EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI

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

Tensor Analysis and Fusion of Multimodal Brain Images

Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D

Modeling sparse connectivity between underlying brain sources for EEG/MEG

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure

Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture