Brain Blogger recently discovered that scientists are poised to deliver the first virtual simulation of the human brain from a truly fascinating talk by Dr. Randy McIn­tosh, Vicepresident of Research and Direc­tor of Baycrest’s Rot­man Research Institute, presented at the cutting-edge 2015 SharpBrains Virtual Summit: Monitoring & Enhancing Brain Health in the Pervasive Neurotechnology Era.

Simulations have proved to be a useful tool in many fields of science. Pharmaceutical companies are now using molecular modelling to understand the behavior of medicines and guide the design of new drugs. When building aircraft, aeronautical engineers rely on modelling so heavily that no airplane prototype goes into real flight without passing the simulation tests first.

The human brain is another complex system that can benefit from modelling and simulations. This effort can profit from the modern neuroimaging techniques that allow us exploring the anatomy and function of brain areas as small as a group of neurons. Moreover, our computational hardware is becoming powerful enough to gather the colossal amounts of data generated by the brain. Nevertheless, acquiring raw brain data in itself is not very useful unless we understand its meaning and can use it to our benefits.

The Virtual Brain Initiative is one of the best known trials to understand and organize brain data in a useful way. It is a neuroinformatics platform that tries to simulate the brain organization on the macroscopic level of detail. This tool is based on the idea of taking advantage of available functional and structural brain data generated by imaging techniques such as MRI, functional MRI and trans-cranial magnetic stimulation.

The virtual brain will try to gather important information related to neuronal connectivity and structure of the brain. It will inform us about activated groups of neurons, their connections, their respective distances, the time and the speed of their communications. This software will also collect data related to the structure of the brain like 3D cortex geometry and the exact location of neuron groups. After identifying the involved population of neurons, they will be assembled in large neuronal networks to finally construct a brain model.

As the model is developed, a knowledge library of brain anatomy and physiology will be used to guide the building process. This later step guarantees that the model reflects the natural phenomena regulating normal brain physiology.

An ideal computational brain model should consider the functions and connections of individual neurons. However, just to assemble this model we will need very powerful computational resources that are not yet available. Even if we achieve this colossal task, we still won’t be able to explain how cognitive and psychological process are born in the mind and how are they related to the organic structure of the brain.

Moreover, some studies showed that the behavior of the single neuron is irrelevant for the prediction of complex functions of the brain and probably even less for understanding the cognitive functions. In this regard, virtual brain has the advantage of working on the macroscopic and mesoscopic levels (the micrometer range) where not so much computer power is needed.

This approach is not new in science, and the attempts to build macroscopic models based on the mesoscopic level of details were shown to be useful. In the virtual brain, this philosophy is used to predict the global brain behavior and function starting from small groups of neurons.

The virtual brain is a great tool for research that will allow us to monitor the dynamics of communications between different brain regions and see how the functions of the brain are related to its structure. Scientists can use the virtual brain to understand how changes in the brain structure affect neuron communications and in turn lead to modifications in behaviour and cognitive processes. Another utility of the virtual brain is the capability to track normal and physiological brain modifications between different life stages, for example to understand how a newborn brain can grow up to become mature and capable of very complex functions and cognitive process.

Applications of the virtual brain can also be extended to serve medical needs through the use of neuroimages and brain data of people suffering from specific diseases. Neuroimaging data of such brains are used to create models and to understand how pathologies alter the normal structure and function of the brain. This is particularly true for pathologies where there is an organic dysfunction such as strokes, Alzheimer’s disease or Parkinson’s disease.

By feeding images of patients suffering from stroke, scientists and doctors will be able to understand how the pathology affects the brain’s communications and disrupts connections between the networks, making it possible to predict how the remaining networks preserve some of the brain functions despite the damage. This is particularly important as it allows to spot the mechanisms that control healing and recovery in injured brains. Psychiatric disorders like autism or schizophrenia can also benefit from the virtual brain simulations by helping scientists to identify the effect of these pathologies on the neuronal network and therefore suggest new therapeutics based on the properties of altered brain regions.

In addition, the virtual brain can be used to collect brain images and data from a specific patient so it opens the way to personalized medicine by simulating how a patient brain should reorganize its networks to obtain an optimal recovery after a brain injury or disease. Such information is extremely valuable to doctors since it will guide the rehabilitation process by favoring some type of therapies over others depending on the patient’s condition.

Modelling tools like the virtual brain are still in the early phase of development and validation; nevertheless the potential is very promising. It is clear that some time will pass before we can see some clinical applications of the brain simulations, but once established we can hope to treat the most difficult brain diseases and injuries.

References

Carter, A., Shulman, G., & Corbetta, M. (2012). Why use a connectivity-based approach to study stroke and recovery of function? NeuroImage, 62 (4), 2271-2280 DOI: 10.1016/j.neuroimage.2012.02.070

D’Angelo E, Solinas S, Garrido J, Casellato C, Pedrocchi A, Mapelli J, Gandolfi D, & Prestori F (2013). Realistic modeling of neurons and networks: towards brain simulation. Functional neurology, 28 (3), 153-66 PMID: 24139652

Dumas, G., Chavez, M., Nadel, J., & Martinerie, J. (2012). Anatomical Connectivity Influences both Intra- and Inter-Brain Synchronizations PLoS ONE, 7 (5) DOI: 10.1371/journal.pone.0036414

Falcon, M., Riley, J., Jirsa, V., McIntosh, A., Shereen, A., Chen, E., & Solodkin, A. (2015). The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke Frontiers in Neurology, 6 DOI: 10.3389/fneur.2015.00228

Ritter, P., Schirner, M., McIntosh, A., & Jirsa, V. (2013). The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging Brain Connectivity, 3 (2), 121-145 DOI: 10.1089/brain.2012.0120

Sanz Leon, P., Knock, S., Woodman, M., Domide, L., Mersmann, J., McIntosh, A., & Jirsa, V. (2013). The Virtual Brain: a simulator of primate brain network dynamics Frontiers in Neuroinformatics, 7 DOI: 10.3389/fninf.2013.00010

Schirner, M., Rothmeier, S., Jirsa, V., McIntosh, A., & Ritter, P. (2015). An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data NeuroImage, 117, 343-357 DOI: 10.1016/j.neuroimage.2015.03.055

Image via agsandrew / Shutterstock.