MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

BACKGROUND: With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS: We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. RESULTS: In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. CONCLUSIONS: Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies

Detecting and tracking early neurodegeneration in familial Alzheimer’s disease

Alzheimer’s disease (AD) is recognized to have a long presymptomatic period, with initial deposition of extracellular amyloid and intracellular tau, followed by downstream neurodegeneration and cognitive decline. There is great interest in testing potential disease-modifying treatments for AD prior to the onset of symptoms, when minimal neuronal loss has occurred. To facilitate this, robust and sensitive methods are needed to identify at-risk individuals, stage their disease, and track progression. Familial Alzheimer’s disease (FAD) shares many features, clinically, radiologically, and neurophysiologically, with the more common sporadic form of disease. Carriers of autosomal dominantly inherited mutations in the presenilin 1, presenilin 2, and amyloid precursor protein genes have relatively predictable ages at symptom onset, based on family history. Study of FAD mutation carriers therefore provides the opportunity for the prospective study of asymptomatic individuals with known underlying AD pathology prior to the onset of clinical disease. The studies presented herein aim to improve the identification and characterization of early FAD neurodegenerative change and its earliest downstream cognitive effects. A multimodal approach is taken, with both presymptomatic and mildly symptomatic individuals included. Chapter one provides an introduction to AD and methods for measuring early neurodegeneration. Chapter two then outlines the general methodological approach across the different studies. Chapters three and four present results of magnetic resonance imaging studies of macrostructural (cortical thickness) and microstructural (diffusion-weighted imaging) cortical change. Chapter five reports results for a new blood-based biomarker of neurodegeneration – serum neurofilamentlight. Chapter six investigates a novel approach to presymptomatic cognitive testing – 6 assessing accelerated long-term forgetting. In all studies, significant differences between mutation carriers and non-carrier controls are detectable during the presymptomatic period. The thesis draws together these different approaches and discusses how they advance our understanding of the neurobiology of AD and their potential utility in both clinical assessment and presymptomatic therapeutic trials

Understanding preclinical dementia : early detection of dementia through cognitive and biological markers

Dementia is becoming a growing healthcare crisis, therefore identifying individuals at risk or in the earliest stages of dementia is essential if prevention or disease modification is to be achieved. The objective of this thesis was to examine cognitive performance and decline during the preclinical phase and explore the ability of cognitive and biological markers to identify those at risk of future dementia. Data from a population-based longitudinal study, SNAC-K, were used to investigate this aim. Study I examined the ability of neuropsychological tests, genetics, and structural MRI volumes to predict dementia six years later. Models were systematically created to identify the best combinations for prediction. A model containing all three modalities: hippocampal volume, a task of category fluency, presence of an APOE ɛ4 allele, white-matter hyperintensities volume, and a task of general knowledge, displayed the most predictive value (AUC=.924; C.I=.883–.965). However, this model did not significantly improve predictive value over one containing only cognitive and genetic markers, suggesting that minor increases in predictivity should be weighed against the costs of additional tests. Study II investigated the benefit of DTI, alongside neuropsychological tests, genetics, and brain volume markers in predicting future dementia. MD values for tracts CHC, CS, FMAJ, and IFOF (AUC=.837– .862) and the FA IFOF latent factor (AUC=.839) were significantly associated with dementia at six years. A final model consisting of a measure of perceptual speed, hippocampal volume, and MD of the FMAJ tract was created with the highest predictive value (AUC=.911). Assessment of microstructural white matter integrity via DTI was associated with future dementia but the additional benefit when combined with other markers was relatively small. Study III narrowed its focus to the ability of cognitive markers alone and the effect of modifying factors (age, sex, education, the presence of an ɛ4 allele, AD–only dementia, and time to diagnosis) on identifying those at risk of dementia. The most predictive model, consisting of category fluency, word recall, and pattern comparison, achieved good prediction values (AUC=.913) for dementia six years later. Tests in the domains of category fluency, episodic memory, and perceptual speed were, in general, good predictors across all subgroups and up to 6 years before a dementia diagnosis. However, cognitive tests became increasingly unreliable at predicting dementia beyond that time. Study IV explored the trajectories of cognitive decline over a 12-year period during the preclinical stage of dementia, before examining the ability of early cognitive decline in identifying those with increased likelihood of future dementia. Persons in the preclinical phase showed increased rate of decline in all cognitive domains compared to those who did not develop dementia (β:-.07 to -.11), this difference was particularly noticeable closer to diagnosis. Those classified as fast decliners for 3 or more cognitive tests demonstrated the highest risk of dementia (HR: 3.38, CI: 1.91-6.01). Although, changes in early rates of decline were small and rates of decline may be more predictive closer to diagnosis. Collectively, these studies confirm a long preclinical period in dementia development, which allows for the use of a wide range of markers (cognitive, genetic, MRI, and DTI) capable of identifying those at high risk of dementia. The ability of these markers to predict future dementia is increased through combining within and between modalities

