Coordinated multivoxel coding beyond univariate effects is not likely to be observable in fMRI data.

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of 'weak classifiers' (i.e., single voxels) in higher dimensions. We propose instead that 'real' multivoxel coding should result in changes in higher-order statistics across voxels between conditions such as second-order multivariate effects (sMVE). Surprisingly, analysis of conditions with robust multivariate effects (MVE) revealed by MVPA failed to show significant sMVE in two species (humans and macaques). Further analysis showed that while both MVE and sMVE can be readily observed in the spiking activity of neuronal populations, the slow and nonlinear hemodynamic coupling and low spatial resolution of fMRI activations make the observation of higher-order statistics between voxels highly unlikely. These results reveal inherent limitations of fMRI signals for studying coordinated coding across voxels. Together, these findings suggest that care should be taken in interpreting significant MVPA results as representing anything beyond a collection of univariate effects.

Spatial and temporal joint, partially-joint and individual sources in independent component analysis: Application to social brain fMRI dataset.

absectionBackground Three types of sources can be considered in the analysis of multi-subject datasets: (i) joint sources which are common among all subjects, (ii) partially-joint sources which are common only among a subset of subjects, and (iii) individual sources which belong to each subject and represent the specific conditions of that subject. Extracting spatial and temporal joint, partially-joint, and individual sources of multi-subject datasets is of significant importance to analyze common and cross information of multiple subjects. NEW METHOD: We present a new framework to extract these three types of spatial and temporal sources in multi-subject functional magnetic resonance imaging (fMRI) datasets. In this framework, temporal and spatial independent component analysis are utilized, and a weighted sum of higher-order cumulants is maximized. RESULTS: We evaluate the presented algorithm by analyzing simulated data and one real multi-subject fMRI dataset. Our results on the real dataset are consistent with the existing meta-analysis studies. We show that spatial and temporal jointness of extracted joint and partially-joint sources in the theory of mind regions of brain increase with the age of subjects. COMPARISON WITH EXISTING METHOD: In Richardson et al. (2018), predefined regions of interest (ROI) have been used to analyze the real dataset, whereas our unified algorithm simultaneously extracts activated and uncorrelated ROIs, and determines their spatial and temporal jointness without additional computations. CONCLUSIONS: Extracting temporal and spatial joint and partially-joint sources in a unified algorithm improves the accuracy of joint analysis of the multi-subject fMRI dataset.

Joint, Partially-Joint, and Individual Independent Component Analysis in Multi-Subject fMRI Data.

OBJECTIVE: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-joint/individual multiple datasets unidimensional (JpJI-MDU), and accordingly, a source extraction method is developed. METHOD: We present a deflation-based algorithm utilizing higher order cumulants to analyze the JpJI-MDU source model. The algorithm maximizes a cost function which leads to an eigenvalue problem solved with thin-SVD (singular value decomposition) factorization. Furthermore, we introduce the JpJI-feature which indicates the spatial shape of each source and the amount of its jointness with other subjects. We use this feature to determine the type of sources. RESULTS: We evaluate our algorithm by analyzing simulated data and two real functional magnetic resonance imaging (fMRI) datasets. In our simulation study, we will show that the proposed algorithm determines the type of sources with the accuracy of 95% and 100% for 2-class and 3-class clustering scenarios, respectively. Furthermore, our algorithm extracts meaningful joint and partially-joint sources from the two real datasets, which are consistent with the existing neuroscience studies. CONCLUSION: Our results in analyzing the real datasets reveal that both datasets follow the JpJI-MDU source model. This source model improves the accuracy of source extraction methods developed for multi-subject datasets. SIGNIFICANCE: The proposed joint blind source separation algorithm is robust and avoids parameters which are difficult to fine-tune.

Extraction and Automatic Grouping of Joint and Individual Sources in Multisubject fMRI Data Using Higher Order Cumulants.

The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of joint sources by applying a simple and efficient strategy to determine the type of sources (joint or individual). The algorithm also categorizes similar sources automatically across data sets through an optimization process. The proposed algorithm is evaluated by analyzing simulated functional magnetic resonance imaging (fMRI) multisubject data sets, and its performance is compared with existing alternatives. We investigate clean and noisy fMRI signals and consider two source models. Our results reveal that the proposed algorithm outperforms its alternatives in terms of the mean joint signal to interference ratio. We also apply the proposed algorithm on a public-available real fMRI multisubject data set, which was acquired during naturalistic auditory experience. The extracted results are in accordance with the previous studies on naturalistic audio listening and results of a recent study investigated this data set, which demonstrates that the JI-ThICA algorithm can be applied to extract reliable and meaningful information from multisubject fMRI data.