Imaging and biomarkers will be used for detection and monitoring progression of early Alzheimer's disease

Diagnosis of Alzheimer's disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features

Background

Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people. Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD.

Methods

In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of resting-state EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation.

Results

The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65, 95.86, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects.

Conclusion

This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.

Current development of biosensing technologies towards diagnosis of mental diseases

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Wavelet skeletons in sleep EEG-monitoring as biomarkers of early diagnostics of mild cognitive impairment

Many neuro-degenerative diseases are difficult to diagnose in their early stages. For example, early diagnosis of Mild Cognitive Impairment (MCI) requires a wide variety of tests to distinguish MCI symptoms and normal consequences of aging. In this article, we use the wavelet-skeleton approach to find some characteristic patterns in the electroencephalograms (EEGs) of healthy adult patients and patients with cognitive dysfunctions. We analyze the EEG activity recorded during natural sleep of 11 elderly patients aged between 60 and 75, six of whom have mild cognitive impairment, and apply a nonlinear analysis method based on continuous wavelet transformskeletons. Our studies show that a comprehensive analysis of EEG signals of the entire sleep state allows us to identify a significant decrease in the average duration of oscillatory patterns in the frequency band [12; 14] Hz in the presence of mild cognitive impairment. Thus, the changes in this frequency range can be interpreted as related to the activity in the motor cortex, as a candidate for developing the criteria for early objective MCI.

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.

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.

Biomarkers for Alzheimer's disease therapeutic trials

The development of disease-modifying treatments for Alzheimer's disease requires innovative trials with large numbers of subjects and long observation periods. The use of blood, cerebrospinal fluid or neuroimaging biomarkers is critical for the demonstration of disease-modifying therapy effects on the brain. Suitable biomarkers are those which reflect the progression of AD related molecular mechanisms and neuropathology, including amyloidogenic processing and aggregation, hyperphosphorylation, accumulation of tau and neurofibrillary tangles, progressive functional, metabolic and structural decline, leading to neurodegeneration, loss of brain tissue and cognitive symptoms. Biomarkers should be used throughout clinical trial phases I-III of AD drug development. They can be used to enhance inclusion and exclusion criteria, or as baseline predictors to increase the statistical power of trials. Validated and qualified biomarkers may be used as outcome measures to detect treatment effects in pivotal clinical trials. Finally, biomarkers can be used to identify adverse effects. Questions regarding which biomarkers should be used in clinical trials, and how, are currently far from resolved. The Oxford Task Force continues and expands the work of our previous international expert task forces on disease-modifying trials and on endpoints for Alzheimer's disease clinical trials. The aim of this initiative was to bring together a selected number of key international opinion leaders and experts from academia, regulatory agencies and industry to condense the current knowledge and state of the art regarding the best use of biological markers in Alzheimer's disease therapy trials and to propose practical recommendations for the planning of future AD trials.

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.

Structural and functional imaging correlates of cognitive and brain reserve hypotheses in healthy and pathological aging

In the field of ageing and dementia, brain- or cognitive reserve refers to the capacity of the brain to manage pathology or age-related changes thereby minimizing clinical manifestations. The brain reserve capacity (BRC) hypothesis argues that this capacity derives from an individual's unique neural profile (e.g., cell count, synaptic connections, brain volume, etc.). Complimentarily, the cognitive reserve (CR) hypothesis emphasizes inter-individual differences in the effective recruitment of neural networks and cognitive processes to compensate for age-related effects or pathology. Despite an abundance of research, there is scarce literature attempting to synthesize the BRC the CR models. In this paper, we will review important aging and dementia studies using structural and functional neuroimaging techniques to investigate and attempt to assimilate both reserve hypotheses. The possibility to conceptualize reserve as reflecting indexes of brain plasticity will be proposed and novel data suggesting an intimate and complex correspondence between active and passive components of reserve will be presented.

