Imaging Of Brain Tissue Research Articles

Structural and functional imaging of brains.

Analyzing the complex structures and functions of brain is the key issue to understanding the physiological and pathological processes. Although neuronal morphology and local distribution of neurons/blood vessels in the brain have been known, the subcellular structures of cells remain challenging, especially in the live brain. In addition, the complicated brain functions involve numerous functional molecules, but the concentrations, distributions and interactions of these molecules in the brain are still poorly understood. In this review, frontier techniques available for multiscale structure imaging from organelles to the whole brain are first overviewed, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), serial-section electron microscopy (ssEM), light microscopy (LM) and synchrotron-based X-ray microscopy (XRM). Specially, XRM for three-dimensional (3D) imaging of large-scale brain tissue with high resolution and fast imaging speed is highlighted. Additionally, the development of elegant methods for acquisition of brain functions from electrical/chemical signals in the brain is outlined. In particular, the new electrophysiology technologies for neural recordings at the single-neuron level and in the brain are also summarized. We also focus on the construction of electrochemical probes based on dual-recognition strategy and surface/interface chemistry for determination of chemical species in the brain with high selectivity and long-term stability, as well as electrochemophysiological microarray for simultaneously recording of electrochemical and electrophysiological signals in the brain. Moreover, the recent development of brain MRI probes with high contrast-to-noise ratio (CNR) and sensitivity based on hyperpolarized techniques and multi-nuclear chemistry is introduced. Furthermore, multiple optical probes and instruments, especially the optophysiological Raman probes and fiber Raman photometry, for imaging and biosensing in live brain are emphasized. Finally, a brief perspective on existing challenges and further research development is provided.

Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling.

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

Multimodal Electrocorticogram Active Electrode Array Based on Zinc Oxide-Thin Film Transistors.

Active electrocorticogram (ECoG) electrodes can amplify weak electrophysiological signals and improve anti-interference ability; however, traditional active electrodes are opaque and cannot realize photoelectric collaborative observation. In this study, an active and fully transparent ECoG array based on zinc oxide thin-film transistors (ZnO TFTs) is developed as a local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array is up to 85%, which is superior to that of the previously reported active electrode arrays. Various electrical characterizations have demonstrated its ability to record electrophysiological signals with a higher signal-to-noise ratio of 19.9dB compared to the Au grid (13.2dB). The high transparency of the ZnO-TFT electrode array allows the concurrent collection of high-quality electrophysiological signals (32.2dB) under direct optical stimulation of the optogenetic mice brain. The ECoG array can also work under 7-Tesla magnetic resonance imaging to record local brain signals without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.

Assessment of Thermal Damage from Robot-Drilled Craniotomy for Cranial Window Surgery in Mice

Cranial window surgery allows for the imaging of brain tissue in live mice with the use of multiphoton or other intravital imaging techniques. However, when performing any craniotomy by hand, there is often thermal damage to brain tissue, which is inherently variable surgery-to-surgery and may be dependent on individual surgeon technique. Implementing a surgical robot can standardize surgery and lead to a decrease in thermal damage associated with surgery. In this study, three methods of robotic drilling were tested to evaluate thermal damage: horizontal, point-by-point, and pulsed point-by-point. Horizontal drilling utilizes a continuous drilling schematic, while point-by-point drills several holes encompassing the cranial window. Pulsed point-by-point adds a "2 s on, 2 s off" drilling scheme to allow for cooling in between drilling. Fluorescent imaging of Evans Blue (EB) dye injected intravenously measures damage to brain tissue, while a thermocouple placed under the drilling site measures thermal damage. Thermocouple results indicate a significant decrease in temperature change in the pulsed point-by-point (6.90 °C ± 1.35 °C) group compared to the horizontal (16.66 °C ± 2.08 °C) and point-by-point (18.69 °C ± 1.75 °C) groups. Similarly, the pulsed point-by-point group also showed significantly less EB presence after cranial window drilling compared to the horizontal method, indicating less damage to blood vessels in the brain. Thus, a pulsed point-by-point drilling method appears to be the optimal scheme for reducing thermal damage. A robotic drill is a useful tool to help minimize training, variability, and reduce thermal damage. With the expanding use of multiphoton imaging across research labs, it is important to improve the rigor and reproducibility of results. The methods addressed here will help inform others of how to better use these surgical robots to further advance the field.

