Neuroscience and Neurobiology Commons^™

Towards Understanding And Improving Speech Processing, Sonia Yasmin

Electronic Thesis and Dissertation Repository

This dissertation explores mechanisms for understanding and improving speech processing. First, I used EEG to investigate the acoustic and semantic processing of continuous naturalistic speech masked by multi-talker babble. I found that different features of the same speech signal are reflected in different aspects of the neural tracking response, which are themselves differentially affected by noise. These findings point to a complex relationship between speech intelligibility and neural speech encoding.

Next, I systematically reviewed the current advancements in speech enhancement technologies. I find that speech enhancement algorithms are limited in their generalizability to speech-noise (i.e., babble). I demonstrate that, for …

Utilizing Ai Integrated Neuroimaging Technology To Expand Upon Machine Learning In Positron Emission Tomography Technology With The Aim Of Detecting Amyloid Beta Biomarkers Early In The Onset Of Alzheimer's., Ethan S. Terman

Undergraduate Research Posters

Early intervention in Alzheimer's is vital for treatment. The earlier a professional can detect symptoms and make a diagnosis the earlier a prognosis can be implemented. With the prevalence of data in our day-to-day world combined with Artificial intelligence (AI), utilizing both for machine learning can pave the way for more accurate and efficient detection of Alzheimer's and other neurodegenerative diseases. AI combined with Machine learning (ML) increases diagnostic efficiency and reduces human errors, making it a valuable resource for physicians and clinicians alike. With the increasing amount of data processing and image interpretation required, the ability to use AI …

Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi

Electronic Thesis and Dissertation Repository

Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques.

To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel …

Neural Correlates Of Post-Traumatic Brain Injury (Tbi) Attention Deficits In Children, Meng Cao

Dissertations

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can …

Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn

Graduate College Dissertations and Theses

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the …

Systems Approaches For Gene And Drug Discovery In Alzheimer’S Disease, Jeffrey Brabec

Graduate College Dissertations and Theses

Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder affecting all tissues and cell types of brain leading to emotional dysregulation and cognitive dysfunction. From genome-wide association studies (GWAS), to date we have identified forty-two genome-wide significant genes for AD that influence overall disease risk or endophenotypes, including neuroimaging and gene expression profiles. Nevertheless, the currently known AD genes do not account for a significant proportion of the heritability of disease risk, implying the existence of many weak-effect variants in potentially thousands of genes as drivers of AD outcomes. This genetic architecture, composed of many small effects, is partly due to …

Brain-Behavior Connections Underlying Emotion And Theory Of Mind In Autism Spectrum Disorder, Yu Han

Graduate College Dissertations and Theses

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that af- fects nearly 1 in 54 children. Children with ASD struggle with social, communication, and behavioral challenges due to deficits in theory of mind (ToM). In addition, diag- nosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. In this study, we conducted two experiments collecting behavioral and neu- roimaging data from 9 children with ASD and 19 neurotypical children (NT) between the age of 7 and 14 years.

The first experiment examined specific …

Using Machine Learning To Conduct A Detailed Behavioral Analysis In An Appetitive Social Learning Task, Thomas Shao

Honors Scholar Theses

Learning by watching others, or observational learning, is important for social development and survival. However, not much is known about the brain mechanisms underlying this type of learning. Since the 1960s, observational learning has been widely studied in humans, but developing and analyzing experiments for animals has been challenging. Here, I explore observational learning using a novel paradigm while performing an analysis that involves tracking the rats using an active learning paradigm called DeepLabCut. In this novel paradigm, customized operant conditioning chambers are used for the rats to observe and learn from another animal repeatedly on multiple trials each day. …

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral measures …

An Integrated Neuroimaging Approach For The Prediction And Analysis Of Alzheimer’S Disease And Its Prodromal Stages, Qi Zhou

FIU Electronic Theses and Dissertations

This dissertation proposes to combine magnetic resonance imaging (MRI), positron emission tomography (PET) and a neuropsychological test, Mini-Mental State Examination (MMSE), as input to a multidimensional space for the classification of Alzheimer’s disease (AD) and it’s prodromal stages including amnestic MCI (aMCI) and non-amnestic MCI (naMCI). An assessment is provided on the effect of different MRI normalization techniques on the prediction of AD. Statistically significant variables selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis …

Modeling Visual Features To Recognize Biological Motion: A Developmental Approach, Giulio Sandini, Nicoletta Noceti, Alessia Vignolo, Alessandra Sciutti, Francesco Rea, Alessandro Verri, Francesca Odone

MODVIS Workshop

In this work we deal with the problem of designing and developing computational vision models – comparable to the early stages of the human development – using coarse low-level information.

More specifically, we consider a binary classification setting to characterize biological movements with respect to non-biological dynamic events. To this purpose, our model builds on top of the optical flow estimation, and abstract the representation to simulate the limited amount of visual information available at birth. We take inspiration from known biological motion regularities explained by the Two-Thirds Power Law, and design a motion representation that includes different low-level features, …

Lexical Mechanics: Partitions, Mixtures, And Context, Jake Ryland Williams

Graduate College Dissertations and Theses

Highly structured for efficient communication, natural languages are complex systems. Unlike in their computational cousins, functions and meanings in natural languages are relative, frequently prescribed to symbols through unexpected social processes. Despite grammar and definition, the presence of metaphor can leave unwitting language users "in the dark," so to speak. This is not problematic, but rather an important operational feature of languages, since the lifting of meaning onto higher-order structures allows individuals to compress descriptions of regularly-conveyed information. This compressed terminology, often only appropriate when taken locally (in context), is beneficial in an enormous world of novel experience. However, what …