Functional near infrared spectroscopy study of language, joint attention and motor skills

Near infrared spectroscopy (NIRS) is an emerging non-invasive optical neuro imaging technique that monitors the hemodynamic response to brain activation with ms-scale temporal resolution and sub-cm spatial resolution. The overall goal of my dissertation was to develop and apply NIRS towards investigation of neurological response to language, joint attention and planning and execution of motor skills in healthy adults. Language studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal and fronto-temporal cortex of healthy adults in response to language reception and expression. The mathematical model developed based on granger causality explicated the directional flow of information during the processing of language stimuli by the fronto-temporal cortex. Joint attention and planning/ execution of motor skill studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal cortex of healthy adults and in children (5-8 years old) with autism (for joint attention studies) and individuals with cerebral palsy (for planning/execution of motor skills studies). The joint attention studies on healthy adults showed differences in activation as well as intensity and phase dependent connectivity in the frontal cortex during joint attention in comparison to rest. The joint attention studies on typically developing children showed differences in frontal cortical activation in comparison to that in children with autism. The planning and execution of motor skills studies on healthy adults and individuals with cerebral palsy (CP) showed difference in the frontal cortical dominance, that is, bilateral and ipsilateral dominance, respectively. The planning and execution of motor skills studies also demonstrated the plastic and learning behavior of brain wherein correlation was found between the relative change in total hemoglobin in the frontal cortex and the kinematics of the activity performed by the participants. Thus, during my dissertation the NIRS neuroimaging technique was successfully implemented to investigate the neurological response of language, joint attention and planning and execution of motor skills in healthy adults as well as preliminarily on children with autism and individuals with cerebral palsy. These NIRS studies have long-term potential for the design of early stage interventions in children with autism and customized rehabilitation in individuals with cerebral palsy.

Funtional Near Infrared Spectroscopy Study of Language, Joint Attention and Motor Skills

Near infrared spectroscopy (NIRS) is an emerging non-invasive optical neuro imaging technique that monitors the hemodynamic response to brain activation with ms-scale temporal resolution and sub-cm spatial resolution. The overall goal of my dissertation was to develop and apply NIRS towards investigation of neurological response to language, joint attention and planning and execution of motor skills in healthy adults. Language studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal and fronto-temporal cortex of healthy adults in response to language reception and expression. The mathematical model developed based on granger causality explicated the directional flow of information during the processing of language stimuli by the fronto-temporal cortex. Joint attention and planning/ execution of motor skill studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal cortex of healthy adults and in children (5-8 years old) with autism (for joint attention studies) and individuals with cerebral palsy (for planning/execution of motor skills studies). The joint attention studies on healthy adults showed differences in activation as well as intensity and phase dependent connectivity in the frontal cortex during joint attention in comparison to rest. The joint attention studies on typically developing children showed differences in frontal cortical activation in comparison to that in children with autism. The planning and execution of motor skills studies on healthy adults and individuals with cerebral palsy (CP) showed difference in the frontal cortical dominance, that is, bilateral and ipsilateral dominance, respectively. The planning and execution of motor skills studies also demonstrated the plastic and learning behavior of brain wherein correlation was found between the relative change in total hemoglobin in the frontal cortex and the kinematics of the activity performed by the participants. Thus, during my dissertation the NIRS neuroimaging technique was successfully implemented to investigate the neurological response of language, joint attention and planning and execution of motor skills in healthy adults as well as preliminarily on children with autism and individuals with cerebral palsy. These NIRS studies have long-term potential for the design of early stage interventions in children with autism and customized rehabilitation in individuals with cerebral palsy.

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

A mathematical resolution to log transformations and the binning effect in applied processing of data in flow cytometry

