REGIONAL HUMAN MODELS
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PHM Repository
The Population Head Model (PHM) Repository: a collection of 50 de-identified head models representing brain variability in males/females aged 22-35 years.

Population Head Model Repository

Developed at Iowa State University, the Population Head Model (PHM) Repository consists of 50 unique head models constructed from MRI images distributed through the Human Connectome Project [1]. The initial reason for creating these head models came from the finding in transcranial magnetic stimulation (TMS) studies that individuals have unique responses to TMS and that differences in response can be explained, in part, by unique geometrical features of the anatomies stimulated by TMS [2]. The creators of these head models wanted to better understand the variation in subject response to stimulation from time varying magnetic fields and created these head models for use in finite element simulations to further understand the biophysical determinants that could be responsible for this variation in subject response [3].

All models in the PHM Repository were developed using the SimNIBS pipeline [4], which utilizes FreeSurfer [5] and FSL [6] to develop heterogeneous head models from T1 and T2 weighted MRI images with 0.7 mm isotropic voxels. SimNIBS utilizes FreeSurfer to generate surfaces for grey matter, white matter, and cerebellum and utilizes FSL to generate surfaces for skin, skull, cerebrospinal fluid, and ventricles. The initial development of these head models was completely autonomous, but each model in the current version of the PHM Repository was checked for unrealistic anatomical features, and small edits were made to improve the accuracy of select areas of the skin and skull mesh.

Models are developed from de-identified images of participants in the Human Connectome Project aged 22-35 years old. All participants are healthy individuals and participants include both males and females.

The PHM Repository is most useful for researchers looking to utilize all the anatomical variability seen in a healthy young adult population. This standard anatomical variation has been shown through simulations to be a very important factor driving the dose of noninvasive brain stimulation [3]. Future studies using simulations to aid in the design of new technologies or assessment of questions related to health, safety, and treatment of disease may find these models useful, especially within the field of neuromodulation. This development aims to provide researchers with a more in depth view of the anatomical variation seen within a healthy population of young adults, providing a broader range of the potential effects of expected anatomical variation between individuals. Further, this development aims to let researchers explore how anatomical features are relevant for computer modeling at the interface of physics and biology.

 

Version History

Version Specifications Download
PHM V1.0  Each model contains following tissues. DOI: 10.13099/VIP-PHM-V1.0

License Agreement

 

Requests & Inquiries

This model is free of charge for everyone (except for handling fees). To obtain the PHM Repository, please click on the DOI number above. Please address all inquiries to the Virtual Population Group.

 

Acknowledgements

This work was supported by Iowa State University, the Palmer Endowment Fund and the Carver Charitable Trust. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

 

Reference for citation

Investigational Effect of Brain-Scalp Distance on the Efficacy of Transcranial Magnetic Stimulation Treatment in Depression

E. G. Lee, W. Duffy, R. L. Hadimani, M. Waris, W. Siddiqui, F. Islam, M. Rajamani, R. Nathan, and D. C. Jiles, in IEEE Trans. Magn., vol. 52, no. 7, pp. 1-4: 2016

Impact of non-brain anatomy and coil orientation on inter-and intra-subject variability in TMS at midline

E. G. Lee, P. Rastogi, R. L. Hadimani, D. C. Jiles, and J. A. Camprodon, in Clinical Neurophysiology, Vol. 129, no. 9, pp 1873-83: 2018


 

Additional References

[1] The Human Connectome Project: A data acquisition perspective

D. C. Van Essen, K. Ugurbil, E. Auerbach, D. Barch, T. E. J. Behrens, R. Bucholz, a. Chang, L. Chen, M. Corbetta, S. W. Curtiss, S. Della Penna, D. Feinberg, M. F. Glasser, N. Harel, a. C. Heath, L. Larson-Prior, D. Marcus, G. Michalareas, S. Moeller, R. Oostenveld, S. E. Petersen, F. Prior, B. L. Schlaggar, S. M. Smith, a. Z. Snyder, J. Xu, and E. Yacoub, Neuroimage, vol. 62, no. 4, pp. 2222–2231, 2012.

[2] Distance-adjusted motor threshold for transcranial magnetic stimulation

M. G. Stokes, C. D. Chambers, I. C. Gould, T. English, E. McNaught, O. McDonald, and J. B. Mattingley, Clin. Neurophysiol., vol. 118, no. 7, pp. 1617–1625, 2007.

[3] Investigational Effect of Brain-Scalp Distance on the Efficacy of Transcranial Magnetic Stimulation Treatment in Depression

E. Lee, W. Duffy, R. Hadimani, M. Waris, W. Siddiqui, F. Islam, M. Rajamani, R. Nathan, and D. Jiles, IEEE Trans. Magn., vol. 9464, no. c, pp. 1–1, 2016.

[4] Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models

M. Windhoff, A. Opitz, and A. Thielscher, Hum. Brain Mapp., vol. 34, no. 4, pp. 923–935, 2013.

[5] Cortical surface-based analysis. II: Inflation, flattening, a surface-based coordinate system

B. Fischl, M. I. Sereno, and A. M. Dale, Neuroimage, 1999, vol. 194, pp. 195–207, 1999.

[6] Fast robust automated brain extraction

S. M. Smith, Hum. Brain Mapp., vol. 17, no. 3, pp. 143–155, 2002.