An Integrated Neuroimaging Approach for the Prediction and Analysis of Alzheimer’s Disease and its Prodromal Stages

This dissertation proposes to combine magnetic resonance imaging (MRI), positron emission tomography (PET) and a neuropsychological test, Mini-Mental State Examination (MMSE), as input to a multidimensional space for the classification of Alzheimer’s disease (AD) and it’s prodromal stages including amnestic MCI (aMCI) and non-amnestic MCI (naMCI). An assessment is provided on the effect of different MRI normalization techniques on the prediction of AD. Statistically significant variables selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis to discriminate between AD, early and late MCI (EMCI and LMCI) from cognitively normal (CN)s. In addition, this dissertation proposes a new effective mean indicator (EMI) method for distinguishing stages of AD from CN. EMI utilizes the mean of specific top-ranked measures, determined by incremental error analysis, to achieve optimal separation of AD and CN. For AD vs. CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores were found to improve classification accuracy by 8.2% and 12% for aMCI vs. CN and naMCI vs. CN, respectively. Brain atrophy was almost evenly seen on both sides of the brain for AD subjects, which was different from right side dominance for aMCI and left side dominance for naMCI. Findings suggest that subcortical volume need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or the mean thickness. Furthermore, MRI and PET had comparable predictive power in separating AD from CN. For the EMCI prediction, cortical thickness was found to be the best predictor, even better than using all features together. Validation with an external test set demonstrated that best of feature-selected models for the LMCI group was able to classify 83% of the LMCI subjects. The EMI-based method achieved an accuracy of 92.7% using only MRI features. The performance of the EMI-based method along with its simplicity suggests great potential for its use in clinical trials

EANM-EAN recommendations for the use of brain 18 F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND: Recommendations for using FDG-PET to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured. METHODS: We defined 21 questions on diagnostic issues and on semi-automated analysis to assist visual reading. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiving operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion. RESULTS: Of the 1435 papers, 58 provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudodementia; differentiating between AD and DLB, FTLD, or vascular dementia, between DLB and FTLD, and between Parkinson's disease (PD) and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in PD; using semi-automated assessment to assist visual reading. Panelists did not support FDG-PET use for preclinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis (ALS) and Huntington disease (HD) diagnoses, and ALS or HD-related cognitive decline. CONCLUSIONS: Despite limited formal evidence, panelists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semiautomated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations. This article is protected by copyright. All rights reserved

The Spatial Evolution of Tau Pathology in Alzheimer’s Disease: Influence of Functional Connectivity and Education

Alzheimer’s disease is neuropathologically characterized by extracellular accumulation of amyloid beta plaques and intracellular aggregation of misfolded tau proteins, which eventually lead to neurodegeneration and cognitive impairment. With the recent advances in neuroimaging, these two proteinopathies can now be studied in vivo using positron emission tomography (PET). Combining this imaging technique with functional magnetic resonance imaging has consistently revealed a spatial overlap between amyloid beta accumulates and functional connectivity networks (Buckner et al., 2009; Grothe et al., 2016), indicating functional connectivity as mechanistic pathway in the distribution of neuropathologies. While the infiltration of these neuronal networks by amyloid beta deposits seems uniform across individuals with Alzheimer’s disease, there nevertheless exists inter-individual differences in the clinical expression of the disease despite similar pathological burden (Stern, 2012). This observation has fuelled the concept of existing resilience mechanisms, which are supported by lifetime and –style factors and, which magnitude varies between individuals, contributing to the clinical heterogeneity seen in Alzheimer’s disease. Even though the spreading and resilience mechanisms in the phase of amyloid beta accumulation are now better understood, no information on tau pathology in vivo were available in this regard until recently. Given the recent introduction of tau PET compounds, this thesis therefore aimed to address two questions: 1) whether functional connectivity contributes to the distribution of tau pathology across brain networks, and 2) whether the consequence of tau pathology on cognitive and neuronal function is mitigated by a resilience proxy, namely education. Using [18F]-AV-1451 PET imaging to quantify tau pathology in a group of Alzheimer’s disease patients, we observed that tau pathology arises synchronously in independent components of the brain, which in turn moderately overlap with known functional connectivity networks. This suggest that functional connectivity may act as contributing factor in the stereotypical distribution of tau pathology. Moreover, the results of this thesis demonstrate that the consequence of regional tau pathology on cognition differs depending on the level of education. Despite equal clinical presentation, higher educated patients can tolerate more tau pathology, already in regions related to advanced disease stage, than lower educated patients. Furthermore, tau pathology is less paralleled by neuronal dysfunction at higher levels of education. Thus, higher educated individuals show a relative preservation of neuronal function despite the aggregation of misfolded tau proteins. This maintenance of neuronal function may in turn explain the relative preservation of cognitive function albeit progressive tau pathology aggregation. Taken together, the results of this thesis provide novel insights into the spreading mechanisms and the role of resilience factors towards tau pathology aggregation, which may not only be relevant for Alzheimer’s disease, but other neurodegenerative diseases, in particular,tauopathies. Better understanding of the spreading mechanisms in these diseases will permit a more precise prediction of disease progression and will thus be valuable for disease monitoring. Concomitantly, the development of sensitive biomarkers for disease monitoring is crucial for the evaluation of anti-tau-based therapies. Regarding the development of pharmacological strategies, the current results also indicate that proxy measures of resilience, such as education, need to be considered when allocating patients to treatment groups. Biased allocation may otherwise lead to a misinterpretation of observed effects that are not due to the drug but the group characteristics. Aside from this, sensitive tools for the early identification of at-risk individuals with high resilience need to be established to allow for a timely intervention. Current hypothesis is that an early intervention has the highest chance of success in modifying the disease course. However, as demonstrated by this work, individuals with high resilience remain undiagnosed until late in the disease course. Further research into resilience mechanisms may thus support the development of sensitive diagnostic tools and additionally offer potential targets that can be harnessed for novel treatment strategies. Hopefully, one day supporting the development of effective disease-modifying treatments