Effects of Diversity and Neuropsychological Performance in an NFL Cohort

Objective: The aim of this study was to examine the effect of ethnicity on neuropsychological test performance by comparing scores of white and black former NFL athletes on each subtest of the WMS. Participants and Methods: Data was derived from a de-identified database in South Florida consisting of 63 former NFL white (n=28, 44.4%) and black (n=35, 55.6%) athletes (Mage= 50.38; SD= 11.57). Participants completed the following subtests of the WMS: Logical Memory I and II, Verbal Paired Associates I and II, and Visual Reproduction I and II. Results: A One-Way ANOVA yielded significant effect between ethnicity and performance on several subtests from the WMS-IV. Black athletes had significantly lower scores compared to white athletes on Logical Memory II: F(1,61) = 4.667, p= .035, Verbal Paired Associates I: F(1,61) = 4.536, p = .037, Verbal Paired Associates: II F(1,61) = 4.677, p = .034, and Visual Reproduction I: F(1,61) = 6.562, p = .013. Conclusions: Results suggest significant differences exist between white and black athletes on neuropsychological test performance, necessitating the need for proper normative samples for each ethnic group. It is possible the differences found can be explained by the psychometric properties of the assessment and possibility of a non-representative sample for minorities, or simply individual differences. Previous literature has found white individuals to outperform African-Americans on verbal and non-verbal cognitive tasks after controlling for socioeconomic and other demographic variables (Manly & Jacobs, 2002). This highlights the need for future investigators to identify cultural factors and evaluate how ethnicity specifically plays a role on neuropsychological test performance. Notably, differences between ethnic groups can have significant implications when evaluating a sample of former athletes for cognitive impairment, as these results suggest retired NFL minorities may be more impaired compared to retired NFL white athletes

Distinguishing Performance on Tests of Executive Functions Between Those with Depression and Anxiety

Objective: To see if there are differences in executive functions between those diagnosed with Major Depressive Disorder (MDD) and those with Generalized Anxiety Disorder (GAD).Participants and Methods: The data were chosen from a de-identified database at a neuropsychological clinic in South Florida. The sample used was adults diagnosed with MDD (n=75) and GAD (n=71) and who had taken the Halstead Category Test, Trail Making Test, Stroop Test, and the Wisconsin Card Sorting Test. Age (M=32.97, SD=11.75), gender (56.7% female), and race (52.7% White) did not differ between groups. IQ did not differ but education did (MDD=13.41 years, SD=2.45; GAD=15.11 years, SD=2.40), so it was ran as a covariate in the analyses. Six ANCOVAs were run separately with diagnosis being held as the fixed factor and executive function test scores held as dependent variables. Results: The MDD group only performed worse on the Category Test than the GAD group ([1,132]=4.022, p\u3c .05). Even though both WCST scores used were significantly different between the two groups, both analyses failed Levene’s test of Equality of Error Variances, so the data were not interpreted. Conclusions: Due to previous findings that those diagnosed with MDD perform worse on tests of executive function than normal controls (Veiel, 1997), this study wanted to compare executive function performance between those diagnosed with MDD and those with another common psychological disorder. The fact that these two groups only differed on the Category Test shows that there may not be much of a difference in executive function deficits between those with MDD and GAD. That being said, not being able to interpret the scores on the WCST test due to a lack of homogeneity of variance indicates that a larger sample size is needed to compare these two types of patients, as significant differences may be found. The results of this specific study, however, could mean that the Category Test could be used in assisting the diagnosis of a MDD patient

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

The Effect of Ethnicity on Neuropsychological Test Performance of Former NFL Athletes

Objective: To investigate the effect of ethnicity on neuropsychological test performance by specifically exploring differences between white and black former NFL athletes on subtests of the WAIS-IV. Participants and Methods: Data was derived from a de-identified database in Florida consisting of 63 former NFL athletes (Mage=50.38; SD=11.57); 28 white and 35 black. Participants completed the following subtests of the WAIS-IV: Block Design, Similarities, Digit Span, Matrix Reasoning, Arithmetic, Symbol Search, Visual Puzzles, Coding, and Cancellation. Results: One-Way ANOVA yielded a significant effect between ethnicity and performance on several subtests. Black athletes had significantly lower scaled scores than white athletes on Block Design F(1,61)=14.266, p\u3c.001, Similarities F(1,61)=5.904, p=.018, Digit Span F(1,61)=8.985, p=.004, Arithmetic F(1,61)=16.07, p\u3c.001 and Visual Puzzles F(1,61)=16.682, p\u3c .001. No effect of ethnicity was seen on performance of Matrix Reasoning F(1,61)=2.937, p=.092, Symbol Search F(1,61)=3.619, p=.062, Coding F(1,61)=3.032, p=.087 or Cancellation F(1,61)=2.289, p=.136. Conclusions: Results reveal significant differences between white and black athletes on all subtests of the WAIS-IV but those from the Processing Speed Scale and Matrix Reasoning. These findings align with previous literature that found white individuals to outperform African-Americans on verbal and non-verbal tasks after controlling for socioeconomic and demographic variables (Manly & Jacobs, 2002). These differences may also be a reflection of the WAIS-IV’s psychometric properties and it is significant to consider the normative sample used may not be appropriate for African-Americans. This study highlights the need for future research to identify how ethnicity specifically influences performance, sheds light on the importance of considering cultural factors when interpreting test results, and serves as a call to action to further understand how and why minorities may not be accurately represented in neuropsychological testing

