Analysis, modeling and visualization of brain physiological data using the multi-scale network platform BrainX3: a novel computational medicine tool for the diagnostics, prognostics and intervention of neurological disorder.

Group: Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS)
Group leader: Paul Verschure (pverschure@ibecbarcelona.eu)

 

Project description:

The World Health Organization (WHO) estimates that about one billion people around the world are affected by neurological disorders, including 60 million who have epilepsy and 24 million with Alzheimer disease and other dementias. Neurological disorders are an important cause of mortality and constitute 12% of total deaths globally [1,2,3].

Epilepsy is a neurological disorder manifested by episodic disruption of brain electrical activity associated with abnormal body movement, loss of consciousness, and sensory disturbances. Neurological disorders such as epilepsy affect the mental health of individual with significant disturbance in an individual’s cognition, emotion regulation, or behavior that reflects a dysfunction in the psychological, biological, or developmental processes underlying mental functioning. Mental disorder is commonly associated with substantial distress or impairment in social, occupational, or other essential activities [4]. Psychiatric and neurological comorbidities are relatively common and often co-exist in people with epilepsy. For example, depression and anxiety disorders are the most common psychiatric comorbidities, and they are particularly common in these patients who also have a neurological comorbidity, such as stroke, traumatic brain injury or dementia. [5]

A considerable number of epileptic patients are pharmaco-resistant and require surgical intervention specifically aimed to identify the location of the epileptic zone and evaluate the dynamics of the epileptic network. To prepare the patients for the surgical intervention, a complex procedure and several diagnostic tests are performed on the patient, with long hospitalization and high costs.

Thus, we identify a need to analyze and improve the current assessment tools and intervention methods taking into account the interrelated nature of neurological symptomatology. Identifying the network of neurological derived symptoms may help to understanding its dynamics and provide better prognosis and intervention after epilepsy and brain damage in general. New technologies are needed to enhance the capacity of the clinicians to plan the surgical intervention, monitoring and identification of the epileptic zones for the upcoming therapeutic outcome.

In this project we want to build a simulation of a damaged brain based on structural and functional connectivity networks using BrainX3, a computational tool for multi-level integrative in-silico modeling of brain networks. Using BrainX3, this project will target the localization and dynamics of brain lesions and the analyses of their impact on brain functionality. The BrainX3 platform is specifically designed for integrating multiple types of data and simulating network dynamics at multiple scales. This is particularly important for simulating detailed disease models and assessing the effects of various interventions. In a large-scale simulation study with BrainX3 we have recently demonstrated the dynamics of the healthy resting-state attractor and compared that to the neural dynamics of brain networks with cortical lesions [6]. We have found that the convergence between the data centric and model-based approach will define the confidence the system has on its prognostics. This project aims at providing in-silico dynamics of neural activity after brain damage and make longitudinal predictions about plasticity changes, neurodynamic reorganization and changing symptomatology. These model predictions about the prognosis after brain damage will be validated on epileptic patients using behavioral data/symptomatology as well as longitudinal EEG/fMRI data. For individual patients this will mean building a simulation of a brain using structural and functional data obtained at the onset of an epileptic crisis for that patient and comparing model predictions about changes in plasticity and brain activity over time with new longitudinal functional data for the same patient. At the population level, we will validate our computational models by analyzing the statistics of model-predicted changes in symptomatology and comparing those to large cohorts of longitudinal behavioral data. In relation to other brain pathologies such as Stroke and Alzheimer disease, the patient diagnostics and prognostics will be defined by running BrainX3 simulations.

The project will be developed in collaboration with:

· Prof. Paul Verschure at IBEC, ICREA, Barcelona. paul.verschure@ibecbarcelona.eu
· Prof. Rodrigo Alberto Rocamora at the Epilepsy Unit, UMIM, Barcelona 99267@parcdesalutmar.cat

The proposed research activity is aligned with the Advanced Societal Health Challenges SHC2 and SHC3, and the Advanced Technological Platform ATP5 of the Severo Ochoa program.

Job position description

PhD position: The group is offering a PhD position for a research study that contributes to uncovering neural and behavioral mechanisms underlying brain changes taking place in health and disease as well as in artificial behaving systems. The research will be within the framework of the Eu project: Virtual Brain Cloud.

The main tasks and responsibilities for the PhD candidate will be

  • Behavioral testing of epileptic patients with electrode implants (learning, memory, cognition)
  • Data analysis and brain modeling with BrainX3 using brain data recorded from intracranially planted electrodes in epileptic patients
  • Translation of the epileptic BrainX3 model to simulate other brain diseases such as Stroke and Alzheimer disease.

The requirement for the candidate are:

Degree and Master on any field of Neuroscience, Computational Neuroscience, Neurorehabilitation. Multidisciplinary qualifications Neuroscience, Psychology, Imaging, Modeling, Statistics etc. Knowledge on EEG analysis and machines learning will be a plus.

Competencies and skills: Programming, Data analysis, Modeling, Communication, Teamwork, Commitment, Critical and Analytical thinking. High level of English.