A comparison of magnetic resonance imaging and neuropsychological examination in the diagnostic distinction of Alzheimer’s disease and behavioral variant frontotemporal dementia

The clinical distinction between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) remains challenging and largely dependent on the experience of the clinician. This study investigates whether objective machine learning algorithms using supportive neuroimaging and neuropsychological clinical features can aid the distinction between both diseases. Retrospective neuroimaging and neuropsychological data of 166 participants (54 AD; 55 bvFTD; 57 healthy controls) was analyzed via a Naïve Bayes classification model. A subgroup of patients (n = 22) had pathologically-confirmed diagnoses. Results show that a combination of gray matter atrophy and neuropsychological features allowed a correct classification of 61.47% of cases at clinical presentation. More importantly, there was a clear dissociation between imaging and neuropsychological features, with the latter having the greater diagnostic accuracy (respectively 51.38 vs. 62.39%). These findings indicate that, at presentation, machine learning classification of bvFTD and AD is mostly based on cognitive and not imaging features. This clearly highlights the urgent need to develop better biomarkers for both diseases, but also emphasizes the value of machine learning in determining the predictive diagnostic features in neurodegeneration

Multimodal phenotyping of synaptic damage in Alzheimer’s disease : translational perspective with focus on quantitative EEG

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common form of dementia. Accumulation of AD-associated pathology in the brain may begin a decade or more before the appearance of the first symptoms of the disease. The pathological-clinical “continuum of AD” therefore encompasses time between the initial neuropathological changes and symptoms of advanced disease. Besides cognitively healthy individuals at risk, it includes subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI) and eventually dementia when the severity of cognitive impairment affects patients’ ability to carry out everyday activities. Timely detection of the disease would therefore recognize patients that are at risk for future cognitive deterioration and provide time window for the prevention and novel therapeutical interventions. Accumulating evidence suggests that degeneration and dysfunction of brain neuronal connections, i.e. synapses, is one of the earliest and best proxies of cognitive deficits in patients along AD continuum. Human electroencephalography (EEG) is a non-invasive and widely available diagnostic method that records real-time large-scale synaptic activity. The commonly used method in research settings is quantitative EEG (qEEG) analysis that provides objective information on EEG recorded at the level of the scalp. Quantitative EEG analysis unravels complex EEG signal and adds relevant information on its spectral components (frequency domain), temporal dynamics (time domain) and topographic estimates (space domain) of brain cortical activity. The general aim of the present thesis was to characterize different aspects of synaptic degeneration in AD, with the focus on qEEG and its relationship to both conventional and novel synaptic markers. In study I, global qEEG measures of power and synchronization were found to correlate with conventional cerebrospinal fluid (CSF) biomarkers of Aβ and tau pathology in patients diagnosed with SCD, MCI and AD, linking the markers of AD pathology to the generalized EEG slowing and reduced brain connectivity in fast frequency bands. In study II, qEEG analysis in the time domain (EEG microstates) revealed alterations in the organization and dynamics of large-scale brain networks in memory clinic patients compared to healthy elderly controls. In study III, topographical qEEG analysis of brain functional connectivity was associated with regionspecific cortical glucose hypometabolism ([18F]Fluorodeoxyglucose positron-emission tomography) in MCI and AD patients. Study IV provided evidence that qEEG measures of global power and synchronization correlate with CSF levels of synaptic marker neurogranin, both modalities being in combination independent predictors of progression to AD dementia in MCI patients. Study V and associated preliminary study introduced in the thesis assessed the translational potential of CSF neurogranin and qEEG as well as their direct relationship to AD neuropathology in App knock-in mouse models of AD. In study V, changes in CSF neurogranin levels and their relationship to conventional CSF markers in App knock-in mice corresponded to the pattern observed in clinical AD cohorts. These findings highlighted the potential use of mouse CSF biomarkers as well as App knock-in mouse models for translational investigation of synaptic dysfunction due to AD. In general, the results of the thesis invite for further clinical validation of multimodal synaptic markers in the context of early AD diagnosis, prognosis, and treatment monitoring in individual patients

