110 research outputs found

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    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’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

    Sleep dependent memory consolidation in mild cognitive impairment subtypes

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    Sleep plays a crucial role in the overnight consolidation of newly learnt information in young adults, however the sleep-memory relationship in older adults is less understood. Age-associated memory decline as well as sleep disturbances are a concern for up to 60% of older people. Greater non-rapid eye movement (NREM) sleep neurophysiology such as slow waves and spindles have been postulated to be important for overnight memory consolidation, however, these associations are unclear in those at greater risk of dementia, namely in Mild Cognitive Impairment (MCI). Furthermore, it is unclear whether structural brain integrity for regions important for sleep and memory in ageing such as the hippocampus and medial prefrontal cortex, are associated with OMC in this ‘at-risk’ population. The overall aims of this study were to determine if there are differences in memory consolidation in older adults with and without MCI (and their subtypes), and examine associations between overnight memory consolidation with NREM sleep neurophysiology, and structural brain integrity using neuroimaging. Using a 256-channel high density EEG and a novel task of spatial navigation memory, the implications of these findings speak to the design of clinical trials targeting sleep in older adults, to determine the impact and functions of sleep as a modifiable risk factor for cognitive decline

    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

    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

    Cardiovascular health and brain aging : a population-based MRI study

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    Deterioration of brain structure and cognitive function occurs as individuals reach advanced age. The former can be observed through various markers of cerebral small vessel disease on magnetic resonance imaging (MRI) scans and the later can be assessed by neuropsychological tests and clinical examinations. In addition, maintaining a favorable cardiovascular health (CVH) status may contribute to delaying brain aging. Having a higher cognitive reserve (CR) capacity may contribute to preserving cognitive function even in the presence of brain damage. In this thesis, we aimed to examine the progression and interrelationships of MRI markers of structural brain aging and the association between the progression of these markers and cognitive decline. Furthermore, we aimed to investigate whether maintaining a favorable CVH status would be related to a slower deterioration of brain structure and whether having a higher CR capacity would be associated with a lower risk of cognitive deterioration and death. Data were derived from the population-based Swedish National study on Aging and Care in Kungsholmen from 2001–2004 to 2016–2019 and the MRI sub-study from 2001–2003 to 2007–2010. Study I: This six-year follow-up study showed that the progression rate of cerebral small vessel disease markers including expansion rates of white matter hyperintensities (WMHs) and lateral ventricles, incidence of lacunes, and shrinkage rate of gray matter volume, but not the progression rate of perivascular spaces (PVSs), steadily increased with aging (P < 0.05). The progression rate of regional WMHs was faster in males than in females and in people without a university degree than those with a degree (P < 0.05). In addition, a higher load of microvascular lesions (i.e., WMHs, PVSs, and lacunes) at baseline was related to faster progression of both microvascular lesions (WMHs and lacunes) and gray matter atrophy (P < 0.05). Study II: This follow-up study showed that a greater burden of WMHs at baseline was associated with a faster decline in executive function, letter fluency, perceptual speed, and global cognition over 15 years (P < 0.05), but not in episodic or semantic memory. The faster deterioration in category fluency was linked to greater periventricular WMHs at baseline only in people carrying the APOE-ε4 allele (multivariable-adjusted β-coefficients and 95% confidence interval [CI]: -0.018, -0.031– -0.004). Accelerated decline in perceptual speed over 15 years was linked to a faster increase in deep and periventricular WMHs during the first six years, and accelerated decline in executive function and global cognition was linked to a faster increase in deep WMHs during the first six years (P < 0.05). Study III: This six-year follow-up study showed that compared to the unfavorable global CVH profile, the intermediate-to-favorable profiles were associated with a slower accumulation of WMHs (multivariable-adjusted β-coefficients and 95% CI: -0.019, -0.035– -0.002 and -0.018, - 0.034– -0.001, respectively). Intermediate-to-favorable biological CVH profiles were associated with a slower WMH increase among people aged 60–72 years, but not in those aged 78 years and above. Furthermore, a higher metabolic genetic risk was linked to a faster accumulation of WMHs in people with intermediate-to-favorable global or behavioral CVH profiles, but not in those with favorable CVH profiles (P for both interactions = 0.001). Study IV: This 15-year follow-up study revealed that a higher composite CR score, which was estimated from early-life education, midlife work complexity, late-life leisure activities, and late-life social network, was associated with a reduced risk of transition from normal cognition to cognitive impairment, no dementia (CIND) (multivariable-adjusted hazards ratio and 95% CI: 0.78, 0.72–0.85) and death (0.85, 0.79–0.93) and from CIND to death (0.82, 0.73–0.91), but not from CIND to dementia neither from CIND to normal cognition (P > 0.05). The risk of transitions from normal cognition to CIND or death did not change after controlling for brain aging markers, while the risk of transition from CIND to death became not significant. Furthermore, a higher CR score was associated with a lower risk of transition from CIND to death among people aged 60–72 years (0.65, 0.54–0.77) while not among those aged 78 years and above (0.87, 0.75–1.01) (P for interaction = 0.010). Conclusions: First, the deterioration of brain structure accelerates with advancing age. Cerebral microvascular lesions are associated with accelerated brain atrophy. Second, WMHs are linked to an accelerated decline in multiple cognitive domains except memory. A faster accumulation of WMHs in deep brain regions is associated with an accelerated decline in perceptual speed and executive function. Third, having a favorable CVH profile is associated with a slower progression of structural brain aging attributable to metabolic genetic risk. Finally, having a greater CR capacity might play a crucial role in preserving cognitive health and reducing mortality rate in the prodromal phase of dementia, independent of brain aging markers. The association between higher CR capacity and lower likelihood of transition from CIND to death exists particularly among people in the early stage of older adulthood

