Introduction to Special Issue on Neurophotonics

Optoelectronics, genomics, and proteomics have ushered in an era of new approaches and tools for neuroscience research, especially optical neuroimaging. “Function follows form”, anatomical structure is the basis for understanding brain function and brain diseases. Brain function depends on neuronal networks, so from a systems biology perspective, not only should the neuronal level be studied, but also the neuronal networks and system levels. Optical imaging can now be applied at multiple levels, from the gene to the molecule, from the cell to the tissue, and from the organ to the system, to provide critical information that bridges molecular structure and physiological function.

This special issue, which includes seven research articles, aims to highlight recent advances in the development of neurophotonic techniques and their applications in biological discovery, disease diagnosis, and treatment. This special issue was also prepared to celebrate the 20th Anniversary of Wuhan National Laboratory for Optoelectronics.

Rabies virus-based retrograde tracers can spread retrogradely across multiple synapses in the nervous systems of rodents and primates, making them powerful tools for determining the structure and function of the brain's intricate neural circuits. However, they have some limitations. Fuqiang Xu's group established a new retrograde trans-multi-synaptic tracing method in combination with the avian tumor virus receptor A and found that efficient retrograde trans-multi-synaptic transduction can be mediated from cell-type-specific starter neurons.1 Alzheimer's disease (AD) is a typical neurodegenerative disorder. β-Amyloid (Aβ) plaque is the most prominent pathological biomarker associated with the progression of AD. Ya-Long Wang's group developed water-soluble AIE-based wash-free Aβ probes, superior to commercial ThT, that can label Aβ plaques in AD brain tissue slices with high SNR without requiring tedious washing procedures.² Obstructive sleep apnea (OSA) and central sleep apnea (CSA) are two major types of sleep-disordered breathing (SDB). While the changes in cerebral hemodynamics induced by OSA events have been well studied using near-infrared spectroscopy (NIRS), they are essentially unknown in adult CSA. Zhongxing Zhang’s group compared changes in cerebral oxygenation between OSA and CSA events in adult patients using NIRS and found that apnea events induced greater cerebral desaturations and blood volume fluctuations compared to hypopneas.³ However, their results showed that there was no difference between OSA and CSA, suggesting that OSA and CSA may have similar effects on cerebral oxygenation during NREM sleep in adult patients with SDB. Hao Lei’s group conducted a study on synchronous measurements of prefrontal activity and pulse rate variability during online video game playing using functional near-infrared spectroscopy (fNIRS).⁴ Their results suggest that it is feasible to use fNIRS to monitor simultaneous brain and autonomic nervous system activations during online video game playing (VGP), facilitating the understanding of VGP-related heart-brain coupling. Jinyan Sun and Aoran Yang's group studied brain activation and network reorganization in the motor cortex using functional near-infrared spectroscopy (fNIRS),⁵ to improve our understanding of neuroplasticity after stroke and to develop effective rehabilitation programs. Multiphoton microscopy is a powerful imaging technique for brain research. To quantitatively analyze the effect of emission wavelength on 3-photon imaging of mouse brain in vivo, Ping Qiu's group used the same excitation wavelength to excite a single fluorescent dye and simultaneously collect near-infrared (NIR) and orange-red emission fluorescence at 828 nm and 620 nm, respectively.⁶ Both experimental and simulation results showed that as the imaging depth increases, the NIR emission decays less than the orange-red fluorescence emission. These results suggest that it is preferable to shift the emission wavelength to NIR to enable more efficient signal collection deep in the brain. Neurons can be represented abstractly as skeletons due to the filamentary nature of neurites. Jingpeng Wu's group presented semantic segmentation of pyramidal neuron skeletons using geometric deep learning, they demonstrated the effectiveness of this method using pyramidal neurons in mammalian brains,⁷ and the results are promising for its application in neuroscience studies.

Finally, we would like to thank all the contributing authors for making this issue possible.