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

The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning

Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges on postmortem verification, and so is not a helpful method for identifying patients and research subjects in the beginning phases of the pathophysiological process. Instead, a new biomarker-based approach provides a more biological understanding of the disease and can detect pathological changes up to 20 years before the clinical symptoms emerge. Subjects are assigned a profile according to their biomarker measures of amyloidosis (A), tauopathy (T) and neurodegeneration (N) that reflects their underlying pathology in vivo. AD is confirmed as the underlying pathology when subjects have abnormal values of both amyloid and tauopathy biomarkers, and so have a biomarker profile of A+T+(N)- or A+T+(N)+. This new biomarker based characterization of AD can be combined with machine learning techniques in multimodal classification studies to shed light on the elements of the AD pathological process and develop early detection paradigms. A guiding research framework is proposed for the development of reliable, biologically-valid and interpretable multimodal classification models

Regional brain hypometabolism is unrelated to regional amyloid plaque burden

In its original form, the amyloid cascade hypothesis of Alzheimer's disease holds that fibrillar deposits of amyloid are an early, driving force in pathological events leading ultimately to neuronal death. Early clinicopathological investigations highlighted a number of inconsistencies leading to an updated hypothesis in which amyloid plaques give way to amyloid oligomers as the driving force in pathogenesis. Rather than focusing on the inconsistencies, amyloid imaging studies have tended to highlight the overlap between regions that show early amyloid plaque signal on positron emission tomography and that also happen to be affected early in Alzheimer's disease. Recent imaging studies investigating the regional dependency between metabolism and amyloid plaque deposition have arrived at conflicting results, with some showing regional associations and other not. We extracted multimodal neuroimaging data from the Alzheimer's disease neuroimaging database for 227 healthy controls and 434 subjects with mild cognitive impairment. We analysed regional patterns of amyloid deposition, regional glucose metabolism and regional atrophy using florbetapir ((18)F) positron emission tomography, (18)F-fluordeoxyglucose positron emission tomography and T1-weighted magnetic resonance imaging, respectively. Specifically, we derived grey matter density and standardized uptake value ratios for both positron emission tomography tracers in 404 functionally defined regions of interest. We examined the relation between regional glucose metabolism and amyloid plaques using linear models. For each region of interest, correcting for regional grey matter density, age, education and disease status, we tested the association of regional glucose metabolism with (i) cortex-wide florbetapir uptake; (ii) regional (i.e. in the same region of interest) florbetapir uptake; and (iii) regional florbetapir uptake while correcting in addition for cortex-wide florbetapir uptake. P-values for each setting were Bonferroni corrected for 404 tests. Regions showing significant hypometabolism with increasing cortex-wide amyloid burden were classic Alzheimer's disease-related regions: the medial and lateral parietal cortices. The associations between regional amyloid burden and regional metabolism were more heterogeneous: there were significant hypometabolic effects in posterior cingulate, precuneus, and parietal regions but also significant positive associations in bilateral hippocampus and entorhinal cortex. However, after correcting for global amyloid burden, few of the negative associations remained and the number of positive associations increased. Given the wide-spread distribution of amyloid plaques, if the canonical cascade hypothesis were true, we would expect wide-spread, cortical hypometabolism. Instead, cortical hypometabolism appears to be linked to global amyloid burden. Thus we conclude that regional fibrillar amyloid deposition has little to no association with regional hypometabolism

Mental stimulation and multimodal trials to prevent cognitive impairment and Alzheimer ́s disease

