Human brain structural change related to acute single exposure to sarin

Objective This study aimed to identify persistent morphological changes subsequent to an acute single-time exposure to sarin, a highly poisonous organophosphate, and the neurobiological basis of long-lasting somatic and cognitive symptoms in victims exposed to sarin.

Methods Thirty-eight victims of the 1995 Tokyo subway sarin attack, all of whom had been treated in an emergency department for sarin intoxication, and 76 matched healthy control subjects underwent T1-weighted and diffusion tensor magnetic resonance imaging (DTI) in 2000 to 2001. Serum cholinesterase (ChE) levels measured immediately and longitudinally after the exposure and the current severity of chronic reports in the victims were also evaluated.

Results The voxel-based morphometry exhibited smaller than normal regional brain volumes in the insular cortex and neighboring white matter, as well as in the hippocampus in the victims. The reduced regional white matter volume correlated with decreased serum cholinesterase levels and with the severity of chronic somatic complaints related to interoceptive awareness. Voxel-based analysis of diffusion tensor magnetic resonance imaging further demonstrated an extensively lower than normal fractional anisotropy in the victims. All these findings were statistically significant (corrected p < 0.05).

Interpretation Sarin intoxication might be associated with structural changes in specific regions of the human brain, including those surrounding the insular cortex, which might be related to elevated subjective awareness of internal bodily status in exposed individuals.

Gender-common and -specific neuroanatomical basis of human anxiety-related personality traits

Exploration of the relationships between regional brain volume and anxiety-related personality traits is important for understanding preexisting vulnerability to depressive and anxiety disorders. However, previous studies on this topic have employed relatively limited sample sizes and/or image processing methodology, and they have not clarified possible gender differences. In the present study, 183 (male/female: 117/66) right-handed healthy individuals in the third and fourth decades of life underwent structural magnetic resonance imaging scans and Temperament and Character Inventory. Neuroanatomical correlates of individual differences in the score of harm avoidance (HA) were examined throughout the entire brain using voxel-based morphometry. We found that higher scores on HA were associated with smaller regional gray matter volume in the right hippocampus, which was common to both genders. In contrast, female-specific correlation was found between higher anxiety-related personality traits and smaller regional brain volume in the left anterior prefrontal cortex. The present findings suggest that smaller right hippocampal volume underlies the basis for higher anxiety-related traits common to both genders, whereas anterior prefrontal volume contributes only in females. The results may have implications for why susceptibility to stress-related disorders such as anxiety disorders and depression shows gender and/or individual differences.

Functional (GT)n polymorphisms in promoter region of N-methyl-d-aspartate receptor 2A subunit (GRIN2A) gene affect hippocampal and amygdala volumes

The glutamate system including N-methyl-d-aspartate (NMDA) affects synaptic formation, plasticity and maintenance. Recent studies have shown a variable (GT)n polymorphism in the promoter region of the NMDA subunit gene (GRIN2A) and a length-dependent inhibition of transcriptional activity by the (GT)n repeat. In the present study, we examined whether the GRIN2A polymorphism is associated with regional brain volume especially in medial temporal lobe structures, in which the NMDA-dependent synaptic processes have been most extensively studied. Gray matter regions of interest (ROIs) for the bilateral amygdala and hippocampus were outlined manually on the magnetic resonance images of 144 healthy individuals. In addition, voxel-based morphometry (VBM) was conducted to explore the association of genotype with regional gray matter volume from everywhere in the brain in the same sample. The manually measured hippocampal and amygdala volumes were significantly larger in subjects with short allele carriers (n = 89) than in those with homozygous long alleles (n = 55) when individual differences in intracranial volume were accounted for. The VBM showed no significant association between the genotype and regional gray matter volume in any brain region. These findings suggest that the functional GRIN2A (GT)n polymorphism could weakly but significantly impact on human medial temporal lobe volume in a length-dependent manner, providing in vivo evidence of the role of the NMDA receptor in human brain development.

