Data visualisation and manifold mapping using the ViSOM.

The self-organising map (SOM) has been successfully employed as a nonparametric method for dimensionality reduction and data visualisation. However, for visualisation the SOM requires a colouring scheme to imprint the distances between neurons so that the clustering and boundaries can be seen. Even though the distributions of the data and structures of the clusters are not faithfully portrayed on the map. Recently an extended SOM, called the visualisation-induced SOM (ViSOM) has been proposed to directly preserve the distance information on the map, along with the topology. The ViSOM constrains the lateral contraction forces between neurons and hence regularises the interneuron distances so that distances between neurons in the data space are in proportion to those in the map space. This paper shows that it produces a smooth and graded mesh in the data space and captures the nonlinear manifold of the data. The relationships between the ViSOM and the principal curve/surface are analysed. The ViSOM represents a discrete principal curve or surface and is a natural algorithm for obtaining principal curves/surfaces. Guidelines for applying the ViSOM constraint and setting the resolution parameter are also provided, together with experimental results and comparisons with the SOM, Sammon mapping and principal curve methods.     

Objective image alignment for three-dimensional reconstruction of digital autoradiograms

Autoradiography can generate large quantities of information related to brain metabolism, blood flow, transport across the blood-brain barrier, neurotransmitter-receptor binding and other aspects of brain function. Three-dimensional (3D) reconstruction of digitized autoradiograms provides a mechanism for efficient analysis of function, in detail, over the entire brain. 3D reconstructions of the mean and variance can be obtained by superimposing data from similar experiments, leading ultimately to 3D reconstructions of differences with statistical tests of significance. Image registration is essential for reconstruction, and this article reports two independent algorithms for coronal image alignment that have been successfully implemented in computer programs. The first algorithm superimposes the centroids and principal axes of serial images; the extent and direction of the translation and rotation required for each image is obtained from an analysis of the inertia matrix of that image. The second algorithm matches the edges of structure features in serial-adjacent images, from analyses of the cross-correlation function of each pair of adjacent images. The cross-correlation method requires a great deal more computation than the principal axes method, but it can align damaged sections not reliably treated by the principal axes method. The methods are described in detail, and a quantitative assessment of the registration of non-identical images is considered.     

A novel tracing algorithm for high throughput imaging Screening of neuron-based assays

High throughput neuron image processing is an important method for drug screening and quantitative neurobiological studies. The method usually includes detection of neurite structures, feature extraction, quantification, and statistical analysis. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center-line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction, and robust enough for usage on images with poor quality, such as those with low contrast or low signal-to-noise ratio. It is able to completely and accurately extract neurite segments in neuron images with highly complicated neurite structures. Robustness comes from the use of 2D smoothening techniques and the idea of center-line extraction by estimating the surrounding edges. Efficiency is achieved by processing only pixels that are close enough to the line structures, and by carefully chosen stopping conditions. These make the proposed approach suitable for demanding image processing tasks in high throughput screening of neuron-based assays. Detailed results on experimental validation of the proposed method and on its comparative performance with other proposed schemes are included.     

NeuriteTracer: a novel ImageJ plugin for automated quantification of neurite outgrowth

In vitro assays to measure neuronal growth are a fundamental tool used by many neurobiologists studying neuronal development and regeneration. The quantification of these assays requires accurate measurements of neurite length and neuronal cell numbers in neuronal cultures. Generally, these measurements are obtained through labor-intensive manual or semi-manual tracing of images. To automate these measurements, we have written NeuriteTracer, a neurite tracing plugin for the freely available image-processing program ImageJ. The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length. We find that NeuriteTracer accurately measures neurite outgrowth from cerebellar, DRG and hippocampal neurons. Values obtained by NeuriteTracer correlate strongly with those obtained by semi-manual tracing with NeuronJ and by using a sophisticated analysis package, MetaXpress. We reveal the utility of NeuriteTracer by demonstrating its ability to detect the neurite outgrowth promoting capacity of the rho kinase inhibitor Y-27632. Our plugin is an attractive alternative to existing tracing tools because it is fully automated and ready for use within a freely accessible imaging program.     

