Bioimaging and Biomedical Optics Commons^™

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 …

Assessing Structural And Functional Brain Alterations And Work-Related Fatigue In Non-Hyposmic And Hyposmic Covid-19 Survivors, Rakibul Hafiz

Dissertations

In the year 2019, life began to change at the advent of a global pandemic caused by the novel coronavirus. Mask mandates and mass vaccinations have mitigated the effects significantly, yet cases keep rising with new variants, especially, in densely populated countries, like India. Recent neuroimaging evidence shows the virus can attack the central nervous system (CNS). However, exactly which brain regions undergo structural and functional changes remain largely unknown. Many patients experience 'loss of/reduced sense of smell' (i.e., hyposmic) and an alarming number of survivors develop persistent symptoms ('long-COVID') for several months after initial infection. Fatigue is the most …

Therapeutic Treatment With The Anti-Inflammatory Drug Candidate Mw151 May Partially Reduce Memory Impairment And Normalizes Hippocampal Metabolic Markers In A Mouse Model Of Comorbid Amyloid And Vascular Pathology, David J. Braun, David K. Powell, Christopher J. Mclouth, Saktimayee M. Roy, D. Martin Watterson, Linda J. Van Eldik

Neuroscience Faculty Publications

Alzheimer's disease (AD) is the leading cause of dementia in the elderly, but therapeutic options are lacking. Despite long being able to effectively treat the ill-effects of pathology present in various rodent models of AD, translation of these strategies to the clinic has so far been disappointing. One potential contributor to this situation is the fact that the vast majority of AD patients have other dementia-contributing comorbid pathologies, the most common of which are vascular in nature. This situation is modeled relatively infrequently in basic AD research, and almost never in preclinical studies. As part of our efforts to develop …

Toward A Multimodal Computer-Aided Diagnostic Tool For Alzheimer’S Disease Conversion, Danilo Pena, Jessika Suescun, Mya Schiess, Timothy M. Ellmore, Luca Giancardo, Alzheimer’S Disease Neuroimaging Initiative

Publications and Research

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current …

Inter-Subject Correlation While Listening To Minimalist Music: A Study Of Electrophysiological And Behavioral Responses To Steve Reich’S Piano Phase, Tysen Dauer, Duc T. Nguyen, Nick Gang, Jacek P. Dmochowski, Jonathan Berger, Blair Kaneshiro

Publications and Research

Musical minimalism utilizes the temporal manipulation of restricted collections of rhythmic, melodic, and/or harmonic materials. One example, Steve Reich’s Piano Phase, offers listeners readily audible formal structure with unpredictable events at the local level. For example, pattern recurrences may generate strong expectations which are violated by small temporal and pitch deviations. A hyper-detailed listening strategy prompted by these minute deviations stands in contrast to the type of listening engagement typically cultivated around functional tonal Western music. Recent research has suggested that the inter-subject correlation (ISC) of electroencephalographic (EEG) responses to natural audio-visual stimuli objectively indexes a state of “engagement,” demonstrating …

Pairwise Correlation Analysis Of The Alzheimer’S Disease Neuroimaging Initiative (Adni) Dataset Reveals Significant Feature Correlation, Erik D. Huckvale, Matthew W. Hodgman, Brianna B. Greenwood, Devorah O. Stucki, Katrisa M. Ward, Mark T. W. Ebbert, John S. K. Kauwe, The Alzheimer’S Disease Neuroimaging Initiative, The Alzheimer’S Disease Metabolomics Consortium, Justin B. Miller

Sanders-Brown Center on Aging Faculty Publications

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the …

Rapid Microscopic Fractional Anisotropy Imaging Via An Optimized Linear Regression Formulation., N J J Arezza, D H Y Tse, C A Baron

Medical Biophysics Publications

Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (μFA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate μFA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire μFA directly from the 2nd order cumulant expansion of the powder averaged dMRI signal obtained from direct linear regression (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than …

When The Brain Plays A Game: Neural Responses To Visual Dynamics During Naturalistic Visual Tasks, Jason Ki

Dissertations and Theses

Many day-to-day tasks involve processing of complex visual information in a continuous stream. While much of our knowledge on visual processing has been established from reductionist approaches in lab-controlled settings, very little is known about the processing of complex dynamic stimuli experienced in everyday scenarios. Traditional investigations employ event-related paradigms that involve presentation of simple stimuli at select locations in visual space and discrete moments in time. In contrast, visual stimuli in real-life are highly dynamic, spatially-heterogeneous, and semantically rich. Moreover, traditional experiments impose unnatural task constraints (e.g., inhibited saccades), thus, it is unclear whether theories developed under the reductionist …

