The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement

Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data

BACKGROUND

Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization.

METHOD

We adapt a newly developed learning-to-rank approach [Formula: see text] to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend [Formula: see text] to better separate the most effective cognitive assessments and the less effective ones.

RESULTS

Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features.

CONCLUSIONS

The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.

Cognitive and neural predictors of speech comprehension in noisy backgrounds in older adults

Older adults often experience difficulties comprehending speech in noisy backgrounds, which hearing loss does not fully explain. It remains unknown how cognitive abilities, brain networks, and age-related hearing loss may uniquely contribute to speech in noise comprehension at the sentence level. In 31 older adults, using cognitive measures and resting-state fMRI, we investigated the cognitive and neural predictors of speech comprehension with energetic (broadband noise) and informational masking (multi-speakers) effects. Better hearing thresholds and greater working memory abilities were associated with better speech comprehension with energetic masking. Conversely, faster processing speed and stronger functional connectivity between frontoparietal and language networks were associated with better speech comprehension with informational masking. Our findings highlight the importance of the frontoparietal network in older adults' ability to comprehend speech in multi-speaker backgrounds, and that hearing loss and working memory in older adults contributes to speech comprehension abilities related to energetic, but not informational masking.

CSF glucose tracks regional tau progression based on Alzheimer's disease risk factors

INTRODUCTION

Glucose hypometabolism and tau formation are key features of Alzheimer's disease (AD). Less is known about the relationship between fasting glucose and regional tau accumulation.

METHODS

Cerebrospinal fluid (CSF) glucose was linearly regressed on regional tau (flortaucipir) among 169 Alzheimer's Disease Neuroimaging Initiative (ADNI3) participants. Flortaucipir uptake was examined by Braak stages and regions of interest (ROIs). Interactions were explored between CSF glucose and AD risk factors including regional amyloid beta (Aβ), sex, Apolipoprotein E ε4 (APOEε4) status, AD parental family history (AD FH), and cognitive impairment (CI).

RESULTS

Interactions found higher CSF glucose tracked less tau in ROIs or Braak stages I/II (women, APOE ε4+, regional Aβ), III/IV (AD FH+, regional Aβ), and V/VI (AD FH+). CI drove Braak III-VI associations.

DISCUSSION

Among women and APOE ε4 carriers, higher CSF glucose tracked less early-stage tau. Higher CSF glucose may reflect compensation against tau spreading in CI, Aβ+, or AD FH+.

Multimodal in vivo and postmortem assessments of tau in Lewy body disorders

We compared regional retention of ¹⁸F-flortaucipir between 20 patients with Lewy body disorders (LBD), 12 Alzheimer's disease patients with positive amyloid positron emission tomography (PET) scans (AD+Aβ) and 15 healthy controls with negative amyloid PET scans (HC-Aβ). In LBD subjects, we compared the relationship between ¹⁸F-flortaucipir retention and cerebrospinal fluid (CSF) tau, cognitive performance, and neuropathological tau at autopsy. The LBD cohort was stratified using an Aβ42 cut-off of 192 pg/mL to enrich for groups likely harboring tau pathology (LBD+Aβ = 11, LBD-Aβ = 9). ¹⁸F-flortaucipir retention was higher in LBD+AB than HC-Aβ in five, largely temporal-parietal regions with sparing of medial temporal regions. Higher retention was associated with higher CSF total-tau levels (p = 0.04), poorer domain-specific cognitive performance (p = 0.02-0.04), and greater severity of neuropathological tau in corresponding regions. While ¹⁸F-flortaucipir retention in LBD is intermediate between healthy controls and AD, retention relates to cognitive impairment, CSF total-tau, and neuropathological tau. Future work in larger autopsy-validated cohorts is needed to define LBD-specific tau biomarker profiles.

Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings

Multicenter diffusion magnetic resonance imaging (MRI) has drawn great attention recently due to the expanding need for large-scale brain imaging studies, whereas the variability in MRI scanners and data acquisition tends to confound reliable individual-based analysis of diffusion measures. In addition, a growing number of multi-shell diffusion models have been shown with the potential to generate various estimates of physio-pathological information, yet their reliability and reproducibility in multicenter studies remain to be assessed. In this article, we describe a multi-shell diffusion dataset collected from three traveling subjects with identical acquisition settings in ten imaging centers. Both the scanner type and imaging protocol for anatomical and diffusion imaging were well controlled. This dataset is expected to replenish individual reproducible studies via multicenter collaboration by providing an open resource for advanced and novel microstructural and tractography modelling and quantification.

Women can bear a bigger burden: ante- and post-mortem evidence for reserve in the face of tau

In this study, we aimed to assess whether women are able to withstand more tau before exhibiting verbal memory impairment. Using data from 121 amyloid-β-positive Alzheimer’s Disease Neuroimaging Initiative participants, we fit a linear model with Rey Auditory Verbal Learning Test score as the response variable and tau-PET standard uptake value ratio as the predictor and took the residuals as an estimate of verbal memory reserve for each subject. Women demonstrated higher reserve (i.e. residuals), whether the Learning (t = 2.78, P = 0.006) or Delay (t = 2.14, P = 0.03) score from the Rey Auditory Verbal Learning Test was used as a measure of verbal memory ability. To validate these findings, we examined 662 National Alzheimer’s Coordinating Center participants with a C2/C3 score (Consortium to Establish a Registry for Alzheimer’s Disease) at autopsy. We stratified our National Alzheimer’s Coordinating Center sample into Braak 1/2, Braak 3/4 and Braak 5/6 subgroups. Within each subgroup, we compared Logical Memory scores between men and women. Men had worse verbal memory scores within the Braak 1/2 (Logical Memory Immediate: β = −5.960 ± 1.517, P < 0.001, Logical Memory Delay: β = −5.703 ± 1.677, P = 0.002) and Braak 3/4 (Logical Memory Immediate: β = −2.900 ± 0.938, P = 0.002, Logical Memory Delay: β = −2.672 ± 0.955, P = 0.006) subgroups. There were no sex differences in Logical Memory performance within the Braak 5/6 subgroup (Logical Memory Immediate: β = −0.314 ± 0.328, P = 0.34, Logical Memory Delay: β = −0.195 ± 0.287, P = 0.50). Taken together, our results point to a sex-related verbal memory reserve.

