Pittsburgh compound B retention and progression of cognitive status--a meta-analysis

Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer's Disease

Alzheimer's disease (AD) is a heterogeneous progressive neurocognitive disorder. Although different neuroimaging modalities have been used for the identification of early diagnostic and prognostic factors of AD, there is no consolidated view of the findings from the literature. Here, we aim to provide a comprehensive account of different neural correlates of cognitive dysfunction via magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI) (resting-state and task-related), positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) modalities across the cognitive groups i.e., normal cognition, mild cognitive impairment (MCI), and AD. A total of 46 meta-analyses met the inclusion criteria, including relevance to MCI, and/or AD along with neuroimaging modality used with quantitative and/or functional data. Volumetric MRI identified early anatomical changes involving transentorhinal cortex, Brodmann area 28, followed by the hippocampus, which differentiated early AD from healthy subjects. A consistent pattern of disruption in the bilateral precuneus along with the medial temporal lobe and limbic system was observed in fMRI, while DTI substantiated the observed atrophic alterations in the corpus callosum among MCI and AD cases. Default mode network hypoconnectivity in bilateral precuneus (PCu)/posterior cingulate cortices (PCC) and hypometabolism/hypoperfusion in inferior parietal lobules and left PCC/PCu was evident. Molecular imaging revealed variable metabolite concentrations in PCC. In conclusion, the use of different neuroimaging modalities together may lead to identification of an early diagnostic and/or prognostic biomarker for AD.

Application of CRISPR genetic screens to investigate neurological diseases

The adoption of CRISPR-Cas9 technology for functional genetic screens has been a transformative advance. Due to its modular nature, this technology can be customized to address a myriad of questions. To date, pooled, genome-scale studies have uncovered genes responsible for survival, proliferation, drug resistance, viral susceptibility, and many other functions. The technology has even been applied to the functional interrogation of the non-coding genome. However, applications of this technology to neurological diseases remain scarce. This shortfall motivated the assembly of a review that will hopefully help researchers moving in this direction find their footing. The emphasis here will be on design considerations and concepts underlying this methodology. We will highlight groundbreaking studies in the CRISPR-Cas9 functional genetics field and discuss strengths and limitations of this technology for neurological disease applications. Finally, we will provide practical guidance on navigating the many choices that need to be made when implementing a CRISPR-Cas9 functional genetic screen for the study of neurological diseases.

Amyloid-β and Tau at the Crossroads of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia characterized neuropathologically by senile plaques and neurofibrillary tangles (NFTs). Early breakthroughs in AD research led to the discovery of amyloid-β as the major component of senile plaques and tau protein as the major component of NFTs. Shortly following the identification of the amyloid-β (Aβ) peptide was the discovery that a genetic mutation in the amyloid precursor protein (APP), a type1 transmembrane protein, can be a cause of autosomal dominant familial AD (fAD). These discoveries, coupled with other breakthroughs in cell biology and human genetics, have led to a theory known as the "amyloid hypothesis", which postulates that amyloid-β is the predominant driving factor in AD development. Nonetheless, more recent advances in imaging analysis, biomarkers and mouse models are now redefining this original hypothesis, as it is likely amyloid-β, tau and other pathophysiological mechanism such as inflammation, come together at a crossroads that ultimately leads to the development of AD.

Hypertension and obesity moderate the relationship between β-amyloid and cognitive decline in midlife

BACKGROUND

This study tested if central obesity, hypertension, or depressive symptoms moderated the relationship between β-amyloid (Aβ) and longitudinal cognitive performance in late middle-aged adults enriched for Alzheimer's disease (AD) risk.

METHODS

Participants (n = 207; ages = 40-70 years; 73% parental AD) in the Wisconsin Registry for Alzheimer's Prevention study completed 3+ neuropsychological evaluations and a [¹¹C]PiB positron emission tomography scan or lumbar puncture. Linear mixed-effects regression models tested interactions of risk factor × Aβ × visit age on longitudinal Verbal Learning & Memory and Speed & Flexibility factor scores.

RESULTS

The relationship between Aβ and Verbal Learning & Memory decline was moderated by hypertension (χ²₍₁₎ = 3.85, P = .04) and obesity (χ²₍₁₎ = 6.12, P = .01); those with both elevated Aβ and the risk factor declined at faster rates than those with only elevated Aβ or elevated risk factors.

