A Dynamic Directional Model for Effective Brain Connectivity Using Electrocorticographic (ECoG) Time Series
| Autor: | Brian Caffo, Tingting Zhang, Dana Boatman-Reich, Jingwei Wu, Fan Li |
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| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Quantitative Biology::Neurons and Cognition Series (mathematics) Computer science business.industry Differential equation Pattern recognition Context (language use) computer.software_genre Brain mapping Article Temporal resolution Premovement neuronal activity Artificial intelligence Data mining Statistics Probability and Uncertainty business Image resolution computer Potts model |
| Zdroj: | Journal of the American Statistical Association. 110:93-106 |
| ISSN: | 1537-274X 0162-1459 |
| DOI: | 10.1080/01621459.2014.988213 |
| Popis: | We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset. |
| Databáze: | OpenAIRE |
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