A Programmable Biomimetic Testbed for Evaluation of Functional Brain Activation

Wednesday, June 13, 2012: 1:30 PM  - 3:30 PM 
Room: Plenary Hall, Level 4 

Poster No:

732 

On Display:

Wednesday, June 13 & Thursday, June 14 

Authors:

Randall Barbour1, Randall Andronica1, S. S. Barbour2, Harry Graber1, Daniel Lee1, Pflieger Mark3, J. D. Nicoles3, Yaling Pei2, Douglas Pfeil1, Christoph Schmitz4, Anandita Tyagi2, Yong Xu1

Institutions:

1SUNY Downstate Medical Center, Brooklyn, NY, 2NIRx Medical Technologies LLC, Glen Head, NY, 3Source Signal Imaging Inc, San Diego, CA, 44NIRx Medizintechnik GmbH, Berlin, Germany

Poster Presenter:

Randall Barbour, Ph. D.   -  Contact Me
SUNY Downstate Medical Center
Brooklyn, NY

Introduction:

Near infrared spectroscopy (NIRS) and electrical encephalography (EEG) are complementary technologies that support exploration of the principal hemodynamic and bioelectric phenomenologies associated with neuroactivation. To harness the full potential of these methods, it is important that elements of data collection, information extraction and feature validation be attended to in a comprehensive manner. While direct measures are well developed and are easily appreciated, extraction and validation of nonobservable features, such as effective connectivity, is considerably more challenging. Ideally, derived information would be mapped to a common brain space, while validation would be supported by resources that mimic the full spectrum of expected behaviors for both sensing domains, as least on a macroscopic level. Here we report on efforts to extend capabilities pertaining to extraction and validation of information derived from measures of these domains. Building on previously reported work [1], validation is accomplished using a longitudinally stabilized, biomimetic head form containing programmable source elements that can be precisely mimic simple or complex hemodynamic and bioelectric behaviors.

Methods:

Anthropomorphic Dynamic Phantom: The earlier approach [1] was extended by introducing a "brain" (Fig. 1A) composed of a hydrogel-based biopolymer with saline added to mimic impedances typical of real tissue. Stabilizers were included to inhibit bacterial and mold growth, and TiO2 and India Ink are added to provide physiologically plausible optical coefficients. The embedded source array (Fig. 1B) comprises electrochromic cells as optical modulators to mimic the hemoglobin signal and dipoles for generation of bioelectric signals.
Analysis and Mapping Environment: For processing of NIRS data, we have introduced NAVI [2,3], a MATLAB-based environment that supports data transformations common to evaluation of bioelectric and hemodynamic studies and atlas-based parametric mapping with full 3D tomographic capabilities. The package includes modules for image formation, display and analysis and a number of utilities modeled principally after strategies supported by SPM8 [4,5]: GLM-based parametric mapping for detected hemodynamic response functions; atlas-based mapping of image findings onto identified brain regions, anatomical labeling functionality; and examination of effective connectivity via strategies such as dynamic causal modeling (DCM) [6].
The analysis environment also includes the EMSE Suite comprising modules for integrating EEG with structural MRI [7]. A flowchart of the integrated analysis environment is shown in Fig. 2. Dotted arrows indicate the points where structural information and functional features derived from inverse-problem computations will feed back into available forward-problem solvers.
Supporting Image: fig1.png
Supporting Image: fig2.png
 

Results:

Solutions to either the NIRS or EEG inverse problem ideally would be based on knowledge of individualized boundary conditions. To support instances where such information is not available, we have implemented an alternative solution wherein a selected atlas is substituted for individual-subject structural information. The developed human-head atlas is summarized in Fig. 3. A montage of standard EEG electrode positions is included (Fig. 3A) to guide placement of NIRS optodes. A depiction of functional image data interpolated onto the cortical surface, along with a montage of the corresponding sensor array, is shown in Fig. 3B. The array information also is displayed the on the atlas segment selected for image reconstruction, as shown in Fig. 3C.
Supporting Image: fig3.png
 

Conclusions:

The full capabilities of the Testbed have been explored by programming the phantom to support causally directed activities involving three different head regions. Volume-averaged image time series from the three colored regions in Fig. 4 were used as input for DCM model-selection computations and showed that the preferred model was selected.
Supporting Image: fig4.png
 

Imaging Methods:

Multi-Modal Imaging

Abstract Information


References

[1] R. L. Barbour, R. Ansari, R. Al abdi, H. L. Graber, M. B. Levin, Y. Pei, C. H. Schmitz, and Y. Xu, “Validation of near infrared spectroscopic (NIRS) imaging using programmable phantoms,” Paper 687002 in Design and Performance Validation of Phantoms Used in Conjunction with Optical Measurements of Tissue (Proceedings of SPIE, Vol. 6870), R.J. Nordstrom, Ed. (2008).
[2] Y. Pei, Z. Wang, and R.L. Barbour, “NAVI: A problem solving environment (PSE) for NIRS data analysis,” Poster No. 685 T-AM at Human Brain Mapping 2006 (Florence, Italy, June 11-15, 2006).
[3] NIRx fNIRS Analysis Environment User’s guide. Available at: http://otg.downstate.edu/Publication/NIRxPackage_02.pdf.
[4] W. D. Penny, K. J. Friston, J. T. Ashburner, S. J. Kiebel, and T. E. Nichols, Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, 2006.
[5] N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” NeuroImage 15, 273-289 (2002).
[6] K. Friston, “Causal modeling and brain connectivity in functional magnetic resonance imaging,” PLoS Biology 7, 0220-0225 (2009).
[7] Source Signal Imaging, Inc., EMSE®Suite User Manual, Version 5.4, San Diego, 2011. Available at: ftp://ftp.sourcesignal.com/manuals/