    Alzheimer Disease Detection Techniques and Methods: A Review

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    Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

    Self-Organized Criticality as a Neurodynamical Correlate of Consciousness: A neurophysiological approach to measure states of consciousness based on EEG-complexity features

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    Background and Objectives This thesis was based on the hypothesis that the physics-derived theoretical framework of self-organized criticality can be applied to the neuronal dynamics of the human brain. From a consciousness science perspective, this is especially appealing as critical brain dynamics imply a vicinity a phase transition, which is associated with optimized information processing functions as well as the largest repertoire of configurations that a system explores throughout its temporal evolution. Hence, self-organised criticality could serve as a neurodynamical correlate for consciousness, which provides the possibility of deriving empirically testable neurophysiological indices suitable to characterise and quantify states of consciousness. The purpose of this work was to experimentally examine the feasibility of the self-organized criticality theory as a correlate for states of consciousness. Therefore, it was aimed at answering the following research questions based on the analysis of three 64 channel EEG datasets: (i) Can signatures of self-organized criticality be found on the level of the EEG in terms of scale-free distribution of neuronal avalanches and the presence of long-range temporal correlations (LRTC) in neuronal oscillations? (ii) Are criticality features suitable to differentiate state of consciousness in the spectrum of wakefulness? (iii) Can the neuronal dynamics be shifted towards the critical point of a phase transition associated with optimized information processing function by mind-body interventions? (iv) Can an explicit relationship to other nonlinear complexity features and power spectral density parameter be identified? (v) Do EEG-based criticality features reflect individual temperament traits? Material and Methods (1): Re-analysis: Thirty participants highly proficient in meditation (mean age 47 years, 11 females/19 males, meditation experience of at least 5 years practice or more than 1000 h of total meditation time) were measured with 64-channel EEG during one session consisting of a task-free baseline resting, a reading condition and three meditation conditions, namely thoughtless emptiness, presence monitoring and focused attention. (2): 64-channel EEG was recorded from 34 participants (mean age 36.0 ±13.4 years, 24 females/ 10 males) before, during and after a professional singing bowl massage. Further, psychometric data was assessed including absorption capacity defined as the individual’s capacity for engaging attentional resources in sensory and imaginative experiences measured by the Tellegen-Absorption Scale (TAS-D), subjective changes in in body sensation, emotional state, and mental state (CSP-14) as well as the phenomenology of consciousness (PCI-K). (3): Electrophysiological data (64 channels of EEG, EOG, ECG, skin conductance, and respiration) was recorded from 116 participants (mean age 40.0 ±13.4 years, 83 females/ 33 males) – in collaboration with the Institute of Psychology, Bundeswehr University Munich - during a task-free baseline resting state. The individual level of sensory processing sensitivity was assessed using the High Sensitive Person Scale (HSPS-G). The datasets were analysed applying analytical tools from self-organized criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), nonlinear complexity algorithms (multiscale entropy, Higuchi’s fractal dimension) and power spectral density. In study 1 and 2, task conditions were contrasted, and effect sizes were compared using a paired two-tailed t-test calculated across participants, and features. T-values were corrected for multiple testing using false discovery rate. To calculate correlations between the EEG features, Spearman’s rank correlation was applied after determining that the distribution was not appropriate for parametric testing by the Shapiro-Wilk test. In addition, in study 1, a discrimination analysis was carried out to determine the classification performance of the EEG features. Here, partial least squares regression and receiver operating characteristics analysis was applied. To determine whether the EEG features reflect individual temperament traits, the individual level of absorption capacity (study 2) and sensory processing sensitivity (study 3) was correlated with the EEG features using Spearman’s rank correlation. Results Signatures of self-organized criticality in the form of scale-free distribution of neuronal avalanches and long-range temporal correlations (LRTCs) in the amplitude of neural oscillations were observed in three distinct EEG-datasets. EEG criticality as well as complexity features were suitable to characterise distinct states of consciousness. In study 1, compared to the task-free resting condition, all three meditative states revealed significantly reduced long-range temporal correlation with moderate effect sizes (presence monitoring: d= -0.49, p<.001; thoughtless emptiness: d= -0.37, p<.001; and focused attention: d= -0.28, p=.003). The critical exponent was suitable to differentiate between focused attention and presence monitoring (d= -0.32, p=.02). Further, in study 2, the criticality features significantly changed during the course of the experiment, whereby values indicated a shift towards the critical regime during the sound condition. Both analyses of the first and second dataset revealed that the critical exponent was significantly negatively correlated with the sample entropy, the scaling exponent resulting from the DFA denoting the amount of long-range temporal correlations as well as Higuchi’s fractal dimension in each condition, respectively. In addition, the critical scaling exponent was found to be significantly negatively correlated with the trait absorption (Spearman's ρ= -0.39, p= .007), whereas an association between critical dynamics and the level of sensory processing sensitivity could not be verified (study 3). Conclusion The findings of this thesis suggest that neuronal dynamics are governed by the phenomena of self-organized criticality. EEG-based criticality features were shown to be sensitive to detect experimentally induced alterations in the state of consciousness. Further, an explicit relationship with nonlinear measures determining the degree of neuronal complexity was identified. Thus, self-organized criticality seems feasible as a neurodynamical correlate for consciousness with the potential to quantify and characterize states of consciousness. Its agreement with the current most influencing theories in the field of consciousness research is discussed
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