scholarly journals Dissecting individual differences in responses to naturalistic stimuli in functional MRI: effects of development and gender

2020 ◽  
Author(s):  
Xin Di ◽  
Bharat B. Biswal

AbstractFunctional MRI (fMRI) study of naturalistic conditions, e.g. movie watching, usually focuses on shared responses across subjects. However, individual differences in the responses have been attracting increasing attention in search of group differences or associations with behavioral outcomes. The individual differences have been studied by directly modeling the cross-subject correlation matrix or projecting the relations into a 1-D space. We contend that it is critical to examine whether there are single or multiple consistent components of responses underlying the whole population, because multiple components may undermine the individual relations using the previous methods. We use principal component analysis (PCA) to examine the heterogeneity of brain responses across subjects in terms of the eigenvalues of the covariance matrix, and utilize this approach to study developmental trajectories and gender effects in a movie watching dataset. We identified several brain networks in the parietal cortex that showed a significant second principal component (PC) of regional responses, which were mainly represented the younger children. The second PCs in some networks, i.e. the supramarginal network, resembled a delayed version of the first PCs for 4 seconds (2 TR), indicating delayed responses in the younger children than the older children and adults. However, no apparent gender effects were found in the first and second PCs. The analyses highlight the importance of identifying multiple consistent responses underlying individual differences in responses to naturalistic stimuli. And the PCA-based approach could be complementary to the commonly used intersubject correlation analysis.HighlightsThere may be multiple consistent responses among subjects during movie watchingPrincipal component analysis can be used to identify the multiple consistent responsesMany brain regions showed two principal components that were separated by ageYounger children showed delayed response in the supramarginal gyrus and precuneus

2010 ◽  
Vol 478 (1) ◽  
pp. 19-23 ◽  
Author(s):  
Argiro E. Maganioti ◽  
Chrissanthi D. Hountala ◽  
Charalambos C. Papageorgiou ◽  
Miltiades A. Kyprianou ◽  
Andreas D. Rabavilas ◽  
...  

Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.


Radiology ◽  
2020 ◽  
pp. 200822
Author(s):  
Benjamin Ades-Aron ◽  
Gregory Lemberskiy ◽  
Jelle Veraart ◽  
John Golfinos ◽  
Els Fieremans ◽  
...  

2019 ◽  
Vol 29 (6) ◽  
pp. 620-627 ◽  
Author(s):  
Rachael L. Thurecht ◽  
Fiona E. Pelly

This study aimed to develop and refine an Athlete Food Choice Questionnaire (AFCQ) to determine the key factors influencing food choice in an international cohort of athletes. A questionnaire that contained 84 items on a 5-point frequency scale was developed for this study. Athletes at the 2017 Universiade, in Taiwan, were invited to participate. Principal component analysis was utilized to identify key factors and to refine the questionnaire. Completed questionnaires were received from 156 athletes from 31 countries and 17 sports. The principal component analysis extracted 36 items organized into nine factors explaining 68.0% of variation. The nine factors were as follows: nutritional attributes of the food, emotional influences, food and health awareness, influence of others, usual eating practices, weight control, food values and beliefs, sensory appeal, and performance. The overall Kaiser–Meyer–Olkin measure was 0.75, the Bartlett test of sphericity was statistically significant, χ2(666) =2,536.50, p < .001, and all of the communalities remained >0.5. Intercorrelations were detected between performance and both nutritional attributes of the food and weight control. The price of food, convenience, and situational influences did not form part of the factorial structure. This research resulted in an AFCQ that includes factors specific to athletic performance and the sporting environment. The AFCQ will enable researchers and sports dietitians to better tailor nutrition education and dietary interventions to suit the individual or team. The next phase will test the accuracy and reliability of the AFCQ both during and outside of competition. The AFCQ is a useful tool to assist with management of performance nutrition for athletes.


2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.


2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
RAHMAD RAHMAD WIDODO ◽  
I PUTU EKA NILA KENCANA ◽  
NI LUH PUTU SUCIPTAWATI

Controlling the quality of learning is very important and influences the accreditation of study programs at the Faculty of Mathematics and Natural Sciences Udayana University, as a guarantor of the quality of graduates. Apply pricipal component analysis to reduce the number of determinant attributes of learning quality, with the aim of looking at the data structure with fewer variables. The control chart is a multivariate control chart that is used to view the potrait of the quality of learning in the Mathematics and Natural Sciences Faculty, using new variables obtained from principal component analysis. The results obtained from principal component analysis show that the contribution of the learning quality indicators is univen. The potrait of the quality of learning at the Faculty of Mathematics and Natural Sciences obtained from the individual-moving range (I-MR) and the control chart shows the need for corrective actions and monitor regularly to improve the quality of learning.


Perception ◽  
10.1068/p5811 ◽  
2008 ◽  
Vol 37 (11) ◽  
pp. 1637-1648 ◽  
Author(s):  
Satoru Kawamura ◽  
Masashi Komori ◽  
Yusuke Miyamoto

We examined the effect of facial expression on the assignment of gender to facial images. A computational analysis of the facial images was applied to examine whether physical aspects of the face itself induced this effect. Thirty-six observers rated the degree of masculinity of the faces of 48 men, and the degree of femininity of the faces of 48 women. Half of the faces had a neutral facial expression, and the other half was smiling. Smiling significantly reduced the perceived masculinity of men's faces, especially for male observers, whereas no effect of smiling on femininity ratings was obtained for women's faces. A principal component analysis was conducted on the matrix of pixel luminance values for each facial image × all the images. The third principle component explained a relatively high proportion of the variance of both facial expressions and gender of face. These results suggest that the effect of smiling on the assignment of gender is caused, at least in part, by the physical relationship between facial expression and face gender.


Author(s):  
Omid Heidari ◽  
John O. Roylance ◽  
Alba Perez-Gracia ◽  
Eydie Kendall

Motion synergies are principal components of the movement, obtained as combinations of joint degrees of freedom, that account for common postures of the human body. These synergies are usually obtained by capturing the motion of the human joints and reducing the dimensionality of the joint space with techniques such as principal component analysis. In this work, an experimental procedure to investigate the synergies of the upper body is developed and the results of the pilot study are shown. The upper-limb kinematics includes the joint complexes of the hand, wrist, forearm, elbow, and shoulder. The different kinematic models in the literature have been reviewed, and a serial chain is considered from the upper arm. A three degree of freedom (3-DOF) linkage containing two revolute joints and one prismatic joint has been chosen to simulate the shoulder motion. A spherical joint represents the Glenohumeral (GH) joint; the elbow and ulna-radius rotations are represented by two revolute joints and the wrist is modeled with two revolute joints. The hand has a tree structure and branches into the individual phalanges, with a 2-dof MCP joint and single R joints for the rest of the phalangeal joints. The data are collected using motion capture and the joint angles are calculated using a combination of dimensional synthesis and inverse kinematics. Principal component analysis can be used to extract the synergies for a set of previously-selected motions. The motions are performed by healthy subjects and subjects who have suffered stroke, in order to see the changes in the motion primitives. It is expected that this study will help quantify and classify some of the loss of motion due to stroke.


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