Assessment of Thermal Damage from Robot-Drilled Craniotomy for Cranial Window Surgery in Mice.

Cranial window surgery allows for the imaging of brain tissue in live mice with the use of multiphoton or other intravital imaging techniques. However, when performing any craniotomy by hand, there is often thermal damage to brain tissue, which is inherently variable surgery-to-surgery and may be dependent on individual surgeon technique. Implementing a surgical robot can standardize surgery and lead to a decrease in thermal damage associated with surgery. In this study, three methods of robotic drilling were tested to evaluate thermal damage: horizontal, point-by-point, and pulsed point-by-point. Horizontal drilling utilizes a continuous drilling schematic, while point-by-point drills several holes encompassing the cranial window. Pulsed point-by-point adds a "2 s on, 2 s off" drilling scheme to allow for cooling in between drilling. Fluorescent imaging of Evans Blue (EB) dye injected intravenously measures damage to brain tissue, while a thermocouple placed under the drilling site measures thermal damage. Thermocouple results indicate a significant decrease in temperature change in the pulsed point-by-point (6.90 °C ± 1.35 °C) group compared to the horizontal (16.66 °C ± 2.08 °C) and point-by-point (18.69 °C ± 1.75 °C) groups. Similarly, the pulsed point-by-point group also showed significantly less EB presence after cranial window drilling compared to the horizontal method, indicating less damage to blood vessels in the brain. Thus, a pulsed point-by-point drilling method appears to be the optimal scheme for reducing thermal damage. A robotic drill is a useful tool to help minimize training, variability, and reduce thermal damage. With the expanding use of multiphoton imaging across research labs, it is important to improve the rigor and reproducibility of results. The methods addressed here will help inform others of how to better use these surgical robots to further advance the field.

Self-labelled encoder-decoder (SLED) for multi-echo gradient echo-based myelin water imaging

PurposeReconstruction of high quality myelin water imaging (MWI) maps is challenging, particularly for data acquired using multi-echo gradient echo (mGRE) sequences. A non-linear least squares fitting (NLLS) approach has often been applied for MWI. However, this approach may produce maps with limited detail and, in some cases, sub-optimal signal to noise ratio (SNR), due to the nature of the voxel-wise fitting. In this study, we developed a novel, unsupervised learning method called self-labelled encoder-decoder (SLED) to improve gradient echo-based MWI data fitting. MethodsUltra-high resolution, MWI data was collected from five mouse brains with variable levels of myelination, using a mGRE sequence. Imaging data was acquired using a 7T preclinical MRI system. A self-labelled, encoder-decoder network was implemented in TensorFlow for calculation of myelin water fraction (MWF) based on the mGRE signal decay. A simulated MWI phantom was also created to evaluate the performance of MWF estimation. ResultsCompared to NLLS, SLED demonstrated improved MWF estimation, in terms of both stability and accuracy in phantom tests. In addition, SLED produced less noisy MWF maps from high resolution MR microscopy images of mouse brain tissue. It specifically resulted in lower noise amplification for all mouse genotypes that were imaged and yielded mean MWF values in white matter ROIs that were highly correlated with those derived from standard NLLS fitting. Lastly, SLED also exhibited higher tolerance to low SNR data. ConclusionDue to its unsupervised and self-labeling nature, SLED offers a unique alternative to analyze gradient echo-based MWI data, providing accurate and stable MWF estimations.