This dissertation develops a new mathematical approach that overcomes the effect of a data processing phenomenon known as "histogram binning" inherent to flow cytometry data. A real-time procedure is introduced to prove the effectiveness and fast implementation of such an approach on real-world data. The histogram binning effect is a dilemma posed by two seemingly antagonistic developments: (1) flow cytometry data in its histogram form is extended in its dynamic range to improve its analysis and interpretation, and (2) the inevitable dynamic range extension introduces an unwelcome side effect, the binning effect, which skews the statistics of the data, undermining as a consequence the accuracy of the analysis and the eventual interpretation of the data. Researchers in the field contended with such a dilemma for many years, resorting either to hardware approaches that are rather costly with inherent calibration and noise effects; or have developed software techniques based on filtering the binning effect but without successfully preserving the statistical content of the original data. The mathematical approach introduced in this dissertation is so appealing that a patent application has been filed. The contribution of this dissertation is an incremental scientific innovation based on a mathematical framework that will allow researchers in the field of flow cytometry to improve the interpretation of data knowing that its statistical meaning has been faithfully preserved for its optimized analysis. Furthermore, with the same mathematical foundation, proof of the origin of such an inherent artifact is provided. These results are unique in that new mathematical derivations are established to define and solve the critical problem of the binning effect faced at the experimental assessment level, providing a data platform that preserves its statistical content. In addition, a novel method for accumulating the log-transformed data was developed. This new method uses the properties of the transformation of statistical distributions to accumulate the output histogram in a non-integer and multi-channel fashion. Although the mathematics of this new mapping technique seem intricate, the concise nature of the derivations allow for an implementation procedure that lends itself to a real-time implementation using lookup tables, a task that is also introduced in this dissertation.

The Processing of Preposition-Stranding Constructions in English

One of the prominent questions in modern psycholinguistics is the relationship between the grammar and the parser. Within the approach of Generative Grammar, this issue has been investigated in terms of the role that Principles of Universal Grammar may play in language processing. The aim of this research experiment is to investigate this topic. Specifically, this experiment aims to test whether the Minimal Structure Principle (MSP) plays a role in the processing of Preposition-Stranding versus Pied-Piped Constructions. This investigation is made with a self-paced reading task, an on-line processing test that measures participants’ unconscious reaction to language stimuli. Monolingual English speakers’ reading times of sentences with Preposition-Stranding and Pied-Piped Constructions are compared. Results indicate that neither construction has greater processing costs, suggesting that factors other than the MSP are active during language processing.

The applied computer science of economic analyses: Internet prioritization and Internet pricing, and software engineering and software pricing for the mass market

This research examines evolving issues in applied computer science and applies economic and business analyses as well. There are two main areas. The first is internetwork communications as embodied by the Internet. The goal of the research is to devise an efficient pricing, prioritization, and incentivization plan that could be realistically implemented on the existing infrastructure. Criteria include practical and economic efficiency, and proper incentives for both users and providers. Background information on the evolution and functional operation of the Internet is given, and relevant literature is surveyed and analyzed. Economic analysis is performed on the incentive implications of the current pricing structure and organization. The problems are identified, and minimally disruptive solutions are proposed for all levels of implementation to the lowest level protocol. Practical issues are considered and performance analyses are done. The second area of research is mass market software engineering, and how this differs from classical software engineering. Software life-cycle revenues are analyzed and software pricing and timing implications are derived. A profit maximizing methodology is developed to select or defer the development of software features for inclusion in a given release. An iterative model of the stages of the software development process is developed, taking into account new communications capabilities as well as profitability. ^

A mathematical resolution to log transformations and the binning effect in applied processing of data in flow cytometry

This dissertation develops a new mathematical approach that overcomes the effect of a data processing phenomenon known as “histogram binning” inherent to flow cytometry data. A real-time procedure is introduced to prove the effectiveness and fast implementation of such an approach on real-world data. The histogram binning effect is a dilemma posed by two seemingly antagonistic developments: (1) flow cytometry data in its histogram form is extended in its dynamic range to improve its analysis and interpretation, and (2) the inevitable dynamic range extension introduces an unwelcome side effect, the binning effect, which skews the statistics of the data, undermining as a consequence the accuracy of the analysis and the eventual interpretation of the data. ^ Researchers in the field contended with such a dilemma for many years, resorting either to hardware approaches that are rather costly with inherent calibration and noise effects; or have developed software techniques based on filtering the binning effect but without successfully preserving the statistical content of the original data. ^ The mathematical approach introduced in this dissertation is so appealing that a patent application has been filed. The contribution of this dissertation is an incremental scientific innovation based on a mathematical framework that will allow researchers in the field of flow cytometry to improve the interpretation of data knowing that its statistical meaning has been faithfully preserved for its optimized analysis. Furthermore, with the same mathematical foundation, proof of the origin of such an inherent artifact is provided. ^ These results are unique in that new mathematical derivations are established to define and solve the critical problem of the binning effect faced at the experimental assessment level, providing a data platform that preserves its statistical content. ^ In addition, a novel method for accumulating the log-transformed data was developed. This new method uses the properties of the transformation of statistical distributions to accumulate the output histogram in a non-integer and multi-channel fashion. Although the mathematics of this new mapping technique seem intricate, the concise nature of the derivations allow for an implementation procedure that lends itself to a real-time implementation using lookup tables, a task that is also introduced in this dissertation. ^

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

From Bilingualism to Interlinguistics: The Case Against the Deficit Model of Language Acquisition

The article argues against an ahistorical deficit model of Spanish/English bilingualism in educational practice based on interlinguistic research. The bidirectional facilitative effects of Hispanic bilingualism allow Spanish-speaking minorities to exploit their language background while learning academic English and integrating their language and culture into the American mainstream.

