Alzheimer’s disease (AD) is a typical neurodegenerative disease. β-amyloid (Aβ) plaque is the most prominent pathological biomarker associated with the progression of AD. Conventional Aβ probes, including commercial probe ThT, usually suffer from tedious washing procedures. Herein, novel AIE-active Aβ probes with excellent water solubility, named DE-V1-PYC3 and DE-V1-PYOH, were developed for the detection and image of Aβ without tedious washing procedures. Compared with commercial probe ThT, the AIE-active Aβ probes exhibited better sensitivity and affinity to Aβ aggregates. Moreover, for ThT, the washing procedures are essential to obtain high signal-to-noise ratio (SNR) images of Aβ plaques in AD brain tissue slices. DE-V1-PYC3 and DE-V1-PYOH can label Aβ plaques in AD brain tissue slices with high SNR even without tedious washing procedures.

Rabies-viruses-based retrograde tracers can spread across multiple synapses in a retrograde direction in the nervous system of rodents and primates, making them powerful tools for determining the structure and function of the complicated neural circuits of the brain. However, they have some limitations, such as posing high risks to human health and the inability to retrograde trans-synaptic label inputs from genetically-defined starter neurons. Here, we established a new retrograde trans-multi-synaptic tracing method through brain-wide rabies virus glycoprotein (RVG) compensation, followed by glycoprotein-deleted rabies virus (RV-ΔG) infection in specific brain regions. Furthermore, in combination with the avian tumor virus receptor A (TVA) controlled by a cell-type-specific promoter, we found that EnvA-pseudotyped RV-ΔG can mediate efficient retrograde trans-multi-synaptic transduction from cell-type-specific starter neurons. This study provides new alternative methods for neuroscience researchers to analyze the input neural networks of rodents and nonhuman primates.

Multiphoton microscopy (MPM) is a powerful imaging technology for brain research. The imaging depth in MPM is partly determined by emission wavelength of fluorescent labels. It has been demonstrated that a longer emission wavelength is favorable for signal detection as imaging depth increases. However, there has been no comparison with near-infrared (NIR) emission. In order to quantitatively analyze the effect of emission wavelength on 3-photon imaging of mouse brains in vivo, we utilize the same excitation wavelength to excite a single fluorescent dye and simultaneously collect NIR and orange-red emission fluorescence at 828nm and 620nm, respectively. Both experimental and simulation results show that as the imaging depth increases, NIR emission decays less than orange-red fluorescent emission. These results show that it is preferable to shift the emission wavelength to NIR to enable more efficient signal collection deep in the brain.

Hemiplegia after stroke has become a major cause of the world’s high disabilities, and it is vital to enhance our understanding of post-stroke neuroplasticity to develop efficient rehabilitation programs. This study aimed to explore the brain activation and network reorganization of the motor cortex (MC) with functional near-infrared spectroscopy (fNIRS). The MC hemodynamic signals were gained from 22 stroke patients and 14 healthy subjects during a shoulder-touching task with the right hand. The MC activation pattern and network attributes analyzed with the graph theory were compared between the two groups. The results revealed that healthy controls presented dominant activation in the left MC while stroke patients exhibited dominant activation in the bilateral hemispheres MC. The MC networks for the two groups had small-world properties. Compared with healthy controls, patients had higher transitivity and lower global efficiency (GE), mean connectivity, and long connections (LCs) in the left MC. In addition, both MC activation and network attributes were correlated with patient’s upper limb motor function. The results showed the stronger compensation of the unaffected motor area, the better recovery of the upper limb motor function for patients. Moreover, the MC network possessed high clustering and relatively sparse inter-regional connections during recovery for patients. Our results promote the understanding of MC reorganization during recovery and indicate that MC activation and network could provide clinical assessment significance in stroke patients. Given the advantages of fNIRS, it shows great application potential in the assessment and rehabilitation of motor function after stroke.

