Volume 11 Issue 4 | Neurophotonics

Neurophotonics

VOL. 11 · NO. 4 | October 2024

CONTENTS

IN THIS ISSUE

Editorial (1)

Research Papers (15)

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Editorial

On the Power of Constructive Criticism

Neurophotonics, Vol. 11, Issue 04, 040101, (December 2024) https://doi.org/10.1117/1.NPh.11.4.040101

TOPICS: Neurophotonics, Engineering, Biomedical engineering, Neuroscience, Medicine, Peer review, Brain, Photonics, Physics, Chemistry

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Research Papers

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data

Costanza Lester, Laura Bonzano, Monica Biggio, Simone Cutini, Marco Bove, Sabrina Brigadoi

Neurophotonics, Vol. 11, Issue 04, 045001, (October 2024) https://doi.org/10.1117/1.NPh.11.4.045001

TOPICS: Wavelets, Contamination, Matrices, Interpolation, Tunable filters, Neurophotonics, Motion detection, Functional magnetic resonance imaging, Error analysis, Databases

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All-optics technique for monitoring absolute cerebral blood flow: validation against magnetic resonance imaging perfusion

Leena Shoemaker, Saeed Samaei, Graham Deller, Danny J. J. Wang, Daniel Milej, Keith St. Lawrence

Neurophotonics, Vol. 11, Issue 04, 045002, (October 2024) https://doi.org/10.1117/1.NPh.11.4.045002

TOPICS: Near infrared spectroscopy, Hypoxia, Cerebral blood flow, Magnetic resonance imaging, Blood circulation, Calibration, Neurophotonics, Tissues, Brain, Oxygen

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In vivo volumetric analysis of retinal vascular hemodynamics in mice with spatio-temporal optical coherence tomography

Piotr Węgrzyn, Wiktor Kulesza, Maciej Wielgo, Sławomir Tomczewski, Anna Galińska, Bartłomiej Bałamut, Katarzyna Kordecka, Onur Cetinkaya, Andrzej Foik, Robert Zawadzki, Dawid Borycki, Maciej Wojtkowski, Andrea Curatolo

Neurophotonics, Vol. 11, Issue 04, 045003, (October 2024) https://doi.org/10.1117/1.NPh.11.4.045003

TOPICS: Eye, Hemodynamics, Retina, Imaging systems, In vivo imaging, Cameras, Optical coherence tomography, Signal to noise ratio, Neurophotonics, Tunable filters

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Significance

Microcirculation and neurovascular coupling are important parameters to study in neurological and neuro-ophthalmic conditions. As the retina shares many similarities with the cerebral cortex and is optically accessible, a special focus is directed to assessing the chorioretinal structure, microvasculature, and hemodynamics of mice, a vital animal model for vision and neuroscience research.

Aim

We aim to introduce an optical imaging tool enabling in vivo volumetric mouse retinal monitoring of vascular hemodynamics with high temporal resolution.

Approach

We translated the spatio-temporal optical coherence tomography (STOC-T) technique into the field of small animal imaging by designing a new optical system that could compensate for the mouse eye refractive error. We also developed post-processing algorithms, notably for the assessment of (i) localized hemodynamics from the analysis of pulse wave–induced Doppler artifact modulation and (ii) retinal tissue displacement from phase-sensitive measurements.

Results

We acquired high-quality, in vivo volumetric mouse retina images at a rate of 113 Hz over a lateral field of view of ∼500 μm. We presented high-resolution en face images of the retinal and choroidal structure and microvasculature from various layers, after digital aberration correction. We were able to measure the pulse wave velocity in capillaries of the outer plexiform layer with a mean speed of 0.35 mm/s and identified venous and arterial pulsation frequency and phase delay. We quantified the modulation amplitudes of tissue displacement near major vessels (with peaks of 150 nm), potentially carrying information about the biomechanical properties of the retinal layers involved. Last, we identified the delays between retinal displacements due to the passing of venous and arterial pulse waves.

Conclusions

The developed STOC-T system provides insights into the hemodynamics of the mouse retina and choroid that could be beneficial in the study of neurovascular coupling and vasculature and flow speed anomalies in neurological and neuro-ophthalmic conditions.

