scholarly journals A unified neurocognitive model of the anterior temporal lobe contributions to semantics, language, social behaviour & face recognition

2019 ◽  
Author(s):  
Junhua Ding ◽  
Keliang Chen ◽  
Haoming Liu ◽  
Lin Huang ◽  
Yan Chen ◽  
...  
Keyword(s):  
Face Recognition ◽  
Social Behaviour ◽  
Region Of Interest ◽  
Principal Component ◽  
Temporal Lobes ◽  
Word Finding ◽  
Cognitive Behavioural ◽  
Patient Groups ◽  
Verbal Production ◽  
Semantic Impairment

AbstractThe anterior temporal lobes (ATL) have become a key brain region of interest in cognitive and clinical neuroscience. Contemporary explorations are founded upon neuropsychological investigations of semantic dementia (SD) that describe the patients’ selective semantic impairment and the variations in their language, behavioural and face recognition abilities. The purpose of this investigation was to generate a single unified model which captures the known cognitive-behavioural variations in SD, and integrates with the considerable database on healthy semantic function and other patient groups. A new analytical approach was able to capture the graded neuropsychological differences and map these to the patients’ distribution of frontotemporal atrophy. Multiple regression and principal component analyses confirmed that the degree of generalised semantic impairment was related to the patients’ total, bilateral ATL atrophy. Verbal production and word-finding abilities were related to total ATL atrophy as well as to the balance of left>right ATL atrophy. Behavioural apathy was found to relate positively to the degree of orbitofrontal atrophy and negatively to total temporal volumes. Disinhibited behaviour was related to right ATL and orbitofrontal atrophy and face recognition to right ATL volumes. Rather than positing mutually-exclusive sub-categories, the data-driven model repositions semantics, language, social behaviour and face recognition into a continuous frontotemporal neurocognitive space.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

scholarly journals PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kanokmon Rujirakul ◽  
Chakchai So-In ◽  
Banchar Arnonkijpanich
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Expectation Maximization ◽  
High Speed ◽  
Expectation Maximization Algorithm ◽  
Parallel Architecture ◽  
Principal Component ◽  
Eigenvalue Decomposition ◽  
Computational Time ◽  
Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

scholarly journals Small Extracellular Vesicles Isolated from Serum May Serve as Signal-Enhancers for the Monitoring of CNS Tumors

2020 ◽  
Vol 21 (15) ◽  
pp. 5359 ◽  
Author(s):  
Gabriella Dobra ◽  
Matyas Bukva ◽  
Zoltan Szabo ◽  
Bella Bruszel ◽  
Maria Harmati ◽  
...  
Keyword(s):  
Extracellular Vesicles ◽  
Serum Proteins ◽  
Lumbar Disc ◽  
Principal Component ◽  
Cns Tumors ◽  
Serum Samples ◽  
Force Microscopy ◽  
Isolation And Characterization ◽  
Atomic Force ◽  
Patient Groups

Liquid biopsy-based methods to test biomarkers (e.g., serum proteins and extracellular vesicles) may help to monitor brain tumors. In this proteomics-based study, we aimed to identify a characteristic protein fingerprint associated with central nervous system (CNS) tumors. Overall, 96 human serum samples were obtained from four patient groups, namely glioblastoma multiforme (GBM), non-small-cell lung cancer brain metastasis (BM), meningioma (M) and lumbar disc hernia patients (CTRL). After the isolation and characterization of small extracellular vesicles (sEVs) by nanoparticle tracking analysis (NTA) and atomic force microscopy (AFM), liquid chromatography -mass spectrometry (LC-MS) was performed on two different sample types (whole serum and serum sEVs). Statistical analyses (ratio, Cohen’s d, receiver operating characteristic; ROC) were carried out to compare patient groups. To recognize differences between the two sample types, pairwise comparisons (Welch’s test) and ingenuity pathway analysis (IPA) were performed. According to our knowledge, this is the first study that compares the proteome of whole serum and serum-derived sEVs. From the 311 proteins identified, 10 whole serum proteins and 17 sEV proteins showed the highest intergroup differences. Sixty-five proteins were significantly enriched in sEV samples, while 129 proteins were significantly depleted compared to whole serum. Based on principal component analysis (PCA) analyses, sEVs are more suitable to discriminate between the patient groups. Our results support that sEVs have greater potential to monitor CNS tumors, than whole serum.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

CONTRIBUTIONS OF THE RIGHT AND LEFT MESIAL TEMPORAL LOBES TO MUSIC MEMORY: EVIDENCE FROM MELODIC LEARNING DIFFICULTIES

2008 ◽  
Vol 25 (4) ◽  
pp. 303-314 ◽  
Author(s):  
SARAH J. WILSON ◽  
MICHAEL M. SALING
Keyword(s):  
Learning Difficulties ◽  
Learning Task ◽  
Learning Curves ◽  
Memory Impairments ◽  
Temporal Lobes ◽  
Patient Groups ◽  
Left And Right ◽  
Mesial Temporal Lobe ◽  
The Right ◽  
Musical Context

THE AIM OF THIS STUDY WAS TO ASSESS the effects of left- and right-sided MTL damage on melodic memory using a newly developed arbitrary relational learning task. Participants included patients with MTL damage, patient controls,musicians, and musician controls. The learning curves of these groups showed striking differences, with right MTL patients failing to learn tonal (easy) melody pairs. Both patient groups had difficulty learning nontonal (hard) pairs. Performance was greatest for the musicians, particularly for the nontonal melody pairs. These differences were not primarily attributable to pitch discrimination or pitch working memory impairments. The findings point to differential contributions of the left and right mesial temporal lobes to melodic memory, with specificity of the right mesial temporal lobe emerging for melodic learning within a tonal musical context.

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