The Influence of Baseline Alzheimer's Disease Severity on Cognitive Decline and CSF Biomarkers in the NILVAD Trial

We examined the effects of a dihydropyridine calcium channel blocker nilvadipine with anti-inflammatory properties on cognition and cerebrospinal fluid (CSF) biomarkers by baseline Alzheimer's disease (AD) severity. Exploratory analyses were performed on the dataset (n = 497) of a phase III randomized placebo-controlled trial to examine the response to nilvadipine in AD subjects stratified by baseline AD severity into very mild (MMSE ≥ 25), mild (MMSE 20-24) and moderate AD (MMSE < 20). The outcome measures included total and subscale scores of the Alzheimer's Disease Assessment Scale Cognitive 12 (ADAS-Cog 12), the Clinical Dementia Rating Scale sum of boxes (CDR-sb) and the AD composite score (ADCOMS). Cerebrospinal fluid biomarkers Aβ38, Aβ40, Aβ42, neurofilament light chain (NFL), neurogranin, YKL-40, total tau and P181 tau (ptau) were measured in a subset of samples (n = 55). Regression analyses were adjusted for confounders to specifically examine the influence of nilvadipine and baseline AD severity on cognitive outcomes over 78-weeks. Compared to their respective placebo-controls, nilvadipine-treated, very mild AD subjects showed less decline, whereas moderate AD subjects showed a greater cognitive decline on the ADAS-Cog 12 test and the ADCOMS. A lower decline was observed after nilvadipine treatment for a composite memory trait in very mild AD subjects and a composite language trait in mild AD subjects. Cerebrospinal fluid Aβ42/Aβ40 ratios were increased in mild AD and decreased in moderate AD patients treated with nilvadipine, compared to their respective controls. Among moderate AD subjects, levels of ptau, total tau, neurogranin and YKL-40 increased in subjects treated with nilvadipine compared to placebo. These studies suggest that baseline AD severity influenced the treatment outcome in the NILVAD trial and that future clinical trials of nilvadipine should be restricted to mild and very mild AD patients.

Trial Registration: NCT02017340 Registered 20 December 2013, https://clinicaltrials.gov/ct2/show/NCT02017340

EUDRACT Reference Number 2012-002764-27 Registered 04 February 2013, https://www.clinicaltrialsregister.eu/ctr-search/search?query=2012-002764-27

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer's disease

BACKGROUND

The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer's disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear.

METHODS

Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer's Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions.

RESULTS

We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10-9). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (Ppermutation = 1.0 × 10-2 and 4.9 × 10-4 for left and right respectively).

CONCLUSIONS

In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other.

Assessment of executive function declines in presymptomatic and mildly symptomatic familial frontotemporal dementia: NIH-EXAMINER as a potential clinical trial endpoint

INTRODUCTION

Identifying clinical measures that track disease in the earliest stages of frontotemporal lobar degeneration (FTLD) is important for clinical trials. Familial FTLD provides a unique paradigm to study early FTLD. Executive dysfunction is a clinically relevant hallmark of FTLD and may be a marker of disease progression.

METHODS

Ninety-three mutation carriers with no symptoms or minimal/questionable symptoms (MAPT, n = 31; GRN, n = 28; C9orf72, n = 34; Clinical Dementia Rating scale plus NACC FTLD Module < 1) and 78 noncarriers enrolled through Advancing Research and Treatment in Frontotemporal Lobar Degeneration/Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects studies completed the Executive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research (NIH-EXAMINER) and the UDS neuropsychological battery. Linear mixed-effects models were used to identify group differences in cognition at baseline and longitudinally. We examined associations between cognition, clinical functioning, and magnetic resonance imaging volumes.

RESULTS

NIH-EXAMINER scores detected baseline and differences in slopes between carriers and noncarriers, even in carriers with a baseline Clinical Dementia Rating scale plus NACC FTLD Module = 0. NIH-EXAMINER declines were associated with worsening clinical symptoms and brain volume loss.

DISCUSSION

The NIH-EXAMINER is sensitive to cognitive changes in presymptomatic familial FTLD and is a promising surrogate endpoint.

Gender medicine: Towards a gender-specific treatment of neuropsychiatric disorders

Sex and gender are increasingly recognized as major influencing factors in disorders across all medical specialties. Even though there is ample evidence of sex and gender differences in neuropsychiatric disorders, a sex and gender-differentiated approach has not yet been sufficiently applied to diagnostics and management. Therefore, there is an urgent need to establish general recommendations and guidelines toward precision and sex/gender medicine, with regard to dosage, tolerability, interactions and side effects, sensitivity of diagnostic tests, and distinct treatment strategies. This chapter illustrates the current knowledge about sex and gender aspects in neuropsychiatric disorders, providing a base not only to assist the clinician in the handling of specific pathologic entities, but also to sensitize medical practitioners to consider sex and gender in clinical decision-making. As such, the chapter is a call to action to physicians and researchers to produce more sex- and gender-stratified evidence, leading to an acceleration of guideline development. Such novel guidelines will provide a base for medical education, of both medical students and specialists, as well as a reference point for practitioners, toward precision medicine.

