Browsing by Subject "Neuroimaging"

Understanding the structure of the brain has been a major goal of neuroscience research over the past century, driven in part by the understanding that brain structure closely follows function. Normative brain maps, or atlases, can be used to understand normal brain structure, and to identify structural differences resulting from disease. Recently, diffusion tensor magnetic resonance imaging has emerged as a powerful tool for brain atlasing; however, its utility is hindered by image resolution and signal limitations. These limitations can be overcome by imaging fixed ex-vivo specimens stained with MRI contrast agents, a technique known as diffusion tensor magnetic resonance histology (DT-MRH). DT-MRH represents a unique, quantitative tool for mapping the brain with unprecedented structural detail. This technique has engendered a new generation of 3D, digital brain atlases, capable of representing complex dynamic processes such as neurodevelopment. This dissertation explores the use of DT-MRH for quantitative brain atlasing in an animal model and initial work in the human brain.

Chapter 1 describes the advantages of the DT-MRH technique, and the motivations for generating a quantitative atlas of rat postnatal neurodevelopment. The second chapter covers optimization of the DT-MRH hardware and pulse sequence design for imaging the developing rat brain. Chapter 3 details the acquisition and curation of rat neurodevelopmental atlas data. Chapter 4 describes the creation and implementation of an ontology-based segmentation scheme for tracking changes in the developing brain. Chapters 5 and 6 pertain to analyses of volumetric changes and diffusion tensor parameter changes throughout rat postnatal neurodevelopment, respectively. Together, the first six chapters demonstrate many of the unique and scientifically valuable features of DT-MRH brain atlases in a popular animal model.

The final two chapters are concerned with translating the DT-MRH technique for use in human and non-human primate brain atlasing. Chapter 7 explores the validity of assumptions imposed by DT-MRH in the primate brain. Specifically, it analyzes computer models and experimental data to determine the extent to which intravoxel diffusion complexity exists in the rhesus macaque brain, a close model for the human brain. Finally, Chapter 8 presents conclusions and future directions for DT-MRH brain atlasing, and includes initial work in creating DT-MRH atlases of the human brain. In conclusion, this work demonstrates the utility of a DT-MRH brain atlasing with an atlas of rat postnatal neurodevelopment, and lays the foundation for creating a DT-MRH atlas of the human brain.

The global population is aging with projections that the number of people over 60 will more than triple by 2050. While many organ systems are impacted by aging, deterioration of the brain is a particularly debilitating form of age-related disease. Alzheimer's disease and related dementias (ADRD) represent neurodegeneration that results in a loss of the ability to perform everyday tasks, maintain independence, and care for oneself. To date, ADRD interventions targeting older adults have largely proven to be ineffective at limiting morbidity and disability, suggesting that interventions may be failing to slow age-related disease because they are implemented too late in the aging process after decline has taken hold. However, to target ADRD interventions to younger adults we will need surrogate biomarkers that track sub-clinical signs of accelerated brain-aging that has yet to be fully cemented. This dissertation consists of 4 original studies that aim to measure and begin to validate magnetic resonance imaging (MRI)-based surrogate biomarkers for accelerated brain-aging in midlife adults. Each of these studies utilizes the Dunedin Study, a population-representative birth cohort of 1,037 adults, who have been followed longitudinally from birth to the most recent wave of data collection, completed when Study members were 45 years old. In Chapter 1, I found that individual differences in WMH volume, an established marker of dementia risk, cognitive decline, and dementia in older adults, were associated with cognitive decline from childhood to age-45. In Chapter 2, I found that individual differences in brainAGE were also associated with cognitive decline from childhood to age-45. In Chapter 3, I found that a known neurotoxicant, lead, was associated with cognitive decline from childhood to age 45, as well as with several MRI measures at age 45 including hippocampal volume, surface area, fractional anisotropy, and brainAGE. In chapter 4, I found that an accelerated Pace of Aging was associated with a thinner cortex, smaller surface area, lower hippocampal volume, higher WMH volume, and older brainAGE. Together, by triangulating evidence from cognitive aging, neurotoxic exposure, and biological aging, these studies help motivate the critical need for researchers to embrace midlife brain aging as a tool for better understanding the aging brain and dementia risk. I conclude with a discussion of the limitations of this research and opportunities for future research to target midlife brain aging itself as a target for clinical translation.