Association between mitochondrial DNA 10398A>G polymorphism and the volume of amygdala

Mitochondrial calcium regulation plays a number of important roles in neurons. Mitochondrial DNA (mtDNA) is highly polymorphic, and its interindividual variation is associated with various neuropsychiatric diseases and mental functions. An mtDNA polymorphism, 10398A>G, was reported to affect mitochondrial calcium regulation. Volume of hippocampus and amygdala is reportedly associated with various mental disorders and mental functions and is regarded as an endophenotype of mental disorders. The present study investigated the relationship between the mtDNA 10398A>G polymorphism and the volume of hippocampus and amygdala in 118 right-handed healthy subjects. The brain morphometry using magnetic resonance images employed both manual tracing volumetry in the native space and voxel-based morphometry (VBM) in the spatially normalized space. Amygdala volume was found to be significantly larger in healthy subjects with 10398A than in those with 10398G by manual tracing, which was confirmed by the VBM. Brain volumes in the other gray matter regions and all white matter regions showed no significant differences associated with the polymorphism. These provocative findings might provide a clue to the complex relationship between mtDNA, brain structure and mental disorders.

Cocaine use severity and cerebellar gray matter are associated with reversal learning deficits in cocaine-dependent individuals

Cocaine addiction involves persistent deficits to unlearn previously rewarded response options, potentially due to neuroadaptations in learning-sensitive regions. Cocaine-targeted prefrontal systems have been consistently associated with reinforcement learning and reversal deficits, but more recent interspecies research has raised awareness about the contribution of the cerebellum to cocaine addiction and reversal. We aimed at investigating the link between cocaine use, reversal learning and prefrontal, insula and cerebellar gray matter in cocaine-dependent individuals (CDIs) varying on levels of cocaine exposure in comparison with healthy controls (HCs). Twenty CDIs and 21 HCs performed a probabilistic reversal learning task (PRLT) and were subsequently scanned in a 3-Tesla magnetic resonance imaging scanner. In the PRLT, subjects progressively learn to respond to one predominantly reinforced stimulus, and thenmust learn to respond according to the opposite, previously irrelevant, stimulus-reward pairing. Performance measureswere errors after reversal (reversal cost), and probability of maintaining response after errors. Voxel-based morphometry was conducted to investigate the association between gray matter volume in the regions of interest and cocaine use and PRLT performance. Severity of cocaine use correlated with gray matter volume reduction in the left cerebellum (lobule VIII), while greater reversal cost was correlated with gray matter volume reduction in a partially overlapping cluster (lobules VIIb and VIII). Right insula/inferior frontal gyrus correlated with probability of maintaining response after errors. Severity of cocaine use detrimentally impacted reversal learning and cerebellar gray matter.

Central pathways causing fatigue in neuro-inflammatory and autoimmune illnesses

The genesis of severe fatigue and disability in people following acute pathogen invasion involves the activation of Toll-like receptors followed by the upregulation of proinflammatory cytokines and the activation of microglia and astrocytes. Many patients suffering from neuroinflammatory and autoimmune diseases, such as multiple sclerosis, Parkinson's disease and systemic lupus erythematosus, also commonly suffer from severe disabling fatigue. Such patients also present with chronic peripheral immune activation and systemic inflammation in the guise of elevated proinflammtory cytokines, oxidative stress and activated Toll-like receptors. This is also true of many patients presenting with severe, apparently idiopathic, fatigue accompanied by profound levels of physical and cognitive disability often afforded the non-specific diagnosis of chronic fatigue syndrome.

Neural correlates of impaired self-awareness of apathy, disinhibition and dysexecutive deficits in cocaine-dependent individuals

Cocaine addiction is characterized by impaired self-awareness about cognitive and motivational deficits, leading to poor treatment outcomes. However, there is still limited understanding of the neurophysiological underpinnings of this impairment. We aimed to establish if impaired self-awareness is underpinned by brain structural phenotypes among cocaine-dependent individuals (CDI). Sixty-five CDI and 65 designated informants completed the Frontal Systems Behavior Scale, and a subsample of 40 CDI were scanned via magnetic resonance imaging. We applied multiple regression models to establish the association between levels of self-awareness indexed by Frontal Systems Behavior Scale's discrepancy scores (i.e. informant ratings minus self-reports of apathy, disinhibition and dysexecutive deficits) and gray matter volumes indexed by magnetic resonance imaging voxel-based measures within five brain regions of interest: anterior cingulate cortex, orbitofrontal cortex (OFC), striatum, insula and dorsolateral prefrontal cortex (DLPFC). We also examined the neural underpinnings of underestimation versus overestimation of deficits, by splitting the CDI group according to the positive or negative value of their discrepancy scores. We found that poorer self-awareness of apathy deficits was associated with greater gray matter volume in the dorsal striatum, and poorer self-awareness of disinhibition deficits was associated with greater gray matter volume in the OFC in the whole sample. More underestimation and more overestimation of executive deficits were linked to lower DLPFC volume. We show that impaired self-awareness of cognitive and motivational deficits in cocaine addiction has a neural underpinning, implicating striatum, OFC and DLPFC structural phenotypes.

