fMRI data;
Modeling and inference of multisubject fMRI data
... The analysis of single-subject fMRI data has drawn heav- ily on signal processing techniques. As discussed in the fol- lowing, linear time invariant systems are the standard way to specify the model for the ...
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Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network
... for fMRI StarPlus dataset for the subject of 04847 data and divided into two experiments involving twenty samples and fifty samples for each class; PicS ...same data obtained from SNR feature ...
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Potential pitfalls when denoising resting state fMRI data using nuisance regression
... state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately re fl ect the underlying intrinsic brain fl uctuations of ...the ...
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Bayesian spatiotemporal model of fMRI data using transfer functions
... problem. It also constitutes a natural but rigorous theory for combining prior and experimental information. Most Bayesian approaches to the modelling of fMRI data use GMRF as prior distributions, in order ...
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Motion related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data
... and fMRI data is challenging owing to increased noise levels in the EEG ...integrate data from these two modalities is to use aspects of the EEG data, such as the amplitudes of event-related ...
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Studies on classification of FMRI data using deep learning approach
... task. fMRI is a non- invasive technique to measure brain activity of a human subject according to various ...the fMRI datasets for each subject is huge and ...in fMRI classifications based on three ...
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The Pipeline of Processing fMRI data with Python Based on the Ecosystem NeuroDebian
... more data is produced from ...traditional data analysis approaches face a big challenge and bottleneck when the big data fade into it, so that’s why deep learning make very popular in the ...
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Prediction of Brain Connectivity Map in Resting-State fMRI Data Using Shrinkage Estimator
... The fMRI is a non-invasive method that uses Blood Oxygen Level Dependent (BOLD) contrast mechanism (Daliri & Behroozi, ...(rs)-fMRI data to understand patterns ofbrainconnectivity and their role ...
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Group analysis based on multilevel Bayesian for FMRI data
... Bayesian methods summarize evidences for statistical inference with conditional or posterior inference based on the posterior distribution of the activations. The first paper based on Bayesian inference was on PET in ...
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A Review on Dependence Measures in Exploring Brain Networks from fMRI Data
... 3D fMRI image, ...using fMRI signals collected from voxel level or ROIs is considered to be features that must be defined before exploring brain ...from fMRI paradigm ...maps fMRI data ...
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Statistical approaches for resting state fMRI data analysis
... EEG data was processed offline to filter out MR artifacts and remove ballistocardiogram artifacts (Brain Vision Analyzer ...resting-state fMRI data was preprocessed in line with the healthy subject, ...
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Artificial Neural Networks for fMRI Data Analysis: A Survey
... raw fMRI data over space and time, averaged fMRI data over a block, Beta values from a GLM analysis or average of several voxels in an ...In fMRI the number of features is usually ...
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A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
... single-subject fMRI data [Flandin and Penny (2007), Harrison and Green (2010), Penny, Kiebel and Friston (2003), Penny, Trujillo-Barreto and Friston (2005), Woolrich, Behrens and Smith ...the data ...
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A Better Looking Brain: Image Pre-Processing Approaches for fMRI Data
... templates, fMRI data is decomposed into multiple spatial inde- pendent components (intrinsic networks) using group spatial ICA implemented within the GIFT toolbox (Calhoun et ...is data- driven ...
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Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework
... (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD ...real fMRI data demonstrates the JPDE performance over standard detection ...
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Detection of Cognitive States from fMRI data using Machine Learning Techniques
... dimensional fMRI data, we have used three feature selection ...single fMRI scan and correlation based features were used when fMRI data from a single time in- terval was ...
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Knowledge Discovery through Computational Methods on EEG and fMRI Data
... or fMRI data, it is required to parametrize the ...the data alone. The authors in [1] study fMRI which is a noninvasive method to understand the state of ones ...But fMRI lacks temporal ...
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Temporally constrained ICA with threshold and its application to fMRI data
... in fMRI data before ICA ...in fMRI data is unknown, the threshold is also used to determine whether the extracted IC is related to the ...real fMRI experiments demon- strate that the ...
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Thought experiment: Decoding cognitive processes from the fMRI data of one individual
... of fMRI, because in the clinical setting making diagnoses for single cases is ...test data, as well as correlations with maps from the NeuroSynth ...single fMRI session and in each of its single ...
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Temporal-spatial modeling for fMRI data
... observed fMRI signal at the jth voxel on the corrected ...the fMRI data and gives the estimation of the variance- covariance matrix W of the estimated ...
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