Augmenting interictal mapping with neurovascular coupling biomarkers by
structured factorization of epileptic EEG and fMRI data
- URL: http://arxiv.org/abs/2004.14185v1
- Date: Wed, 29 Apr 2020 13:27:45 GMT
- Title: Augmenting interictal mapping with neurovascular coupling biomarkers by
structured factorization of epileptic EEG and fMRI data
- Authors: Simon Van Eyndhoven, Patrick Dupont, Simon Tousseyn, Nico Vervliet,
Wim Van Paesschen, Sabine Van Huffel, Borb\'ala Hunyadi
- Abstract summary: We develop a novel structured matrix-tensor factorization for EEG-fMRI analysis.
We show that the extracted source signatures provide a sensitive localization of the ictal onset zone.
We also show that complementary localizing information can be derived from the spatial variation of the hemodynamic response.
- Score: 3.2268407474728957
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: EEG-correlated fMRI analysis is widely used to detect regional blood oxygen
level dependent fluctuations that are significantly synchronized to interictal
epileptic discharges, which can provide evidence for localizing the ictal onset
zone. However, such an asymmetrical, 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. We show
that the extracted source signatures provide a sensitive localization of the
ictal onset zone, and, moreover, that complementary localizing information can
be derived from the spatial variation of the hemodynamic response. Hence, this
multivariate, multimodal factorization provides two useful sets of EEG-fMRI
biomarkers, which can inform the presurgical evaluation of epilepsy. We make
all code required to perform the computations available.
Related papers
- CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation [6.682531937245544]
This paper proposes the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging.
The proposed framework establishes a new paradigm for cross-modal synthesis of neuroimaging.
It shows promise in medical applications such as improving Parkinson's disease prediction and identifying abnormal brain regions.
arXiv Detail & Related papers (2024-07-16T11:31:38Z) - Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
In this study, we first construct the brain-effective network via the dynamic causal model.
We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE)
This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
arXiv Detail & Related papers (2024-05-21T20:37:07Z) - A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics.
We generate high-quality synthetic fMRI data based on user-supplied demographics.
arXiv Detail & Related papers (2024-05-13T17:49:20Z) - DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for
Structural Connectivity Prediction from Brain fMRI [15.40111168345568]
It is challenging to bridge the reliable non-linear mapping relations between structural connectivity (SC) and functional magnetic resonance imaging (fMRI)
A novel diffusision generative adversarial network-based fMRI-to-SC model is proposed to predict SC from brain fMRI in an end-to-end manner.
arXiv Detail & Related papers (2023-09-28T06:55:50Z) - Inferring latent neural sources via deep transcoding of simultaneously
acquired EEG and fMRI [12.588880677192975]
Simultaneous EEG-fMRI is a neuroimaging technique that provides complementary spatial and temporal resolution.
We develop a symmetric method consisting of a cyclic convolutional transcoder that transcodes EEG to fMRI.
We quantify, for both the simulated and real EEG-fMRI data, how well the modalities can be transcoded from one to another.
arXiv Detail & Related papers (2022-11-27T23:44:16Z) - fMRI from EEG is only Deep Learning away: the use of interpretable DL to
unravel EEG-fMRI relationships [68.8204255655161]
We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data.
We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
arXiv Detail & Related papers (2022-10-23T15:11:37Z) - Spatio-temporally separable non-linear latent factor learning: an
application to somatomotor cortex fMRI data [0.0]
Models of fMRI data that can perform whole-brain discovery of latent factors are understudied.
New methods for efficient spatial weight-sharing are critical to deal with the high dimensionality of the data and the presence of noise.
Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task.
arXiv Detail & Related papers (2022-05-26T21:30:22Z) - G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for
Biomarker Identification and Disease Classification [49.53651166356737]
We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers.
We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
arXiv Detail & Related papers (2021-01-27T19:28:04Z) - Statistical control for spatio-temporal MEG/EEG source imaging with
desparsified multi-task Lasso [102.84915019938413]
Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques.
The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge.
We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
arXiv Detail & Related papers (2020-09-29T21:17:16Z) - Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR
Images using a GAN [59.60954255038335]
The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators.
Experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
arXiv Detail & Related papers (2020-06-26T02:50:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.