Regional Cerebral Blood Flow Patterns in Children vs. Adults with ADHD Combined and Inattentive Types: A SPECT Study

Objective: The current study sought to determine whether ADHD Combined Type (ADHD-C) and ADHD Primarily Inattentive Type (ADHD-PI) showed differential regional cerebral blood flow (rCBF) patterns in children vs. adults. Participants and Methods: The overall sample (N=1484) was effectively split into four groups: adults with ADHD-PI (n=519), adults with ADHD-C (n=405), children with ADHD-PI (n=192), children with ADHD-C (n=368). All participants were void of bipolar, schizophrenia, autism, neurocognitive disorders, and TBI. The data were collected from a de-identified archival database of individuals who underwent SPECT scans at rest. Results: Using αConclusions: Overall, the current study suggested that children may show rCBF differences between different ADHD subtypes, but adults may not. The current study did not find significance in any of the 17 brain regions examined when comparing adults with ADHD-C to adults with ADHD-PI. All significant findings were attributed to the children with ADHD-C group showing aberrant blood flow rate than at least one other group. Previous research has supported that the differentiation of these subtypes as distinctive disorders is difficult to make in adults (Sobanski et al., 2006). Other research has indicated the potential of imaging techniques to differentiate the two in children (Al-Amin, Zinchenko, & Geyer, 2018). The current findings support nuanced ways in which rCBF patterns of ADHD-C and ADHD-PI differ between children and adults

Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Blood and cerebrospinal fluid biomarkers for Alzheimer’s disease: from clinical to preclinical cohorts

Dementia is a major contributor to global morbidity, mortality and costs associated with health and social care. Alzheimer’s disease (AD) is a common pathology culminating in dementia, but it has a preclinical phase of one to two decades, with early brain deposition of amyloid and tau, followed by synaptic and neuronal degeneration. Early detection during the preclinical phase of AD might enable disease-modifying therapies to be applied during a window of opportunity in which they would be more likely to work. Currently the main biomarkers of AD pathology are neuroimaging markers, which can be costly, or cerebrospinal fluid markers, which require invasive sampling. Blood biomarkers would be relatively less invasive and could be a more cost-effective means for risk stratification, early detection, monitoring progression and measuring response to treatment. The work described here used sensitive assay technology including the Simoa digital immunoassay platform, in large and well-characterised cohorts, to examine candidate blood biomarkers linked to the core AD pathologies of amyloid, tau and neurodegeneration, as specified by the National Institute on Aging and Alzheimer’s Association 2018 research framework. Firstly, experiments on samples from a cognitive clinic cohort established the stability of the blood biomarkers Aβ40, Aβ42, total tau and neurofilament light chain (NFL – a marker of neurodegeneration) to multiple freeze-thaw cycles, and the optimal blood fraction to use for quantifying each of these biomarkers in onward studies. Secondly, an unique large preclinical cohort with life course data (Insight 46, the neuroscience sub-study of 502 individuals from the MRC National Survey of Health and Development; the 1946 British birth cohort) was used to examine the cross-sectional relationships between these blood biomarkers, neuroimaging biomarkers (18F-florbetapir amyloid PET, whole brain and hippocampal volumes, white matter hyperintensity volume and cortical thickness in an AD signature region) and cognitive performance (PACC: preclinical Alzheimer’s composite and its constituents). Through a collaboration with the University of Gothenburg, a novel liquid chromatography-mass spectrometry (LC-MS) method for quantification of plasma amyloid-β species was compared with the commercial Simoa assays in Insight 46. This was the first direct method comparison study of plasma amyloid-β species for the detection of preclinical cerebral amyloid deposition. It showed that the LC-MS method, when combined with age, sex and APOE #-4 carrier status, was able to distinguish PET amyloid status with an optimal (Youden’s cut point) sensitivity of 85.7% and specificity of 72.7%. The Simoa biomarkers of plasma total tau and serum NFL were confirmed to be potentially useful prognostic markers, as lower AD signature cortical thickness was associated with higher plasma total tau and serum NFL, lower whole brain volume was associated with higher plasma total tau, and higher ventricular volume was associated with higher serum NFL. Lower PACC scores were associated with higher serum NFL and lower scores for a paired associative memory test in particular were associated with higher plasma total tau and serum NFL. Thirdly, through a collaboration with Harvard University and the University of California San Diego, a new N-terminal tau biomarker was developed in CSF and plasma that showed good accuracy in distinguishing individuals with symptomatic CSF-defined AD pathology from healthy controls. Taken together, this work has demonstrated the impact of pre-analytical factors on measurements of AD blood biomarkers, validated these biomarkers as indicators of the core pathologies of AD and helped to develop a new tau blood biomarker in AD