Mild cognitive impairment (MCI) is a medical diagnosis that is conceptualized as existing on a continuum between normal cognitive aging and dementia. While a growing body of research has established the impact of this condition on family members' emotional well-being, as well as the quality of family relationships, the reciprocal impact of family dynamics and the family environment on illness course has received much less attention. Expressed emotion (EE) is a measure of the family emotional climate that has been established as being highly predictive of relapse and symptom exacerbation for a variety of mental health disorders. The recent integration of attribution theory with EE has offered new insights into the underlying attitudes and beliefs that give rise to it. This mixed methods study applied the attribution model of EE to test the validity of EE in predicting the illness course of MCI, and to identify family members' attributions regarding MCI-related behaviors and symptoms that underlie their EE status. The study sample included 57 family dyads consisting of a person with MCI and a family member providing primary care or assistance. The results of the ANCOVA did not support the hypothesis that EE status would predict changes in the non-cognitive features of MCI over time. However, methods of thematic analysis revealed four major themes, or care partner attributional stances: (a) non-blaming, (b) blaming, (c) variable, and (d) no identified. The analysis also revealed three subthemes, or attributional styles, within the variable stance: (a) ambivalent, (b) mixed, and (c) complex. These attributional stances and styles intersected with family EE status in notable ways and form the basis for future research in this area, as well as clinical interventions with these families that promote adaptation to the illness. / Ph. D.

BACKGROUND: Mild cognitive impairment (MCI) is a clinical condition that is characterized by mild changes in cognition. The amnestic form of MCI (aMCI) primarily affects memory and is thought to represent a stage between healthy aging and Alzheimer’s disease (AD). The medial temporal lobe (MTL) and the limbic system are two areas of the brain that have been implicated in the amnestic form of MCI. While MCI represents a risk factor for AD, it does not always lead to dementias. Being a carrier of the APOE Ɛ4 allele has also shown to increase chances of progression from MCI to AD. OBJECTIVE: To determine whether the subnuclei of the amygdala, along with other specific regions within the MTL, can differentiate between cognitively normal individuals and age-matched subjects with aMCI. METHODS: T1-weighted magnetic resonance imaging (MRI) data from two sources, the Boston University Alzheimer’s Disease Center (BU-ADC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), was compiled for cross-sectional analysis. 95 scans in total from 45 cognitively normal participants and 50 diagnosed with aMCI were analyzed and the volumes of interest were automatically generated by the developmental version of FreeSurfer v6.0. To evaluate how well the volumes could predict either group membership (i.e. control group or MCI group) or APOE Ɛ4 status (i.e. carrier or noncarrier), the variables were assessed by nominal logistic regression models. RESULTS: Six of the nine nuclei of the amygdala had significantly reduced volumes in the aMCI group compared to controls. The whole amygdala and the perirhinal cortex also demonstrated reduced volumes in the aMCI group compared to the control group. The whole amygdala was a good predictor of group membership (R2 = 0.1386, whole model test chi square = 18.21558, p = 0.0004), but none of the subnuclei were good predictors individually. A model containing the 9 nuclei, the entorhinal cortex, and the perirhinal cortex provided a good fit for predicting APOE Ɛ4 status fit (R2 = 0.3000, whole model test chi square = 36.29563, p = 0.0002) and the best predictor was the corticoamygdaloid transition area of the amygdala. CONCLUSIONS: The results of our study confirm previous findings of reduced whole amygdala volume and add to the limited literature of reduced perirhinal cortex and amygdaloid nuclei volumes in MCI compared to healthy controls. To the best of our knowledge, this was the first time the automatic segmentation atlas was used to analyze the volumes of nine subnuclei of the amygdala in a population of aMCI. Our model testing the volume of the whole amygdala accurately predicted aMCI subjects with 58% accuracy and controls with 70% accuracy; the accuracy rose to 69% when the entorhinal cortex and the perirhinal cortex were added to the model to predict aMCI subjects from controls. Additionally, the model for predicting APOE Ɛ4 status identified noncarriers of the allele at 85% accuracy. Future studies should consider increasing the sample size to better assess small ROIs and assess for differences in the separate hemispheres.