Bayesian Scalar on Image Regression With Nonignorable Nonresponse

Medical imaging has become an increasingly important tool in screening, diagnosis, prognosis, and treatment of various diseases given its information visualization and quantitative assessment. The aim of this article is to develop a Bayesian scalar-on-image regression model to integrate high-dimensional imaging data and clinical data to predict cognitive, behavioral, or emotional outcomes, while allowing for nonignorable missing outcomes. Such a nonignorable nonresponse consideration is motivated by examining the association between baseline characteristics and cognitive abilities for 802 Alzheimer patients enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1), for which data are partially missing. Ignoring such missing data may distort the accuracy of statistical inference and provoke misleading results. To address this issue, we propose an imaging exponential tilting model to delineate the data missing mechanism and incorporate an instrumental variable to facilitate model identifiability followed by a Bayesian framework with Markov chain Monte Carlo algorithms to conduct statistical inference. This approach is validated in simulation studies where both the finite sample performance and asymptotic properties are evaluated and compared with the model with fully observed data and that with a misspecified ignorable missing mechanism. Our proposed methods are finally carried out on the ADNI1 dataset, which turns out to capture both of those clinical risk factors and imaging regions consistent with the existing literature that exhibits clinical significance. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals-including known population brain networks and subject-specific signals-are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.

Microglia in Brain Development, Homeostasis, and Neurodegeneration

Advances in human genetics have implicated a growing number of genes in neurodegenerative diseases, providing insight into pathological processes. For Alzheimer disease in particular, genome-wide association studies and gene expression studies have emphasized the pathogenic contributions from microglial cells and motivated studies of microglial function/dysfunction. Here, we summarize recent genetic evidence for microglial involvement in neurodegenerative disease with a focus on Alzheimer disease, for which the evidence is most compelling. To provide context for these genetic discoveries, we discuss how microglia influence brain development and homeostasis, how microglial characteristics change in disease, and which microglial activities likely influence the course of neurodegeneration. In all, we aim to synthesize varied aspects of microglial biology and highlight microglia as possible targets for therapeutic interventions in neurodegenerative disease.

Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data

We propose an innovative machine learning paradigm enabling precision medicine for prioritizing cognitive assessments according to their relevance to Alzheimer's disease at the individual patient level. The paradigm tailors the cognitive biomarker discovery and cognitive assessment selection process to the brain morphometric characteristics of each individual patient. We implement this paradigm using a newly developed learning-to-rank method PLTR. Our empirical study on the ADNI data yields promising results to identify and prioritize individual-specific cognitive biomarkers as well as cognitive assessment tasks based on the individual's structural MRI data. The resulting top ranked cognitive biomarkers and assessment tasks have the potential to aid personalized diagnosis and disease subtyping.

Automated Multi-Atlas Segmentation of Hippocampal and Extrahippocampal Subregions in Alzheimer's Disease at 3T and 7T: What Atlas Composition Works Best?

BACKGROUND

Multi-atlas segmentation, a popular technique implemented in the Automated Segmentation of Hippocampal Subfields (ASHS) software, utilizes multiple expert-labelled images ("atlases") to delineate medial temporal lobe substructures. This multi-atlas method is increasingly being employed in early Alzheimer's disease (AD) research, it is therefore becoming important to know how the construction of the atlas set in terms of proportions of controls and patients with mild cognitive impairment (MCI) and/or AD affects segmentation accuracy.

OBJECTIVE

To evaluate whether the proportion of controls in the training sets affects the segmentation accuracy of both controls and patients with MCI and/or early AD at 3T and 7T.

METHODS

We performed cross-validation experiments varying the proportion of control subjects in the training set, ranging from a patient-only to a control-only set. Segmentation accuracy of the test set was evaluated by the Dice similarity coeffiecient (DSC). A two-stage statistical analysis was applied to determine whether atlas composition is linked to segmentation accuracy in control subjects and patients, for 3T and 7T.

RESULTS

The different atlas compositions did not significantly affect segmentation accuracy at 3T and for patients at 7T. For controls at 7T, including more control subjects in the training set significantly improves the segmentation accuracy, but only marginally, with the maximum of 0.0003 DSC improvement per percent increment of control subject in the training set.

CONCLUSION

ASHS is robust in this study, and the results indicate that future studies investigating hippocampal subfields in early AD populations can be flexible in the selection of their atlas compositions.

Frequency and longitudinal clinical outcomes of Alzheimer's AT(N) biomarker profiles: A longitudinal study

INTRODUCTION

We aimed to estimate the frequency of each AT(N) (β-amyloid deposition [A], pathologic tau [T], and neurodegeneration [N]) profile in different clinical diagnosis groups and to describe the longitudinal change in clinical outcomes of individuals in each group.

METHODS

Longitudinal change in clinical outcomes and conversion risk of AT(N) profiles are assessed using linear mixed-effects models and multivariate Cox proportional-hazard models, respectively.

RESULTS

Participants with A+T+N+ showed faster clinical progression than those with A-T-N- and A+T±N-. Compared with A-T-N-, participants with A+T+N± had an increased risk of conversion from cognitively normal (CN) to incident prodromal stage of Alzheimer's disease (AD), and from MCI to AD dementia. A+T+N+ showed an increased conversion risk when compared with A+T±N-.

DISCUSSION

The 2018 research framework may provide prognostic information of clinical change and progression. It may also be useful for targeted recruitment of participants with AD into clinical trials.

Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals.

