A small subset of individuals that have experienced mild traumatic brain injury (mTBI) may experience persistent cognitive deficits more than a year following the head injury. Neuroimaging studies reveal structural and functional changes in frontal areas of the brain, exacerbated when loss of consciousness is experienced, and indicate that these changes may be progressive in nature for some people. Social support and social participation have, however, been suggested to confer cognitive reserve - neurocognitive protection against cognitive decline. Analyses were run on Canadian Longitudinal Study on Aging (CLSA) neuropsychological data, consisting of individuals who experienced mTBI with loss of consciousness (n = 536 for less than 1 minute, and n = 435 for unconsciousness between 1 and 20 minutes) more than a year prior, and 13,163 no-head injury comparisons. These same individuals were re-assessed three years later. The results presented in this thesis suggest that at a year or more after a single mTBI with loss of consciousness, a small subset of individuals are more likely to be impaired on prospective memory and other executive functioning tasks, relative to comparisons. In addition, when examined at three-year follow-up, those who experienced mTBI with longer duration of unconsciousness were more likely to exhibit cognitive decline relative to those who experienced less unconsciousness or comparisons. Moreover, greater social participation over the past year, and more perceived social support were predictive of lessened cognitive deterioration in those individuals.

BACKGROUND: Early detection of individuals at risk for converting to Alzheimer’s disease (AD) can potentially lead to more efficient treatment and better disease management. A well-known approach has aimed at identifying individuals at the prodromal stage of dementia; namely, Mild Cognitive Impairment (MCI). Past studies showed that MCI subjects often have accelerated rates of conversion to AD, or to other types of dementia compared to healthy controls (HCs). However, with more investigations of the MCI population, it became evident that a high level of heterogeneity exists within this group: many remain clinically stable even after 10 years. MCI subtypes defined by the conventional classification criteria showed inconsistent results for determining an individual's risk of AD. As another approach, neuroimaging techniques such as magnetic resonance imaging (MRI) are able to successfully identify neurological changes during early AD. MRI markers including morphological, connectional and abnormal signal patterns in the brain have been shown to have good sensitivity for classifying AD. Based on these findings, recent studies started implementing these imaging markers to create computer-aided classification models for predicting the risk of conversion to AD. Most of these studies enrolled MCI subjects who remained stable or converted to AD within 3 years, and generated computer-aided classification models to predict conversion using various imaging markers and clinical data. To our knowledge, no classification models proposed achieved an accuracy of higher than 80% for predicting MCI-AD conversion earlier than 3 years with only using structural MRI features. In this paper, we tested the prediction range beyond 3 years, and suggested new candidate imaging measures for earlier prediction. METHODS: The subjects included in the current study are n=51 MCI non-converter, n=157 MCI converter (115 fast converters and 42 slow converters) and n=38 AD, selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using subjects' baseline T1-weighted MRI scans, we combined conventional morphometric measures (e.g. cortical thickness, surface area, volume, etc.) with novel intensity measures to differentiate MCI converters from non-converters. We additionally applied a machine learning approach to classify MCI subgroups by combining features in multiple measurement domains. RESULTS: Based on group comparison using independent t-test, we found that while MCI fast converters (conversion within 0-2 years) were highly distinct from MCI non-converters across many cortical and subcortical regions, MCI slow converters (conversion within 3-5 years) demonstrated more focal differences from MCI non-converters mainly in the temporal regions and hippocampal subfields. We identified unique imaging features associated with each converter group and had improved classification performance on both MCI converter groups by adding those markers. The best performing classifiers combined conventional imaging features, novel intensity features and neuropsychological features. For our best performing classification models, we were able to classify MCI fast converters (0-2 years) from non-converter with an average accuracy of 86.1%, sensitivity of 85.5%, and specificity of 89.8%, and to classify MCI slow converters (3-5 years) from non-converters with an accuracy of 80.5%, sensitivity of 75.7%, and specificity of 82.3%. CONCLUSION: Our results demonstrated the potential of the suggested approach for predicting the conversion from MCI to AD at an even earlier time point (3-5 years) before the onset of AD. The combination of standard morphometric features and proposed novel intensity features improved the sensitivity of using T1-weighted MRI for describing the heterogeneity between MCI subgroups.