Theoretical models of dynamic biomarkers underlying the development of Alzheimer´s Disease (AD) acknowledge that there is inter-individual variability in the cognitive performance associated with any level of AD pathology. Mentally stimulating activities such as schooling, occupation, and leisure activities, may contribute to this variability, but it is yet unclear how this can be best assessed, and how such effects can vary across AD severity and among individuals at-risk for cognitive impairment. The association between mental stimulation and cognitive performance also suggests that it is important to account for mental stimulation levels in randomized clinical trials (RCTs) comparing rates of cognitive change between interventions (i.e., drugs, lifestyle interventions) and controls. The aim of this thesis was to investigate a) how pre-existing levels of occupational complexity affect the cognitive outcomes of a multimodal lifestyle-based RCT among older adults at increased risk for dementia based on a validated risk score b) if occupational complexity is associated to cognitive performance among individuals at-risk for dementia, including individuals in the early stages of symptomatic AD (prodromal AD) and c) if occupational complexity is associated with resilience to AD pathology, measured with validated biomarkers and neuroimaging among individuals at-risk for cognitive impairment and with prodromal AD. The four studies in this thesis were based on data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), the Karolinska University Hospital electronic database and biobank for clinical research (GEDOC) and The Multimodal Prevention Trial for Alzheimer´s Disease (MIND-ADmini). Study I. This study used data from the FINGER study (N=1026) to investigate if preexisting levels of occupational complexity were associated with cognitive function at baseline, and if occupational complexity was associated with the rate of change in cognition during the 2-year intervention period. For all measures of occupational complexity, higher levels of complexity were associated with better cognitive outcomes at baseline. Occupational complexity was not associated with the rate of cognitive change during the intervention, except for the executive function outcome, for which higher levels of complexity with data predicted increased improvement ((ß[SE]: .028[.014], p=.044). Study II. This study used data from the FINGER neuroimaging cohort, to investigate if the association between occupational complexity and cognition was moderated by measures of brain integrity, both in terms of magnetic resonance imaging (MRI, N=126) and Pittsburgh-B Compound – Positron Emission Tomography (PiB-PET, N=41). The results showed that higher levels of occupational complexity were associated with better cognitive performance for some outcomes after adjusting for Alzheimer’s Disease Signature (ADS) and medial temporal atrophy (MTA). However, for most types of neuropathology and cognitive outcomes, moderation effects indicated that higher occupational complexity levels were associated with better cognitive performance only in people with higher brain integrity, suggesting lack of occupational complexity-related resilience mechanisms. Study III. This study investigated the association between mental stimulation (occupational complexity and education) and validated AD biomarkers, Aβ1–42, p-tau and t-tau measured in cerebrospinal fluid (CSF). Using data from the GEDOC database, 174 individuals with prodromal AD were included, and analyses were adjusted for cognitive function. The results indicated that both higher occupational complexity and education were associated with higher levels of p-tau and t-tau. For education the association with tau pathology was age dependent. No association was found with Aβ1– 42. This suggests that higher education and occupational complexity may provide resilience against tau-related pathology in prodromal AD. Study IV. This study used data from FINGER, GEDOC, and MIND-ADmini, thus including a total of 1410 individuals, 1207 at-risk for dementia and 203 with Prodromal AD. The aim was to to compare the two most common rating systems for occupational complexity, the Occupation Information Network (O*NET) and the Dictionary of Occupational Titles (DOT) and assess if there was an association between occupational complexity and episodic memory performance among individuals at-risk for dementia. The study found that higher occupational complexity was only associated with memory performance in the FINGER cohort but not the two prodromal AD cohorts. The correlation between the two rating systems was moderate to strong, and highly significant (Spearman’s rho = 0.5-0.6, p <.001). Conclusions. Higher levels of Occupational complexity are associated with better cognitive performance among older individuals at-risk for dementia (and with no substantial cognitive impairment), but does not affect the intervention effect in the FINGER multidomain lifestyle-based RCT, apart from the effect on executive function. Occupational complexity does not seem to provide strong resilience against neuropathology among individuals at-risk for cognitive impairment. Among individuals with prodromal AD, higher levels of occupational complexity do seem to provide resilience to tau-related pathology measured with CSF markers but is not associated with better episodic memory performance. Measuring occupational complexity with the DOT or O*NET system seems to yield similar results, as the two systems scores are correlated

The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment

Objectives: Diminished function of the posterior cingulate cortex (PCC) is a typical finding in early Alzheimer’s disease (AD). It is hypothesized that in early stage AD, PCC functioning relates to or reflects hippocampal dysfunction or atrophy. The aim of this study was to examine the relationship between hippocampus function, volume and structural connectivity, and PCC activation during an episodic memory task-related fMRI study in mild cognitive impairment (MCI). Method: MCI patients (n = 27) underwent episodic memory task-related fMRI, 3D-T1w MRI, 2D T2-FLAIR MRI and diffusion tensor imaging. Stepwise linear regression analysis was performed to examine the relationship between PCC activation and hippocampal activation, hippocampal volume and diffusion measures within the cingulum along the hippocampus. Results: We found a significant relationship between PCC and hippocampus activation during successful episodic memory encoding and correct recognition in MCI patients. We found no relationship between the PCC and structural hippocampal predictors. Conclusions: Our results indicate a relationship between PCC and hippocampus activation during episodic memory engagement in MCI. This may suggest that during episodic memory, functional network deterioration is the most important predictor of PCC functioning in MCI. Key Points: • PCC functioning during episodic memory relates to hippocampal functioning in MCI. • PCC functioning during episodic memory does not relate to hippocampal structure in MCI. • Functional network changes are an important predictor of PCC functioning in MCI