CONCLUSION

In this cohort, hypertension and obesity moderated the relationship between Aβ and cognitive decline.

A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models

Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models.

Structural MRI and Amyloid PET Imaging for Prediction of Conversion to Alzheimer's Disease in Patients with Mild Cognitive Impairment: A Meta-Analysis

OBJECTIVE

The aim of this study was to explore the prognostic values of biomarkers of neurodegeneration as measured by magnetic resonance imaging (MRI) and amyloid burden as measured by amyloid positron emission tomography (PET) in predicting conversion to Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI).

METHODS

PubMed and EMBASE databases were searched for structural MRI or amyloid PET imaging studies published between January 2000 and July 2014 that reported conversion to AD in patients with MCI. Means and standard deviations or individual numbers of biomarkers with positive or negative status at baseline and corresponding numbers of patients who had progressed to AD at follow-up were retrieved from each study. The effect size of each biomarker was expressed as Hedges's g.

RESULTS

Twenty-four MRI studies and 8 amyloid PET imaging studies were retrieved. 674 of the 1741 participants (39%) developed AD. The effect size for predicting conversion to AD was 0.770 [95% confidence interval (CI) 0.607-0.934] for across MRI and 1.316 (95% CI 0.920-1.412) for amyloid PET imaging (p<0.001). The effect size was 1.256 (95% CI 0.902-1.609) for entorhinal cortex volume from MRI.

CONCLUSION

Our study suggests that volumetric MRI measurement may be useful for the early detection of AD.

The case for rejecting the amyloid cascade hypothesis

Alzheimer's disease (AD) is a biologically complex neurodegenerative dementia. Nearly 20 years ago, with the combination of observations from biochemistry, neuropathology and genetics, a compelling hypothesis known as the amyloid cascade hypothesis was formulated. The core of this hypothesis is that it is pathological accumulations of amyloid-β, a peptide fragment of a membrane protein called amyloid precursor protein, that act as the root cause of AD and initiate its pathogenesis. Yet, with the passage of time, growing amounts of data have accumulated that are inconsistent with the basically linear structure of this hypothesis. And while there is fear in the field over the consequences of rejecting it outright, clinging to an inaccurate disease model is the option we should fear most. This Perspective explores the proposition that we are over-reliant on amyloid to define and diagnose AD and that the time has come to face our fears and reject the amyloid cascade hypothesis.

Three dimensions of the amyloid hypothesis: time, space and 'wingmen'

The amyloid hypothesis, which has been the predominant framework for research in Alzheimer's disease (AD), has been the source of considerable controversy. The amyloid hypothesis postulates that amyloid-β peptide (Aβ) is the causative agent in AD. It is strongly supported by data from rare autosomal dominant forms of AD. However, the evidence that Aβ causes or contributes to age-associated sporadic AD is more complex and less clear, prompting criticism of the hypothesis. We provide an overview of the major arguments for and against the amyloid hypothesis. We conclude that Aβ likely is the key initiator of a complex pathogenic cascade that causes AD. However, we argue that Aβ acts primarily as a trigger of other downstream processes, particularly tau aggregation, which mediate neurodegeneration. Aβ appears to be necessary, but not sufficient, to cause AD. Its major pathogenic effects may occur very early in the disease process.

The relative importance of imaging markers for the prediction of Alzheimer's disease dementia in mild cognitive impairment - Beyond classical regression

Selecting a set of relevant markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has become a challenging task given the wealth of regional pathologic information that can be extracted from multimodal imaging data.

Here, we used regularized regression approaches with an elastic net penalty for best subset selection of multiregional information from AV45-PET, FDG-PET and volumetric MRI data to predict conversion from MCI to AD. The study sample consisted of 127 MCI subjects from ADNI-2 who had a clinical follow-up between 6 and 31 months. Additional analyses assessed the effect of partial volume correction on predictive performance of AV45- and FDG-PET data.

Predictor variables were highly collinear within and across imaging modalities. Penalized Cox regression yielded more parsimonious prediction models compared to unpenalized Cox regression. Within single modalities, time to conversion was best predicted by increased AV45-PET signal in posterior medial and lateral cortical regions, decreased FDG-PET signal in medial temporal and temporobasal regions, and reduced gray matter volume in medial, basal, and lateral temporal regions. Logistic regression models reached up to 72% cross-validated accuracy for prediction of conversion status, which was comparable to cross-validated accuracy of non-linear support vector machine classification. Regularized regression outperformed unpenalized stepwise regression when number of parameters approached or exceeded the number of training cases. Partial volume correction had a negative effect on the predictive performance of AV45-PET, but slightly improved the predictive value of FDG-PET data.

