Filters








83 Hits in 7.8 sec

Sparse plus low-rank autoregressive identification in neuroimaging time series [article]

Raphaël Liégeois, Bamdev Mishra, Mattia Zorzi, Rodolphe Sepulchre
2015 arXiv   pre-print
This paper considers the problem of identifying multivariate autoregressive (AR) sparse plus low-rank graphical models.  ...  We apply this decomposition on synthetic and real neuroimaging datasets with a specific focus on the information encoded in the low-rank structure of our model.  ...  Finally, [11] presents a unifying framework allowing sparse plus low-rank identification of inverse power spectral densities in multivariate time series.  ... 
arXiv:1503.08639v1 fatcat:5jnbo6a2andvnlamxgbfnjtclq

Sparse plus low-rank autoregressive identification in neuroimaging time series

Raphael Liegeois, Bamdev Mishra, Mattia Zorzi, Rodolphe Sepulchre
2015 2015 54th IEEE Conference on Decision and Control (CDC)  
This paper considers the problem of identifying multivariate autoregressive (AR) sparse plus low-rank graphical models.  ...  We apply this decomposition on synthetic and real neuroimaging datasets with a specific focus on the information encoded in the low-rank structure of our model.  ...  Finally, [11] presents a unifying framework allowing sparse plus low-rank identification of inverse power spectral densities in multivariate time series.  ... 
doi:10.1109/cdc.2015.7402835 dblp:conf/cdc/LiegeoisMZS15 fatcat:tgiulxpl3fambiknikd4d2qnmi

Causality as a unifying approach between activation and connectivity analysis of fMRI data [article]

Nevio Dubbini
2011 arXiv   pre-print
We test our method on finger tapping data, in which GLM and Granger Causality approaches are compared in finding activations.  ...  The algorithm accepts as inputs: the stimulus timing time series { time series of the BOLD signal of the voxel.  ...  to the bootstrapped time series.  ... 
arXiv:1102.5021v1 fatcat:pusvedu4rfa23kfde75jkog5lq

Rate-Optimal Robust Estimation of High-Dimensional Vector Autoregressive Models [article]

Di Wang, Ruey S. Tsay
2022 arXiv   pre-print
High-dimensional time series data appear in many scientific areas in the current data-rich environment.  ...  The proposed methodology enjoys both statistical optimality and computational efficiency, and can handle many popular high-dimensional models, such as sparse, reduced-rank, banded, and network-structured  ...  The approximately low-dimensional structure, such as weak sparsity and approximate low-rankness, is general and natural in high-dimensional time series modeling.  ... 
arXiv:2107.11002v2 fatcat:qo2iv7widzd45fwdagko2xp2si

Bayesian Time-Varying Tensor Vector Autoregressive Models for Dynamic Effective Connectivity [article]

Wei Zhang, Ivor Cribben, sonia Petrone, Michele Guindani
2021 arXiv   pre-print
In this paper, we propose a computationally efficient Bayesian time-varying VAR approach for modeling high-dimensional time series.  ...  quadratically with the number of time series.  ...  Supplemental Material We provide a proof of Proposition 1 and full conditional distributions in the MCMC algorithm to draw posterior samples.  ... 
arXiv:2106.14083v1 fatcat:snos7yn6hnawfaplk2o4zn4pv4

Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

Giulia Prando, Mattia Zorzi, Alessandra Bertoldo, Maurizio Corbetta, Marco Zorzi, Alessandro Chiuso
2019 NeuroImage  
Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role  ...  in the emergent brain dynamics is still lacking.  ...  Thus, we regard as a plus the ability to recover a model which is close to some ground truth for "typical" sparse network topologies.  ... 
doi:10.1016/j.neuroimage.2019.116367 pmid:31812714 fatcat:qfeo2piy2rhqtbdeenv42igtvm

A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering

Andreas Galka, Okito Yamashita, Tohru Ozaki, Rolando Biscay, Pedro Valdés-Sosa
2004 NeuroImage  
By fitting linear autoregressive models with neighbourhood interactions to EEG time series, new classes of inverse solutions with improved resolution and localisation ability can be explored.  ...  In contrast to previous approaches, we reinterpret this problem as a filtering problem in a state space framework; for the purpose of its solution, we propose a new extension of Kalman filtering to the  ...  While providing important results and methodology, so far none of these studies has addressed the problem of reconstruction of distributed sources from EEG (or MEG) times series in the context of identification  ... 
doi:10.1016/j.neuroimage.2004.02.022 pmid:15488394 fatcat:vecvm5mzwzhzhd2nyhij7h2ha4

