The tentorium and anterior head sulci in Dictyoptera and Mantophasmatodea (Insecta)

The tentorium, the anterior sulci of the head capsule (epistomal, subgenal, subantennal, circumantennal, and circumocular sulci), and the extension of the anterior tentorial pit were studied in 26 species of Blattaria (representing most principal lineages), 4 species of Mantodea (including the basal Mantoida schraderi), and 1 species each of Isoptera (the basal Mastotermes darwiniensis) and Mantophasmatodea (Austrophasma caledonense). The morphology of these head structures is compared with literature data on other insect orders, mainly Phasmatodea, Orthoptera, Dermaptera, Embioptera, and Plecoptera, and partly Odonata and Zygentoma. Characters are defined, presented in a matrix, and evaluated with regard to phylogenetic implications and homoplastic evolution. The structural relationships of the subantennal sulcus to the subgenal, circumocular, and circumantennal sulci, which are highly variable and strongly homoplastic (depending much on the size of the compound eyes) are a focal issue; several types of subantennal sulci are defined. The presence of an anterior transverse bridge in the tentorium (“perforated tentorium”) of all Dictyoptera here studied confirms the monophyly of this group. Mantophasmatodea lacks this element.     

Cortical gyrification and sulcal spans in early stage Alzheimer's disease

Alzheimer's disease (AD) is characterized by an insidious onset of progressive cerebral atrophy and cognitive decline. Previous research suggests that cortical folding and sulcal width are associated with cognitive function in elderly individuals, and the aim of the present study was to investigate these morphological measures in patients with AD. The sample contained 161 participants, comprising 80 normal controls, 57 patients with very mild AD, and 24 patients with mild AD. From 3D T1-weighted brain scans, automated methods were used to calculate an index of global cortex gyrification and the width of five individual sulci: superior frontal, intra-parietal, superior temporal, central, and Sylvian fissure. We found that global cortex gyrification decreased with increasing severity of AD, and that the width of all individual sulci investigated other than the intra-parietal sulcus was greater in patients with mild AD than in controls. We also found that cognitive functioning, as assessed by Mini-Mental State Examination (MMSE) scores, decreased as global cortex gyrification decreased. MMSE scores also decreased in association with a widening of all individual sulci investigated other than the intra-parietal sulcus. The results suggest that abnormalities of global cortex gyrification and regional sulcal span are characteristic of patients with even very mild AD, and could thus facilitate the early diagnosis of this condition.     

Sex differences in regional brain response to aversive pelvic visceral stimuli

To explore sex differences in the response of seven brain regions to an aversive pelvic visceral stimulus, functional magnetic resonance images were acquired from 13 healthy adults (6 women) during 15 s of cued rectal distension at two pressures: 25 mmHg (uncomfortable), and 45 mmHg (mild pain), as well as during an expectation condition (no distension). Random-effects analyses combining subject data voxelwise found 45-mmHg pressure significantly activated the insular and anterior cingulate cortices in both sexes. In men only, the left thalamus and ventral striatum were also activated. Although all activations appeared more extensive in men, no sex difference attained significance. To explore the presence of deactivations, which are generally cancelled by more numerous activations when subjects are combined for each voxel, the number of activated voxels, number of deactivated voxels, and ratio of deactivated voxels to total voxels affected were assessed via random-effects, mixed-model analyses combining subject data at the region level. Greater insula activation in men compared with women was seen during the expectation condition and during the 25-mmHg distension. Greater deactivations in women were seen in the amygdala (25-mmHg distension) and midcingulate (45-mmHg distension). Women had a significantly higher proportion of deactivated voxels than men in all four subcortical structures during 25-mmHg distension. Greater familiarity of females with physiological pelvic visceral discomfort may have enhanced brain systems that dampen arousal networks during lower levels of discomfort.     

Measuring Granger Causality between cortical regions from voxelwise fMRI BOLD signals with LASSO

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis.     

Evaluating the Accuracy of Diffusion MRI Models in White Matter

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of commonly used models have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM model-accuracy, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking.     

