Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset

Multivariate decoding of brain images using ordinal regression.

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection

Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats

[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures. Altogether reflects the high complexity of alcohol-related disorders suggesting the involvement of multiple brain systems. With the emergence of non-invasive diagnosis techniques such as neuroimaging or EEG, many neurobiological factors have been evidenced to be fundamental in the acquisition and maintenance of addictive behaviors, relapsing risk, and validity of available treatment alternatives. Alterations in brain structure and function reflected in non-invasive imaging studies have been repeatedly investigated. However, the extent to which imaging measures may precisely characterize and differentiate pathological stages of the disease often accompanied by other pathologies is not clear. The use of animal models has elucidated the role of neurobiological mechanisms paralleling alcohol misuses. Thus, combining animal research with non-invasive neuroimaging studies is a key tool in the advance of the disorder understanding. As the volume of data from very diverse nature available in clinical and research settings increases, an integration of data sets and methodologies is required to explore multidimensional aspects of psychiatric disorders. Complementing conventional mass-variate statistics, interests in predictive power of statistical machine learning to neuroimaging data is currently growing among scientific community. This doctoral thesis has covered most of the aspects mentioned above. Starting from a well-established animal model in alcohol research, Marchigian Sardinian rats, we have performed multimodal neuroimaging studies at several stages of alcohol-experimental design including the etiological mechanisms modulating high alcohol consumption (in comparison to Wistar control rats), alcohol consumption, and treatment with the opioid antagonist Naltrexone, a well-established drug in clinics but with heterogeneous response. Multimodal magnetic resonance imaging acquisition included Diffusion Tensor Imaging, structural imaging, and the calculation of magnetic-derived relaxometry maps. We have designed an analytical framework based on widely used algorithms in neuroimaging field, Random Forest and Support Vector Machine, combined in a wrapping fashion. Designed approach was applied on the same dataset with two different aims: exploring the validity of the approach to discriminate experimental stages running at subject-level and establishing predictive models at voxel-level to identify key anatomical regions modified during the experiment course. As expected, combination of multiple magnetic resonance imaging modalities resulted in an enhanced predictive power (between 3 and 16%) with heterogeneous modality contribution. Surprisingly, we have identified some inborn alterations correlating high alcohol preference and thalamic neuroadaptations related to Naltrexone efficacy. As well, reproducible contribution of DTI and relaxometry -related biomarkers has been repeatedly identified guiding further studies in alcohol research. In summary, along this research we demonstrate the feasibility of incorporating multimodal neuroimaging, machine learning algorithms, and animal research in the advance of the understanding alcohol-related disorders.[ES] El abuso de alcohol es una de las mayores preocupaciones de las autoridades sanitarias en la Unión Europea. El consumo de alcohol en exceso afecta en mayor o menor medida la totalidad del organismo siendo el páncreas e hígado los más severamente afectados. Además de estos, el sistema nervioso central sufre deterioros relacionados con el alcohol y con frecuencia se presenta en paralelo con otras patologías psiquiátricas como la depresión u otras adicciones como la ludopatía. La presencia de estas comorbidades demuestra la complejidad de la patología en la que multitud de sistemas neuronales interaccionan entre sí. El uso imágenes de resonancia magnética (RM) han ayudado en el estudio de enfermedades psiquiátricas facilitando el descubrimiento de mecanismos neurológicos fundamentales en el desarrollo y mantenimiento de la adicción al alcohol, recaídas y el efecto de los tratamientos disponibles. A pesar de los avances, todavía se necesita investigar más para identificar las bases biológicas que contribuyen a la enfermedad. En este sentido, los modelos animales sirven, por lo tanto, a discriminar aquellos factores únicamente relacionados con el alcohol controlando otros factores que facilitan el desarrollo del alcoholismo. Estudios de resonancia magnética en animales de laboratorio y su posterior evaluación en humanos juegan un papel fundamental en el entendimiento de las patologías psiquatricas como la addicción al alcohol. La imagen por resonancia magnética se ha integrado en entornos clínicos como prueba diagnósticas no invasivas. A medida que el volumen de datos se va incrementando, se necesitan herramientas y metodologías capaces de fusionar información de muy distinta naturaleza y así establecer criterios diagnósticos cada vez más exactos. El poder predictivo de herramientas derivadas de la inteligencia artificial como el aprendizaje automático sirven de complemento a tradicionales métodos estadísticos. En este trabajo se han abordado la mayoría de estos aspectos. Se han obtenido datos multimodales de resonancia magnética de un modelo validado en la investigación de patologías derivadas del consumo del alcohol, las ratas Marchigian-Sardinian desarrolladas