The white matter fibers of the human brain are primarily composed of myelinated axons, which connect different brain regions, transmit neural signals, and form efficient communication pathways that shape the neural systems responsible for higher-order functioning. The fatty myelin sheath protects and insulates the axons and acts as an electrical insulator that facilitates the electrical flow through the axons, and is crucial in the transmission of nerve impulses. Human cognition, sensation and motor functions all rely on the efficient transmission of neural signals, where compromised myelin integrity may lead to severe neurological and physical disorders. Myelin abnormality can be a hallmark of numerous neurological disorders such as cerebral palsy, multiple sclerosis, and autism. Abnormal myelination can be a result of direct damages to the myelin sheath, or indirect causes such as neuro-inflammation which affects the oligodendrocytes that generate the myelin sheath, or even genetic disorders.To approach the pathology and potential therapeutic effects for these neurological disorders, studies have been directed towards the remyelination or repair of the myelin in the central nervous system (CNS). Previously, myelin in the CNS can only be reliably quantified by in vitro methods such as myelin staining and measuring myelin basic protein. Magnetic Resonance Imaging (MRI), with its excellent soft tissue contrast and non-invasive nature, has revolutionized the ways to investigate white matter properties. Several methods in effort to assess the white matter have been developed, such as diffusion tensor imaging (DTI), which has been used to quantify the water diffusion in white matter and thus the connectivity of the brain. However, DTI-derived measurements, while sensitive to white matter microstructural changes, are difficult to interpret due to multiple factors that can alter water diffusion, including axonal membrane, neural tubules, crossing fibers, and myelin. It is possible that either axonal or myelin alternations could impact the conductivity of the fibers and further affect the diffusion measures. Therefore, DTI does not have the specificity to single out the origins of the connectivity change behind neurodegenerative diseases or brain development. Prior studies using quantitative susceptibility mapping (QSM) have shown its unique sensitivity to myelin. However, due to the cylindrical structure of myelin sheaths wrapping around axons, the magnetic susceptibility measured by QSM of the white matter has been found to be dependent on the angular orientations of white matter fibers. Susceptibility Tensor Imaging (STI) has been developed to address this orientation-dependence of susceptibility values in white matter, which requires images acquired from at least 6 non-colinear orientations to solve the susceptibility tensor, and is not practical in clinical settings. Therefore, the goal of this dissertation work is to develop a clinically practical MR susceptibility mapping method to quantitatively assess the magnetic susceptibility anisotropy (MSA) of white matter, which will greatly help us understand the role of myelination in the treatment of neurological diseases and in normal brain development. The work presented here includes the development of the methodology and two in vivo studies to prove its efficacy: (1) The magnetic susceptibility anisotropy in white matter was observed and measured by relating the apparent tissue susceptibility as a function of the white matter angle with respect to the applied magnetic field. A clinically practical solution to estimate the MSA of white matter fibers with QSM images acquired from a single orientation is proposed using prior information obtained through DTI, namely DTI-guided QSM. (2) The DTI-guided QSM methodology was used to investigate the potential mechanism behind the motor function improvement of cerebral palsy (CP) patients who underwent autologous stem cell therapy. Results showed that this motor function improvement was correlated with the connectivity increase in the motor network, and was further traced down to a focal increase of the magnetic susceptibility at the periventricular corticospinal tract (CST), which may indicate an increase in the local myelin content after treatment. (3) This methodology was then applied to profile the myelin maturation pattern of the white matter fiber bundles in pediatric subjects. Results revealed a spatio-temporal myelination pattern of the corpus callosal fibers, which follows a posterior to anterior myelination trajectory with the peak developmental rate spurts at around 2-3 years of age. This result is consistent with previous studies using histological methods and relaxometry-based methods, with better specificity to myelin, and improved consistency across subjects. In conclusion, the proposed DTI-guided QSM has shown its ability to accurately quantify the magnetic susceptibility anisotropy of major fiber tracts with high spatial accuracy and minimal angle dependence, and has addressed its potential in delineating the underlying neural mechanism in neurodevelopmental disorders such as CP, as well as in profiling the myelination pattern during normal brain development. It is anticipated that this quantitative approach may find broader applications to help characterize white matter properties in both healthy and diseased brains across the life span.

