Small Animal Brain Research Articles

Abstract 22: Evaluation of the effects of CD8 knock-out on cognitive decline in the TAC-induced hypertension mouse model

Hypertension is one of the main risk factors for cognitive impairment and dementia. We and others demonstrated the role of immunoinflammatory challenges in the establishment of cerebral injury following hypertension, highlighting a major role of inflammatory cytokines as interferon-gamma (IFN-γ) and finding increased parenchymal infiltration of CD8+ T cells. To establish the mechanistic role of these cells in cognitive impairment we subjected mice lacking CD8 cells (CD8 KO) and matched controls to aortic coarctation (TAC) and performed advanced neuroimaging analyses with a dedicated 7T small animal MRI, evaluating perfusion, brain volumes, and white matter microstructural integrity. Cognitive performance was evaluated at 4 weeks using the Morris Water Maze (MWM) test. Brain perfusion analyses shown protection from cerebral hypoperfusion in TAC CD8 KO mice compared to TAC WT mice (TAC WT vs TAC KO: 108 vs 148 ml/100g/min, p=0.0298) (Figure A). CD8 KO show preserved microstructural structure at DTI MRI, a typical trait of cerebral injury in clinical and experimental hypertension (Fimbria Fractional Anisotropy, TAC WT vs TAC KO: 0.45 vs 0.54, p=0.0210) , and show no a preserved cognition at MWM (Figure B), with a preserved learning curve at acquisition trial and a preserved memory retention capability evidenced at the probe trial (TAC WT vs TAC KO: 16,4 vs 32.2 seconds spent in platform quadrant, p=0.0014). The rescue from cerebral injury evidenced at MRI and the absence of cognitive impairment in CD8 KO mice demonstrate the pivotal role of CD8+ lymphocytes in the pathogenesis of hypertension-induced cognitive impairment.

Small animal brain surgery with neither a brain atlas nor a stereotaxic frame

BackgroundStereotaxic surgery is a cornerstone in brain research for the precise positioning of electrodes and probes, but its application is limited to species with available brain atlases and tailored stereotaxic frames. Addressing this limitation, we introduce an alternative technique for small animal brain surgery that requires neither an aligned brain atlas nor a stereotaxic frame. New methodThe new method requires an ex-vivo high-contrast MRI brain scan of one specimen and access to a micro-CT scanner. The process involves attaching miniature markers to the skull, followed by CT scanning of the head. Subsequently, MRI and CT images are co-registered using standard image processing software and the targets for brain recordings are marked in the MRI image. During surgery, the animal's head is stabilized in any convenient orientation, and the probe’s 3D position and angle are tracked using a multi-camera system. We have developed a software that utilizes the on-skull markers as fiducial points to align the CT/MRI 3D model with the surgical positioning system, and in turn instructs the surgeon how to move the probe to reach the targets within the brain. ResultsOur technique allows the execution of insertion tracks connecting two points in the brain. We successfully applied this method for neuropixels probe positioning in owls, quails, and mice, demonstrating its versatility. Comparison with existing methodsWe present an alternative to traditional stereotaxic brain surgeries that does not require established stereotaxic tools. Thus, this method is especially of advantage for research in non-standard and novel animal models.

Dynamic light scattering and laser speckle contrast imaging of the brain: theory of the spatial and temporal statistics of speckle pattern evolution.

Dynamic light scattering (DLS) and laser speckle contrast imaging (LSCI) are closely related techniques that exploit the statistics of speckle patterns, which can be utilized to measure cerebral blood flow (CBF). Conventionally, the temporal speckle intensity auto-correlation function is calculated in DLS, while the spatial speckle contrast Ks is calculated in LSCI measurements. Due to the rapid development of CMOS detection technology with increased camera frame rates while still maintaining a large number of pixels, the ensemble or spatial average of as well as the temporal contrast Kt can be easily calculated and utilized to quantify CBF. Although many models have been established, a proper summary is still lacking to fully characterize DLS and LSCI measurements for spatial and temporal statistics, laser coherence properties, various motion types, etc. As a result, there are many instances where theoretical models are misused. For instance, mathematical formulas derived in the diffusive regime or for ergodic systems are sometimes applied to small animal brain measurements, e.g., mice brains, where the assumptions are not valid. Therefore, we aim to provide a review of the speckle theory for both DLS and LSCI measurements with detailed derivations from first principles, taking into account non-ergodicity, spatial and temporal statistics of speckles, scatterer motion types, and laser coherence properties. From these calculations, we elaborate on the differences between spatial and temporal averaging for DLS and LSCI measurements that are typically ignored but can result in inaccurate measurements of blood flow, particularly the spatially varying nature of the static component in and Kt. We also obtained maps in in vivo mouse brain measurements using high frame rate CMOS cameras which have not been demonstrated before, and compared with and Ks,t. This work provides a useful guide for choosing the correct model to analyze spatial and temporal speckle statistics in in-vivo DLS and LSCI measurements.

