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Description
Exome sequencing was used to identify novel variants linked to amyotrophic lateral sclerosis (ALS), in a family without mutations in genes previously linked to ALS. A F115C mutation in the gene MATR3 was identified, and further examination of other ALS kindreds identified an additional three mutations in MATR3; S85C, P154S and T622A. Matrin 3 is an RNA/DNA binding protein as well as part of the nuclear matrix. Matrin 3 interacts with TDP-43, a protein that is both mutated in some forms of ALS, and found in pathological inclusions in most ALS patients. Matrin 3 pathology, including mislocalization and rare cytoplasmic inclusions, was identified in spinal cord tissue from a patient carrying a mutation in Matrin 3, as well as sporadic ALS patients. In an effort to determine the mechanism of Matrin 3 linked ALS, the protein interactome of wild-type and ALS-linked MATR3 mutations was examined. Immunoprecipitation followed by mass spectrometry experiments were performed using NSC-34 cells expressing human wild-type or mutant Matrin 3. Gene ontology analysis identified a novel role for Matrin 3 in mRNA transport centered on proteins in the TRanscription and EXport (TREX) complex, known to function in mRNA biogenesis and nuclear export. ALS-linked mutations in Matrin 3 led to its re-distribution within the nucleus, decreased co-localization with endogenous Matrin 3 and increased co-localization with specific TREX components. Expression of disease-causing Matrin 3 mutations led to nuclear mRNA export defects of both global mRNA and more specifically the mRNA of TDP-43 and FUS. Our findings identify ALS-causing mutations in the gene MATR3, as well as a potential pathogenic mechanism attributable to MATR3 mutations and further link cellular transport defects to ALS.
ContributorsBoehringer, Ashley (Author) / Bowser, Robert (Thesis advisor) / Liss, Julie (Committee member) / Jensen, Kendall (Committee member) / Ladha, Shafeeq (Committee member) / Arizona State University (Publisher)
Created2018
Description
The activation of the primary motor cortex (M1) is common in speech perception tasks that involve difficult listening conditions. Although the challenge of recognizing and discriminating non-native speech sounds appears to be an instantiation of listening under difficult circumstances, it is still unknown if M1 recruitment is facilitatory of second language speech perception. The purpose of this study was to investigate the role of M1 associated with speech motor centers in processing acoustic inputs in the native (L1) and second language (L2), using repetitive Transcranial Magnetic Stimulation (rTMS) to selectively alter neural activity in M1. Thirty-six healthy English/Spanish bilingual subjects participated in the experiment. The performance on a listening word-to-picture matching task was measured before and after real- and sham-rTMS to the orbicularis oris (lip muscle) associated M1. Vowel Space Area (VSA) obtained from recordings of participants reading a passage in L2 before and after real-rTMS, was calculated to determine its utility as an rTMS aftereffect measure. There was high variability in the aftereffect of the rTMS protocol to the lip muscle among the participants. Approximately 50% of participants showed an inhibitory effect of rTMS, evidenced by smaller motor evoked potentials (MEPs) area, whereas the other 50% had a facilitatory effect, with larger MEPs. This suggests that rTMS has a complex influence on M1 excitability, and relying on grand-average results can obscure important individual differences in rTMS physiological and functional outcomes. Evidence of motor support to word recognition in the L2 was found. Participants showing an inhibitory aftereffect of rTMS on M1 produced slower and less accurate responses in the L2 task, whereas those showing a facilitatory aftereffect of rTMS on M1 produced more accurate responses in L2. In contrast, no effect of rTMS was found on the L1, where accuracy and speed were very similar after sham- and real-rTMS. The L2 VSA measure was indicative of the aftereffect of rTMS to M1 associated with speech production, supporting its utility as an rTMS aftereffect measure. This result revealed an interesting and novel relation between cerebral motor cortex activation and speech measures.
ContributorsBarragan, Beatriz (Author) / Liss, Julie (Thesis advisor) / Berisha, Visar (Committee member) / Rogalsky, Corianne (Committee member) / Restrepo, Adelaida (Committee member) / Arizona State University (Publisher)
Created2018
Description
Studies in Second Language Acquisition and Neurolinguistics have argued that adult learners when dealing with certain phonological features of L2, such as segmental and suprasegmental ones, face problems of articulatory placement (Esling, 2006; Abercrombie, 1967) and somatosensory stimulation (Guenther, Ghosh, & Tourville, 2006; Waldron, 2010). These studies have argued that adult phonological acquisition is a complex matter that needs to be informed by a specialized sensorimotor theory of speech acquisition. They further suggested that traditional pronunciation pedagogy needs to be enhanced by an approach to learning offering learners fundamental and practical sensorimotor tools to advance the quality of L2 speech acquisition.
