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

Neuroscience learning from longitudinal cohort studies of Alzheimer's disease: Lessons for disease-modifying drug programs and an introduction to the Center for Neurodegeneration and Translational Neuroscience

The development of disease-modifying therapies for Alzheimer's disease is an urgent public health emergency. Recent failures have highlighted the significant challenges faced by drug-development programs. Longitudinal cohort studies are ideal for promoting understanding of this multifactorial, slowly progressive disease. In this section of the special edition, we review several important lessons from longitudinal cohort studies which should be considered in disease-modifying therapy development. In the final section, we introduce the clinical cohort of the Center for Neurodegeneration and Translational Neuroscience. This newly established longitudinal study aims to provide new insights into the neuroimaging and biological marker (biomarkers) correlates of cognitive decline in early Alzheimer's disease and Parkinson's disease (PD).

Gray Matter Volume and Cognitive Performance During Normal Aging. A Voxel-Based Morphometry Study

Normal aging is characterized by decline in cognitive functioning in conjunction with extensive gray matter (GM) atrophy. A first aim of this study was to determine GM volume differences related to aging by comparing two groups of participants, middle-aged group (MAG, mean age 41 years, N = 16) and older adults (OG, mean age 71 years, N = 14) who underwent an magnetic resonance images (MRI) voxel-based morphometry (VBM) evaluation. The VBM analyses included two optimized pipelines, for the cortex and for the cerebellum. Participants were also evaluated on a wide range of cognitive tests assessing both domain-general and language-specific processes, in order to examine how GM volume differences between OG and MAG relate to cognitive performance. Our results show smaller bilateral GM volume in the OG relative to the MAG, in several cerebral and right cerebellar regions involved in language and executive functions. Importantly, our results also revealed smaller GM volume in the right cerebellum in OG relative to MAG, supporting the idea of a complex cognitive role for this structure. This study provides a broad picture of cerebral, but also cerebellar and cognitive changes associated with normal aging.

Plasma levels of soluble amyloid precursor protein β in symptomatic Alzheimer's disease

The established biomarkers of Alzheimer's disease (AD) require invasive endeavours or presuppose sophisticated technical equipment. Consequently, new biomarkers are needed. Here, we report that plasma levels of soluble amyloid precursor protein β (sAPPβ), a protein of the initial phase of the amyloid cascade, were significantly lower in patients with symptomatic AD (21 with mild cognitive impairment due to AD and 44 with AD dementia) with AD-typical cerebral hypometabolic pattern compared with 27 cognitively healthy elderly individuals without preclinical AD. These findings yield further evidence for the potential of sAPPβ in plasma as an AD biomarker candidate.

External validation of change formulae in neuropsychology with neuroimaging biomarkers: A methodological recommendation and preliminary clinical data

OBJECTIVE

Within neuropsychology, a number of mathematical formulae (e.g. reliable change index, standardized regression based) have been used to determine if change across time has reliably occurred. When these formulae have been compared, they often produce different results, but 'different' results do not necessarily indicate which formulae are 'best.' The current study sought to further our understanding of change formulae by comparing them to clinically relevant external criteria (amyloid deposition and hippocampal volume).

METHOD

In a sample of 25 older adults with varying levels of cognitive intactness, participants were tested twice across one week with a brief cognitive battery. Seven different change scores were calculated for each participant. An amyloid PET scan (to get a composite of amyloid deposition) and an MRI (to get hippocampal volume) were also obtained.

RESULTS

Deviation-based change formulae (e.g. simple discrepancy score, reliable change index with or without correction for practice effects) were all identical in their relationship to the two neuroimaging biomarkers, and all were non-significant. Conversely, regression-based change formulae (e.g. simple and complex indices) showed stronger relationships to amyloid deposition and hippocampal volume.

CONCLUSIONS

These results highlight the need for external validation of the various change formulae used by neuropsychologists in clinical settings and research projects. The findings also preliminarily suggest that regression-based change formulae may be more relevant than deviation-based change formulae in this context.

