Multi-subject fMRI Data Analysis Research Articles

A Novel Method for Multi-subject fMRI Data Analysis: Independent Component Analysis with Clustering Embedded (ICA-CE).

The analysis of multi-subject functional magnetic resonance imaging (fMRI) data and the extraction of accurate brain functional networks (FNs) are of great importance. However, traditional independent component analysis (ICA) methods perform analysis on multi-subject fMRI data under the condition of known or assumed classes of subjects, which may decrease its ability to extract accurate individual brain FNs. Although a previous method named clusterwise ICA (C-ICA) clusters subjects and obtains shared FNs in group-level for each class, its clustering performance on complex data is not ideal. To address the issues, we propose a novel method called independent component analysis with clustering embedded (ICA-CE) that can achieve both the estimation of individual FNs and the clustering of subjects in an unsupervised or semi-supervised manner. Using the simulated data with different properties, ICA-CE achieved better clustering performance than group ICA followed by K-means and C-ICA, and the mean accuracy of extracted individual FNs obtained by ICA-CE was greater than 90%. Using the task-related fMRI data from Human Connectome Project (HCP), our method also achieved higher clustering accuracy, while extracting task-related class-specific FNs. In summary, ICA-CE is effective in estimating accurate brain FNs while achieving the clustering of multiple subjects.Clinical Relevance- Our method is promising in estimating accurate brain functional networks for patients with brain disorders and outputting related class label for each subject.

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.

A dictionary learning algorithm for multi-subject fMRI analysis based on a hybrid concatenation scheme

Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and recently, the approaches based on dictionary learning (DL) are noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multi-subject analysis. In this paper, we propose a DL algorithm for multi-subject fMRI data analysis which is derived using a hybrid (temporal and spatial) concatenation scheme. It differs from existing DL methods in both its sparse coding and dictionary update stages. It has the advantage of learning a dictionary common to all subjects as well as a set of subject-specific dictionaries, as a result, it is able to generate both group-level spatial activation maps as well as group-level temporal dynamics, which are particularly attractive for task-based fMRI studies. In addition, by simultaneously learning multiple sub-specific dictionaries, it also provides us with unique sub-specific features as well. Performance of the proposed DL method is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract common as well as sub-specific latent components.

The adaptive block sparse PCA and its application to multi-subject FMRI data analysis using sparse mCCA

Abstract Motivated by the problem of multi-subject functional magnetic resonance imaging (fMRI) data sets analysis using multiple-set canonical correlation analysis (mCCA), in this paper we propose a new variant of the principal component analysis (PCA) method, namely the adaptive block sparse PCA. It has the advantage to produce modified principal components with block sparse loadings. It is derived using penalized rank one matrix approximation where the penalty is introduced in the minimization problem to promote block sparsity of the loading vectors. An efficient algorithm is proposed for its computation. The effectiveness of the proposed method is illustrated on the problem of multi-subject fMRI data sets analysis using mCCA which is a generalization of canonical correlation analysis (CCA) to three or more sets of variables. This application is obtained by deriving the connection between mCCA and the singular value decomposition (SVD).

Shared and Subject-Specific Dictionary Learning (ShSSDL) Algorithm for Multisubject fMRI Data Analysis.

Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and approaches based on dictionary learning (DL) are recently noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multisubject analysis. In this paper, we propose a DL algorithm for multisubject fMRI data analysis. The proposed algorithm [named shared and subject-specific dictionary learning (ShSSDL)] is derived based on a temporal concatenation, which is particularly attractive for the analysis of multisubject task-related fMRI datasets. It differs from existing DL algorithms in both its sparse coding and dictionary update stages and has the advantage of learning a dictionary shared by all subjects as well as a set of subject-specific dictionaries. The performance of the proposed DL algorithm is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract shared as well as subject-specific latent components. In addition to offering a new DL approach, when applied on multisubject fMRI data analysis, the proposed algorithm generates a group level as well as a set of subject-specific spatial maps. The proposed algorithm has the advantage of learning simultaneously multiple dictionaries providing us with a shared as well discriminative source of information about the analyzed fMRI datasets.

SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis

Background and ObjectiveWith the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. MethodsIn this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. ResultsThe experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. ConclusionsThese results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group.

Blind source separation of functional MRI scans of the human brain based on canonical correlation analysis

A novel approach for multi-subject blind source separation (BSS) of brain functional magnetic resonance imaging (fMRI) data is proposed. Group-level comparison analysis is common in the human brain fMRI analysis. Canonical correlation analysis (CCA) for BSS (BSS-CCA) relies on the basis that all meaningful real signals are auto-correlated compared with white noise, which should generally not be considered. By merely requiring that the second-order statistic be zero, BSS-CCA is more relaxed than independent component analysis (ICA), which demands mutual statistics of all orders to be zero. Based on spatial BSS-CCA, we propose an approach termed group BSS-CCA for the analysis of multi-subject fMRI data. In terms of the simulated situation, in which “sources” were partially overlapping in space, we determined that identification using group BSS-CCA was more efficient than that using group ICA. The results from a real data experiment revealed that the proposed group BSS-CCA approach was effective for extracting functional brain networks that were functionally distinct and spatially overlapping from the fMRI data of the human brain.

Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach

ABSTRACTStroke is a disturbance in blood supply to the brain resulting in the loss of brain functions, particularly motor function. A study was conducted by the UCI Neurorehabilitation Lab to investigate the impact of stroke on motor-related brain regions. Functional MRI (fMRI) data were collected from stroke patients and healthy controls while the subjects performed a simple motor task. In addition to affecting local neuronal activation strength, stroke might also alter communications (i.e., connectivity) between brain regions. We develop a hierarchical Bayesian modeling approach for the analysis of multi-subject fMRI data that allows us to explore brain changes due to stroke. Our approach simultaneously estimates activation and condition-specific connectivity at the group level, and provides estimates for region/subject-specific hemodynamic response functions. Moreover, our model uses spike-and-slab priors to allow for direct posterior inference on the connectivity network. Our results indicate that motor-control regions show greater activation in the unaffected hemisphere and the midline surface in stroke patients than those same regions in healthy controls during the simple motor task. We also note increased connectivity within secondary motor regions in stroke subjects. These findings provide insight into altered neural correlates of movement in subjects who suffered a stroke. Supplementary materials for this article are available online.

A novel fMRI group data analysis method based on data-driven reference extracting from group subjects

Group-independent component analysis (GICA) is a well-established blind source separation technique that has been widely applied to study multi-subject functional magnetic resonance imaging (fMRI) data. The group-independent components (GICs) represent the commonness of all of the subjects in the group. Similar to independent component analysis on the single-subject level, the performance of GICA can be improved for multi-subject fMRI data analysis by incorporating a priori information; however, a priori information is not always considered while looking for GICs in existing GICA methods, especially when no obvious or specific knowledge about an unknown group is available. In this paper, we present a novel method to extract the group intrinsic reference from all of the subjects of the group and then incorporate it into the GICA extraction procedure. Comparison experiments between FastICA and GICA with intrinsic reference (GICA-IR) are implemented on the group level with regard to the simulated, hybrid and real fMRI data. The experimental results show that the GICs computed by GICA-IR have a higher correlation with the corresponding independent component of each subject in the group, and the accuracy of activation regions detected by GICA-IR was also improved. These results have demonstrated the advantages of the GICA-IR method, which can better reflect the commonness of the subjects in the group.

A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies

In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An efficient estimation algorithm is presented, as is an inferential framework that allows for not only tests of activation, but also tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain.

