Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

Abstract

In the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Frequency‐dependent modulation of neural oscillations across the gait cycle

“…The temporal jitter between hdEEG data and acceleration signals was quantified experimentally and resulted in being below 5 ms. Immediately after the experiment, we acquired a 3D scan of the participant's head using an iPad (Apple Inc., USA) equipped with Structure Sensor (Occipital Inc., Boulder, CO) to extract the locations of the hdEEG electrodes (Taberna, Marino, et al, 2019 ; Taberna, Samogin, & Mantini, 2021 ).…”

“…In recent years, novel technical developments have opened the way for the use of EEG as a brain imaging tool (Michel & Murray, 2012 ). Specifically, the application of high‐density electroencephalography (hdEEG) (Liu et al, 2015 ) in combination with novel realistic head modeling methods (Taberna, Guarnieri, & Mantini, 2019 ; Taberna, Marino, et al, 2019 ; Taberna, Samogin, & Mantini, 2021 ) has permitted more accurate characterization of neural oscillations in the source space. For instance, our group has used hdEEG in previous studies for analyzing the functional dissociation of body movements (Zhao et al, 2019 ), the topology of resting‐state brain networks (Liu et al, 2017 ; Liu et al, 2018 ), and the assessment of frequency‐dependent connectivity in the human brain (Samogin et al, 2019 , 2020 ).…”

Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques

“…To perform accurate EEG/MEG source imaging, a realistic head model of the subject undergoing the experimental investigation is needed (Cho et al, 2015;Morales et al, 2019). This head model is derived from a whole-head image of the subject, and it relies on multiple sources of information including electrode positions (Marino et al, 2016;Taberna et al, 2019), tissue geometry (Taberna et al, 2021), and conductive properties. With this work, we aim at contributing to the optimization of the latest aspect, by proposing a methodology for subject-specific head-modeling.…”

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging

“…For each participant, a 12-layer realistic head volume conduction model was created for the neural activity reconstruction step. Specifically, three steps were followed: electrodes position detection and coregistration; head tissue segmentation; and leadfield matrix calculation (Taberna et al, 2021). (1) Electrodes position detection and coregistration.…”

“…(2) Head tissue segmentation. Using the MR-TIM software (Taberna et al, 2021), we segmented the individual MR image to 12 tissue layers: skin, eyes, muscle, fat, spongy bone, compact bone, cortical/subcortical gray matter, cerebellar gray matter, cortical/subcortical white matter, cerebellar white matter, cerebrospinal fluid, and brain stem. The conductivity value of each tissue layer was defined according to relevant studies (Haueisen et al, 1997;Holdefer et al, 2006).…”