Functional data analysis in shape analysis

Mid-level processes on images often return outputs in functional form. In this context the use of functional data analysis (FDA) in image analysis is considered. In particular, attention is focussed on shape analysis, where the use of FDA in the functional approach (contour functions) shows its superiority over other approaches, such as the landmark based approach or the set theory approach, on two different problems (principal component analysis and discriminant analysis) in a well-known database of bone outlines. Furthermore, a problem that has hardly ever been considered in the literature is dealt with: multivariate functional discrimination. A discriminant function based on independent component analysis for indicating where the differences between groups are and what their level of discrimination is, is proposed. The classification results obtained with the methodology are very promising. Finally, an analysis of hippocampal differences in Alzheimer’s disease is carried out

Hippocampal shape analysis in Alzheimer’s disease using Functional Data Analysis

The hippocampus is one of the first affected regions in Alzheimer's disease. The left hippocampi of control subjects, patients with mild cognitive impairment and patients with Alzheimer's disease are represented by spherical harmonics. Functional data analysis is used in the hippocampal shape analysis. Functional principal component analysis and functional independent component analysis are defined for multivariate functions with two arguments. A functional linear discriminant function is also defined. Comparisons with other approaches are carried out. Our functional approach gives promising results, especially in shape classification. Copyright © 2013 John Wiley & Sons, Ltd

A neuroimaging data set on problem solving in the case of the reversal error: Putamen data

Structural Magnetic Resonance Images (sMRI) for a sample of university students were recorded. Out of magnetic resonance, students performed a test of algebra problem solving. As we are interested in reversal errors, the test was prepared to detect this kind of error. Depending on the number of mistakes made, students were divided into two groups: one group contains 15 students that responded erroneously to more than 60% of the 16 questions, and the other group contains 18 students that did not make any mistake. We are interested in the more relevant brain structures for this neuroeducation problem. The analysis of these data can be found in Ferrando et al. (2020) [1]. The results of the volumetric analysis showed differences between groups in the right and left putamen. Therefore, both putamens were pre-processed and segmented to use them in the shape analysis. The dataset contains the slices of the left and right putamen and the left putamen of each of 33 subjects, 20 females. It also contains a vector that indicates the group to each subject belongs to

Detecting and visualizing differences in brain structures with SPHARM and functional data analysis

A new procedure for classifying brain structures described by SPHARM is presented. We combine a dimension reduction technique (functional principal component analysis or functional independent component analysis) with stepwise variable selection for linear discriminant classification. This procedure is compared with many well-known methods in a novel classification problem in neuroeducation, where the reversal error (a common error in mathematical problem solving) is analyzed by using the left and right putamens of 33 participants. The comparison shows that our proposal not only provides outstanding performance in terms of predictive power, but it is also valuable in terms of interpretation, since it yields a linear discriminant function for 3D structures

Ordinal classification of 3D brain structures by functional data analysis

We introduce several ordinal classification methods for functional data, specificallymultiargument and multivariate functional data. Their performance is analyzed in fourreal data sets that belong to a neuroeducational problem and a neuropathologicalproblem.Funding for open access charge: CRUE-Universitat Jaume

The underlying neural bases of the reversal error while solving algebraic word problems

Problem solving is a core element in mathematical learning. The reversal error in problem solving occurs when students are able to recognize the information in the statement of comparison word problems, but they reverse the relationship between two variables when building the equations. Functional magnetic resonance images were acquired to identify for the first time the neural bases associated with the reversal error. The neuronal bases linked to this error have been used as inputs in 13 classifiers to discriminate between reversal error and non-reversal error groups. We found brain activation in bilateral fronto-parietal areas in the participants who committed reversal errors, and only left fronto-parietal activation in those who did not, suggesting that the reversal error group needed a greater cognitive demand. Instead, the non-reversal error group seems to show that they have developed solid algebraic knowledge. Additionally, the results showed brain activation in the right middle temporal gyrus when comparing the reversal error vs non-reversal error groups. This activation would be associated with the semantic processing which is required to understand the statement and build the equation. Finally, the classifier results show that the brain areas activated could be considered good biomarkers to help us identify competent solvers

