Subtypes based on cerebrospinal fluid and magnetic resonance imaging markers in normal elderly predict cognitive decline

Cluster-Based White Matter Signatures and the Risk of Dementia, Stroke, and Mortality in Community-Dwelling Adults

BACKGROUND AND OBJECTIVES

Markers of white matter (WM) injury on brain MRI are important indicators of brain health. Different patterns of WM atrophy, WM hyperintensities (WMHs), and microstructural integrity could reflect distinct pathologies and disease risks, but large-scale imaging studies investigating WM signatures are lacking. This study aims to identify distinct WM signatures using brain MRI in community-dwelling adults, determine underlying risk factor profiles, and assess risks of dementia, stroke, and mortality associated with each signature.

METHODS

Between 2005 and 2016, we measured WMH volume, WM volume, fractional anisotropy (FA), and mean diffusivity (MD) using automated pipelines on structural and diffusion MRI in community-dwelling adults aged older than 45 years of the Rotterdam study. Continuous surveillance was conducted for dementia, stroke, and mortality. We applied hierarchical clustering to identify separate WM injury clusters and Cox proportional hazard models to determine their risk of dementia, stroke, and mortality.

RESULTS

We included 5,279 participants (mean age 65.0 years, 56.0% women) and identified 4 distinct data-driven WM signatures: (1) above-average microstructural integrity and little WM atrophy and WMH; (2) above-average microstructural integrity and little WMH, but substantial WM atrophy; (3) poor microstructural integrity and substantial WMH, but little WM atrophy; and (4) poor microstructural integrity with substantial WMH and WM atrophy. Prevalence of cardiovascular risk factors, lacunes, and cerebral microbleeds was higher in clusters 3 and 4 than in clusters 1 and 2. During a median 10.7 years of follow-up, 291 participants developed dementia, 220 had a stroke, and 910 died. Compared with cluster 1, dementia risk was increased for all clusters, notably cluster 3 (hazard ratio [HR] 3.06, 95% CI 2.12-4.42), followed by cluster 4 (HR 2.31, 95% CI 1.58-3.37) and cluster 2 (HR 1.67, 95% CI 1.17-2.38). Compared with cluster 1, risk of stroke was higher only for clusters 3 (HR 1.55, 95% CI 1.02-2.37) and 4 (HR 1.94, 95% CI 1.30-2.89), whereas mortality risk was increased in all clusters (cluster 2: HR 1.27, 95% CI 1.06-1.53, cluster 3: HR 1.65, 95% CI 1.35-2.03, cluster 4: HR 1.76, 95% CI 1.44-2.15), compared with cluster 1. Models including clusters instead of an individual imaging marker showed a superior goodness of fit for dementia and mortality, but not for stroke.

DISCUSSION

Clustering can derive WM signatures that are differentially associated with dementia, stroke, and mortality risk. Future research should incorporate spatial information of imaging markers.

Unexpected cognitive similarities between older adults and young people: Scores variability and cognitive performances

BACKGROUND

Increased interindividual variability in cognitive performance during aging has been proposed as an indicator of cognitive reserve.

OBJECTIVE

To determine if interindividual variability performance in episodic memory (PAL), working memory (SWM), reaction time (RTI), and sustained attention (RVP) could differentiate clusters of differential cognitive performance in healthy young and older adults and search for cognitive tests that most contribute to these differential performances.

METHODS

We employed hierarchical cluster and canonical discriminant function analyses of cognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) to identify cognitive variability in older and young adults using the coefficient of variability of cognitive performances between and within groups. We also analyzed potential influences of age, education, and physical activity.

RESULTS

Cluster analysis distinguished groups with differential cognitive performance and correlation analysis revealed coefficient of variability and cognitive performance associations. The greater the coefficient of variability the poorer was cognitive performance in RTI but not in PAL and SWM. Older adults showed diverse trajectories of cognitive decline, and better education or higher percentage of physically active individuals exhibited better cognitive performance in both older and young adults.

CONCLUSION

PAL and SWM are the most sensitive tests to investigate the wide age range encompassing older and young adults. In older adults' intragroup analysis PAL showed greater discriminatory capacity, indicating its potential for clinical applications late in life. Our data underscore the importance of studying variability as a tool for early detection of subtle cognitive declines and for interpreting results that deviate from normality.

Detection and treatment of Alzheimer's disease in its preclinical stage

Longitudinal multimodal biomarker studies reveal that the continuum of Alzheimer's disease (AD) includes a long latent phase, referred to as preclinical AD, which precedes the onset of symptoms by decades. Treatment during the preclinical AD phase offers an optimal opportunity for slowing the progression of disease. However, trial design in this population is complex. In this Review, we discuss the recent advances in accurate plasma measurements, new recruitment approaches, sensitive cognitive instruments and self-reported outcomes that have facilitated the successful launch of multiple phase 3 trials for preclinical AD. The recent success of anti-amyloid immunotherapy trials in symptomatic AD has increased the enthusiasm for testing this strategy at the earliest feasible stage. We provide an outlook for standard screening of amyloid accumulation at the preclinical stage in clinically normal individuals, during which effective therapy to delay or prevent cognitive decline can be initiated.

Transferability of Alzheimer's disease progression subtypes to an independent population cohort

In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models. We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset. The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: 'typical', 'cortical' and 'subcortical'. Next, the subtype agreement was further supported by high consistency in individuals' subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications. In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD.

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes

BACKGROUND

Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses.

OBJECTIVE

To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers.

METHODS

We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class.

RESULTS

For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index.

CONCLUSION

Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.

Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data

Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets. SuStaIn has been used to identify data-driven subgroups and perform patient stratification in neurodegenerative diseases and in lung diseases from continuous biomarker measurements predominantly obtained from imaging. However, the SuStaIn algorithm is not currently applicable to discrete ordinal data, such as visual ratings of images, neuropathological ratings, and clinical and neuropsychological test scores, restricting the applicability of SuStaIn to a narrower range of settings. Here we propose 'Ordinal SuStaIn', an ordinal version of the SuStaIn algorithm that uses a scored events model of disease progression to enable the application of SuStaIn to ordinal data. We demonstrate the validity of Ordinal SuStaIn by benchmarking the performance of the algorithm on simulated data. We further demonstrate that Ordinal SuStaIn out-performs the existing continuous version of SuStaIn (Z-score SuStaIn) on discrete scored data, providing much more accurate subtype progression patterns, better subtyping and staging of individuals, and accurate uncertainty estimates. We then apply Ordinal SuStaIn to six different sub-scales of the Clinical Dementia Rating scale (CDR) using data from the Alzheimer's disease Neuroimaging Initiative (ADNI) study to identify individuals with distinct patterns of functional decline. Using data from 819 ADNI1 participants we identified three distinct CDR subtype progression patterns, which were independently verified using data from 790 ADNI2 participants. Our results provide insight into patterns of decline in daily activities in Alzheimer's disease and a mechanism for stratifying individuals into groups with difficulties in different domains. Ordinal SuStaIn is broadly applicable across different types of ratings data, including visual ratings from imaging, neuropathological ratings and clinical or behavioural ratings data.

Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment

Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity.

Patterns of CSF Inflammatory Markers in Non-demented Older People: A Cluster Analysis

Objective

In this study, we aimed to examine if patterns of CSF inflammatory markers are correlated with global cognition, episodic memory, hippocampal volume, and CSF AD-related pathologies among non-demented older people.

Methods

We included 217 non-demented older individuals, including 87 subjects with normal cognition (NC) and 130 subjects with mild cognitive impairment (MCI) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Hierarchical cluster analysis including nine inflammatory markers in CSF [Tumor necrosis factor-α(TNF-α), TNF-R1, TNF-R2, transforming growth factor-β1 (TGF-β1), TGF-β2, TGF-β3, Interleukin-21 (IL-21), IL-6, and IL-7] was conducted.

Results

We identified two clusters among non-demented older people based on nine inflammatory markers in CSF. Compared to the first cluster, the second cluster showed significantly higher levels of CSF inflammatory markers (TNF-R1, TNF-R2, TGF-β1, TGF-β3, and IL-6). Further, the second cluster was also associated with higher levels of t-tau and p-tau levels in CSF.

Conclusion

We observed a subgroup of non-demented older people characterized by increased levels of inflammatory markers in CSF. Further, this subgroup showed higher levels of t-tau and p-tau levels in CSF.

Biomarker profiling beyond amyloid and tau: cerebrospinal fluid markers, hippocampal atrophy, and memory change in cognitively unimpaired older adults

Brain changes occurring in aging can be indexed by biomarkers. We used cluster analysis to identify subgroups of cognitively unimpaired individuals (n = 99, 64-93 years) with different profiles of the cerebrospinal fluid biomarkers beta amyloid 1-42 (Aβ42), phosphorylated tau (P-tau), total tau, chitinase-3-like protein 1 (YKL-40), fatty acid binding protein 3 (FABP3), and neurofilament light (NFL). Hippocampal volume and memory were assessed across multiple follow-up examinations covering up to 6.8 years. Clustering revealed one group (39%) with more pathological concentrations of all biomarkers, which could further be divided into one group (20%) characterized by tauopathy and high FABP3 and one (19%) by brain β-amyloidosis, high NFL, and slightly higher YKL-40. The clustering approach clearly outperformed classification based on Aβ42 and P-tau alone in prediction of memory decline, with the individuals with most tauopathy and FABP3 showing more memory decline, but not more hippocampal volume change. The results demonstrate that older adults can be classified based on biomarkers beyond amyloid and tau, with improved prediction of memory decline.

Nodal Global Efficiency in Front-Parietal Lobe Mediated Periventricular White Matter Hyperintensity (PWMH)-Related Cognitive Impairment

White matter hyperintensity (WMH) is widely observed in the elderly population and serves as a key indicator of cognitive impairment (CI). However, the underlying mechanism remains to be elucidated. Herein, we investigated the topological patterns of resting state functional networks in WMH subjects and the relationship between the topological measures and CI. A graph theory-based analysis was employed in the resting-state functional magnetic resonance scans of 112 subjects (38 WMH subjects with cognitive impairment without dementia (CIND), 36 WMH subjects with normal cognition and 38 healthy controls (HCs), and we found that WMH-CIND subjects displayed decreased global efficiency at the levels of the whole brain, specific subnetworks [fronto-parietal network (FPN) and cingulo-opercular network (CON)] and certain nodes located in the FPN and CON, as well as decreased local efficiency in subnetworks. Our results demonstrated that nodal global efficiency in frontal and parietal regions mediated the impairment of information processing speed related to periventricular WMH (PWMH). Additionally, we performed support vector machine (SVM) analysis and found that altered functional efficiency can identify WMH-CIND subjects with high accuracy, sensitivity and specificity. These findings suggest impaired functional networks in WMH-CIND individuals and that decreased functional efficiency may be a feature of CI in WMH subjects.

Subtypes Based on Six Apolipoproteins in Non-Demented Elderly Are Associated with Cognitive Decline and Subsequent Tau Accumulation in Cerebrospinal Fluid

Apolipoproteins (APOs) have been implicated in the pathogenesis of Alzheimer's disease (AD). In the present study, we aimed to investigate if patterns of cerebrospinal fluid (CSF) APOs (APOA-I, APOC-III, APOD, APOE, APOH, and APOJ) levels are associated with changes over time in cognition, memory performance, neuroimaging markers, and AD-related pathologies (CSF Aβ42, t-tau, and p-tau) in non-demented older adults. At baseline, a total of 241 non-demented older adults with CSF APOs data was included in the present analysis. Hierarchical agglomerative cluster analysis including the six CSF APOs was carried out. Among non-demented older adults, we identified two clusters. Compare with the first cluster, the second cluster had higher levels of APOs in CSF. Additionally, the second cluster showed a more benign disease course, including slower cognitive decline and slower p-tau accumulation in CSF. Our data highlight the importance of APOs in the pathogenesis of AD.

