FMRI Data Analysis
How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection
... reproducible fMRI data ...underfit data and leave real effects ...overfit data and also reduce statistical ...given fMRI data ...simulated data and demonstrate potential ...
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How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging
... In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: “How to avoid mismodelling in ...
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Statistical approaches for resting state fMRI data analysis
... state fMRI data. As these data are typically characterized by high dimensionality and small sample size, bivariate analysis is the most employed approach to reconstruct functional architecture ...
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Artificial Neural Networks for fMRI Data Analysis: A Survey
... The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes ...
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Data-driven fMRI data analysis based on parcellation
... We argue that our multi-subject data-driven parcellation approach improves over (1) standard voxel-wise £MRI analysis in terms of both robustness and sensi- tivity to normalization i[r] ...
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Novel data-driven analysis methods for real-time fMRI and simultaneous EEG-fMRI neuroimaging
... component analysis (ICA) is a data-driven blind source separation (BSS) method widely used in brain functional magnetic resonance imaging (fMRI) data analysis (Calhoun and Adali, 2006; ...
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Temporally constrained ICA with threshold and its application to fMRI data
... Task fMRI data generally contain one or more spatially independent components that are related to the same task ...task fMRI data in the above ...task fMRI data, the temporal ...
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Statistical Analysis Methods for the fMRI Data
... the analysis of neuroimag- ing data was first proposed by Friston et ...of fMRI data analysis techniques employed by neuroscientists use a GLM of one form or ...in fMRI ...
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Group analysis based on multilevel Bayesian for FMRI data
... BOLD fMRI characterizes hemodynamic response function (HRF) to measure brain spatial distribution based on neural activity by vascular hemodynamic ...the analysis of fMRI ...on fMRI ...
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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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Bayesian spatiotemporal model of fMRI data using transfer functions
... The fMRI data analysis involves the spatiotemporal relationship between a stimulus or cognitive task and the cerebral response measured with ...
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A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
... subject analysis, in multi-subject studies two-stage “group analysis” approaches are often adopted as computationally attractive methods where summary estimates of model parameters are obtained at the ...
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Nonlinear complexity analysis of brain fMRI signals in schizophrenia
... A weakness of our study is that patients with schizophrenia were not drug naive. All patients with schizophrenia were taking some form of anti-psychotic medication, reflecting standard psychiatric practice. We found no ...
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Analysis of fMRI Single Subject Data in the Fourier Domain Acquired Using a Multiple Input Stimulus Experimental Design
... A more recent paper, also based on the work by Brill- inger, is that by Bai et al. [30]. It focused on obtaining unbiased estimates of the HRF using stochastic rather than deterministic input stimuli (the usual design ...
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Studies on classification of FMRI data using deep learning approach
... Common deep learning network architectures include deep belief network (DBN) [19], autoencoders [20], and convolutional neural network (ConvNet) [21]. DBN consists of restricted Boltzmann Machine (RBM). It is a Markov ...
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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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A Review on Dependence Measures in Exploring Brain Networks from fMRI Data
... This approach also concerns about fitting a model that embeds the function describing the causal struc- ture in the estimation process. The estimation problem becomes an optimization problem with con- straints or an ...
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Neuroimaging With Calibrated fmri
... tion among a large number of neurons within a functional unit or column, some of which are firing more quickly and others more slowly, is crucial for signaling in the brain. The ideal way to probe the activity of ...
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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 ...person. Analysis methods were based on similarity metrics, including correlations between training and test data, as ...
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Detection of Cognitive States from fMRI data using Machine Learning Techniques
... An fMRI scanner measures the value of the fMRI signal at all the points in a three dimensional grid, or image every few seconds (6 sec in our ...typical fMRI study have a volume of a few tens of ...
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