The search for reliable early indicators of age-related cognitive decline represents an important avenue in aging research. Most research on late-life development charts cognitive change as a function of chronological age (CA), however, although CA is a commonly used developmental index, it offers little insight into the mechanisms underlying cognitive decline. In contrast, biological age (BioAge), reflecting the vitality of essential biological processes, represents a promising operationalization of developmental time. My overall programmatic doctoral research interests involve the identification of biological risk factors that predict age-related cognitive decline, impairment and dementia. In this dissertation document, I present: an overview of my empirical contributions to the BioAge and cognitive aging literature throughout my doctoral training; the dissertation project which uses preliminary data from the PREVENT study and provides evidence that elevated plasma pro-inflammatory proteins are associated with cognitive status (healthy controls (HC) vs Alzheimer’s disease dementia (AD)), cognitive performance and are related to poorer cognitive performance in amnestic mild cognitive impairment (a-MCI); and a discussion on the broad implications of the project results and future directions in BioAge research. / Graduate

Introdução: O Questionário do Informante sobre o Declínio Cognitivo do Idoso (IQCODE) é um instrumento de rastreio que se baseia nas informações fornecidas por familiares ou cuidadores acerca de um possível declínio cognitivo do paciente. Embora tenha boa sensibilidade para a identificação de casos suspeitos de demência, poucos estudos avaliaram as propriedades diagnósticas do IQCODE no rastreio do comprometimento cognitivo leve (CCL). O CCL corresponde a uma condição de risco para o desenvolvimento de demência, sendo caracterizado pela presença de alterações cognitivas que podem ser mensuradas objetivamente, indicando um declínio em relação ao desempenho esperado para indivíduos da mesma faixa etária e nível de instrução. Tais alterações cognitivas (ou déficits) são insuficientes para o diagnóstico de demência, no caso de um funcionamento cognitivo global preservado e da capacidade de desempenhar as atividades da vida diária (Winblad, 2004). Objetivos: Examinar as propriedades diagnósticas do IQCODE no rastreio do CCL, identificando os pontos de corte do teste IQCODE que melhor separam indivíduos idosos cognitivamente normais dos indivíduos com CCL; correlacionar os resultados obtidos com outros testes de rastreio cognitivo amplamente utilizados em nosso meio, como o Mini-Exame do Estado Mental (MEEM), o Teste do Desenho do Relógio (TDR) e o Teste Cognitivo de Cambridge (CAMCOG); identificar entre os 26 itens do IQCODE os agrupamentos (clusters) que contribuem para a identificação dos casos de CCL. Métodos: Estudo de corte transversal em amostra de 167 indivíduos idosos (Controles n=51, CCL n=58 e Demência de Alzheimer (DA) n=58) acompanhados no Ambulatório de Psicogeriatria do LIM-27, Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo. O diagnóstico do estado cognitivo (padrão-ouro) estabelecido por meio de consenso multidisciplinar, levando-se em consideração as informações clínicas e o desempenho em testes neuropsicológicos: A idade média dos indivíduos de cada grupo foi, respectivamente, de 67,5 (±5,6), 70,2 (±6,1) e 75,5 (±8,4) anos, e a escolaridade média foi de 12,6 (±5,4), 9,6 (±5,7) e 8,5 (±5,5) anos. Análises de curvas ROC (Receiver Operating Characteristics) foram realizadas para avaliar a acurácia diagnóstica do IQCODE e demais testes comparativos na separação dos pacientes de cada grupo diagnóstico, comparados dois a dois (CCL versus Controles, CCL versus DA, DA versus Controles); por meio de regressão logística, avaliou-se o potencial do uso combinado do IQCODE em conjunto com os demais instrumentos de rastreio para predizer a ocorrência de CCL e DA; finalmente, por meio de análise de clusters, avaliou-se a distribuição