Network-Level Mechanisms Underlying Effects Of Transcranial Direct Current Stimulation (Tdcs) On Visuomotor Learning, Pejman Sehatpour, Clément Dondé, Matthew J. Hoptman, Johanna Kreither, Devin Adair, Elisa Dias, Blair Vail, Stephanie Rohrig, Gail Silipo, Javier Lopez-Calderon, Antigona Martinez, Daniel C. Javitt

Publications and Research

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation approach in which low level currents are administered over the scalp to influence underlying brain function. Prevailing theories of tDCS focus on modulation of excitation-inhibition balance at the local stimulation location. However, network level effects are reported as well, and appear to depend upon differential underlying mechanisms. Here, we evaluated potential network-level effects of tDCS during the Serial Reaction Time Task (SRTT) using convergent EEG- and fMRI-based connectivity approaches. Motor learning manifested as a significant (p <.0001) shift from slow to fast responses and corresponded to a significant increase in beta-coherence (p <.0001) and fMRI connectivity (p <.01) particularly within the visual-motor pathway. Differential patterns of tDCS effect were observed within different parametric task versions, consistent with network models. Overall, these findings demonstrate objective physiological effects of tDCS at the network level that result in effective behavioral modulation when tDCS parameters are matched to network-level requirements of the underlying task.

Multimodal Computational Modeling Of Visual Object Recognition Deficits But Intact Repetition Priming In Schizophrenia, Pejman Sehatpour, Anahita Bassir Nia, Devin Adair, Zhishun Wang, Heloise M. Debaun, Gail Silipo, Antigona Martinez, Daniel C. Javitt

Publications and Research

The term perceptual closure refers to the neural processes responsible for “filling-in” missing information in the visual image under highly adverse viewing conditions such as fog or camouflage. Here we used a closure task that required the participants to identify barely recognizable fragmented line-drawings of common objects. Patients with schizophrenia have been shown to perform poorly on this task. Following priming, controls and importantly patients can complete the line-drawings at greater levels of fragmentation behaviorally, suggesting an improvement in their ability to performthe task. Closure phenomena have been shown to involve a distributed network of cortical regions, notably the lateral …

Pathophysiology And Proteogenomics Of Post-Infectious And Post-Hemorrhagic Hydrocephalus In Infants, Albert M. Isaacs

Arts & Sciences Electronic Theses and Dissertations

Post-infectious (PIH) and post-hemorrhagic (PHH) hydrocephalus occur as sequalae of neonatal sepsis or intraventricular hemorrhage (IVH) of prematurity, respectively. Together, PIH and PHH represent the most common form of infantile hydrocephalus, the most common indication for neurosurgery in children globally, and the leading cause of neurological morbidity and mortality worldwide. The lack of understanding of the pathophysiology of PIH and PHH, particularly with regards to the host central nervous system response to the antecedent infection and hemorrhage, perturbation of differentiating neural stems in the ventricular (VZ) and subventricular (SVZ) zones, and damage to periventricular white matter (PVWM) tracts carrying sensorimotor …

Olfaction Modulates Inter-Subject Correlation Of Neural Responses, Paul Deguzman, Anshul Jain, Matthias H. Tabert, Lucas C. Parra

Publications and Research

Odors can be powerful stimulants. It is well-established that odors provide strong cues for recall of locations, people and events. The effects of specific scents on other cognitive functions are less well-established. We hypothesized that scents with different odor qualities will have a different effect on attention. To assess attention, we used Inter-Subject Correlation of the EEG because this metric is strongly modulated by attentional engagement with natural audiovisual stimuli.We predicted that scents known to be “energizing” would increase Inter-Subject Correlation during watching of videos as compared to “calming” scents. In a first experiment, we confirmed this for eucalyptol and …

3d Architectural Analysis Of Neurons, Astrocytes, Vasculature & Nuclei In The Motor And Somatosensory Murine Cortical Columns, Jared Leichner

FIU Electronic Theses and Dissertations

Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, …

Functional Connectivity Of Eeg Is Subject-Specific, Associated With Phenotype, And Different From Fmri, Maximilian Nentwich, Lei Ai, Jens Madsen, Qawi K. Telesford, Stefan Haufe, Michael P. Milham, Lucas C. Parra

Publications and Research

A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ‘’functional connectivity’’ -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest …

Can Transcranial Electrical Stimulation Motor Threshold Estimate Individualized Tdcs Doses Over The Prefrontal Cortex? Evidence From Reverse-Calculation Electric Field Modeling, Kevin A. Caulfield, Bashar W. Badran, Xingbao Li, Marom Bikson, Mark S. George

Publications and Research

No abstract provided.

