Feature selection/visualisation of ADNI data with iterative partial least squares

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.

To realize precision medicine, it is important to realize the detection of the individual atrophy of Alzheimer's disease (AD) patients. Our objective is to find individual brain regions of interest (ROIs) in AD patients via an unsupervised deep learning network.This study used structural Magnetic Resonance Imaging (sMRI) scans with the 732 healthy control (HC) subjects and 202 AD patients from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the 105 HC subjects were collected at the Xuanwu Hospital. An unsupervised deep learning network based on Adversarial Autoencoders (AAE) was proposed to delineate the individual atrophy of AD patients. In the proposed model, Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) were combined to learn the potential distribution and train a generator. In this step, the 530 HCs from ADNI were applied as the training dataset and the 105 HCs from Xuanwu Hospital were applied as an external validation dataset. The structural similarity (SSIM) was used to judge the robustness of the proposed model. Then, ROIs of the 202 AD patients were detected. In order to verify the clinical performance of these ROIs, other 202 HCs were selected from ADNI and a multilayer perceptron (MLP) was used to classify AD versus HC by 5 folder cross-validation. In the comparative experiments, we compared our model with three other previous models.The SSIM reached 0.86 in both training and external validation datasets. Eventually, the classification accuracy of our model achieved 0.94±0.02. In the meanwhile, the classification accuracies were 0.89±0.01, 0.85±0.04 and 0.91±0.03 for the three previous methods.Our deep learning model could detect individual atrophy in AD patients. It may be a useful tool for AD diagnosis in clinics.

In this work we exploited Partial Least Squares (PLS) model for analyzing the genetic underpinning of grey matter atrophy in Alzheimer's Disease (AD). To this end, 42 features derived from T1-weighted Magnetic Resonance Imaging, including cortical thicknesses and subcortical volumes were considered to describe the imaging phenotype, while the genotype information consisted of 14 recently proposed AD related Polygenic Risk Scores (PRS), calculated by including Single Nucleotide Polymorphism passing different significance thresholds. The PLS model was applied on a large study cohort obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database including both healthy individuals and AD patients, and validated on an independent ADNI Mild Cognitive Impairment (MCI) cohort, including Early (EMCI) and Late MCI (LMCI). The experimental results confirm the existence of a joint dynamics between brain atrophy and genotype data in AD, while providing important generalization results when tested on a clinically heterogeneous cohort. In particular, less AD specific PRS scores were negatively correlated with cortical thicknesses, while highly AD specific PRSs showed a peculiar correlation pattern among specific subcortical volumes and cortical thicknesses. While the first outcome is in line with the well known neurodegeneration process in AD, the second could be revealing of different AD subtypes.

Develop a cerebrospinal fluid biomarker signature for mild Alzheimer's disease (AD) in Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects. Amyloid-beta 1 to 42 peptide (A beta(1-42)), total tau (t-tau), and tau phosphorylated at the threonine 181 were measured in (1) cerebrospinal fluid (CSF) samples obtained during baseline evaluation of 100 mild AD, 196 mild cognitive impairment, and 114 elderly cognitively normal (NC) subjects in ADNI; and (2) independent 56 autopsy-confirmed AD cases and 52 age-matched elderly NCs using a multiplex immunoassay. Detection of an AD CSF profile for t-tau and A beta(1-42) in ADNI subjects was achieved using receiver operating characteristic cut points and logistic regression models derived from the autopsy-confirmed CSF data. CSF A beta(1-42) was the most sensitive biomarker for AD in the autopsy cohort of CSF samples: receiver operating characteristic area under the curve of 0.913 and sensitivity for AD detection of 96.4%. In the ADNI cohort, a logistic regression model for A beta(1-42), t-tau, and APO epsilon 4 allele count provided the best assessment delineation of mild AD. An AD-like baseline CSF profile for t-tau/A beta(1-42) was detected in 33 of 37 ADNI mild cognitive impairment subjects who converted to probable AD during the first year of the study. The CSF biomarker signature of AD defined by A beta(1-42) and t-tau in the autopsy-confirmed AD cohort and confirmed in the cohort followed in ADNI for 12 months detects mild AD in a large, multisite, prospective clinical investigation, and this signature appears to predict conversion from mild cognitive impairment to AD.

The Alzheimer’s disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) encompasses members from industry, biotechnology, diagnostic, and non-profit organizations that have until recently been managed by the Foundation for the National Institute of Health (FNIH) and provided financial and scientific support to ADNI programs. In this article, we review some of the major activities undertaken by the PPSB, focusing on those supporting the most recently completed National Institute on Aging grant, ADNI3, and the impact it has had on streamlining biomarker discovery and validation in Alzheimer’s disease. We also provide a perspective on the gaps that may be filled with future PPSB activities as part of ADNI4 and beyond.