This foundational study designs a sensorimotor approach to pronunciation pedagogy and tests its effect on the L2 speech of five adult (late) learners of American English. Throughout an eight week classroom experiment, participants from different first language backgrounds received instruction on Articulatory Settings (Honickman, 1964) and the sensorimotor mechanism of speech acquisition (Waldron 2010; Guenther et al., 2006). In addition, they attended five adapted lessons of the Feldenkrais technique (Feldenkrais, 1972) designed to develop sensorimotor awareness of the vocal apparatus and improve the quality of L2 speech movement. I hypothesize that such sensorimotor learning triggers overall positive changes in the way L2 learners deal with speech articulators for L2 and that over time they develop better pronunciation.
After approximately eight hours of intervention, analysis of results shows participants’ improvement in speech rate, degree of accentedness, and speaking confidence, but mixed changes in word intelligibility and vowel space area. Albeit not statistically significant (p >.05), these results suggest that such a sensorimotor approach to L2 phonological acquisition warrants further consideration and investigation for use in the L2 classroom.
This foundational study designs a sensorimotor approach to pronunciation pedagogy and tests its effect on the L2 speech of five adult (late) learners of American English. Throughout an eight week classroom experiment, participants from different first language backgrounds received instruction on Articulatory Settings (Honickman, 1964) and the sensorimotor mechanism of speech acquisition (Waldron 2010; Guenther et al., 2006). In addition, they attended five adapted lessons of the Feldenkrais technique (Feldenkrais, 1972) designed to develop sensorimotor awareness of the vocal apparatus and improve the quality of L2 speech movement. I hypothesize that such sensorimotor learning triggers overall positive changes in the way L2 learners deal with speech articulators for L2 and that over time they develop better pronunciation.
After approximately eight hours of intervention, analysis of results shows participants’ improvement in speech rate, degree of accentedness, and speaking confidence, but mixed changes in word intelligibility and vowel space area. Albeit not statistically significant (p >.05), these results suggest that such a sensorimotor approach to L2 phonological acquisition warrants further consideration and investigation for use in the L2 classroom.
ContributorsLima, J. Alberto S., Jr (Author) / Pruitt, Kathryn (Thesis advisor) / Gelderen, Elly van (Thesis advisor) / Liss, Julie (Committee member) / James, Mark (Committee member) / Arizona State University (Publisher)
Created2015
Description
Audiovisual (AV) integration is a fundamental component of face-to-face communication. Visual cues generally aid auditory comprehension of communicative intent through our innate ability to “fuse” auditory and visual information. However, our ability for multisensory integration can be affected by damage to the brain. Previous neuroimaging studies have indicated the superior temporal sulcus (STS) as the center for AV integration, while others suggest inferior frontal and motor regions. However, few studies have analyzed the effect of stroke or other brain damage on multisensory integration in humans. The present study examines the effect of lesion location on auditory and AV speech perception through behavioral and structural imaging methodologies in 41 left-hemisphere participants with chronic focal cerebral damage. Participants completed two behavioral tasks of speech perception: an auditory speech perception task and a classic McGurk paradigm measuring congruent (auditory and visual stimuli match) and incongruent (auditory and visual stimuli do not match, creating a “fused” percept of a novel stimulus) AV speech perception. Overall, participants performed well above chance on both tasks. Voxel-based lesion symptom mapping (VLSM) across all 41 participants identified several regions as critical for speech perception depending on trial type. Heschl’s gyrus and the supramarginal gyrus were identified as critical for auditory speech perception, the basal ganglia was critical for speech perception in AV congruent trials, and the middle temporal gyrus/STS were critical in AV incongruent trials. VLSM analyses of the AV incongruent trials were used to further clarify the origin of “errors”, i.e. lack of fusion. Auditory capture (auditory stimulus) responses were attributed to visual processing deficits caused by lesions in the posterior temporal lobe, whereas visual capture (visual stimulus) responses were attributed to lesions in the anterior temporal cortex, including the temporal pole, which is widely considered to be an amodal semantic hub. The implication of anterior temporal regions in AV integration is novel and warrants further study. The behavioral and VLSM results are discussed in relation to previous neuroimaging and case-study evidence; broadly, our findings coincide with previous work indicating that multisensory superior temporal cortex, not frontal motor circuits, are critical for AV integration.
