The present series of investigations examined the diagnostic accuracy of the Cognitive Examination (CAMCOG) from the Cambridge Mental Disorders of the Elderly Examination (CAMDEX) in the differential diagnosis of various dementing conditions. Specifically, this study examined: (a) the degree to which the CAMCOG would differentiate normal individuals from patients with Alzheimer's Disease (AD) and from those suffering from non-AD dementing conditions, (b) the extent to which the CAMCOG would distinguish between patients suffering from organic dementing conditions, those having functional psychiatric disorders, and normal persons, and (c) whether the CAMCOG would offer an improvement in diagnostic accuracy over a widely used screening instrument (i.e., the Mini-Mental Status Examination, MMSE) when attempting to differentially diagnose dementing patients and normal cohorts.A review of the literature was presented with an emphasis on the difficulties in establishing differential diagnosis, inaccuracies in diagnosis, the importance of improved diagnostic accuracy, and the use of neuropsychological measures in the assessment and diagnosis of patients suffering from dementing illnesses. Further, research relevant to ancillary diagnostic techniques, the various neuropsychologicalapproaches used in evaluating and diagnosing mental disorders in the elderly, and studies investigating the utility of specific cognitive/neuropsychological measures in the differential diagnosis of dementing diseases was presented.The results of these investigations revealed that the CAMCOG provides excellent diagnostic sensitivity and specificity when differentiating normal persons from clinically diagnosed AD patients and when distinguishing between individuals with an organic-dementing condition and normal adults. The CAMCOG was found to be less effective in differentiating AD and non-AD dementia patients and in distinguishing between patients suffering from organic dementia versus specified psychiatric disorders. Finally, the CAMCOG demonstrated a slight improvement in diagnostic accuracy over the Mini-Mental Status Examination. These results were discussed in terms of their support for the utility of the CAMCOG as an excellent screening measure when used to differentiate patients suffering from various dementia-producing disease states and normal persons. / Department of Educational Psychology

Magnetic Resonance Imaging (MR Imaging, or MRI) offers superior soft-tissue contrast compared to other medical imaging modalities. However, access to MRI across developing countries ranges from prohibitive to scarcely available. The lack of educational facilities and the excessive costs involved in imparting technical training have resulted in a lack of skilled human resources required to operate MRI systems in developing countries. While diagnostic medical imaging improves the utilization of facility-based rural health services and impacts management decisions, MRI requires technical expertise to set up the patient, acquire, visualize, and interpret data. The availability of such local expertise in underserved geographies is challenging. Inefficient workflows and usage of MRI result in challenges related to financial and temporal access in countries with higher scanner densities than the global average of 5.3 per million people. MRI is routinely employed for neuroimaging and, in particular, for dementia screening. Dementia affected 50 million people worldwide in 2018, with an estimated economic impact of US $1 trillion a year, and Alzheimer’s Disease (AD) accounts for up to 60–80% of dementia cases. However, AD-imaging using MRI is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for acquisition time, image contrast, and image quality. The lack of this expertise contributes to the highly inefficient utilization of MRI services, diminishing their clinical value. Augmenting human capabilities can tackle these challenges and standardize the practice. Autonomous and time-efficient acquisition, reconstruction, and visualization schemes to maximize MRI hardware usage and solutions that reduce reliance on human operation of MRI systems could alleviate some of the challenges associated with the requirement/absence of skilled human resources. We first present a preliminary demonstration of AMRI that simplifies the end-to-end MRI workflow of registering the subject, setting up and invoking an imaging session, acquiring and reconstructing the data, and visualizing the images. Our initial implementation of AMRI separates the required intelligence and