Transcranial Electrical Stimulation Motor Threshold Can Estimate Individualized Tdcs Dosage From Reverse-Calculation Electric-Field Modeling, Kevin A. Caulfield, Bashar W. Badran, William H. Devries, Philipp M. Summers, Emma Kofmehl, Xingbao Li, Jeffrey J. Borckardt, Marom Bikson, Mark S. George

Publications and Research

Background

Unique amongst brain stimulation tools, transcranial direct current stimulation (tDCS) currently lacks an easy or widely implemented method for individualizing dosage.

Objective

We developed a method of reverse-calculating electric-field (E-field) models based on Magnetic Resonance Imaging (MRI) scans that can estimate individualized tDCS dose. We also evaluated an MRI-free method of individualizing tDCS dose by measuring transcranial magnetic stimulation (TMS) motor threshold (MT) and single pulse, suprathreshold transcranial electrical stimulation (TES) MT and regressing it against E-field modeling. Key assumptions of reverse-calculation E-field modeling, including the size of region of interest (ROI) analysis and the linearity of multiple E-field …

Resolving Intravoxel White Matter Structures In The Human Brain Using Regularized Regression And Clustering, Andrea Hart, Brianna Smith, Sean Smith, Elijah Sales, Jacqueline Hernandez-Camargo, Yarlin Mayor Garcia, Felix Zhan, Lori Griswold, Brian Dunkelberger, Michael R. Schwob, Sharang Chaudhry, Justin Zhan, Laxmi Gewali, Paul Oh

Computer Science Faculty Research

The human brain is a complex system of neural tissue that varies significantly between individuals. Although the technology that delineates these neural pathways does not currently exist, medical imaging modalities, such as diffusion magnetic resonance imaging (dMRI), can be leveraged for mathematical identification. The purpose of this work is to develop a novel method employing machine learning techniques to determine intravoxel nerve number and direction from dMRI data. The method was tested on multiple synthetic datasets and showed promising estimation accuracy and robustness for multi-nerve systems under a variety of conditions, including highly noisy data and imprecision in parameter assumptions.

Objectively Measuring Effects Of Electro-Acupuncture In Parkinsonian Rhesus Monkeys, Rui Zhang, Anders H. Andersen, Peter A. Hardy, Eric Forman, April Evans, Yi Ai, Jin Yue, Guihua Yue, Don M. Gash, Richard Grondin, Zhiming Zhang

Magnetic Resonance Imaging and Spectroscopy Center Faculty Publications

Acupuncture has increasingly been used as an alternative therapy for treatment of Parkinson’s disease (PD). However, the efficacy of acupunture for PD still remains unclear. The present study was designed to objectively and safely monitor anti-parkinsonian effects of electroacupuncture (EA) and brain activity in nonhuman primates modeling human PD. Six middle-aged rhesus monkeys were extensively studied by a computerized behavioral testing battery and by pharmacological MRI (phMRI) scans with specific dopaminergic drug stimulations. All animals were evaluated for behavior and phMRI responses under normal, parkinsonian, parkinsonian with EA treatment and parkinsonian after EA treatment conditions. Stable parkinsonian features were observed …

Characterization Of Structural Dynamics Of The Human Head Using Magnetic Resonance Elastography, Andrew Arun Badachhape

McKelvey School of Engineering Theses & Dissertations

In traumatic brain injury (TBI), the skull-brain interface, composed of three meningeal layers: the dura mater, arachnoid mater, and pia mater, along with cerebrospinal fluid (CSF) between the layers, plays a vital role in transmitting motion from the skull to brain tissue. Magnetic resonance elastography (MRE) is a noninvasive imaging modality capable of providing in vivo estimates of tissue motion and material properties. The objective of this work is to augment human and phantom MRE studies to better characterize the mechanical contributions of the skull-brain interface to improve the parameterization and validation of computational models of TBI. Three specific aims …

Pattern Discovery In Brain Imaging Genetics Via Scca Modeling With A Generic Non-Convex Penalty, Lei Du, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen, Michael W. Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, Charles D. Smith, Gregory Jicha, Peter A. Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad

Neurology Faculty Publications

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose ℓ₁-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the ℓ₁-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce …

Resting State Functional Network Disruptions In A Kainic Acid Model Of Temporal Lobe Epilepsy., Ravnoor Singh Gill, Seyed M Mirsattari, L Stan Leung

Physiology and Pharmacology Publications

We studied the graph topological properties of brain networks derived from resting-state functional magnetic resonance imaging in a kainic acid induced model of temporal lobe epilepsy (TLE) in rats. Functional connectivity was determined by temporal correlation of the resting-state Blood Oxygen Level Dependent (BOLD) signals between two brain regions during 1.5% and 2% isoflurane, and analyzed as networks in epileptic and control rats. Graph theoretical analysis revealed a significant increase in functional connectivity between brain areas in epileptic than control rats, and the connected brain areas could be categorized as a limbic network and a default mode network (DMN). The …

Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li

Computer Science Theses & Dissertations

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries …

Lesion Identification And The Effect Of Lesion On Motor Mapping After Stroke, Ruixi Zhou

McKelvey School of Engineering Theses & Dissertations

Stroke is the most common cause of long-term severe disability and the motor system that is most commonly affected in stroke. One of the mechanisms that underlies recovery of motor deficits is reorganization or remapping of functional representations around the motor cortex. This mechanism has been shown in monkeys, but results in human subjects have been variable. In this thesis, I used a database that includes longitudinal behavioral and multimodal imaging data in both stroke patients and healthy controls for two research projects. Firstly, I improved an automatic lesion segmentation method to aid in the identification of the location and …

The Connectivity Domain: Analyzing Resting State Fmri Data Using Feature-Based Data-Driven And Model-Based Methods, Armin Iraji, Vince D. Calhoun, Natalie M. Wiseman, Esmaeil Davoodi-Bojd, Mohammad R. N. Avanaki, Zhifeng Kou

Biomedical Engineering Faculty Research Publications

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into …

Resting-State Connectivity Identifies Distinct Functional Networks In Macaque Cingulate Cortex., R Matthew Hutchison, Thilo Womelsdorf, Joseph S Gati, L Stan Leung, Ravi S Menon, Stefan Everling

Physiology and Pharmacology Publications

Subregions of the cingulate cortex represent prominent intersections in the structural networks of the primate brain. The relevance of the cingulate to the structure and dynamics of large-scale networks ultimately requires a link to functional connectivity. Here, we map fine-grained functional connectivity across the complete extent of the macaque (Macaca fascicularis) cingulate cortex and delineate subdivisions pertaining to distinct identifiable networks. In particular, we identified 4 primary networks representing the functional spectrum of the cingulate: somatomotor, attention-orienting, executive, and limbic. The cingulate nodes of these networks originated from separable subfields along the rostral-to-caudal axis and were characterized by positive and …

Multimodal Imaging Evidence For Axonal And Myelin Deterioration In Amnestic Mild Cognitive Impairment, Brian T. Gold, Yang Jiang, David K. Powell, Charles D. Smith

Neuroscience Faculty Publications

White matter (WM) microstructural declines have been demonstrated in Alzheimer's disease and amnestic mild cognitive impairment (aMCI). However, the pattern of WM microstructural changes in aMCI after controlling for WM atrophy is unknown. Here, we address this issue through joint consideration of aMCI alterations in fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, as well as macrostructural volume in WM and gray matter compartments. Participants were 18 individuals with aMCI and 24 healthy seniors. Voxelwise analyses of diffusion tensor imaging data was carried out using tract-based spatial statistics (TBSS) and voxelwise analyses of high-resolution structural data was conducted using …

Alterations In Multiple Measures Of White Matter Integrity In Normal Women At High Risk For Alzheimer's Disease, Brian T. Gold, David K. Powell, Anders H. Andersen, Charles D. Smith

Neuroscience Faculty Publications

There is evidence that disruption of white matter (WM) microstructure is an early event in the course of Alzheimer's disease (AD). However, the neurobiological bases of WM microstructural declines in presymptomatic AD are unknown. In the present study we address this issue using a multimodal imaging approach to the study of presymptomatic AD. Participants were 37 high-risk (both family history of dementia and one or more APOE4 alleles) women and 20 low-risk (neither family history nor APOE4) women. Groups were matched for age, education, neuropsychological performance, and vascular factors that could affect white matter. Whole-brain analyses of diffusion tensor imaging …