Brain Biomarkers in Familial Alzheimer's Disease Mouse Models

Alzheimer's disease (AD) is characterized by progressive loss of memory and cognitive deterioration. It is thought that the onset of the disease takes place several decades before memory deficits are apparent. Reliable biomarkers for the diagnosis or prognostication of the disease are highly desirable. Neural stem cells (NSC) exist in the adult brain throughout life and give rise to neural progenitor cells (NPC), which differentiate into neurons or glia. The level of NPC proliferation and new neuron formation is significantly compromised in mouse models of familial Alzheimer's disease (FAD). These deficits are readily detected in young adults, at 2-3 months of age, preceding amyloid deposition and cognitive impairments, which may indicate that impaired neurogenesis can be an early biomarker for cognitive deficits in AD. Recent studies suggest that NSC can be detected in live rodents, noninvasively, using proton magnetic resonance spectroscopy (1H-MRS) signal at 1.28 ppm. Here we examined the use of 1H-MRS for determining the extent of neurogenesis in the brains of FAD mice. We observed that the reduction in neurogenesis in the FAD mice as observed by immunohistochemistry, was not manifested by a reduction in the 1.28 ppm signal, suggesting that this marker is either not specific for neurogenesis or not sensitive enough for the detection of alterations in hippocampal neurogenesis in the brains of FAD mice.

MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer's Dementia 24 Months Before Probable Diagnosis

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests.

Japanese and North American Alzheimer's Disease Neuroimaging Initiative studies: Harmonization for international trials

INTRODUCTION

We conducted Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) and compared the basic characteristics and progression profiles with those of ADNI in North America.

METHODS

A total of 537 Japanese subjects with normal cognition, late amnestic mild cognitive impairment (LMCI), or mild Alzheimer's disease (AD) were enrolled using the same criteria as ADNI. Rates of changes in representative cognitive or functional measures were compared for amyloid positron emission tomography- or cerebrospinal fluid amyloid β(1-42)-positive LMCI and mild AD between J-ADNI and ADNI.

RESULTS

Amyloid positivity rates were significantly higher in normal cognition of ADNI but at similar levels in LMCI and mild AD between J-ADNI and ADNI. Profiles of decline in cognitive or functional measures in amyloid-positive LMCI in J-ADNI (n = 75) and ADNI (n = 269) were remarkably similar, whereas those in mild AD were milder in J-ADNI (n = 73) compared with ADNI (n = 230).

DISCUSSION

These results support the feasibility of bridging of clinical trials in the prodromal stage of AD between Asia and western countries.

Telomere attrition is associated with declines in medial temporal lobe volume and white matter microstructure in functionally independent older adults

Although leukocyte telomere length (TL) shortens over the lifespan and is associated with diseases of aging, little is known about the relationships between TL, memory, and brain structure. Sixty-nine functionally normal older adults (mean age = 71.7) were assessed at 2 time points (mean interval = 2.9 years). Linear mixed models assessed relationships between TL and hippocampal volume, fractional anisotropy, and mean diffusivity (MD) of the fornix and verbal and visual episodic memory. Unstandardized coefficients are reported in the following, and p values are not corrected for multiple comparisons. A negative baseline trend was observed between TL and fornix MD (b = -0.01, p = 0.06), but no other cross-sectional associations were significant (ps > 0.16). Greater TL shortening at follow-up was associated with greater hippocampal volume loss (b = 27.09, p < 0.001), even after controlling for global volume loss (b = 10.83, p = 0.002). Greater telomere attrition was also associated with larger increases in fornix MD (b = -0.01, p = 0.012) and decreases in fornix fractional anisotropy (b = 0.004, p = 0.002). TL was not associated with changes in episodic memory (ps > 0.23). These relationships may reflect neurobiological influences that affect both TL and brain structure, as well as the effect of TL on brain aging via mechanisms such as cellular senescence and inflammation.

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.

Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.

