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Page 755 of Educational and Psychological Measurement Vol. 74, Issue 5 [page]

2014 Educational and Psychological Measurement  
Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis. Computers in Biology and Medicine, 41, 1142-1155. Cheung, G. W., & Rensvold, R. B. (2002).  ...  Structural Equation Modeling, 9, 233-255. Chin, W., & Newstead, P. (1999). Structural equation modeling analysis with small samples using partial least squares. In R.  ... 

The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution

Alard Roebroeck, Elia Formisano, Rainer Goebel
2011 NeuroImage  
Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of  ...  of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in  ...  We have argued here that useful models for brain connectivity have well justified assumptions, both in their structural model and in their dynamical model.  ... 
doi:10.1016/j.neuroimage.2009.09.036 pmid:19786106 fatcat:5duffr7hrzc5dfw7stzyy5oh64

Functional and Effective Connectivity: A Review

Karl J. Friston
2011 Brain Connectivity  
Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience.  ...  One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles  ...  This leads to structural equation modeling with time-lagged data and related autoregression models, such as those employed by Granger causality.  ... 
doi:10.1089/brain.2011.0008 pmid:22432952 fatcat:lwczplky2jb6zn3uwlgauks7pa

Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications

Ljubiša Stanković, Danilo Mandic, Miloš Daković, Miloš Brajović, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
2020 Foundations and Trends® in Machine Learning  
"A tutorial on sparse signal reconstruction and its applications in signal processing". Circuits, Systems, and Signal Processing. 38(3): 1206-1263. Stoer, M. and F. Wagner (1997).  ...  Sejdić, and M. Daković (2018). "Reduced interference vertex-frequency distributions". IEEE Signal Processing Letters. 25(9): 1393-1397. Stanković, L., E. Sejdić, S. Stanković, M. Daković, and I.  ...  Gotlib, and R. W. Cox (2011). "Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis". Computers in Biology and Medicine. 41(12): 1142-1155.  ... 
doi:10.1561/2200000078-3 fatcat:el57qukkrzgbjekeocn24d5fti

Effect of hemodynamic variability on Granger causality analysis of fMRI

Gopikrishna Deshpande, K. Sathian, Xiaoping Hu
2010 NeuroImage  
Generically, under all conditions, faster sampling and low measurement noise improved the sensitivity of GC analysis of fMRI data to neuronal causality.  ...  In this work, we investigated the effect of the regional variability of the hemodynamic response on the sensitivity of Granger causality (GC) analysis of functional magnetic resonance imaging (fMRI) data  ...  Acknowledgments The authors acknowledge support by the Georgia Research Alliance and NIH grants R01EB002009 (to XH), R01EY12440 (to KS) and K24EY17332 (to KS).  ... 
doi:10.1016/j.neuroimage.2009.11.060 pmid:20004248 pmcid:PMC3098126 fatcat:b4qyqi7tivcl7nbp254itwaqga

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models

Roger T. Dean, William T. M. Dunsmuir
2015 Behavior Research Methods  
model development.  ...  We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain.  ...  The resulting "discrete control equation" is normally an example of a vector (multivariate) autoregression time series analysis model of the paired (or several) continuous response/performance time series  ... 
doi:10.3758/s13428-015-0611-2 pmid:26100765 fatcat:ymgle3ubpfgrviim25kgzelfgi

Deep Markov Spatio-Temporal Factorization [article]

Amirreza Farnoosh, Behnaz Rezaei, Eli Zachary Sennesh, Zulqarnain Khan, Jennifer Dy, Ajay Satpute, J Benjamin Hutchinson, Jan-Willem van de Meent, Sarah Ostadabbas
2020 arXiv   pre-print
We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data.  ...  This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal  ...  [4] enforced a low rank assumption on coefficient tensor of vector autoregressive models, and used a spatial Laplacian regularization for prediction in spatio-temporal data. Cai et al.  ... 
arXiv:2003.09779v2 fatcat:cwkuwhrznjc57dhffkf7bkgy4a

Translational Perspectives for Computational Neuroimaging

Klaas E. Stephan, Sandra Iglesias, Jakob Heinzle, Andreea O. Diaconescu
2015 Neuron  
of neuroimaging data.  ...  Promising new avenues for translation are provided by computational modeling of neuroimaging data.  ...  ,'' and the Deutsche Forschungsgemeinschaft (TR-SFB 134).  ... 
doi:10.1016/j.neuron.2015.07.008 pmid:26291157 fatcat:kexghlnh5jdoteeptmb36pggre