Ecological functional near-infrared spectroscopy in mobile children: using short separation channels to correct for systemic contamination during naturalistic neuroimaging

Paola Pinti, Larisa Dinu, Tim Smith

Neurophotonics, Vol. 11, Issue 04, 045004, (October 2024) https://doi.org/10.1117/1.NPh.11.4.045004

TOPICS: Virtual reality, Brain, Hemodynamics, Near infrared spectroscopy, Head, Neurophotonics, Neuroimaging, Contamination, Anatomy, Tunable filters

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How the heterogeneity of the severely injured brain affects hybrid diffuse optical signals: case examples and guidelines

Susanna Tagliabue, Michał Kacprzak, Anna Rey-Perez, Jacinto Baena, Marilyn Riveiro, Federica Maruccia, Jonas Fischer, Maria Poca, Turgut Durduran

Neurophotonics, Vol. 11, Issue 04, 045005, (October 2024) https://doi.org/10.1117/1.NPh.11.4.045005

TOPICS: Brain, Tissues, Computed tomography, Neurophotonics, Data modeling, Autocorrelation, Head, Traumatic brain injury, Photons, Skull

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Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy–based brain network analysis

Shuo Guan, Yuhang Li, Yuxi Luo, Haijing Niu, Yuanyuan Gao, Dalin Yang, Rihui Li

Neurophotonics, Vol. 11, Issue 04, 045006, (October 2024) https://doi.org/10.1117/1.NPh.11.4.045006

TOPICS: Brain, Tunable filters, Wavelets, Signal filtering, Electronic filtering, Data modeling, Principal component analysis, Interpolation, Computer simulations, Interference (communication)

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DeepVID v2: self-supervised denoising with decoupled spatiotemporal enhancement for low-photon voltage imaging

Chang Liu, Jiayu Lu, Yicun Wu, Xin Ye, Allison Ahrens, Jelena Platisa, Vincent Pieribone, Jerry Chen, Lei Tian

Neurophotonics, Vol. 11, Issue 04, 045007, (October 2024) https://doi.org/10.1117/1.NPh.11.4.045007

TOPICS: Denoising, Signal to noise ratio, Video, Spatial resolution, Neurophotonics, Education and training, Spatial learning, Performance modeling, Data modeling, Neuroimaging

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Significance

Voltage imaging is a powerful tool for studying the dynamics of neuronal activities in the brain. However, voltage imaging data are fundamentally corrupted by severe Poisson noise in the low-photon regime, which hinders the accurate extraction of neuronal activities. Self-supervised deep learning denoising methods have shown great potential in addressing the challenges in low-photon voltage imaging without the need for ground-truth but usually suffer from the trade-off between spatial and temporal performances.

Aim

We present DeepVID v2, a self-supervised denoising framework with decoupled spatial and temporal enhancement capability to significantly augment low-photon voltage imaging.

Approach

DeepVID v2 is built on our original DeepVID framework, which performs frame-based denoising by utilizing a sequence of frames around the central frame targeted for denoising to leverage temporal information and ensure consistency. Similar to DeepVID, the network further integrates multiple blind pixels in the central frame to enrich the learning of local spatial information. In addition, DeepVID v2 introduces a new spatial prior extraction branch to capture fine structural details to learn high spatial resolution information. Two variants of DeepVID v2 are introduced to meet specific denoising needs: an online version tailored for real-time inference with a limited number of frames and an offline version designed to leverage the full dataset, achieving optimal temporal and spatial performances.

Results

We demonstrate that DeepVID v2 is able to overcome the trade-off between spatial and temporal performances and achieve superior denoising capability in resolving both high-resolution spatial structures and rapid temporal neuronal activities. We further show that DeepVID v2 can generalize to different imaging conditions, including time-series measurements with various signal-to-noise ratios and extreme low-photon conditions.

Conclusions

Our results underscore DeepVID v2 as a promising tool for enhancing voltage imaging. This framework has the potential to generalize to other low-photon imaging modalities and greatly facilitate the study of neuronal activities in the brain.

NiReject: toward automated bad channel detection in functional near-infrared spectroscopy

Christian Gerloff, Meryem Yücel, Lena Mehlem, Kerstin Konrad, Vanessa Reindl

Neurophotonics, Vol. 11, Issue 04, 045008, (November 2024) https://doi.org/10.1117/1.NPh.11.4.045008

TOPICS: Sensors, Machine learning, Signal detection, Contamination, Education and training, Neurophotonics, Signal attenuation, Near infrared spectroscopy, Signal to noise ratio, Interference (communication)

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Cerebral baseline optical and hemodynamic properties in pediatric population: a large cohort time-domain near-infrared spectroscopy study

Valeria Calcaterra, Michele Lacerenza, Caterina Amendola, Mauro Buttafava, Davide Contini, Virginia Rossi, Lorenzo Spinelli, Sara Zanelli, Gianvincenzo Zuccotti, Alessandro Torricelli

Neurophotonics, Vol. 11, Issue 04, 045009, (November 2024) https://doi.org/10.1117/1.NPh.11.4.045009

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Significance

Reference cerebral near-infrared spectroscopy (NIRS) data on the pediatric population are scarce, and in most cases, only cerebral oxygen saturation (SO2) measured by continuous wave spatially resolved spectroscopy NIRS is reported. Absolute data for baseline optical and hemodynamic parameters are missing.