Reconciling root plasticity and architectural ground rules in tree root growth models with voxel automata

Dynamic models of tree root growth and function have to reconcile the architectural rules for coarse root topology with the dynamics of fine root growth (and decay) in order to predict the strategic plus opportunistic behaviour of a tree root system in a heterogeneous soil. We present an algorithm for a 3D model based on both local (soil voxel level) and global (tree level) controls of root growth, with development of structural roots as a consequence of fine root function, rather than as driver. The suggested allocation rules of carbon to fine root growth in each rooted voxel depend on the success in water uptake in this voxel during the previous day, relative to overall supply and demand at plant level. The allocated C in each voxel is then split into proliferation (within voxel growth) and extension into neighbouring voxels (colonisation), with scale-dependent thresholds and transfer coefficients. The fine root colonisation process defines a dynamic and spatially explicit demand for transport functions. C allocation to development of a coarse root infrastructure linking all rooted voxels depends on the apparent need for adjustment of root diameter to meet the topologically defined sap flow through this voxel during the previous day. The allometric properties of the coarse root system are maintained to be in line with fractal branching theory. The model can predict the dynamics of the shape and structure (fine root density, coarse root topology and biomass) of the root system either independently of soil conditions (purely genetically-driven) or including both the genetic and environmental effects of roots interacting with soil water supply and its external replenishment, linking in with existing water balance models. Sensitivity of the initial model to voxel dimensions was addressed through explicit scaling rules resulting in scale-independent parameters. The model was parameterised for two tree species: hybrid walnut (Juglans nigra × regia) and wild cherry (Prunus avium L.) using results of a pot experiment. The model satisfactorily predicted the root growth behaviour of the two species. The model is sparse in parameters and yet applicable to heterogeneous soils, and could easily be upgraded to include additional local influences on root growth (and decay) such as local success in nutrient uptake or dynamic soil physical properties.     

金丝猴(RHINOPITHECUS)脑的外部形态

本文是对三种金丝猴脑的外部形态的观察结果表明:金丝猴的大脑皮层除存在猴科固有的全部沟裂外,还较其他猴科动物具有更多的副沟。大脑的沟型与疣猴亚科的特征完全吻合,但是很多特点都较叶猴更近似于长臂猿。另外,除了蚓叶和蚓结节较小,四叠体的下丘较猕猴发达以外,金丝猴的小脑和脑干均与猴科的一般特征无明显差异。     

Three-dimensional reconstruction and characterization of human external shapes from two-dimensional images using volumetric methods

This work presents a volumetric approach to reconstruct and characterise 3D models of external anatomical structures from 2D images. Volumetric methods represent the final volume using a finite set of 3D geometric primitives, usually designed as voxels. Thus, from an image sequence acquired around the object to reconstruct, the images are calibrated and the 3D models of the referred object are built using different approaches of volumetric methods. The final goal is to analyse the accuracy of the obtained models when modifying some of the parameters of the considered volumetric methods, such as the type of voxel projection (rectangular or accurate), the way the consistency of the voxels is tested (only silhouettes or silhouettes and photo-consistency) and the initial size of the reconstructed volume.     

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data

Background

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming.

Methodology/Principal Findings

Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time.

Conclusions/Significance

The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.     

Modified test statistics by inter-voxel variance shrinkage with an application to f MRI

Functional magnetic resonance imaging (f MRI) is a noninvasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of f MRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels is desirable in order to improve efficiency. Here, we construct a hierarchical model and apply an empirical Bayesian framework for the analysis of group f MRI data, employing techniques used in high-throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels and subsequently to construct an improved test statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances toward a common value. The shrunken estimator for voxel-specific variance components on the group analyses outperforms the classical residual error estimator in terms of mean-squared error. Moreover, the shrunken test statistic decreases false-positive rates when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.     

Septation of the anorectal and genitourinary tracts in the human embryo: crucial role of the catenoidal shape of the urorectal sulcus

BACKGROUND: Previous studies of the tracheoesophageal sulcus and the sulci of the developing heart have suggested that the catenoidal or saddle-shaped configuration of the sulcus had mechanical properties that were important to developmental processes by causing regional growth limitation. We examined the development of the human perineal region to determine if a similar configuration exists in relation to the urorectal septum. We wished to re-examine the controversial issue of the role of the urorectal sulcus in the partitioning of the cloaca. METHODS: Digitally scanned photomicrographs of serial histologic sections of embryos from Carnegie stages 13, 15, 18, and 22, obtained from the Carnegie Embryological Collection were used. Each image was digitally stacked, aligned, and isolated using image-editing software. Images were compiled using 3-D image-visualization software (T-Vox), into full 3-D voxel-based volume renderings. Similarly, digital models were made of the urogenital sinus, anorectum, cloaca, allantois, mesonephric ducts, ureters, and kidneys by isolating their associated epithelium in each histologic section and compiling the data in T-Vox. Methods were developed to create registration models for determining the exact position and orientation of outlined structures within the embryos. RESULTS: Models were oriented and resectioned to determine the configuration of the urorectal sulcus. The results show that the urorectal sulcus maintains a catenoidal configuration during the developmental period studied and, thus, would be expected to limit caudal growth of the urorectal septum. CONCLUSION: The observations support the concept that the urorectal septum is a passive structure that does not actively divide the cloaca into urogenital and anorectal components.     

