107 research outputs found

    Association Between False Memories and Delusions in Alzheimer Disease

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    IMPORTANCE: Understanding the mechanisms of delusion formation in Alzheimer disease (AD) could inform the development of therapeutic interventions. It has been suggested that delusions arise as a consequence of false memories. OBJECTIVE: To investigate whether delusions in AD are associated with false recognition, and whether higher rates of false recognition and the presence of delusions are associated with lower regional brain volumes in the same brain regions. DESIGN, SETTING, AND PARTICIPANTS: Since the Alzheimer's Disease Neuroimaging Initiative (ADNI) launched in 2004, it has amassed an archive of longitudinal behavioral and biomarker data. This cross-sectional study used data downloaded in 2020 from ADNI participants with an AD diagnosis at baseline or follow-up. Data analysis was performed between June 24, 2020, and September 21, 2021. EXPOSURE: Enrollment in the ADNI. MAIN OUTCOMES AND MEASURES: The main outcomes included false recognition, measured with the 13-item Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog 13) and the Rey Auditory Verbal Learning Test (RAVLT) and volume of brain regions corrected for total intracranial volume. Behavioral data were compared for individuals with delusions in AD and those without using independent-samples t tests or Mann-Whitney nonparametric tests. Significant findings were further explored using binary logistic regression modeling. For neuroimaging data region of interest analyses using t tests, Poisson regression modeling or binary logistic regression modeling and further exploratory, whole-brain voxel-based morphometry analyses were carried out to explore the association between regional brain volume and false recognition or presence of delusions. RESULTS: Of the 2248 individuals in the ADNI database, 728 met the inclusion criteria and were included in this study. There were 317 (43.5%) women and 411 (56.5%) men. Their mean (SD) age was 74.8 (7.4) years. The 42 participants with delusions at baseline had higher rates of false recognition on the ADAS-Cog 13 (median score, 3; IQR, 1 to 6) compared with the 549 control participants (median score, 2; IQR, 0 to 4; U = 9398.5; P = .04). False recognition was not associated with the presence of delusions when confounding variables were included in binary logistic regression models. An ADAS-Cog 13 false recognition score was inversely associated with left hippocampal volume (odds ratio [OR], 0.91 [95% CI, 0.88-0.94], P < .001), right hippocampal volume (0.94 [0.92-0.97], P < .001), left entorhinal cortex volume (0.94 [0.91-0.97], P < .001), left parahippocampal gyrus volume (0.93 [0.91-0.96], P < .001), and left fusiform gyrus volume (0.97 [0.96-0.99], P < .001). There was no overlap between locations associated with false recognition and those associated with delusions. CONCLUSIONS AND RELEVANCE: In this cross-sectional study, false memories were not associated with the presence of delusions after accounting for confounding variables, and no indication for overlap of neural networks for false memories and delusions was observed on volumetric neuroimaging. These findings suggest that delusions in AD do not arise as a direct consequence of misremembering, lending weight to ongoing attempts to delineate specific therapeutic targets for treatment of psychosis

    Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records

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    Introduction Clustering algorithms are a class of algorithms that can discover groups of observations in complex data and are often used to identify subtypes of heterogeneous diseases in electronic health records (EHR). Evaluating clustering experiments for biological and clinical significance is a vital but challenging task due to the lack of consensus on best practices. As a result, the translation of findings from clustering experiments to clinical practice is limited. Aim The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of clustering experiments using EHR. Methods We conducted a scoping review of clustering studies in EHR to identify common evaluation approaches. We systematically investigated the performance of the identified approaches using a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER) that tested whether clusterable structures exist in EHR. To develop this method we tested several cluster validation indexes and methods of generating null data to see which are the best at discovering clusters. In order to enable the robust benchmarking of evaluation approaches, we created a tool that generated synthetic EHR data that contain known cluster labels across a range of clustering scenarios. Results Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing cluster results across multiple algorithms (30% of studies). We examined this approach conducting a clustering experiment on AD patients using a population of 10,065 AD patients and 21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means 4 was found to have the best clustering solution with the highest silhouette score (0.19) and was more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD (n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of mental health issues, smoking and early disease onset (n=1528), which has been found in previous research as well as in the results of other clustering methods. We created a synthetic data generation tool which allows for the generation of realistic EHR clusters that can vary in separation and number of noise variables to alter the difficulty of the clustering problem. We found that decreasing cluster separation did increase cluster difficulty significantly whereas noise variables increased cluster difficulty but not significantly. To develop the tool to assess clusters existence we tested different methods of null dataset generation and cluster validation indices, the best performing null dataset method was the min max method and the best performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters were identified using the Calinski Harabasz index they were more likely to have significantly different outcomes between clusters. Lastly we repeated the initial clustering experiment, comparing 10 different pre-processing methods. The three best performing methods were RBF kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters; heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory loss (n = 1823), female with more problem (n=2244). Conclusion We have developed and tested a series of methods and tools to enable the evaluation of EHR clustering experiments. We developed and proposed a novel cluster evaluation metric and provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR

    Pursuit of precision medicine: Systems biology approaches in Alzheimer\u27s disease mouse models.

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    Alzheimer\u27s disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD

    DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

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    In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model&#8217;s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

    False memory and delusions in Alzheimer's disease

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    Aims: This thesis aimed to investigate the relationship between memory errors and delusions in Alzheimer’s disease (AD), in order to further elucidate the mechanisms underlying delusion formation. This was achieved by undertaking narrative and systematic review of relevant literature, by exploring the relationship between performance on memory and metamemory tasks and delusions in AD patient populations and by investigating the neuroanatomical correlates of memory errors and delusions in AD patient populations. // Methods: I recruited 27 participants with and without delusions in AD and compared performance on measures of context memory, false memory and metamemory. I explored statistically significant behavioural findings further in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort of participants with AD (n = 733). I then conducted hypothesis-driven region of interest and exploratory voxel-based morphometric analyses to determine the relationship between false memory and delusions and regional brain volume in the ADNI cohort. This informed similar analyses of neuroimaging data in my own participants (n = 8). // Results: In both samples, individuals with delusions in AD had higher false recognition rates on recognition memory tasks than those without delusions. False recognition was inversely correlated with volume of medial temporal lobe, ventral visual stream and prefrontal cortex in both samples. In the ADNI sample, false recognition was also inversely correlated with anterior cingulate cortex (ACC) volume bilaterally. Participants with delusions had reduced volume of right ACC and increased volume of right parahippocampal gyrus compared to the control group. // Conclusions: These two complementary studies provide evidence of specific memory impairments associated with both delusions and a distinct pattern of brain atrophy in AD. Simple cognitive interventions can reduce false recognition rates in AD. Given the significant risks associated with antipsychotic drug treatment of delusions, exploring how these non-pharmacological interventions potentially affect psychosis symptoms in AD is an important next step

    Hippocampus

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    The hippocampus is a bicortical structure with extensive fiber connections with multiple brain regions. It is involved in several functions, such as learning, memory, attention, emotion, and more. This book covers various aspects of the hippocampus including cytoarchitecture, functions, diseases, and treatment. It highlights the most advanced findings in research on the hippocampus. It discusses circuits, pattern formation process of grid cells, and zinc dynamics of the hippocampus. The book also addresses the tau pathology and circRNAs related to Alzheimer’s disease and potential treatment strategies. It is a useful resource for general readers, students, and researchers

    Prediction and Monitoring of Progression of Alzheimer’s Disease : Multivariable approaches for decision support