Measurements of slow tissue dynamics with short-separation speckle contrast optical spectroscopy.

Laser speckle contrast imaging (LSCI) measures 2D maps of cerebral blood flow (CBF) in small animal brains such as mice. The contrast measured in LSCI also includes the static and slow-varying components that contain information about brain tissue dynamics. But these components are less studied as compared to the fast dynamics of CBF. In traditional wide-field LSCI, the contrast measured in the tissue is largely contaminated by neighboring blood vessels, which reduces the sensitivity to these static and slow components. Our goal is to enhance the sensitivity of the contrast to static and slow tissue dynamics and test models to quantify the characteristics of these components. To achieve this, we have developed a short-separation speckle contrast optical spectroscopy (ss-SCOS) system by implementing point illumination and point detection using multi-mode fiber arrays to enhance the static and slow components in speckle contrast measurements as compared to traditional wide-field LSCI (WF-LSCI). We observed larger fractions of the static and slow components when measured in the tissue using ss-SCOS than in traditional LSCI for the same animal and region of interest. We have also established models to obtain the fractions of the static and slow components and quantify the decorrelation time constants of the intensity auto-correlation function for both fast blood flow and slower tissue dynamics. Using ss-SCOS, we demonstrate the variations of fast and slow brain dynamics in animals before and post-stroke, as well as within an hour post-euthanasia. This technique establishes the foundation to measure brain tissue dynamics other than CBF, such as intracellular motility.

DAEGAN: Generative adversarial network based on dual-domain attention-enhanced encoder-decoder for low-dose PET imaging

BackgroundPositron emission tomography (PET) is a valuable medical imaging modality utilized in both clinical and preclinical settings. There is a growing concern regarding the potential radiation exposure associated with the administration of radiotracers. This concern has led to a focus on improving the quality of PET images obtained from low-dose radiotracer injections. MethodsIn this study, we propose a novel generative adversarial network (GAN) architecture called dual-domain attention-enhanced encoder-decoder GAN (DAEGAN) for low-dose PET imaging. The DAEGAN architecture incorporates a dual-domain encoder-decoder-based generator, which enables the network to focus simultaneously on the structure information and content features. Additionally, attention modules are integrated into the discriminator and the generator to effectively aggregate meaningful features. A significant contribution of this study is the incorporation of the classification activation map (CAM) loss as a component of the generator loss function for the denoising task.Experiments: Experiments on datasets of small animal and human brain PET scans including normal-dose images and low-dose images for the proposed method and other published methods were carried out. A cross-dataset validation based on a homemade Derenzo phantom was also implemented. ResultsIn the qualitative evaluation experiments, the proposed model can generate denoised images approaching normal-dose images with better visual structural details. Meanwhile, the proposed method achieved the best score in metrics on the test datasets and cross-dataset validation. ConclusionsThe results suggest that the proposed DAEGAN architecture is a promising approach for improving the quality of low-dose PET images compared to state-of-the-art methods.

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains.