Biomarker-guided clustering of Alzheimer's disease clinical syndromes

Alzheimer's disease (AD) neuropathology is extremely heterogeneous, and the evolution from preclinical to mild cognitive impairment until dementia is driven by interacting genetic/biological mechanisms not fully captured by current clinical/research criteria. We characterized the heterogeneous "construct" of AD through a cerebrospinal fluid biomarker-guided stratification approach. We analyzed 5 validated pathophysiological cerebrospinal fluid biomarkers (Aβ_1-42, t-tau, p-tau₁₈₁, NFL, YKL-40) in 113 participants (healthy controls [N = 20], subjective memory complainers [N = 36], mild cognitive impairment [N = 20], and AD dementia [N = 37], age: 66.7 ± 10.4, 70.4 ± 7.7, 71.7 ± 8.4, 76.2 ± 3.5 years [mean ± SD], respectively) using Density-Based Spatial Clustering of Applications with Noise, which does not require a priori determination of the number of clusters. We found 5 distinct clusters (sizes: N = 38, 16, 24, 14, and 21) whose composition was independent of phenotypical groups. Two clusters showed biomarker profiles linked to neurodegenerative processes not associated with classical AD-related pathophysiology. One cluster was characterized by the neuroinflammation biomarker YKL-40. Combining nonlinear data aggregation with informative biomarkers can generate novel patient strata which are representative of cellular/molecular pathophysiology and may aid in predicting disease evolution and mechanistic drug response.

Revealing heterogeneity of brain imaging phenotypes in Alzheimer's disease based on unsupervised clustering of blood marker profiles

Alzheimer's disease (AD) affects millions of people and is a major rising problem in health care worldwide. Recent research suggests that AD could have different subtypes, presenting differences in how the disease develops. Characterizing those subtypes could be key to deepen the understanding of this complex disease. In this paper, we used a multivariate, non-supervised clustering method over blood-based markers to find subgroups of patients defined by distinctive blood marker profiles. Our analysis on ADNI database identified 4 possible subgroups, each with a different blood profile. More importantly, we show that subgroups with different profiles have a different relationship between brain phenotypes detected in magnetic resonance imaging and disease condition.

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10⁻⁴) or temporal stage (p = 3.96 × 10⁻⁵). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.

Clinical dementia severity associated with ventricular size is differentially moderated by cognitive reserve in men and women

Background

Interindividual differences in cognitive reserve (CR) are associated with complex and dynamic clinical phenotypes observed in cognitive impairment and dementia. We tested whether (1) CR early in life (E-CR; measured by education and IQ), (2) CR later in life (L-CR; measured by occupation), and (3) CR panel (CR-P) with the additive effects of E-CR and L-CR, act as moderating factors between baseline ventricular size and clinical dementia severity at baseline and across 2 years. We further examined whether this moderation is differentially represented by sex.

Methods

We examined a longitudinal model using patients (N = 723; mean age = 70.8 ± 9.4 years; age range = 38–90 years; females = 374) from the Sunnybrook Dementia Study. The patients represented Alzheimer’s disease (n = 439), mild cognitive impairment (n = 77), vascular cognitive impairment (n = 52), Lewy body disease (n = 30), and frontotemporal dementia (n = 125). Statistical analyses included (1) latent growth modeling to determine how clinical dementia severity changes over 2 years (measured by performance on the Dementia Rating Scale), (2) confirmatory factor analysis to establish a baseline E-CR factor, and (3) path analysis to predict dementia severity. Baseline age (continuous) and Apolipoprotein E status (ɛ4−/ɛ4+) were included as covariates.

Results

The association between higher baseline ventricular size and dementia severity was moderated by (1) E-CR and L-CR and (2) CR-P. This association was differentially represented in men and women. Specifically, men in only the low CR-P had higher baseline clinical dementia severity with larger baseline ventricular size. However, women in the low CR-P showed the (1) highest baseline dementia severity and (2) fastest 2-year decline with larger baseline ventricular size.

Conclusions

Clinical dementia severity associated with ventricular size may be (1) selectively moderated by complex and additive CR networks and (2) differentially represented by sex.

Trials registration

ClinicalTrials.gov, NCT01800214. Registered on 27 February 2013.

Electronic supplementary material

The online version of this article (10.1186/s13195-018-0419-2) contains supplementary material, which is available to authorized users.

Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers

INTRODUCTION

Models characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD.

METHODS

We used a large multimodal longitudinal data set of biomarkers and demographic and genotype information from 1624 participants from the Alzheimer's Disease Neuroimaging Initiative. Using hidden Markov models, we characterized intermediate disease stages. We validated inferred disease trajectories by comparing time to first clinical AD diagnosis. We trained an L2-regularized logistic regression model to predict disease trajectory and evaluated its discriminative performance on a test set.

RESULTS

We identified 12 distinct disease states. Progression to AD occurred most often through one of two possible paths through these states. Paths differed in terms of rate of disease progression (by 5.44 years on average), amyloid and total-tau (t-tau) burden (by 10% and 69%, respectively), and hippocampal neurodegeneration (P < .001). On the test set, the predictive model achieved an area under the receiver operating characteristic curve of 0.85.

DISCUSSION

Progression to AD, in terms of biomarker trajectories, can be predicted based on participant-specific factors. Such disease staging tools could help in targeting high-risk patients for therapeutic intervention trials. As longitudinal data with richer features are collected, such models will help increase our understanding of the factors that drive the different trajectories of AD.

Alzheimer's Disease Biomarkers and Future Decline in Cognitive Normal Older Adults

BACKGROUND

Identifying older adults at risk of cognitive decline represents a challenge as Alzheimer's disease (AD) modifying therapies move toward preclinical stages.

OBJECTIVE

To investigate the relationship between AD biomarkers and subsequent change in cognition in a cohort of cognitively intact older adults.

METHODS

84 cognitively normal subjects (mean age 72.0 years, 59% women) were recruited through the Massachusetts Alzheimer's Disease Research Center and the Harvard Aging Brain Study and followed over 3 years. Measurements of amyloid-β 1-42 (Aβ42), total Tau (t-Tau), and Tau phosphorylated at threonine 181 (p-Tau181) in the cerebrospinal fluid (CSF) at study entry were available in all cases. Baseline brain MRI, FDG-PET, and PiB-PET data were available in the majority of participants. Relationship between baseline AD biomarkers and longitudinal change in cognition was assessed using Cox proportional hazard regression and linear mixed models.

