 SDGs, in addition to SDG5, through a literature review and case study analysis, the basis for the newly developed framework, highlighting specific actions associated to each SDG. The 13 countries addressed in the 16 case studies include China, India, or Australia and illustrate the inclusion of SDG5 into the SDGs. Using an SDG matrix, the SDG targets are analysed. Those where an emphasis on gender equality is important in allowing them to be achieved are listed. The novelty of our approach resides in offering an in-depth analysis of how gender issues interact with the other SDGs, proposing a new analysis framework clearly identifying SDGs 1, 4, 11, 12, 14 and 16 demanding further attention for successful SD gender implementation and illustrating specific areas where further actions may be necessary, which may be used by policy-makers, raising further awareness on gender equality contribution to achieve the SDGs. A set of recommendations aimed at placing gender matters more centrally in the SDGs delivery are presented as a final contribution. These focus on the need for greater awareness and attention to good practices, to achieve successful implementation initiatives.peer-reviewe

Promoting gender equality across the sustainable development goals.

Gender issues, and gender equality in particular, can be regarded as cross-cutting issues in the implementation of the Sustainable Development Goals (SDGs), even though it is unclear how they are taken into account. This study addresses this information gap by performing an assessment of the emphasis on gender issues across all the other 16 SDGs, in addition to SDG5, through a literature review and case study analysis, the basis for the newly developed framework, highlighting specific actions associated to each SDG. The 13 countries addressed in the 16 case studies include China, India, or Australia and illustrate the inclusion of SDG5 into the SDGs. Using an SDG matrix, the SDG targets are analysed. Those where an emphasis on gender equality is important in allowing them to be achieved are listed. The novelty of our approach resides in offering an in-depth analysis of how gender issues interact with the other SDGs, proposing a new analysis framework clearly identifying SDGs 1, 4, 11, 12, 14 and 16 demanding further attention for successful SD gender implementation and illustrating specific areas where further actions may be necessary, which may be used by policy-makers, raising further awareness on gender equality contribution to achieve the SDGs. A set of recommendations aimed at placing gender matters more centrally in the SDGs delivery are presented as a final contribution. These focus on the need for greater awareness and attention to good practices, to achieve successful implementation initiatives. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-022-02656-1