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The Study of Neural Mechanism of Top-down Letter Processing Based on fMRI

LIU Jian-Gang,TIAN Jie,Kang Lee,
,田捷,Kang Lee

心理科学进展 , 2011,
Abstract: People can efficiently recognize an object even under some extremely poor viewing conditions.Such efficient visual processing depends on both bottom-up input and top-down regulations.The present study used a novel illusory detection paradigm to investigate the neural network involved in top-down letter processing.This paradigm required participants to detect letters in pure noise images that actually contained no letters.By using of psychophysiological interaction(PPI) analysis,it revealed that some regions...
Abnormal Left-Sided Orbitomedial Prefrontal Cortical–Amygdala Connectivity during Happy and Fear Face Processing: A Potential Neural Mechanism of Female MDD  [PDF]
Jorge Renner Cardoso de Almeida,Etienne L. Sibille,Mary Louise Phillips
Frontiers in Psychiatry , 2011, DOI: 10.3389/fpsyt.2011.00069
Abstract: Background: Pathophysiologic processes supporting abnormal emotion regulation in major depressive disorder (MDD) are poorly understood. We previously found abnormal inverse left-sided ventromedial prefrontal cortical–amygdala effective connectivity to happy faces in females with MDD. We aimed to replicate and expand this previous finding in an independent participant sample, using a more inclusive neural model, and a novel emotion processing paradigm. Methods: Nineteen individuals with MDD in depressed episode (12 females), and 19 healthy individuals, age, and gender matched, performed an implicit emotion processing and automatic attentional control paradigm to examine abnormalities in prefrontal cortical–amygdala neural circuitry during happy, angry, fearful, and sad face processing measured with functional magnetic resonance imaging in a 3-T scanner. Effective connectivity was estimated with dynamic causal modeling in a trinodal neural model including two anatomically defined prefrontal cortical regions, ventromedial prefrontal cortex, and subgenual cingulate cortex (sgACC), and the amygdala. Results: We replicated our previous finding of abnormal inverse left-sided top-down ventromedial prefrontal cortical–amygdala connectivity to happy faces in females with MDD (p = 0.04), and also showed a similar pattern of abnormal inverse left-sided sgACC–amygdala connectivity to these stimuli (p = 0.03). These findings were paralleled by abnormally reduced positive left-sided ventromedial prefrontal cortical–sgACC connectivity to happy faces in females with MDD (p = 0.008), and abnormally increased positive left-sided sgACC–amygdala connectivity to fearful faces in females, and all individuals, with MDD (p = 0.008; p = 0.003). Conclusion: Different patterns of abnormal prefrontal cortical–amygdala connectivity to happy and fearful stimuli might represent neural mechanisms for the excessive self-reproach and comorbid anxiety that characterize female MDD.
Neural Basis of Self and Other Representation in Autism: An fMRI Study of Self-Face Recognition  [PDF]
Lucina Q. Uddin, Mari S. Davies, Ashley A. Scott, Eran Zaidel, Susan Y. Bookheimer, Marco Iacoboni, Mirella Dapretto
PLOS ONE , 2008, DOI: 10.1371/journal.pone.0003526
Abstract: Background Autism is a developmental disorder characterized by decreased interest and engagement in social interactions and by enhanced self-focus. While previous theoretical approaches to understanding autism have emphasized social impairments and altered interpersonal interactions, there is a recent shift towards understanding the nature of the representation of the self in individuals with autism spectrum disorders (ASD). Still, the neural mechanisms subserving self-representations in ASD are relatively unexplored. Methodology/Principal Findings We used event-related fMRI to investigate brain responsiveness to images of the subjects' own face and to faces of others. Children with ASD and typically developing (TD) children viewed randomly presented digital morphs between their own face and a gender-matched other face, and made “self/other” judgments. Both groups of children activated a right premotor/prefrontal system when identifying images containing a greater percentage of the self face. However, while TD children showed activation of this system during both self- and other-processing, children with ASD only recruited this system while viewing images containing mostly their own face. Conclusions/Significance This functional dissociation between the representation of self versus others points to a potential neural substrate for the characteristic self-focus and decreased social understanding exhibited by these individuals, and suggests that individuals with ASD lack the shared neural representations for self and others that TD children and adults possess and may use to understand others.
Neural Mechanism of Self-Face Recognition

GUAN Li-Li,QI Ming-Ming,ZHANG Qing-Lin,YANG Juan,

心理科学进展 , 2011,
Abstract: Self-face recognition is an experimental paradigm of self-referential processing.It reflects the process that someone can recognize one's own face by distinguishing self from others.The brain regions involving in self-face recognition include the prefrontal cortex,insula,cingulated cortex,temporal and parietal areas.There are three stages involving in the neural mechanism of self-face recognition,including low-level sensory processing,self-referential information processing,and identity discrimination.The f...
Mechanism of Case Processing in the Brain: An fMRI Study  [PDF]
Satoru Yokoyama, Hideki Maki, Yosuke Hashimoto, Masahiko Toma, Ryuta Kawashima
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0040474
Abstract: In sentence comprehension research, the case system, which is one of the subsystems of the language processing system, has been assumed to play a crucial role in signifying relationships in sentences between noun phrases (NPs) and other elements, such as verbs, prepositions, nouns, and tense. However, so far, less attention has been paid to the question of how cases are processed in our brain. To this end, the current study used fMRI and scanned the brain activity of 15 native English speakers during an English-case processing task. The results showed that, while the processing of all cases activates the left inferior frontal gyrus and posterior part of the middle temporal gyrus, genitive case processing activates these two regions more than nominative and accusative case processing. Since the effect of the difference in behavioral performance among these three cases is excluded from brain activation data, the observed different brain activations would be due to the different processing patterns among the cases, indicating that cases are processed differently in our brains. The different brain activations between genitive case processing and nominative/accusative case processing may be due to the difference in structural complexity between them.
Studies of Fusiform Face Area in People with Autism Spectrum Disorders

