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

Two decades of [11C]PiB synthesis, 2003-2023: a review

Because carbon-11 (11C) radiotracers cannot be shipped over long distances, their use in routine positron emission tomography (PET) studies is dependent on the production capabilities of individual radiochemistry laboratories. Since 2003, 11C-labeled Pittsburgh compound B ([11C]PiB) has been the gold standard PET radiotracer for in vivo imaging of amyloid β (Aβ) plaques. For more than two decades, researchers have been working to develop faster, higher-yielding, more robust, and optimized production methods with higher radiochemical yields for various imaging applications. This review evaluates progress in [11C]PiB radiochemistry. An introductory overview assesses how it has been applied in clinical neurologic imaging research. We examine the varying approaches reported for radiolabeling, purification, extraction, and formulation. Further considerations for QC methods, regulatory considerations, and optimizations were also discussed.

Case of Autosomal Dominant Alzheimer Disease With Negative Findings From PiB-PET Examination

Background and Objectives

This study reports an uncommon case of autosomal dominant Alzheimer disease (AD) with negative PiB-PET findings.

Methods

A 55-year-old woman was admitted to the hospital due to a progressive cognitive decline for over 9 years, along with a possible dementia family history. The patient underwent routine laboratory tests, neuropsychological assessments, and neuroimaging examinations. Additionally, the cerebrospinal fluid sample was analyzed for AD biomarkers using the Single Molecular Array (Simoa) technique. A targeted Next-Generation-Sequencing (NGS) panel screening was also conducted.

Results

Routine blood and CSF laboratory tests, as well as CSF tap test, yielded negative results. Cranial MRI showed atrophy of the whole brain. PiB-PET scanning indicated that PiB was merely retained in her brain with an overall standard uptake value ratio (SUVR) <1.0. Simoa analysis showed an increase in the level of CSF t-tau and a decrease in that of CSF Aβ42. NGS panel screening detected a c.G2032A (p.D678N) heterozygous mutation in APP. Consequently, the patient was diagnosed with autosomal dominant AD.

Discussion

We have reported an uncommon case of autosomal dominant AD carrying p.D678N variant in APP with negative PiB-PET results. The diagnosis of AD should not be directly excluded solely based on the negative PiB-PET results.

Friends and Foes in Alzheimer's Disease

Alzheimer's disease (AD) is a disabling neurodegenerative disease. The prognosis is poor, and currently there are no proven effective therapies. Most likely, the etiology is related to cerebral inflammatory processes that cause neuronal damage, resulting in dysfunction and apoptosis of nerve cells. Pathogens that evoke a neuroinflammatory response, collectively activate astrocytes and microglia, which contributes to the secretion of pro-inflammatory cytokines. This leads to the deposit of clustered fragments of beta-amyloid and misfolded tau proteins which do not elicit an adequate immune reaction. Apart from the function of astrocytes and microglia, molecular entities such as TREM2, SYK, C22, and C33 play a role in the physiopathology of AD. Furthermore, bacteria and viruses may trigger an overactive inflammatory response in the brain. Pathogens like Helicobacter pylori, Chlamydia pneumonia, and Porphyromonas gingivalis (known for low-grade infection in the oral cavity) can release gingipains, which are enzymes that can damage and destroy neurons. Chronic infection with Borrelia burgdorferi (the causative agent of Lyme disease) can co-localize with tau tangles and amyloid deposits. As for viral infections, herpes simplex virus 1, cytomegalovirus, and Epstein-Barr virus can play a role in the pathogenesis of AD. Present investigations have resulted in the development of antibodies that can clear the brain of beta-amyloid plaques. Trials with humanized aducanumab, lecanemab, and donanemab revealed limited success in AD patients. However, AD should be considered as a continuum in which the initial preclinical phase may take 10 or even 20 years. It is generally thought that this phase offers a window for efficacious treatment. Therefore, research is also focused on the identification of biomarkers for early AD detection. In this respect, the plasma measurement of neurofilament light chain in patients treated with hydromethylthionine mesylate may well open a new way to prevent the formation of tau tangles and represents the first treatment for AD at its roots.