user interaction from the acquisition hardware. AMRI performs intelligent protocolling and intelligent slice planning. Intelligent protocolling optimizes contrast value while satisfying signal-to-noise ratio and acquisition time constraints. We acquired data from four healthy volunteers across three experiments that differed in acquisition time constraints. AMRI achieved comparable image quality across all experiments despite optimizing for acquisition duration, therefore indirectly optimizing for MR Value – a metric to quantify the value of MRI. We believe we have demonstrated the first Autonomous MRI of the brain. We also present preliminary results from a deep learning (DL) tool for generating first-read text-based radiological reports directly from input brain images. It can potentially alleviate the burden on radiologists who experience the seventh-highest levels of burnout among all physicians, according to a 2015 survey. Next, we accelerate the routine brain imaging protocol employed at the Columbia University Irving Medical Center and leverage DL methods to boost image quality via image-denoising. Since MR physics dictates that the volume of the object being imaged influences the amount of signal received, we also demonstrate subject-specific image-denoising. The accelerated protocol resulted in a factor of 1.94 gain in imaging throughput, translating to a 72.51% increase in MR Value. We also demonstrate that this accelerated protocol can potentially be employed for AD imaging. Finally, we present ArtifactID – a DL tool to identify Gibbs ringing in low-field (0.36 T) and high-field (1.5 T and 3.0 T) brain MRI. We train separate binary classification models for low-field and high-field data, and visual explanations are generated via the Grad-CAM explainable AI method to help develop trust in the models’ predictions. We also demonstrate detecting motion using an accelerometer in a low-field MRI scanner since low-field MRI is prone to artifacts. In conclusion, our novel contributions in this work include: i) a software framework to demonstrate an initial implementation of autonomous brain imaging; ii) an end-to-end framework that leverages intelligent protocolling and DL-based image-denoising that can potentially be employed for accelerated AD imaging; and iii) a DL-based tool for automated identification of Gibbs ringing artifacts that may interfere with diagnosis at the time of radiological reading. We envision AMRI augmenting human expertise to alleviate the challenges associated with the scarcity of skilled human resources and contributing to globally accessible MRI.

Rural West Virginia has a very high percentage of older adults. The age-related disease of Alzheimer’s threatens the health of older Appalachians, yet research on Alzheimer’s disease (AD) in this population is scarce. In order to improve screening rates for cognitive impairment, Appalachians need to understand their vulnerability. The first step would be to assess their knowledge about AD but a suitable AD knowledge test has not been developed. The purpose of this study was to test the reliability and validity of a new measure of knowledge about AD that is culturally congruent, and to examine factors that may predict AD knowledge in this rural population. A correlational descriptive study was conducted with 240 participants from four samples of older adults in south central rural Appalachian West Virginia using surveys and face-to-face interviews. Results from tests for stability, reliability including Rasch modeling, discrimination and point biserial indices, and concurrent, divergent, and construct validity were favorable. Findings were that although more diversity in test item difficulty is needed, the test discriminated well between persons with higher and lower levels of education [F(2, 226) = 170.51, p = .001]. Using multiple regression, the predictors of AD knowledge included caregiver status, miles from a healthcare provider, gender, and education; (R2=.05, F(4,187) = 2.65, p =. 04). Only years of education accounted for a significant proportion of unique variance in predicting the total BKAD score (t = 2.14, p =. 03). Implications include the need for further tool refinement, testing for health literacy, coordination with recent statewide efforts to educate the public regarding AD, and community based participatory research in designing culturally effective education programs that will ultimately increase screening and detection of Alzheimer’s disease in rural populations. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2013.

