Periodic Reporting for period 3 - BrainModes (Personalized whole brain simulations: linking connectomics and dynamics in the human brain)
Reporting period: 2019-08-01 to 2021-01-31
In the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational brain network modeling to reveal fundamental principles of how the human brain generates large-scale activity observable by noninvasive neuroimaging. We used structural and functional neuroimaging data to construct whole- brain models.
The human brain’s very complexity makes it difficult to come up with theories about its workings through thought alone. Computer models can simulate the consequences of theories, identifying problems and formulating new ones for neuroscience testing. Virtual Brain Simulation platform (thevirtualbrain.org) affords a better understanding of the emergence of brain network activity reconfigurations. Electroencephalography (EEG) and magnetic resonance imaging (MRI) show that brains are active even when people are not engaged in specific activities. Brain simulations explain these rhythmic networks as spontaneously emerging from the interaction of large groups of nerve cells via the brain's so-called white-matter. The brain is made up of approximately 100 billion nerve cells and one quadrillion connections between them. Nerve cells and connections are in turn made up of even smaller elements, like ion channels and spines, that can have a range of functional properties. To accurately simulate the brain, would require the measurement of all the different subtle properties of these components. But computer processing power still too limited to perform the necessary calculations in a practical amount of time. So, we are not trying to accurately simulate the brain, but rather reveal the larger scale patterns that emerge from the interaction of these elements, like those that emerge in a flock of birds.
Dividing the entire brain into areas, we formulate theories testable by computer modelling. As many details of the equations are little understood or only vaguely specified, we use brain imaging to constrain the models. Brain simulations using EEG and fMRI derived data enable us to estimate the connectivity between brain areas yielding so-called connectomes (strengths of interaction between different brain areas) and accurately predict brain activity. We are interested in high-level cognitive functions like intelligence, decision-making, memory and learning, to work out the cause of impairments and to map out strategies for improvements.
So far, amongst other findings, the project has improved understanding of the healthy brain and aging and neurodegeneration.
a. Healthy individuals across a wide age range
We revealed that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local brain region revealed the dynamical cortical core of the human brain, which is driving the activity of the rest of the whole brain (Deco et al. 2017 Scientific Reports). We present evidence that in most standard datasets, the subject variation in structural connectivity (SC) may be too weak to be reflected in the functional connectivity (FC) variability. However, subject specificity of SC-FC can be captured via fine, multimodally parcellated data because of greater SC variability across subjects. Nonetheless, SC and FC each show a large component that is common across subjects, which sets limitations on the extent of SC-FC subject specificity. Implications of these findings for personalized medicine should be considered. Namely, attention to the quality of processing and parcellation methods is critical for furthering our understanding of the relationship between individual SC and FC (Zimmermann et al. 2018 Network Neuroscience).
b. Patients with Brain Tumors
We simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance. We demonstrated individual specificity of biophysical model parameters, differences in local model parameters dependent on distance from a tumor, and associations between model parameters and structural network topology and cognitive performance (Aerts et al. 2018 eNeuro)
c. Patients with Neurodegenerative Disease
Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes (Zimmermann 2018 Neuroimage Clin).
Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multiscale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data (Shen et al. 2019 Scientific Data).
2) We have identified a role of intrinsic plasticity for network reconfigurations in the resting state.
The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects’ individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between a- rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies (Schirner et al. 2018 eLife). To achieve this, we the models contain a plasticity mechanism that adjusts local inhibitory coupling strengths in order to obtain biologically plausible firing rates in excitatory populations. For this form of plasticity, termed feedback inhibition control (FIC), average population firing rates were the sole optimization criterion. FIC modulates the strengths of inhibitory connections that is required to compensate for excess or lack of excitation resulting from the large variability in white-matter coupling strengths obtained by MRI tractography, which is a prerequisite to obtain plausible ranges of population activity that is relevant for some results.
3) Educational tools that provides access to the dynamical regimes library and makes pre-computed simulations easily accessible allowing researchers to benefit and learn from existing work.
TVB EduPack: https://brainmodes.github.io/TVB_EduPack/
INCF Training Space: https://training.incf.org/courses?title=TVB&field_course_category_target_id=All&field_course_level_value=All
In addition, for broad public, have developed an interactive The Virtual Brain Software that can be operated via touch screen: https://www.brainsimulation.org/atlasweb/
Aerts, Schirner, Jeurissen, Van Roost, Achten, Ritter, Marinazzo (2018) Modeling brain dynamics in brain tumor patients using The Virtual Brain. eNeuro
Schirner, McIntosh, Jirsa, Deco, Ritter (2018) Inferring multi-scale neural mechanisms with brain network modelling. eLife
Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR (2019) A macaque connectome for large-scale network simulations in TheVirtualBrain Nature Scientific Data
Stefanovski, Triebkorn, Spiegler, Diaz-Cortes, Solodkin, Jirsa, McIntosh, Ritter; for the Alzheimer’s Disease Neuroimaging Initiative (2019). Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer’s disease. Frontiers Computational Neuroscience
Zimmermann, Griffiths, Schirner, Ritter, McIntosh (2018) Subject-specificity of the correlation between large-scale structural and functional connectivity. Network Neuroscience
Zimmermann, Perry, Breakspear, Schirner, Sachdev, Wen, Kochan, Mapstone, Ritter, McIntosh, Solodkin (2018) Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models. Neuroimage Clinical
As the Virtual Brain is open source, it is freely available for download and even modification, with data obtained from brain imaging converted to formats that can be interpreted by a range of software packages. The team has also advanced software that simplifies the construction of individual brain models from MRI data.
Neurodegenerative disorders are one of the most pressing problems facing modern societies. In addition to the individual burden, with 14 million people predicted to have dementia in 2030 alone, the estimated cost for that year is over 250 billion Euro. Additionally, mental health conditions such as bipolar disorder, Schizophrenia, depression, anxiety, PTSD, ADHD, alcohol and drug use disorders currently affects one in six people across the EU, and is rising. The cost of healthcare, social security and decreased employment/productivity is 620 billion Euros annually. Existing treatments for these conditions usually rely on medication, which suppresses symptoms rather than cure illness.
While the underlying mechanisms of these disorders remain unclear, evidence increasingly points to complex systematic physiological connections which are hard to study with experimental methods alone. With full-brain simulation, and in the future full-body simulation, we will better understand the whole human system. ‘Virtual humans’ would allow us to develop customized interventions targeting the combination of genetic, metabolic and neuronal factors responsible for brain disorders.
Individualized medicine is increasingly put forward as an important means to advance medical care. In this regard, the neuroinformatics platform The Virtual Brain holds great promise, given its direct focus on simulation of subject-specific brain activity. Reliable prediction of patient-specific large-scale brain dynamics would open up the possibility to virtually lesion structural connectomes, making computational models unique predictive tools to investigate the impact of diverse structural connectivity alterations on brain functioning, including those purposefully induced by surgery.
Understanding development, aging and brain disorders from the perspective of disruption of information processing architectures provides an opportunity for new interventions that re-establish control in brain pathology hence posing a breakthrough in the health and biotech sector.