Early Prediction of Progression to Alzheimer's Disease using Multi-Modality Neuroimages by a Novel Ordinal Learning Model ADPacer

Machine learning has shown great promise for integrating multi-modality neuroimaging datasets to predict the risk of progression/conversion to Alzheimer's Disease (AD) for individuals with Mild Cognitive Impairment (MCI). Most existing work aims to classify MCI patients into converters versus non-converters using a pre-defined timeframe. The limitation is a lack of granularity in differentiating MCI patients who convert at different paces. Progression pace prediction has important clinical values, which allow from more personalized interventional strategies, better preparation of patients and their caregivers, and facilitation of patient selection in clinical trials. We proposed a novel ADPacer model which formulated the pace prediction into an ordinal learning problem with a unique capability of leveraging training samples with label ambiguity to augment the training set. This capability differentiates ADPacer from existing ordinal learning algorithms. We applied ADPacer to MCI patient cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), and demonstrated the superior performance of ADPacer compared to existing ordinal learning algorithms. We also integrated the SHapley Additive exPlanations (SHAP) method with ADPacer to assess the contributions from different modalities to the model prediction. The findings are consistent with the AD literature.

Advances in diagnosing mild cognitive impairment and Alzheimer's disease using 11C-PIB- PET/CT and common neuropsychological tests

Alzheimer's disease (AD) is a critical health issue worldwide that has a negative impact on patients' quality of life, as well as on caregivers, society, and the environment. Positron emission tomography (PET)/computed tomography (CT) and neuropsychological scales can be used to identify AD and mild cognitive impairment (MCI) early, provide a differential diagnosis, and offer early therapies to impede the course of the illness. However, there are few reports of large-scale ¹¹C-PIB-PET/CT investigations that focus on the pathology of AD and MCI. Therefore, further research is needed to determine how neuropsychological test scales and PET/CT measurements of disease progression interact.

PET molecular imaging for pathophysiological visualization in Alzheimer's disease

Alzheimer's disease (AD) is the most common dementia worldwide. The exact etiology of AD is unclear as yet, and no effective treatments are currently available, making AD a tremendous burden posed on the whole society. As AD is a multifaceted and heterogeneous disease, and most biomarkers are dynamic in the course of AD, a range of biomarkers should be established to evaluate the severity and prognosis. Positron emission tomography (PET) offers a great opportunity to visualize AD from diverse perspectives by using radiolabeled agents involved in various pathophysiological processes; PET imaging technique helps to explore the pathomechanisms of AD comprehensively and find out the most appropriate biomarker in each AD phase, leading to a better evaluation of the disease. In this review, we discuss the application of PET in the course of AD and summarized radiolabeled compounds with favorable imaging characteristics.

Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis

INTRODUCTION

Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD.

AREAS COVERED

This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments.

EXPERT OPINION

It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.

Neuronal ER-Signalosome Proteins as Early Biomarkers in Prodromal Alzheimer's Disease Independent of Amyloid-β Production and Tau Phosphorylation

