The Structural Basis for Brain Health

Cardiovascular disease (CVD) remains the leading cause of mortality in the United States. Stroke and dementia are the leading causes of adult disability worldwide, and the 5th and 6th leading causes of mortality respectively in the United States. Furthermore, CVD annually accounts for approximately $330 billion in direct and indirect costs in the United States: approximately one in seven health care dollars is spent on CVD. While these diseases have different etiologies, and present with different clinical manifestations and prognosis, converging evidence increasingly supports the idea of CVD as a common pathophysiological origin of cerebrovascular disease, potentially indicating a complex interplay between brain health and cardiovascular health. In this thesis, we leverage methodological advancements in systems and computational neurosciences related to the human brain connectome to assess individual topological network organization and integrity in acute and chronic stroke cohorts, and in a non-stroke cohort with varying CV risk factor burden, using graph theory and network analysis. We propose measures that underly neuroanatomical mechanisms that constitute efficient transfer of information and brain health. We demonstrate the impact of cardiovascular risk factors on brain health, even before overt clinical manifestation, and the resulting impact on cognitive performance, and further determine the underlying pathophysiology relating white matter disease and post-stroke outcomes

CEREBROVASCULAR RISK FACTORS, ARTERIOLAR SCLEROSIS, AND COGNITIVE DECLINE IN THE KENTUCKY APPALACHIAN “STROKE-BELT”

The relationship between cerebrovascular disease (CVD) risk factors and cognitive impairment or dementia has been widely studied with significant variability in findings between groups. We hypothesized that chronic small vessel injury in the form of arteriolar sclerosis, measured quantitatively using MRI to measure total white matter hyperintensity (WMH) volumes, would identify specific association of CVD risk factors and patterns of cognitive decline, associated with mild cognitive impairment of the cerebrovascular type, that represent the core features of vascular cognitive impairment in our cohort. A Cross-sectional analysis of clinical and quantitative MRI data on 114 subjects with normal cognitive function (n=52) and mild cognitive impairment (MCI; n=62) was performed. Quantitative total WMH volumes were examined in relation to potentially causative CVD risk factors and resultant test scores across cognitive domains using linear regression models adjusted for age, gender, and education. Among CVD risk factors analyzed, age (p\u3c 0.001), education (p= 0.003), hypertension (p= 0.012), and hyperlipidemia (p= 0.008) demonstrated the strongest associations with WMH volumes. Conversely, diabetes, smoking, history of heart attacks, atrial fibrillation, and history of stroke that have shown associations with CVD pathology on imaging in other studies were not statistically associated with increased WMH in this cohort. WMH volumes were associated with decrease performance on the Trial Making Test type A & B and long delayed free recall on the California Verbal Learning Test. Our findings suggest similarities and yet differences in comparison to other studies. Hypertension and hyperlipidemia appear to represent common shared risks across geographically disparate groups. Our findings, like others, suggest CVD pathology impact processing speed and executive function and provide further evidence for CVD effects on short-term memory in those at risk for cognitive decline and the future development of dementia in our cohort

Evidence of endothelial dysfunction in the development of Alzheimer's disease : Is Alzheimer's a vascular disorder?

Acknowledgements Dr Soiza is funded by an NRS Career Research Fellowship. The authors are grateful to Alzheimer’s Research UK for providing funding.Peer reviewedPublisher PD

Disconnected aging: cerebral white matter integrity and age-related differences in cognition.

Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging

Improving the clinico-radiological association in neurological diseases

Despite the key role of magnetic resonance imaging (MRI) in the diagnosis and monitoring of multiple sclerosis (MS) and cerebral small vessel disease (SVD), the association between clinical and radiological disease manifestations is often only moderate, limiting the use of MRI-derived markers in the clinical routine or as endpoints in clinical trials. In the projects conducted as part of this thesis, we addressed this clinico-radiological gap using two different approaches. Lesion-symptom association: In two voxel-based lesion-symptom mapping studies, we aimed at strengthening lesion-symptom associations by identifying strategic lesion locations. Lesion mapping was performed in two large cohorts: a dataset of 2348 relapsing-remitting MS patients, and a population-based cohort of 1017 elderly subjects. T2-weighted lesion masks were anatomically aligned and a voxel-based statistical approach to relate lesion location to different clinical rating scales was implemented. In the MS lesion mapping, significant associations between white matter (WM) lesion location and several clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers. In the SVD lesion mapping, the same WM fibers and the caudate nucleus were identified to significantly relate to the subjects’ cerebrovascular risk profiles, while no other locations were found to be associated with cognitive impairment. Atrophy-symptom association: With the construction of an anatomical physical phantom, we aimed at addressing reliability and robustness of atrophy-symptom associations through the provision of a “ground truth” for atrophy quantification. The built phantom prototype is composed of agar gels doped with MRI and computed tomography (CT) contrast agents, which realistically mimic T1 relaxation times of WM and grey matter (GM) and showing distinguishable attenuation coefficients using CT. Moreover, due to the design of anatomically simulated molds, both WM and GM are characterized by shapes comparable to the human counterpart. In a proof-of-principle study, the designed phantom was used to validate automatic brain tissue quantification by two popular software tools, where “ground truth” volumes were derived from high-resolution CT scans. In general, results from the same software yielded reliable and robust results across scans, while results across software were highly variable reaching volume differences of up to 8%

The Role of Frontal Lobe White Matter Integrity and Executive Functioning in Predicting Adaptive Functioning in Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia and is characterized by a gradual deterioration of the patients’ ability to independently perform day to day activities. Researchers have discovered significant changes in neuroanatomy, cognition and behavior that are related to the disease process of AD and researchers continue to uncover new variables, such as the presence of vascular risk factors, which may further increase our ability to understand and characterize the disease. The purpose of this study is to identify the neuroanatomical, cognitive and behavioral variables that best predict impairment of instrumental activities of daily living in individuals with probable AD. Reduced white matter integrity in the dorsolateral prefrontal cortex as well as the presence of vascular risk factors significantly predicted impairments in activities of daily living (ADLs). Executive functioning skills, typically described as frontal lobe system behaviors, were positively associated with ADLs. Further, executive functions fully mediated the relationship between frontal lobe white matter integrity and ADLs. A better understanding of the variables responsible for diminished ADLs in AD will allow researchers and clinicians to better target prevention and intervention strategies and ultimately help individuals with AD to maintain their independence for a longer duration

Peripheral (Deep) but Not Periventricular MRI White Matter Hyperintensities Are Increased in Clinical Vascular Dementia Compared to Alzheimer\u27s Disease

Background and purpose: Vascular dementia (VAD) is a complex diagnosis at times difficult to distinguish from Alzheimer\u27s disease (AD). MRI scans often show white matter hyperintensities (WMH) in both conditions. WMH increase with age, and both VAD and AD are associated with aging, thus presenting an attribution conundrum. In this study, we sought to show whether the amount of WMH in deep white matter (dWMH), versus periventricular white matter (PVH), would aid in the distinction between VAD and AD, independent of age. Methods: Blinded semiquantitative ratings of WMH validated by objective quantitation of WMH volume from standardized MRI image acquisitions. PVH and dWMH were rated separately and independently by two different examiners using the Scheltens scale. Receiver operator characteristic (ROC) curves were generated using logistic regression to assess classification of VAD (13 patients) versus AD (129 patients). Clinical diagnoses were made in a specialty memory disorders clinic. Results: Using PVH rating alone, overall classification (area under the ROC curve, AUC) was 75%, due only to the difference in age between VAD and AD patients in our study and not PVH. In contrast, dWMH rating produced 86% classification accuracy with no independent contribution from age. A global Longstreth rating that combines dWMH and PVH gave an 88% AUC. Conclusions: Increased dWMH indicate a higher likelihood of VAD versus AD. Assessment of dWMH on MRI scans using Scheltens and Longstreth scales may aid the clinician in distinguishing the two conditions