The studies presented in this work were completed with the goal ofgaining greater insight into the roles of protein oxidation in brain aging and age-relatedcognitive impairment. Aging is associated with the impairment of physiological systemssuch as the central nervous system (CNS), homeostatic system, immune system, etc.Functional impairments of the CNS is associated with increased susceptibility to developmany neurodegenerative diseases such as Alzheimer's diseases (AD), Parkinson's disease(PD), and amyotrophic lateral sclerosis (ALS). One of the most noticeable functionalimpairments of the CNS is manifested by cognitive decline. In the past three decades, thefree radical theory of aging has gained relatively strong support in this area. Excessiveproduction reactive oxygen species (ROS) was demonstrated as a contributing factor inage-related memory and synaptic plasticity dysfunction. This dissertation use proteomicsto identify the proteins that are oxidatively modified and post-translationally altered inaged brain with cognitive impairment and normal aging brain.Ongoing research is being pursued for development of regime to preventoxidative damage by age-related oxidative stress. Among which are those that scavengefree radicals by antioxidants, i.e. ??-lipoic acid (LA), and protecting the brains byreducing production of neurotoxic substance, i.e. reducing production of amyloid ??(A??).Therefore, proteomics were also used to identify the alteration of specific proteins in agedbrain treated with LA and antisense oligonucleotides again amyloid protein precursor.This dissertation provides evidences that certain proteins are less oxidatively modifiedand post-translationally altered in cognitively impaired aged brain treated with LA andantisense oligonucleotides against the A?? region of amyloid precursor protein (APP)(AO).Together, the studies in this dissertation demonstrated that increased oxidativestress in brain play a significant role in age-related cognitive impairment. Moreover, suchincreased oxidative stress leads to specific protein oxidation in the brain of cognitiveimpaired subject, thereby leading to cognitive function impairment. Moreover, thefunctional alterations of the proteins identified by proteomics in this dissertation mayleads to impaired metabolism, decline antioxidant system, and damaged synapticcommunication. Ultimately, impairment of these processes lead to neuronal damages andcognitive decline. This dissertation also show that several of the up-regulated andoxidized proteins in the brains of normal aging mice identified are known to be oxidizedin neurodegenerative diseases as well, suggesting that the expression levels of certainproteins may increase as a compensatory response to oxidative stress. This compensationwould allow for the maintenance of proper molecular functions in normal aging brainsand protection against neurodegeneration.

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.

Background: Mild Cognitive Impairment (MCI) is a clinical construct reputed to represent an intermediate stage on a continuum between normal aging and cognitive decline. Conceptual and prognostic ambiguity can lead to significant diagnostic challenges and there is a need for accurate screening tests which can assist clinicians with decision-making. A diagnosis of MCI is also associated with considerable uncertainty for patients who may be adjusting to cognitive difficulties along with an increased risk of developing dementia. Beliefs about MCI may influence psychosocial adjustment, and individual differences in ‘psychological flexibility (PF)’, as conceptualised by the Acceptance and Commitment Therapy (ACT) model, may also be involved in this process. Objectives: In order to evaluate the accuracy and clinical utility of a recently developed screening tool for MCI, the Montreal Cognitive Assessment (MoCA), a systematic review of validation and diagnostic test accuracy (DTA) studies for this measure was conducted. Psychosocial adjustment to a diagnosis of MCI was also a key focus. An empirical study was therefore carried out with the aim of evaluating the possible relationships between cognitive impairment, illness representations about MCI, psychological wellbeing and quality of life (QoL), and to assess the potential involvement of PF. Method: Following a systematic search of relevant electronic databases and reference lists, validation and DTA studies of the MoCA were identified and evaluated for methodological quality. For the empirical study, patients recently diagnosed with MCI were recruited from local NHS memory clinic services and completed the MoCA and a questionnaire pack assessing illness representations, PF, mood, anxiety and QoL. Results: The systematic review identified 18 validation and DTA studies. Few of the studies achieved high ratings for methodological quality and problems with representativeness and generalisability were identified. Nevertheless, sensitivity levels appeared robust across studies, though specificity was variable. For the present empirical study, participants reported a spectrum of positive and negative beliefs about MCI. Distress attributed to MCI was associated with anxiety, along with perceptions of more serious illness consequences, while higher PF was associated with higher perceived QoL and mood. Lived experience of MCI appeared to have more relevance to psychosocial adjustment than objective cognitive impairment. Conclusions: The results of the systematic review indicate that while the MoCA is a robust tool overall in the identification of cognitive impairment, estimates of accuracy may be exaggerated by inter-study variation and bias. More rigorous validation studies are therefore needed. Implications for clinical decision-making regarding MCI are discussed and recommendations for future accuracy studies are outlined. The empirical study supported the findings of previous studies of the relevance of illness representations to psychosocial adjustment in MCI and added to the evidence base by providing preliminary support for the possible involvement of PF. The results suggest that both cognitive content and PF may represent possible vehicles for therapeutic change in patients with adjustment difficulties, and indicate that further investigation of these factors is warranted. Conclusions are limited, however, by small sample size and low statistical power. Replication of these findings with a larger and more representative sample is therefore recommended.