Magnetron-Sputtered Niobium Nanoparticles for Molecular Imaging of Brain Tissues through Surface-Assisted Laser Desorption/Ionization Mass Spectrometry

Magnetron-Sputtered Niobium Nanoparticles for Molecular Imaging of Brain Tissues through Surface-Assisted Laser Desorption/Ionization Mass Spectrometry

Learning to detect boundary information for brain image segmentation

MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to 5.3% compared to the state-of-the-art models) in detecting and segmenting brain tissue images.

How innovations in methodology offer new prospects for volume electron microscopy

Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.

Depth random-access two-photon Bessel light-sheet imaging in brain tissue.

Two-photon light-sheet fluorescence microscopy enables high-resolution imaging of neural activity in brain tissue at a high frame rate. Traditionally, light-sheet microscopy builds up a 3D stack by multiple depth scans with uniform spatial intervals, which substantially limits the volumetric imaging speed. Here, we introduce the depth random-access light-sheet microscopy, allowing rapid switching scanning depth for light-sheet imaging. With a low-cost electrically tunable lens and minimum modification of an existing two-photon light-sheet imaging instrument, we demonstrated fast random depth hopping light-sheet imaging at 100 frames per second in the live brain slice. Through depth random-access, calcium activities for an astrocyte were recorded on four user-selected detection planes at a refreshing rate of 25 Hz.

Diagnostic accuracy of MRI in internal derangement of knee in comparison to arthroscopy MRI vs Arthroscopy in knee injury

Background: The knee joint (KJ) is one of the largest as well as most complicated joints in the human body. The knee is one of the most frequently injured joints, either as a separated injury or as a common portion in patients with multiple traumas. MRI has completely transformed the field of radio diagnosis because it provides superior soft tissue (ST) contrast. Also, it has appeared to be highly useful in muscle, brain, abdominal, and soft tissue imaging. The MRI is non-invasive which provides significantly better ST contrast, and requires less imaging time while having fewer artefacts. Aims & Objectives: To compare the accuracy of diagnostic [True Positive (TP), True Negative (TN), False Positive (FP), as well as False Negative (FN)] of MRI & arthroscopy in diagnosing internal derangement (ID) of the knee. To determine the function of MRI in selecting patients for arthroscopy. Material and Methods: This research is a cross-sectional investigation wherein patients who were thought to have an internal knee dislocation (IKD) were given an MRI and some were chosen for an arthroscopy. Most MRIs were done on a 1.5 tesla MRI scanner. The results of the MRI were written down.

A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images

Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.

Differentiation of Meningiomas and Gliomas by Amide Proton Transfer Imaging: A Preliminary Study of Brain Tumour Infiltration.

BackgroundGliomas are more malignant and invasive than meningiomas.ObjectiveTo distinguish meningiomas from low-grade/high-grade gliomas (LGGs/HGGs) using amide proton transfer imaging (APT) combined with conventional magnetic resonance imaging (MRI) and to explore the application of APT in evaluating brain tumour invasiveness.Materials and MethodsThe imaging data of 50 brain tumors confirmed by pathology in patients who underwent APT scanning in our centre were retrospectively analysed. Of these tumors, 25 were meningiomas, 10 were LGGs, and 15 were HGGs. The extent of the tumour-induced range was measured on APT images, T2-weighted imaging (T2WI), and MRI enhancement; additionally, and the degree of enhancement was graded. Ratios (RAPT/T2 and RAPT/E) were obtained by dividing the range of changes observed by APT by the range of changes observed via T2WI and MR enhancement, respectively, and APTmean values were measured. The Mann–Whitney U test was used to compare the above measured values with the pathological results obtained for gliomas and meningiomas, the Kruskal-Wallis test was used to compare LGGs, HGGs and meningiomas, and Dunn’s test was used for pairwise comparisons. In addition, receiver operating characteristic (ROC) curves were drawn.ResultsThe Mann–Whitney U test showed that APTmean (p=0.005), RAPT/T2 (p<0.001), and RAPT/E (p<0.001) values were statistically significant in the identification of meningioma and glioma. The Kruskal-Wallis test showed that the parameters APTmean, RAPT/T2, RAPT/E and the degree of enhancement are statistically significant. Dunn’s test revealed that RAPT/T2 (p=0.004) and RAPT/E (p=0.008) could be used for the identification of LGGs and meningiomas. APTmean (p<0.001), RAPT/T2 (p<0.001), and RAPT/E (p<0.001) could be used for the identification of HGGs and meningiomas. APTmean (p<0.001) was statistically significant in the comparison of LGGs and HGGs. ROC curves showed that RAPT/T2 (area under the curve (AUC)=0.947) and RAPT/E (AUC=0.919) could be used to distinguish gliomas from meningiomas.ConclusionAPT can be used for the differential diagnosis of meningioma and glioma, but APTmean values can only be used for the differential diagnosis of HGGs and meningiomas or HGGs and LGGs. Gliomas exhibit more obvious changes than meningiomas in APT images of brain tissue; this outcome may be caused by brain infiltration.