RESULTS

14% participants increased their global Clinical Dementia Rating (CDR) score from 0 to 0.5 during follow-up. A CDR score increase was associated with higher baseline CSF t-Tau and p-Tau181, higher global cortical PiB retention, and lower hippocampal volume. The combination of high CSF t-Tau and low Aβ42 or low hippocampal volume was more strongly related to cognitive outcome than each single biomarker. Higher CSF t-Tau was the only biomarker associated with subsequent decline in MMSE score.

CONCLUSIONS

Baseline CSF t-Tau and p-Tau181, in vivo amyloid load, and hippocampal volume were all independently associated with future decline in cognition. The discriminatory ability of these biomarkers to predict risk of cognitive decline, however, was only modest.

Prediction of Alzheimer's Dementia in Patients with Amnestic Mild Cognitive Impairment in Clinical Routine: Incremental Value of Biomarkers of Neurodegeneration and Brain Amyloidosis Added Stepwise to Cognitive Status

The aim of this study was to evaluate the incremental benefit of biomarkers for prediction of Alzheimer's disease dementia (ADD) in patients with mild cognitive impairment (MCI) when added stepwise in the order of their collection in clinical routine. The model started with cognitive status characterized by the ADAS-13 score. Hippocampus volume (HV), cerebrospinal fluid (CSF) phospho-tau (pTau), and the FDG t-sum score in an AD meta-region-of-interest were compared as neurodegeneration markers. CSF-Aβ1-42 was used as amyloidosis marker. The incremental prognostic benefit from these markers was assessed by stepwise Kaplan-Meier survival analysis in 402 ADNI MCI subjects. Predefined cutoffs were used to dichotomize patients as 'negative' or 'positive' for AD characteristic alteration with respect to each marker. Among the neurodegeneration markers, CSF-pTau provided the best incremental risk stratification when added to ADAS-13. FDG PET outperformed HV only in MCI subjects with relatively preserved cognition. Adding CSF-Aβ provided further risk stratification in pTau-positive subjects, independent of their cognitive status. Stepwise integration of biomarkers allows stepwise refinement of risk estimates for MCI-to-ADD progression. Incremental benefit strongly depends on the patient's status according to the preceding diagnostic steps. The stepwise Kaplan-Meier curves might be useful to optimize diagnostic workflow in individual patients.

A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers

Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.

Cerebrospinal Biomarker Cut-off Methods Defined Only by Alzheimer's Disease Predict More Precisely Conversions of Mild Cognitive Impairment

Background and Purpose

The cerebrospinal fluid (CSF) biomarkers play an important supportive role as diagnostic and predictive indicators of Alzheimer's disease (AD). About 30% of controls in old age show abnormal values of CSF biomarkers and display a higher risk for AD compared with those showing normal values. The cut-off values are determined by their diagnostic accuracy. However, the current cut-off values may be less accurate, because controls include high-risk groups of AD. We sought to develop models of patients with AD, who are homogenous for CSF biomarkers.

Methods

We included participants who had CSF biomarker data in the Alzheimer's Disease Neuroimaging Initiative database. We investigated the factors related to CSF biomarkers in patients with AD using linear mixed models. Using the factors, we developed models corresponding to CSF biomarkers to classify patients with mild cognitive impairment (MCI) into high risk and low risk and analyzed the conversion from MCI to AD using the Cox proportional hazards model.

Results

APOE ε4 status and age were significantly related to CSF Aβ_1-42. CSF t-tau, APOE ε2 status and sex were significant factors. The CSF p-tau₁₈₁ was associated with age and frequency of diagnosis. Accordingly, we modeled the three CSF biomarkers of AD. In MCI without APOE ε4, our models were better predictors of conversion.

Conclusions

We can interpret CSF biomarkers based on the models derived from the data obtained from patients with AD.

Cognitive Variability during Middle-Age: Possible Association with Neurodegeneration and Cognitive Reserve

Objective: Increased variability in cognition with age has been argued as an indication of pathological processes. Focusing on early detection of neurodegenerative disorders, we investigated variability in cognition in healthy middle-aged adults. In order to understand possible determinants of this variability, we also investigated associations with cognitive reserve, neuroimaging markers, subjective memory complaints, depressive symptomatology, and gender.

Method: Thirty-one 50 ± 2 years old individuals were investigated as target group and deviation was studied in comparison to a reference younger group of 30 individuals 40 ± 2 years old. Comprehensive neuropsychological and structural imaging protocols were collected. Brain regional volumes and cortical thickness were calculated with FreeSurfer, white matter hyperintensities with CASCADE, and mean diffusivity with FSL.

Results: Across-individuals variability showed greater dispersion in lexical access, processing speed, executive functions, and memory. Variability in global cognition correlated with, reduced cortical thickness in the right parietal-temporal-occipital association cortex, and increased mean diffusivity in the cingulum bundle and right inferior fronto-occipital fasciculus. A trend was also observed for the correlation between global cognition and hippocampal volume and female gender. All these associations were influenced by cognitive reserve. No correlations were found with subjective memory complaints, white matter hyperintensities and depressive symptomatology. Across-domains and across-tasks variability was greater in several executive components and cognitive processing speed.

Conclusion: Variability in cognition during middle-age is associated with neurodegeneration in the parietal–temporal–occipital association cortex and white matter tracts connecting this to the prefrontal dorsolateral cortex and the hippocampus. Moreover, this effect is influenced by cognitive reserve. Studying variability offers valuable information showing that differences do not occur in the same magnitude and direction across individuals, cognitive domains and tasks. These findings may have important implications for early detection of subtle cognitive impairment and clinical interpretation of deviation from normality.

