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Title

NEUROPATHOLOGIC CORRELATES OF BRAIN MACROSTRUCTURE

Creator

Kotrotsou, Aikaterini

Date

2014, 2014-07

Description

Alzheimer’s disease, the most common form of dementia, is a degenerative disorder of the brain that leads to memory loss. Clinical diagnostic...
Show moreAlzheimer’s disease, the most common form of dementia, is a degenerative disorder of the brain that leads to memory loss. Clinical diagnostic techniques in use today rely on mental and behavioral tests and physical examinations and only provide diagnoses of possible or probable Alzheimer’s disease. However, lately it has become clear that clinical-pathological correspondence is not always consistent. A definitive diagnosis of Alzheimer’s disease is only possible via histology, when the density of neurofibrillary tangles and amyloid plaques is measured. Therefore, the development of a reliable neuroimaging technique that allows detection of Alzheimer’s pathology during life is needed. This method would be noninvasive, and could allow the detection of Alzheimer’s disease in the early stages, and could be also used to monitor the progression of the disease through time. The purpose of this work was to investigate the use of magnetic resonance imaging (MRI) as diagnostic tool for Alzheimer’s pathology and other age-related neurodegenerative pathologies that are common in older persons. To uncover the anatomical origins and determine the macrostructural signatures of age-related neuropathologies, it is necessary to link MRI findings with pathologic information on the same individuals. In this work, we focused on imaging cerebral hemispheres ex-vivo, when a complete pathology report was available from a board-certified neuropathologist. The main difference between this work and any other study is the abundance of postmortem imaging data paired with neuropathology data in a relatively large pool of subjects. First, we developed and validated a protocol to perform ex-vivo MR volumetry. By using this protocol we observed the longitudinal behavior of the volume of different brain regions. Furthermore, we tested the hypothesis that volumetric measurements performed ex-vivo are associated with in-vivo measurements. It was shown that: (a) regional brain volumes measured with this approach for ex-vivo MR volumetry remain relatively unchanged for a period of 6 months postmortem, and (b) a linear correspondence was detected between in-vivo and ex-vivo measurements, suggesting that this approach captures information linked to antemortem macrostructural brain characteristics. Using the approach for ex-vivo MR volumetry, we combined ex-vivo MR volumetry with pathology on the same adults. AD pathology was significantly negatively correlated with volumes of cortical gray matter regions, mainly in the temporal, frontal, parietal and cingulate cortices, subcortical gray matter, and whole-hemisphere white matter. A significant negative correlation was shown between hippocampal sclerosis and volumes of the hippocampus, as well as other temporal and frontal gray matter regions. Finally, we performed a morphometric MRI study to investigate associations of brain volumes with pathology using voxel-based analysis. This technique allows the assessment of gray and white matter volumes in subjects with different pathologies compared with controls in an automated fashion, across the whole brain. AD pathology was negatively associated with regions of gray matter and white matter located in temporal and frontal lobes, and orbitofrontal cortex. This work examined the associations of brain volumes with Alzheimer’s pathology and other typed of age-related neurodegenerative pathologies. Combining histological result with MR images requires that the time elapsed between imaging and histology is minimal. Ex-vivo MRI provides images at essentially the same time-point as histological examination of the tissue, and this study is unique in that it involves a large number of cadaveric brain hemispheres. The findings of this ex-vivo study could allow for future standardization of MRI as a biomarker of neurodegenerative diseases, and also allow in identification and classification of subjects in groups for tests of new drugs.
Ph.D. in Biomedical Engineering, July 2014
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