Vascular risk factors affect cerebral blood flow (CBF) and cerebral vascular reactivity, contributing to cognitive decline. Hippocampus is vulnerable to both Alzheimer's disease (AD) pathology and ischemia; nonetheless, the information about the impact of vascular risk on hippocampal perfusion is minimal. Cognitively, healthy elderly (NL18, 69.96.7 years) and subjects with mild cognitive impairment (MCI15, 74.98.1 years) were evaluated for the Framingham cardiovascular risk profile (FCRP). All underwent structural imaging and resting CBF assessment with arterial spin labeling (ASL) at 3T magnetic resonance imaging (MRI). In 24 subjects (NL17, MCI7), CBF was measured after a carbon dioxide rebreathing challenge. Across all subjects, FCRP negatively correlated with hippocampal (0.41, P0.049) and global cortical (0.46, P0.02) vasoreactivity to hypercapnia (VRh). The FCRP-VRh relationships were most pronounced in the MCI group: hippocampus (0.77, P=0.04); global cortex (0.83, P=0.02). The FCRP did not correlate with either volume or resting CBF. The hippocampal VR h was lower in MCI than in NL subjects (Z2.0, P=0.047). This difference persisted after age and FCRP correction (F 3,20 4.6, P0.05). An elevated risk for vascular pathology is associated with a reduced response to hypercapnia in both hippocampal and cortical tissue. The VR h is more sensitive to vascular burden than either resting CBF or brain volume.

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Alzheimer's Disease (AD) is a neurodegenerative disease that causes complications with thinking capability, memory and behavior. AD is a major public health problem among the elderly in developed and developing countries. With the growth of AD around the world, there is a need to further expand our understanding of the roles different clinical measurements can have in the diagnosis of AD. In this work, we propose a machine learning-based technique to distinguish control subjects with no cognitive impairments, AD subjects, and subjects with mild cognitive impairment (MCI), often seen as precursors of AD. We utilized several machine learning (ML) techniques and found that Gradient Boosting Decision Trees achieved the highest performance above 84% classification accuracy. Also, we determined the importance of the features (clinical biomarkers) contributing to the proposed multi-class classification system. Further investigation on the biomarkers will pave the way to introduce better treatment plan for AD patients.

Thesis (Ph.D.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / The goals of the present research were two fold: first to examine an acute head injury sample to test memory functions, second to study what parts of the memory process are most affected during post traumatic amnesia with special emphasis on retrieval of structured material in delayed recall. Subsidiary interests include studying recovery and the relationship between memory functioning and severity of injury. After head injury a common complaint is a transitory period of amnesia for recent events (PTA). PTA is often thought of as one stage in the recovery of consciousness and is believed to be an index of neurological severity. A model was proposed to account for amnesia. Two separate memory mechanisms prior to permanent storage were hypothesized, one for short term and the other for long term storage. Inputs are coded on the basis of recurrent patterns of common features. Retrieval occurs by means of the coded representation. Amnesia is viewed as a malfunctioning of the coding mechanism. Amnesia is thus held to be an inability to fully utilize coding of stimulus material as an aid in recall. Following this theory, it was predicted that the perceptive and cognitive functions were operating in amnesia and that immediate recall was also unimpaired. The major prediction was that retrieval of structured stimulus material by delayed recall would be impaired compared to normals, while retrieval of unrelated stimulus material would be unimpaired. Retrieval by recognition would only be mildly impaired because less information is required for recognition than for recall. Hence, the memory event can be reconstructed in recognition on the basis of partial coding. It was further predicted that, with recovery, there would be improvement of memory functioning and that there would be a relationship between severity of injury and memory functioning. A Memory Scale was constructed which included four subtests designed to test the above theory. Each subtest included both related and unrelated stimulus material. An additional test (a Picture Similarities Test) was employed to measure conceptualization. Forty seven acute head injury patients were tested as soon after injury as possible and matched with forty four control subjects (patients with acute trauma, but no head injury) on the basis of age, education, occupation and performance on the Ammons Picture Vocabulary Test. Head injury patients with approximately normal intelligence (Ammons I.Q. 80 or above) followed the predictions with these exceptions: Immediate recall and recognition of related stimulus material showed a trend toward impairment, though immediate recall and recognition of unrelated stimulus material did not. The findings with the patients with normal intelligence suggest, that while cognitive and perceptual abilities are not affected by trauma, utilization of organization as an aid in recall of related stimulus material is not as effective in the experimental· as in the control group. Head injury patients with low I.Q.'s (79 or below on the Ammons) demonstrated impairment of perception and immediate recall as well as the predicted impairment of delayed recall. These patients appeared to exhibit a generalized cognitive disturbance. No definite trends toward recovery were observed on any of the memory tests. There was also no relationship between severity of injury and performance on the Memory Scale. However, there was a significant correlation between performance on the Ammons and Picture Similarities tests and neurological severity. Patients with the lowest scores on these tests were most impaired neurologically. Intelligence thus appears to be more closely associated with severity of injury than is memory functioning per se. / 2031-01-01

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