张耀心, 吴坤, 尤日虹, 蔡启文
Advances in Psychology (AP) , 2016, DOI: 10.12677/AP.2016.68110
This paper reviews the studies of Fusiform Face Area (FFA) in patients with Autism Spectrum Dis-orders (ASD) from two perspectives. First, we reviewed fMRI researches about face processing and FFA on patients with ASD. It’s found that face processing in ASD appears to rely on FFA as in typical individuals, differing quantitatively but not qualitatively. That is, face processing deficits of ASD may correlate with FFA neural selectivity and the little selectivity, the worse performance. In ad-dition, previous study extended the function of FFA from face processing to expertise. Recent re-search showed that restricted interest in patients with ASD could enhance expertise through FFA study. From the perspective of face processing of FFA, future research could combine FFA with other brain regions to explore the network mechanism of face processing in patients with ASD. Also, researchers can integrate FFA with reward circuits or other related brain areas to explore corporate mechanism of face processing and visual expertise by recruiting ASD patients.
The Cognitive Neuroscience Of Face Processing: A Review

Xu Yan,Zhang Yaxu,Zhou Xiaolin,

心理科学进展 , 2003,
Abstract: Is there a face-specific processing module which is cognitively and/or neurally dissociable with the module of object processing? And how is face processing system organized? Both of these questions are the core issues in the current cognitive neuroscience of face processing. With evidence from both neuropsychology and neuroimaging studies using ERPs, PET or fMRI, researchers had found a face-specific brain area, which was named fusiform face area (FFA). In this paper, we first review some critical studies investigating both the specificity and the multiple components of face processing. We then evaluate the popular cognitive and neural models of face recognition and speculate on the direction of future studies.
The Neurophysiological Mechanism of Racial Prejudice

富云露, 胡金生, 王鸽
Advances in Psychology (AP) , 2014, DOI: 10.12677/AP.2014.46095
Early ERP researches on racial prejudice concerned later components of processing, while more attention is paid on early stages of cognitive processing (P100, N170, P200, SPCN) recently. Race-related fMRI researches mainly examine how racial bias is related to processes such as face processing, evaluation and so on. The neural processing of racial bias, which is not unalterable, is also under the influence of psychological factors, individual experience, intergroup relationship and social environment. In future, combining with iterative reprocessing model, based on neurophysiological researches on amygdala and anterior temporal lobe activity, the formation and degradation mechanism of racial prejudice and the regulatory mechanism of motivation to racial prejudice will be investigated in depth.
Neural tuning size is a key factor underlying holistic face processing  [PDF]
Cheston Tan,Tomaso Poggio
Computer Science , 2014,
Abstract: Faces are a class of visual stimuli with unique significance, for a variety of reasons. They are ubiquitous throughout the course of a person's life, and face recognition is crucial for daily social interaction. Faces are also unlike any other stimulus class in terms of certain physical stimulus characteristics. Furthermore, faces have been empirically found to elicit certain characteristic behavioral phenomena, which are widely held to be evidence of "holistic" processing of faces. However, little is known about the neural mechanisms underlying such holistic face processing. In other words, for the processing of faces by the primate visual system, the input and output characteristics are relatively well known, but the internal neural computations are not. The main aim of this work is to further the fundamental understanding of what causes the visual processing of faces to be different from that of objects. In this computational modeling work, we show that a single factor - "neural tuning size" - is able to account for three key phenomena that are characteristic of face processing, namely the Composite Face Effect (CFE), Face Inversion Effect (FIE) and Whole-Part Effect (WPE). Our computational proof-of-principle provides specific neural tuning properties that correspond to the poorly-understood notion of holistic face processing, and connects these neural properties to psychophysical behavior. Overall, our work provides a unified and parsimonious theoretical account for the disparate empirical data on face-specific processing, deepening the fundamental understanding of face processing.
Face Recognition using Neural Network and Eigenvalues with Distinct Block Processing
Amit Kumar, Prashant Sharma, Shishir Kumar
International Journal of Computer Trends and Technology , 2011,
Abstract: –Human face recognition has been employed in different commercial and law enforcement applications. It has also been employed for mug shots matching, bank-store security, crowd surveillance, expert identification, witness face reconstruction, electronics mug shots book, and electronic lineup. A face recognition system based on principal component analysis and neural networks has been developed. The system consists of three stages; preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were performed. Principal component analysis is applied to obtained the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set with the help of distinct block processing. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces.Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this paper, a separate network was developed for each person. The input face has been projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person’s network, trained earlier. The one with maximum output is selected and reported as the equivalent image.
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