Neuroimages are a valuable tool for studying brain morphology in experiments using animal models. Magnetic resonance imaging (MRI) has become the standard method for soft tissues, although its low spatial resolution poses some limits for small animals. Here, we describe a protocol for obtaining high-resolution three-dimensional (3D) information on mouse neonate brains and skulls using micro-computed tomography (micro-CT). The protocol includes those steps needed to dissect the samples, stain and scan the brain, and obtain morphometric measurements of the whole organ and regions of interest (ROIs). Image analysis includes the segmentation of structures and the digitization of point coordinates. In sum, this work shows that the combination of micro-CT and Lugol's solution as a contrast agent is a suitable alternative for imaging the perinatal brains of small animals. This imaging workflow has applications in developmental biology, biomedicine, and other sciences interested in assessing the effect of diverse genetic and environmental factors on brain development.

Transfer Learning Approach to Vascular Permeability Changes in Brain Metastasis Post-Whole-Brain Radiotherapy.

The purpose of this study is to further validate the utility of our previously developed CNN in an alternative small animal model of BM through transfer learning. Unlike the glioma model, the BM mouse model develops multifocal intracranial metastases, including both contrast enhancing and non-enhancing lesions on DCE MRI, thus serving as an excellent brain tumor model to study tumor vascular permeability. Here, we conducted transfer learning by transferring the previously trained GBM CNN to DCE MRI datasets of BM mice. The CNN was re-trained to learn about the relationship between BM DCE images and target permeability maps extracted from the Extended Tofts Model (ETM). The transferred network was found to accurately predict BM permeability and presented with excellent spatial correlation with the target ETM PK maps. The CNN model was further tested in another cohort of BM mice treated with WBRT to assess vascular permeability changes induced via radiotherapy. The CNN detected significantly increased permeability parameter Ktrans in WBRT-treated tumors (p < 0.01), which was in good agreement with the target ETM PK maps. In conclusion, the proposed CNN can serve as an efficient and accurate tool for characterizing vascular permeability and treatment responses in small animal brain tumor models.

Android mobile-platform-based image reconstruction for photoacoustic tomography.

In photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications. To implement PAT image reconstruction in Android-based mobile platforms. For implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones. The performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of in vivo small animal brain dataset in 2.4s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation. We proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4s is achieved without hampering the quality of the reconstructed image.

Small animal cerebral microvascular imaging with super-resolution ultrasound: Techniques and applications

Super-resolution ultrasound (SR-US) is an emerging microvascular imaging technology that provides a micron-scale spatial resolution with tens of millimeters of depth of imaging penetration. The unmatched combination of high spatial resolution and deep imaging penetration opened new doors for many brain imaging applications that benefit from cerebrovascular biomarkers. Recently, SR-US has found its niche in small animal brain applications thanks to its unique capabilities of extending the optical-imaging-level spatial resolution to subcortical, deep brain regions. In this presentation, I will first introduce existing SR-US imaging techniques that are tailored to small animal brain imaging applications. Topics include fast SR-US methods, 3D SR-US imaging, and phase aberration correction for intact skull imaging. I will then focus on reviewing brain imaging applications that use SR-US to characterize cerebral mirovasculature in the aging, stroke, and Alzheimer’s Disease brain. Finally, I will introduce the new functional super-resolution imaging technique that combines SR-US with functional ultrasound (fUS) to realize whole-brain neural activity recording at a micron-scale spatial resolution.

Signal-to-Noise Ratio Enhancement of Single-Voxel In Vivo 31P and 1H Magnetic Resonance Spectroscopy in Mice Brain Data Using Low-Rank Denoising.

Magnetic resonance spectroscopy (MRS) is a noninvasive technique for measuring metabolite concentration. It can be used for preclinical small animal brain studies using rodents to provide information about neurodegenerative diseases and metabolic disorders. However, data acquisition from small volumes in a limited scan time is technically challenging due to its inherently low sensitivity. To mitigate this problem, this study investigated the feasibility of a low-rank denoising method in enhancing the quality of single voxel multinuclei (31P and 1H) MRS data at 9.4 T. Performance was evaluated using in vivo MRS data from a normal mouse brain (31P and 1H) and stroke mouse model (1H) by comparison with signal-to-noise ratios (SNRs), Cramer-Rao lower bounds (CRLBs), and metabolite concentrations of a linear combination of model analysis results. In 31P MRS data, low-rank denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared with the original data. In 1H MRS data, the method also improved the SNRs, CRLBs, but it performed better for 31P MRS data with relatively simpler patterns compared to the 1H MRS data. Therefore, we suggest that the low-rank denoising method can improve spectra SNR and metabolite quantification uncertainty in single-voxel in vivo 31P and 1H MRS data, and it might be more effective for 31P MRS data. The main contribution of this study is that we demonstrated the effectiveness of the low-rank denoising method on small-volume single-voxel MRS data. We anticipate that our results will be useful for the precise quantification of low-concentration metabolites, further reducing data acquisition voxel size, and scan time in preclinical MRS studies.