Aim

We aimed at collecting baseline cerebral optical parameters [absorption coefficient, μa; reduced scattering coefficient, μs′; differential pathlength factor (DPF)] and hemodynamic parameters [oxy-hemoglobin content (HbO2), deoxyhemoglobin content (HHb), total hemoglobin content (tHB), SO2] in a large cohort of pediatric patients. The objectives are to establish reference optical values in this population and evaluate the reproducibility of a commercial time domain (TD) NIRS tissue oximeter.

Approach

TD NIRS measurements were performed in the prefrontal cortex at 686 and 830 nm with a 2.5-cm source–detector distance and 1-Hz acquisition rate. Five independent measurements (after probe replacement) were taken for every subject. TD NIRS data were fitted to a photon diffusion model to estimate the optical parameters. From the absorption coefficients, the hemodynamic parameters were derived by Beer’s law. Auxological and physiological information was also collected to explore the potential correlations with NIRS data.

Results

We measured 305 patients in the age range of 2 to 18 years. Absolute values for baseline optical and hemodynamic parameters were shown as a function of age and auxological variables. From the analysis of the repositioning after probe replacement, the time-domain near-infrared spectroscopy device exhibited an average precision (intended as coefficient of variation) of <5% for μs′, DPF, HbO2, HHb, and tHb, whereas precision was <2% for SO2.

Conclusions

We provided baseline values for optical and hemodynamic parameters in a large cohort of healthy pediatric subjects with good precision, providing a foundation for future investigations into clinically relevant deviations in these parameters.

Mapping multi-regional functional connectivity of astrocyte-neuronal networks during behaviors

Haoyu Wang, Mingzhu Huang, Shaofan Yang, Jiameng Xu, Jin Li, Han Qin, Shanshan Liang, Teng Teng, Chuanyan Yang, Mingyue Gong, Yong He, Xingyi Li, Huiquan Wang, Xiang Liao, Xiaowei Chen, Zhiqi Yang, Kuan Zhang

Neurophotonics, Vol. 11, Issue 04, 045010, (November 2024) https://doi.org/10.1117/1.NPh.11.4.045010

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Customizable optode attachments to improve hair clearance timing and inclusiveness in functional near-infrared spectroscopy research

Seth Crawford, Tiffany-Chau Le, Audrey Bowden

Neurophotonics, Vol. 11, Issue 04, 045011, (November 2024) https://doi.org/10.1117/1.NPh.11.4.045011

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Mid-infrared photoacoustic brain imaging enabled by cascaded gas-filled hollow-core fiber lasers

Cuiling Zhang, Kunyang Sui, Marcello Meneghetti, Jose Enrique Antonio-Lopez, Manoj Kumar Dasa, Rune Berg, Rodrigo Amezcua-Correa, Yazhou Wang, Christos Markos

Neurophotonics, Vol. 11, Issue 04, 045012, (November 2024) https://doi.org/10.1117/1.NPh.11.4.045012

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Significance

Extending the photoacoustic microscopy (PAM) into the mid-infrared (MIR) molecular fingerprint region constitutes a promising route toward label-free imaging of biological molecular structures. Realizing this objective requires a high-energy nanosecond MIR laser source. However, existing MIR laser technologies are limited to either low pulse energy or free-space structure that is sensitive to environmental conditions. Fiber lasers are promising technologies for PAM for their potential to offer both high pulse energy and robust performance, which however have not yet been used for PAM because it is still at the infant research stage.

Aim

We aim to employ the emerging gas-filled anti-resonant hollow-core fiber (ARHCF) laser technology for MIR-PAM for the purpose of imaging myelin-rich regions in a mouse brain.

Approach

This laser source is developed with a high-pulse-energy nanosecond laser at 3.4μm, targeting the main absorption band of myelin sheaths, the primary chemical component of axons in the central nervous system. The laser mechanism relies on two-order gas-induced vibrational stimulated Raman scattering for non-linear wavelength conversion, starting from a 1060-nm pump laser to 3.4 μm through the two-stage gas-filled ARHCFs.