Multimodal 18F-AV-1451 and MRI Findings in Nonfluent Variant of Primary Progressive Aphasia: Possible Insights on Nodal Propagation of Tau Protein Across the Syntactic Network

Although abnormally folded tau protein has been found to self-propagate from neuron to connected neuron, similar propagation through human brain networks has not been fully documented. We studied tau propagation in the left hemispheric syntactic network, which comprises an anterior frontal node and a posterior temporal node connected by the white matter of the left arcuate fasciculus. This network is affected in the nonfluent variant of primary progressive aphasia, a neurodegenerative disorder with tau accumulation. Methods: Eight patients with the nonfluent variant of primary progressive aphasia (age, 67.0 ± 7.4 y; 4 women) and 8 healthy controls (age, 69.6 ± 7.0 y; 4 women) were scanned with ¹⁸F-AV-1451 tau PET to determine tau deposition in the brain and with MRI to determine the fractional anisotropy of the arcuate fasciculus. Normal syntactic network characteristics were confirmed with structural MRI diffusion imaging in our healthy controls and with blood oxygenation level-dependent functional imaging in 35 healthy participants from the Alzheimer Disease Neuroimaging Initiative database. Results: Language scores in patients indicated dysfunction of the anterior node. ¹⁸F-AV-1451 deposition was greatest in the 2 nodes of the syntactic network. The left arcuate fasciculus had decreased fractional anisotropy, particularly near the anterior node. Normal MRI structural connectivity from an area similar to the one containing tau in the anterior frontal node projected to an area similar to the one containing tau in the patients in the posterior temporal node. Conclusion: Tau accumulation likely started in the more affected anterior node and, at the disease stage at which we studied these patients, appeared as well in the brain region (in the temporal lobe) spatially separate from but most connected with it. The arcuate fasciculus, connecting both of them, was most severely affected anteriorly, as would correspond to a loss of axons from the anterior node. These findings are suggestive of tau propagation from node to connected node in a natural human brain network and support the idea that neurons that wire together die together.

Tau and atrophy: domain-specific relationships with cognition

BACKGROUND

Late-onset Alzheimer's disease (AD) is characterized by primary memory impairment, which then progresses towards severe deficits across cognitive domains. Here, we report how performance in cognitive domains relates to patterns of tau deposition and cortical thickness.

METHODS

We analyzed data from 131 amyloid-β positive participants (55 cognitively normal, 46 mild cognitive impairment, 30 AD) of the Alzheimer's Disease Neuroimaging Initiative who underwent magnetic resonance imaging (MRI), flortaucipir (FTP) positron emission tomography, and neuropsychological testing. Surface-based vertex-wise and region-of-interest analyses were conducted between FTP and cognitive test scores, and between cortical thickness and cognitive test scores.

RESULTS

FTP and thickness were differentially related to cognitive performance in several domains. FTP-cognition associations were more widespread than thickness-cognition associations. Further, FTP-cognition patterns reflected cortical systems that underlie different aspects of cognition.

CONCLUSIONS

Our findings indicate that AD-related decline in domain-specific cognitive performance reflects underlying progression of tau and atrophy into associated brain circuits. They also suggest that tau-PET may have better sensitivity to this decline than MRI-derived measures of cortical thickness.

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

INTRODUCTION

It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.

METHODS

A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.

RESULTS

The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning-based progression risk was combined with baseline clinical measures.

DISCUSSION

Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.

Enrichment factors for clinical trials in mild-to-moderate Alzheimer's disease

Introduction

Heterogeneity of outcomes in Alzheimer's disease (AD) clinical trials necessitates large sample sizes and contributes to study failures. This analysis determined whether mild-to-moderate AD populations could be enriched for cognitive decline based on apolipoprotein (APOE) ε4 genotype, family history of AD, and amyloid abnormalities.

Methods

Modeling estimated the number of randomized patients needed to detect a 2-point treatment difference on the AD Assessment Scale–Cognitive subscale using placebo data from three randomized, double-blind trials (ClinicalTrials.gov Identifiers: NCT01955161, NCT02006641, and NCT02006654).

Results

An 80% power to detect a 2-point treatment effect required the randomization of 148 amyloid-positive patients; 178 ε4 homozygous or amyloid-positive patients; and 231 ε4 homozygous, family history-positive, or amyloid-positive patients, compared with 1619 unenriched patients (per arm).

Discussion

Enrichment in mild-to-moderate AD clinical trials can be achieved using combinations of biomarkers/risk factors to increase the likelihood of observing potential treatment effects.

•

APOE ɛ4, family history, and amyloid status can enrich mild-to-moderate AD samples.

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Enrichment increases the likelihood of detecting a treatment effect on cognition.

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Enrichment may reduce sample sizes in clinical trials of symptomatic drugs in AD.

Perivascular space fluid contributes to diffusion tensor imaging changes in white matter

Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from non-parenchymal fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.

Nonparenchymal fluid is the source of increased mean diffusivity in preclinical Alzheimer's disease

Introduction

Although increased mean diffusivity of the white matter has been repeatedly linked to Alzheimer’s disease pathology, the underlying mechanism is not known.

Methods

Here, we used ADNI-3 multishell diffusion magnetic resonance imaging data to separate the diffusion signal of the parenchyma from less hindered fluid pools within the white matter such as perivascular space fluid and fluid-filled cavities.

Results

We found that the source of the pathological increase of the mean diffusivity is the increased nonparenchymal fluid, often found in lacunes and perivascular spaces. In this cohort, the cognitive decline was significantly associated with the fluid increase and not with the microstructural changes of the white matter parenchyma itself. The white matter fluid increase was dominantly observed in the sagittal stratum and anterior thalamic radiation.

Discussion

These findings are positive steps toward understanding the pathophysiology of white matter alteration and its role in the cognitive decline.

