Dr. Theodore J. Huppert Profile

Dr. Theodore J. Huppert

Associate Professor at University of Pittsburgh

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Author

Publications (18)

SPIE Journal Paper | 10 February 2023 Open Access

Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model

Xuetong Zhai, Hendrik Santosa, Robert T. Krafty, Theodore J. Huppert

NPh, Vol. 10, Issue 02, 023516, (February 2023) https://doi.org/10.1117/12.10.1117/1.NPh.10.2.023516

KEYWORDS: Image restoration, Brain, Voxels, Neuroimaging, Data modeling, Near infrared spectroscopy, Computer simulations, Neurophotonics, Statistical inference, Statistical analysis

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SPIE Journal Paper | 9 December 2022 Open Access

Introduction to the shared near infrared spectroscopy format

Stephen Tucker, Jay Dubb, Sreekanth Kura, Alexander von Lühmann, Robert Franke, Jörn M. Horschig, Samuel Powell, Robert Oostenveld, Michael Lührs, Édouard Delaire, Zahra Aghajan, Hanseok Yun, Meryem Yücel, Qianqian Fang, Theodore Huppert, Blaise deB. Frederick, Luca Pollonini, David Boas, Robert Luke

NPh, Vol. 10, Issue 01, 013507, (December 2022) https://doi.org/10.1117/12.10.1117/1.NPh.10.1.013507

KEYWORDS: Near infrared spectroscopy, Data acquisition, Standards development, Software development, Neuroimaging, Neurophotonics, Design and modelling, MATLAB, Data storage, Compliance

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SPIE Journal Paper | 18 August 2022 Open Access

Correction of global physiology in resting-state functional near-infrared spectroscopy

Pradyumna Lanka, Heather Bortfeld, Theodore Huppert

NPh, Vol. 9, Issue 03, 035003, (August 2022) https://doi.org/10.1117/12.10.1117/1.NPh.9.3.035003

KEYWORDS: Autoregressive models, Physiology, Data modeling, Motion models, Statistical analysis, Interference (communication), Statistical modeling, Neurophotonics, Brain, Signal to noise ratio

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Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear.Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data.Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance.Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution.Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

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SPIE Journal Paper | 1 January 2021 Open Access

Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography

Jiaming Cao, Theodore Huppert, Pulkit Grover, Jana Kainerstorfer

NPh, Vol. 8, Issue 01, 015002, (January 2021) https://doi.org/10.1117/12.10.1117/1.NPh.8.1.015002

KEYWORDS: Electroencephalography, Reconstruction algorithms, Resolution enhancement technologies, Electrodes, Brain, Image resolution, Spatial resolution, Sensors, Hemodynamics, Neurophotonics

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SPIE Journal Paper | 23 September 2020 Open Access

Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies

Hendrik Santosa, Xuetong Zhai, Frank Fishburn, Patrick Sparto, Theodore Huppert

NPh, Vol. 7, Issue 03, 035009, (September 2020) https://doi.org/10.1117/12.10.1117/1.NPh.7.3.035009

KEYWORDS: Data modeling, Statistical analysis, Brain, Autoregressive models, Error analysis, Neurophotonics, Linear filtering, Principal component analysis, Statistical modeling, Performance modeling

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SPIE Journal Paper | 23 September 2020 Open Access

Using anatomically defined regions-of-interest to adjust for head-size and probe alignment in functional near-infrared spectroscopy

Xuetong Zhai, Hendrik Santosa, Theodore Huppert

NPh, Vol. 7, Issue 03, 035008, (September 2020) https://doi.org/10.1117/12.10.1117/1.NPh.7.3.035008

KEYWORDS: Head, Error analysis, Brain, Statistical analysis, Neurophotonics, Sensors, Image registration, Tissue optics, Channel projecting optics, Monte Carlo methods

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Significance: Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim: We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task. Approach: We use the optical forward model to project the underlying brain-space ROI into a tapered contrast vector, which defines the relative weighting of the fNIRS channels contributing to the ROI and allows us to test the null hypothesis of no brain activity in this region during a functional task. We demonstrate this method through simulation and compare the sensitivity-specificity of this approach to other conventional methods. Results: We examine the performance of this method in the scenario where head size and probe registration are both an accurately known parameters and where this is subject to unknown experimental errors. This method is compared with the performance of the conventional method using 364 different simulation parameter combinations. Conclusion: The proposed method is always recommended in ROI-based analysis, since it significantly improves the analysis performance without a localizer task, wherever the fNIRS probe registration is known or unknown.

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SPIE Journal Paper | 30 May 2019 Open Access

Investigation of the sensitivity-specificity of canonical- and deconvolution-based linear models in evoked functional near-infrared spectroscopy

Hendrik Santosa, Frank Fishburn, Xuetong Zhai, Theodore Huppert

NPh, Vol. 6, Issue 02, 025009, (May 2019) https://doi.org/10.1117/12.10.1117/1.NPh.6.2.025009

KEYWORDS: Signal to noise ratio, Autoregressive models, Data modeling, Hemodynamics, Deconvolution, Brain, Statistical analysis, Statistical modeling, Functional magnetic resonance imaging, Performance modeling

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SPIE Journal Paper | 18 September 2017 Open Access

Investigation of the sensitivity of functional near-infrared spectroscopy brain imaging to anatomical variations in 5- to 11-year-old children

Ashley Whiteman, Hendrik Santosa, Daniel Chen, Susan Perlman, Theodore Huppert

NPh, Vol. 5, Issue 01, 011009, (September 2017) https://doi.org/10.1117/12.10.1117/1.NPh.5.1.011009

KEYWORDS: Brain, Head, Near infrared spectroscopy, Neurophotonics, Visualization, Monte Carlo methods, Tissue optics, Magnetic resonance imaging, Skull, Sensors

