This title appears in the Scientific Report :
2021
Please use the identifier:
http://hdl.handle.net/2128/29326 in citations.
Efficient validation of dynamical whole-brain models via mathematical optimization algorithms
Efficient validation of dynamical whole-brain models via mathematical optimization algorithms
INTRODUCTION: Investigating the resting-state brain dynamics involves its simulation by dynamical whole-brain models that have attracted a great interest over the last years [1]. With a rising number of utilized mathematical models and their complexity, the challenge of adequate model fitting to emp...
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Personal Name(s): | Wischnewski, Kevin |
---|---|
Eickhoff, Simon / Popovych, Oleksandr (Corresponding author) | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Imprint: |
2021
|
Conference: | The 27th Annual Meeting of the Organization for Human Brain Mapping, Virtual (Virtual), 2021-06-21 - 2021-06-25 |
Document Type: |
Poster |
Research Program: |
Personalized Recommendations for Neurodegenerative Disease Human Brain Project Specific Grant Agreement 3 Human Brain Project Specific Grant Agreement 2 Computational Principles |
Link: |
OpenAccess |
Publikationsportal JuSER |
INTRODUCTION: Investigating the resting-state brain dynamics involves its simulation by dynamical whole-brain models that have attracted a great interest over the last years [1]. With a rising number of utilized mathematical models and their complexity, the challenge of adequate model fitting to empirical data has become apparent. An intuitive approach is to optimize model parameters by a grid search. However, an exploration of the entire parameter space on a dense grid becomes computationally expensive for models with many free parameters. In search of alternatives, mathematical optimization algorithms have received increased attention [2]. These methods can outperform the grid search approach regarding computation time and result quality. In this work, we investigate several such optimization schemes for the validation of whole-brain models and compare their outcomes as well as computational costs with each other and the grid search. We suggest two most efficient algorithms for the optimization of the correspondence between simulated and empirical data. METHODS: Neuroimaging data of 105 subjects of the Human Connectome Project [3] were used for the extraction of structural and resting-state functional connectivity (SC and FC, respectively). Schaefer’s functional atlas [4] with 100 cortical regions was chosen as a brain parcellation. Additionally, an MRtrix-based probabilistic tractography [5] was applied to compute atlas-based SC. It was used to determine the coupling weights and delays between units of the dynamical model of coupled phase oscillators [6]. The model was deployed to simulate resting-state brain dynamics and eventually generate simulated FC (sFC). This in turn was fitted to empirical FC (eFC) by simultaneously adjusting up to 3 model parameters: global coupling and delay (2Dim parameter space) as well as noise intensity (3Dim parameter space). In addition to the grid search, the following derivative-free methods were tested: Nelder Mead Algorithm (NMA, [7]), Particle Swarm Optimization (PSO, [8]), Covariance Matrix Adaptation Evolution Strategy (CMAES, [9]) and Bayesian Optimization (BO, [10]). NMA is a deterministic local search method, the others represent global stochastic approaches. PSO and CMAES share the feature of being population-based. RESULTS: For all tested algorithms, the detected goodness-of-fit values range from -5% to +11% around those found by the grid search. Among the methods, the order PSO > CMAES > BO > NMA in respect of the goodness-of-fit (larger is better) can be observed. The values of the best fit in the 3Dim parameter space are on average around 16% higher than those obtained in the 2Dim case, regardless of applied algorithm. We found that the algorithms reliably detect global maxima in the parameter space, where the spread of solutions was tested for multiple random initial conditions. This effect is most pronounced for CMAES, while NMA demonstrates a high susceptibility to local optima. Comparing the computation time required by the investigated methods to the grid search (100%) yielded relative values from 3500% (PSO) to 87% (NMA) in 2Dim and from 73% (PSO) to 2% (BO) in 3Dim. To evaluate methods, we analyzed a cost function that included the goodness-of-fit values, spread of algorithm solutions as well as the required computation time. We identified CMAES and BO as the most efficient approaches that may be used for the validation of dynamical models. CONCLUSIONS: We showed that some available mathematical optimization algorithms can be used as an efficient tool to improve and accelerate the search for the parameters that maximize the similarity between empirical and simulated data. The tested methods perform a continuous search and do not rely on a selected grid granularity in order to detect optimal solutions. Thus, our findings may contribute to a more efficient validation of complex models with high-dimensional parameter spaces and facilitate precise and personalized modeling of brain dynamics. REFERENCES: [1] doi:10.3389/fnsys.2018.00068, [2] doi:10.1007/s12021-018-9369-x, [3] doi:10.1016/j.neuroimage.2013.05.041, [4] doi:10.1093/cercor/bhx179, [5] doi:10.1016/j.neuroimage.2019.116137, [6] doi:10.1016/j.neuroimage.2011.04.010, [7] doi:10.1093/comjnl/7.4.308, [8] doi:10.1007/s11721-007-0002-0, [9] doi:10.1145/2330784.2330919, [10] doi:10.1287/educ.2018.0188 |