Effects of scale on segmentation of Nissl–stained rat brain tissue images via convolutional neural networks

There are numerous efforts to automate the delineation of rat brain regions from rat brain histology. A leading approach uses convolutional neural networks which model anatomical variability and determine cytoarchitectonic boundaries. Currently, it is not clear what scale of the input tissue images offers the most information for these models to exploit. In this work, we test a fully convolutional architecture, U–Net, with Nissl–stained rat brain tissue images of different scales. We show that the networks obtain a lower precision and higher recall when trained on large-scale images. Conversely, networks trained with small-scale images produce fewer false-positive predictions and more false-negative predictions. Our work provides valuable insight into the optimal scale needed for convolutional neural networks to segment brain regions from Nissl-based images of the brain.

Metabolic state oscillations in cerebral nuclei detected using two-photon fluorescence lifetime imaging microscopy

The fluorescence lifetime of nicotinamide adenine dinucleotide (NADH), a key endogenous coenzyme and metabolic biomarker, can reflect the metabolic state of cells. To implement metabolic imaging of brain tissue at high resolution, we assembled a two-photon fluorescence lifetime imaging microscopy (FLIM) platform and verified the feasibility and stability of NADH-based two-photon FLIM in paraformaldehyde-fixed mouse cerebral slices. Furthermore, NADH based metabolic state oscillation was observed in cerebral nuclei suprachiasmatic nucleus (SCN). The free NADH fraction displayed a relatively lower level in the daytime than at the onset of night, and an ultradian oscillation at night was observed. Through the combination of high-resolution imaging and immunostaining data, the metabolic tendency of different cell types was detected after the first two hours of the day and at night. Thus, two-photon FLIM analysis of NADH in paraformaldehyde-fixed cerebral slices provides a high-resolution and label-free method to explore the metabolic state of deep brain regions.

Plug-and-play adaptive optics for two photon high-speed volumetric imaging

To understand brain functions, it is important to study functional connectivity among stereoscopically distributed neurons. Since the brain is composed of 3D neuron networks, volumetric imaging with high spatiotemporal resolution is highly desirable. Two-photon microscopy (2PM) conveniently offers 3D tissue imaging with sub-micrometer resolution based on its intrinsic optical sectioning and deep penetration capabilities. However, the main challenge lies in the volumetric imaging speed and contrast reduction in deep tissue due to aberration. In this study, we integrate a tunable acoustic gradient lens and a plug-and-play adaptive-optics lens into 2PM. The former provides ∼100 kHz axial scan rate, achieving volumetric imaging rate in 1–10 Hz range, while the latter enhances image contrast by nearly two-fold in deep brain regions via correcting both systematic and sample aberrations. The combination offers a practical approach toward high-speed, high-contrast optical volumetric imaging of brain tissues.