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SPIE Journal Paper | 10 May 2017 Open Access

Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy

Hendrik Santosa, Ardalan Aarabi, Susan Perlman, Theodore Huppert

JBO, Vol. 22, Issue 05, 055002, (May 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.5.055002

KEYWORDS: Near infrared spectroscopy, Statistical analysis, Neuroimaging, Optical testing, Blood, Cognitive neuroscience, Neural networks, Brain, Motion analysis, Physiology

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SPIE Journal Paper | 6 June 2016 Open Access

Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis

Ardalan Aarabi, Theodore Huppert

NPh, Vol. 3, Issue 02, 025004, (June 2016) https://doi.org/10.1117/12.10.1117/1.NPh.3.2.025004

KEYWORDS: Brain, Physiology, Independent component analysis, Hemodynamics, Interference (communication), Blood pressure, Principal component analysis, Near infrared spectroscopy, Neurophotonics, Electronic filtering

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SPIE Journal Paper | 23 May 2016 Open Access

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

Jeffrey Barker, Andrea Rosso, Patrick Sparto, Theodore Huppert

NPh, Vol. 3, Issue 03, 031410, (May 2016) https://doi.org/10.1117/12.10.1117/1.NPh.3.3.031410

KEYWORDS: Autoregressive models, Data modeling, Filtering (signal processing), Brain, Motion models, Performance modeling, Error analysis, Statistical analysis, Computer simulations, Statistical modeling

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SPIE Journal Paper | 2 March 2016 Open Access

Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy

Theodore Huppert

NPh, Vol. 3, Issue 01, 010401, (March 2016) https://doi.org/10.1117/12.10.1117/1.NPh.3.1.010401

KEYWORDS: Data modeling, Statistical analysis, Autoregressive models, Statistical modeling, Motion models, Brain, Functional magnetic resonance imaging, Near infrared spectroscopy, Linear filtering, Error analysis

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SPIE Journal Paper | 1 July 2009 Open Access

Sensitivity of neural-hemodynamic coupling to alterations in cerebral blood flow during hypercapnia

Theodore Huppert, Phill Jones, Anna Devor, Andrew Dunn, Ivan Teng, Anders Dale, David Boas

JBO, Vol. 14, Issue 04, 044038, (July 2009) https://doi.org/10.1117/12.10.1117/1.3210779

KEYWORDS: Blood circulation, Oxygen, Hemodynamics, Blood, Cerebral blood flow, Brain, Mode conditioning cables, Data modeling, Physiology, Resistance

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SPIE Journal Paper | 1 September 2008 Open Access

Direct estimation of evoked hemoglobin changes by multimodality fusion imaging

Solomon Diamond, David Boas, Theodore Huppert

JBO, Vol. 13, Issue 05, 054031, (September 2008) https://doi.org/10.1117/12.10.1117/1.2976432

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SPIE Journal Paper | 1 January 2007 Open Access

Near-infrared spectroscopy measurement of the pulsatile component of cerebral blood flow and volume from arterial oscillations

George Themelis, Helen D'Arceuil, Solomon Diamond, Sonal Thaker, Theodore Huppert, David Boas, Maria Angela Franceschini

JBO, Vol. 12, Issue 01, 014033, (January 2007) https://doi.org/10.1117/12.10.1117/1.2710250

KEYWORDS: Near infrared spectroscopy, Blood circulation, Skin, Doppler effect, Blood, Cerebral blood flow, Brain, Hemodynamics, Head, Oximeters

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SPIE Journal Paper | 1 November 2006 Open Access

Quantitative spatial comparison of diffuse optical imaging with blood oxygen level-dependent and arterial spin labeling-based functional magnetic resonance imaging

Theodore Huppert, Richard Hoge, Anders Dale, Maria Angela Franceschini, David Boas

JBO, Vol. 11, Issue 06, 064018, (November 2006) https://doi.org/10.1117/12.10.1117/1.2400910

KEYWORDS: Functional magnetic resonance imaging, Optical testing, Geometrical optics, Diffuse optical imaging, Magnetic resonance imaging, Monte Carlo methods, Absorption, Hemodynamics, Head, Inverse optics

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SPIE Journal Paper | 1 September 2006 Open Access

Diffuse optical imaging of the whole head

Maria Angela Franceschini, Danny Joseph, Theodore Huppert, Solomon Diamond, David Boas

JBO, Vol. 11, Issue 05, 054007, (September 2006) https://doi.org/10.1117/12.10.1117/1.2363365

KEYWORDS: Sensors, Head, Visualization, Diffuse optical imaging, Hemodynamics, Brain, Principal component analysis, Blood pressure, Signal to noise ratio, Imaging systems

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Proceedings Article | 28 April 2005 Paper

A spatial-temporal comparison of fMRI and NIRS hemodynamic responses to motor stimuli in adult humans

R. Hoge, M. Franceschini, T. Huppert, D. Boas

Proceedings Volume 5693, (2005) https://doi.org/10.1117/12.612143

KEYWORDS: Near infrared spectroscopy, Functional magnetic resonance imaging, Hemodynamics, Magnetic resonance imaging, Brain, Monte Carlo methods, Sensors, Spatial resolution, Neuroimaging, Photons

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Showing 5 of 18 publications

Conference Committee Involvement (4)

Multimodal Biomedical Imaging XII

28 January 2017 | San Francisco, California, United States

Multimodal Biomedical Imaging XI

13 February 2016 | San Francisco, California, United States

Multimodal Biomedical Imaging X

7 February 2015 | San Francisco, California, United States

Multimodal Biomedical Imaging IX

1 February 2014 | San Francisco, California, United States

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