3-D Image Pre-processing Algorithms for Improved Automated Tracing of Neuronal Arbors

The accuracy and reliability of automated neurite tracing systems is ultimately limited by image quality as reflected in the signal-to-noise ratio, contrast, and image variability. This paper describes a novel combination of image processing methods that operate on images of neurites captured by confocal and widefield microscopy, and produce synthetic images that are better suited to automated tracing. The algorithms are based on the curvelet transform (for denoising curvilinear structures and local orientation estimation), perceptual grouping by scalar voting (for elimination of non-tubular structures and improvement of neurite continuity while preserving branch points), adaptive focus detection, and depth estimation (for handling widefield images without deconvolution). The proposed methods are fast, and capable of handling large images. Their ability to handle images of unlimited size derives from automated tiling of large images along the lateral dimension, and processing of 3-D images one optical slice at a time. Their speed derives in part from the fact that the core computations are formulated in terms of the Fast Fourier Transform (FFT), and in part from parallel computation on multi-core computers. The methods are simple to apply to new images since they require very few adjustable parameters, all of which are intuitive. Examples of pre-processing DIADEM Challenge images are used to illustrate improved automated tracing resulting from our pre-processing methods.     

A comparison of manual tracing and FreeSurfer for estimating hippocampal volume over the adult lifespan

MRI has become an indispensable tool for brain volumetric studies, with the hippocampus an important region of interest. Automation of the MRI segmentation process has helped advance the field by facilitating the volumetric analysis of larger cohorts and more studies. FreeSurfer has emerged as the de facto standard tool for these analyses, but studies validating its output are all based on older versions. To characterize FreeSurfer's validity, we compare several versions of FreeSurfer software with traditional hand‐tracing. Using MRI images of 262 males and 402 females aged 38 to 84, we directly compare estimates of hippocampal volume from multiple versions of FreeSurfer, its hippocampal subfield routines, and our manual tracing protocol. We then use those estimates to assess asymmetry and atrophy, comparing performance of different estimators with each other and with brain atrophy measures. FreeSurfer consistently reports larger volumes than manual tracing. This difference is smaller in larger hippocampi or older people, with these biases weaker in version 6.0.0 than prior versions. All methods tested agree qualitatively on rightward asymmetry and increasing atrophy in older people. FreeSurfer saves time and money, and approximates the same atrophy measures as manual tracing, but it introduces biases that could require statistical adjustments in some studies.     

Posterior cervical triangle approach for carotid endarterectomy: Technical note and results

IntroductionCarotid endarterectomy (CEA) is usually performed using the anterior cervical triangle as a surgical corridor but, when needed, the retromandibular space makes dissection of higher structures difficult in some cases. The posterior cervical triangle (PCT) can be useful in these demanding cases.MethodsWe retrospectively reviewed cases from July 2013 to November 2019 in which PCT was used as an approach for CEA. The surgical technique used was explained, and the complications and evolution of the patients were analysed.ResultsWe found 7 CEAs performed through this approach, of which 2 presented transient trapezius paresis. There were no cases of severe complications in this series.ConclusionThe PCT approach for performing CEA represents a useful and easy technique that avoids the need for mandibular mobilisation or osteotomies for lesions located in anatomically high carotid bifurcations.     

A Broadly Applicable 3-D Neuron Tracing Method Based on Open-Curve Snake

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.     

A rapid and reliable procedure to localize subdural electrodes in presurgical evaluation of patients with drug-resistant focal epilepsy.

OBJECTIVES: To evaluate a novel method for localization of subdural electrodes in presurgical assessment of patients with drug-resistant focal epilepsy. METHODS: We studied eight consecutive patients with posterior epilepsy in whom subdural electrodes were implanted for presurgical evaluation. Electrodes were detected on post-implantation brain CT scans through a semiautomated procedure based on a MATLAB routine. Then, post-implantation CT scans were fused with pre-implantation MRI to localize the electrodes in relation to the underlying cortical structures. The reliability of this procedure was tested by comparing 3D-rendered MR images of the electrodes with electrode position as determined by intraoperative digital photography. RESULTS: In each patient, all electrodes could be correctly localized and visualized in a stereotactic space, thus allowing optimal surgery planning. The agreement between the procedure-generated images and the digital photographs was good according to two independent raters. The mean mismatch between the 3D images and the photographs was 2 mm. CONCLUSIONS: While our findings need confirmation on larger samples including patients with anterior epilepsy, this procedure allowed to localize subdural electrodes and to establish the spatial relationship of each electrode to the underlying brain structure, either normal or damaged, on brain convessity, basal and medial cortex. SIGNIFICANCE: Being simple, rapid, unexpensive, and reliable, this procedure holds promise to be useful to optimize epilepsy surgery planning.     

Mapping the oculomotor system: the power of transneuronal labelling with rabies virus

Neuronal networks underlying and related to horizontal eye movements were visualized by retrograde transneuronal tracing with rabies virus from the left medial rectus muscle in guinea pigs. Time-sequenced labelling revealed distinct circuitries involved in particular oculomotor functions, i.e. vestibulo-ocular reflex and saccade generation (brainstem circuitry), adaptive plasticity (cerebellar modules) and possibly motivation and navigation (limbic, hippocampal and cortical structures). Our results provide a first comprehensive road map of the oculomotor system that is unsurpassed by any previous tracing study. We report a number of unexpected findings that illustrate a much vaster and more complicated network for the control of the relatively simple horizontal eye movements than had been envisioned previously.     

