An aphasia database on the internet: a model for computer-assisted analysis in aphasiology

A web-based software model was developed as an example for data mining in aphasiology. It is used for educating medical and engineering students. It is based upon a database of 254 aphasic patients which contains the diagnosis of the aphasia type, profiles of an aphasia test battery (Aachen Aphasia Test), and some further clinical information. In addition, the cerebral lesion profiles of 147 of these cases were standardized by transferring the coordinates of the lesions to a 3D reference brain based upon the ACPC coordinate system. Two artificial neural networks were used to perform a classification of the aphasia type. First, a coarse classification was achieved by using an assessment of spontaneous speech of the patient which produced correct results in 87% of the test cases. Data analysis tools were used to select four features of the 30 available test features to yield a more accurate diagnosis. This classifier produced correct results in 92% of the test cases. The neural network approach is similar to grouping performed in group studies, while the nearest-neighbor method shows a design more similar to case studies. It finds the neurolinguistic and the lesion data of patients whose AAT profiles are most similar to the user's input. This way lesion profiles can be compared to each other interindividually. The Aphasia Diagnoser is available on the Web address http://fuzzy.iau.dtu.dk/aphasia.nsf and thus should facilitate a discussion about the reliability and possibilities of data-mining techniques in aphasiology.     

Differential activity profile of cAMP-dependent protein kinase isoforms during long-term memory consolidation in the crab Chasmagnathus

The isoforms of cAMP-dependent protein kinase (PKA) show distinct biochemical properties and subcellular localization, suggesting different physiological functions, and conferring the fine-tuning between the activation of cAMP-PKA cascade and the cellular response. The critical role of PKA in memory and synaptic plasticity has been extensively demonstrated both in vertebrates and invertebrates, but the role of PKA isoforms is a matter of debate. Here we present experimental data showing differential PKA activation profiles after two different experiences: an instance of associative contextual learning (context-signal learning) and a single exposure to a novel context, both in the learning and memory model of the crab Chasmagnathus. Differences were found in the temporal course of activation and in the involvement of PKA isoforms. We found increased PKA activity immediately and 6 h after context-signal training correlating with the critical periods during which pharmacological inhibition of PKA disrupts memory formation. In contrast, PKA activity increased immediately but not 6 h after single exposure to a novel context. The amounts of PKA I and PKA II holoenzymes were analyzed to determine changes in holoenzyme levels and/or differential activation induced by both experiences. Results indicate that context-induced PKA activation is at least in part due to PKA II, and that PKA activation 6 h after context-signal learning coincides with an increase in the total level of PKA I. Considering the higher sensitivity of PKA I to cAMP, its increment can account for the PKA activation found 6 h after training and is proposed as a novel mechanism providing the prolonged PKA activation during memory consolidation.     

Genetic counseling for a family with two distinct anomalies: A case report of a neural tube defect and 5p- syndrome in a fetus

A couple presented for genetic counseling because of an elevated maternal serum alpha fetoprotein. Ultrasound examination revealed the presence of a neural tube defect. The couple declined an amniocentesis, but chose serial ultrasound evaluations instead. Ultrasounds eventually identified microcephaly, but the couple continued to decline amniocentesis. After the child's birth, the diagnosis of 5p- syndrome was made. The couple's decision not to have an amniocentesis allowed the family their right to autonomy; however, prenatal chromosome analysis would have provided this couple with a great deal more prognostic information. We discuss the conflict between a counselor's duty to respect a client's freedom vs. duty to care for a client's welfare. We address issues of nondirective counseling and the need for more studies looking at the decision-making process in prenatal diagnosis.     

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

BackgroundThe Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.ResultsTraining four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.LimitationsThis study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.ConclusionsBased on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.     

探究事件相关脑电/脑磁信号中的神经表征模式：基于分类解码和表征相似性分析的方法

探究不同心智活动下的神经表征差异, 是认知神经科学关注的核心问题之一。早期的脑电/脑磁分析方法主要关注组平均后的神经响应水平, 这要求在关注的时间进程上, 各个被试在相同刺激条件下事件相关电位/事件相关磁场的振幅大小和方向、以及地形图分布和极性均要有较高的一致性。近些年来, 研究者们将功能性磁共振成像研究中常用到的两种技术——机器学习中的分类算法(即基于分类的解码)和表征相似性分析——引入到了脑电/脑磁数据分析中。这两种新技术可以克服传统脑电/脑磁数据基于具体电压/磁感应强度波形平均分析的缺点, 具有在个体水平上探究神经表征编码的特点, 为人们探究大脑在不同时间进程上如何对特定的神经表征信息进行动态编码提供了新的思路。两种技术基于不同的方法学原理来抽提个体间一致的脑认知加工机制, 还为脑电/脑磁研究开展跨时域、跨任务、跨模态、跨群体比较不同认知过程中的表征差异提供了更多新颖的途径。我们首先通过与传统的脑电/脑磁分析方法进行比较, 系统性介绍了基于分类的解码和表征相似性分析的原理和操作流程, 之后对两种方法的应用场景进行了梳理, 并在最后对未来可供研究的方向提出了我们的见解。     

Hierarchical growing grid networks for skeleton based action recognition

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is compared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.     

Brain tumor detection using fusion of hand crafted and deep learning features

The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.     

空间频率影响恐惧面孔表情加工的神经通路

恐惧情绪由于其具有威胁性而优先得到有效的加工。其中空间频率作为处理面孔信息的基础成分, 通过不同的神经通路影响恐惧面孔表情的加工。双通路观点认为在皮层下通路上, 低空间频率的恐惧面孔表情存在优先传递性, 高空间频率则主要通过皮层通路对恐惧面孔表情进行精细化加工; 而多通路则能够更加灵活地处理空间频率对情绪加工的影响。未来研究应明确脑区及其子区域在多条通路上的作用, 从而进一步验证视觉信息是如何影响情绪加工的。     

信任的认知神经网络模型

信任是指一方在基于对另一方行为期望的基础上愿意冒一定的风险, 以期在将来得到积极结果的心理过程。近年, 认知神经取向的研究对信任行为引起的特定脑区激活进行了考察, 却忽略了大规模脑网络在信任过程中的整体作用。在总结前人研究的基础上提出信任的认知神经网络模型, 并从认知神经网络视角对信任行为进行解释和整合。在模型中, 信任行为是动力系统、情感系统和认知系统相互作用的结果, 并分别与奖励网络、显著网络、中央执行网络和默认网络等神经网络激活有关。此外, 模型还强调信任行为的反馈机制, 以此构成完整的建构模型。模型阐明了心理系统与中枢神经网络之间的对应关系, 从认知神经角度解释了信任行为发生的心理机制和神经基础。     

时距知觉的动物研究范式及相关神经机制

在探索时距知觉的脑机制的过程中, 相对于人类被试相关研究, 动物研究可以提供较多的药理学、分子生物学、单个神经元电生理学以及光遗传等方面的研究证据。目前较为常用的时距知觉动物研究范式包括时间二分法、峰值间隔法以及低比率差别强化法等。根据不同的研究需求, 动物研究的范式常会进行调整。对时距知觉的动物研究的探讨将基于两方面展开：(1) 常用的时距知觉动物研究范式的介绍及比较; (2) 基于动物研究范式的时距知觉神经机制研究进展, 旨在为深入探索时间知觉的心理学相关研究提供参考。