Results

The developed fiber Raman laser was used for the first time for MIR-PAM of mouse brain regions containing structures rich in myelin. The high peak power of ∼1.38 kW and robust performance of the generated MIR Raman pulse addressed the challenge faced by the commonly used MIR lasers.

Conclusions

We pioneered the potential use of high-energy and nanosecond gas-filled ARHCF laser source to MIR-PAM, with a first attempt to report this kind of fiber laser source for PAM of lipid-rich myelin regions in a mouse brain. We also open up possibilities for expanding into a versatile multiwavelength laser source covering multiple biomarkers and being employed to image other materials such as plastics.

Assessment of residual awareness in patients with disorders of consciousness using functional near-infrared spectroscopy–based connectivity: a pilot study

Yifang He, Nan Wang, Dongsheng Liu, Hao Peng, Shaoya Yin, Xiaosong Wang, Yong Wang, Yi Yang, Juanning Si

Neurophotonics, Vol. 11, Issue 04, 045013, (December 2024) https://doi.org/10.1117/1.NPh.11.4.045013

TOPICS: Brain, Consciousness, Traumatic brain injury, Neurophotonics, Neurological disorders, Cerebral cortex, Brain diseases, Lab on a chip, Machine learning, Thalamus

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Effects of red blood cell transfusion on cerebral hemodynamics of preterm neonates

Caterina Amendola, Agnese De Carli, Tiziana Boggini, Davide Contini, Sofia Passera, Nicola Pesenti, Lorenzo Spinelli, Martina Giovannella, Turgut Durduran, Udo M. Weigel, Alessandro Torricelli, Gorm Greisen, Monica Fumagalli

Neurophotonics, Vol. 11, Issue 04, 045014, (December 2024) https://doi.org/10.1117/1.NPh.11.4.045014

TOPICS: Red blood cells, Oxygen, Hemodynamics, Near infrared spectroscopy, Blood, Oxygenation, Tissues, Neurophotonics, Blood circulation, Mode conditioning cables

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Significance

Anemia is a common problem in preterm neonates, and red blood cell transfusion (RBCT) is used to improve oxygen delivery. However, RBCT is associated with complications, although an increase in cerebral oxygenation has been documented, and no universally accepted biomarker for the need for transfusion (i.e., the concentration of hemoglobin in the blood) has been defined.

Aim

We used a hybrid optical device (BabyLux device) that merges time-domain near-infrared spectroscopy (TD-NIRS) and diffuse correlation spectroscopy (DCS) to potentially obtain a better assessment of the cerebral effects of RBCT compared with previous studies using continuous wave (CW) spatially resolved NIRS.

Approach

Eighteen clinically stable preterm neonates were assessed before and after RBCT by the BabyLux device as five repetitions of 60 s measurement (with 1 s acquisition time), estimating the cerebral blood flow (CBF) as a blood flow index (BFI), the total hemoglobin concentration (tHb), and the cerebral tissue oxygen saturation (StO2). StO2 was also continuously monitored by a commercial CW-NIRS device, as well as peripheral saturation, SpO2. Tissue oxygen extraction (TOE) and cerebral metabolic rate of oxygen consumption (tCMRO2) were computed, and the Wilcoxon signed-rank test for paired data was performed, comparing the data acquired before and after RBCT.

Results

The BabyLux data from four neonates did not meet quality criteria and were discarded. After the transfusion, tHb and StO2 (measured both with TD-NIRS and CW-NIRS devices) significantly increased, causing a significant decrease in TOE. CW-NIRS showed a wider dispersion of StO2 data compared with TD-NIRS. However, CBF did not decrease proportionally but the variation was high, as well as for tCMRO2.

Conclusions

The results confirm previous CW-NIRS studies, but the wide variability of BFI makes the effects of RBCT on cerebral metabolism uncertain.

Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach

Aykut Eken, Murat Yüce, Gülnaz Yükselen, Sinem Erdogan

Neurophotonics, Vol. 11, Issue 04, 045015, (December 2024) https://doi.org/10.1117/1.NPh.11.4.045015

TOPICS: Data modeling, Performance modeling, Education and training, Hemodynamics, Staring arrays, Neurophotonics, Deep learning, Statistical modeling, Brain, Design

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Significance

Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.

Aim

We aim to propose a deep learning (DL)–based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)–derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.

Approach

A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90 min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.

Results

Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.

Conclusions

The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain–computer interface (BCI) system designs that target other application areas.

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