en la Universidad de Camerino (Italia) y con consumos de alcohol comparables a los humanos. Para cada animal se han adquirido datos antes y después del consumo de alcohol y bajo dos condiciones de abstinencia (con y sin tratamiento de Naltrexona, una medicaciones anti-recaídas usada como farmacoterapia en el alcoholismo). Los datos de resonancia magnética multimodal consistentes en imágenes de difusión, de relaxometría y estructurales se han fusionado en un esquema analítico multivariable incorporando dos herramientas generalmente usadas en datos derivados de neuroimagen, Random Forest y Support Vector Machine. Nuestro esquema fue aplicado con dos objetivos diferenciados. Por un lado, determinar en qué fase experimental se encuentra el sujeto a partir de biomarcadores y por el otro, identificar sistemas cerebrales susceptibles de alterarse debido a una importante ingesta de alcohol y su evolución durante la abstinencia. Nuestros resultados demostraron que cuando biomarcadores derivados de múltiples modalidades de neuroimagen se fusionan en un único análisis producen diagnósticos más exactos que los derivados de una única modalidad (hasta un 16% de mejora). Biomarcadores derivados de imágenes de difusión y relaxometría discriminan estados experimentales. También se han identificado algunos aspectos innatos que están relacionados con posteriores comportamientos con el consumo de alcohol o la relación entre la respuesta al tratamiento y los datos de resonancia magnética. Resumiendo, a lo largo de esta tesis, se demuestra que el uso de datos de resonancia magnética multimodales en modelos animales combinados en esquemas analíticos multivariados es una herramienta válida en el entendimiento de patologías[CAT] L'abús de alcohol es una de les majors preocupacions per part de les autoritats sanitàries de la Unió Europea. Malgrat la dificultat de establir xifres exactes, se estima que uns 23 milions de europeus actualment sofreixen de malalties derivades del alcoholisme amb un cost que supera els 150.000 milions de euros per a la societat. Un consum de alcohol en excés afecta en major o menor mesura el cos humà sent el pàncreas i el fetge el més afectats. A més, el cervell sofreix de deterioraments produïts per l'alcohol i amb freqüència coexisteixen amb altres patologies com depressió o altres addiccions com la ludopatia. Tot aquest demostra la complexitat de la malaltia en la que múltiple sistemes neuronals interactuen entre si. Tècniques no invasives com el encefalograma (EEG) o imatges de ressonància magnètica (RM) han ajudat en l'estudi de malalties psiquiàtriques facilitant el descobriment de mecanismes neurològics fonamentals en el desenvolupament i manteniment de la addició, recaiguda i la efectivitat dels tractaments disponibles. Tot i els avanços, encara es necessiten més investigacions per identificar les bases biològiques que contribueixen a la malaltia. En aquesta direcció, el models animals serveixen per a identificar únicament dependents del abús del alcohol. Estudis de ressonància magnètica en animals de laboratori i posterior avaluació en humans jugarien un paper fonamental en l' enteniment de l'ús del alcohol. L'ús de probes diagnostiques no invasives en entorns clínics has sigut integrades. A mesura que el volum de dades es incrementa, eines i metodologies per a la fusió d' informació de molt distinta natura i per tant, establir criteris diagnòstics cada vegada més exactes. La predictibilitat de eines desenvolupades en el camp de la intel·ligència artificial com la aprenentatge automàtic serveixen de complement a mètodes estadístics tradicionals. En aquesta investigació se han abordat tots aquestes aspectes. Dades multimodals de ressonància magnètica se han obtingut de un model animal validat en l'estudi de patologies relacionades amb el consum d'alcohol, les rates Marchigian-Sardinian desenvolupades en la Universitat de Camerino (Italià) i amb consums d'alcohol comparables als humans. Per a cada animal es van adquirir dades previs i després al consum de alcohol i dos condicions diferents de abstinència (amb i sense tractament anti-recaiguda). Dades de ressonància magnètica multimodal constituides per imatges de difusió, de relaxometria magnètica i estructurals van ser fusionades en esquemes analítics multivariats incorporant dues metodologies validades en el camp de neuroimatge, Random Forest i Support Vector Machine. Nostre esquema ha sigut aplicat amb dos objectius diferenciats. El primer objectiu es determinar en quina fase experimental es troba el subjecte a partir de biomarcadors obtinguts per neuroimatge. Per l'altra banda, el segon objectiu es identificar el sistemes cerebrals susceptibles de ser alterats durant una important ingesta de alcohol i la seua evolució durant la fase del tractament. El nostres resultats demostraren que l'ús de biomarcadors derivats de varies modalitats de neuroimatge fusionades en un anàlisis multivariat produeixen diagnòstics més exactes que els derivats de una única modalitat (fins un 16% de millora). Biomarcadors derivats de imatges de difusió i relaxometria van contribuir de distints estats experimentals. També s'han identificat aspectes innats que estan relacionades amb posterior preferències d'alcohol o la relació entre la resposta al tractament anti-recaiguda i les dades de ressonància magnètica. En resum, al llarg de aquest treball, es demostra que l'ús de dades de ressonància magnètica multimodal en models animals combinats en esquemes analítics multivariats són una eina molt valida en l'enteniment i avanç de patologies psiquiàtriques com l'alcoholisme.Cosa Liñán, A. (2017). Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90523TESI