Potential fluid biomarkers for pathological brain changes in Alzheimer's disease: Implication for the screening of cognitive frailty

Cognitive frailty (CF) overlaps with early neuropathological alterations associated with aging-related major neurocognitive disorders, including Alzheimer's disease (AD). Fluid biomarkers for these pathological brain alterations allow for early diagnosis in the preclinical stages of AD, and for objective prognostic assessments in clinical intervention trials. These biomarkers may also be helpful in the screening of CF. The present study reviewed the literature and identified systematic reviews of cohort studies and other authoritative reports. The selection criteria for potentially suitable fluid biomarkers included: i) Frequent use in studies of fluid-derived markers and ii) evidence of novel measurement techniques for fluid-derived markers. The present study focused on studies that assessed these biomarkers in AD, mild cognitive impairment and non-AD demented subjects. At present, widely used fluid biomarkers include cerebrospinal fluid (CSF), total tau, phosphorylated tau and amyloid-β levels. With the development of novel measurement techniques and improvements in understanding regarding the mechanisms underlying aging-related major neurocognitive disorders, numerous novel biomarkers associated with various aspects of AD neuropathology are being explored. These include specific measurements of Aβ oligomer or monomer forms, tau proteins in the peripheral plasma and CSF, and novel markers of synaptic dysfunction, neuronal damage and apoptosis, neuronal activity alteration, neuroinflammation, blood brain barrier dysfunction, oxidative stress, metabolites, mitochondrial function and aberrant lipid metabolism. The proposed panels of fluid biomarkers may be useful in the early diagnosis of AD, prediction of the progression of AD from preclinical stages to the dementia stage, and the differentiation of AD from non-AD dementia. In combination with physical frailty, the present study surmised that these biomarkers may also be used as biomarkers for CF, thus contribute to discovering causes and informing interventions for cognitive impairment in individuals with CF.

Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife

The ability to detect preclinical Alzheimer's disease is of great importance, as this stage of the Alzheimer's continuum is believed to provide a key window for intervention and prevention. As Alzheimer's disease is characterized by multiple pathological changes, a biomarker panel reflecting co-occurring pathology will likely be most useful for early detection. Towards this end, 175 late middle-aged participants (mean age 55.9 ± 5.7 years at first cognitive assessment, 70% female) were recruited from two longitudinally followed cohorts to undergo magnetic resonance imaging and lumbar puncture. Cluster analysis was used to group individuals based on biomarkers of amyloid pathology (cerebrospinal fluid amyloid-β42/amyloid-β40 assay levels), magnetic resonance imaging-derived measures of neurodegeneration/atrophy (cerebrospinal fluid-to-brain volume ratio, and hippocampal volume), neurofibrillary tangles (cerebrospinal fluid phosphorylated tau181 assay levels), and a brain-based marker of vascular risk (total white matter hyperintensity lesion volume). Four biomarker clusters emerged consistent with preclinical features of (i) Alzheimer's disease; (ii) mixed Alzheimer's disease and vascular aetiology; (iii) suspected non-Alzheimer's disease aetiology; and (iv) healthy ageing. Cognitive decline was then analysed between clusters using longitudinal assessments of episodic memory, semantic memory, executive function, and global cognitive function with linear mixed effects modelling. Cluster 1 exhibited a higher intercept and greater rates of decline on tests of episodic memory. Cluster 2 had a lower intercept on a test of semantic memory and both Cluster 2 and Cluster 3 had steeper rates of decline on a test of global cognition. Additional analyses on Cluster 3, which had the smallest hippocampal volume, suggest that its biomarker profile is more likely due to hippocampal vulnerability and not to detectable specific volume loss exceeding the rate of normal ageing. Our results demonstrate that pathology, as indicated by biomarkers, in a preclinical timeframe is related to patterns of longitudinal cognitive decline. Such biomarker patterns may be useful for identifying at-risk populations to recruit for clinical trials.

Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer's disease: a systematic review

Background

Neuroimaging-biomarkers of Mild Cognitive Impairment (MCI) allow an early diagnosis in preclinical stages of Alzheimer’s disease (AD). The goal in this paper was to review of biomarkers for Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), with emphasis on neuroimaging biomarkers.

Methods

A systematic review was conducted from existing literature that draws on markers and evidence for new measurement techniques of neuroimaging in AD, MCI and non-demented subjects. Selection criteria included: 1) age ≥ 60 years; 2) diagnosis of AD according to NIAAA criteria, 3) diagnosis of MCI according to NIAAA criteria with a confirmed progression to AD assessed by clinical follow-up, and 4) acceptable clinical measures of cognitive impairment, disability, quality of life, and global clinical assessments.

Results

Seventy-two articles were included in the review. With the development of new radioligands of neuroimaging, today it is possible to measure different aspects of AD neuropathology, early diagnosis of MCI and AD become probable from preclinical stage of AD to AD dementia and non-AD dementia.

Conclusions

The panel of noninvasive neuroimaging-biomarkers reviewed provides a set methods to measure brain structural and functional pathophysiological changes in vivo, which are closely associated with preclinical AD, MCI and non-AD dementia. The dynamic measures of these imaging biomarkers are used to predict the disease progression in the early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials.

Cortical Amyloid β Deposition and Current Depressive Symptoms in Alzheimer Disease and Mild Cognitive Impairment

Depressive symptoms are frequently seen in patients with dementia and mild cognitive impairment (MCI). Evidence suggests that there may be a link between current depressive symptoms and Alzheimer disease (AD)-associated pathological changes, such as an increase in cortical amyloid-β (Aβ). However, limited in vivo studies have explored the relationship between current depressive symptoms and cortical Aβ in patients with MCI and AD. Our study, using a large sample of 455 patients with MCI and 153 patients with AD from the Alzheimer's disease Neuroimaging Initiatives, investigated whether current depressive symptoms are related to cortical Aβ deposition. Depressive symptoms were assessed using the Geriatric Depression Scale and Neuropsychiatric Inventory-depression/dysphoria. Cortical Aβ was quantified using positron emission tomography with the Aβ probe(18)F-florbetapir (AV-45).(18)F-florbetapir standardized uptake value ratio (AV-45 SUVR) from the frontal, cingulate, parietal, and temporal regions was estimated. A global AV-45 SUVR, defined as the average of frontal, cingulate, precuneus, and parietal cortex, was also used. We observed that current depressive symptoms were not related to cortical Aβ, after controlling for potential confounds, including history of major depression. We also observed that there was no difference in cortical Aβ between matched participants with high and low depressive symptoms, as well as no difference between matched participants with the presence and absence of depressive symptoms. The association between depression and cortical Aβ deposition does not exist, but the relationship is highly influenced by stressful events in the past, such as previous depressive episodes, and complex interactions of different pathways underlying both depression and dementia.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

INTRODUCTION

Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders.