Medial temporal lobe atrophy scores translated to clinical practice: Editorial comment on ‘Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut‐offs'

PURPOSEChronic neuro‐inflammation is an important mechanism underlying normal aging and Alzheimer's disease (AD) that is thought to potentiate medial temporal lobe (MTL) atrophy and memory decline. Currently, there are not enough prognostic biomarkers that can track the development and progression of normal aging versus AD, as well as differing effects on the brain. Our study examined which pro‐ or anti‐inflammatory cerebrospinal fluid (CSF) biomarkers best predicted AD neuropathology over 24 months.METHODSBaseline mass spectrometry data from two hundred and eighty‐five adults was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our focus was on examining biomarkers directly associated with inflammatory processes. For our study, we utilized T1‐weighted medial temporal gray matter volumes at baseline and months 6, 12, and 24 after baseline. Statistical analyses were conducted using SPSS 23.0 software. We used linear mixed models as well as least significant differences (LSD) post‐hoc tests to analyze how variables differed by baseline diagnosis (normal aging, pre‐AD, AD) or pre‐AD patients that did or did not convert to AD. We used stepwise regression analyses to analyze which inflammatory biomarkers best predicted atrophy and memory decline over 24 months. We specifically focused on CSF levels of Neuronal Pentraxin 2 (NPTX2) and Chitinase‐3‐like‐protein‐1 (C3LP1), as they are the inflammatory biomarkers that significantly predicted MTL atrophy and memory decline. NPTX2 is a biomarker of synaptic plasticity. C3LP1 is a biomarker of microglial activation and chronic neuro‐inflammation. We also analyzed relative risk ratios for diagnosis with NPTX2 and C3LP1 with multinomial logistic regression analyses. Preliminary results for memory indicate that, regardless of baseline diagnosis, higher NPTX2 levels strongly predict higher memory scores by month 24 (Figure 1).RESULTSNPTX2 and C3LP1, respectively beneficial and adverse biomarkers of synaptic plasticity and microglial activation, loaded significantly. By month 24, higher baseline NPTX2 corresponded to less MTL atrophy [R2= .287] and less memory decline [R2= .560]. Conversely, higher C3LP1 modestly predicted more MTL atrophy [R2=.083].CONCLUSIONSOur study suggests that NPTX2 is a promising biomarker for predicting longitudinal changes in medial temporal volume, especially memory performance across the AD spectrum. Baseline NPTX2 performs better than C3LP1 in predicting MTL atrophy and memory decline over time. C3LP1 was a comparatively modest predictor of medial temporal atrophy and memory decline. For future directions, studies should investigate if clinical trials using anti‐inflammatory drugs/nutritional therapy increase or inhibit NPTX2 and/or C3LP1.Support or Funding InformationThis study was funded by Iowa State University and NIH K99 AG047282. Data collection and sharing for this project were funded by the ADNI (National Institutes of Health Grant U01‐AG‐024904) and Department of Defense ADNI (award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private‐sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

Alzheimer's Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans

INTRODUCTIONBiomarkers for Alzheimer's disease neuropathologic change (ADNC) have been instrumental in developing effective disease‐modifying therapeutics. However, to prevent/treat dementia effectively, we require biomarkers for non‐AD neuropathologies; for this, neuropathologic examinations and annotated tissue samples are essential.METHODSWe conducted clinicopathologic correlation for the first 100 Alzheimer's Disease Neuroimaging Initiative (ADNI) Neuropathology Core (NPC) cases.RESULTSClinical syndromes in this cohort showed 95% sensitivity and 79% specificity for predicting high/intermediate ADNC, a 21% false positive rate, and a ∼44% false negative rate. In addition, 60% with high/intermediate ADNC harbored additional potentially dementing co‐pathologies.DISCUSSIONThese results suggest that clinical presentation imperfectly predicts ADNC and that accurate prediction of high/intermediate ADNC does not exclude co‐pathology that may modify presentation, biomarkers, and therapeutic responses. Therefore, new biomarkers are needed for non‐AD neuropathologies. The ADNI NPC supports this mission with well‐characterized tissue samples (available through ADNI and the National Institute on Aging) and “gold‐standard” diagnostic information (soon to include digital histology).HighlightsThe Alzheimer's Disease Neuroimaging Initiative (ADNI) Neuropathology Core (NPC) brain donation cohort now exceeds 200 cases.ADNI NPC data in National Alzheimer's Coordinating Center format are available through the Laboratory of Neuro Imaging.Digitized slide files from the ADNI NPC will be available in 2025.Requests for ADNI brain tissue samples can be submitted online for ADNI/National Institute on Aging evaluation.Clinical diagnoses of Alzheimer's disease (AD)/AD and related dementias (ADRD) do not always predict post mortem neuropathology.Neuropathology is essential for the development of novel AD/ADRD biomarkers.