Development of Gaussian Learning Algorithms for Early Detection of Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer\u27s Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot be reversed or stopped. This research aims to develop Gaussian learning algorithms, natural language processing (NLP) techniques, and mathematical models to effectively delineate the MCI participants from the cognitively normal (CN) group, and identify the most significant brain regions and patterns of changes associated with the progression of AD. The focus will be placed on the earliest manifestations of the disease (early MCI or EMCI) to plan for effective curative/therapeutic interventions and protocols. Multiple modalities of biomarkers have been found to be significantly sensitive in assessing the progression of AD. In this work, several novel multimodal classification frameworks based on proposed Gaussian Learning algorithms are created and applied to neuroimaging data. Classification based on the combination of structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers is seen as the most reliable approach for high-accuracy classification. Additionally, changes in linguistic complexity may provide complementary information for the diagnosis and prognosis of AD. For this research endeavor, an NLP-oriented neuropsychological assessment is developed to automatically analyze the distinguishing characteristics of text data in MCI group versus those in CN group. Early findings suggest significant linguistic differences between CN and MCI subjects in terms of word usage, vocabulary, recall, fragmented sentences. In summary, the results obtained indicate a high potential of the neuroimaging-based classification and NLP-oriented assessment to be utilized as a practically computer aided diagnosis system for classification and prediction of AD and its prodromal stages. Future work will ultimately focus on early signs of AD that could help in the planning of curative and therapeutic intervention to slow the progression of the disease

Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease

Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression. The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy. This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant. With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.

AbstractAutomated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy)

Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging

Alzheimer’s disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitive impairment (MCI) is considered to be a prodromal stage of AD. The ability to identify subjects with MCI will be increasingly important as disease modifying therapies for AD are developed. We propose a semi-supervised learning method based on robust optimization for the identification of MCI from [18F]Fluorodeoxyglucose PET scans. We extracted three groups of spatial features from the cortical and subcortical regions of each FDG-PET image volume. We measured the statistical uncertainty related to these spatial features via transformation using an incomplete random forest and formulated the MCI identification problem under a robust optimization framework. We compared our approach to other state-of-the-art methods in different learning schemas. Our method outperformed the other techniques in the ability to separate MCI from normal controls

Brain‐wide associations between white matter and age highlight the role of fornix microstructure in brain ageing

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6–82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.publishedVersio