Music-mathematical teaching-learning using educational robotics

El presente proyecto combina las áreas de matemáticas y música a través de la robótica educativa, proponiendo una metodología diferente para aprender música utilizando nuevas tecnologías. En la intervención se utilizan los robots Bee-Bots, los cuales son unos robots educativos indicados para el trabajo con alumnos/as de 3 a 7 años. Los Bee-Bots son programados por el alumnado para que se desplacen por unos tableros que hemos creado y adaptado para trabajar los contenidos musicales propuestos. Estos tableros forman parte de una de las innovaciones de este proyecto, ya que se han creado para enlazar y aunar el pensamiento lógico-matemático con la enseñanza-aprendizaje musical. Para acercar la robótica a nuestro alumnado se han realizado varias sesiones divididas en 3 fases. Éstas son: familiarización y manejo de los robots; desplazamiento de los robots por un camino marcado para la resolución de problemas propuestos; y toma de decisiones del alumnado para el desplazamiento del robot, sin un camino marcado (Diago y Arnau, 2017). Además, para facilitar los caminos, se han utilizado las cajas de secuenciación con las tarjetas de comandos para observar las estrategias utilizadas por el alumnado en la resolución de problemas, e implementar el método Polya (1945). Como conclusiones podemos decir que esta metodología parece mejorar el aprendizaje de los conceptos musicales trabajados, desarrollar el pensamiento lógico-matemático, y aumentar la motivación de nuestro alumnado.The present project combines the areas of mathematics and music through educational robotics, proposing a different methodology to learn music using new technologies. Bee-Bots robots are used in the intervention, which are educational robots indicated for working with students from 3 to 7 years old. The Bee-Bots are programmed by the students to move through some boards that we have created and adapted to work on the proposed musical contents. These boards are part of one of the innovations of this project, since they have been created to link and combine logical-mathematical thinking with musical teaching-learning. To bring robotics closer to our students, several sessions have been divided into 3 phases. These are: familiarization and management of the robots; displacement of the robots along a marked path for the resolution of proposed problems; and student decision-making for the robot’s displacement, without a marked path (Diago & Arnau, 2017). In addition, to facilitate the roads, the sequencing boxes with the command cards have been used to observe the strategies used by the students in the resolution of problems, and to implement the Polya method (1945). As conclusions we can say that this methodology seems to improve the learning of the musical concepts studied, develop the logical-mathematical thinking, and increase the motivation of our students

Brain networks involved in accented speech processing

We investigated the neural correlates of accented speech processing (ASP) with an fMRI study that overcame prior limitations in this line of research: we preserved intelligibility by using two regional accents that differ in prosody but only mildly in phonetics (Latin American and Castilian Spanish), and we used independent component analysis to identify brain networks as opposed to isolated regions. ASP engaged a speech perception network composed primarily of structures related with the processing of prosody (cerebellum, putamen, and thalamus). This network also included anterior fronto-temporal areas associated with lexical-semantic processing and a portion of the inferior frontal gyrus linked to executive control. ASP also recruited domain-general executive control networks related with cognitive demands (dorsal attentional and default mode networks) and the processing of salient events (salience network). Finally, the reward network showed a preference for the native accent, presumably revealing people's sense of social belonging

The role of protest scenario in the neural response to the supportive communication

People are capable of understanding the suffering of distant others and of their personal responsibility in this suffering. The communication of harm and self-responsibility in the suffering of others leads to greater moral sensitivity. Two studies were carried out to test our hypotheses. In Study 1 we analyse the emotional response to the scripts using a correlational study. In Study 2 we use functional MRI to investigate brain activation associated with the communication of harm and self-responsibility in a moral scenario on supportive communication. Direct comparison between donor and protest scenarios yielded a significant activation in the left amygdala usually associated with moral emotions. Responses in supportive communication scenarios show that donors can feel personally involved in a moral issue if they perceive the harm and their selfresponsibility. Our results suggest that the creation of a communications structure based on social condemnation increases moral sensitivity to poverty

Neuroticism predisposes to donation more than agrecableness: An fMRI study

This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.Using functional MRI (fMRI) in Study 1, we examined the effect of 2 personality dimensions related to altruism, such as Agreeableness and Neuroticism, on the neural response to videos, including images of situations from developing countries and audio recordings of sentences employed by Non-governmental Organizations (NGOs) to request help. For all the participants, the brain response across the whole brain was not significantly different in the donor and control videos. Multiple regression analyses revealed that while Agreeableness was related to the activation of mentalizing brain areas (i.e., the precuneus), Neuroticism was related more to the activation of the brain areas related to reward and donation. Study 2 was a psychometric study and confirmed that Neuroticism showed a greater association with donation behavior and sponsoring children from developing countries than Agreeableness. Our results may serve to gain a better understanding of the relationship between personality traits and altruistic behavior. (PsycINFO Database Record (c) 2016 APA, all rights reserved)Sponsor: Spanish Ministry of Science and Technology, Spain Recipient: No recipient indicated Grant Number: CSD2007-00012 Sponsor: Spanish Ministry of Science and Technology Recipient: Ávila, César Grant Number: PSI2013-45378-R and P1·1B2013-6