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    Alzheimerin tauti, yksi yleisimmistä muistisairauksista, on hitaasti etenevä aivoja rappeuttava tauti, jolle ei ole vielä parantavaa hoitoa. Tietyt lääkkeet ja elämäntapainterventiot voivat kuitenkin hidastaa taudin etenemistä ja lievittää sen oireita, mikä parantaa potilaiden elämänlaatua ja terveydenhuollon kustannusvaikuttavuutta. Alzheimerin taudin varhainen diagnostiikka on erittäin tärkeää, koska erilaiset interventiot pitäisi aloittaa jo taudin varhaisessa vaiheessa, jotta niillä saataisiin aikaan paras mahdollinen vaikutus. Taudin varhainen diagnostiikka on kuitenkin haastavaa, koska muutokset aivoissa alkavat vuosia tai vuosikymmeniä ennen ensimmäisten oireiden ilmaantumista. Lisäksi viime vuosien tutkimus on tuottanut tietoa suuresta määrästä erilaisia testejä ja biomarkkereita, jotka voivat vaikuttaa taudin diagnoosiin ja prognoosiin. Tiedon suuri määrä saattaa aiheuttaa informaatioähkyä kliinikoille vaikeuttaen heidän päätöksentekoaan. Datalähtöiset analytiikka- ja visualisointimenetelmät voivat auttaa suuren ja heterogeenisen tietomäärän tulkinnassa ja hyödyntämisessä. Ne voivat siten tukea kliinikkoa hänen päätöksenteossaan. Lisäksi nämä menetelmät voivat auttaa tunnistamaan sopivia potilaita kliinisiin lääketutkimuksiin, joiden tavoitteena on kehittää Alzheimerin taudin etenemistä hidastavia lääkkeitä. Tämän väitöskirjan tavoitteena oli kehittää datalähtöisiä menetelmiä Alzheimerin taudin etenemisen ennustamiseen ja seurantaan taudin eri vaiheisiin alkaen normaalista kognitiosta ja edeten kuolemaan. Mallien kehittämisessä hyödynnettiin kognitiivisten ja neuropsykologisten testien tuloksia, magneettikuvantamista (MRI), selkäydinnestenäytteitä, ja genetiikkaa (apolipoproteiini E). Väitöskirja koostuu neljästä alkuperäisestä tutkimuksesta, jotka on julkaistu kansainvälisissä tieteellisissä lehdissä. Ensimmäinen osatutkimus keskittyi Alzheimerin taudin varhaiseen vaiheeseen. Tutkimuksessa käytettiin ohjattua koneoppimisen menetelmää Disease State Index (DSI, taudin tilan indeksi) ennustamaan, kenellä subjektiivisesti koettu kognition heikkeneminen etenee taudin vakavampaan vaiheeseen eli lievään kognition heikentymiseen (mild cognitive impairment, MCI) tai dementiaan. Tutkimuksen aineisto koostui 647 henkilöstä kolmesta eurooppalaisesta muisti- klinikkakohortista. Kun yhdistettiin useita eri muuttujia DSI-menetelmällä, ROC- käyrän (engl. Receiver Operating Characteristic curve) alle jäävä pinta-ala (AUC) oli 0.81 ja tasapainotettu tarkkuus oli 74%. Negatiivinen ennustearvo oli korkea (93%) ja positiivinen ennustearvo oli matala (38%). Kun DSI-malli validoitiin erillisellä testikohortilla, mallin AUC huononi 11%. Lisäanalyysit osoittivat, että useat erot kohorttien välillä voivat selittää suorituskyvyn alenemista. Toinen osatutkimus keskittyi taudin myöhäisempään vaiheeseen. DSI-menetelmällä analysoitiin pitkittäistä dataa, joka koostui 273 henkilön MCI-kohortista. Kohortti hankittiin Alzheimer’s Disease and Neuroimaging (ADNI 1) tietokannasta. DSI-arvojen muutokset ajan kuluessa olivat erilaiset niillä, joiden tauti eteni Alzheimerin taudin dementiaksi, ja niillä, joilla tauti pysyi MCI-vaiheessa. Lisäksi huomattiin, että stabiilina pysynyt MCI-ryhmä koostui kahdesta aliryhmästä: ensimmäisessä ryhmässä DSI-arvot pysyivät vakaina ja toisessa ryhmässä DSI-arvot kohosivat. Tämä indikoi, että toisessa ryhmässä tauti saattaa edetä dementiaksi tulevaisuudessa. Näiden analyysien lisäksi DSI:in oleellisesti liittyvä Disease State Fingerprint (DSF, taudin tilan sormenjälki) -visualisointimenetelmä laajennettiin pitkittäiselle datalle. Kolmas osatutkimus ennusti hippokampuksen surkastumista 24 kuukauden ai- kana lähtötilanteen mittausten perusteella. Tutkimuskohortti koostui henkilöistä, joilla oli normaali kognitio, MCI tai Alzheimerin taudin dementia, ja se hankittiin ADNI 1 (n=530) ja Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL, n=176) tutkimuksista. Useita eri datatyyppejä sisältävät mallit ennustivat hippokampuksen surkastumista tarkemmin kuin pelkistä MRI-muuttujista koostuvat mallit. Kuitenkin molemmat mallit aliarvioivat todellista surkastumista erityisesti suuremmilla surkastumisnopeuksilla, aliarviointi oli suurempaa pelkästään MRI-muuttujiin perustuvilla malleilla. Kun ennustettiin kaksiluokkaista vastemuuttujaa, eli nopea vs. hidas surkastuminen, mallien tarkkuus oli 79-87%. MRI-mallien suorituskyky oli hyvä, kun testauksessa käytettiin erillistä AIBL-aineistoa. Viimeinen osatutkimus keskittyi Alzheimerin taudin viimeisimpiin vaiheisiin. Siinä tutkittiin, mitkä tautiin liittyvät tekijät ovat yhteydessä kuolleisuuteen potilailla, joilla oli Alzheimerin taudin dementia. Aineisto koostui 616 henkilöstä Amsterdam Dementia Cohort -aineistosta. Iällä ja sukupuolella vakioidun Coxin suhteellisen vaaran mallin mukaan vanhempi ikä, miessukupuoli, huonommat pisteet kognitiivisessa toimintakyvyssä, ja aivojen kuoriosien ja mediaalisen ohimolohkon surkastuminen olivat yhteydessä kuolleisuuteen. Optimaalinen muuttujien yhdistelmä sisälsi iän, sukupuolen, tulokset kahdesta kognitiivisesta testistä (digit span backward, Trail Ma- king Test A), mediaalisen ohimolohkon surkastumisen ja selkäydinnestenäytteestä mitatun kohdasta 181 (treoniini) fosforyloidun tau-proteiinin määrän. Yhteenvetona todetaan, että datalähtöisillä menetelmillä voidaan ennustaa ja seu- rata Alzheimerin taudin etenemistä varhaisesta vaiheesta myöhäiseen vaiheeseen. Yhdistämällä useita eri datatyyppejä saadaan parempia tuloksia kuin käyttämällä vain yhtä datatyyppiä. Tulokset korostavat myös, että