Dr. Yuanyuan Gao Profile

Dr. Yuanyuan Gao

at Boston Univ

SPIE Involvement:

Author

Publications (5)

SPIE Journal Paper | 23 May 2023 Open Access

Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy

Yuanyuan Gao, De’Ja Rogers, Alexander von Lühmann, Antonio Ortega-Martinez, David A. Boas, Meryem Yücel

NPh, Vol. 10, Issue 02, 025007, (May 2023) https://doi.org/10.1117/12.10.1117/1.NPh.10.2.025007

KEYWORDS: Data modeling, Brain, Head, Image restoration, Diffuse optical tomography, Matrices, Performance modeling, Lithium, Neurophotonics, Brain activation

Read Abstract +

DOWNLOAD PAPER

SPIE Journal Paper | 22 October 2022 Open Access

How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS?

Antonio Ortega-Martinez, De'Ja Rogers, Jessica Anderson, Parya Farzam, Yuanyuan Gao, Bernhard Zimmermann, Meryem Yücel, David Boas

NPh, Vol. 10, Issue 01, 013504, (October 2022) https://doi.org/10.1117/12.10.1117/1.NPh.10.1.013504

KEYWORDS: Brain, Near infrared spectroscopy, Sensors, Absorption, Continuous wave operation, Monte Carlo methods, Neurophotonics, Hemodynamics, Statistical analysis, Photons

Read Abstract +

SignificanceAdvances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF.AimsWe compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM.ApproachWe augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with “short-separation regression” designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests.ResultsWe found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment.ConclusionsProperly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.

DOWNLOAD PAPER

SPIE Journal Paper | 23 April 2022 Open Access

Deep learning-based motion artifact removal in functional near-infrared spectroscopy

Yuanyuan Gao, Hanqing Chao, Lora Cavuoto, Pingkun Yan, Uwe Kruger, Jack Norfleet, Basiel Makled, Steven Schwaitzberg, Suvranu De, Xavier Intes

NPh, Vol. 9, Issue 04, 041406, (April 2022) https://doi.org/10.1117/12.10.1117/1.NPh.9.4.041406

KEYWORDS: Data modeling, Motion models, Principal component analysis, Autoregressive models, Wavelets, Near infrared spectroscopy, Performance modeling, Denoising, Computer simulations, Amplifiers

Read Abstract +

DOWNLOAD PAPER

SPIE Journal Paper | 27 July 2021 Open Access

Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments (Erratum)

Arun Nemani, Anil Kamat, Yuanyuan Gao, Meryem Yucel, Denise Gee, Clairice Cooper, Steven Schwaitzberg, Xavier Intes, Anirban Dutta, Suvranu De

NPh, Vol. 8, Issue 03, 039801, (July 2021) https://doi.org/10.1117/12.10.1117/1.NPh.8.3.039801

KEYWORDS: Virtual reality, Brain, Surgery, Spectroscopy, Neurophotonics, Medicine, Biomedical optics, Neuroimaging

Read Abstract +

DOWNLOAD PAPER

SPIE Journal Paper | 3 March 2021 Open Access

Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments

Arun Nemani, Anil Kamat, Yuanyuan Gao, Meryem Yucel, Denise Gee, Clairice Cooper, Steven Schwaitzberg, Xavier Intes, Anirban Dutta, Suvranu De

NPh, Vol. 8, Issue 01, 015008, (March 2021) https://doi.org/10.1117/12.10.1117/1.NPh.8.1.015008

KEYWORDS: Brain, Shape memory alloys, Surgery, Laparoscopy, Wavelets, Prefrontal cortex, Neurophotonics, Virtual reality, Neurovascular coupling, Sensors

Read Abstract +

Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent. Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill. Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively. Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk’s Λ for expert versus novice, F ( 10,1 ) = 7495.5, p < 0.01. Partial eta squared effect size for the inter-regional WCO metrics was found to be highest between the central prefrontal cortex (CPFC) and SMA, CPFC-SMA (η2 = 0.257). Two-tailed Mann–Whitney U tests with a 95% confidence interval showed baseline equivalence and a statistically significant (p < 0.001) difference in the CPFC-SMA WPCO metrics for the physical simulator training group (0.960 ± 0.045) versus the untrained control group (0.735 ± 0.177) following training for 10 consecutive days in addition to the pretest and posttest days. Conclusion: We show that brain functional connectivity WCO metric corresponds to surgical motor skills in the laparoscopic physical simulators. Functional connectivity between the CPFC and the SMA is lower for subjects that exhibit expert surgical motor skills than untrained subjects in laparoscopic physical simulators.

DOWNLOAD PAPER

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years