Cargando…

A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic...

Descripción completa

Detalles Bibliográficos
Autores principales:	Cao, Xuefei, Sandstede, Björn, Luo, Xi
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2019
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402339/ https://www.ncbi.nlm.nih.gov/pubmed/30872989 http://dx.doi.org/10.3389/fnins.2019.00127

Ejemplares similares

Assessing parameter identifiability for dynamic causal modeling of fMRI data
por: Arand, Carolin, et al.
Publicado: (2015)

Missing data estimation in fMRI dynamic causal modeling
por: Zaghlool, Shaza B., et al.
Publicado: (2014)

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task
por: Vargas, Gabriela, et al.
Publicado: (2023)

Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data
por: Sharaev, Maksim G., et al.
Publicado: (2016)

Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations
por: Sadeghi, Sadjad, et al.
Publicado: (2020)

Variational Bayesian causal connectivity analysis for fMRI
por: Luessi, Martin, et al.
Publicado: (2014)

Prediction of Task-Related BOLD fMRI with Amplitude Signatures of Resting-State fMRI
por: Kannurpatti, Sridhar S., et al.
Publicado: (2012)

High Classification Accuracy for Schizophrenia with Rest and Task fMRI Data
por: Du, Wei, et al.
Publicado: (2012)

Identifying Abnormal Connectivity in Patients Using Dynamic Causal Modeling of fMRI Responses
por: Seghier, Mohamed L., et al.
Publicado: (2010)

Toward open sharing of task-based fMRI data: the OpenfMRI project
por: Poldrack, Russell A., et al.
Publicado: (2013)

Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG
por: Vaudano, Anna Elisabetta, et al.
Publicado: (2013)

Aging into Perceptual Control: A Dynamic Causal Modeling for fMRI Study of Bistable Perception
por: Dowlati, Ehsan, et al.
Publicado: (2016)

Network Connectivity in Epilepsy: Resting State fMRI and EEG–fMRI Contributions
por: Centeno, Maria, et al.
Publicado: (2014)

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data
por: Ge, Bao, et al.
Publicado: (2018)

Brain motion networks predict head motion during rest- and task-fMRI
por: Tomasi, Dardo, et al.
Publicado: (2023)

GLMdenoise: a fast, automated technique for denoising task-based fMRI data
por: Kay, Kendrick N., et al.
Publicado: (2013)

EEG-fMRI Methods for the Study of Brain Networks during Sleep
por: Duyn, Jeff H.
Publicado: (2012)

Effect of Spatial Smoothing on Task fMRI ICA and Functional Connectivity
por: Chen, Zikuan, et al.
Publicado: (2018)

Reproducibility of Graph Metrics in fMRI Networks
por: Telesford, Qawi K., et al.
Publicado: (2010)

EEG-Informed fMRI: A Review of Data Analysis Methods
por: Abreu, Rodolfo, et al.
Publicado: (2018)

fMRI in Non-human Primate: A Review on Factors That Can Affect Interpretation and Dynamic Causal Modeling Application
por: Jovellar, D. Blair, et al.
Publicado: (2019)

DC Shifts-fMRI: A Supplement to Event-Related fMRI
por: Li, Qiang, et al.
Publicado: (2019)

Regression dynamic causal modeling for resting‐state fMRI
por: Frässle, Stefan, et al.
Publicado: (2021)

Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation
por: Parlak, Fatma, et al.
Publicado: (2023)

Listening to real-world sounds: fMRI data for analyzing connectivity networks
por: Kuo, Po-Chih, et al.
Publicado: (2019)

Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics
por: Sip, Viktor, et al.
Publicado: (2023)

Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI
por: Schaefer, Alexander, et al.
Publicado: (2014)

The impact of vasomotion on analysis of rodent fMRI data
por: Lambers, Henriette, et al.
Publicado: (2023)

ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks
por: Khullar, Siddharth, et al.
Publicado: (2011)

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task
por: Perronnet, Lorraine, et al.
Publicado: (2017)

Evaluation of Task fMRI Decoding With Deep Learning on a Small Sample Dataset
por: Yotsutsuji, Sunao, et al.
Publicado: (2021)

Reinforcement and Punishment Shape the Learning Dynamics in fMRI Neurofeedback
por: Klöbl, Manfred, et al.
Publicado: (2020)

Arousal Contributions to Resting-State fMRI Connectivity and Dynamics
por: Gu, Yameng, et al.
Publicado: (2019)

Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data
por: Vergun, Svyatoslav, et al.
Publicado: (2016)

Brain Network to Placebo and Nocebo Responses in Acute Experimental Lower Back Pain: A Multivariate Granger Causality Analysis of fMRI Data
por: Shi, Yu, et al.
Publicado: (2021)

Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data
por: Kyathanahally, Sreenath P., et al.
Publicado: (2017)

Multisensory integration of dynamic emotional faces and voices: method for simultaneous EEG-fMRI measurements
por: Schelenz, Patrick D., et al.
Publicado: (2013)

Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data
por: Xu, Yuting, et al.
Publicado: (2015)

Exploring fMRI Results Space: 31 Variants of an fMRI Analysis in AFNI, FSL, and SPM
por: Pauli, Ruth, et al.
Publicado: (2016)

A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction
por: Cury, Claire, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_t894ne8ours4bmt5cm417ueemq