Diagnosing and treating brain diseases

The holy grail of brain simulation is to offer personalized diagnosis and treatment of brain diseases:

  1. A patient with a pathological condition indicating a perturbed brain function (e.g. from head injury, seizures, failure of motoric and/or cognitive functions) is undergoing multi-modal brain scans.
  2. The neuroimaging data is automatically analyzed and processed to construct a virtual model of the patient’s brain.
  3. By comparing the patient’s digital “doppelgänger” to a vast library of healthy and pathological virtual brains, first inferences can be made about the nature and the gravity of the condition.
  4. Clinicians can discuss and dry-run different therapy approaches using the patient’s virtual brain, e.g. medication, physical/cognitive training or surgery.
  5. Using the simulation’s response to different therapy approaches, the patient receives the therapy which promises the most likely positive outcome – individually tailored to their condition.

Proof of concept

Since the large-scale brain network simulator The Virtual Brain (TVB) became available in 2012, the Brain Simulation Section and many other international research centers have been working towards this goal: Peer-reviewed clinical studies covering smaller patient groups (between 20 and 40 subjects) suffering from stroke (including prenatal and perinatal), epilepsy, mild amnesic MCI, Alzheimer’s disease and brain tumors have shown results which improved diagnostic validity, therapeutic approaches and chances of recovery.

In 2017, the largest clinical trial for TVB-based brain simulation has begun in France: The Epinov project (Improving EPilepsy surgery management and progNOsis using Virtual brain technology) covers 400 patients with drug-resistant epilepsy in 10 surgery centers, guiding surgical strategies to improve success rates and refine diagnostic prognosis.

More data, more problems

In order to accelerate clinical brain modeling initiatives, the Brain Simulation Section developed an automated pipeline for constructing personalized virtual brains from multi-modal neuroimaging data. This open-source software can be run on average computers or HPC clusters and takes in raw structural, functional and diffusion-weighted MRI data, as well as optional EEG data.

The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface tesselations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated BOLD fMRI time-series.

The output files of the pipeline can be directly loaded into TVB to create and simulate individualized large-scale network models which incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. The entire source code for this pipeline is available on GitHub.

The first cloud-based decision-making system

Together with 17 European research and neuroinformatics partners, the Brain Simulation Section has set out to develop and validate all components of a decision-making system for early diagnosis and therapy development of neurodegenerative diseases (NDD): the VirtualBrainCloud.

This project is funded by the prestigious EU H2020 program for societal challenges because NDD affect every third senior and kill more people than breast cancer and prostate cancer combined. The causes for this group of diseases (predominantly Alzheimer’s and Parkinson’s) are still not understood well, there are no reliable therapies and, just in the United States alone, the cost for disease management will balloon to $1.1 trillion per year.

Earlier research clearly suggests that NDD have no single cause but rather a complex network of interactions between scales and systems that define disease risk and mitigation. Combining the granularity of semantic network modeling on the subcellular level with the wholistic integration of TVB’s large-scale brain dynamics modeling will lead to a better understanding of how the balance between local and global brain activity relates to cognitive changes in NDD.

In this way, the VirtualBrainCloud brings together several influential research streams and initiatives:

  • Integration of TVB’s large-scale brain network simulator with specialized cellular and subcellular simulators from the EU flagship Human Brain Project (HBP), to achieve higher simulation fidelity in particular brain regions
  • Bridging the individual achievments of HBP with the EU/EFPIA Innovative Medicine Initiative (IMI) for the first time, to add Big Data capabilities, large-scale biomarker discovery activities and innovative adaptive trials of new drugs

The implementation of standardized workflows for processing medical imaging data and biomarker trajectories, as well as the possibility to access derivative data and final outcomes in the cloud will facilitate data integration and usage by a wide community:

  • Researchers:

    Translational biomedical research using multi-scale, multi-omics, individualized whole brain models, disease progression models and interactive exploration analysis

  • Clinicians:

    Biomarker profile medical checkup using access to patient’s status in the cloud, disease progression models, CVB’s analysis report

  • Patients:

    Intervention using personalized therapeutic recommendations and monitoring

  • Students:

    Biomedical education with interactive and dynamic exploration of disease progress and mechanistic properties