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Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number117814
Pages (from-to)117814
Early online date4 Feb 2021
E-pub ahead of print4 Feb 2021
Published1 May 2021

Bibliographical note

Funding Information: This work was supported by the MRC (MR/J01107X/1), the EPSRC (EP/H046410/1, the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging ( EP/L016478/1 ), the Wolfson Foundation, Centre for Medical Engineering King’s College London, and the Wellcome Trust. Funding Information: This work was supported by the MRC (MR/J01107X/1), the EPSRC (EP/H046410/1, the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1), the Wolfson Foundation, Centre for Medical Engineering King's College London, and the Wellcome Trust. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.

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