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An asymptotic theory for cross-correlation between auto-correlated sequences and its application on neuroimaging data.
Zhou, Yunyi; Tao, Chenyang; Lu, Wenlian; Feng, Jianfeng.
Afiliación
  • Zhou Y; School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, PR China.
  • Tao C; Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States.
  • Lu W; School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, PR China; Shanghai Key Laboratory for Contemporary Applied Mathematics and Laboratory of Mathematics for Nonlinear Sci
  • Feng J; School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, PR China; Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, PR China; Department of Comput
J Neurosci Methods ; 304: 52-65, 2018 07 01.
Article en En | MEDLINE | ID: mdl-29684465
BACKGROUND: Functional connectivity is among the most important tools to study brain. The correlation coefficient, between time series of different brain areas, is the most popular method to quantify functional connectivity. Correlation coefficient in practical use assumes the data to be temporally independent. However, the time series data of brain can manifest significant temporal auto-correlation. NEW METHOD: A widely applicable method is proposed for correcting temporal auto-correlation. We considered two types of time series models: (1) auto-regressive-moving-average model, (2) nonlinear dynamical system model with noisy fluctuations, and derived their respective asymptotic distributions of correlation coefficient. These two types of models are most commonly used in neuroscience studies. We show the respective asymptotic distributions share a unified expression. RESULT: We have verified the validity of our method, and shown our method exhibited sufficient statistical power for detecting true correlation on numerical experiments. Employing our method on real dataset yields more robust functional network and higher classification accuracy than conventional methods. COMPARISON WITH EXISTING METHODS: Our method robustly controls the type I error while maintaining sufficient statistical power for detecting true correlation in numerical experiments, where existing methods measuring association (linear and nonlinear) fail. CONCLUSIONS: In this work, we proposed a widely applicable approach for correcting the effect of temporal auto-correlation on functional connectivity. Empirical results favor the use of our method in functional network analysis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Neuroimagen / Modelos Neurológicos / Vías Nerviosas Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: J Neurosci Methods Año: 2018 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Neuroimagen / Modelos Neurológicos / Vías Nerviosas Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: J Neurosci Methods Año: 2018 Tipo del documento: Article Pais de publicación: Países Bajos