A Wavelet-Based Bayesian Approach to Regression Models with Long Memory Errors and Its Application to fMRI Data

Jaesik Jeong, Marina Vannucci, Kyungduk Ko
2013 Biometrics  
These models have been proven useful for application in many areas, such as medical imaging, signal processing, and econometrics.  ...  We explore performances on simulated data and present an application to an fMRI data set.  ...  Wavelets have been extremely successful as a tool for the analysis and synthesis of discrete data.  ... 
doi:10.1111/j.1541-0420.2012.01819.x pmid:23379536 fatcat:hwtyv6giyfaxjlyxuaydmxwhaa

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Stefan Frässle, Eduardo A. Aponte, Saskia Bollmann, Kay H. Brodersen, Cao T. Do, Olivia K. Harrison, Samuel J. Harrison, Jakob Heinzle, Sandra Iglesias, Lars Kasper, Ekaterina I. Lomakina, Christoph Mathys (+9 others)
2021 Frontiers in Psychiatry  
to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition.  ...  , electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice.  ...  modeling of neuroimaging and behavioral data.  ... 
doi:10.3389/fpsyt.2021.680811 pmid:34149484 pmcid:PMC8206497 fatcat:ichfqltpdbdfflh2ozfk4lduda

A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis

Christian Habeck, John W. Krakauer, Claude Ghez, Harold A. Sackeim, David Eidelberg, Yaakov Stern, James R. Moeller
2005 Neural Computation  
In sum, OrT has potential applications to not only studies of young adults and their cognitive abilities, but also studies of normal aging and neurological and psychiatric disease.  ...  We designed ordinal trend analysis (OrT) to identify activation patterns that increase monotonically in their expression as the experimental task parameter increases, while the correlative relationships  ...  Acknowledgments We thank Eric Zarahn and two anonymous reviewers for a critical reading of the manuscript for this article and many helpful questions and suggestions.  ... 
doi:10.1162/0899766053723023 pmid:15901409 fatcat:d67o62lskvf7tim4icjdtcwr3m

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

Cristina Mollica, Lea Petrella
2016 Journal of Applied Statistics  
Specialized teams Currently the ERCIM WG has over 1150 members and the following specialized teams BM: Bayesian Methodology CODA: Complex data structures and Object Data Analysis CPEP: Component-based  ...  methods for Predictive and Exploratory Path modeling  ...  in the estimation of large vector autoregressive models (VARs).  ... 
doi:10.1080/02664763.2016.1263835 fatcat:l5eyielgxrct7hq5ljqeej5ccy

The Role of Generative Adversarial Network in Medical Image Analysis: An in-depth survey

Manal AlAmir, Manal AlGhamdi
2022 ACM Computing Surveys  
Third, the extension models of GANs were classified and introduced one by one.  ...  A generative adversarial network (GAN) is one of the most significant research directions in the field of artificial intelligence, and its superior data generation capability has garnered wide attention  ...  This motivates us to describe GANs briely and provides a high-level glance of their usage and role in medical image analysis.  ... 
doi:10.1145/3527849 fatcat:m5yjmhlxjrfoblw6cxwaqbb774

Robust Cyber–Physical Systems: Concept, models, and implementation

Fei Hu, Yu Lu, Athanasios V. Vasilakos, Qi Hao, Rui Ma, Yogendra Patil, Ting Zhang, Jiang Lu, Xin Li, Neal N. Xiong
2016 Future generations computer systems  
Here raw physical processes (RPP) data is collected, and the system is controlled by an intelligent computational world.  ...  For example, we may build a regression model or hidden Markov model to describe the CPS time evolution dynamics.  ...  Acknowledgement: The authors thank the following people for their valuable comments and inputs: Tony Huynh, Ahmed Alsadah, Michael Johnson, Tony Randolph, Steven Guy, Erica Boyle, Rebecca Landrum, and  ... 
doi:10.1016/j.future.2015.06.006 fatcat:uu4jvdxq3rbh7jstt4btdku5c4

Neural Fields, Masses and Bayesian Modelling [chapter]

Dimitris A. Pinotsis, Karl J. Friston
2014 Neural Fields  
This chapter considers the relationship between neural field and mass models and their application to modelling empirical data.  ...  We describe these models and show how Bayesian inference can be used to assess the validity of their field and mass variants, given empirical data.  ...  Frequency domain data-features were obtained from this epoch using a vector autoregression model of order eight.  ... 
doi:10.1007/978-3-642-54593-1_17 fatcat:mb3mfu4dgffltcb62syr5oeqqa
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