"What is in a word? No versus Yes differentially engage the lateral orbitofrontal cortex": Correction.

Reports an error in "What is in a word? No versus Yes differentially engage the lateral orbitofrontal cortex" by Nelly Alia-Klein, Rita Z. Goldstein, Dardo Tomasi, Lei Zhang, Stephanie Fagin-Jones, Frank Telang, Gene-Jack Wang, Joanna S. Fowler and Nora D. Volkow (Emotion, 2007[Aug], Vol 7[3], 649-659). The supplemental materials link should appear as follows: http://dx.doi.org/10.1037/1528-3542.7.3.649.supp. (The following abstract of the original article appeared in record 2007-11660-018.) The words "No" and "Yes" are involved in conditioning to prohibit or encourage behavior, respectively. The authors, therefore, hypothesized that these words would be attributed to endogenous valence, activating neuronal circuits involved with valence and emotional control. Functional MRI (fMRI) at 4 Tesla was used to record regional brain activity while participants were exposed to emphatic vocalizations of the words. Results showed that No and Yes were associated with opposite brain-behavior responses; while No was negatively valenced, produced slower response times, and evoked a negative signal in the right lateral orbitofrontal cortex (OFC), Yes was positively valenced, produced faster response times, and evoked a positive signal in a contiguous region of the OFC. Attribution of negative valence to No and trait anger control were associated with increased responsivity of the OFC to No. Inasmuch as sensitivity to the prohibitive command No develops during childhood through interaction with primary caregivers as the first social objects, our findings may implicate the lateral OFC in the neurobiology of emotion regulation and subsequent social development. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Altered mRNA and Protein Expression of Monocarboxylate Transporter MCT1 in the Cerebral Cortex and Cerebellum of Prion Protein Knockout Mice

The effect of a cellular prion protein (PrP^c) deficiency on neuroenergetics was primarily analyzed via surveying the expression of genes specifically involved in lactate/pyruvate metabolism, such as monocarboxylate transporters (MCT1, MCT2, MCT4). The aim of the present study was to elucidate a potential involvement of PrP^c in the regulation of energy metabolism in different brain regions. By using quantitative real-time polymerase chain reaction (qRT-PCR), we observed a marked reduction in MCT1 mRNA expression in the cortex of symptomatic Zürich I Prnp^−/− mice, as compared to their wild-type (WT) counterparts. MCT1 downregulation in the cortex was accompanied with significantly decreased expression of the MCT1 functional interplayer, the Na⁺/K⁺ ATPase α2 subunit. Conversely, the MCT1 mRNA level was significantly raised in the cerebellum of Prnp^−/− vs. WT control group, without a substantial change in the Na⁺/K⁺ ATPase α2 subunit expression. To validate the observed mRNA findings, we confirmed the observed change in MCT1 mRNA expression level in the cortex at the protein level. MCT4, highly expressed in tissues that rely on glycolysis as an energy source, exhibited a significant reduction in the hippocampus of Prnp^−/− vs. WT mice. The present study demonstrates that a lack of PrP^c leads to altered MCT1 and MCT4 mRNA/protein expression in different brain regions of Prnp^−/− vs. WT mice. Our findings provide evidence that PrP^c might affect the monocarboxylate intercellular transport, which needs to be confirmed in further studies.     

基于视觉感知机制的织物疵点轮廓检测

针对传统人工织物疵点检测存在的误检及低效等问题,提出了一种基于视觉感知机制的自适应织物疵点轮廓检测方法.首先,模拟视觉系统中视网膜感受野对视觉信息的处理机制对织物疵点图像进行滤波及疵点增强;其次,依据初级视皮层(V1)区对视觉信息响应的方向选择性机制构建织物疵点图像边缘检测模型,实现对织物疵点图像的边缘检测.最后,采用自适应阈值选择的方法对检测到的边缘进行二次处理,获得织物图像疵点的轮廓.为验证本文方法的有效性和准确性,对4类织物疵点图像进行测试,并定性和定量两方面进行比较分析,结果表明文中提出的方法能够较好地检测出织物疵点轮廓信息,不仅可以得到质量较高的织物疵点轮廓图像,而且在整个检测过程中能够自适应的选择参数,避免受人的主观因素影响,具有实际的应用价值.     

不同产地黄柏的近红外指纹图谱鉴别分析

建立用近红外漫反射光谱鉴别不同产地黄柏药材的新方法.采集不同产地的黄柏药材及其伪品的近红外漫反射光谱,用模式识别方法进行聚类分析,建立判别模型并用三重交叉验证的方法对模型稳定性进行验证.黄柏样品与伪品能较好地区分开;建立模型后对已知训练集样本的分类精度高达100%,对未知样本的预测精度达到100%,该模型具有很好的预测性能,这说明了所建方法性能优良,能够适用于黄柏药材的不同种植产地分类鉴别.近红外光谱法简便、快速、不破坏样品,结合模式识别方法能够准确鉴别正品、伪品以及不同产地的黄柏药材.     