ContributorsCai, Julia (Author) / Rogalsky, Corianne (Thesis advisor) / Azuma, Tamiko (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2017
Description
Speech intelligibility measures how much a speaker can be understood by a listener. Traditional measures of intelligibility, such as word accuracy, are not sufficient to reveal the reasons of intelligibility degradation. This dissertation investigates the underlying sources of intelligibility degradations from both perspectives of the speaker and the listener. Segmental phoneme errors and suprasegmental lexical boundary errors are developed to reveal the perceptual strategies of the listener. A comprehensive set of automated acoustic measures are developed to quantify variations in the acoustic signal from three perceptual aspects, including articulation, prosody, and vocal quality. The developed measures have been validated on a dysarthric speech dataset with various severity degrees. Multiple regression analysis is employed to show the developed measures could predict perceptual ratings reliably. The relationship between the acoustic measures and the listening errors is investigated to show the interaction between speech production and perception. The hypothesize is that the segmental phoneme errors are mainly caused by the imprecise articulation, while the sprasegmental lexical boundary errors are due to the unreliable phonemic information as well as the abnormal rhythm and prosody patterns. To test the hypothesis, within-speaker variations are simulated in different speaking modes. Significant changes have been detected in both the acoustic signals and the listening errors. Results of the regression analysis support the hypothesis by showing that changes in the articulation-related acoustic features are important in predicting changes in listening phoneme errors, while changes in both of the articulation- and prosody-related features are important in predicting changes in lexical boundary errors. Moreover, significant correlation has been achieved in the cross-validation experiment, which indicates that it is possible to predict intelligibility variations from acoustic signal.
ContributorsJiao, Yishan (Author) / Berisha, Visar (Thesis advisor) / Liss, Julie (Thesis advisor) / Zhou, Yi (Committee member) / Arizona State University (Publisher)
Created2019
Description
The brain uses the somatosensory system to interact with the environment and control movements. Additionally, many movement disorders are associated with deficits in the somatosensory sensory system. Thus, understanding the somatosensory system is essential for developing treatments for movement disorders. Previous studies have extensively examined the role of the somatosensory system in controlling the lower and upper extremities; however, little is known about the contributions of the orofacial somatosensory system. The overall goal of this study was to determine factors that influence the sensitivity of the orofacial somatosensory system. To measure the somatosensory system's sensitivity, transcutaneous electrical current stimulation was applied to the skin overlaying the trigeminal nerve on the lower portion of the face. After applying stimulation, participants' sensitivity was determined through the detection of the electrical stimuli (i.e., perceptual threshold). The data analysis focused on the impact of (1) stimulation parameters, (2) electrode placement, and (3) motor tasks on the perceptual threshold. The results showed that, as expected, stimulation parameters (such as stimulation frequency and duration) influenced perceptual thresholds. However, electrode placement (left vs. right side of the face) and motor tasks (lip contraction vs. rest) did not influence perceptual thresholds. Overall, these findings have important implications for designing and developing therapeutic neuromodulation techniques based on trigeminal nerve stimulation.
ContributorsKhoury, Maya Elie (Author) / Daliri, Ayoub (Thesis advisor) / Patten, Jake (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2022
Description
Stroke is the leading cause of long-term disability in the U.S., with up to 60% of strokescausing speech loss. Individuals with severe stroke, who require the most frequent, intense speech therapy, often cannot adhere to treatments due to high cost and low success rates. Therefore, the ability to make functionally significant changes in individuals with severe post- stroke aphasia remains a key challenge for the rehabilitation community. This dissertation aimed to evaluate the efficacy of Startle Adjuvant Rehabilitation Therapy (START), a tele-enabled, low- cost treatment, to improve quality of life and speech in individuals with severe-to-moderate stroke. START is the exposure to startling acoustic stimuli during practice of motor tasks in individuals with stroke. START increases the speed and intensity of practice in severely impaired post-stroke reaching, with START eliciting muscle activity 2-3 times higher than maximum voluntary contraction. Voluntary reaching distance, onset, and final accuracy increased after a session of START, suggesting a rehabilitative effect. However, START has not been evaluated during impaired speech. The objective of this study is to determine if impaired speech can be elicited by startling acoustic stimuli, and if three days of START training can enhance clinical measures of moderate to severe post-stroke aphasia and apraxia of speech. This dissertation evaluates START in 42 individuals with post-stroke speech impairment via telehealth in a Phase 0 clinical trial. Results suggest that impaired speech can be elicited by startling acoustic stimuli and that START benefits individuals with severe-to-moderate post-stroke impairments in both linguistic and motor speech domains. This fills an important gap in aphasia care, as many speech therapies remain ineffective and financially inaccessible for patients with severe deficits. START is effective, remotely delivered, and may likely serve as an affordable adjuvant to traditional therapy for those that have poor access to quality care.