datalähtöiset menetelmät on tärkeä arvioida erillisellä aineistolla, jota ei ole käytetty menetelmien kehittämiseen. Lisäksi näiden menetelmien käyttöönotto eri ympäristöissä tai maissa saattaa vaatia potilaan tutkimusmenetelmien ja diagnoosikriteereiden harmonisointia.Alzheimer’s disease (AD), the most common form of dementia, is a slowly progressing neurodegenerative disease, which cannot be cured yet. However, certain medications and lifestyle interventions can delay progression of the disease and its symptoms, thereby positively influencing both quality of life of patients as well as cost- effectiveness of healthcare. Early diagnosis of AD is important because such interventions should be started already at an early phase of the disease to have the best effect. However, early diagnosis is challenging because pathological changes in the brain occur years before the clinical symptoms become visible. In addition, the re- search during the past years has produced information from a large number of different tests and biomarkers that can potentially contribute to diagnosis and prognosis of AD. This excessive amount of data can cause information overload for clinicians, thus hampering the clinicians’ decision making. Data-driven analysis and visualization methods may help with interpretation and utilization of large amounts of heterogeneous patient data and support the clinicians’ decision-making process. Furthermore, the methods may aid in identifying suitable patients for clinical drug trials. The aim of the work described in this thesis was to develop and validate data- driven methods for predicting and monitoring progression of Alzheimer’s disease at the different phases of the disease spectrum, starting from normal cognition and ending to death, using data from neuropsychological and cognitive tests, magnetic resonance imaging (MRI), cerebrospinal fluid samples (CSF), comorbidities, and genetics (apolipoprotein E). The thesis consists of four original studies published as international journal articles. The first study focused on the early phase of AD. A supervised machine learning method called Disease State Index (DSI) was utilized to predict who of the individuals with subjective cognitive decline (SCD) will progress to a more severe condition, i.e., mild cognitive impairment (MCI) or dementia. The study population included 647 subjects from three different memory clinic-based cohorts in Europe. When all data modalities were combined, the area under the receiver operating characteristic curve (AUC) was 0.81 and balanced accuracy was 74%. Negative predictive value was high (93%), whereas positive predictive value was low (38%). Performance of the DSI method in terms of AUC decreased by 11% when validated with an in- dependent test set. Additional analyses suggested that several differences between the cohorts may explain the decrease in the performance. The second study focused on a more advanced disease stage. The DSI method was applied to longitudinal data collected from an MCI cohort of 273 subjects obtained from the Alzheimer’s Disease and Neuroimaging (ADNI 1) study. Longitudinal profiles of the DSI values differed between the subjects progressing to dementia due to AD and subjects remaining as MCI. In addition, two subgroups were found in the group remaining as MCI: one group with stable DSI values over time and another group with increasing DSI values, suggesting the latter group may progress to dementia due to AD in the future. This study also extended the Disease State Fingerprint (DSF) data visualization method for longitudinal data. The third study predicted hippocampal atrophy over 24 months using baseline data and penalized linear regression. The cohorts consisted of subjects with normal cognition, MCI, and dementia due to AD and were obtained from the ADNI 1 (n=530) and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL, n=176) studies. The models including different data modalities per- formed better than the models including only MRI features. However, both models underestimated the real change at higher atrophy rate levels, the MRI-only models showing a greater underestimation. When predicting dichotomized outcome, i.e., fast vs. slow atrophy, the models obtained a prediction accuracy of 79-87%. The MRI-only models performed well when evaluated with an independent validation cohort (AIBL). The last study focused on the latest phase of AD by identifying which disease- related determinants are associated with mortality in patients with dementia due to AD. The cohort included 616 patients from the Amsterdam Dementia Cohort. Age- and sex-adjusted Cox proportional hazards models revealed that older age, male sex, and worse scores on cognitive functioning, as well as more severe medial temporal lobe and global cortical atrophy were associated with an increased risk of mortality. An optimal combination of variables comprised age, sex, performance on digit span backward test and Trail Making Test A, medial temporal lobe atrophy, and tau phosphorylated at threonine 181 in CSF. In conclusion, data-driven methods can be used for predicting and monitoring progression of AD from the mildest stages to the more advanced stages. Combining information from several data modalities provides better prediction performance than individual data modalities alone. The results also highlight the importance of the validation of the methods with independent validation cohorts. Introduction of these methods to different environments and countries may require harmonization of patient examination methods and diagnostic criteria

