Flexible fusion of electroencephalography and functional magnetic resonance imaging: Revealing neural-hemodynamic coupling through structured matrix-tensor factorization

Eyndhoven, Simon Van; Hunyadi, Borbála; Lathauwer, Lieven De; Huffel, Sabine Van

doi:10.23919/eusipco.2017.8081162

Cited by 23 publications

(38 citation statements)

References 18 publications

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“…On the other hand, tensors conveniently present multidimensional data and tensor decomposition techniques such as PARAFAC or Tucker decomposition do not impose constraints in the optimization process. Tensor-based analysis of concurrent EEG-fMRI have received increasing attention in recent years (Vanderperren et al 2010;Karahan et al 2015;Ferdowsi et al 2015;Hunyadi et al 2016Hunyadi et al , 2017Acar et al 2017a,b;Deshpande et al 2017;Sen and Parhi 2017;Van Eyndhoven et al 2017;Chatzichristos et al 2018;Kinney-Lang et al 2019). However, all the tensor-based fusion of the EEG-fMRI methods, except (Chatzichristos et al 2018), has been in fact under the matrixtensor factorization framework, i.e., fMRI in a matrix and EEG in a 3rd order tensor, and only one mode of variability such as participant or time have been used as the common loading vectors for the decomposition of the two modalities.…”

Section: Discussionmentioning

confidence: 99%

Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

Jonmohamadi

Muthukumaraswamy²,

Chen³

et al. 2019

Preprint

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The fusion of simultaneously recorded EEG and fMRI data is of great value to neuroscience research due to the complementary properties of the individual modalities. Traditionally, techniques such as PCA and ICA, which rely on strong strong nonphysiological assumptions such as orthogonality and statistical independence, have been used for this purpose. Recently, tensor decomposition techniques such as parallel factor analysis have gained more popularity in neuroimaging applications as they are able to inherently contain the multidimensionality of neuroimaging data and achieve uniqueness in decomposition without imposing strong assumptions. Previously, the coupled matrix-tensor decomposition (CMTD) has been applied for the fusion of the EEG and fMRI. Only recently the coupled tensor-tensor decomposition (CTTD) has been proposed. Here for the first time, we propose the use of CTTD of a 4th order EEG tensor (space, time, frequency, and participant) and 3rd order fMRI tensor (space, time, participant), coupled partially in time and participant domains, for the extraction of the task related features in both modalities. We used both the sensor-level and source-level EEG for the coupling. The phase shifted paradigm signals were incorporated as the temporal initializers of the CTTD to extract the task related features. The validation of the approach is demonstrated on simultaneous EEG-fMRI recordings from six participants performing an N-Back memory task. The EEG and fMRI tensors were coupled in 9 components out of which 7 components had a high correlation (more than 0.85) with the task. The result of the fusion recapitulates the well-known attention network as being positively, and the default mode network working negatively time-locked to the memory task.

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Section: Discussionmentioning

confidence: 99%

Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

Jonmohamadi

Muthukumaraswamy²,

Chen³

et al. 2019

Preprint

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“…The coupled CP decomposition jointly analyzes heterogeneous data sets or signals and identifies their shared underlying components. The facts that the heterogeneous signals can have different nature and dimensions and that the uniqueness properties are relaxed make the coupled CP decomposition a very practical tool for array (Sørensen et al, 2015(Sørensen et al, , 2018Sørensen and De Lathauwer, 2017a,b), audio (Zou et al, 2017), and biomedical signal progressing (Becker et al, 2012;Acar et al, 2013Acar et al, , 2015Papalexakis et al, 2014;Rivet et al, 2015;Naskovska et al, 2017a,b;Van Eyndhoven et al, 2017).…”

Section: If Two Low-rank Noiseless Tensorsmentioning

confidence: 99%

Coupled CP Decomposition of Simultaneous MEG-EEG Signals for Differentiating Oscillators During Photic Driving

et al. 2020

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Magnetoencephalography (MEG) and electroencephalography (EEG) are contemporary methods to investigate the function and organization of the brain. Simultaneously acquired MEG-EEG data are inherently multi-dimensional and exhibit coupling. This study uses a coupled tensor decomposition to extract the signal sources from MEG-EEG during intermittent photic stimulation (IPS). We employ the Coupled Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (C-SECSI). After comparing its performance with alternative methods using simulated benchmark data, we apply it to MEG-EEG recordings of 12 participants during IPS with fractions of the individual alpha frequency between 0.4 and 1.3. In the benchmark tests, C-SECSI is more accurate than SECSI and alternative methods, especially in ill-conditioned scenarios, e.g., involving collinear factors or noise sources with different variances. The component field-maps allow us to separate physiologically meaningful oscillations of visually evoked brain activity from background signals. The frequency signatures of the components identify either an entrainment to the respective stimulation frequency or its first harmonic, or an oscillation in the individual alpha band or theta band. In the group analysis of both, MEG and EEG data, we observe a reciprocal relationship between alpha and theta band oscillations. The coupled tensor decomposition using C-SECSI is a robust, powerful method for the extraction of physiologically meaningful sources from multidimensional biomedical data. Unsupervised signal source extraction is an essential solution for rendering advanced multi-modal signal acquisition technology accessible to clinical diagnostics, pre-surgical planning, and brain computer interface applications.

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“…The rationale behind these methods is to employ objective functions for decomposition of the fMRI signal with constraints based on information from EEG (or vice versa). Recently, the emphasis has been turned to "true" fusion, e.g., [19,20,21,22,23,5], where the decomposition of the data from each modality can influence the other using all the common information that may exist. During optimization, the factors, which have been identified as shared, are appropriately "coupled" and thus a bridge between the two modalities is established.…”

Section: Categorization Of Data Fusionmentioning

confidence: 99%

“…Furthermore, different methods can be used to account for a possible misspecification of the HRF. Constraining the HRF to a class of "plausible" waveforms and estimating the optimal one from the data itself has been proposed in [22] for the single-subject case. Such approaches will be called "flexible".…”

Section: High Levelmentioning

confidence: 99%

Fusion of EEG and fMRI via Soft Coupled Tensor Decompositions

Chatzichristos

Davies

Escudero

et al. 2018

2018 26th European Signal Processing Conference (EUSIPCO)

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Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this preprint, the problem of jointly analyzing electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) data is considered. Jointly analyzing EEG and fMRI measurements is highly beneficial for studying brain function because these modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The fusion methods reported so far ignore the underlying multi-way nature of the data in at least one of the modalities and/or rely on very strong assumptions about the relation of the two datasets. In this preprint, these two points are addressed by adopting for the first time tensor models in the two modalities while also exploring double coupled tensor decompositions and by following soft and flexible coupling approaches to implement the multi-modal analysis. To cope with the Event Related Potential (ERP) variability in EEG, the PARAFAC2 model is adopted. The results obtained are compared against those of parallel Independent Component Analysis (ICA) and hard coupling alternatives in both simulated and real data. Our results confirm the superiority of tensorial methods over methods based on ICA. In scenarios that do not meet the assumptions underlying hard coupling, the advantage of soft and flexible coupled decompositions is clearly demonstrated.

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Flexible fusion of electroencephalography and functional magnetic resonance imaging: Revealing neural-hemodynamic coupling through structured matrix-tensor factorization

Cited by 23 publications

References 18 publications

Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

Coupled CP Decomposition of Simultaneous MEG-EEG Signals for Differentiating Oscillators During Photic Driving

Fusion of EEG and fMRI via Soft Coupled Tensor Decompositions

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