dos diferentes itens do IQCODE nos pacientes com CCL e seus subtipos. Resultados: Os pontos de corte do IQCODE para a separação dos grupos diagnósticos foram: (a) DA versus Controles: 3,3 (AUC=0,90; sensibilidade: 84,5%; especificidade: 82,4%); (b) CCL versus Controles: 3,1 (AUC=0,73; sensibilidade: 77,6%; especificidade: 60,8%); (c) CCL versus DA: 3,4 (AUC=0,81; sensibilidade: 79,3%; especificidade: 70,7%). O IQCODE apresentou melhor correlação com o CAMCOG (=0,542; p<0,001). Com base na análise de cluster, estimou-se que o agrupamento que contém itens relacionados à memória episódica foi o mais relevante para identificar os pacientes portadores de CCL amnéstico. Conclusões: O uso do IQCODE obteve melhores resultados para diferenciar idosos cognitivamente normais de CCL quando utilizado em conjunto com o CAMCOG. A análise de cluster do IQCODE melhor prediz CCL e seus subtipos / Introduction: The Informant Questionnaire of Cognitive Decline in the Elderly is a screening diagnostic instrument which is based on given information from family members and caregivers regarding a possible patients cognitive impairment. Despite its good sensitivity for suspected dementia caseness, few studies have been carried out using the diagnosis properties of the IQCODE to screen for Mild Cognitive Impairment (MCI). MCI corresponds to a condition of a risk factor for dementia outcome and is characterized by the presence of cognitive changes measured objectively, indicating an impairment in comparison with the expected performance for individuals at the same age and years of schooling. These deficits are insufficient for dementia diagnosis in case of preserved global cognitive functioning as well as in the capacity to perform daily activities (Winblad, 2004). Objectives: Examine diagnostic properties of the IQCODE in identifying cut-off scores which best distinguish the cognitively normal elderly from those with MCI; to correlate these results with other widely used cognitive tests, such as the Mini Mental State Examination (MMSE), the Clock Drawing Test (CDT) and the Cambridge Cognitive Test (CAMCOG); to identify among the 26 items in the IQCODE those clusters which best contribute to the identification of the cases. Methods: Cross-sectional study in a sample of 167 elderly subjects (Controls: n=51, MCI: n=58 and Alzheimer Disease (AD): n=58) followed at the Psychogeriatric Clinic of the Laboratory of Neuroscience (LIM-27), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo. The cognitive diagnosis was reached by consensus at expert multi-disciplinary meetings (gold standard), taking into account clinical and neuropsychological evaluation. The mean age in each group was respectively: 67.5(±5.6), 70.2 (±6.1) and 75.5 (±8.4) years, and mean of years of schooling were 12.6(±5.4), 9.6(±5.7) and 8.5(±5.5) years. ROC (Receiver Operating Characteristics) Curve analysis were carried out to determine diagnostic accuracy of the IQCODE and the comparative tests in paired sets (MCI versus Controls, MCI versus AD, AD versus Controls); by logistic regression analysis it was evaluated the prediction of MCI and AD with the IQCODE and its combined usage with the comparative tests; finally by cluster analysis it was evaluated the different distribution of the IQCODE items in MCI patients and its subtypes. Results: The IQCODE cut-off scores for diagnostic groups separation were: (a) AD versus Controls: 3.3 (AUC=0.90; sensitivity: 84.5%; specificity: 82.4 %( b) MCI versus Controls: 3.1 (AUC=0.73; sensitivity: 77.6%; specificity%: 60.8); (c) MCI versus AD: 3.4 (AUC=0.81; sensitivity: 79.3%; specificity: 70.7%). The IQCODE had the best correlation with the CAMCOG (=0.542; p<0.001). According to cluster analysis, the episodic memory grouping was the most relevant in identifying amnestic MCI. Conclusions: The IQCODE achieved best results to discriminate cognitively unimpaired elderly from MCI when combined with the CAMCOG. Cluster analysis of the IQCODE better predicts MCI and its subtypes.

Alzheimer's disease (AD) is the most common form of dementia and results in progressive cognitive decline, particularly in regards to memory (National Institute on Aging, 2012). Prior research has shown sex differences in brain-atrophy rates of AD patients, with women experiencing a higher rate of progression in volume reduction (Skup et al., 2011). This suggests that there may also be differences in cognitive functioning between sexes, particularly in the rate of cognitive decline with a more rapid disease progression for dementing females compared to dementing males. The current study monitored memory function longitudinally in approximately 200 total participants, 100 with Mild Cognitive Impairment (MCI) or probable AD and 100 healthy controls enrolled in an aging study through the Arizona Alzheimer's Disease Research Consortium. Memory performance was evaluated with two memory tests, the Rey Auditory Verbal Learning Test (RAVLT; Rey, 1941) and the Brief Visuospatial Memory Test-Revised (BVMT-R; Benedict, 1997). Memory function was evaluated in participants with at least three data points over a five-year span. A multivariate regression model was used that includes controls for disease severity, age, age at disease onset, education, ethnicity, and medical comorbidities. Results indicated that females in the MCI and AD groups initially performed better than the males, but that over time, female scores had dropped significantly lower than male scores, suggesting a more rapid decline in females. Significant sex differences in cognitive decline may yield a deeper understanding of the development and progression of AD and aid in more effective and sex-specific treatment.

Although there are ample studies confirming that memory self-efficacy (MSE) declines with age, less is known about what factors account for the variation in MSE among older adults. The purpose of this study was to examine the relationship between MSE, diagnostic and clinical characteristics, and subsequent episodic memory performance in older adults. A nonprobability sample of 200 cognitively normal and older adults with mild cognitive impairment (MCI) participating in a longitudinal population-based study investigating the incidence, prevalence and risk factors for MCI completed a questionnaire about self-referent beliefs of MSE. Bandura's (1989) selfefficacy theory and the Integration Model (Whittemore, 2005) informed the descriptive study. Pearson product-moment correlations, a general linear model and a multiple linear regression analysis were conducted. The difference in MSE ratings between the cognitively normal group and the MCI group tested as a whole was significant when adjusting for age, gender and educational attainment (p < .001; ES= 0.585). The overall regression model explained 17 % of the variance of MSE (p < .001) and included age, gender, educational attainment, APOE 4 genotype, family history of dementia, cognitive diagnosis and depressive symptoms. After controlling for age and the other variables of interest, cognitive classification and depression were significant predictors of MSE. Higher MSE ratings were correlated with better episodic memory performance for both groups (r = .273, p < .001). Memory training that capitalizes on the benefits accruing from higher MSE is needed for cognitively normal older adults and older adults with MCI.

This dissertation is a critical examination of the relationship between sleep problems and/or disorders, particularly Obstructive Sleep Apnea (OSA) and Alzheimer Disease (AD). First, I conducted an exhaustive systematic review of existing literature, and identified gaps in research that led to specific research aims. For the first aim, I conducted the first ever-published meta-analysis examining sleep, cognitive decline and AD, providing an aggregate effect of sleep on AD. Second, focusing on OSA, I conducted a study examining OSA’s effect on longitudinal changes on AD biomarkers in cognitive normal, MCI and AD subjects, using data from the Alzheimer Disease Neuroimaging Initiative (ADNI). Lastly, I conducted a review, integrating over 3 decades of research examining OSA and cognition; OSA and subsequent cognitive decline; and OSA and AD; with particular focus in appreciating the heterogeneity of OSA and its outcomes in distinct age groups. Results and implications from my research indicate that ample evidence exists linking sleep impairments and circadian regulating mechanisms directly to clinical symptoms in AD. Sleep problems and/or disorders increases your risk of cognitive decline and AD. OSA is associated with increased AD biomarker burden over time, and effects longitudinal changes in these biomarkers, such that OSA subjects progress faster than non-OSA subjects do. OSA may be age-dependent in older adults (60 – 70 years old) and the elderly (70 years and above) and is associated with neurodegenerative diseases particularly, cognitive decline and AD. Intermittent hypoxia and sleep fragmentation are two main processes by which OSA induces neurodegenerative changes. Therefore, clinical interventions aimed at OSA, such as treatment with CPAP or dental appliances, in cognitive normal and MCI patients, could possibly slow the progression of cognitive impairment to AD.