A critique of Tensor Probabilistic Independent Component Analysis: Implications and recommendations for multi-subject fMRI data analysis

Tensor Probabilistic Independent Component Analysis (TPICA) is a popular tool for analyzing multi-subject fMRI data (voxels×time×subjects) because of TPICA's supposed robustness. In this paper, we show that TPICA is not as robust as its authors claim. Specifically, we discuss why TPICA's overall objective is questionable, and we present some flaws related to the iterative nature of the TPICA algorithm. To demonstrate the relevance of these issues, we present a simulation study that compares TPICA versus Parallel Factor Analysis (Parafac) for analyzing simulated multi-subject fMRI data. Our simulation results demonstrate that TPICA produces a systematic bias that increases with the spatial correlation between the true components, and that the quality of the TPICA solution depends on the chosen ICA algorithm and iteration scheme. Thus, TPICA is not robust to small-to-moderate deviations from the model's spatial independence assumption. In contrast, Parafac produces unbiased estimates regardless of the spatial correlation between the true components, and Parafac with orthogonality-constrained voxel maps produces smaller biases than TPICA when the true voxel maps are moderately correlated. As a result, Parafac should be preferred for the analysis multi-subject fMRI data where the underlying components may have spatially overlapping voxel activation patterns.

Estimating and testing variance components in a multi-level GLM

Most analysis of multi-subject fMRI data is concerned with determining whether there exists a significant population-wide 'activation' in a comparison between two or more conditions. This is typically assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. However, important information can also be obtained by testing whether there exist significant individual differences in effect magnitude between subjects, i.e. whether the variance of a COPE is significantly different from zero. Intuitively, such a test amounts to testing whether inter-individual differences are larger than would be expected given the within-subject error variance. We compare several methods for estimating variance components, including a) a naïve estimate using ordinary least squares (OLS); b) linear mixed effects in R (LMER); c) a novel Matlab implementation of iterative generalized least squares (IGLS) and its restricted maximum likelihood variant (RIGLS). All methods produced reasonable estimates of within- and between-subject variance components, with IGLS providing an attractive balance between sensitivity and appropriate control of false positives. Finally, we use the IGLS method to estimate inter-subject variance in a perfusion fMRI study (N=18) of social evaluative threat, and show evidence for significant inter-individual differences in ventromedial prefrontal cortex (VMPFC), amygdala, hippocampus and medial temporal lobes, insula, and brainstem, with predicted inverse coupling between VMPFC and the midbrain periaqueductal gray only when high inter-individual variance was used to define the seed for functional connectivity analyses. In sum, tests of variance provides a way of selecting regions that show significant inter-individual variability for subsequent analyses that attempt to explain those individual differences.

Frequency domain connectivity identification: An application of partial directed coherence in fMRI

Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity.

Fixed and random effect analysis of multi-subject fMRI data using wavelet transform

We propose a new method to estimate the random effect variance in group analysis of fMRI data. In the first level of analysis, general linear model (GLM) is used to estimate a parameter map (“effect”) for each subject. After applying discrete wavelet transform to the “effect” maps, noise is reduced through a vertical energy thresholding (VET). The fixed effect component in each coefficient is derived by averaging the wavelet coefficients along all subjects. Then, the wavelet coefficients containing significant random effect are identified by their higher sample variance along the subjects. Wavelet coefficients containing random effect component in each subject are used to reconstruct the random effect maps through an inverse wavelet transform. Random effect variance is obtained from random effect maps for use in random effect analysis. The proposed method and other methods like GLM group analysis and variance ratio smoothing are applied to both experimental and artificial fMRI data. ROC curves, obtained from the simulated data, show improved group activation detection compared to existing random effect analysis methods. For the experimental data, the proposed method shows its high sensitivity by detecting multiple activation regions, namely visual cortex, cuneus, precuneus, thalamus, and cerebellum. From these regions, precuneus and cerebellum are not detected by majority of the previously published methods.

Statistical analysis of multi-subject fMRI data: assessment of focal activations.

A simple procedure for analyzing multi-subject functional magnetic resonance imaging (fMRI) data is proposed. In the first step, a voxel-wise t-test across standardized z-maps is performed to identify areas that are consistently activated across subjects. In the second step, for each area, individual mean z-scores are calculated and subsequently subjected to an analysis of variance. An example is provided.