Complement activation as a biomarker for Alzheimer's disease

There is increasing evidence from genetic, immunohistochemical, proteomic and epidemiological studies as well as in model systems that complement activation has an important role in the pathogenesis of Alzheimer's disease (AD). The complement cascade is an essential element of the innate immune response. In the brain complement proteins are integral components of amyloid plaques and complement activation occurs at the earliest stage of the disease. The complement cascade has been implicated as a protective mechanism in the clearance of amyloid, and in a causal role through chronic activation of the inflammatory response. In this review we discuss the potential for complement activation to act as a biomarker for AD at several stages in the disease process. An accurate biomarker that has sufficient predictive, diagnostic and prognostic value would provide a significant opportunity to develop and test for effective novel therapies in the treatment of AD.

Alzheimer disease identification using amyloid imaging and reserve variables: proof of concept

OBJECTIVE

Several factors may influence the relationship between Alzheimer disease (AD) lesions and the expression of dementia, including those related to brain and cognitive reserve. Other factors may confound the association between AD pathology and dementia. We tested whether factors thought to influence the association of AD pathology and dementia help to accurately identify dementia of the Alzheimer type (DAT) when considered together with amyloid imaging.

METHODS

Participants with normal cognition (n = 180) and with DAT (n = 25), aged 50 years or older, took part in clinical, neurologic, and psychometric assessments. PET with the Pittsburgh compound B (PiB) tracer was used to measure brain amyloid, yielding a mean cortical binding potential (MCBP) reflecting PiB uptake. Logistic regression was used to generate receiver operating characteristic curves, and the areas under those curves (AUC), to compare the predictive accuracy of using MCBP alone vs MCBP together with other variables selected using a stepwise selection procedure to identify participants with DAT vs normal cognition.

RESULTS

The AUC resulting from MCBP alone was 0.84 (95% confidence interval [CI] = 0.73-0.94; cross-validated AUC = 0.80, 95% CI = 0.68-0.92). The AUC for the predictive equation generated by a stepwise model including education, normalized whole brain volume, physical health rating, gender, and use of medications that may interfere with cognition was 0.94 (95% CI = 0.90-0.98; cross-validated AUC = 0.91, 95% CI = 0.85-0.96), an improvement (p = 0.025) over that yielded using MCBP alone.

CONCLUSION

Results suggest that factors reported to influence associations between AD pathology and dementia can improve the predictive accuracy of amyloid imaging for the identification of symptomatic AD.

The Alzheimer's disease neuroimaging initiative: progress report and future plans

The Alzheimer's Disease Neuroimaging Initiative (ADNI) beginning in October 2004, is a 6-year research project that studies changes of cognition, function, brain structure and function, and biomarkers in elderly controls, subjects with mild cognitive impairment, and subjects with Alzheimer's disease (AD). A major goal is to determine and validate MRI, PET images, and cerebrospinal fluid (CSF)/blood biomarkers as predictors and outcomes for use in clinical trials of AD treatments. Structural MRI, FDG PET, C-11 Pittsburgh compound B (PIB) PET, CSF measurements of amyloid beta (Abeta) and species of tau, with clinical/cognitive measurements were performed on elderly controls, subjects with mild cognitive impairment, and subjects with AD. Structural MRI shows high rates of brain atrophy, and has high statistical power for determining treatment effects. FDG PET, C-11 Pittsburgh compound B PET, and CSF measurements of Abeta and tau were significant predictors of cognitive decline and brain atrophy. All data are available at UCLA/LONI/ADNI, without embargo. ADNI-like projects started in Australia, Europe, Japan, and Korea. ADNI provides significant new information concerning the progression of AD.

Identifying early markers of Alzheimer's disease using quantitative multiplex proteomic immunoassay panels

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder with incidence expected to increase four-fold over the next decade. Extensive research efforts are focused upon identifying new treatments, and early diagnosis is considered key to successful intervention. Although imaging and cerebrospinal fluid biomarkers have shown promise in identifying patients in very early stages of the disease, more noninvasive cost-effective tools have remained elusive. Recent studies have reported that an 18-analyte multiplexed plasma panel can differentiate AD from controls suggesting plasma-based screening tools for early AD diagnosis exists. The current study tested the reproducibility of a subset of the original 18-analyte panel using a bead-based multiplex technology. Preliminary results suggest diagnostic accuracy using the subset was 61%. Multivariate analysis of an 89-analyte multivariate panel yielded a diagnostic accuracy of 70% suggesting a plasma-based AD signature that may be a useful screening tool.