To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis.@en

EEG-correlated fMRI analysis is widely used to detect regional BOLD fluctuations that are synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, the typical, asymmetrical and mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular coupling over patients and brain regions. We aim to overcome these drawbacks in a data-driven manner by means of a novel structured matrix-tensor factorization: the single-subject EEG data (represented as a third-order spectrogram tensor) and fMRI data (represented as a spatiotemporal BOLD signal matrix) are jointly decomposed into a superposition of several sources, characterized by space-time-frequency profiles. In the shared temporal mode, Toeplitz-structured factors account for a spatially specific, neurovascular ‘bridge’ between the EEG and fMRI temporal fluctuations, capturing the hemodynamic response's variability over brain regions. By analyzing interictal data from twelve patients, we show that the extracted source signatures provide a sensitive localization of the ictal onset zone (10/12). Moreover, complementary parts of the IOZ can be uncovered by inspecting those regions with the most deviant neurovascular coupling, as quantified by two entropy-like metrics of the hemodynamic response function waveforms (9/12). Hence, this multivariate, multimodal factorization provides two useful sets of EEG-fMRI biomarkers, which can assist the presurgical evaluation of epilepsy. We make all code required to perform the computations available at https://github.com/svaneynd/structured-cmtf.@en

Functional ultrasound (fUS) is an exciting new neuroimaging technique that is able to record brain activity similar to functional magnetic resonance imaging, yet with higher spatiotemporal resolution and at lower cost. We consider the problem of jointly estimating the underlying neural sources and the hemodynamic response function (HRF) from fUS recordings. We propose to model the measured voxel time-series as a convolutive mixture of multiple source signals and solve the blind deconvolution problem via block-term decomposition. This allows us to estimate both the source time courses and a different HRF for each voxel and source combination, which accounts for the variability of HRF across different brain regions and events respectively. The proposed approach is proven to be robust against noise via simulations and further validated on real fUS data by performing a visual experiment on a mouse. The obtained results show that the proposed method is able to recover the timings of the visual paradigm.@en