Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling

Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can help in understanding the dynamics of disease progression and facilitate early diagnosis and prognosis. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate than existing state-of-the-art EBM methods. The method first estimates for each subject an approximate ordering of events. Subsequently, the central ordering over all subjects is estimated by fitting a generalized Mallows model to these approximate subject-specific orderings. We also introduce the concept of relative distance between events which helps in creating a disease progression timeline. Subsequently, we propose a method to stage subjects by placing them on the estimated disease progression timeline. We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods. We also performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease. The event orderings obtained on ADNI data seem plausible and are in agreement with the current understanding of progression of AD. The proposed patient staging algorithm performed consistently better than that of state-of-the-art EBM methods. Event orderings obtained in simulation experiments were more accurate than those of other EBM methods and the estimated disease progression timeline was observed to correlate with the timeline of actual disease progression. The results of these experiments are encouraging and suggest that discriminative EBM is a promising approach to disease progression modeling

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

Several CSF and blood biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), astrogliosis (glial fibrillary acidic protein (GFAP)), and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage FTD, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic FTD using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. 275 presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialised DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on prior diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF NfL, blood pNfH, blood GFAP, and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve (AUC) of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The AUC to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic FTD revealed that NPTX2 and NfL are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions

Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease

Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. // Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. // Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM = 0.866; R2DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. // Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice

Prognostic value of single-subject grey matter networks in early multiple sclerosis

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict five-year EDSS progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from magnetic resonance imaging (MRI), outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for five years (mean follow-up = 5.0 ± 0.6 years). Expanded Disability Status Scale (EDSS) was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again one year after baseline. Grey matter (GM) atrophy over one year and white matter (WM) lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on GM atrophy measures derived from a statistical parameter mapping (SPM)-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for GM atrophy, WM lesion load and the network measures, and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over five years through lower values for network degree [H(2)=30.0, p<0.001] and global efficiency [H(2)=31.3, p<0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups (H(2)= 1.5, p=0.474). Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of GM atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over GM atrophy and WM lesion load in predicting EDSS worsening (all p-values < 0.05). Our findings provide evidence that GM network reorganization over one year discloses relevant information about subsequent clinical worsening in RRMS. Early GM restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors

Alzheimer’s disease heterogeneity assessment with MRI biomarkers and unsupervised statistical learning

Alzheimer’s disease (AD) is the most common cause of dementia. It is characterized by loss of memory and other cognitive functions. Although it is a heterogeneous condition, it has been studied as one disease for many decades. Neuropathological data and a large body of in vivo neuroimaging literature challenge the hypothesis that AD is a single entity, supporting the hypothesis of AD as a heterogeneous disease. In this thesis, we set out to understand some aspects of the heterogeneity in AD and aging with the help of atrophy and WM integrity markers from magnetic resonance imaging (MRI). The main aim of the thesis was to investigate the potential use of statistical and machine learning models for the assessment of heterogeneous conditions. In Study I, we utilized whole brain atrophy markers and cross-sectional cluster analysis to characterize the neurodegeneration variability in a large AD dementia cohort (299 amnestic AD patients). The clusters of patients that we discovered presented with distinct atrophy patterns. Some of them exist due to disease severity, but we identified topologically variable atrophy patterns too. Patients of the different clusters had distinct cognitive symptoms and clinical progression. Then, we designed a pipeline that will help us to assess heterogeneous populations when longitudinal neuroimaging and clinical data are available (Study II).We tested this pipeline in atrophy data from a small dataset of AD patients to assess its usefulness in MRI data and heterogeneous conditions. The model fitted the data well and we concluded that it can be used in larger scale analyses. Moreover, larger numbers of participants with long follow-up period should increase its freedom in searching for heterogeneity in longitudinal neuroimaging trajectories. After this methodological study, we used a very large dataset that consisted of neuroimaging, cerebrospinal fluid (CSF), and clinical data. We split our data in discovery and prediction datasets. The discovery dataset included positive clinically diagnosed AD dementia patients and negative cognitively unimpaired individuals (CU). Based on this dataset (Study III), we aimed to understand whether the observed heterogeneity in AD is caused by sampling patient’s data at different disease stages, or if it resembles distinct neurodegeneration subtypes. We modelled longitudinal brain atrophy data anchored to the clinical dementia onset. Our findings show that all the previously reported atrophy subtypes do exist but some of them reflect disease stages rather than subtypes. Most importantly, our modeling managed to summarize the observed heterogeneity in neurodegeneration with two unique pathways (mediotemporal and cortical). These two pathways have distinct cognitive signatures and were evaluated in a large independent AD dataset. Heterogeneity within the pathways exist and is likely caused by a complex interaction between protective/risk factors and concomitant non-AD pathologies. Some findings indicate that WM changes may precede grey matter atrophy in AD. In Study IV we investigated whether more than one WM profile exists in the aging population. We wanted to understand their association with AD pathophysiological changes and relate them to the risk of developing dementia. We discovered four distinct WM integrity patterns with different spatial WM integrity distribution in aging. Those patterns were related to different longitudinal cognitive profiles and specific white matter tracts informed about cluster assignments. In conclusion, heterogeneity can be observed not only in AD, but also in the population including healthy individuals. In this thesis, we identified distinct pathways of brain atrophy and WM integrity. Understanding the heterogeneous patterns of the different pathophysiological markers during ageing and the course of AD, will ultimately lead to the development of disease modifying (personalized) treatments