ContributorsSwann, Zoe Elisabeth (Author) / Honeycutt, Claire F (Thesis advisor) / Daliri, Ayoub (Committee member) / Rogalsky, Corianne (Committee member) / Liss, Julie (Committee member) / Schaefer, Sydney (Committee member) / Arizona State University (Publisher)
Created2022
Description
In many biological research studies, including speech analysis, clinical research, and prediction studies, the validity of the study is dependent on the effectiveness of the training data set to represent the target population. For example, in speech analysis, if one is performing emotion classification based on speech, the performance of the classifier is mainly dependent on the number and quality of the training data set. For small sample sizes and unbalanced data, classifiers developed in this context may be focusing on the differences in the training data set rather than emotion (e.g., focusing on gender, age, and dialect).
This thesis evaluates several sampling methods and a non-parametric approach to sample sizes required to minimize the effect of these nuisance variables on classification performance. This work specifically focused on speech analysis applications, and hence the work was done with speech features like Mel-Frequency Cepstral Coefficients (MFCC) and Filter Bank Cepstral Coefficients (FBCC). The non-parametric divergence (D_p divergence) measure was used to study the difference between different sampling schemes (Stratified and Multistage sampling) and the changes due to the sentence types in the sampling set for the process.
This thesis evaluates several sampling methods and a non-parametric approach to sample sizes required to minimize the effect of these nuisance variables on classification performance. This work specifically focused on speech analysis applications, and hence the work was done with speech features like Mel-Frequency Cepstral Coefficients (MFCC) and Filter Bank Cepstral Coefficients (FBCC). The non-parametric divergence (D_p divergence) measure was used to study the difference between different sampling schemes (Stratified and Multistage sampling) and the changes due to the sentence types in the sampling set for the process.
ContributorsMariajohn, Aaquila (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2020
Description
Diffusion Tensor Imaging may be used to understand brain differences within PD. Within the last couple of decades there has been an explosion of learning and development in neuroimaging techniques. Today, it is possible to monitor and track where a brain is needing blood during a specific task without much delay such as when using functional Magnetic Resonance Imaging (fMRI). It is also possible to track and visualize where and at which orientation water molecules in the brain are moving like in Diffusion Tensor Imaging (DTI). Data on certain diseases such as Parkinson’s Disease (PD) has grown considerably, and it is now known that people with PD can be assessed with cognitive tests in combination with neuroimaging to diagnose whether people with PD have cognitive decline in addition to any motor ability decline. The Montreal Cognitive Assessment (MoCA), Modified Semantic Fluency Test (MSF) and Mini-Mental State Exam (MMSE) are the primary tools and are often combined with fMRI or DTI for diagnosing if people with PD also have a mild cognitive impairment (MCI). The current thesis explored a group of cohort of PD patients and classified based on their MoCA, MSF, and Lexical Fluency (LF) scores. The results indicate specific brain differences in whether PD patients were low or high scorers on LF and MoCA scores. The current study’s findings adds to the existing literature that DTI may be more sensitive in detecting differences based on clinical scores.
ContributorsAndrade, Eric (Author) / Oforoi, Edward (Thesis advisor) / Zhou, Yi (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2022
Description
Speech analysis for clinical applications has emerged as a burgeoning field, providing valuable insights into an individual's physical and physiological state. Researchers have explored speech features for clinical applications, such as diagnosing, predicting, and monitoring various pathologies. Before presenting the new deep learning frameworks, this thesis introduces a study on conventional acoustic feature changes in subjects with post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI). This work demonstrates the effectiveness of using speech signals to assess the pathological status of individuals. At the same time, it highlights some of the limitations of conventional acoustic and linguistic features, such as low repeatability and generalizability. Two critical characteristics of speech features are (1) good robustness, as speech features need to generalize across different corpora, and (2) high repeatability, as speech features need to be invariant to all confounding factors except the pathological state of targets. This thesis presents two research thrusts in the context of speech signals in clinical applications that focus on improving the robustness and repeatability of speech features, respectively. The first thrust introduces a deep learning framework to generate acoustic feature embeddings sensitive to vocal quality and robust across different corpora. A contrastive loss combined with a classification loss is used to train the model jointly, and data-warping techniques are employed to improve the robustness of embeddings. Empirical results demonstrate that the proposed method achieves high in-corpus and cross-corpus classification accuracy and generates good embeddings sensitive to voice quality and robust across different corpora. The second thrust introduces using the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. A novel regularizer, the ICC regularizer, is proposed to regularize deep neural networks to produce embeddings with higher repeatability. This ICC regularizer is implemented and applied to three speech applications: a clinical application, speaker verification, and voice style conversion. The experimental results reveal that the ICC regularizer improves the repeatability of learned embeddings compared to the contrastive loss, leading to enhanced performance in downstream tasks.
ContributorsZhang, Jianwei (Author) / Jayasuriya, Suren (Thesis advisor) / Berisha, Visar (Thesis advisor) / Liss, Julie (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2023