The integrity of brain white matter is vital for the interneuronal signalling between distinct brain regions required for normal cognitive function. White matter integrity is compromised with ageing and could contribute to age-related cognitive decline. Chronic cerebral hypoperfusion is thought to underlie the development of white matter pathology and cognitive changes, often seen in the elderly. Additionally, the development of regional hypoperfusion and white matter damage are thought to be early events in Alzheimer’s disease (AD) pathogenesis. This thesis set out to test the hypothesis that chronic cerebral hypoperfusion underlies the development of white matter pathology and cognitive decline and also that chronic cerebral hypoperfusion causes the development of Ab pathology in AD. The first aim was to investigate the impact of hypoperfusion on the development of white matter damage and different aspects of cognition in a mouse model of chronic cerebral hypoperfusion. Two studies were undertaken to address this. The first study examined the temporal development of pathology following hypoperfusion induced by bilateral carotid artery stenosis (BCAS) using microcoils Hypoperfusion was induced in wild type (WT) mice and the pathological changes examined at one week, two weeks, one month and two months. Hypoperfused animals developed a diffuse and widespread white matter pathology, present from one week, which occurred predominantly in the myelin component of white matter; this was accompanied by minimal axonal damage. A second study examined the impact of hypoperfusion on different aspects of spatial memory and further investigated pathological changes in the model at one and two months. Behavioural testing revealed a significant impairment in spatial working memory but not episodic memory or spatial reference memory in hypoperfused animals. In the same mice, pathological assessment indicated that there was a significant increase in levels of myelin damage and elevated levels of microglial activation as compared to shams. These results demonstrate that modest reductions in cerebral blood flow are sufficient to cause the development of white matter damage and the development of cognitive deficits. The second aim was to investigate the impact of hypoperfusion on the development of white matter and amyloid pathology in a mouse model (3xTg-AD) of AD. To address this, using 2 different sizes of microcoils (0.18mm and 0.16mm internal diameter) BCAS of varying severities was induced in 3xTg-AD mice and white matter and Ab pathology were assessed at one month. Circle of Willis (CoW) architecture was also compared between WT and 3xTg-AD mice. Overall white matter pathology was not exacerbated in experimental 3xTg-AD mice with BCAS induced by 0.18mm coils. However with a greater level of stenosis (0.16mm coil) ischaemic damage to neuronal perikarya was present in most experimental animals. In addition to ischaemic damage, localised areas of severe white matter pathology were also observed in conjunction with subtle changes to white matter Ab levels. Hypoperfusion did not impact on the development of intraneuronal Ab pathology, other than in the presence of ischaemic damage when levels were reduced. Comparison of CoW architecture between WT and 3xTg-AD mice revealed strain specific differences in the presence and morphology of the posterior communicating artery which may explain the lack of pathology in 3xTg-AD mice as compared to WT following BCAS induced using 0.18mm dia. microcoils. The third aim was to investigate whether white matter protein composition changed with age and also whether ageing conferred increased vulnerability to hypoperfusion. To address this, white matter protein levels were compared between young (3-4 months) and old (12-13 months) 3xTg-AD mice. White matter pathology was compared between sham and hypoperfused animals in the aged cohort. Levels of myelin basic protein and 2', 3'-cyclic nucleotide 3'- phosphodiesterase were found to be significantly increased whilst levels of myelin associated glycoprotein were significantly reduced with ageing. These results suggest that changes in myelin protein composition may contribute to the development of age related white matter pathology. White matter pathology was not exacerbated in aged hypoperfused animals following one month of hypoperfusion as compared to shams. The results presented within the thesis demonstrate that chronic cerebral hypoperfusion precipitates the development of selective white matter damage and impacts on cognition. Also it has been shown that where hypoperfusion is severe enough to cause ischaemic damage to neuronal perikarya and localised areas of severe white matter pathology, alterations in white matter Ab levels can occur. Hypoperfusion does not impact on APP processing or on intraneuronal levels of APP or Ab, other than in the presence of ischaemic damage to neuronal perikarya, when levels are reduced. These findings highlight the importance of early intervention strategies in the treatment of vascular risk factors which can lead to hypoperfusion and the development of white matter damage and a decline in cognitive function in later life. These findings also suggest that repair or prevention of white matter damage may be an appropriate strategy for the attenuation of cognitive decline following onset of hypoperfusion. This thesis also highlights some of the limitations of animal models of human disease.

