Holistic Brain Analysis – HoliBrain

When a complex system is studied, it is usually broken down into smaller and simpler subsystems in order to facilitate its analysis. Such a reductionist paradigm is powerful; making the analysis easier, but ignores the interactions and relationships that exist between the parts and scales of the whole system. Faced with the complexity of brain anatomy, human experts (e.g., neuroanatomists) follow this paradigm when analyzing the cerebral organization using 3D Magnetic Resonance Imaging (MRI). Therefore, human experts specialize in specific brain structures (i.e., task) at a given anatomical level (i.e., scale). This specialization is accentuated at the substructure level – the analysis of which is now possible with high- resolution multimodal MRI – because more information needs to be processed simultaneously. This reductionist strategy results in numerous task-specific image processing methods ignoring task relationships and scale interdependencies. Consequently, these tools produce partial brain representations, preventing global analysis of the brain. HoliBrain introduces a shift towards a novel holistic paradigm able to process high-resolution multimodal 3D MRI in order to generate whole brain analysis a consistent manner. Previously beyond the capabilities of both computational tools and human experts, the recent advances of deep learning open up new opportunities to tackle this great challenge. To this end, HoliBrain proposes to develop new image processing methods based on a large number of networks sharing information between tasks and scales. The main idea is to divide this intractable problem for a single network into multiple simpler tasks processed by a large ensemble of collaborating networks. HoliBrain will produce novel knowledge on cerebral organization and new biomarkers for early diagnostic of neurological diseases. Consequently, HoliBrain will have a tremendous impact on health, patient care and society.