There exists considerable interest to unveil preclinical period and prodromal stages of Alzheimer's disease (AD). The mild cognitive impairment (MCI) is characterized by significant memory and/or other cognitive domains impairments, and is often considered the prodromal phase of AD. The cerebrospinal fluid (CSF) levels of β-amyloid (βA), total tau (t-tau), and phosphorylated tau (p-tau) have been used as biomarkers of AD albeit their significance as indicators during early stages of AD remains far from accurate. The new biomarkers are being intensively sought as to allow identification of pathological processes underlying early stages of AD. Fifty-three participants (75.4 ± 8.3 years) were classified in three groups as cognitively normal healthy controls (HC), MCI, and subjective memory complaints (SMC). The subjects were subjected to a battery of neurocognitive tests and underwent lumbar puncture for CSF extraction. The CSF levels of estrogen-receptor (ER)-signalosome proteins, βA, t-tau and p-tau, were submitted to univariate, bivariate, and multivariate statistical analyses. We have found that the components of the ER-signalosome, namely, caveolin-1, flotilin-1, and estrogen receptor alpha (ERα), insulin growth factor-1 receptor β (IGF1Rβ), prion protein (PrP), and plasmalemmal voltage dependent anion channel 1 (VDAC) could be detected in the CSF from all subjects of the HC, MCI, and SMC groups. The six proteins appeared elevated in MCI and slightly increased in SMC subjects compared to HC, suggesting that signalosome proteins undergo very early modifications in nerve cells. Using a multivariate approach, we have found that the combination of ERα, IGF-1Rβ, and VDAC are the main determinants of group segregation with resolution enough to predict the MCI stage. The analyses of bivariate relationships indicated that collinearity of ER-signalosome proteins vary depending on the stage, with some pairs displaying opposed relationships between HC and MCI groups, and the SMC stage showing either no relationships or behaviors similar to either HC or MCI stages. The multinomial logistic regression models of changes in ER-signalosome proteins provide reliable predictive criteria, particularly for the MCI. Notably, most of the statistical analyses revealed no significant relationships or interactions with classical AD biomarkers at either disease stage. Finally, the multivariate functions were highly correlated with outcomes from neurocognitive tests for episodic memory. These results demonstrate that alterations in ER-signalosome might provide useful diagnostic information on preclinical stages of AD, independently from classical biomarkers.

Systematic Review: microRNAs as Potential Biomarkers in Mild Cognitive Impairment Diagnosis

The rate of progression from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) is estimated at >10% per year, reaching up to 80-90% after 6 years. MCI is considered an indicator of early-stage AD. In this context, the diagnostic screening of MCI is crucial for detecting individuals at high risk of AD before they progress and manifest further severe symptoms. Typically, MCI has been determined using neuropsychological assessment tools such as the Montreal Cognitive Assessment (MoCA) or Mini-Mental Status Examination (MMSE). Unfortunately, other diagnostic methods are not available or are unable to identify MCI in its early stages. Therefore, identifying new biomarkers for MCI diagnosis and prognosis is a significant challenge. In this framework, miRNAs in serum, plasma, and other body fluids have emerged as a promising source of biomarkers for MCI and AD-related cognitive impairments. Interestingly, miRNAs can regulate several signaling pathways via multiple and diverse targets in response to pathophysiological stimuli. This systematic review aims to describe the current state of the art regarding AD-related target genes modulated by differentially expressed miRNAs in peripheral fluids samples in MCI subjects to identify potential miRNA biomarkers in the early stages of AD. We found 30 articles that described five miRNA expression profiles from peripheral fluid in MCI subjects, showing possible candidates for miRNA biomarkers that may be followed up as fluid biomarkers or therapeutic targets of early-stage AD. However, additional research is needed to validate these miRNAs and characterize the precise neuropathological mechanisms.

Partial Volume Correction Increases the Sensitivity of 18F-Florbetapir-Positron Emission Tomography for the Detection of Early Stage Amyloidosis

Purpose: To test whether correcting for unspecific signal from the cerebral white matter increases the sensitivity of amyloid-PET for early stages of cerebral amyloidosis.

Methods: We analyzed 18F-Florbetapir-PET and cerebrospinal fluid (CSF) Aβ42 data from 600 older individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including people with normal cognition, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) dementia. We determined whether three compartmental partial volume correction (PVC-3), explicitly modeling signal spill-in from white matter, significantly improved the association of CSF Aβ42 levels with global 18F-Florbetapir-PET values compared with standard processing without PVC (non-PVC) and a widely used two-compartmental PVC method (PVC-2). In additional voxel-wise analyses, we determined the sensitivity of PVC-3 compared with non-PVC and PVC-2 for detecting early regional amyloid build-up as modeled by decreasing CSF Aβ42 levels. For replication, we included an independent sample of 43 older individuals with subjective memory complaints from the INveStIGation of AlzHeimer’s PredicTors cohort (INSIGHT-preAD study).

Results: In the ADNI sample, PVC-3 18F-Florbetapir-PET values normalized to whole cerebellum signal showed significantly stronger associations with CSF Aβ42 levels than non-PVC or PVC-2, particularly in the lower range of amyloid levels. These effects were replicated in the INSIGHT-preAD sample. PVC-3 18F-Florbetapir-PET data detected regional amyloid build-up already at higher (less abnormal) CSF Aβ42 levels than non-PVC or PVC-2 data.