Obstructive sleep apnea (OSA) and central sleep apnea (CSA) are two main types of sleep disordered breathing (SDB). While the changes in cerebral hemodynamics triggered by OSA events have been well studied using near-infrared spectroscopy (NIRS), they are essentially unknown in CSA in adults. Therefore, in this study, we compared the changes in cerebral oxygenation between OSA and CSA events in adult patients using NIRS. Cerebral tissue oxygen saturation (StO₂) in 13 severe SDB patients who had both CSA and OSA events was measured using frequency-domain NIRS. The changes in cerebral StO₂ desaturation and blood volume (BV) in the first hour of natural sleep were compared between different types of respiratory events (i.e., 277 sleep hypopneas, 161 OSAs and 113 CSAs) with linear mixed-effect models controlling for confounders. All respiratory events occurred during non-rapid eye movement (NREM) sleep. We found that apnea events induced greater cerebral desaturations and BV fluctuations compared to hypopneas, but there was no difference between OSA and CSA. These results suggest that cerebral autoregulation in our patients are still capable to counteract the pathomechanisms of apneas, in particularly the negative intrathoracic pressure (ITP) caused by OSA events. Otherwise larger BV fluctuations in OSA compared to CSA should be observed due to the negative ITP that reduces cardiac stroke volume and leads to lower systematic blood supply. Our study suggests that OSA and CSA may have similar impact on cerebral oxygenation during NREM sleep in adult patients with SDB.

Interactions between the central nervous system (CNS) and autonomic nervous system (ANS) play a crucial role in modulating perception, cognition, and emotion production. Previous studies on CNS–ANS interactions, or heart–brain coupling, have often used heart rate variability (HRV) metrics derived from electrocardiography (ECG) recordings as empirical measurements of sympathetic and parasympathetic activities. Functional near-infrared spectroscopy (fNIRS) is a functional brain imaging modality that is increasingly used in brain and cognition studies. The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms. Therefore, fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously, allowing analysis of CNS–ANS interactions. In this proof-of-concept study, fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena (MOBA) game. From the fNIRS recordings, neural and heartbeat frequency bands were extracted to assess prefrontal activities and short-term pulse rate variability (PRV), an approximation for short-term HRV, respectively. Under the experimental conditions used, fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards, in terms of absolute measurements and video game playing (VGP)-related changes. It was also observed that, similar to previous studies on physical activity and exercise, the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP. It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP, facilitating the understanding of VGP-related heart–brain coupling.

Neurons can be abstractly represented as skeletons due to the filament nature of neurites. With the rapid development of imaging and image analysis techniques, an increasing amount of neuron skeleton data is being produced. In some scientific studies, it is necessary to dissect the axons and dendrites, which is typically done manually and is both tedious and time-consuming. To automate this process, we have developed a method that relies solely on neuronal skeletons using Geometric Deep Learning (GDL). We demonstrate the effectiveness of this method using pyramidal neurons in mammalian brains, and the results are promising for its application in neuroscience studies.

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

In ophthalmology, retinal optical coherence tomography (OCT) images with noticeable structural features help identify human eyes as healthy or diseased. The recently hot artificial intelligence (AI) realized this recognition process automatically. However, speckle noise in the original retinal OCT image reduces the accuracy of disease classification. This study presents a time-saving approach based on deep learning to improve classification accuracy by removing the noise from the original dataset. Firstly, four pre-trained convolutional neural networks (CNNs) from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) were trained to classify the original images into two categories: The noise reduction required (NRR) and the noise-free (NF) images. Among the CNNs, VGG19_BN performed best with 98% accuracy and 99% recall. Then, we used the block-matching and 3D filtering (BM3D) algorithm to denoise the NRR images. Those noise-removed NRR and the NF images form the processed dataset. The quality of images in the dataset is prominently ameliorated after denoising, which is valid to improve the models’ performance. The original and processed datasets were tested on the four pre-trained CNNs to evaluate the effectiveness of our proposed approach. We have compared the CNNs, and the results show the performance of the CNNs trained with the processed dataset is improved by an average of 2.04%, 5.19%, and 5.10% under overall accuracy (OA), Macro F1-score, and Micro F1-score, respectively. Especially for DenseNet161, the OA is improved to 98.14%. Our proposed method demonstrates its effectiveness in improving classification accuracy and opens a new solution to reduce denoising time-consuming for large datasets.

Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications. The absorption (μa) and scattering (μs) coefficients of biological tissues are inversely analyzed from their diffuse reflectance (R) and total transmittance (T), which are measured using a double integrating spheres (DIS) system. The inversion algorithms, for example, inverse adding doubling method and inverse Monte Carlo method, are sensitive to noise signals during the DIS measurements, resulting in reduced accuracy during determination. In this study, we propose an artificial neural network (ANN) to estimate μa and μs at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties. Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprising μa, μs, R and T. Measurement noise signals were added to R and T, and the ANN model was then trained using the noise-added datasets. Numerical results showed that the trained ANN model reduced the effects of noise in μa and μs estimation. Experimental verification indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average, resulting in measurement time reduction. The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times, thus reducing changes in the optical properties due to desiccation of the samples.