Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM- MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22

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

Medial Temporal Lobe Does Not Tell The Whole Story: Episodic Memory In ‘atypical’ Variants Of Alzheimer’s Disease

Alzheimer’s disease is the most common form of dementia, which is globally epidemic and well-known by the general public. Episodic memory, a conscious recollection of a particular event in spatial and temporal context, is the most prominent deficit in the early stage of clinical amnestic AD, and reflected by the shrinkage of structures in medial temporal lobe (MTL), including the hippocampus. According to Braak staging, tangles begin in the transentorhinal cortex of the MTL, which then spreads to hippocampal subfields, and later to neocortical areas. Cases that are less recognized by the general public are patients with the atypical variants of AD. Interestingly, many of the atypical cases of AD appear to share the same histopathological features with clinical amnestic AD. According to the diagnostic criteria for these atypical variants of AD, episodic memory should be relatively preserved. However, inconsistent reports on the episodic memory performance and the hippocampal involvement in these atypical cases pose challenges for accurately diagnosing these patients. The two kinds of atypical variants of AD that I focused here are logopenic variant of Primary Progressive Aphasia (lvPPA) and posterior cortical atrophy (PCA). The overarching theme of my thesis is to examine 1) whether the atypical cases of AD have episodic memory difficulty, and if so, 2) what brain areas are responsible for this difficulty. Chapter 2 and 3 of the current thesis show that 1) episodic memory difficulty is observed in lvPPA and PCA cases and 2) this impairment is modulated by deficit in other cognitive domains and associated with disease in non-MTL brain regions. This would be consistent with the ‘hippocampal-sparing’ hypothesis that not all AD histopathology begins in the MTL, and these hippocampal-sparing conditions suggest that additional mechanisms must be considered in the genesis of spreading pathology in AD

Unconventional markers of Alzheimer Disease

Although typically conceptualized as a cortical disease, recent neuropathological and neuroimaging investigations on Alzheimer Disease suggest that other brain structures play an important role in the pathogenesis and progression of this devastating condition. In this thesis, we explored novel markers of Alzheimer Disease beyond the classical cortical pathology measures of amyloid, tau, and neurodegeneration. We focused on the role of white matter abnormalities, assessed with magnetic resonance imaging but also with amyloid positron emission tomography, in predicting early pathologic changes and disease progression, as well as on the added value of cognition to amyloid, tau, and neurodegeneration biomarkers. Overall, we found that these unconventional markers provide useful information to detect the earliest pathological changes of the disease, providing a better understanding of the mechanisms that lead to amyloid deposition and cognitive decline

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools

Alzheimer PEThology

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

Small-World Network Analysis of Cortical Connectivity in Chronic Fatigue Syndrome using EEG

The primary aim of this thesis was to explore the relationship between electroencephalography (qEEG) and brain system dysregulation in people with Chronic Fatigue Syndrome (CFS). EEG recordings were taken from an archival dataset of 30 subjects, 15 people with CFS and 15 healthy controls (HCs), evaluated during an eye-closed resting state condition. Exact low resolution electromagnetic tomography (eLORETA) was applied to the qEEG data to estimate cortical sources and perform functional connectivity analysis assessing the strength of time-varying signals between all pairwise cortical regions of interest. To obtain a comprehensive view of local and global processing, eLORETA lagged coherence was computed on 84 regions of interest representing 42 Brodmann areas for the left and right hemispheres of the cortex, for the delta (1-3 Hz) and alpha-1 (8-10 Hz) and alpha-2 (10-12 Hz) frequency bands. Graph theory analysis of eLORETA coherence matrices for each participant was conducted to derive the “small-worldness” index, a measure of the optimal balance between the functional integration (global) and segregation (local) properties known to be present in brain networks. The data were also associated with the cognitive impairment composite score on the DePaul Symptom Questionnaire (DSQ), a patient-reported symptom outcome measure of frequency and severity of cognitive symptoms. Results showed that small-worldness for the delta band was significantly lower for patients with CFS compared to HCs. Small-worldness for delta, alpha-1, and alpha-2 were associated with higher cognitive composite scores on the DSQ. Finally, small-worldness in all 3 frequency bands correctly distinguished those with CFS from HCS with a classification rate of nearly 87 percent. These preliminary findings suggest disease processes in CFS may be functionally disruptive to small-world characteristics, especially in the delta frequency band, resulting in cognitive impairments. In turn, these findings may help to confirm a biological basis for cognitive symptoms, providing clinically relevant diagnostic indicators, and characterizing the neurophysiological status of people with CFS

Bundle-specific associations between white matter microstructure and Aβ and tau pathology in preclinical Alzheimer\u27s disease

Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of Alzheimer\u27s disease (AD), are believed to spread through connected regions of the brain. Combining diffusion imaging and positron emission tomography, we investigated associations between white matter microstructure specifically in bundles connecting regions where Aβ or tau accumulates and pathology. We focused on free-water-corrected diffusion measures in the anterior cingulum, posterior cingulum, and uncinate fasciculus in cognitively normal older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD. In Aβ-positive or tau-positive groups, lower tissue fractional anisotropy and higher mean diffusivity related to greater Aβ and tau burden in both cohorts. Associations were found in the posterior cingulum and uncinate fasciculus in preclinical sporadic AD, and in the anterior and posterior cingulum in presymptomatic mutation carriers. These results suggest that microstructural alterations accompany pathological accumulation as early as the preclinical stage of both sporadic and autosomal dominant AD

Cognitive and neural mechanisms of sense of self in neurodegenerative disorders

The ability to maintain a coherent and continuous ‘sense of self’ is a fundamental component of being human, enabling us to interact and function successfully in everyday life. While a sense of self is commonly accepted to involve both ‘extended’ (i.e., memories) and ‘interpersonal’ (i.e., social) elements, the precise cognitive and neural mechanisms underlying these aspects of the self remain poorly understood. This thesis draws upon theory and methods from contemporary cognitive neuroscience to examine the neurocognitive underpinnings of the extended and interpersonal self in Alzheimer’s disease (AD), semantic dementia (SD), and the behavioural variant of frontotemporal dementia (bvFTD): neurodegenerative disorders involving progressive cognitive and behavioural change as the result of degeneration to distinct brain networks. Employing a novel experimental method (the ‘NExt’ taxonomy), Part 1 of the thesis (Chapters 3 and 4) reveals how a full spectrum of episodic and semantic memory representations may be drawn upon to support one’s past and future life stories, giving rise to a sense of continuity of the extended self. Part 2 (Chapters 5 and 6) illustrates how the complex social interactions that comprise the interpersonal self may be deconstructed into several distinct, yet interacting, psychological components. Furthermore, neuroimaging analyses uncover widespread neural regions to be associated with both the extended and interpersonal aspects of the self, incorporating brain networks beyond those typically implicated in self-related processing. The improved neurocognitive characterisation of the self provided by this thesis highlights the complex, multifaceted nature of this construct. Moreover, from a clinical perspective, distinct profiles of the self unveiled across AD, SD, and bvFTD reveal how ultimately, ‘all is not lost’ in neurodegeneration