METHODS

We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3.

RESULTS

Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials.

DISCUSSION

Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

Detection of Motor Changes in Huntington's Disease Using Dynamic Causal Modeling

Deficits in motor functioning are one of the hallmarks of Huntington's disease (HD), a genetically caused neurodegenerative disorder. We applied functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to assess changes that occur with disease progression in the neural circuitry of key areas associated with executive and cognitive aspects of motor control. Seventy-seven healthy controls, 62 pre-symptomatic HD gene carriers (preHD), and 16 patients with manifest HD symptoms (earlyHD) performed a motor finger-tapping fMRI task with systematically varying speed and complexity. DCM was used to assess the causal interactions among seven pre-defined regions of interest, comprising primary motor cortex, supplementary motor area (SMA), dorsal premotor cortex, and superior parietal cortex. To capture heterogeneity among HD gene carriers, DCM parameters were entered into a hierarchical cluster analysis using Ward's method and squared Euclidian distance as a measure of similarity. After applying Bonferroni correction for the number of tests, DCM analysis revealed a group difference that was not present in the conventional fMRI analysis. We found an inhibitory effect of complexity on the connection from parietal to premotor areas in preHD, which became excitatory in earlyHD and correlated with putamen atrophy. While speed of finger movements did not modulate the connection from caudal to pre-SMA in controls and preHD, this connection became strongly negative in earlyHD. This second effect did not survive correction for multiple comparisons. Hierarchical clustering separated the gene mutation carriers into three clusters that also differed significantly between these two connections and thereby confirmed their relevance. DCM proved useful in identifying group differences that would have remained undetected by standard analyses and may aid in the investigation of between-subject heterogeneity.

Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials.

METHODS

We searched for ADNI publications using established methods.

RESULTS

ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis.

DISCUSSION

ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.

The Alzheimer's Disease Neuroimaging Initiative phase 2: Increasing the length, breadth, and depth of our understanding

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study designed to characterize the trajectories of biomarkers across the aging process. We present ADNI Biostatistics Core analyses that integrate data over the length, breadth, and depth of ADNI.

METHODS

Relative progression of key imaging, fluid, and clinical measures was assessed. Individuals with subjective memory complaints (SMC) and early mild cognitive impairment (eMCI) were compared with normal controls (NC), MCI, and individuals with Alzheimer's disease. Amyloid imaging and magnetic resonance imaging (MRI) summaries were assessed as predictors of disease progression.

RESULTS

Relative progression of markers supports parts of the amyloid cascade hypothesis, although evidence of earlier occurrence of cognitive change exists. SMC are similar to NC, whereas eMCI fall between the cognitively normal and MCI groups. Amyloid leads to faster conversion and increased cognitive impairment.

DISCUSSION

Analyses support features of the amyloid hypothesis, but also illustrate the considerable heterogeneity in the aging process.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Monitoring the early signs of cognitive decline in elderly by computer games: an MRI study

Background

It is anticipated that current and future preventive therapies will likely be more effective in the early stages of dementia, when everyday functioning is not affected. Accordingly the early identification of people at risk is particularly important. In most cases, when subjects visit an expert and are examined using neuropsychological tests, the disease has already been developed. Contrary to this cognitive games are played by healthy, well functioning elderly people, subjects who should be monitored for early signs. Further advantages of cognitive games are their accessibility and their cost-effectiveness.

Purpose

The aim of the investigation was to show that computer games can help to identify those who are at risk. In order to validate games analysis was completed which measured the correlations between results of the 'Find the Pairs' memory game and the volumes of the temporal brain regions previously found to be good predictors of later cognitive decline.

Participants and Methods

34 healthy elderly subjects were enrolled in the study. The volume of the cerebral structures was measured by MRI. Cortical reconstruction and volumetric segmentation were performed by Freesurfer.

Results

There was a correlation between the number of attempts and the time required to complete the memory game and the volume of the entorhinal cortex, the temporal pole, and the hippocampus. There was also a correlation between the results of the Paired Associates Learning (PAL) test and the memory game.

Conclusions

The results gathered support the initial hypothesis that healthy elderly subjects achieving lower scores in the memory game have increased level of atrophy in the temporal brain structures and showed a decreased performance in the PAL test. Based on these results it can be concluded that memory games may be useful in early screening for cognitive decline.

Multimodality imaging of Alzheimer disease and other neurodegenerative dementias

Neurodegenerative diseases, such as Alzheimer disease, result in cognitive decline and dementia and are a leading cause of mortality in the growing elderly population. These progressive diseases typically have an insidious onset, with overlapping clinical features early in the disease course that make diagnosis challenging. The neurodegenerative diseases are associated with characteristic, although not completely understood, changes in the brain: abnormal protein deposition, synaptic dysfunction, neuronal injury, and neuronal death. Neuroimaging biomarkers-principally regional atrophy on structural MR imaging, patterns of hypometabolism on (18)F-FDG PET, and detection of cerebral amyloid plaque on amyloid PET--are able to evaluate the patterns of these abnormalities in the brain to improve early diagnosis and help predict the disease course. These techniques have unique strengths and synergies in multimodality evaluation of the patient with cognitive decline or dementia. This review discusses the key imaging biomarkers from MR imaging, (18)F-FDG PET, and amyloid PET; the imaging features of the most common neurodegenerative dementias; the role of various neuroimaging studies in differential diagnosis and prognosis; and some promising imaging techniques under development.

Temporal evolution of biomarkers and cognitive markers in the asymptomatic, MCI, and dementia stage of Alzheimer's disease

BACKGROUND

We investigated the pattern of disease progression in the asymptomatic, mild cognitive impairment (MCI), and dementia stage of Alzheimer's disease (AD).

METHODS

We selected 284 subjects with AD pathology, defined as abnormal levels of amyloid beta 1-42 (Aβ1-42) in cerebrospinal fluid (CSF). Disease outcome measures included six biomarkers and five cognitive markers. We compared differences in baseline measures and decline over 4 years between the AD stages and tested whether these changes differed from subjects, without AD pathology (N = 132).

