• What is the problem/issue being addressed?
Modern network science has introduced exciting new opportunities for understanding the brain as a complex system of interacting units in both health and disease and across the human lifespan. Despite the rapidly growing interdisciplinary science of complex networks, the brain remains one of greatest challenges for network science. In the face of the oncoming ‘tsunami’ of disordered neuroimaging datasets on brain networks, namely the ongoing 14 connectomic brain data collection studies for Connectome Related to Human Disease (CRHD) initiative funded by the National Institutes of Health, there is an urgent need for handling connectomic datasets with unprecedented scale and heterogeneity via developing for the first time advanced learning-based models of multimodal brain networks that will enable accurate mapping of disordered neural pathways, and a rational approach to designing effective interventions and treatments in the context of public health.
Creating a connectional brain template (CBT) for a population of subjects can be a powerful tool to integrate complementary information from different neuroimaging modalities in order to understand different connectional patterns of both the healthy and the disordered brain. CBTs can be the basis for automated brain analysis in clinical practices when used as references in comparative studies and classification. However, it is not always obvious how to integrate multimodal connectomic data together, nor easy to do so in practice, in order to first understand how the brain's structural, morphological and functional levels interlink to form this integrated complex system, and then identify typical and atypical connectional trends fingerprinting the human brain. This is substantially due to the large variability in brain connectivity across individuals, which limits our ability to distinguish between `healthy' brain connectional variability and `pathological' variability. To disentangle such connectional variability, we need to define a `normalization' or `standardization' process of brain networks. Here, we set out to design geometric deep learning-based methods that can meet such challenges by normalizing a population of multimodal brain networks and thereby using the learned template in downstream brain state classification and prediction tasks.
• Why is it important for society?
With yearly costs of about 800 billion euros and an estimated 179 million people afflicted in 2010 in Europe, neurological disorders are an unquestionable emergency and a grand challenge for neuroscientists. The NormNets Project has contributed to better understanding the underpinning of neurological disorders by providing population-driven connectional brain templates (CBTs) that allow for an easy identification of neurological biomarkers. We also demonstrated that the learned CBTs have prognostic predictive potential that can translate into the development of brain connectivity-targeting clinical trials for treating neurological disorders in addition to integral mapping of the brain across its lifespan.
In addition to that, using these compact and easy to process and load brain templates, one can train and design artificial intelligence (AI) methods that are affordable —i.e. do not require high-computation resources for training. We have shown this “affordable” aspect of CBT-based geometric deep learning model training in our recent published works. This has a strong societal contribution in terms of democratizing AI with application to network neuroscience in low-middle income countries and low-regime environments.
• What are the overall objectives?
The NormNets Project has:
(i) produced the most centered and representative connectional brain template that consistently captures the unique and distinctive traits of a population of multimodal brain networks,
(ii) reliably revealed the integral signature of a particular brain disorder by comparing the estimated disordered CBT with a healthy CBT, and
(iii) helped better predict integral changes in brain connectivity for an individual patient from first brain scan (i.e. baseline acquisition) for early diagnosis.
• Conclusions
We conclude this action with a set of geometric deep learning methods for CBT learning published in high-impact journals and conferences/workshops. More importantly, all source codes and estimated CBTs be publicly shared via GitHub to facilitate the reproducibility of our findings and diffusing the created knowledge by NormNets globally and openly.
GitHub channel:
https://github.com/basiralab(se abrirá en una nueva ventana) YouTube presentation channel:
https://bit.ly/3lt7Su7(se abrirá en una nueva ventana)YouTube code demo channel:
https://bit.ly/3wL2Q1s(se abrirá en una nueva ventana)