Disease progression and genetic risk factors in the primary tauopathies

The primary tauopathies are a group of progressive neurodegenerative diseases within the frontotemporal lobar degeneration spectrum (FTLD) characterised by the accumulation of misfolded, hyperphosphorylated microtubule-associated tau protein (MAPT) within neurons and glial cells. They can be classified according to the underlying ratio of three-repeat (3R) to four-repeat (4R) tau and include Pick’s disease (PiD), which is the only 3R tauopathy, and the 4R tauopathies the most common of which are progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). There are no disease modifying therapies currently available, with research complicated by the wide variability in clinical presentations for each underlying pathology, with presentations often overlapping, as well as the frequent occurrence of atypical presentations that may mimic other non-FTLD pathologies. Although progress has been made in understanding the genetic contribution to disease risk in the more common 4R tauopathies (PSP and CBD), very little is known about the genetics of the 3R tauopathy PiD. There are two broad aims to this thesis; firstly, to use data-driven generative models of disease progression to try and more accurately stage and subtype patients presenting with PSP and corticobasal syndrome (CBS, the most common presentation of CBD), and secondly to identify genetic drivers of disease risk and progression in PiD. Given the rarity of these disorders, as part of this PhD I had to assemble two large cohorts through international collaboration, the 4R tau imaging cohort and the Pick’s disease International Consortium (PIC), to build large enough sample sizes to enable the required analyses. In Chapter 3 I use a probabilistic event-based modelling (EBM) approach applied to structural MRI data to determine the sequence of brain atrophy changes in clinically diagnosed PSP - Richardson syndrome (PSP-RS). The sequence of atrophy predicted by the model broadly mirrors the sequential spread of tau pathology in PSP post-mortem staging studies, and has potential utility to stratify PSP patients on entry into clinical trials based on disease stage, as well as track disease progression. To better characterise the spatiotemporal heterogeneity of the 4R tauopathies, I go on to use Subtype and Stage Inference (SuStaIn), an unsupervised machine algorithm, to identify population subgroups with distinct patterns of atrophy in PSP (Chapter 4) and CBS (Chapter 5). The SuStaIn model provides data-driven evidence for the existence of two spatiotemporal subtypes of atrophy in clinically diagnosed PSP, giving insights into the relationship between pathology and clinical syndrome. In CBS I identify two distinct imaging subtypes that are differentially associated with underlying pathology, and potentially a third subtype that if confirmed in a larger dataset may allow the differentiation of CBD from both PSP and AD pathology using a baseline MRI scan. In Chapter 6 I investigate the association between the MAPT H1/H2 haplotype and PiD, showing for the first time that the H2 haplotype, known to be strongly protective against developing PSP or CBD, is associated with an increased risk of PiD. This is an important finding and has implications for the future development of MAPT isoform-specific therapeutic strategies for the primary tauopathies. In Chapter 7 I perform the first genome wide association study (GWAS) in PiD, identifying five genomic loci that are nominally associated with risk of disease. The top two loci implicate perturbed GABAergic signalling (KCTD8) and dysregulation of the ubiquitin proteosome system (TRIM22) in the pathogenesis of PiD. In the final chapter (Chapter 8) I investigate the genetic determinants of survival in PiD, by carrying out a Cox proportional hazards genome wide survival study (GWSS). I identify a genome-wide significant association with survival on chromosome 3, within the NLGN1 gene. which encodes a synaptic scaffolding protein located at the neuronal pre-synaptic membrane. Loss of synaptic integrity with resulting dysregulation of synaptic transmission leading to increased pathological tau accumulation is a plausible mechanism though which NLGN1 dysfunction could impact on survival in PiD

Cognition and Motor Function: A Novel Outcome Measure for Studies on Pre-Dementia Syndromes

Advances in dementia research have shifted attention towards earlier stages in the natural history, such as Mild Cognitive Impairment. The current gold standard outcome measure, the Alzheimer’s Disease Assessment Scale-Cognitive Subscale, is not optimally responsive to changes in pre-dementia populations. Modifications to scoring methodology and content have improved the measurement performance of the ADAS-Cog. However, no published modifications have addressed a second key shift in the field towards understanding motor function as an important component of dementia and pre-dementia syndromes. This thesis used a Pooled Index approach to combine an ADAS-Cog-Proxy measure with assessments of gait velocity and dual-task cost. The responsiveness of the PI to baseline discrimination between older adults with normal cognition, Subjective Cognitive Impairment, and MCI was similar to the ADAS-Cog-Proxy. The PI demonstrated greater responsiveness than the ADAS-Cog-Proxy to change over 6mo. and 48mo., but not 36mo. of follow-up. Overall, motor function assessments improve ADAS-Cog responsiveness