Rationale and methods for a multicenter clinical trial assessing exercise and intensive vascular risk reduction in preventing dementia (rrAD Study)

Alzheimer's Disease (AD) is an age-related disease with modifiable risk factors such as hypertension, hypercholesterolemia, obesity, and physical inactivity influencing the onset and progression. There is however, no direct evidence that reducing these risk factors prevents or slows AD. The Risk Reduction for Alzheimer's Disease (rrAD) trial is designed to study the independent and combined effects of intensive pharmacological control of blood pressure and cholesterol and exercise training on neurocognitive function. Six hundred and forty cognitively normal older adults age 60 to 85 years with hypertension and increased risk for dementia will be enrolled. Participants are randomized into one of four intervention group for two years: usual care, Intensive Reduction of Vascular Risk factors (IRVR) with blood pressure and cholesterol reduction, exercise training (EX), and IRVR+EX. Neurocognitive function is measured at baseline, 6, 12, 18, and 24 months; brain MRIs are obtained at baseline and 24 months. We hypothesize that both IRVR and EX will improve global cognitive function, while IRVR+EX will provide a greater benefit than either IRVR or EX alone. We also hypothesize that IRVR and EX will slow brain atrophy, improve brain structural and functional connectivity, and improve brain perfusion. Finally, we will explore the mechanisms by which study interventions impact neurocognition and brain. If rrAD interventions are shown to be safe, practical, and successful, our study will have a significant impact on reducing the risks of AD in older adults. NCT Registration: NCT02913664.

High Selectivity and Sensitivity of Oligomeric p-Phenylene Ethynylenes for Detecting Fibrillar and Prefibrillar Amyloid Protein Aggregates

Misfolding and aggregation of amyloid proteins into fibrillar aggregates is a central pathogenic event in neurodegenerative disorders such as Alzheimer's (AD) and Parkinson's diseases (PD). Currently, there is a lack of reliable sensors for detecting the range of protein aggregates involved in disease etiology, particularly the prefibrillar aggregate conformations that are more neurotoxic. In this study, the fluorescent sensing of two novel oligomeric p-phenylene ethynylenes (OPEs), anionic OPE1- and cationic OPE2+, for detecting prefibrillar and fibrillar aggregates of AD-associated amyloid-β (Aβ40 and Aβ42) and PD-associated α-synuclein proteins (wildtype, and single mutants A30P, E35K, and A53T) over their monomeric counterparts, were tested. Furthermore, the performance of OPEs was evaluated and compared to thioflavin T (ThT), the most widely used fibril dye. Our results show that OPE1- and OPE2+ exhibited aggregate-specific binding inducing large fluorescence turn-on and spectral shifts based on a combination of backbone planarization, hydrophobic unquenching, and superluminescent OPE complex formation sensing modes. OPEs exhibited higher selectivity, higher binding affinity, and comparable limits of detection for Aβ40 fibrils compared to ThT. OPE2+ exhibited the largest fluorescence turn-on and highest sensitivity. Significantly, OPEs detected prefibrillar aggregates of Aβ42 and α-synuclein that ThT failed to detect. The superior sensing performance, the nonprotein specific detection, and the ability to selectively detect fibrillar and prefibrillar amyloid protein aggregates point to the potential of OPEs to overcome the limitations of existing probes and promise significant advancement in the detection of the myriad of protein aggregates involved in the early stages of AD and PD.

Gene-environment interactions in Alzheimer's disease: A potential path to precision medicine

Alzheimer's disease (AD) is the leading cause of dementia in the United States and afflicts >5.7 million Americans in 2018. Therapeutic options remain extremely limited to those that are symptom targeting, while no drugs have been approved for the modification or reversal of the disease itself. Risk factors for AD including aging, the female sex, as well as carrying an APOE4 genotype. These risk factors have been extensively examined in the literature, while less attention has been paid to modifiable risk factors, including lifestyle, and environmental risk factors such as exposures to air pollution and pesticides. This review highlights the most recent data on risk factors in AD and identifies gene by environment interactions that have been investigated. It also provides a suggested framework for a personalized therapeutic approach to AD, by combining genetic, environmental and lifestyle risk factors. Understanding modifiable risk factors and their interaction with non-modifiable factors (age, susceptibility alleles, and sex) is paramount for designing personalized therapeutic interventions.

European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (EPAD LCS): study protocol

INTRODUCTION

The European Prevention of Alzheimer's Dementia (EPAD) project is funded initially by the Innovative Medicines Initiative and has been established to overcome the major hurdles hampering drug development for secondary prevention of Alzheimer's dementia, by conducting the EPAD Longitudinal Cohort Study (LCS) in alignment with the Bayesian adaptive designed EPAD Proof-of-Concept (PoC) trial.

METHODS AND ANALYSIS

EPAD LCS is an ongoing prospective, multicentre, pan-European longitudinal cohort study. Participants are recruited mainly from existing parent cohorts across Europe to form a 'probability-spectrum' population covering the entire continuum of anticipated probability for Alzheimer's dementia development. The primary objective of the EPAD LCS is to be a readiness cohort for the EPAD PoC trial though a second major objective is to generate a comprehensive and large data set for disease modelling of preclinical and prodromal Alzheimer's disease. This characterisation of cognitive, biomarker and risk factor (genetic and environmental) status of research participants over time will provide the necessary well-phenotyped population for developing accurate longitudinal models for Alzheimer's disease covering the entire disease course and concurrently create a pool of highly characterised individuals for the EPAD PoC trial.

ETHICS AND DISSEMINATION

The study has received the relevant approvals from numerous Institutional Review Boards across Europe. Findings will be disseminated to several target audiences, including the scientific community, research participants, patient community, general public, industry, regulatory authorities and policy-makers. Regular and coordinated releases of EPAD LCS data will be made available for analysis to help researchers improve their understanding of early Alzheimer's disease stages and facilitate collaborations.

TRIAL REGISTRATION NUMBER

NCT02804789.

Perspectives on ethnic and racial disparities in Alzheimer's disease and related dementias: Update and areas of immediate need

Alzheimer's disease and related dementias (ADRDs) are a global crisis facing the aging population and society as a whole. With the numbers of people with ADRDs predicted to rise dramatically across the world, the scientific community can no longer neglect the need for research focusing on ADRDs among underrepresented ethnoracial diverse groups. The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART; alz.org/ISTAART) comprises a number of professional interest areas (PIAs), each focusing on a major scientific area associated with ADRDs. We leverage the expertise of the existing international cadre of ISTAART scientists and experts to synthesize a cross-PIA white paper that provides both a concise "state-of-the-science" report of ethnoracial factors across PIA foci and updated recommendations to address immediate needs to advance ADRD science across ethnoracial populations.