RESULTS

CSF Aβ1-42 reached the maximum abnormality level in the asymptomatic stage and tau in the MCI stage. The imaging and cognitive markers started to decline in the asymptomatic stage, and decline accelerated with advancing clinical stage.

CONCLUSION

This study provides further evidence for a temporal evolution of AD biomarkers. Our findings may be helpful to determine stage specific outcome measures for clinical trials.

Assessment of CSF Aβ42 as an aid to discriminating Alzheimer's disease from other dementias and mild cognitive impairment: a meta-analysis of 50 studies

Mild Alzheimer's disease (AD) is usually difficult to differentiate from other dementias or mild cognitive impairment (MCI). The aim of our study is to evaluate the clinical importance of cerebrospinal fluid (CSF) β-amyloid 42 (Aβ42) in MCI, AD and other dementias, more specifically: frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), Parkinson's disease (PD) with dementia (PDD) and vascular dementia (VaD). Fifty eligible articles were identified by search of databases including PubMed, EMBASE, Elsevier, Springer Link and the Cochrane Library, from January 1990 to May 2014. The random effects model was used to calculate the standardized mean difference (SMD) with corresponding 95% CI by STATA 9.0 software. The subgroup analyses were made on the method (ELISA, xMAP). We found that CSF Aβ42 concentrations were significantly lower in AD compared to MCI (SMD: -0.68, 95% CI: [-0.80, -0.56], z=11.34, P<0.001), FTD (SMD: -1.09, 95% CI: [-1.41, -0.76], z=6.62, P<0.001), PDD (SMD: -0.75, 95% CI: [-1.39, -0.10], z=2.27, P=0.023), VaD (SMD: -0.95, 95% CI: [-1.30, -0.61], z=5.43, P<0.001). In addition, compared to DLB, Aβ42 concentrations are moderately lower in AD (SMD: -0.27, 95% CI: [-0.51, -0.03], z=2.20, P=0.028). Results from this meta-analysis hinted that CSF Aβ42 is a good biomarker for discriminating Alzheimer's disease from other dementias and MCI.

Does CSF p-tau181 help to discriminate Alzheimer's disease from other dementias and mild cognitive impairment? A meta-analysis of the literature

To evaluate the clinical importance of cerebrospinal fluid (CSF) phosphorylated tau 181 (p-tau181) in mild cognitive impairment (MCI), Alzheimer's disease (AD) and other dementias, more specifically: frontotemporal degeneration (FTD), dementia with Lewy bodies (DLB), vascular dementia (VaD) and Parkinson's disease (PD) with dementia (PDD). Fifty eligible articles were identified by search of databases including PubMed, EMBASE, Elsevier, Springer Link and the Cochrane Library, up to December 2013. The random effects model was used to calculate the standardized mean difference (SMD) with corresponding 95% CI by STATA 9.0 software. The subgroup analyses were made on the methods or PD with dementia. We found that CSF p-tau181 concentrations were significantly higher in AD compared to MCI [SMD: 0.61, 95% CI: (0.46, 0.76), z = 8.07, P < 0.001], FTD [SMD: 1.23, 95% CI: (0.89, 1.56), z = 7.19, P < 0.001], DLB [SMD: 1.08, 95% CI: (0.80, 1.37), z = 7.41, P < 0.001], PDD [SMD: 1.05, 95% CI: (0.02, 2.07), z = 2.00, P = 0.045] and VaD [SMD: 1.28, 95% CI: (0.68, 1.88), z = 4.19, P < 0.001]. Results from this meta-analysis implied that CSF p-tau181 is a good biomarker for discriminating Alzheimer's disease from other dementias and mild cognitive impairment.

A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative

In recent years, numerous laboratories and consortia have used neuroimaging to evaluate the risk for and progression of Alzheimer's disease (AD). The Alzheimer's Disease Neuroimaging Initiative is a longitudinal, multicenter study that is evaluating a range of biomarkers for use in diagnosis of AD, prediction of patient outcomes, and clinical trials. These biomarkers include brain metrics derived from magnetic resonance imaging (MRI) and positron emission tomography scans as well as metrics derived from blood and cerebrospinal fluid. We focus on Alzheimer's Disease Neuroimaging Initiative studies published between 2011 and March 2013 for which structural MRI was a major outcome measure. Our main goal was to review key articles offering insights into progression of AD and the relationships of structural MRI measures to cognition and to other biomarkers in AD. In Supplement 1, we also discuss genetic and environmental risk factors for AD and exciting new analysis tools for the efficient evaluation of large-scale structural MRI data sets such as the Alzheimer's Disease Neuroimaging Initiative data.

Meta-Review of CSF Core Biomarkers in Alzheimer's Disease: The State-of-the-Art after the New Revised Diagnostic Criteria

BACKGROUND

Current research criteria for Alzheimer's disease (AD) include cerebrospinal fluid (CSF) biomarkers into the diagnostic algorithm. However, spreading their use to the clinical routine is still questionable.

OBJECTIVE

To provide an updated, systematic and critical review on the diagnostic utility of the CSF core biomarkers for AD.

DATA SOURCES

MEDLINE, PreMedline, EMBASE, PsycInfo, CINAHL, Cochrane Library, and CRD.

ELIGIBILITY CRITERIA

(1a) Systematic reviews with meta-analysis; (1b) Primary studies published after the new revised diagnostic criteria; (2) Evaluation of the diagnostic performance of at least one CSF core biomarker.

RESULTS

The diagnostic performance of CSF biomarkers is generally satisfactory. They are optimal for discriminating AD patients from healthy controls. Their combination may also be suitable for mild cognitive impairment (MCI) prognosis. However, CSF biomarkers fail to distinguish AD from other forms of dementia.

LIMITATIONS

(1) Use of clinical diagnosis as standard instead of pathological postmortem confirmation; (2) variability of methodological aspects; (3) insufficiently long follow-up periods in MCI studies; and (4) lower diagnostic accuracy in primary care compared with memory clinics.