The role of *language in early childhood mathematics

Math literacy is imperative to succeed in society. Experience is key for acquiring math literacy. A preschooler's world is full of mathematical experiences. Children are continually counting, sorting and comparing as they play. As children are engaged in these activities they are using language as a tool to express their mathematical thinking. If teachers are aware of these teachable moments and help children bridge their daily experiences to mathematical concepts, math literacy may be enhanced. This study described the interactions between teachers and preschoolers, determining the extent to which teachers scaffold children's everyday language into expressions of mathematical concepts. Of primary concern were the teachers' responsive interactions to children's expressions of an implicit mathematical utterance made while engaged in block play. The parallel mixed methods research design consisted of two strands. Strand 1 of the study focused on preschoolers' use of everyday language and the teachers' responses after a child made a mathematical utterance. Twelve teachers and 60 students were observed and videotaped while engaged in block play. Each teacher worked with five children for 20 minutes, yielding 240 minutes of observation. Interaction analysis was used to deductively analyze the recorded observations and field notes. Using a priori codes for the five mathematical concepts, it was found children produced 2,831 mathematical utterances. Teachers ignored 60% of these utterances and responded to, but did not mediate 30% of them. Only 10% of the mathematical utterances were mediated to a mathematical concept. Strand 2 focused on the teacher's view of the role of language in early childhood mathematics. The 12 teachers who had been observed as part of the first strand of the study were interviewed. Based on a thematic analysis of these interviews three themes emerged: (a) the importance of a child's environment, (b) the importance of an education in society, and (c) the role of math in early childhood. Finally, based on a meta-inference of both strands, three themes emerged: (a) teacher conception of math, (b) teacher practice, and (c) teacher sensitivity. Implications based on the findings involve policy, curriculum, and professional development.

Intuition and its component parts in the heuristic processing of deception detection: Hunch, *evidence, and reliance on intuitive efficacy

Rated trust in intuitive efficacy (measured as trust, belief, use, accuracy and weighting of intuition) was investigated as a predictor of self-designated use of intuitive (hunch and hunch plus evidential belief) vs. deliberative (evidential belief and evidential belief plus hunch) deception detection judgments and actual accuracy. Twenty-nine student participants were filmed as they made true and deceptive statements about their everyday activities on a given evening (last Friday night), and college students (N=238) judged 20 (10=true, 10=deceptive) of these filmed statements as truthful or deceptive. Participants provided ratings of reliance on hunches vs. evidential belief, confidence in film judgments, intuitive efficacy, accuracy in deception detection, reliance on cues to deception, and experiences with intuition. Generalized estimated equation modeling using binary logistics demonstrated accuracy in identifying true vs. deceptive statements was predicted by film number, hunch-evidence ratings, weighting of intuition, and total cues cited. Weighting of intuition was predictive of accuracy across participants, with higher weighting predictive of higher accuracy in general. Participants who cited evidential belief plus hunch and moderate to high weighting incorrectly reversed their true vs. deceptive judgments. Accuracy for true statements was higher for hunches and hunch plus evidential belief, whereas accuracy for deceptive statements was higher for evidential belief Accuracy for participants who relied on evidential belief plus hunch was at chance. Subjective experiences underlying judgments differed by participant and type of film viewed (true vs. deceptive) and were predicted by hunch-evidence ratings, trust, use, intuitive accuracy, and total cues cited. Trust predicted increases in judging films to be true, whereas use and accuracy predicted increases in judging films as deceptive; none were predictive of accuracy. Increased number of cues cited predicted judgments of deception, whereas decreased number of cues cited predicted truth. The study concluded that participants have the capacity to self-define their judgments as subjectively vs. deliberately based, provide subjective assessments of the influence of intuitive vs. objective information on their judgments, and can apply this self-knowledge, through effective weighting of intuition vs. other types of information, in making accurate judgments of true and deceptive everyday statements.

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.