Cognitive Performance in Parkinson's Disease in the Brain Health Registry

The study of cognition in Parkinson's disease (PD) traditionally requires exhaustive recruitment strategies. The current study examines data collected by the Brain Health Registry (BHR) to determine whether ongoing efforts to improve the recruitment base for therapeutic trials in Alzheimer's disease may be similarly effective for PD research, and whether online cognitive measurements can discriminate between participants who do and do not report a PD diagnosis. Participants enrolled in the BHR (age ≥50) with self-reported PD data and online cognitive testing available were included (n = 11,813). Associations between baseline cognitive variables and diagnostic group were analyzed using logistic regression. Linear mixed effects models were used to analyze longitudinal data. A total of 634 participants reported PD diagnosis at baseline with no self-reported cognitive impairment and completed cognitive testing. Measures of visual learning and memory, processing speed, attention, and working memory discriminated between self-reported PD and non-PD participants after correcting for multiple comparisons (p values < 0.006). Scores on all cognitive tests improved over time in PD and controls with the exception of processing speed, which remained stable in participants with PD while improving in those without. We demonstrate that a novel online approach to recruitment and longitudinal follow-up of study participants is effective for those with self-reported PD, and that significant differences exist between those with and without a reported diagnosis of PD on computerized cognitive measures. These results have important implications for recruitment of participants with PD into targeted therapeutic trials or large-scale genetic and cognitive studies.

Amyloid, tau, pathogen infection and antimicrobial protection in Alzheimer's disease -conformist, nonconformist, and realistic prospects for AD pathogenesis

BACKGROUND

Alzheimer's disease (AD) is a fatal disease that threatens the quality of life of an aging population at a global scale. Various hypotheses on the etiology of AD have been developed over the years to guide efforts in search of therapeutic strategies.

MAIN BODY

In this review, we focus on four AD hypotheses currently relevant to AD onset: the prevailing amyloid cascade hypothesis, the well-recognized tau hypothesis, the increasingly popular pathogen (viral infection) hypothesis, and the infection-related antimicrobial protection hypothesis. In briefly reviewing the main evidence supporting each hypothesis and discussing the questions that need to be addressed, we hope to gain a better understanding of the complicated multi-layered interactions in potential causal and/or risk factors in AD pathogenesis. As a defining feature of AD, the existence of amyloid deposits is likely fundamental to AD onset but is insufficient to wholly reproduce many complexities of the disorder. A similar belief is currently also applied to hyperphosphorylated tau aggregates within neurons, where tau has been postulated to drive neurodegeneration in the presence of pre-existing Aβ plaques in the brain. Although infection of the central nerve system by pathogens such as viruses may increase AD risk, it is yet to be determined whether this phenomenon is applicable to all cases of sporadic AD and whether it is a primary trigger for AD onset. Lastly, the antimicrobial protection hypothesis provides insight into a potential physiological role for Aβ peptides, but how Aβ/microbial interactions affect AD pathogenesis during aging awaits further validation. Nevertheless, this hypothesis cautions potential adverse effects in Aβ-targeting therapies by hindering potential roles for Aβ in anti-viral protection.

CONCLUSION

AD is a multi-factor complex disorder, which likely requires a combinatorial therapeutic approach to successfully slow or reduce symptomatic memory decline. A better understanding of how various causal and/or risk factors affecting disease onset and progression will enhance the likelihood of conceiving effective treatment paradigms, which may involve personalized treatment strategies for individual patients at varying stages of disease progression.

Fluid and PET biomarkers for amyloid pathology in Alzheimer's disease

Alzheimer's disease (AD) is characterized by amyloid plaques and tau pathology (neurofibrillary tangles and neuropil threads). Amyloid plaques are primarily composed of aggregated and oligomeric β-amyloid (Aβ) peptides ending at position 42 (Aβ42). The development of fluid and PET biomarkers for Alzheimer's disease (AD), has allowed for detection of Aβ pathology in vivo and marks a major advancement in understanding the role of Aβ in Alzheimer's disease (AD). In the recent National Institute on Aging and Alzheimer's Association (NIA-AA) Research Framework, AD is defined by the underlying pathology as measured in patients during life by biomarkers (Jack et al., 2018), while clinical symptoms are used for staging of the disease. Therefore, sensitive, specific and robust biomarkers to identify brain amyloidosis are central in AD research. Here, we discuss fluid and PET biomarkers for Aβ and their application.

Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer's Disease in the Aging Brain

Brain aging is a multifaceted process that remains poorly understood. Despite significant advances in technology, progress toward identifying reliable risk factors for suboptimal brain health requires realistically complex analytic methods to explain relationships between genetics, biology, and environment. Here we show the utility of a novel unsupervised machine learning technique - Correlation Explanation (CorEx) - to discover how individual measures from structural brain imaging, genetics, plasma, and CSF markers can jointly provide information on risk for Alzheimer's disease (AD). We examined 829 participants (M age: 75.3 ± 6.9 years; 350 women and 479 men) from the Alzheimer's Disease Neuroimaging Initiative database to identify multivariate predictors of cognitive decline and brain atrophy over a 1-year period. Our sample included 231 cognitively normal individuals, 397 with mild cognitive impairment (MCI), and 201 with AD as their baseline diagnosis. Analyses revealed latent factors based on data-driven combinations of plasma markers and brain metrics, that were aligned with established biological pathways in AD. These factors were able to improve disease prediction along the trajectory from normal cognition and MCI to AD, with an area under the receiver operating curve of up to 99%, and prediction accuracy of up to 89.9% on independent "held out" testing data. Further, the most important latent factors that predicted AD consisted of a novel set of variables that are essential for cardiovascular, immune, and bioenergetic functions. Collectively, these results demonstrate the strength of unsupervised network measures in the detection and prediction of AD.

