Volume 11 Issue 4 | Neurophotonics

Neurophotonics

VOL. 11 · NO. 4 | October 2024

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Research Papers (8)

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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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