藏北发现的大型猫科动物脑化石

本文主要报道采自西藏(Xizang)北部,伦波拉盆地南缘,色林错东南岸的一块大型猫科(Felidae)动物的脑化石。因为没有发现任何与其共生的哺乳动物化石,其时代只能依据猫科动物中虎豹属(Panthera)的生活时代,定为上新世至更新世(Pliocene-Pleistocene)。本文可能是中国哺乳动物脑化石的首次研究报道。     

Modeling the effect of the layer thickness and tissue conductivities of the head and the brain on the EEG potentials using finite element method

The influence of the thickness and specific resistance of the brain tissues and its surrounding coverings, brain sulci and anisotropy of white matter electric conductivity on the EEG potentials were modeled. The finite element method was used for simulation, which is realized in the Femlab module. During the comparison of two models with different thickness of the layers was shown the change of potentials by 40% for scull, which thickness varied by 9 mm, and for the gray matter and spinal fluid the change of potentials amounted to 15% by 3 mm variable thickness. The change of conductivity by 25% resulted in the difference of potentials amounted to 15% for the white matter and 1.5% for the cortex of the brain. During the assignment of the anisotropy in the model, by the whole volume of the white matter, sixfold difference of the potentials in comparison with application of isotropic parameters was discovered. The difference of the potentials for single heterogeneity, simulated the sulci of the brain, amounted to 10% in the heterogeneity projection point and increased during the drawing near to the "referent electrode". The model complex of the "sulci of cerebrum" gave significant contribution to the difference of the potentials and depended on the depth of dipole occurrence and nearness of the sulci complex. Significant influence of brain sulci on the EEG-potentials distribution, which can give rise to great artifacts, was discovered.     

Identifying Cognitive States Using Regularity Partitions

Functional Magnetic Resonance (fMRI) data can be used to depict functional connectivity of the brain. Standard techniques have been developed to construct brain networks from this data; typically nodes are considered as voxels or sets of voxels with weighted edges between them representing measures of correlation. Identifying cognitive states based on fMRI data is connected with recording voxel activity over a certain time interval. Using this information, network and machine learning techniques can be applied to discriminate the cognitive states of the subjects by exploring different features of data. In this work we wish to describe and understand the organization of brain connectivity networks under cognitive tasks. In particular, we use a regularity partitioning algorithm that finds clusters of vertices such that they all behave with each other almost like random bipartite graphs. Based on the random approximation of the graph, we calculate a lower bound on the number of triangles as well as the expectation of the distribution of the edges in each subject and state. We investigate the results by comparing them to the state of the art algorithms for exploring connectivity and we argue that during epochs that the subject is exposed to stimulus, the inspected part of the brain is organized in an efficient way that enables enhanced functionality.     

Voxel-Wise Perfusion Assessment in Cerebral White Matter with PCASL at 3T; Is It Possible and How Long Does It Take?

Purpose

To establish whether reliable voxel-wise assessment of perfusion in cerebral white matter (WM) is possible using arterial spin labeling (ASL) at 3T in a cohort of healthy subjects.

Materials and Methods

Pseudo-continuous ASL (PCASL) with background suppression (BS) optimized for WM measurements was performed at 3T in eight healthy male volunteers aged 25–41. Four different labeling schemes were evaluated by varying the labeling duration (LD) and post-labeling delay (PLD). Eight slices with voxel dimension 3.75x3.75x5 mm³ were acquired from the anterosuperior aspect of the brain, and 400 image/control pairs were collected for each run. Rigid head immobilization was applied using individually fitted thermoplastic masks. For each voxel in the resulting ASL time series, the time needed to reach a 95% significance level for the ASL signal to be higher than zero (paired t-test), was estimated.

Results

The four protocols detected between 88% and 95% (after Bonferroni correction: 75% and 88%) of WM voxels at 95% significance level. In the most efficient sequence, 80% was reached after 5 min and 95% after 53 min (after Bonferroni correction 40% and 88% respectively). For all protocols, the fraction of significant WM voxels increased in an asymptotic fashion with increasing scan time. A small subgroup of voxels was shown to not benefit at all from prolonged measurement.

Conclusion

Acquisition of a significant ASL signal from a majority of WM voxels is possible within clinically acceptable scan times, whereas full coverage needs prohibitively long scan times, as a result of the asymptotic trajectory.     