The Lifespan Human Connectome Project in Aging: An overview

The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al., 2018), and the image acquisition protocol common to both studies (Harms et al., 2018).

Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative

INTRODUCTION

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials.

METHODS

We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/).

RESULTS

(1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies.

DISCUSSION

ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.

MR approaches in neurodegenerative disorders

Neurodegenerative disease is the umbrella term which refers to a range of clinical conditions causing degeneration of neurons within the central nervous system leading to loss of brain function and eventual death. The most prevalent of these is Alzheimer's disease (AD), which affects approximately 50 million people worldwide and is predicted to reach 75 million by 2030. Neurodegenerative diseases can only be fully diagnosed at post mortem by neuropathological assessment of the type and distribution of protein deposits which characterise each different condition, but there is a clear role for imaging technologies in aiding patient diagnoses in life. Magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques have been applied to study these conditions for many years. In this review, we consider the range of MR-based measurements and describe the findings in AD, but also contrast these with the second most common dementia, dementia with Lewy bodies (DLB). The most definitive observation is the major structural brain changes seen in AD using conventional T1-weighted (T1w) MRI, where medial temporal lobe structures are notably atrophied in most symptomatic patients with AD, but often preserved in DLB. Indeed these findings are sufficiently robust to have been incorporated into clinical diagnostic criteria. Diffusion tensor imaging (DTI) reveals widespread changes in tissue microstructure, with increased mean diffusivity and decreased fractional anisotropy reflecting the degeneration of the white matter structures. There are suggestions that there are subtle differences between AD and DLB populations. At the metabolic level, atrophy-corrected MRS demonstrates reduced density of healthy neurons in brain areas with altered perfusion and in regions known to show higher deposits of pathogenic proteins. As studies have moved from patients with advanced disease and clear dysfunction to patients with earlier presentation such as with mild cognitive impairment (MCI), which in some represents the first signs of their ensuing dementia, the ability of MRI to detect differences has been weaker and further work is still required, ideally in much larger cohorts than previously studied. The vast majority of imaging research in dementia populations has been univariate with respect to the MR-derived parameters considered. To date, none of these measurements has uniquely replicated the patterns of tissue involvement seen by neuropathology, and the ability of MR techniques to deliver a non-invasive diagnosis eludes us. Future opportunities may lie in combining MR and nuclear medicine approaches (position emission tomography, PET) to provide a more complete view of structural and metabolic changes. Such developments will require multi-variate analyses, possibly combined with artificial intelligence or deep learning algorithms, to enhance our ability to combine the array of image-derived information, genetic, gender and lifestyle factors.

Derivation of cutoffs for the Elecsys® amyloid β (1-42) assay in Alzheimer's disease

Introduction

An Elecsys® Amyloid β (Aβ [1–42]) immunoassay cutoff for classification of patients with Alzheimer's disease was investigated.

Methods

Cerebrospinal fluid samples collected from patients with mild-to-moderate Alzheimer's disease were analyzed by Elecsys® immunoassays: (1) Aβ (1–42), (2) total tau, and (3) phosphorylated tau. Cutoffs (Aβ [1–42] and ratios with tau) were estimated by method comparison between AlzBio3 (n = 206), mixture modeling (n = 216), and concordance with florbetapir F 18 imaging-based classification (n = 75).

Results

A 1065-pg/mL (95% confidence interval: 985–1153) Elecsys® Aβ (1–42) cutoff provided 94% overall percentage agreement with AlzBio3. Comparable cutoff estimates (95% confidence interval) were derived from mixture modeling (equally weighted: 1017 [949–1205] pg/mL; prevalence weighted: 1172 [1081–1344] pg/mL) and concordance with florbetapir F 18 imaging (visual read: 1198 [998–1591] pg/mL; automated: 1198 [1051–1638] pg/mL).

Discussion

Based on three approaches, a 1100-pg/mL Elecsys® Aβ (1–42) cutoff is suitable for clinical trials with similar populations and preanalytical handling.

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Biomarkers can facilitate appropriate patient recruitment into clinical trials.

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Amyloid beta measurement can aid the identification of amyloid-positive patients.

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Similar cutoff estimates were derived by three different approaches.

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.

Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)

In this paper, we introduce the use of a personalized Gaussian Process pGP model to predict per-patient changes in ADAS-Cog13-a significant predictor of Alzheimer's Disease (AD) in the cognitive domain - using data from each patient's previous visits, and testing on future (held-out) data. We start by learning a population-level model using multi- modal data from previously seen patients using a base Gaussian Process (GP) regression. The pGP is then formed by adapting the base GP sequentially over time to a new (target) patient using domain adaptive GPs [1]. We extend this personalized approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24 months. We compare this approach to a GP model trained only on past data of the target patients tGP, as well as to a new approach that combines pGP with tGP. We find that this new approach (pGP+tGP) leads to significant improvements in accurately forecasting future ADAS-Cog13 scores.