Diffusion imaging-based subdivision of the human hypothalamus: a magnetic resonance study with clinical implications

The hypothalamus and its subdivisions are involved in many neuropsychiatric conditions such as affective disorders, schizophrenia, or narcolepsy, but parcellations of hypothalamic subnuclei have hitherto been feasible only with histological techniques in postmortem brains. In an attempt to map subdivisions of the hypothalamus in vivo, we analyzed the directionality information from high-resolution diffusion-weighted magnetic resonance images of healthy volunteers. We acquired T1-weighted and diffusion-weighted scans in ten healthy subjects at 3 T. In the T1-weighted images, we manually delineated an individual mask of the hypothalamus in each subject and computed in the co-registered diffusion-weighted images the similarity of the principal diffusion direction for each pair of mask voxels. By clustering the similarity matrix into three regions with a k-means algorithm, we obtained an anatomically coherent arrangement of subdivisions across hemispheres and subjects. In each hypothalamus mask, we found an anterior region with dorsoventral principal diffusion direction, a posteromedial region with rostro-caudal direction, and a lateral region with mediolateral direction. A comparative analysis with microstructural hypothalamus parcellations from the literature reveals that each of these regions corresponds to a specific group of hypothalamic subnuclei as defined in postmortem brains. This is to our best knowledge the first in vivo study that attempts a delineation of hypothalamic subdivisions by clustering diffusion-weighted magnetic resonance imaging data. When applied in a larger sample of neuropsychiatric patients, a structural analysis of hypothalamic subnuclei should contribute to a better understanding of the pathogenesis of neuropsychiatric conditions such as affective disorders.     

Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: Detecting,quantifying, and predicting

This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects. Hum Brain Mapp 35:3701–3725, 2014. © 2014 Wiley Periodicals, Inc.     

基于YCbCr空间与柔性数学形态学结合在人脸检测中的算法分析

人脸检测通常是人机系统中人机交互的第1步，在远程诊疗系统中起重要的作用。人脸检测研究不仅能为临床诊断提供客观标准，也能为面部诊断客观化和建立中医远程诊疗系统发挥重要作用。但是传统的检测方法会损失图像中有用的部分信息，而且光照对图片检测效果的影响也很大。文章对输入的图片进行从RGB空间到YCbCr空间的转换，并在YCbCr彩色空间进行色差补偿后，排除背景的干扰，分别对Y，Cb，Cr分量分别进行柔性数学形态学滤波，提取与目标有关的信息，利用在排序加权统计的方法实现形态学的运算。将图片转换为RGB图像后进行二值化得到二值图像，进行形态学滤波得到人脸检测图像。实验结果表明，该算法与传统法的人脸检测方法比较，该方法尤其适合背景较暗的图像，准确率较高，鲁棒性比较好，提取的特征点比较完全，检测效率也比较高。     

The neural basis of mental imagery

Visual mental imagery, or 'seeing with the mind's eye', has been the subject of considerable controversy in cognitive science. At issue is whether images are fundamentally different from verbal thoughts, whether they share underlying mechanisms with visual perception, and whether information in images is represented in a spatial (i.e. map-like) format. Research on the neural systems underlying imagery brings a new source of evidence to bear on these cognitive science controversies, as well as on the cerebral localization of imagery processes. Emerging from this work is the view that mental imagery involves the efferent activation of visual areas in prestriate occipital cortex, parietal and temporal cortex, and that these areas represent the same kinds of specialized visual information in imagery as they do in perception. In addition, different components of imagery processing appear to be differentially lateralized, with the generation of mental images from memory depending primarily upon structures in the posterior left hemisphere, and the rotation of mental images depending primarily upon structures in the posterior right hemisphere.     

降钙素原对接受静脉溶栓的急性脑梗死患者发生卒中相关性肺炎的预测价值

目的 探讨降钙素原(Procalcitonin,PCT)对接受静脉溶栓治疗的急性脑梗死患者发生卒中相关性肺炎(Stroke-associated pneumonia,SAP)的预测价值.方法 回顾性收集2016年1月-2019年12月在本院接受静脉溶栓的急性脑梗死患者的临床资料;采用单因素及多因素Logistic回归分...     

