714 research outputs found
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
Tensor-based fusion of EEG and FMRI to understand neurological changes in Schizophrenia
Neuroimaging modalities such as functional magnetic resonance imaging (fMRI)
and electroencephalography (EEG) provide information about neurological
functions in complementary spatiotemporal resolutions; therefore, fusion of
these modalities is expected to provide better understanding of brain activity.
In this paper, we jointly analyze fMRI and multi-channel EEG signals collected
during an auditory oddball task with the goal of capturing brain activity
patterns that differ between patients with schizophrenia and healthy controls.
Rather than selecting a single electrode or matricizing the third-order tensor
that can be naturally used to represent multi-channel EEG signals, we preserve
the multi-way structure of EEG data and use a coupled matrix and tensor
factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our
analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can
capture meaningful temporal and spatial signatures of patterns that behave
differently in patients and controls, and (ii) these differences and the
interpretability of the associated components increase by including multiple
electrodes from frontal, motor and parietal areas, but not necessarily by
including all electrodes in the analysis
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