This dissertation describes separate but related studies that explore the prospective memory abilities of older adults and individuals with Mild Cognitive Impairment/Early Alzheimer’s disease. Prospective memory (PM) refers to the type of memory utilized to execute planned actions in accordance with a specific event. PM is critical to maintaining functional independence in older adults, as it can refer to such basic acts as remembering to turn off a stove or taking one’s medication. Research suggests PM abilities decline within normal aging and to a greater extent in Mild Cognitive Impairment (MCI) and early Alzheimer’s Disease (AD). Together, the studies assessed and compared the PM abilities across healthy younger and older adults, individuals with MCI, and individuals with early AD while exploring two major theories that seek to explain PM retrieval. The preparatory attentional and memory process theory of PM (PAM) assumes that PM retrieval requires resource-demanding preparatory attentional processes, whereas the Dynamic Multiprocess theory (DMPT) assumes that retrieval can also occur spontaneously (Scullin, McDaniel, & Shelton, 2013; Smith & Bayen, 2006). Study 1 used a novel laboratory PM task in which the focality and the frequency of PM cues were manipulated to compare the PM abilities of cognitively healthy younger and older adults. The results revealed significant differences in the patterns of performance between the younger and older adults based on the focality and frequency of cues which indicated different attentional allocation strategies. Study 2 examined the impact of cognitive impairment on PM abilities by using the same paradigm to compare the performance of cognitively healthy older adults to individuals with MCI and early AD. The results again revealed significant differences in the patterns of performance which indicated that these groups may have used different strategies of attentional allocation depending on the focality and cue frequency. Taken together, the findings in Studies 1 and 2 were mixed with respect to the predictions of the DMPT and PAM. The MCI group, in particular, demonstrated a unique performance profile that suggests the neuropathophysiological changes associated with this diagnosis may lead to the reliance on different PM retrieval processes compared to healthy older adults. Finally, Study 3 explored the use of a more naturalistic and ecologically valid PM task to compare the PM performance of individuals with MCI and early AD to healthy older adults without cognitive impairment. The results showed that, after taking the learning and retrospective memory scores into account, the significant differences between groups in PM accuracy on this task can mostly be accounted for by these factors. Nevertheless, the AD group was found to display significantly lower PM accuracy with event-based cues with a weak association between cue and action compared to the older adult and MCI groups after controlling for these factors. These findings provide valuable theoretical, methodological, and clinical contributions which will be discussed.

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.

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

No description available.

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).

Alzheimer's disease~(AD) is the most frequent neurodegenerative dementia and a growing health problem. Early and accurate diagnosis and prediction of AD is crucial because treatment may be most efficacious if introduced as early as possible. Neuropsychological testing, which is clinically used, sometimes fails to recognize probable dementia, especially to recognize the disease at an early time point such as the mild cognitive impairment~(MCI), which is the prodromal stage of AD. Recently, there has been a realization that magnetic resonance imaging~(MRI) may help diagnoses of AD and MCI. In this dissertation, we introduce an MRI-analysis based algorithm to help diagnose the illness before irreversible neuronal loss has set in, and to help detect brain changes between MCI patients who may convert and may not convert to AD. Given a set of brain MR images, the algorithm first calculates the distance between each pair of images via a registration process. Then images are projected from a high dimensional Euclidean space to a low dimensional Euclidean subspace based on the calculated distances, with a dimension reduction method. Finally classical supervised classification approaches are employed to assign images to appropriate groups in the low dimensional space. The classification accuracy rates we obtained in our experiments are higher than, or at least comparable to, those reported in recently published papers. Moreover, this algorithm can be extended to explore the pathology distribution of AD. Exploring the distribution of AD pathology is of great importance to reveal AD related regional atrophy at specific stages of the disease and provide insight into longitudinal sequence of disease progression. Calculating distances between different brain structures produces different classification accuracy. Those structures yielding higher classification accuracy are considered as pathological regions. Our experimental results on pathology localization are also compared with the reproduced results using other existing popular algorithms; the observations are consistent. / Ph. D.