Gender-common and -specific neuroanatomical basis of human anxiety-related personality traits.
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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. (+info)
Genetic algorithms for finite mixture model based voxel classification in neuroimaging.
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Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging, positron emission tomography, and mouse brain MRI. The tissue classification results by our method are shown to be consistently more reliable and accurate than with the competing parameter estimation methods. (+info)
Dynamic visualization of the developing nervous system of the bullfrog, Rana catesbeiana.
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Anuran amphibians undergo a rapid and dramatic process of metamorphosis featuring widespread structural reorganization of the central nervous system. Although morphological changes during embryonic stages of anuran development have been well documented, much less information is available describing structural changes in the brain during larval (tadpole) stages. Using still images from cresyl-violet-stained material, we present an adaptation of the digital image and video manipulation technique of morphing that allows these images to be compiled in such a manner as to highlight key periods in tadpole brain development in a dynamic fashion. We present three morphed video data sets from ranid tadpoles that facilitate the identification of developmental changes in nuclear boundaries at different levels of the neuraxis. The use of animation allows dynamic examination of anatomical changes across long developmental spans without requiring additional anatomical preparations or specialized expensive equipment. (+info)
The neuron classification problem.
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A systematic account of neuron cell types is a basic prerequisite for determining the vertebrate nervous system global wiring diagram. With comprehensive lineage and phylogenetic information unavailable, a general ontology based on structure-function taxonomy is proposed and implemented in a knowledge management system, and a prototype analysis of select regions (including retina, cerebellum, and hypothalamus) presented. The supporting Brain Architecture Knowledge Management System (BAMS) Neuron ontology is online and its user interface allows queries about terms and their definitions, classification criteria based on the original literature and "Petilla Convention" guidelines, hierarchies, and relations-with annotations documenting each ontology entry. Combined with three BAMS modules for neural regions, connections between regions and neuron types, and molecules, the Neuron ontology provides a general framework for physical descriptions and computational modeling of neural systems. The knowledge management system interacts with other web resources, is accessible in both XML and RDF/OWL, is extendible to the whole body, and awaits large-scale data population requiring community participation for timely implementation. (+info)
A multiarchitectonic approach for the definition of functionally distinct areas and domains in the monkey frontal lobe.
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Over the last century, anatomical studies have shown that the cerebral cortex can be subdivided into structurally distinct regions, giving rise to a new branch of neuroanatomy: 'architectonics'. Since then, architectonics has been often accused of being overly subjective, and its validity for the definition of functionally different cortical fields has been seriously questioned. Since the late 1980s, however, the problem of localization has become particularly important in functional studies of the primate motor cortex, because of evidence that (1) the primate motor cortex is made up of a mosaic of functionally specialized areas and (2) the human motor cortex shares several general organizational principles with the monkey motor cortex. Studies of the macaque agranular frontal cortex that used a multimodal cyto-, myelo- and immuno-architectonic approach have shown that architectonic borders can be reliably and consistently defined across different individuals, even at a qualitative level of analysis. The validity of this approach has been confirmed by its ability to localize functionally distinct areas precisely and to predict the existence of new functional areas. After more than a century, architectonics as a discipline goes far beyond its original aim of generating cortical maps. (+info)
Neuromuscular anatomy and evolution of the cetacean forelimb.
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The forelimb of cetaceans (whales, dolphins, and porpoises) has been radically modified during the limb-to-flipper transition. Extant cetaceans have a soft tissue flipper encasing the manus and acting as a hydrofoil to generate lift. The neuromuscular anatomy that controls flipper movement, however, is poorly understood. This study documents flipper neuromuscular anatomy and tests the hypothesis that antebrachial muscle robustness is related to body size. Data were gathered during dissections of 22 flippers, representing 15 species (7 odontocetes, 15 mysticetes). Results were compared with published descriptions of both artiodactyls and secondarily aquatic vertebrates. Results indicate muscle robustness is best predicted by taxonomic distribution and is not a function of body size. All cetaceans have atrophied triceps muscles, an immobile cubital joint, and lack most connective tissue structures and manus muscles. Forelimbs retain only three muscle groups: triceps (only the scapular head is functional as the humeral heads are vestigal), and antebrachial extensors and flexors. Well-developed flexor and extensor muscles were found in mysticetes and basal odontocetes (i.e., physeterids, kogiids, and ziphiids), whereas later diverging odontocetes (i.e., monodontids, phocoenids, and delphinids) lack or reduce these muscles. Balaenopterid mysticetes (e.g., fin and minke whales) may actively change flipper curvature, while basal odontocetes (e.g., sperm and beaked whales) probably stiffen the flipper through isometric contraction. Later diverging odontocetes lack musculature supporting digital movements and are unable to manipulate flipper curvature. Cetacean forelimbs are unique in that they have lost agility and several soft tissue structures, but retain sensory innervations. (+info)
The dentate gyrus: fundamental neuroanatomical organization (dentate gyrus for dummies).
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The dentate gyrus is a simple cortical region that is an integral portion of the larger functional brain system called the hippocampal formation. In this review, the fundamental neuroanatomical organization of the dentate gyrus is described, including principal cell types and their connectivity, and a summary of the major extrinsic inputs of the dentate gyrus is provided. Together, this information provides essential information that can serve as an introduction to the dentate gyrus--a "dentate gyrus for dummies." (+info)
Optimally wired subnetwork determines neuroanatomy of Caenorhabditis elegans.
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Wiring cost minimization has successfully explained many structures of nervous systems. However, in the nematode Caenorhabditis elegans, for which anatomical data are most detailed, wiring economy is thought to play only a partial role and alone has failed to account for the grouping of neurons into ganglia [Chen BL, Hall DH, Chklovskii DB (2006) Proc Natl Acad Sci USA 103:4723-4728; Kaiser M, Hilgetag CC (2006) PLoS Comput Biol 2:e95; Ahn Y-Y, Jeong H, Kim BJ (2006) Physica A 367:531-537]. Here, we test the hypothesis that optimally wired subnetworks can exist within nonoptimal networks, thus allowing wiring economy to give an improved prediction of spatial structure. We show in C. elegans that the small subnetwork of wires connecting sensory and motor neurons with sensors and muscles, comprising only 15% of connections, is close to optimal and alone predicts the main features of the spatial segregation of neurons into ganglia and encephalization. Moreover, a method to dissect networks into optimal and nonoptimal components is shown to find a large near-optimal subnetwork of 84% of neurons with a very low position error of 5.4%, and that explains clustering of neurons into ganglia and encephalization to fine detail. In general, we expect realistic networks not to be globally optimal in wire cost. We thus propose the strategy of using near-optimal subnetworks to understand neuroanatomical structure. (+info)