Mr. Hendrik Santosa Profile

Hendrik Santosa

at Univ of Pittsburgh

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Publications (6)

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 | 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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