Conclusion: A PVC approach that explicitly models unspecific white matter binding improves the sensitivity of amyloid-PET for identifying the earliest stages of cerebral amyloid pathology which has implications for future primary prevention trials.

Cognitive and Behavior Deficits in Parkinson's Disease with Alteration of FDG-PET Irrespective of Age

Significant progress has been made in our understanding of the neurobiology of Parkinson’s disease (PD). Post-mortem studies are an important step and could help to comprehend not only the progression of motor symptoms, but also the involvement of other clinical domains, including cognition, behavior and impulse control disorders (ICDs). The correlation of neuropathological extension of the disease with the clinical stages remains challenging. Molecular imaging, including positron emission tomography (PET) and single photon computed tomography (SPECT), could allow for bridging the gap by providing in vivo evidence of disease extension. In the last decade, we have observed a plethora of reports describing improvements in the sensitivity of neuroimaging techniques. These data contribute to increasing the accuracy of PD diagnosis, differentiating PD from other causes of parkinsonism and also obtaining a surrogate marker of disease progression. FDG-PET has been used to measure cerebral metabolic rates of glucose, a proxy for neuronal activity, in PD. Many studies have shown that this technique could be used in early PD, where reduced metabolic activity correlates with disease progression and could predict histopathological diagnosis. The aim of this work is to report two particular cases of PD in which the assessment of brain metabolic activity (from FDG-PET) has been combined with clinical aspects of non-motor symptoms. Integration of information on neuropsychological and metabolic imaging allows us to improve the treatment of PD patients irrespective of age.

A2A Adenosine Receptor as a Potential Biomarker and a Possible Therapeutic Target in Alzheimer's Disease

Alzheimer’s disease (AD) is one of the most common neurodegenerative pathologies. Its incidence is in dramatic growth in Western societies and there is a need of both biomarkers to support the clinical diagnosis and drugs for the treatment of AD. The diagnostic criteria of AD are based on clinical data. However, it is necessary to develop biomarkers considering the neuropathology of AD. The A_2A receptor, a G-protein coupled member of the P1 family of adenosine receptors, has different functions crucial for neurodegeneration. Its activation in the hippocampal region regulates synaptic plasticity and in particular glutamate release, NMDA receptor activation and calcium influx. Additionally, it exerts effects in neuroinflammation, regulating the secretion of pro-inflammatory cytokines. In AD patients, its expression is increased in the hippocampus/entorhinal cortex more than in the frontal cortex, a phenomenon not observed in age-matched control brains, indicating an association with AD pathology. It is upregulated in peripheral blood cells of patients affected by AD, thus reflecting its increase at central neuronal level. This review offers an overview on the main AD biomarkers and the potential role of A_2A adenosine receptor as a new marker and therapeutic target.

Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI)

BACKGROUND

Dementia is a progressive global cognitive impairment syndrome. In 2010, more than 35 million people worldwide were estimated to be living with dementia. Some people with mild cognitive impairment (MCI) will progress to dementia but others remain stable or recover full function. There is great interest in finding good predictors of dementia in people with MCI. The Mini-Mental State Examination (MMSE) is the best-known and the most often used short screening tool for providing an overall measure of cognitive impairment in clinical, research and community settings.

OBJECTIVES

To determine the accuracy of the Mini Mental State Examination for the early detection of dementia in people with mild cognitive impairment SEARCH METHODS: We searched ALOIS (Cochrane Dementia and Cognitive Improvement Specialized Register of diagnostic and intervention studies (inception to May 2014); MEDLINE (OvidSP) (1946 to May 2014); EMBASE (OvidSP) (1980 to May 2014); BIOSIS (Web of Science) (inception to May 2014); Web of Science Core Collection, including the Conference Proceedings Citation Index (ISI Web of Science) (inception to May 2014); PsycINFO (OvidSP) (inception to May 2014), and LILACS (BIREME) (1982 to May 2014). We also searched specialized sources of diagnostic test accuracy studies and reviews, most recently in May 2014: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). No language or date restrictions were applied to the electronic searches and methodological filters were not used as a method to restrict the search overall so as to maximize sensitivity. We also checked reference lists of relevant studies and reviews, tracked citations in Scopus and Science Citation Index, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on MMSE for dementia diagnosis to try to locate possibly relevant but unpublished data.