Culture-based fisheries in non-perennial reservoirs of Sri Lanka: influence of reservoir morphometry and stocking density on yield

Culture-based fish yield in non-perennial reservoirs of Sri Lanka was related to reservoir morphometry and stocking density. The reservoirs were stocked mainly with fingerlings of one Chinese and three Indian major carp species, common carp, Cyprinus carpio L., and the genetically improved farmed tilapia strain of Nile tilapia, Oreochromis niloticus (L.), at four pre-determined species combinations and a range of stocking densities [SD (fingerlings ha⁻¹)]. Twenty-three reservoirs were harvested successfully at the end of the culture period of 2002–2003. Basic limnological and morphometric parameters, including shoreline development (D_L) and shoreline area ratio (R_LA), were estimated for each of the 23 reservoirs. Bray–Curtis similarity and non-metric multidimensional scaling using mean values of limnological data revealed that reservoirs could be ordinated into two major clusters, one with intact sample distribution due to similar trophic characteristics and the other with scattered sample distribution. Reservoirs in the cluster with similar trophic characteristics showed significant correlation (P < 0.05) between R_LA and total fish yield (Y). A multiple regression equation, Y = −693 + 4810 R_LA + 0.484 SD, was generated to estimate fish harvest in relation to SD.

3D haptic needle insertion simulator utilising an agent based spherical voxel model

This paper describes a technique for the real-time modeling of deformable tissue. Specifically geared towards needle insertion simulation, the low computational requirements of the model enable highly accurate haptic feedback to a user without introducing noticeable time delay or buzzing generally associated with haptic surgery simulation. Using a spherical voxel array combined with aspects of computational geometry and agent communication and interaction principals, the model is capable of providing haptic update rates of over 1000Hz with real-time visual feedback. Iterating through over 1000 voxels per millisecond to determine collision and haptic response while making use of Vieta’s Theorem for extraneous force culling.

Function-based approach to mixed haptic effects rendering

Commonly, surface and solid haptic effects are defined in such a way that they hardly can be rendered together. We propose a method for defining mixed haptic effects including surface, solid, and force fields. These haptic effects can be applied to virtual scenes containing various objects, including polygon meshes, point clouds, impostors, and layered textures, voxel models as well as function-based shapes. Accordingly, we propose a way how to identify location of the haptic tool in such virtual scenes as well as consistently and seamlessly determine haptic effects when the haptic tool moves in the scenes with objects having different sizes, locations, and mutual penetrations. To provide for an efficient and flexible rendering of haptic effects, we propose to concurrently use explicit, implicit and parametric functions, and algorithmic procedures.

Function-based single and dual point haptic interaction in Cyberworlds

Polygon and point based models dominate virtual reality. These models also affect haptic rendering algorithms, which are often based on collision with polygons. With application to dual point haptic devices for operations like grasping, complex polygon and point based models will make the collision detection procedure slow. This results in the system not able to achieve interactivity for force rendering. To solve this issue, we use mathematical functions to define and implement geometry (curves, surfaces and solid objects), visual appearance (3D colours and geometric textures) and various tangible physical properties (elasticity, friction, viscosity, and force fields). The function definitions are given as analytical formulas (explicit, implicit and parametric), function scripts and procedures. We proposed an algorithm for haptic rendering of virtual scenes including mutually penetrating objects with different sizes and arbitrary location of the observer without a prior knowledge of the scene to be rendered. The algorithm is based on casting multiple haptic rendering rays from the Haptic Interaction Point (HIP), and it builds a stack to keep track on all colliding objects with the HIP. The algorithm uses collision detection based on implicit function representation of the object surfaces. The proposed approach allows us to be flexible when choosing the actual rendering platform, while it can also be easily adopted for dual point haptic collision detection as well as force and torque rendering. The function-defined objects and parts constituting them can be used together with other common definitions of virtual objects such as polygon meshes, point sets, voxel volumes, etc. We implemented an extension of X3D and VRML as well as several standalone application examples to validate the proposed methodology. Experiments show that our concern about fast, accurate rendering as well as compact representation could be fulfilled in various application scenarios and on both single and dual point haptic devices.

Fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images via a learning-based method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.