Penalized regression yielded more parsimonious models than unpenalized stepwise regression for the integration of multiregional and multimodal imaging information. The advantage of penalized regression was particularly strong with a high number of collinear predictors.

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Use of regularized Cox and logistic regression for dementia prediction

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Regularized regression deals with a high number of highly collinear predictors.

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Regularized regression yields a parsimonious and plausible prediction model.

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Prediction accuracy of regularized regression is superior to machine learning.

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Partial volume correction of PET data modulates prediction accuracy.

(11)C-PIB-PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

According to the latest revised National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (now known as the Alzheimer's Association) (NINCDS-ADRDA) diagnostic criteria for Alzheimer's disease dementia, the confidence in diagnosing mild cognitive impairment (MCI) due to Alzheimer's disease dementia is raised with the application of imaging biomarkers. These tests, added to core clinical criteria, might increase the sensitivity or specificity of a testing strategy. However, the accuracy of biomarkers in the diagnosis of Alzheimer's disease dementia and other dementias has not yet been systematically evaluated. A formal systematic evaluation of the sensitivity, specificity, and other properties of positron emission tomography (PET) imaging with the (11)C-labelled Pittsburgh Compound-B ((11)C-PIB) ligand was performed.

OBJECTIVES

To determine the diagnostic accuracy of the (11)C- PIB-PET scan for detecting participants with MCI at baseline who will clinically convert to Alzheimer's disease dementia or other forms of dementia over a period of time.

SEARCH METHODS

The most recent search for this review was performed on 12 January 2013. We searched MEDLINE (OvidSP), EMBASE (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science and Conference Proceedings (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We also requested a search of the Cochrane Register of Diagnostic Test Accuracy Studies (managed by the Cochrane Renal Group).No language or date restrictions were applied to the electronic searches and methodological filters were not used so as to maximise sensitivity.

SELECTION CRITERIA

We selected studies that had prospectively defined cohorts with any accepted definition of MCI with baseline (11)C-PIB-PET scan. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis for example NINCDS-ADRDA or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. The identified full papers were assessed for eligibility and data were extracted to create two by two tables. Two independent assessors performed quality assessment using the QUADAS 2 tool. We used the hierarchical summary receiver operating characteristic (ROC) model to produce a summary ROC curve.

MAIN RESULTS

Conversion from MCI to Alzheimer's disease dementia was evaluated in nine studies. The quality of the evidence was limited. Of the 274 participants included in the meta-analysis, 112 developed Alzheimer's dementia. Based on the nine included studies, the median proportion converting was 34%. The studies varied markedly in how the PIB scans were done and interpreted.The sensitivities were between 83% and 100% while the specificities were between 46% and 88%. Because of the variation in thresholds and measures of (11)C-PIB amyloid retention, we did not calculate summary sensitivity and specificity. Although subject to considerable uncertainty, to illustrate the potential strengths and weaknesses of (11)C-PIB-PET scans we estimated from the fitted summary ROC curve that the sensitivity was 96% (95% confidence interval (CI) 87 to 99) at the included study median specificity of 58%. This equated to a positive likelihood ratio of 2.3 and a negative likelihood ratio of 0.07. Assuming a typical conversion rate of MCI to Alzheimer's dementia of 34%, for every 100 PIB scans one person with a negative scan would progress and 28 with a positive scan would not actually progress to Alzheimer's dementia.There were limited data for formal investigation of heterogeneity. We performed two sensitivity analyses to assess the influence of type of reference standard and the use of a pre-specified threshold. There was no effect on our findings.

AUTHORS' CONCLUSIONS

Although the good sensitivity achieved in some included studies is promising for the value of (11)C-PIB-PET, given the heterogeneity in the conduct and interpretation of the test and the lack of defined thresholds for determination of test positivity, we cannot recommend its routine use in clinical practice.(11)C-PIB-PET biomarker is a high cost investigation, therefore it is important to clearly demonstrate its accuracy and standardise the process of the (11)C-PIB diagnostic modality prior to it being widely used.