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

Probability distribution function-based classification of structural MRI for the detection of Alzheimer’s disease

ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease

Due to a change in disease spectrum in aged countries, the primary role of geriatricians should be directed to an appropriate management and prevention of 1) cognitive decline and dementia, 2) swallowing and aspiration pneumonia and 3) falls and fractures. Management of dementia constitutes a central part in the practice of geriatric medicine in order to support independence of life in elderly people. The current paradigm of cognitive function-based testing for the diagnosis and treatment of Alzheimer's disease (AD) is going to drastically shift to a biomarker-based test approach, a shift that will correspond to the emergence of disease-modifying drugs. In addition, a new molecular imaging technique that visualizes neuronal protein deposits or pathological features has been developed in Japan and the U.S.A. Based on these achievements, the Alzheimer's Disease Neuroimaging Initiative (ADNI) was proposed and initiated in 2005. The ADNI is a long-term observational study being conducted in the U.S.A., Europe, Australia, and Japan using identical protocols. The objectives of ADNI are: 1) to establish methodology which will allow standard values related to long-term changes in imaging data, such as MRI and PET, in patients with AD and mild cognitive impairment and normal elderly persons; 2) to obtain clinical indices, psychological test data, and blood/cerebrospinal fluid biomarkers to demonstrate the validity of image-based surrogate markers; and 3) to establish optimum methods to monitor the therapeutic effects of disease-modifying drugs for AD. Patient enrollment in the Japanese ADNI has begun in July 2008. Imaging of AD pathology not only acts as a reliable biomarker with which to assay curative drug development by novel pharmaceutical companies, but it also helps health promotion toward AD prevention.

Cross-sectional studies suggest self-reported cancer history is associated with decreased risk of Alzheimer's disease (AD). However, little is known about how self-reported cancer affects longitudinal AD progression, the primary outcome in clinical trials and observational studies. To determine self-reported cancer history's effect on longitudinal AD progression in an observational study. We utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate progression to AD by self-reported all-cancer, breast, prostate, colorectal, or non-melanoma skin cancer history. Linear mixed effects models were used to examine baseline differences and rates of progression on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) by self-reported cancer history. Age at AD onset was examined using consensus clinical diagnoses with Cox proportional hazards regression. Among 1,271 participants, models revealed no significant differences in progression over time but did reveal significantly lower baseline ADAS-Cog score, indicating better cognition at a given age in those with self-reported cancer history. Cox models indicated those with self-reported cancer history had significantly later age of AD onset (HR: 0.67, 95% CI: 0.53-0.85) after adjustment for covariates. Participants with self-reported cancer history entered ADNI with better cognition and later age of AD onset, but progressed similarly to participants without such history, indicating differences in AD between those with and without self-reported cancer history emerge early in the disease course. Such differences in longitudinal progression by self-reported cancer history could affect AD trials and observational studies, given the current focus on early disease course. Further investigation is warranted with detailed longitudinal assessment of cancer and AD.

Aim: We investigated the performance of FDG PET using an automated procedure for discrimination between Alzheimer’s disease (AD) and controls, and studied the influence of demographic and technical factors. Methods: FDG PET data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [102 controls (76.0 ± 4.9 years) and 89 AD patients (75.7 ± 7.6 years, MMSE 23.5 ± 2.1) and the Network for Standardisation of Dementia Diagnosis (NEST-DD) [36 controls (62.2 ± 5.0 years) and 237 AD patients (70.8 ± 8.3 years, MMSE 20.9 ± 4.4). The procedure created t-maps of abnormal voxels. The sum of t-values in predefined areas that are typically affected by AD (AD t-sum) provided a measure of scan abnormality associated with a preset threshold for discrimination between patients and controls. Results: AD patients had much higher AD t-sum scores compared to controls (p < 0.01), which were significantly related to dementia severity (ADNI: r = –0.62, p < 0.01; NEST-DD: r = –0.59, p < 0.01). Early-onset AD patients had significantly higher AD t-sum scores than late-onset AD patients (p < 0.01). Differences between databases were mainly due to different age distributions. The predefined AD t-sum threshold yielded a sensitivity and specificity of 83 and 78% in ADNI and 78 and 94% in NEST-DD, respectively. Conclusion: The automated FDG PET analysis procedure provided good discrimination power, and was most accurate for early-onset AD.