    Quantification of diverse cerebrovascular pathologies on MRI in older individuals and their relationships to cognition in a multimodal MRI index.

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    Cerebrovascular disease (CVD) is a leading cause of morbidity and mortality, with manifold clinical consequences including vascular dementia (VaD), the second most common dementia subtype. CVD also worsens the risk and expression of Alzheimer’s disease (AD). Diagnosing VaD requires determining whether a patient’s cognitive deficits can be explained by the current CVD burden. However, CVD is markedly pleomorphic and its full extent has been difficult to determine. Traditional markers of CVD such as white matter hyperintensities (WMH) are inconsistently associated with clinical outcomes. There is an unmet need to better quantify total CVD burden and relate it to cognition and dementia. CVD indices have been published, which combine information from several neuroimaging markers, in the hope to better capture the variability seen on neuroimaging and lead to more robust associations with cognition. In this thesis, the existing literature on neuroimaging and neuropathological indices of CVD was systematically reviewed. The contributions of two common but under-researched MRI CVD markers, cerebral microbleeds (CMB) and dilated perivascular spaces (PVS), were then assessed. The second chapter examines the association of CMB with both cross sectional and longitudinal impairment, finding associations with executive function and visuospatial function respectively. There was no existing reliable rating scale for quantification of PVS and the development of this is the theme of the third chapter. The fourth chapter examines the associations of dilated PVS with longitudinal cognitive impairment and incident dementia. I found that individuals with the most severe PVS had declines in Global Cognition and their presence triples the risk of developing dementia over 8 years. The fifth chapter describes the development of an MRI-based composite CVD index and its validation in two independent cohorts. Peak skeletonised mean diffusivity, a DTI measure, and WMH volume contributed most to the variability seen in Global Cognition. The Index explained 9% of the proportion of the variance seen in the development sample and 5 and 13 % in the validation cohorts respectively. It performed better than the most widely used CVD index in all cohorts examined. This work broadens the range of vascular pathologies used in the determination of the total CVD burden. The resulting composite index, while needing further refinement, represents a promising step-forward in the assessment of such burden in an individual, which could potentially help clinicians and researchers in determining the vascular contributions to dementia
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