Abstract
In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semi-automated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an ‘arbitrary’ individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck—two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5mm3 resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the ‘ground truth’) is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an ‘individualized’ BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption.
Highlights
Today, a multitude of tools are available to non-invasively ‘read and write the brain.’ Brain imaging technologies such as electroencephalography (EEG) allow one to track the activity of neuronal populations with millisecond precision
Besides the ICBM-NY, we evaluate the following head models against the ground truth provided by REF Finite element models (FEM): a Boundary element models (BEM) and a spherical harmonics expansions (SHE) electrical model of the reference anatomy, FEMs of three other individuals' anatomies, a BEM of the ICBM152 anatomy (ICBM BEM), and an ‘individualized’ BEM of the ICBM152 anatomy
ICBM-NY provides the performance of the proposed New York Head when tested on the four individual FEMs
Summary
A multitude of tools are available to non-invasively ‘read and write the brain.’ Brain imaging technologies such as electroencephalography (EEG) allow one to track the activity of neuronal populations with millisecond precision. A multitude of tools are available to non-invasively ‘read and write the brain.’ Brain imaging technologies such as electroencephalography (EEG) allow one to track the activity of neuronal populations with millisecond precision. Laboratory for Intelligent Imaging and Neural Computing, Columbia University, New York, NY 10027, USA They rely on a volume conductor model of the human head to establish the connection between structures in the brain and electrodes located on the scalp. According to the reciprocity theorem, the two forward models are identical (Rush and Driscoll, 1969), so that the terms ‘forward model’ and ‘lead field,’ as well as ‘volume conductor model’ and ‘head model,’ are interchangeable The accuracy of such a model determines the precision of both source localization in EEG and targeting of specific brain structures using tES
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