Voxel-Based Sensitivity of Flat-Panel CT for the Detection of Intracranial Hemorrhage: Comparison to Multi-Detector CT

ObjectivesFlat-panel CT (FPCT) allows cross-sectional parenchymal, vascular and perfusion imaging within the angiography suite, which could greatly facilitate acute stroke management. We hypothesized that FPCT offers equal diagnostic accuracy compared to multi-detector CT (MDCT) as a primary tool to exclude intracranial hemorrhage.Methods22 patients with intracranial hematomas who had both MDCT and FPCT performed within 24 hours were retrospectively identified. Patients with visible change in hematoma size or configuration were excluded. Two raters independently segmented hemorrhagic lesions. Data sets and corresponding binary lesion maps were co-registered to compare hematoma volume. Diagnostic accuracy of FPCT to detect hemorrhage was calculated from voxel-wise analysis of lesion overlap compared to reference MDCT.ResultsMean hematoma size was similar between MDCT (16.2±8.9 ml) and FPCT (16.1±8.6 ml), with near perfect correlation of hematoma sizes between modalities (ρ = 0.95, p<0.001). Sensitivity and specificity of FPCT to detect hemorrhagic voxels was 61.6% and 99.8% for intraventricular hematomas and 67.7% and 99.5% for all other intracranial hematomas.ConclusionsIn this small sample containing predominantly cases with subarachnoid hemorrhage, FPCT based assessment of hemorrhagic volume in brain yields acceptable accuracy compared to reference MDCT, albeit with a limited sensitivity on a voxel level. Further assessment and improvement of FPCT is necessary before it can be applied as a primary imaging modality to exclude intracranial hemorrhage in acute stroke patients.     

The radiographic preauricular groove: its non-relationship to past parity

Deep preauricular sulci were identified on abdominal radiographs in 29 of 190 (15%) adult females and in none of 110 adult males. To assess the value of the deep preauricular sulcus as an index of past pregnancy, we examined gravidity and parity records of 190 women, using standard films that included the sacroiliac region. Deep, radiographic preauricular grooves were identified in 4 of 41 (10%) nulliparous women and in 25 of 149 (17%) women with positive pregnancy histories. We also examined radiographs obtained before and after pregnancy in six primigravidas. No evidence of radiographic changes in the preauricular grooves was seen in any of the six women. We conclude that the presence of a deep, radiographic preauricular sulcus is not necessarily an indication of past pregnancy.     

Frequency-Dependent Modulation of Regional Synchrony in the Human Brain by Eyes Open and Eyes Closed Resting-States

The eyes-open (EO) and eyes-closed (EC) states have differential effects on BOLD-fMRI signal dynamics, affecting both the BOLD oscillation frequency of a single voxel and the regional homogeneity (ReHo) of several neighboring voxels. To explore how the two resting-states modulate the local synchrony through different frequency bands, we decomposed the time series of each voxel into several components that fell into distinct frequency bands. The ReHo in each of the bands was calculated and compared between the EO and EC conditions. The cross-voxel correlations between the mean frequency and the overall ReHo of each voxel’s original BOLD series in different brain areas were also calculated and compared between the two states. Compared with the EC state, ReHo decreased with EO in a wide frequency band of 0.01–0.25 Hz in the bilateral thalamus, sensorimotor network, and superior temporal gyrus, while ReHo increased significantly in the band of 0–0.01 Hz in the primary visual cortex, and in a higher frequency band of 0.02–0.1 Hz in the higher order visual areas. The cross-voxel correlations between the frequency and overall ReHo were negative in all the brain areas but varied from region to region. These correlations were stronger with EO in the visual network and the default mode network. Our results suggested that different frequency bands of ReHo showed different sensitivity to the modulation of EO-EC states. The better spatial consistency between the frequency and overall ReHo maps indicated that the brain might adopt a stricter frequency-dependent configuration with EO than with EC.     

Micromechanical analysis of brain’s diffuse axonal injury

Computational models are important tools which help researchers understand traumatic brain injury (TBI). A mechanistic multi-scale numerical approach is introduced to quantify diffuse axonal injury (DAI), the most important mechanism of TBI, induced by a mechanical insult at micro-scale regions of the white matter or voxels where fiber orientations are the same. Using the mechanical properties of a single axon with a viscoelastic constitutive relation and its functional failure in terms of electrophysiological impairment, a numerical 2D micro-level lattice method is implemented to directly analyze the percentage of injured axons in a voxel containing a bundle of axons all with the same orientation under biaxial stretches. Reference micro-injury maps are then developed with the input parameters based on the principal strain or stretch values and their direction with respect to axons, which provide the percentage of injured axons in the voxel of interest as the output. The methodology is independent of any statistical analyses of the accident data and medical reports to derive probabilistic injury risk curves for DAI. Avoiding a structurally detailed full finite element head model, this study proposes a micro-mechanical approach which considers the anatomical structure of neural axons in the white matter together with their mechanical properties using a numerical lattice method to analyze the brain’s diffuse axonal injury. This work has the potential to help develop safer prevention tools and more effective diagnosis methods for DAI.