Targeted Therapies for Parkinson's Disease: From Genetics to the Clinic

The greatest unmet medical need in Parkinson's disease (PD) is treatments that slow the relentless progression of symptoms. The discovery of genetic variants causing and/or increasing the risk for PD has provided the field with a new arsenal of potential therapies ready to be tested in clinical trials. We highlight 3 of the genetic discoveries (α-synuclein, glucocerebrosidase, and leucine-rich repeat kinase) that have prompted new therapeutic approaches now entering the clinical stages. We are at an exciting juncture in the journey to developing disease-modifying treatments based on knowledge of PD genetics and pathology. This review focuses on therapeutic paradigms that are under clinical development and highlights a wide range of key outstanding questions in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Utilizing the Centiloid scale in cross-sectional and longitudinal PiB PET studies

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. Successful use of this tool is limited by the lack of a common standard in the quantification of amyloid imaging data. The Centiloid approach was recently proposed to address this problem and in this work, we report our implementation of this approach and evaluate the impact of differences in underlying image analysis methodologies using both cross-sectional and longitudinal datasets. The Centiloid approach successfully converts quantitative amyloid burden measurements into a common Centiloid scale (CL) and comparable dynamic range. As expected, the Centiloid values derived from different analytical approaches inherit some of the inherent benefits and drawbacks of the underlying approaches, and these differences result in statistically significant (p < 0.05) differences in the variability and group mean values. Because of these differences, even after expression in CL, the 95% specificity amyloid positivity thresholds derived from different analytic approaches varied from 5.7 CL to 11.9 CL, and the reliable worsening threshold varied from −2.0 CL to 11.0 CL. Although this difference is in part due to the dependency of the threshold determination methodology on the statistical characteristics of the measurements. When amyloid measurements obtained from different centers are combined for analysis, one should not expect Centiloid conversion to eliminate all the differences in amyloid burden measurements due to variabilities in underlying acquisition protocols and analysis techniques.

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The Centiloid approach brings amyloid burden measurements into a common scale.

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The Centiloid value inherits the characteristics of the underlying method.

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The Centiloid value derived from different analysis techniques remains different.

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The amyloid positivity thresholds in Centiloid are sensitive to implementation.

A Systematic Review of Antiamyloidogenic and Metal-Chelating Peptoids: Two Structural Motifs for the Treatment of Alzheimer's Disease

Alzheimer's disease (AD) is an incurable form of dementia affecting millions of people worldwide and costing billions of dollars in health care-related payments, making the discovery of a cure a top health, societal, and economic priority. Peptide-based drugs and immunotherapies targeting AD-associated beta-amyloid (Aβ) aggregation have been extensively explored; however, their therapeutic potential is limited by unfavorable pharmacokinetic (PK) properties. Peptoids (N-substituted glycine oligomers) are a promising class of peptidomimetics with highly tunable secondary structures and enhanced stabilities and membrane permeabilities. In this review, the biological activities, structures, and physicochemical properties for several amyloid-targeting peptoids will be described. In addition, metal-chelating peptoids with the potential to treat AD will be discussed since there are connections between the dysregulation of certain metals and the amyloid pathway.

Mobile and pervasive computing technologies and the future of Alzheimer's clinical trials

The rapid growth of mobile phones, automated speech recognizing personal assistants, and internet access among the elderly provides new opportunities for incorporating such technologies into clinical research and personalized medical care. Alzheimer's disease is a good test case given the need for early detection, the high rate of clinical trial failures, the need to more efficiently recruit patients for trials, and the need for sensitive and ecologically valid trial outcomes.

Longitudinal Modeling of Functional Decline Associated with Pathologic Alzheimer's Disease in Older Persons without Cognitive Impairment

BACKGROUND

Therapeutic research on Alzheimer's disease (AD) has moved to intercepting the disease at the preclinical phase. Most drugs in late development have focused on the amyloid hypothesis.

OBJECTIVE

To understand the magnitude of amyloid-related functional decline and to identify the functional domains sensitive to decline in a preclinical AD population.

METHODS

Data were from the Religious Orders Study and the Rush Memory and Aging Project. Cognitive decline was measured by a modified version of the Alzheimer's Disease Cooperative Study Preclinical Alzheimer Cognitive Composite. The trajectories of functional decline, as measured by the instrumental and basic activities of daily living, were longitudinally modeled in 484 participants without cognitive impairment at baseline and having both a final clinical and a postmortem neuropathology assessment of AD.

RESULTS

Individuals with different final clinical diagnoses had different trajectories of cognitive and functional decline. Individuals with AD dementia, minor cognitive impairment, and no cognitive impairment had the most, intermediate, and least declines. While individuals with pathologic AD had significantly more cognitive decline over time than those without, the magnitude of difference in functional decline between these two groups was small. Functional domains such as handling finance and handling medications were more sensitive to decline.

CONCLUSION

Demonstrating the functional benefit of an amyloid-targeting drug represents a significant challenge as elderly people experience functional decline due to a wide range of reasons with limited manifestation attributable to AD neuropathology. More sensitive functional scales focusing on the functional domains sensitive to decline in preclinical AD are needed.

Amyloid Network Topology Characterizes the Progression of Alzheimer's Disease During the Predementia Stages

There is increasing evidence showing that the accumulation of the amyloid-β (Aβ) peptide into extracellular plaques is a central event in Alzheimer's disease (AD). These abnormalities can be detected as lowered levels of Aβ42 in the cerebrospinal fluid (CSF) and are followed by increased amyloid burden on positron emission tomography (PET) several years before the onset of dementia. The aim of this study was to assess amyloid network topology in nondemented individuals with early stage Aβ accumulation, defined as abnormal CSF Aβ42 levels and normal Florbetapir PET (CSF+/PET-), and more advanced Aβ accumulation, defined as both abnormal CSF Aβ42 and Florbetapir PET (CSF+/PET+). The amyloid networks were built using correlations in the mean 18F-florbetapir PET values between 72 brain regions and analyzed using graph theory analyses. Our findings showed an association between early amyloid stages and increased covariance as well as shorter paths between several brain areas that overlapped with the default-mode network (DMN). Moreover, we found that individuals with more advanced amyloid accumulation showed more widespread changes in brain regions both within and outside the DMN. These findings suggest that amyloid network topology could potentially be used to assess disease progression in the predementia stages of AD.