2021 Index IEEE Transactions on Signal Processing Vol. 69

2021 IEEE Transactions on Signal Processing  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  ., +, TSP 2021 5638-5650 Causality Online Topology Identification From Vector Autoregressive Time Series.  ... 
doi:10.1109/tsp.2022.3162899 fatcat:kcubj566gzb4zkj7xb5r5we3ri

Model-based whole-brain effective connectivity to study distributed cognition in health and disease

Matthieu Gilson, Gorka Zamora-López, Vicente Pallarés, Mohit H Adhikari, Mario Senden, Adrià Tauste Campo, Dante Mantini, Maurizio Corbetta, Gustavo Deco, Andrea Insabato
2019 Network Neuroscience  
This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity.  ...  Neuroimaging techniques are now widely used to study human cognition.  ...  It corresponds to a network with linear feedback that is the equivalent in continuous time of the discrete-time multivariate autoregressive (MAR) process.  ... 
doi:10.1162/netn_a_00117 pmid:32537531 pmcid:PMC7286310 fatcat:jflwomj6vjdajfi3byxstj6niu

Connecting the Dots: Identifying Network Structure via Graph Signal Processing [article]

Gonzalo Mateos, Santiago Segarra, Antonio G. Marques, Alejandro Ribeiro
2018 arXiv   pre-print
arising in networked systems that evolve over time.  ...  Network topology inference is a prominent problem in Network Science.  ...  In this section we first consider identification of digraphs given nodal time series, which is intimately related to the problem of causal inference.  ... 
arXiv:1810.13066v1 fatcat:7ub2dgol7vhtxnwwgelficghz4

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

Cristina Mollica, Lea Petrella
2016 Journal of Applied Statistics  
The working group (WG) CMStatistics comprises a number of specialized teams in various research areas of computational and methodological statistics.  ...  The teams act autonomously within the framework of the WG in order to promote their own research agenda. Their activities are endorsed by the WG.  ...  time series in terms of a structured model encopassing a latent (low-rank) and a sparse component.  ... 
doi:10.1080/02664763.2016.1263835 fatcat:l5eyielgxrct7hq5ljqeej5ccy

A sequential distance-based approach for imputing missing data: Forward Imputation

Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
2016 Advances in Data Analysis and Classification  
strategy to detect and impose reduced-rank restrictions in large multivariate time series models is proposed.  ...  non-linearity in a time series.  ... 
doi:10.1007/s11634-016-0243-0 fatcat:yvrqlgllsbesbnvnzzci2egpl4

Spatiotemporal wavelet resampling for functional neuroimaging data

Michael Breakspear, Michael J. Brammer, Ed T. Bullmore, Pritha Das, Leanne M. Williams
2004 Human Brain Mapping  
This is achieved through spatiotemporal resampling of the data in the wavelet domain.  ...  In this report, we present a wavelet-based non-parametric technique for testing the null hypothesis that the correlations are typical of the data set and not unique to the regions of interest.  ...  Functional neuroimaging data consists of a series of slices S i (x,t) recorded over a time window t ϭ {t 1 ,t 2 ,. . .  ... 
doi:10.1002/hbm.20045 pmid:15281138 pmcid:PMC6871944 fatcat:lx6ghyny7remvbs7aui3u3ehia

Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models

Chee-Ming Ting, Hernando Ombao, S. Balqis Samdin, Sh-Hussain Salleh
2018 IEEE Transactions on Medical Imaging  
We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity.  ...  We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR  ...  Future work can incorporate sparsity based on the idea of low-rank plus sparse modeling [36] to explain a broader class of network topologies.  ... 
doi:10.1109/tmi.2017.2780185 pmid:29610078 fatcat:txvbli3fbrfovpaybs2tnbeqdu

Dynamic causal modelling

K.J. Friston, L. Harrison, W. Penny
2003 NeuroImage  
In this paper we present an approach to the identification of nonlinear input-state-output systems.  ...  Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system.  ...  In this sense DCM is much closer to conventional analyses of neuroimaging time series because the causal or explanatory variables enter as known fixed quantities.  ... 
doi:10.1016/s1053-8119(03)00202-7 pmid:12948688 fatcat:sby6njo3snce3iuetqjqc7ict4
« Previous Showing results 1 — 15 out of 83 results