Understanding Cognitive Variability in Alzheimer’s Disease

Alzheimer’s Disease (AD) is highly heterogenous, both clinically and biologically. This variability is exacerbated by the ways within which, the clinical presentation is assessed with cognitive measures. This inhibits clinical trial success and earlier diagnosis of individuals. Marrying the clinical presentation to the pathology of the disease has so far proved troublesome. This thesis will look at how cognitive measures can best capture the clinical presentation of AD and how these measures can link to the underlying pathology using machine learning methods. This thesis studied this problem across four analyses and two cohorts. Each study looked at a different aspect of cognitive testing within AD. This was done with the overarching aim to interrogate the cognitive variability across the spectrum of AD. Study 1 showed a novel discrepancy score is different to memory measures at screening for AD. It also showed it tracks with AD severity, in the same way memory recall does. Studies 2 & 3 uncovered broad psychometric variance within amnestic measurement of impairment due to AD. This was done in two different populations across two different constructs of amnestic measurement, story recall and verbal list learning. These tests are frequently used interchangeably. These two studies show they should not be. Finally, Study 4 built models from cognitive measures to predict AD pathology. The performance of these models was moderate showing that even with novel cognitive measures, further work is needed to link the clinical and amyloid related biological presentations of AD. Bridging the gap between clinical presentation and pathology of AD using clinical and cognitive markers alone is not possible. Even when using a novel measure of discrepancy score. The discrepancy measure shows promise but was limited due to the inability of the MMSE to measure verbal ability. Conceptually a discrepancy score remains a promising avenue of research for screening, but broader language measures, as well as other AD biomarkers are needed to further test the construct validity of this measure

Association between anxiety and vascular dementia risk: new evidence and an updated meta-analysis

The association between anxiety and vascular dementia (VaD) is unclear. We aimed to reliably estimate the association between anxiety and VaD risk using meta-analysis to pool new results from a large community-based cohort (Zaragoza Dementia and Depression (ZARADEMP) study) and results from previous studies. ZARADEMP participants (n = 4057) free of dementia were followed up on for up to 12 years. Cases and subcases of anxiety were determined at baseline. A panel of four psychiatrists diagnosed incident cases of VaD by consensus. We searched for similar studies published up to October 2019 using PubMed and Web of Science. Observational studies reporting associations between anxiety and VaD risk, and adjusting at least for age, were selected. Odds ratios (ORs) from each study were combined using fixed-effects models. In the ZARADEMP study, the risk of VaD was 1.41 times higher among individuals with anxiety (95% CI: 0.75-2.68) compared with non-cases (p = 0.288). Pooling this result with results from two previous studies yielded an OR of 1.65 (95% CI: 1.07-2.53; p = 0.022). These findings indicate that anxiety is associated with an increased risk of VaD. Taking into account that anxiety is commonly observed in the elderly, treating and preventing it might reduce the prevalence and incidence of VaD. However, whether anxiety is a cause of a prodrome of VaD is still unknown, and future research is needed to clarify this

Multimodal Identification of Alzheimer's Disease: A Review

Alzheimer's disease is a progressive neurological disorder characterized by cognitive impairment and memory loss. With the increasing aging population, the incidence of AD is continuously rising, making early diagnosis and intervention an urgent need. In recent years, a considerable number of teams have applied computer-aided diagnostic techniques to early classification research of AD. Most studies have utilized imaging modalities such as magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalogram (EEG). However, there have also been studies that attempted to use other modalities as input features for the models, such as sound, posture, biomarkers, cognitive assessment scores, and their fusion. Experimental results have shown that the combination of multiple modalities often leads to better performance compared to a single modality. Therefore, this paper will focus on different modalities and their fusion, thoroughly elucidate the mechanisms of various modalities, explore which methods should be combined to better harness their utility, analyze and summarize the literature in the field of early classification of AD in recent years, in order to explore more possibilities of modality combinations

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods

Transkraniaalne aju ultraheliuuring Eesti parkinsoni tõve haigetel

Väitekirja elektrooniline versioon ei sisalda publikatsiooneEestis läbiviidud uurimustöö eesmärgiks oli näidata aju ultraheli uuringu abil Parkinsoni tõve korral tekkivaid muutusi keskajus asuvas musttuumas, mis aitaks seda haigust diagnoosida. Täiendavalt uuriti ka ultraheliuuringu tulemuste seoseid depressiivsete sümptomite esinemisega. Tegemist on innovaatilise meetodiga Parkinsoni tõve diagnoosimiseks, mida Eestis pole varem uuritud. Uuringus osales 300 PT patsienti ja 200 kontrollisikut. Aju musttuum sisaldab dopamiinirakke, mis Parkinsoni tõvega haigetel haiguse kulu jooksul hävinevad. Läbi kolju tehtava ultraheliuuringu käigus on võimalik mõõta musttuuma piirkonna suurust ja selle kajarikkuse (hüperehhogeensuse) asümmeetria alusel kinnitada Parkinsoni tõve diagnoosi. Uuringul määrati ultraheli diagnostiline väärtus haigete eristamiseks tervetest, mis ühtib varasemalt teistes riikides leitud tulemustega; kõige olulisemaks tulemuse mõjutajaks on vanus. Lisaks näidati erinevust vasaku ja parema ajupoole vahel, mis on seoses esmassümptomite tekkimise poolega. Lisaks aitab ultraheliuuring kirjeldada depressiivsete sümptomite ilmnemist Parkinsoni tõvega haigetel ajutüves asuvate Raphe tuumade ehhogeensuse esinemise põhjal. Raphe tuumad osalevad virgatsaine serotoniini tootmisel, mille vähenemisel kahjustuse korral võivad tekkida depressiooni sümptomid. Uuring näitas, et ultraheli uuring võimaldab hinnata Raphe tuumade terviklikkust ja on hea meetod varjatud depressiooni sümptomite väljaselgitamiseks. Läbiviidud uuring oli esimene ulatuslik aju ultraheliuuring Eestis, mis kinnitas ultraheliuuringu diagnostilist väärtust Parkinsoni tõve diagnoosimiseks. Lisaks teaduslikule väärtusele on sellel oluline kliiniline tähtsus seoses uue meetodi rakendamisega Parkinsoni tõve käsitluses.The aim of the research carried out in Estonia was to show by transcranial brain sonography the changes in Parkinson's disease in substantia nigra, which would help diagnose the disease. Transcranial brain sonography is an innovative method for diagnosing Parkinson's disease, which has not been studied in Estonia before. By sonography examination of the skull, it is possible to measure the size of the substantia nigra echogenicity and confirm the diagnosis of Parkinson's disease on the basis of its asymmetry in substantia nigra hyperechogenicity. The study determined the diagnostic value of SN+ to distinguish patients from healthy ones, which is consistent with the results previously found in other countries. There was a difference between the left and right sides of the SN+ that are related to the onset of the Parkinson’s disease initial symptoms. In addition, age was the most important factor influencing the asymmetry of SN+. The ultrasound study helps to describe the appearance of depressive symptoms in PD patients based on the occurrence of echogenicity of Raphe nuclei in the brainstem. The study showed that the transcranial brain sonography examination provides an estimate of the integrity of the Raphe nuclei and is a good method for detecting the symptoms of hidden depression. The conducted study was the first extensive transcranial brain sonography study in Estonia confirming the diagnostic value of ultrasound in the diagnosis of Parkinson's disease.  https://www.ester.ee/record=b5250326~S