Constructing brain connectivity group graphs from EEG time series
| Autor: | Andrew T. Walden, L. Zhuang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology multivariable power spectra Statistics & Probability 02 engineering and technology Electroencephalography 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine COHERENCE medicine Graphical model spectral matrix clustering Science & Technology EEG time series Quantitative Biology::Neurons and Cognition medicine.diagnostic_test Functional connectivity Brain functional connectivity 0104 Statistics graphical model FUNCTIONAL CONNECTIVITY NETWORKS schizophrenia Physical Sciences Graphical analysis Statistics Probability and Uncertainty Neuroscience Mathematics 030217 neurology & neurosurgery |
| Zdroj: | Journal of Applied Statistics. 46:1107-1128 |
| ISSN: | 1360-0532 0266-4763 |
| Popis: | Graphical analysis of complex brain networks is a fundamental area of modern neuroscience. Functional connectivity is important since many neurological and psychiatric disorders, including schizophrenia, are described as ‘dys-connectivity’ syndromes. Using electroencephalogram time series collected on each of a group of 15 individuals with a common medical diagnosis of positive syndrome schizophrenia we seek to build a single, representative, brain functional connectivity group graph. Disparity/distance measures between spectral matrices are identified and used to define the normalized graph Laplacian enabling clustering of the spectral matrices for detecting ‘outlying’ individuals. Two such individuals are identified. For each remaining individual, we derive a test for each edge in the connectivity graph based on average estimated partial coherence over frequencies, and associated p-values are found. For each edge these are used in a multiple hypothesis test across individuals and the proportion rejecting the hypothesis of no edge is used to construct a connectivity group graph. This study provides a framework for integrating results on multiple individuals into a single overall connectivity structure. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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