生物视觉系统的神经网络编码模型综述

生物视觉系统的研究一直是计算机视觉算法的重要灵感来源。有许多计算机视觉算法与生物视觉研究具有不同程度的对应关系,包括从纯粹的功能启发到用于解释生物观察的物理模型的方法。从视觉神经科学向计算机视觉界传达的经典观点是视觉皮层分层层次处理的结构。而人工神经网络设计的灵感来源正是视觉系统中的分层结构设计。深度神经网络在计算机视觉和机器学习等领域都占据主导地位。许多神经科学领域的学者也开始将深度神经网络应用在生物视觉系统的计算建模中。深度神经网络多层的结构设计加上误差的反向传播训练,使得它可以拟合绝大多数函数。因此,深度神经网络在学习视觉刺激与神经元响应的映射关系并取得目前性能最好的模型同时,网络内部的单元甚至学习出生物视觉系统子单元的表达。本文将从视网膜等初级视觉皮层和高级视觉皮层(如,视觉皮层第4区(visual area 4,V4)和下颞叶皮层(inferior temporal,IT))分别介绍基于神经网络的视觉系统编码模型。主要内容包括:1)有关视觉系统模型的概念与定义;2)初级视觉系统的神经网络预测模型;3)任务驱动的高级视觉皮层编码模型。最后本文还将介绍最新有关无监督学习的神经编码...     

Cognitive-bias toward gaming-related words and disinhibition in World of Warcraft gamers

This study investigated cognitive biases toward gaming-related words and differences in cognitive performance among twelve World of Warcraft players (WWP) and thirty non-players (NP). We measured response to valenced common English and WoW jargon words using a computer-based go/no-go task. Sometimes positive valence words were the targets for the ‘go’ response, with negative-valence words as the distracters, sometimes the reverse. Target discrimination (d′) and response disinhibition (C) were calculated using a signal detection analysis. Based on questionnaire responses, there were no differences between groups in depression, anxiety, smoking or drinking, but WWP reported significantly more screen and gaming time (17.31 h/week versus 4.12 among NP). WWP had faster reaction time (RT) and better discrimination of targets from distracters (high d′) but also showed higher disinhibition (low C). WWP also showed cognitive-bias toward game-related words in the form of higher d′ for WoW jargon than common English and more disinhibition to positive-valence WoW jargon. Similar to past studies which have found alcoholics to have cognitive biases toward alcohol-related words, WWP who play frequently showed cognitive biases toward words related to the World of Warcraft game.     

Hypercolumn-array based image representation and its application to shape-based object detection

Biological and psychological evidence increasingly reveals that high-level geometrical and topological features are the keys to shape-based object recognition in the brain. Attracted by the excellent performance of neural visual systems, we simulate the mechanism of hypercolumns in the mammalian cortical area V1 that selectively responds to oriented bar stimuli. We design an orderly-arranged hypercolumn array to extract and represent linear or near-linear stimuli in an image. Each unit of this array covers stimuli of various orientations in a small area, and multiple units together produce a low-dimensional vector to describe shape. Based on the neighborhood of units in the array, we construct a graph whose node represents a short line segment with a certain position and slope. Therefore, a contour segment in the image can be represented with a route in this graph. The graph converts an image, comprised of typically unstructured raw data, into structured and semantic-enriched data. We search along the routes in the graph and compare them with a shape template for object detection. The graph greatly upgrades the level of image representation, remarkably reduces the load of combinations, significantly improves the efficiency of object searching, and facilitates the intervening of high-level knowledge. This work provides a systematic infrastructure for shape-based object recognition.     

A model of cerebral cortex formation during fetal development using reaction-diffusion-convection equations with Turing space parameters

The cerebral cortex is a gray lamina formed by bodies of neurons covering the cerebral hemispheres, varying in thickness from 1.25 mm in the occipital lobe to 4 mm in the anterior lobe. The brain's surface is about 30 times greater that of the skull because of its many folds; such folds form the gyri, sulci and fissures and mark out areas having specific functions, divided into five lobes. Convolution formation may vary between individuals and is an important feature of brain formation; such patterns can be mathematically represented as Turing patterns. This article describes how a phenomenological model was developed by describing the formation pattern for the gyri occurring in the cerebral cortex by reaction diffusion equations with Turing space parameters. Numerical examples for simplified geometries of a brain were solved to study pattern formation. The finite element method was used for the numerical solution, in conjunction with the Newton–Raphson method. The numerical examples showed that the model can represent cerebral cortex fold formation and reproduce pathologies related to gyri formation, such as polymicrogyria and lissencephaly.     

A proteome analysis of the dorsolateral prefrontal cortex in human alcoholic patients

Alcoholic patients commonly experience cognitive decline. It is postulated that cognitive dysfunction is caused by an alcohol-induced region-selective brain damage, particularly to the prefrontal region, and grey and white matter may be affected differently. We used a proteomics-based approach to compare protein expression profiles of the dorsolateral prefrontal cortex (Brodmann area 9 (BA9)) from human alcoholic and healthy control brains. Changes in the relative expression of 110 protein 'spots' were identified in the BA9 grey matter, of which 54 were identified as 44 different proteins. In our recent article, 60 protein spots were differentially expressed in the BA9 white matter and 18 of these were identified (Alexander-Kaufman, K., James, G., Sheedy, D., Harper, C., Matsumoto, I., Mol. Psychiatry 2006, 11, 56-65). Additional BA9 white matter proteins are identified here and discussed in conjunction to our grey matter results. Thiamine-dependent enzymes transketolase and pyruvate dehydrogenase (E1β ubunit) were among the proteins identified. To our knowledge, this is the first time a disruption in thiamine-dependent enzymes has been demonstrated in the brains of 'neurologically uncomplicated' alcoholics. By identifying protein expression changes in prefrontal grey and white matter separately, hypotheses may draw upon more mechanistic explanations as to how alcoholism causes the structural alterations associated with alcohol-related brain damage and cognitive dysfunction.