ICA-based sparse feature recovery from fMRI datasets

Spatial Independent Components Analysis (ICA) is increasingly used in the context of functional Magnetic Resonance Imaging (fMRI) to study cognition and brain pathologies. Salient features present in some of the extracted Independent Components (ICs) can be interpreted as brain networks, but the segmentation of the corresponding regions from ICs is still ill-controlled. Here we propose a new ICA-based procedure for extraction of sparse features from fMRI datasets. Specifically, we introduce a new thresholding procedure that controls the deviation from isotropy in the ICA mixing model. Unlike current heuristics, our procedure guarantees an exact, possibly conservative, level of specificity in feature detection. We evaluate the sensitivity and specificity of the method on synthetic and fMRI data and show that it outperforms state-of-the-art approaches

A Spatially Robust ICA Algorithm for Multiple fMRI Data Sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model extracts independent components common to all data sets and independent data-set-specific components. We use time-delayed autocorrelations to obtain independent signal components and base our algorithm on prediction analysis. We applied this method to functional brain mapping using functional magnetic resonance imaging (fMRI). The results of our 3-subject analysis demonstrate the robustness of the algorithm to the spatial misalignment intrinsic in multiple-subject fMRI data sets. 1

A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

An ica algorithm for analyzing multiple data sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI). 1

Auto detection in autism

Autism is a neurobiological disorder in which, certain regions of the brain are affected. The main features of autism are impairment in communication, social interaction, language and deficit in imitation and theory of mind. Using Functional Magnetic Resonance Imaging (fMRI), haemodynamic responses during a bilateral finger tapping task are analyzed for both autistic subjects and normal control subjects. fMRI is a noninvasive technique to image the activity of the brain related to a specific task. Generally, the active voxels in the IMRI images are detected using parametric or non-parametric statistical methods in which the fMRI response is assumed to have a model. Such methods are not applicable to detect the active voxels when the fMRI response is unknown. The data driven methods are also used for analyzing the fMRI data. The data driven methods are computationally expensive. In this study, a method for detecting activated voxels without using prior knowledge of the input stimulus is presented. The assumption in this method is that the activation typically involves larger region comprising of several voxels and that these neighboring activated voxels are also temporally correlated. To validate the accuracy of this method, Principal component Analysis and Independent Component Analysis are also performed. A significant overlap in the sensorimotor cortex is found between the various methods suggesting that the automatic detecting method presented does provide accurate detection and localization

A Gaussian-mixed Fuzzy Clustering Model on Valence-Arousal-related fMRI Data-Set

Previous medical experiments illustrated that Valence and Arousal were high corresponded to brain response by amygdala and orbital frontal cortex through observation by functional magnetic resonance imaging (fMRI). In this paper, Valence-Arousal related fMRI data-set were acquired from the picture stimuli experiments, and finally the relative Valence -Arousal feature values for a given word that corresponding to a given picture stimuli were calculated. Gaussian bilateral filter and independent components analysis (ICA) based Gaussian component method were applied for image denosing and segmenting; to construct the timing signals of Valence and Arousal from fMRI data-set separately, expectation maximal of Gaussian mixed model was addressed to calculate the histogram, and furthermore, Otsu curve fitting algorithm was introduced to scale the computational complexity; time series based Valence -Arousal related curve were finally generated. In Valence-Arousal space, a fuzzy c-mean method was applied to get typical point that represented the word relative to the picture. Analyzed results showed the effectiveness of the proposed methods by comparing with other algorithms for feature extracting operations on fMRI data-set including power spectrum density (PSD), spline, shape-preserving and cubic fitting methods

Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging

Independent component analysis (ICA) is a powerful computational tool for separating independent source signals from their linear mixtures. ICA has been widely applied in neuroimaging studies to identify and characterize underlying brain functional networks. An important goal in such studies is to assess the effects of subjects' clinical and demographic covariates on the spatial distributions of the functional networks. Currently, covariate effects are not incorporated in existing group ICA decomposition methods. Hence, they can only be evaluated through ad-hoc approaches which may not be accurate in many cases. In this paper, we propose a hierarchical covariate ICA model that provides a formal statistical framework for estimating and testing covariate effects in ICA decomposition. A maximum likelihood method is proposed for estimating the covariate ICA model. We develop two expectation-maximization (EM) algorithms to obtain maximum likelihood estimates. The first is an exact EM algorithm, which has analytically tractable E-step and M-step. Additionally, we propose a subspace-based approximate EM, which can significantly reduce computational time while still retain high model-fitting accuracy. Furthermore, to test covariate effects on the functional networks, we develop a voxel-wise approximate inference procedure which eliminates the needs of computationally expensive covariance estimation. The performance of the proposed methods is evaluated via simulation studies. The application is illustrated through an fMRI study of Zen meditation.Comment: 36 pages, 5 figure