Combined proton radiography and irradiation for high-precision preclinical studies in small animals.

Proton therapy has become a popular treatment modality in the field of radiooncology due to higher spatial dose conformity compared to conventional radiotherapy, which holds the potential to spare normal tissue. Nevertheless, unresolved research questions, such as the much debated relative biological effectiveness (RBE) of protons, call for preclinical research, especially regarding in vivo studies. To mimic clinical workflows, high-precision small animal irradiation setups with image-guidance are needed. A preclinical experimental setup for small animal brain irradiation using proton radiographies was established to perform planning, repositioning, and irradiation of mice. The image quality of proton radiographies was optimized regarding the resolution, contrast-to-noise ratio (CNR), and minimal dose deposition in the animal. Subsequently, proof-of-concept histological analysis was conducted by staining for DNA double-strand breaks that were then correlated to the delivered dose. The developed setup and workflow allow precise brain irradiation with a lateral target positioning accuracy of<0.26mm for in vivo experiments at minimal imaging dose of<23mGy per mouse. The custom-made software for image registration enables the fast and precise animal positioning at the beam with low observer-variability. DNA damage staining validated the successful positioning and irradiation of the mouse hippocampus. Proton radiography enables fast and effective high-precision lateral alignment of proton beam and target volume in mouse irradiation experiments with limited dose exposure. In the future, this will enable irradiation of larger animal cohorts as well as fractionated proton irradiation.

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics.

Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

Background:Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time consuming. This study is aimed at developing a deep learning approach to fully automate the generation of kinetic parameter maps, Ktrans (volume transfer coefficient) and Vp (blood plasma volume ratio), as a potential surrogate to conventional PK modeling in mouse brain tumor models based on DCE MRI.Methods:Using a 7T MRI, DCE MRI was conducted in U87 glioma xenografts growing orthotopically in nude mice. Vascular permeability Ktrans and Vp maps were generated using the classical Tofts model as well as the extended-Tofts model. These vascular permeability maps were then processed as target images to a twenty-four layer convolutional neural network (CNN). The CNN was trained on T1-weighted DCE images as source images and designed with parallel dual pathways to capture multiscale features. Furthermore, we performed a transfer study of this glioma trained CNN on a breast cancer brain metastasis (BCBM) mouse model to assess the potential of the network for alternative brain tumors.Results:Our data showed a good match for both Ktrans and Vp maps generated between the target PK parameter maps and the respective CNN maps for gliomas. Pixel-by-pixel analysis revealed intratumoral heterogeneous permeability, which was consistent between the CNN and PK models. The utility of the deep learning approach was further demonstrated in the transfer study of BCBM.Conclusions:Because of its rapid and accurate estimation of vascular PK parameters directly from the DCE dynamic images without complex mathematical modeling, the deep learning approach can serve as an efficient tool to assess tumor vascular permeability to facilitate small animal brain tumor research.

Biomarkers to Evaluate Androgen Deprivation Therapy for Prostate Cancer and Risk of Alzheimer's Disease and Neurodegeneration: Old Drugs, New Concerns.

Androgen deprivation therapy (ADT) is a standard treatment for prostate cancer patients, routinely used in the palliative or in the curative setting in association with radiotherapy. Among the systemic long-term side effects of ADT, growing data suggest a potentially increased risk of dementia/Alzheimer’s disease in prostate cancer patients treated with hormonal manipulation. While pre-clinical data suggest that androgen ablation may have neurotoxic effects due to Aβ accumulation and increased tau phosphorylation in small animal brains, clinical studies have measured the impact of ADT on long-term cognitive function, with conflicting results, and studies on biological changes after ADT are still lacking. The aim of this review is to report on the current evidence on the association between the ADT use and the risk of cognitive impairment in prostate cancer patients. We will focus on the contribution of Alzheimer’s disease biomarkers, namely through imaging, to investigate potential ADT-induced brain modifications. The evidence from these preliminary studies shows brain changes in gray matter volume, cortical activation and metabolism associated with ADT, however with a large variability in biomarker selection, ADT duration and cognitive outcome. Importantly, no study investigated yet biomarkers of Alzheimer’s disease pathology, namely amyloid and tau. These preliminary data emphasize the need for larger targeted investigations.