SELECTION CRITERIA

We considered longitudinal studies in which results of the MMSE administered to MCI participants at baseline were obtained and the reference standard was obtained by follow-up over time. We included participants recruited and clinically classified as individuals with MCI under Petersen and revised Petersen criteria, Matthews criteria, or a Clinical Dementia Rating = 0.5. We used acceptable and commonly used reference standards for dementia in general, Alzheimer's dementia, Lewy body dementia, vascular dementia and frontotemporal dementia.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create two by two tables for dementia in general and other dementias. Two authors independently performed quality assessment using the QUADAS-2 tool. Due to high heterogeneity and scarcity of data, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic curve.

MAIN RESULTS

In this review, we included 11 heterogeneous studies with a total number of 1569 MCI patients followed for conversion to dementia. Four studies assessed the role of baseline scores of the MMSE in conversion from MCI to all-cause dementia and eight studies assessed this test in conversion from MCI to Alzheimer´s disease dementia. Only one study provided information about the MMSE and conversion from MCI to vascular dementia. For conversion from MCI to dementia in general, the accuracy of baseline MMSE scores ranged from sensitivities of 23% to 76% and specificities from 40% to 94%. In relationship to conversion from MCI to Alzheimer's disease dementia, the accuracy of baseline MMSE scores ranged from sensitivities of 27% to 89% and specificities from 32% to 90%. Only one study provided information about conversion from MCI to vascular dementia, presenting a sensitivity of 36% and a specificity of 80% with an incidence of vascular dementia of 6.2%. Although we had planned to explore possible sources of heterogeneity, this was not undertaken due to the scarcity of studies included in our analysis.

AUTHORS' CONCLUSIONS

Our review did not find evidence supporting a substantial role of MMSE as a stand-alone single-administration test in the identification of MCI patients who could develop dementia. Clinicians could prefer to request additional and extensive tests to be sure about the management of these patients. An important aspect to assess in future updates is if conversion to dementia from MCI stages could be predicted better by MMSE changes over time instead of single measurements. It is also important to assess if a set of tests, rather than an isolated one, may be more successful in predicting conversion from MCI to dementia.

Clinical diagnosis of Alzheimer's disease: recommendations of the International Working Group

In 2018, the US National Institute on Aging and the Alzheimer's Association proposed a purely biological definition of Alzheimer's disease that relies on biomarkers. Although the intended use of this framework was for research purposes, it has engendered debate and challenges regarding its use in everyday clinical practice. For instance, cognitively unimpaired individuals can have biomarker evidence of both amyloid β and tau pathology but will often not develop clinical manifestations in their lifetime. Furthermore, a positive Alzheimer's disease pattern of biomarkers can be observed in other brain diseases in which Alzheimer's disease pathology is present as a comorbidity. In this Personal View, the International Working Group presents what we consider to be the current limitations of biomarkers in the diagnosis of Alzheimer's disease and, on the basis of this evidence, we propose recommendations for how biomarkers should and should not be used for diagnosing Alzheimer's disease in a clinical setting. We recommend that Alzheimer's disease diagnosis be restricted to people who have positive biomarkers together with specific Alzheimer's disease phenotypes, whereas biomarker-positive cognitively unimpaired individuals should be considered only at-risk for progression to Alzheimer's disease.

Heterogeneity of Amyloid Binding in Cognitively Impaired Patients Consecutively Recruited from a Memory Clinic: Evaluating the Utility of Quantitative 18F-Flutemetamol PET-CT in Discrimination of Mild Cognitive Impairment from Alzheimer's Disease and Other Dementias

BACKGROUND

With the more widespread use of 18F-radioligand-based amyloid-β (Aβ) PET-CT imaging, we evaluated Aβ binding and the utility of neocortical 18F-Flutemetamol standardized uptake value ratio (SUVR) as a biomarker.