Chang, Jianfang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 183-217). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

As the most prevalent cause of dementia worldwide, Alzheimer’s disease (AD) has become a global issue of public health. By current criteria, diagnosis of this neurodegenerative disorder requires both clinical confirmation of dementia and post-mortem detection of the so-called neurofibrillary tangles and senile plaques in the brain. Yet the main proteinaceous component of these plaques, the amyloid beta peptide (Abeta) is now widely believed to initiate a cascade of events that ultimately leads to Alzheimer’s disease. Besides, extensive evidence supports a pathogenic role of soluble oligomers formed upon Abeta aggregation in the onset of the disease, which, unlike Abeta fibrils, present distinct neurotoxic properties and correlate well with disease progression. Their detrimental effects have been suggested to appear decades before the first signs of cognitive impairment, making them biomarkers of choice in the study of the pathology. <p>Given that present guidelines for AD diagnosis are increasingly considered as ill-defined, reliable and early-stage detection methods taking into account the presence of toxic Abeta species are highly awaited by the medical community. In this regard, this thesis work describes the development of a sensing device aiming at the specific detection of the amyloid beta peptide in solution via recognition by antibodies grafted at the surface of functionalized germanium crystals. This new type of BIA-ATR (Biospecific Interaction Analysis - Attenuated Total Reflection) biosensor resorts on ATR-FTIR (Attenuated Total Reflection - Fourier Transform Infrared) spectroscopy, which is extremely sensitive to the secondary structure of proteins. The ATR mode uses germanium as optical transduction element combined to the evanescent wave principle to allow selective online monitoring of peptide-antibody binding events. <p>In the first part of this work, evaluation of the photochemistry on germanium optical elements have been the subject of intense research focus. Our investigations led to the elaboration of a quality control of functionalization efficiency based on infrared spectroscopy. We also set up in the lab an original ELISA method for selecting antibodies in terms of their true affinity for the Abeta peptide. <p>Thereafter binding experiments were carried out on the BIA-ATR sensor using different antibodies and Abeta isoforms, leading to the establishing of a standardized protocol for the detection of molecules of interest. Our results showed that Abeta detected on the biosensor corresponded precisely to antibody-bound peptide, whereas Abeta assemblies, and especially Abeta 1-42 oligomeric conformations, could be discriminated with respect to their spectral signature. This point, which was later confirmed by unsupervised statistical analysis, could be considered as particularly interesting and innovative, since to our knowledge, such conformation-sensitivity has never been observed with existing AD diagnostic methods. Moreover, effective recycling of the functionalized crystals has been demonstrated, which confers thereby a second major advantage to the biosensor. <p>In parallel to these experiments, a structural characterization study of Abeta species was undertaken in order to generate a database of IR spectra, as reference for future comparative analysis of physiological fluids on the biosensor. ATR-FTIR measurements revealed a strong dependency on the ratio between oligomers and fibrils within a mixture and their relative ratio in antiparallel and parallel beta-sheet content. Interestingly, separation trials of oligomeric entities demonstrated a specific effect of Cu2+ ions on Abeta aggregation. Stabilization of small oligomeric aggregates at equimolar Cu2+:Abeta ratios, which had never been clearly evidenced so far, could help to unravel some aspects of the complex role of copper in AD development. <p>These investigations illustrate the applicability of the so-called BIA-ATR methodology to online detection of different forms of the Abeta peptide in solution and the potential of this new sensor technology to fulfill current pitfalls in providing a reliable and comprehensive approach of AD diagnosis. / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer's disease (AD) is the most common form of dementia defined by loss in memory and cognitive abilities severe enough to interfere significantly with daily life activities. Amnestic mild cognitive impairment (MCI) is a clinical condition in which an individual has memory deficits not normal for the individual's age, but not severe enough to interfere significantly with daily functioning. Every year, approximately 10-15% of individuals with MCI will progress to dementia. Currently, there is no treatment to slow or halt AD progression, but research studies are being conducted to identify causes that can lead to its earlier diagnosis and treatment. Genetic variation plays a key role in the development of AD, but not all genetic factors associated with the disease have been identified. Copy number variants (CNVs), a form of genetic variation, are DNA regions that have added genetic material (duplications) or loss of genetic material (deletions). The regions may overlap one or more genes possibly affecting their function. CNVs have been shown to play a role in certain diseases. At the start of this work, only one published study had examined CNVs in late-onset AD and none had examined MCI. In order to determine the possible involvement of CNVs in AD and MCI susceptibility, genome-wide CNV analyses were performed in participants from three cohorts: the ADNI cohort, the NIA-LOAD/NCRAD Family Study cohort, and a unique cohort of clinically characterized and neuropathologically verified individuals. Only participants with DNA samples extracted from blood/brain tissue were included in the analyses. CNV calls were generated using genome-wide array data available on these samples. After detailed quality review, case (AD and/or MCI)/control association analyses including candidate gene and genome-wide approaches were performed. Although no excess CNV burden was observed in cases compared to controls in the three cohorts, gene-based association analyses identified a number of genes including the AD candidate genes CHRFAM7A, RELN and DOPEY2. Thus, the present work highlights the possible role of CNVs in AD and MCI susceptibility warranting further investigation. Future work will include replication of the findings in independent samples and confirmation by molecular validation experiments.