CONCLUSION

Additional work needs to be done to validate the application of CSF core biomarkers as they are proposed in the new revised diagnostic criteria. The use of CSF core biomarkers in clinical routine is more likely if these limitations are overcome. Early diagnosis is going to be of utmost importance when effective pharmacological treatment will be available and the CSF core biomarkers can also be implemented in clinical trials for drug development.

Biological heterogeneity in ADNI amnestic mild cognitive impairment

BACKGROUND

Previous work examining normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) identified substantial biological heterogeneity. We hypothesized that ADNI mild cognitive impairment (MCI) subjects would also exhibit heterogeneity with possible clinical implications.

METHODS

ADNI subjects diagnosed with amnestic MCI (n=138) were clustered based on baseline magnetic resonance imaging, cerebrospinal fluid, and serum biomarkers. The clusters were compared with respect to longitudinal atrophy, cognitive trajectory, and time to conversion.

RESULTS

Four clusters emerged with distinct biomarker patterns: The first cluster was biologically similar to normal controls and rarely converted to Alzheimer's disease (AD) during follow-up. The second cluster had characteristics of early Alzheimer's pathology. The third cluster showed the most severe atrophy but barely abnormal tau levels and a substantial proportion converted to clinical AD. The fourth cluster appeared to be pre-AD and nearly all converted to AD.

CONCLUSIONS

Subjects with MCI who were clinically similar showed substantial heterogeneity in biomarkers.

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

Recently, there have been great interests for computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.

Subgroup of ADNI normal controls characterized by atrophy and cognitive decline associated with vascular damage

Previous work examining Alzheimer's Disease Neuroimaging Initiative (ADNI) normal controls using cluster analysis identified a subgroup characterized by substantial brain atrophy and white matter hyperintensities (WMH). We hypothesized that these effects could be related to vascular damage. Fifty-three individuals in the suspected vascular cluster (Normal 2) were compared with 31 individuals from the cluster characterized as healthy/typical (Normal 1) on a variety of outcomes, including magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) biomarkers, vascular risk factors and outcomes, cognitive trajectory, and medications for vascular conditions. Normal 2 was significantly older but did not differ on ApoE4+ prevalence. Normal 2 differed significantly from Normal 1 on all MRI measures but not on Amyloid-Beta1-42 or total tau protein. Normal 2 had significantly higher body mass index (BMI), Hachinksi score, and creatinine levels, and took significantly more medications for vascular conditions. Normal 2 had marginally significantly higher triglycerides and blood glucose. Normal 2 had a worse cognitive trajectory on the Rey's Auditory Verbal Learning Test (RAVLT) 30-min delay test and the Functional Activity Questionnaire (FAQ). Cerebral atrophy associated with multiple vascular risks is common among cognitively normal individuals, forming a distinct subgroup with significantly increased cognitive decline. Further studies are needed to determine the clinical impact of these findings.

Understanding cognitive deficits in Alzheimer's disease based on neuroimaging findings

Brain amyloid can be measured using positron emission tomography (PET). There are mixed reports regarding whether amyloid measures are correlated with measures of cognition (in particular memory), depending on the cohorts and cognitive domains assessed. In Alzheimer's disease (AD) patients and those at heightened risk for AD, cognitive performance may be related to the level and extent of classical AD pathology (amyloid plaques and neurofibrillary angles), but it is also influenced by neurodegeneration, neurocognitive reserve, and vascular health. We discuss what recent neuroimaging research has discovered about cognitive deficits in AD and offer suggestions for future research.

Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients

Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Recent advances in imaging Alzheimer's disease

Advances in brain imaging technology in the past five years have contributed greatly to the understanding of Alzheimer's disease (AD). Here, we review recent research related to amyloid imaging, new methods for magnetic resonance imaging analyses, and statistical methods. We also review research that evaluates AD risk factors and brain imaging, in the context of AD prediction and progression. We selected a variety of illustrative studies, describing how they advanced the field and are leading AD research in promising new directions.

Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines

Pattern recognition methods have demonstrated to be suitable analyses tools to handle the high dimensionality of neuroimaging data. However, most studies combining neuroimaging with pattern recognition methods focus on two-class classification problems, usually aiming to discriminate patients under a specific condition (e.g., Alzheimer’s disease) from healthy controls. In this perspective paper we highlight the potential of the one-class support vector machines (OC-SVM) as an unsupervised or exploratory approach that can be used to create normative rules in a multivariate sense. In contrast with the standard SVM that finds an optimal boundary separating two classes (discriminating boundary), the OC-SVM finds the boundary enclosing a specific class (characteristic boundary). If the OC-SVM is trained with patterns of healthy control subjects, the distance to the boundary can be interpreted as an abnormality score. This score might allow quantification of symptom severity or provide insights about subgroups of patients. We provide an intuitive description of basic concepts in one-class classification, the foundations of OC-SVM, current applications, and discuss how this tool can bring new insights to neuroimaging studies.

Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer's disease

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are associated with a progressive loss of cognitive abilities. In the present report, we assessed the relationship of memory and executive function with brain structure in a sample of 810 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, including 188 AD, 396 MCI, and 226 healthy older adults (HC). Composite scores of memory (ADNI-Mem) and executive function (ADNI-Exec) were generated by applying modern psychometric theory to item-level data from ADNI's neuropsychological battery. We performed voxel-based morphometry (VBM) and surface-based association (SurfStat) analyses to evaluate relationships of ADNI-Mem and ADNI-Exec with grey matter (GM) density and cortical thickness across the whole brain in the combined sample and within diagnostic groups. We observed strong associations between ADNI-Mem and medial and lateral temporal lobe atrophy. Lower ADNI-Exec scores were associated with advanced GM and cortical atrophy across broadly distributed regions, most impressively in the bilateral parietal and temporal lobes. We also evaluated ADNI-Exec adjusted for ADNI-Mem, and found associations with GM density and cortical thickness primarily in the bilateral parietal, temporal, and frontal lobes. Within-group analyses suggest these associations are strongest in patients with MCI and AD. The present study provides insight into the spatially unbiased associations between brain atrophy and memory and executive function, and underscores the importance of structural brain changes in early cognitive decline.