The aim of this study is to establish a brief screening tool to classify PD patients as PD with normal cognition (PD-N), PD patients with mild cognitive impairment (PD-MCI) and PD patients with dementia (PD-D). There has been emerging evidence that the MoCA (Montreal Cognitive Assessment) shows potential for the brief assessment of cognition to differentiate among PD patients. One possible solution to further improve the discrimination among PD-D, PD-MCI and PD-N groups is to examine Instrumental Activities of Daily Living (IADL) measures in conjunction with the MoCA. A convenience sample of 162 patients suffering from PD and 53 volunteer control subjects were examined in a movement disorders center. Extensive neuropsychological testing was done to classify the PD patients into PD-N, PD-MCI or PD-D. The 24 patients were diagnosed as PD-D based on the Movement Disorders Society Task Force criteria. For PD-MCI, two criteria were used: 1.5SD:2 in one-domain (1.5 SD below the norms on two measures in at least one of four cognitive domains) and 1.5SD:1 in two-domains (1.5 SD below normative data in at least one measure but in two domains) which made a diagnosis of 34 and 39 PD-MCI patients respectively. The remaining patients were classified as PD-N. For both the MCI criteria, the results suggest that 1) for discriminating PD-MCI from PD-N, the MoCA is a sufficiently suitable screening measure that is not improved by adding ADL measures, 2) for distinguishing PD-D from PD-MCI, the MoCA and the full ADL-IS questionnaire can be administered to a patient suffering from PD. When time is limited and depending on the possibility of answering the questions regarding the ADL-IS items, the MoCA along with the Muddled and Complex Medication ADL-IS items should be administered. When no scores are obtained for Muddled, then MoCA along with Complex Medication ADL-IS item is sufficient to discriminate PD-D from PD-MCI. However, if no scores are obtained for Complex Medication item, then an average of four ADL-IS items should be taken along with the MoCA. This attractive brief screening tool helps in detection of cognitive impairment in the elderly.

Individuals with mild cognitive impairment (MCI) show variable impairment in autobiographical memory function, source memory function and reduced integrity in the brain’s default mode network (DMN). There is overlap between the DMN, such as the medial posterior cortical hub, and brain regions that are active when participants recall autobiographical memories. To assess the association between autobiographical memory and the DMN, 14 MCI and eleven age and education-matched healthy control participants were assessed using the autobiographical memory interview (AMI) and underwent resting state fMRI scans. The same participants underwent a test of source memory which assessed both recognition and source memory. The MCI group showed significantly increased semantic as well episodic memory impairments using the AMI, evident across the lifespan for episodic memory but not for childhood semantic memory. Significantly poorer DMN connectivity, using a goodness of fit index (GOF) of the DMN template, was evident in the MCI group. MCI participants showed poorer performance on both recognition and source memory relative to HC participants. A modest association between AMI semantic memory (r=0.4) scores, but not episodic memory scores (r=0.09), and DMN connectivity was found in these participants. For future study the predictive value of MR imaging in the DMN of MCI participants should be explored.