A penny for your thoughts! patterns of fMRI activity reveal the content and the spatial topography of visual mental images

Visual mental imagery is a complex process that may be influenced by the content of mental images. Neuropsychological evidence from patients with hemineglect suggests that in the imagery domain environments and objects may be represented separately and may be selectively affected by brain lesions. In the present study, we used functional magnetic resonance imaging (fMRI) to assess the possibility of neural segregation among mental images depicting parts of an object, of an environment (imagined from a first‐person perspective), and of a geographical map, using both a mass univariate and a multivariate approach. Data show that different brain areas are involved in different types of mental images. Imagining an environment relies mainly on regions known to be involved in navigational skills, such as the retrosplenial complex and parahippocampal gyrus, whereas imagining a geographical map mainly requires activation of the left angular gyrus, known to be involved in the representation of categorical relations. Imagining a familiar object mainly requires activation of parietal areas involved in visual space analysis in both the imagery and the perceptual domain. We also found that the pattern of activity in most of these areas specifically codes for the spatial arrangement of the parts of the mental image. Our results clearly demonstrate a functional neural segregation for different contents of mental images and suggest that visuospatial information is coded by different patterns of activity in brain areas involved in visual mental imagery. Hum Brain Mapp 36:945–958, 2015. © 2014 Wiley Periodicals, Inc.     

血清降钙素原在鉴别小儿不典型化脓性脑膜炎和病毒性脑炎的作用

目的 对比血清降钙素原(PCT)、C反应蛋白(CRP)、白细胞数(WBC)在鉴别不典型化脓性脑膜炎和病毒性脑炎中的价值.方法 把研究对象分为3组,分别为病毒性脑炎组(viral encephalitis)、不典型化脓性脑膜炎组(atypical bacterial meningitis)和典型化脓性脑膜炎组(typical bacterial meningitis),另选20名性别年龄匹配的正常儿童作为正常对照组(normal control group),分别测PCT值、CRP值和WBC值,各组指标比较应用独立样本t检验及卡方检验,并应用ROC曲线比较三者在鉴别病毒性脑炎和不典型化脓性脑膜炎的敏感性和特异性.结果 血清PCT在不典型化脓性脑膜炎、化脓性脑膜炎组与病毒性脑膜炎组、正常对照组之间差异有统计学意义;血清CRP在病毒性脑炎与化脓性脑膜炎之间差异有统计学意义,但在不典型化脓性脑膜炎与病毒性脑膜炎组之间差异无统计学意义;血清WBC在病毒性脑炎组和不典型化脓性脑膜炎组之间差异无统计学意义,但在典型化脓性脑膜炎组与病毒性脑膜炎组差异有统计学意义.不典型化脓性脑膜炎组与病毒性脑炎组中,PCT对不典型化脓性脑膜炎诊断的ROC曲线下面积93.9%,大于WBC和CRP.结论 不典型化脓性脑膜炎时,PCT升高,病毒性脑炎时PCT升高不明显.PCT在鉴别不典型化脓性脑膜炎和病毒性脑炎时有一定临床应用价值.     

Soma Detection in 3D Images of Neurons using Machine Learning Technique

Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust.     

Classification of neuronal spikes over the reconstructed phase space

Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences.     

Automated analysis of fundamental features of brain structures

Automated image analysis of the brain should include measures of fundamental structural features such as size and shape. We used principal axes (P-A) measurements to measure overall size and shape of brain structures segmented from MR brain images. The rationale was that quantitative volumetric studies of brain structures would benefit from shape standardization as had been shown for whole brain studies. P-A analysis software was extended to include controls for variability in position and orientation to support individual structure spatial normalization (ISSN). The rationale was that ISSN would provide a bias-free means to remove elementary sources of a structure’s spatial variability in preparation for more detailed analyses. We studied nine brain structures (whole brain, cerebral hemispheres, cerebellum, brainstem, caudate, putamen, hippocampus, inferior frontal gyrus, and precuneus) from the 40-brain LPBA40 atlas. This paper provides the first report of anatomical positions and principal axes orientations within a standard reference frame, in addition to “shape/size related” principal axes measures, for the nine brain structures from the LPBA40 atlas. Analysis showed that overall size (mean volume) for internal brain structures was preserved using shape standardization while variance was reduced by more than 50%. Shape standardization provides increased statistical power for between-group volumetric studies of brain structures compared to volumetric studies that control only for whole brain size. To test ISSN’s ability to control for spatial variability of brain structures we evaluated the overlap of 40 regions of interest (ROIs) in a standard reference frame for the nine different brain structures before and after processing. Standardizations of orientation or shape were ineffective when not combined with position standardization. The greatest reduction in spatial variability was seen for combined standardizations of position, orientation and shape. These results show that ISSNs automated processing can be a valuable asset for measuring and controlling variability of fundamental features of brain structures.