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.

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.

Researchers studying the long-term cognitive sequelae of mild traumatic brain injury (mTBI) have produced disparate results. Some studies have shown little to no long-term cognitive effects while others have shown that persistent cognitive sequelae continue to affect a subgroup of patients. Meta-analysis has been used to try to integrate these contrasting results to foster a coherent understanding of the cognitive outcomes following mTBI. However, previous meta-analyses of long-term cognitive sequelae have used studies from a period of mTBI research where methodological rigor has been called into question (Carroll, Cassidy, Holm, Kraus, & Coronado, 2004). Using studies from this period, meta-analysts found little to no effect for long-term cognitive sequelae after mTBI: g = 0.07, d = 0.12 (Binder, Rohling, & Larrabee, 1997), g = 0.11(Frencham, Fox, & Mayberry, 2005), and d = -0.07 (Rohling et al., 2011). The present meta-analysis was conducted to address problems with methodological rigor in the studies used in these previous meta-analyses and address differences in meta-analytic methodology (Pertab, James, & Bigler, 2009). Studies published between January 2003 and August 2010 were rated using the 4-tiered American Academy Neurology (AAN) guidelines for methodological rigor to ensure homogeneity and the methodological rigor of included studies. Seven studies were identified that met criteria for a rating of I or II and five met criteria for the lower ratings of III or IV. When studies of all ratings were combined, a significant effect of g = 0.45 was observed. When only studies rated I and II were combined, a significant effect of g = 0.52 was observed while a significant effect of g = 0.38 was observed when only studies rated III and IV were combined. These effect sizes for long-term cognitive sequelae are much larger than those found in previous meta-analyses. Based on these results, it is likely that methodological rigor and/or heterogeneity amongst included studies can impact meta-analytic effect sizes associated with long-term cognitive sequelae following mTBI. However, analyses did not show that more rigorous studies (i.e., those rated I or II) had significantly higher effect sizes than less rigorous studies (i.e., those rated III or IV), t(10) = .636, p = .845. This non-significant finding may be a result of the analysis being underpowered given the small k. Significant effects for neuropsychological domain were also observed and are reported. Additionally, a conceptual critique of mTBI is made with recommendations for future development of the rating system that Cappa, Conger, and Conger (2011) have put forth for objectively rating the methodological rigor of neuropsychological studies. Concerns are addressed related to the mTBI literature in the areas of mTBI definition, definition of cognitive impairment, problems with the constructs of post-concussion syndrome (PCS) and persistent post-concussion symptoms (PPCS), heterogeneity of outcome measurement, and unaccounted for variables.

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Yes / Early diagnosis of Alzheimer’s disease (AD) is essential if treatments are to be administered at an earlier point in time before neurons degenerate to a stage beyond repair. In order for early detection to occur tools used to detect the disorder must be sensitive to the earliest of cognitive impairments. Virtual reality (VR) technology offers opportunities to provide products which attempt to mimic daily life situations, as much as is possible, within the computational environment. This may be useful for the detection of cognitive difficulties. We develop a virtual simulation designed to assess visuospatial memory in order to investigate cognitive function in a group of healthy elderly participants and those with a mild cognitive impairment. Participants were required to guide themselves along a virtual path to reach a virtual destination which they were required to remember. The preliminary results indicate that this virtual simulation has the potential to be used for detection of early AD since significant correlations of scores on the virtual environment with existing neuropsychological tests were found. Furthermore, the test discriminated between healthy elderly participants and those with a mild cognitive impairment (MCI).