A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI)

Alzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation

Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach

Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease

A Multi-tracer PET approach to study early-onset familial and sporadic Alzheimer's disease

Cumulated scientific evidence suggests that the pathology causing Alzheimer's disease (AD) occurs many years or even decades before memory impairment and other clinical symptoms arise. Tangible and detailed knowledge about different pathological processes, their interactions, and time course is therefore of the essence both for the development of potentially successful treatments and a reliable early diagnosis of this relentless disorder. The past decade has thus seen an explosion in research on biomarkers that could provide in vivo evidence for these pathological processes, involving β-amyloid (Aβ) production and aggregation into plaques, neurofibrillary tangle formation, neuroinflammation, and eventually neurodegeneration. The rare form of dominantly-inherited early-onset familial AD (eoFAD), with almost complete mutation penetrance and defined age of disease onset, has been proposed as a model to study the very early disease mechanisms that are also supposed to underlie the common sporadic form (sAD). However, more than 200 mutations in three different genes (PSEN1 and 2, APP) have been identified as causing eoFAD, some of which have been shown to differ substantially from others. This work employed multi-tracer positron emission tomography (PET), using the tracers 2-[18F]‐fluoro-2‐deoxy‐D‐glucose (FDG), N-methyl-[11C] 2-(4'- methylaminophenyl)-6-hydroxy-benzothiazole (PIB), and [11C]-L-deuterium-deprenyl (DED) to explore the characteristics, time course and interrelationship of cerebral glucose metabolism, fibrillar Aβ burden, and astrocyte activation (astrocytosis) at different presymptomatic and symptomatic disease stages of eoFAD and sAD, in relationship to cognition, other AD biomarkers, and/or post-mortem pathology. Thalamic hypometabolism in PSEN1 eoFAD mutation carriers was demonstrated in this thesis nearly 20 years before they were expected to develop clinical symptoms. The pattern of hypometabolism studied in several mutation carriers spread subsequently to regions that are also typically affected in sAD, correlating well with cognitive decline at symptomatic disease stages. Regional hypometabolism was furthermore found to correlate with typical AD pathology, namely neuritic Aβ plaques at post-mortem examination, suggesting that FDG PET is an excellent marker of disease progression from early presymptomatic stages to terminal disease. One particular eoFAD mutation, the Arctic APP mutation, has been reported to modify amyloid processing in a way that obviates the formation of fibrillar Aβ, the form of Aβ most prone to aggregate into neuritic plaques. In contrast to carriers of other eoFAD mutations and sAD patients, we found that carriers of the Arctic APP mutation showed no cortical PIB PET retention as a measure of fibrillar Aβ load, while Aβ and tau in cerebral spinal fluid and glucose metabolism, and in advanced disease also medial temporal lobe atrophy as measured by magnetic resonance imaging and cognition were clearly pathological and typical of AD. The findings imply that clinical AD can be caused by forms of Aβ, supposedly oligomeric or protofibrillar, which cannot be detected by PIB PET. Very little is still known from in vivo studies about when and where in the brain neuroinflammation occurs in AD. Here, it could be shown that DED binding as a measure of astrocytosis was elevated in prodromal AD patients, whereas binding levels in AD were comparable to those in controls. PIB PET retention was increased and glucose metabolism decreased in both groups and there was no regional relationship between the three tracers, indicating that astrocytosis is an early phenomenon in AD that follows a different spatial and temporal pattern than Aβ plaque deposition and impaired synaptic activity as measured by glucose metabolism. Multi-tracer PET is in this work proven to provide novel insights in eoFAD and sAD pathogenesis with processes such as astrocytosis and the potential role of different Aβ species. This knowledge is of significance for the understanding of disease mechanisms as well as the comparability of the purely genetic and the sporadic form of AD

Alzheimer PEThology

Scheltens, P. [Promotor]Lammertsma, A.A. [Promotor]Berckel, B.N.M. van [Copromotor]Flier, W.M. van der [Copromotor

Functional connectivity differences in Alzheimer's disease and amnestic mild cognitive impairment associated with AT(N) classification and anosognosia

Alzheimer's continuum biological profiles (A+T-N-, A+T+N-, A+T-N+, and A+T+N+) were established in the 2018 National Institute on Aging and Alzheimer's Association research framework for Alzheimer's disease (AD). We aim to assess the relation between AT(N) biomarker profiles and brain functional connectivity (FC) and assess the neural correlates of anosognosia. We assessed local functional coupling and between-network connectivity through between-group intrinsic local correlation and independent component analyses. The neural correlates of anosognosia were assessed via voxel-wise linear regression analysis in prodromal AD. Statistical significance for the FC analysis was set at p ≤ 0.05 false discovery rate (FDR)-corrected for cluster size. One hundred and twenty-one and 73 participants were included in the FC and the anosognosia analysis, respectively. The FC in the default mode network is greater in prodromal AD than AD with dementia (i.e., local correlation: T = 8.26, p-FDR &lt; 0.001, k = 1179; independent component analysis: cerebellar network, T = 4.01, p-FDR = 0.0012, k = 493). The default mode network is persistently affected in the early stages of Alzheimer's biological continuum. The anterior cingulate cortex (T = 2.52, p-FDR = 0.043, k = 704) is associated with anosognosia in prodromal AD.</p