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This dissertation examines the utility of Transcranial Magnetic Stimulation (TMS) in enhancing intervention strategies for mild cognitive impairment (MCI). The research begins by exploring the foundations of TMS and its potential to alter disease progression through neuroplastic mechanisms. It highlights how advancements in neuroimaging significantly improve the specificity of TMS interventions, supporting its application in cognitive enhancement for AD and MCI patients. A significant focus is placed on the evaluation of sensitive cognitive outcome assessments (COAs) that can detect early pathological changes before clinical symptoms become evident. The study particularly emphasizes the role of the Face-Name Associative Memory Exam (FNAME) in identifying subtle cognitive deviations across the MCI spectrum. Through comprehensive analyses, this work demonstrates how functional and structural connectivity influence associative memory, revealing potential biomarkers for early AD intervention(Chen et al., 2022). Further investigations assess the reliability of different rTMS targeting strategies, illustrating that Diffusion Tensor Imaging (DTI)-guided methods provide more consistent outcomes than those guided by resting-state fMRI. This underscores the critical importance of precision in personalizing rTMS therapy to enhance treatment efficacy. Additionally, the responsiveness of rTMS in older adults is explored, with a particular emphasis on the predictive value of baseline functional connectivity within the sensorimotor cortex. This approach highlights the inter-individual variability in TMS effects and the potential for tailored therapeutic strategies based on individual neurophysiological profiles. The dissertation concludes with a synthesis of findings and a discussion of future directions for rTMS research in the management of neurodegenerative diseases. It advocates for the standardization of protocols and the integration of rTMS with other treatment modalities to

The urgency to develop a treatment for memory decline in pre-clinical Alzheimer's disease (AD), such as mild cognitive impairment (MCI), has prompted multiple researchers to explore transcranial magnetic stimulation (TMS) as a potential therapeutic approach. However, the outcomes have been mixed. While TMS holds promise as a tool for addressing memory decline, its mechanisms and which protocols are reliable are still the subject of ongoing investigation. In this dissertation, Chapter 1 provides a general introduction to TMS, AD, and MCI, highlighting previous TMS studies that have shown memory improvement. Specifically, we aimed to utilize a time-efficient TMS protocol known as theta-burst stimulation (TBS) across three projects. Chapter 2 features the first TMS treatment study at the University of Arizona. This study was novel in that it involved the use of connectivity-based MRI-guided TMS, with a focus on structural connectivity derived from diffusion-weighted imaging. This allowed the targeting the hippocampus, a deeply-seated memory center of the brain, which is typically beyond the reach of TMS. The results of this study suggested a probable mechanism for TMS, indicating that intermittent TBS (iTBS) stimuli may enhance memory function by transmitting its effects from the superficial cortex to the hippocampus through the inferior longitudinal fasciculus (ILF) white matter tract. Chapter 3 explores a feasibility study where we targeted the dorsolateral prefrontal cortex (DLPFC) in individuals with chemobrain, a type of MCI. This strategy is commonly employed in MCI research to ameliorate poor memory function, and we observed promising results in memory improvement, which led to the development of related superficial frontal stimulation protocols in our lab. Our findings, however, also demonstrated that the mechanism by which TMS on DLPFC enhances memory may not involve the hippocampus, and these findings prompted a departure from this approach. Chapter 4 details

The overall aim of this dissertation is to characterize the macrostructural (i.e. anatomical) and microstructural correlates of healthy cognitive aging in a rat model using high-resolution T2-weighted MRI and diffusion MRI (DMRI) of the brain, respectively. The cohort of rats in this work is the largest to date measuring behavior and carrying out non-invasive MRI at three distinct adult age groups (6, 15 and 23-months-old) and three cognitive groups (low, average and high) at each age. High-resolution 3D T2-weighted MRI images were analyzed in two different ways, namely, atlas-based region-of-interest analysis (ABA) and voxel-wise deformation based morphometry (DBM). The DMRI images were also analyzed in two different ways in this study, namely, diffusion tensor imaging (DTI) and fixel based analysis (FBA). Both macrostructural and microstructural MRI data are used to quantify any differences in healthy cognitive aging within and across age and cognitive groups. Neither macrostructural nor microstructural differences were observed with respect to cognition. However, both macrostructural and microstructural differences were see with respect to age. Interestingly, ABA and DBM identified both expansions and contractions with respect to age, where the deep brain structures are expanding and the neocortex is contracting. Microstructural analysis measured decreases in the apparent diffusion coefficient (ADC), and both decreases and increases FA in different parts of the brain, along with increases in the apparent fiber density in deep white matter structures. When coupled with the volumetry data the implications are that deep brain structures are growing in size and becoming more organized across the lifespan with the majority differences found between 6 month and 15-month age groups. Interestingly, differences between 15-month and 23-month-old groups, do continue, suggesting plasticity into old age. The findings of this study will further advance research on aging and ma