Emerging scaffold- and cellular-based strategies for brain tissue regeneration and imaging

Abstract Stimulating brain tissue regeneration is a major challenge after central nervous system (CNS) injury, such as those observed from trauma or cerebrovascular accidents. Full regeneration is difficult even when a neurogenesis-associated repair response may occur. Currently, there are no effective treatments to stimulate brain tissue regeneration. However, biomaterial scaffolds are showing promising results, where hydrogels are the materials of choice to develop these supportive scaffolds for cell carriers. Their combination with growth factors, such as brain-derived neurotrophic factor (BDNF), basic fibroblast growth factor (bFGF), or vascular endothelial growth factor (VEGF), together with other cell therapy strategies allows the prevention of further neuronal death and can potentially lead to the direct stimulation of neurogenesis and vascularisation at the injured site. Imaging of the injured site is particularly critical to study the reestablishment of neural cell functionality after brain tissue injury. This review outlines the latest key advances associated with different strategies aiming to promote the neuroregeneration, imaging, and functional recovery of brain tissue. Graphical abstract

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis.

Stimulated Raman scattering (SRS) microscopy has emerged as a powerful optical imaging technique for tissue diagnosis. In recent years, two-color SRS has been shown to be able to provide hematoxylin and eosin (H&E)-equivalent images that allow fast and reliable diagnosis of brain cancer. Such capability has enabled exciting intraoperative cancer diagnosis applications. Two-color SRS imaging of tissue can be done with either a picosecond or femtosecond laser source. Femtosecond lasers have the advantage of enabling flexible imaging modes, including fast hyperspectral imaging and real-time, two-color SRS imaging. A spectral-focusing approach with chirped laser pulses is typically used with femtosecond lasers to achieve high spectral resolution. Two-color SRS acquisition can be realized with orthogonal modulation and lock-in detection. The complexity of pulse chirping, modulation, and characterization is a bottleneck for the widespread adoption of this method. This article provides a detailed protocol to demonstrate the implementation and optimization of spectral-focusing SRS and real-time, two-color imaging of mouse brain tissue in the epi-mode. This protocol can be used for a broad range of SRS imaging applications that leverage the high speed and spectroscopic imaging capability of SRS.

Applications of photoacoustic technology in brain tissue imaging (invited)

Applications of photoacoustic technology in brain tissue imaging (invited)

Finding the best clearing approach - Towards 3D wide-scale multimodal imaging of aged human brain tissue

The accessibility of new wide-scale multimodal imaging techniques led to numerous clearing techniques emerging over the last decade. However, clearing mesoscopic-sized blocks of aged human brain tissue remains an extremely challenging task. Homogenizing refractive indices and reducing light absorption and scattering are the foundation of tissue clearing. Due to its dense and highly myelinated nature, especially in white matter, the human brain poses particular challenges to clearing techniques.Here, we present a comparative study of seven tissue clearing approaches and their impact on aged human brain tissue blocks (> 5 mm). The goal was to identify the most practical and efficient method in regards to macroscopic transparency, brief clearing time, compatibility with immunohistochemical processing and wide-scale multimodal microscopic imaging. We successfully cleared 26 × 26 × 5 mm3-sized human brain samples with two hydrophilic and two hydrophobic clearing techniques. Optical properties as well as light and antibody penetration depths highly vary between these methods. In addition to finding the best clearing approach, we compared three microscopic imaging setups (the Zeiss Laser Scanning Microscope (LSM) 880 , the Miltenyi Biotec Ultramicroscope ll (UM ll) and the 3i Marianas LightSheet microscope) regarding optimal imaging of large-scale tissue samples.We demonstrate that combining the CLARITY technique (Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel) with the Zeiss LSM 880 and combining the iDISCO technique (immunolabeling-enabled three-dimensional imaging of solvent-cleared organs) with the Miltenyi Biotec UM ll are the most practical and efficient approaches to sufficiently clear aged human brain tissue and generate 3D microscopic images. Our results point out challenges that arise from seven clearing and three imaging techniques applied to non-standardized tissue samples such as aged human brain tissue.