An optimized registration workflow and standard geometric space for small animal brain imaging

The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We introduce four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 2-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, all of which are indispensable prerequisites for the comparability of scientific results across experiments and centers.

252 Non-Invasive Quantification of Human Brain Lactate Concentrations Across Sleep-Wake Cycles

Abstract Introduction Cellular mechanisms underlying changes in small animal brain lactate concentrations have been investigated for more than 70 years and report sharp reductions in lactate (12-35%) during sleep or anesthesia relative to wakefulness. The goal of this study was to investigate alterations in human cerebral lactate concentrations across sleep-wake cycles. Toward this goal, we developed a novel non-invasive methodology, quantified changes in human cerebral lactate during sleep stages, and investigated potential mechanisms associated with changes in lactate. Methods Nine subjects (four females, five males; 21-27 y-o, mean age 24.2 ±2) were sleep deprived overnight, and underwent (5:45~11:00 am) experiments combining simultaneous MR-spectroscopy (MRS) and polysomnography (PSG) in a 3 T MR instrument using a 64-channel head/neck coil. A single voxel MRS (1H-MRS) acquired signals from a volume of interest (12~24 cm3) for every 7.5-s for 88~180-min. Lactate signal intensity was determined from each 7.5-s spectrum, normalized to corresponding water signal, and averaged over 30-s for each PSG epochs. Artifact corrected PSG data were scored for each 30-s epoch using the standard criteria and classified into one of four stages: W, N1, N2 and N3. Group mean lactate levels were quantified using LCModel. Three subjects returned for lactate diffusivity measurements using diffusion-sensitized PRESS MRS sequence. Results Compared to W, group mean lactate levels within each sleep stage showed a reduction of [4.9 ± 4.9] % in N1, [10.4 ± 5.2] % in N2, and [24.0 ± 5.8] % in N3. We observed a significant decrease in lactate apparent diffusion coefficient (ADC) accompanied by reduced brain lactate in sleep compared to wake (P&amp;lt;0.002). There were no differences in ADC values between wake and sleep for H2O, NAA, tCr, or Cho. Conclusion This is the first in-vivo report of alterations in human brain lactate concentrations across sleep-wake cycles. Observed decline in lactate levels during sleep compared to wakefulness is consistent with, and extends results from invasive small animal brain studies first reported more than 70 years ago, and support the notion of altered lactate metabolism and/or increased glymphatic activity in sleeping human brain. Support (if any) The Paul. G. Allen Family Foundation funded the study.

A Dual-Mode 2D Matrix Array for Ultrasound Image-Guided Noninvasive Therapy.

Focused ultrasound (FUS) lacks reliable real-time image guidance, which hinders the development of non-invasive ultrasound treatment in many important clinical applications. A dual-mode ultrasound array, capable of both imaging and therapy offers a new and reliable strategy for image-guided ultrasound therapy applications. The strategy has the advantages of real-time use, low cost, portability and inherent registration between imaging and therapeutic coordinate systems. In this work, a dual-mode two-dimensional (2D) matrix array with 1 MHz center frequency and 256 elements for ultrasound image-guided non-invasive therapy is reported. The array can provide three-dimensional (3D) volumetric ultrasound imaging and 3D focus control. Ultrasound imaging and therapeutic applications for the brain of small animals demonstrated the multi-functional capability of the dual-mode 2D matrix array. A method of rat brain positioning based on ultrasound imaging was proposed and verified. Transcranial ultrasound image-guided blood-brain barrier (BBB) opening of multiple-targets was achieved in vivo, using the proposed dual-mode 2D array. The obtained results indicate that the dual-mode 2D matrix array is a promising method for practical use in ultrasound image-guided non-invasive therapy applications.

