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Methods for computing the maximum performance of computational models of fMRI responses [article]

Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino
2018 bioRxiv   pre-print
The bound to the performance of a computational model to the prediction of brain responses has been referred to as the noise ceiling.  ...  Computational neuroimaging methods aim to predict brain responses (measured e.g. with functional magnetic resonance imaging [fMRI]) on the basis of stimulus features obtained through computational models  ...  analysis), it is often recommended to report the performances (e.g. 588 out of sample prediction in the case of fMRI encoding) with respect to the maximum 589 performance that a "perfect" model would  ... 
doi:10.1101/377101 fatcat:ip62u3ie5rhjfjp2mgwytqc4xq

Methods for computing the maximum performance of computational models of fMRI responses

Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, Jörn Diedrichsen
2019 PLoS Computational Biology  
Computational neuroimaging methods aim to predict brain responses (measured e.g. with functional magnetic resonance imaging [fMRI]) on the basis of stimulus features obtained through computational models  ...  This bound to the performance of a computational model has been referred to as the noise ceiling.  ...  Methods We consider a two-level procedure to fit a computational model to fMRI responses.  ... 
doi:10.1371/journal.pcbi.1006397 pmid:30849071 pmcid:PMC6426260 fatcat:ea7jlcg5izfzxaqo6v22p74ama

Quantitative comparison of functional MRI and direct electrocortical stimulation for functional mapping

S. Larsen, R. Kikinis, I.-F. Talos, D. Weinstein, W. Wells, A. Golby
2007 International Journal of Medical Robotics and Computer Assisted Surgery  
Methods-A quantitative comparison of DECS and fMRI mapping techniques was performed, using a patient-specific conductivity model to find the current distribution resulting from each stimulation site.  ...  With the increased use of functional magnetic resonance imaging (fMRI) for presurgical planning, there is a need to validate that fMRI activation mapping is consistent with the mapping obtained during  ...  The study was made possible in part by software from the NIH/NCRR Center for Integrative Biomedical Computing (P41-RR12553).  ... 
doi:10.1002/rcs.149 pmid:17763497 pmcid:PMC3733359 fatcat:xu6unaqcp5bwviybzffg7uafk4

Bayesian source separation of fMRI signals

Daniel B. Rowe
2001 AIP Conference Proceedings  
In computing the activation level, a standard method is to select an assumed to be known reference function and perform a multiple regression of the time courses on it and a linear trend.  ...  This underlying reference function is the unobserved response due the presentation of the experimental stimulus.  ...  It can be seen that the Bayesian method of determining the reference function for computation of voxel activation performed well especially with only sixteen voxels.  ... 
doi:10.1063/1.1381901 fatcat:6x3lxnfgofe3rbkffizcrlctbu

Extracting The Haemodynamic Response Function From Fmri Time Series Using Fourier-Wavelet Regularised Deconvolution With Orthogonal Spline Wavelets

Jos B.T.M. Roerdink, Alle Meije Wink
2006 Zenodo  
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006  ...  ACKNOWLEDGEMENTS This research is part of the project "Wavelets and their applications", funded by the Dutch National Science Foundation (NWO), project no. 613.006.570.  ...  An important tool for fMRI analysis is statistical hypothesis testing, where the fMRI signal is predicted using the stimulus pattern and a response model.  ... 
doi:10.5281/zenodo.52872 fatcat:cbwvk57nxna2xjxnrtqcjityhy

Exploratory Parcellation of fMRI Data Based on Finite Mixture Models and Self-annealing Expectation Maximization [chapter]

S. Maleki Balajoo, G. A. Hossein-Zadeh, H. Soltanian-Zadeh
2010 International Federation for Medical and Biological Engineering Proceedings  
We present a new exploratory method for brain parcellation based on a probabilistic model in which anatomical and functional features of fMRI data are used.  ...  The goal of this procedure is to segregate the brain into spatially connected and functionally homogeneous components, and to account for variability of the fMRI response in different brain regions.  ...  MATERIALS AND METHODS In this work, we propose a new method of brain parcellation based on a probabilistic model in which anatomical and functional features of fMRI data are used.  ... 
doi:10.1007/978-3-642-14998-6_100 fatcat:o3zdgnbigjcmpow57wogawzi3a

A General Probabilistic Model for Group Independent Component Analysis and Its Estimation Methods

Ying Guo
2011 Biometrics  
A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates.  ...  An fMRI data example is used to illustrate application of the proposed methods.  ...  The author thanks Dr. Giuseppe Pagnoni for the Zen meditation data.  ... 
doi:10.1111/j.1541-0420.2011.01601.x pmid:21517789 pmcid:PMC3412593 fatcat:tg3go5iv3fba5p7riffjvsk7qy