Identification of Plasma Metabolites Associated with Breast and Ovarian Cancer and Breast Cancer Prognosis

As two leading female cancers, breast cancer, especially metastatic breast cancer, and ovarian cancer, have brought an increasing health and economic burden globally. Biomarkers could improve patient outcomes and quality of life because they play vital roles in cancer screening, diagnosis, prognosis, and prediction. Metabolites are promising cancer biomarkers, as they represent the ultimate phenotypic alteration of the organism and are closely related to cancer. Plasma metabolites can be accessed with minimally invasive procedures. Using plasma metabolites as biomarkers for cancer and other diseases has been widely explored because of the possibility of repeated sampling and periodic monitoring of blood samples. However, metabolic studies are still in their infancy, and only a few studies with large sample sizes are available so far. In this thesis project, we explored the potential of metabolites as putative diagnostic and prognostic markers in breast and ovarian cancer. Plasma metabolite profiling and subsequent validation in primary breast cancer patients and healthy controls identified 18 metabolites that were significantly differentially represented (FDR < 0.05). Multivariate logistic regression analysis selected a panel of seven metabolites to discriminate primary breast cancer patients from healthy controls with an AUC of 0.80. If this panel of metabolites identified here could be verified in large prospective study cohorts, this panel, including Glu, Orn, Thr, Trp, Met-SO, C2, and C3, might add value to multi-molecular diagnostic marker sets for breast cancer early detection. The association of plasma metabolites with metastatic breast cancer was investigated as well. Metastatic breast cancer patients with high numbers of circulating tumor cells (termed CTC-positive) and those with low numbers or without CTCs (termed CTC-negative) were analyzed and compared to healthy controls as well as primary breast cancer patients. Lists of 19 and 12 metabolites were identified to significantly distinguish CTC-positive and CTC-negative samples from healthy controls, respectively. A panel comprising His, C4:0, C18:1, lysoPC a C18:2, PC aa C40:6, and PC ae C42:3 for CTC-positive patients with AUC = 0.92, and a combination of Asn, Glu, His, Thr, Trp, C16:0, C18:0, C18:1, C18:2, lysoPC a C18:2, and PC aa C40:6 for CTC-negative patients with AUC = 0.89 were selected to distinguish from healthy controls. Significantly different metabolites between CTC-positive/CTC-negative and primary breast cancer patients exhibited significant overlaps with those between CTC-positive/CTC-negative patients and healthy controls. We also investigated the prognostic value of metabolites in metastatic breast cancer patients. After successive analysis of the discovery and validation cohorts, four metabolites were found to be significantly negatively correlated with progression-free survival, while 12 metabolites were negatively correlated with overall survival. Amongst these metabolites associated with survival, LASSO Cox regression analysis selected a combination of PC ae C36:1 and PC ae C38:3 to predict progression-free survival, and a combination of lysoPC a C20:3, lysoPC a C20:4, PC aa C38:5, PC ae C38:3, and SM (OH) C22:2 to predict overall survival. Even though the proposed metabolic signatures showed a lower prognostic power than the CTC status, an FDA-approved prognostic marker, the combination of the Cox selected metabolites with the CTC status displayed a lower integrated prediction error than CTC status alone. Therefore, the identified metabolic markers might add prognostic value in combination with other biomarkers such as CTC status determination. The majority of the here identified metabolites have previously shown functional roles in cancer and metastasis development, thus laying a supposed mechanistic basis for their differential levels observed in plasma. Lastly, comparative profiling of plasma metabolites in ovarian cancer patients and healthy controls were applied to identify metabolites associated with ovarian cancer. Remarkably, 71 significantly differentially expressed metabolites were identified (FDR < 0.05). Most of them were down-regulated in ovarian cancer patients. A combination of seven metabolites, including His, Trp, C18:1, lysoPC a C18:2, PC aa C32:2, PC aa C34:4, PC ae C34:3, were identified to differentiate ovarian cancer cases from healthy controls with an AUC of 0.95. Furthermore, this panel could distinguish ovarian cancer from primary breast cancer patients with an AUC of 0.93. In conclusion, we identified specific signatures of plasma metabolites associated with primary breast cancer, metastatic breast cancer, and ovarian cancer. Further, we identified sets of metabolites correlated with the prognosis of metastatic breast cancer patients. If these identified metabolic marker signatures can be verified in large, multi-centric, prospective studies, they might add value to the development of blood-based diagnostic tests

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

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