Acute ischemic stroke (AIS) is a leading cause of death and disability with 15 million incidences and 5 million deaths annually, worldwide. Clinical AIS triage has to be both rapid and accurate to efficiently reestablish flow in the tissue-at-risk and improve patient outcomes, since, as is often said in acute stroke settings, ‘time is brain’. Response to treatment and clinical outcomes, however, vary significantly between patients since patient-specific cerebrovascular anatomy and collateral circulation, play a vital role in governing flow and reperfusion. This dissertation presents novel methods for automatic end-to-end stroke triage using image processing and machine learning techniques, based on cerebrovascular morphology, extracted from baseline imaging scans. This dissertation consists of three consecutive studies. In the first study, we developed and validated a method for accurate and fully automatic segmentation of the cranial vessel network from magnetic resonance angiography (MRA) or computed tomography angiography (CTA) scans and defined quantitative features of the vasculature. We combine a probabilistic vessel-enhancing filtering with an active-contour-based technique to segment angiograms and subsequently extract the vessel centerlines to calculate the geometrical properties of the vasculature, namely – total length, diameter, branching pattern, total volume, tortuosity and fractal dimension. Our method was extensively validated with image matching and registration performance metrics, using a biofidelic 3D phantom of the Circle-of-Willis region. It outperformed other commonly available segmentation tools and software. In the second study, we created a probabilistic atlas of human cerebrovasculature using MRA scans of 175 healthy adults and also extracted the corresponding geometric properties. These features were then used to study morphological changes in the brain vessels with normal aging, and the intersubject variations in the Circle of Willis (Co

Neuroimaging data are an important source for clinicians to prognosticate and diagnose different brain diseases and disorders. Among neurodegenerative diseases, Alzheimer’s Disease (AD) contribute to one of the leading causes of death in the United States. Given the chronic nature of the disease, prognosis during the early stages of disease development is critical where some clinical or non-pharmaceutical interventions may be possible. In terms of analyzing neuroimaging data, Convolutional Neural Networks (CNN) have demonstrated strong performance in prognosis and diagnosis Alzheimer’s Disease. In this dissertation, we propose an End-to-End CNN that can integrate and automate both pre-processing and post-processing of imaging data such as Magnetic Resonance Imaging (MRI). The specific objectives of this work are: (1) To design a CNN based autoencoder-decoder, called ConvoPrep, to pre-process neuroimaging data, (2) To design and evaluate a lightweight 3D CNN, called GreenLite, to post-process 3D neuroimaging data, and (3) To integrate both ConvoPrep and GreenLite architecture to form an end-to-end CNN architecture.Autoencoder Decoder for Pre-processing of Neuroimaging Data: The acquisition of Magnetic Resonance Imaging (MRI) data introduces several artifacts that need to be addressed before analyzing the data. These artifacts include misorientation, tilt, magnetic field inhomogeneity, and redundant tissues. These issues are usually addressed by preprocessing procedures using software such as FSL and MATLAB. As an alternative, we propose to design a 3D CNN based autoencoder-decoder architecture (ConvoPrep) consisting of four convolution and four max pooling layers in autoencoder phase, and four deconvolution and four up-sampling in decoder phase. ConvoPrep is trained and tested on 650 subjects selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, using 80% of data for training and rest for testing. As a comparison, the MATLAB Conn library was used

Major depressive disorder (MDD) is a common mental health condition (Kessler & Bromet, 2013) and the 3rd leading cause of disability worldwide (James et al., 2018). MDD history is a significant risk factor for relapse and recurrence of depression (Buckman et al., 2018; Burcusa & Iacono, 2007). The current study investigated resting state effective connectivity among 13 brain regions from three resting state networks (i.e., default, salience, and central executive), which had been implicated in the pathophysiology of MDD from previous studies (Kaiser et al., 2015; Mulders et al., 2015). In the current study, both within- and between-networks effective connectivity were found to be different in those with a MDD history (N=29) compared to the healthy controls (N=28), through spectral dynamic causal modeling (Friston, Kahan, Biswal, et al., 2014), Bayesian model reduction (Friston et al., 2016), and parametric empirical Bayes (Zeidman, Jafarian, Seghier, et al., 2019) analyses. Of particular interest is the finding that there is more negative effective connectivity from right anterior insula to left dorsolateral prefrontal cortex and left inferior parietal lobe in MDD history. Previous studies have found less causal influence from anterior insula to prefrontal cortex in currently depressed individuals (Hyett et al., 2015; Iwabuchi et al., 2014; Kandilarova et al., 2018). Given the importance of anterior insular in interoception and subjective feelings (Craig & Craig, 2009), the current study provides some preliminary evidence that altered effective connectivity between anterior insula and prefrontal cortex may be related to MDD history as well.

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.