A deconvolution-based approach to identifying large-scale effective connectivity

Keith Bush, Suijian Zhou, Josh Cisler, Jiang Bian, Onder Hazaroglu, Keenan Gillispie, Kenji Yoshigoe, Clint Kilts
2015 Magnetic Resonance Imaging  
We then validated the ability for the proposed method to reliably detect effective connectivity in whole-brain fMRI signal parcellated into networks of viable size.  ...  We then test, both in simulation as well as whole-brain fMRI BOLD signal, the viability of this approach.  ...  Acknowledgements This work was supported in part by National Institutes of Health grants R21MH097784-01 and R01DA036360-01 as well as by as the National Science Foundation grants CRI CNS-0855248 and MRI  ... 
doi:10.1016/j.mri.2015.07.015 pmid:26248273 pmcid:PMC4658309 fatcat:aa3adnv7mzaxfpmgxgmgpnpxge

A computational hierarchy in human cortex [article]

Andreea O. Diaconescu, Vladimir Litvak, Christoph Mathys, Lars Kasper, Karl J. Friston, Klaas E. Stephan
2017 arXiv   pre-print
We found that the temporal sequence of neuronal activity matched the order of computations as predicted by the theory.  ...  Furthermore, our approach offers a novel strategy for the combined computational-physiological phenotyping of patients with disorders of perception, such as schizophrenia or autism.  ...  Acknowledgements: We acknowledge support by the UZH Forschungskredit (AOD), SNF Ambizione  ... 
arXiv:1709.02323v1 fatcat:p4iaoyyhlbcfthcxdt6k6a2y6u

Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering

G.-A. Hossein-Zadeh, B.A. Ardekani, H. Soltanian-Zadeh
2003 IEEE Transactions on Medical Imaging  
Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value.  ...  The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis.  ...  Simulated fMRI Data For a realistic simulation of fMRI data, computer-generated activation time series were added to the measured time series of a single slice of a resting-state experimental fMRI data  ... 
doi:10.1109/tmi.2003.815074 pmid:12906234 fatcat:mtibqi5prndllj3j3cucz6afla

Neural Decoding Technique to Reconstruct Stimulus from the Evoked fMRI Voxel Responses

2016 International Journal of Science and Research (IJSR)  
Using the Euclidean distance calculation, the maximum probable category is chosen using which the encoding model is designed for further reconstruction.  ...  At the decoding end, a hybrid Bayesian framework is formed by gating the clustered image priors and the selected voxels to reconstruct the stimulus from the evoked fMRI voxel responses.  ...  Model Parameter Estimation The encoding model is designed for the selected category of natural images and the associated fMRI voxel responses of the brain.  ... 
doi:10.21275/v5i2.nov161137 fatcat:5yghmmuxerd6vjcsaaypls6lhm

Estimation and detection of event-related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach

Marc A. Burock, Anders M. Dale
2000 Human Brain Mapping  
As with selective averaging methods used in event-related potential (ERP) research, these methods allow for the estimation of the average time-locked response to particular event-types, even when these  ...  Recent developments in analysis methods for event-related functional magnetic resonance imaging (fMRI) has enabled a wide range of novel experimental designs.  ...  ACKNOWLEDGMENTS We thank Doug Greve, Emery Brown, and Rick Buxton for helpful discussions.  ... 
doi:10.1002/1097-0193(200012)11:4<249::aid-hbm20>;2-5 pmid:11144754 fatcat:i64asspvjvaijey72rlvf2qsom

A systematic framework for functional connectivity measures

Huifang E. Wang, Christian G. Bénar, Pascale P. Quilichini, Karl J. Friston, Viktor K. Jirsa, Christophe Bernard
2014 Frontiers in Neuroscience  
We then evaluated the performance of the methods on data simulated with different types of models.  ...  In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable  ...  Valdés-Sosa, Fabrice Wendling, Marmaduke Woodman, Mathieu Golos for helpful discussions, and Andrea Brovelli for useful discussions and technical help.  ... 
doi:10.3389/fnins.2014.00405 pmid:25538556 pmcid:PMC4260483 fatcat:ehfzt5xymrhrbhcr4qmypkfhhm

Multivariate group-level analysis for task fMRI data with canonical correlation analysis

Xiaowei Zhuang, Zhengshi Yang, Karthik R. Sreenivasan, Virendra R. Mishra, Tim Curran, Rajesh Nandy, Dietmar Cordes
2019 NeuroImage  
Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method.  ...  The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques.  ...  We also would like to thank the anonymous reviewers for their helpful comments, which have allowed us to significantly improve our manuscript.  ... 
doi:10.1016/j.neuroimage.2019.03.030 pmid:30894332 pmcid:PMC6536339 fatcat:6vlmkbppkbaotno4uoz6e6l2ey

Estimating Reproducible Functional Networks Associated with Task Dynamics Using Unsupervised LSTMS

Nicha C. Dvornek, Pamela Ventola, James S. Duncan
2020 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)  
The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest.  ...  More reproducible functional networks are essential for better characterizing the neural correlates of a target task.  ...  First, the task stimulus design is convolved with a canonical hemodynamic response function. We also compute the temporal derivative of the expected fMRI response.  ... 
doi:10.1109/isbi45749.2020.9098377 pmid:34422224 pmcid:PMC8375550 dblp:conf/isbi/DvornekVD20 fatcat:5gcqqmbiwzdyxknw7vuklwocoy
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