Multi-Layer Multi-View Classification for Alzheimer's Disease Diagnosis

In this paper, we propose a novel multi-view learning method for Alzheimer's Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i.e., multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.

Nursing Home Use Across The Spectrum of Cognitive Decline: Merging Mayo Clinic Study of Aging With CMS MDS Assessments

BACKGROUND/OBJECTIVES

Objective, complete estimates of nursing home (NH) use across the spectrum of cognitive decline are needed to help predict future care needs and inform economic models constructed to assess interventions to reduce care needs.

DESIGN

Retrospective longitudinal study.

SETTING

Olmsted County, MN.

PARTICIPANTS

Mayo Clinic Study of Aging participants assessed as cognitively normal (CN), mild cognitive impairment (MCI), previously unrecognized dementia, or prevalent dementia (age = 70-89 years; N = 3,545).

MEASUREMENTS

Participants were followed in Centers for Medicare and Medicaid Services (CMS) Minimum Data Set (MDS) NH records and in Rochester Epidemiology Project provider-linked medical records for 1-year after assessment of cognition for days of observation, NH use (yes/no), NH days, NH days/days of observation, and mortality.

RESULTS

In the year after cognition was assessed, for persons categorized as CN, MCI, previously unrecognized dementia, and prevalent dementia respectively, the percentages who died were 1.0%, 2.6%, 4.2%, 21%; the percentages with any NH use were 3.8%, 8.7%, 19%, 40%; for persons with any NH use, median NH days were 27, 38, 120, 305, and median percentages of NH days/days of observation were 7.8%, 12%, 33%, 100%. The year after assessment, among persons with prevalent dementia and any NH use, >50% were a NH resident all days of observation. Pairwise comparisons revealed that each increase in cognitive impairment category exhibited significantly higher proportions with any NH use. One-year mortality was especially high for persons with prevalent dementia and any NH use (30% vs 13% for those with no NH use); 58% of all deaths among persons with prevalent dementia occurred while a NH resident.

CONCLUSIONS

Findings suggest reductions in NH use could result from quality alternatives to NH admission, both among persons with MCI and persons with dementia, together with suitable options for end-of-life care among persons with prevalent dementia.

Alzheimer's disease CSF biomarkers: clinical indications and rational use

This review focusses on the validation and standardization of Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers, as well as on the current clinical indications and rational use of CSF biomarkers in daily clinical practice. The validated AD CSF biomarkers, Aβ1-42, T-tau, and P-tau181, have an added value in the (differential) diagnosis of AD and related disorders, including mixed pathologies, atypical presentations, and in case of ambiguous clinical dementia diagnosis. CSF biomarkers should not be routinely used in the diagnostic work-up of dementia and cannot be used to diagnose non-AD dementias. In cognitively healthy subjects, CSF biomarkers can only be applied for research purposes, e.g., to identify pre-clinical AD in the context of clinical trials with potentially disease-modifying drugs. Therefore, biomarker-based early diagnosis of AD offers great opportunities for preventive treatment development in the near future.

Unlocking the mystery of biomarkers: A brief introduction, challenges and opportunities in Parkinson Disease

First described 200 years ago, Parkinson Disease (PD) exhibits considerable heterogeneity in clinical presentation, as well as trajectory of motor and non-motor decline. This heterogeneity, in turn, complicates the planning of clinical research, particularly trials of disease-modifying therapies, as well as the care of PD patients. While clinical features have been used to delineate subgroups of PD patients, clinical subtyping is hampered by change in features over time, and clinical subtyping may fail to capture the biological processes underlying heterogeneity. In contrast, biomarkers - objective measures that serve as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic interventions - have promise to delineate molecularly-defined subgroups of PD patients who may be most likely to benefit from specific therapeutic interventions. Here we review the present role of genetic and biochemical biomarkers in PD. Moreover, we highlight areas where the use of biomarkers may benefit clinical trial planning, as well as clinical care through the application of a "precision medicine" approach, in the near term.