Functional quantitative susceptibility mapping (fQSM) of rat brain during flashing light stimulation

Functional magnetic resonance imaging (fMRI) based on the blood oxygenation level-dependent (BOLD) contrast has become an indispensable tool in neuroscience. However, the BOLD signal is nonlocal, lacking quantitative measurement of oxygenation fluctuation. This preclinical study aimed to introduced functional quantitative susceptibility mapping (fQSM) to complement BOLD-fMRI to quantitatively assess the local susceptibility and venous oxygen saturation (SvO2). Rats were subjected to a 5 Hz flashing light and the different inhaled oxygenation levels (30% and 100%) were used to observe the venous susceptibility to quantify SvO2. Phase information was extracted to produce QSM, and the activation responses of magnitude (conventional BOLD) and the QSM time-series were analyzed. During light stimulation, the susceptibility change of fQSM was four times larger than the BOLD signal change in both inhalation oxygenation conditions. Moreover, the responses in the fQSM map were more restricted to the visual pathway, such as the lateral geniculate nucleus and superior colliculus, compared with the relatively diffuse distributions in the BOLD map. Also, the calibrated SvO2 was approximately 84% (88%) when the task was on, 83% (87%) when the task was off during 30% (and during 100%) oxygen inhalation. This is the first fQSM study in a small animal model and increases our understanding of fQSM in the brains of small animals. This study demonstrated the feasibility, sensitivity, and specificity of fQSM using light stimulus, as fQSM provides quantitative clues as well as localized information, complementing the defects of BOLD-fMRI. In addition to neural activity, fQSM also assesses SvO2 as supplementary information while BOLD-fMRI dose not. Accordingly, the fQSM technique could be a useful quantitative tool for functional studies, such as longitudinal follow up of neurodegenerative diseases, functional recovery after brain surgery, and negative BOLD studies.

Investigation of the Effect of the Skull in Transcranial Photoacoustic Imaging: A Preliminary Ex Vivo Study.

Although transcranial photoacoustic imaging (TCPAI) has been used in small animal brain imaging, in animals with thicker skull bones or in humans both light illumination and ultrasound propagation paths are affected. Hence, the PA image is largely degraded and in some cases completely distorted. This study aims to investigate and determine the maximum thickness of the skull through which photoacoustic imaging is feasible in terms of retaining the imaging target structure without incorporating any post processing. We identify the effect of the skull on both the illumination path and acoustic propagation path separately and combined. In the experimental phase, the distorting effect of ex vivo sheep skull bones with thicknesses in the range of 0.7~1.3 mm are explored. We believe that the findings in this study facilitate the clinical translation of TCPAI.

Cerebellum: What is in a Name? Historical Origins and First Use of This Anatomical Term

In this paper, we study who first used the Latin anatomical term “cerebellum” for the posterior part of the brain. The suggestion that this term was introduced by Leonardo da Vinci is unlikely. Just before the start of the da Vinci era in the fifteenth century, several authors referred to the cerebellum as “cerebri posteriorus.” Instead, in his translation of Galen’s anatomical text De utilitare particularum of 1307, Nicolo da Reggio used the Latinized Greek word “parencephalon.” More peculiar was the Latin nautical term “puppi,” referring to the stern of a ship, that was applied to the cerebellum by Constantine the African in his translation of the Arabic Liber regius in the eleventh century. The first to use the term “cerebellum” appears to be Magnus Hundt in his Anthropologia from 1501. Like many of the anatomists of this period, he was a humanist with an interest in classical literature. They may have encountered the term “cerebellum” in the writings by classical authors such as Celsus, where it was used as the diminutive of “cerebrum” for the small brains of small animals, and, subsequently, applied the term to the posterior part of the brain. In the subsequent decades of the sixteenth century, an increasing number of pre-Vesalian authors of anatomical texts started to use the name “cerebellum,” initially often combined with one or more of the earlier terms, but eventually more frequently in isolation. We found that a woodcut in Dryander’s Anatomia capitis humani of 1536 is the first realistic picture of the cerebellum.