Few aspects of human cognition are more personal than the choices we make. Our decisions — from the mundane to the impossibly complex — continually shape the courses of our lives. In recent years, researchers have applied the tools of neuroscience to understand the mechanisms that underlie decision making, as part of the new discipline of decision neuroscience. A primary goal of this emerging field has been to identify the processes that underlie specific decision variables, including the value of rewards, the uncertainty associated with particular outcomes, and the consequences of social interactions. Here, across three independent studies, I focus on the neural circuitry supporting social valuation — which shapes our social interactions and interpersonal choices. In the first study (Chapter 2), I demonstrate that social valuation relies on the posterior ventromedial prefrontal cortex (pVMPFC). Extending these findings, I next show that idiosyncratic responses within pVMPFC predict individual differences in complex social decision scenarios (Chapter 3). In addition, I also demonstrate that decisions involving other people (e.g., donations to a charitable organization) produce increased activation in brain regions associated with social cognition, particularly the temporal-parietal junction (TPJ). Finally, in my last study (Chapter 4), I employ functional connectivity analyses and show that social cognition regions — including the TPJ — exhibit increased connectivity with pVMPFC during social valuation, an effect that depends upon individual differences in preferences for social stimuli. Collectively, these results demonstrate that the computation of social value relies on distributed neural circuitry, including both value regions and social cognition regions. Future research on social valuation and interpersonal choice must build upon this emerging theme by linking neural circuits and behavior.

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Research on extreme deviations in early life caregiving has provided valuable insight into the effects of early adversity on brain development and risk for psychopathology. However, much remains unknown about the impact of normative range variation in parenting on these same processes. The primary aim of this dissertation is to begin to address this gap in the literature.

I first examined associations between variability in family functioning and threat-related amygdala reactivity. Analyses revealed that greater familial affective responsiveness was associated with increased amygdala reactivity to explicit, interpersonal threat. Moreover, this association was moderated by the experience of recent stressful life events such that higher affective responsiveness was associated with higher amygdala reactivity in adolescents reporting low but not high stress. I hypothesized that these paradoxical associations may suggest a mechanism through which parental overprotection manifests as psychosocial dysfunction. Centering this hypothesis as the focus of my next study, I examined more detailed aspects of both early caregiving experiences and corticolimbic circuitry. Analyses revealed that participants who reported higher maternal control exhibited increased amygdala reactivity to explicit, interpersonal threat and decreased structural integrity of the uncinate fasciculus. While not a direct replication, these findings supported my hypotheses regarding parental overprotection and expanded Study 1 findings into structural connectivity between the amygdala and regulatory regions of the prefrontal cortex.

I next conducted a scoping review of the extant literature centered on the question, “Is variability in normative range parenting associated with variability in brain structure and function?” This review yielded 23 records for qualitative review and revealed not only how few studies have explored associations between brain development and normative range parenting, but also how little methodological consistency exists across published studies. In light of these limitations, I proposed recommendations for future research on normative range parenting and brain development and highlighted a path forward. Lastly, I applied these recommendations to my own empirical analyses. In the same sample of young adults used in Study 2, I examined associations among parental care and control, neural structural phenotypes, and mood and anxiety symptoms. Analyses revealed no significant associations among parenting and structural indices of interest, suggesting that neural structure is robust to more subtle variability in parenting even while neural function is not.

This dissertation provides critical first steps in empirically investigating how normative parenting shapes brain development with the data currently available. Further, it highlights the work of others similarly investigating this question and establishes an agenda for advancing future research on this topic.

The ability to influence how we feel – affect regulation – is a fundamental psychological process essential for everyday functioning and maintaining well-being. To ultimately develop interventions for improving affect regulation abilities, research investigating the diverse influences that determine individual variability in affect regulation are needed. I focus on two forms of affect regulation of particular importance for mental health outcomes and goal-driven behavior – the downregulation of negative affect and the upregulation of motivation. I approach this challenge by examining how trait factors predict affect regulation outcomes and how affect relation may be learned over time. More specifically, in Chapter 2 I investigate how beliefs about motivation relate to goal-oriented behavior, reward experiences, and motivation regulation through the development of a novel self-report questionnaire. In Chapter 3, I demonstrate that individuals can learn to use motivation regulation strategies to engage brain activity in the VTA (a region critically involved in motivated behavior), when presented with real-time fMRI neurofeedback, and characterize a functional brain network that underlies this process. In Chapter 4, I investigate how trait intolerance of uncertainty influences situational appraisals and coping behaviors to predict anxiety about the COVID-19 pandemic. And lastly in Chapter 5, I reviewed evidence for learning in emotion regulation and proposed a novel conceptual model for investigating how specific learning processes may support components of the emotion regulation process to produce learned changes in regulatory behavior. Collectively, this body of work demonstrates specific ways that affect regulation is shaped through trait characteristics and learning experiences.