OBJECTIVE

18F-Flutemetamol SUVR was used to differentiate 1) mild cognitive impairment (MCI) from Alzheimer's disease (AD), and 2) MCI from other non-AD dementias (OD).

METHODS

109 patients consecutively recruited from a University memory clinic underwent clinical evaluation, neuropsychological test, MRI and 18F-Flutemetamol PET-CT. The diagnosis was made by consensus of a panel consisting of 1 neuroradiologist and 2 geriatricians. The final cohort included 13 subjective cognitive decline (SCD), 22 AD, 39 MCI, and 35 OD. Quantitative analysis of 16 region-of-interests made by Cortex ID software (GE Healthcare).

RESULTS

The global mean 18F-Flutemetamol SUVR in SCD, MCI, AD, and OD were 0.50 (SD-0.08), 0.53 (SD-0.16), 0.76 (SD-0.10), and 0.56 (SD-0.16), respectively, with SUVR in SCD and MCI and OD being significantly lower than AD. Aβ binding in SCD, MCI, and OD was heterogeneous, being 23%, 38.5%, and 42.9% respectively, as compared to 100% amyloid positivity in AD. Using global SUVR, ROC analysis showed AUC of 0.868 and 0.588 in differentiating MCI from AD and MCI from OD respectively.

CONCLUSION

18F-Flutemetamol SUVR differentiated MCI from AD with high efficacy (high negative predictive value), but much lower efficacy from OD. The major benefit of the test was to differentiate cognitively impaired patients (either SCD, MCI, or OD) without AD-related-amyloid-pathology from AD in the clinical setting, which was under-emphasized in the current guidelines proposed by Amyloid Imaging Task Force.

18F-THK 5351 and 11C-PiB PET of the Thai normal brain template

Objectives

The aim of the study was to create a local normal database brain template of Thai individuals for ¹¹C-Pittsburgh compound B (¹¹C-PiB) and ¹⁸F-THK 5351 depositions using statistical parametric mapping (SPM) software, and to validate and optimize the established specific brain template for use in clinical practice with a highly reliability and reproducibility.

Methods

This prospective study was conducted in 24 healthy right-handed volunteers (13 men, 11 women; aged: 42-79 years) who underwent ¹⁸F-THK 5351 and ¹¹C-PiB PET/CT scans. SPM was used for the ¹⁸F-THK 5351 and ¹¹C-PiB PET/CT image analysis. All PET images were processed individually using Diffusion Tensor Image -Magnetic Resonance Imaging-weighted images (DTI-MRI images), which involved: (1) conversion of Digital Imaging and Communications in Medicine (DICOM) files into an analyzable file extension (.NIFTI) for statistical parametric mapping, (2) setting of the origin (the anterior commissure was used as the anatomical landmark), (3) re-alignment, (4) co-registration of PET with B0 (T1W) and DTI-MRI images, (5) normalization, and (6) normal verification using the Thai MRI standard. We then compared the normal PET template with the abnormal deposition area of different dementia syndromes, including Alzheimer's disease and progressive supranuclear palsy.

Results

This method was able to differentiate cognitively normal from Alzheimer's disease and progressive supranuclear palsy subjects.

Conclusions

This normal brain template was able to be integrated into clinical practice and research using PET analyses at our center.

[Early detection of Alzheimer's disease and dementia prediction in patients with mild cognitive impairment : Summary of current recommendations]

Mild cognitive impairment (MCI) is characterized by cognitive deficits but essentially preserved competence in activities of daily living. It is a risk factor for the development of dementia and can reflect a prodromal predementia state of Alzheimer's disease (AD). The pathology of AD is defined by cerebral deposition of amyloid-beta-1-42 protein and aggregation of phosphorylated tau protein, which can be identified in vivo by biomarkers for these alterations. As a result of advances in the field of biomarker-based early detection of AD, it is possible to differentiate between MCI patients with and without a pathological AD condition and therefore, between patients with a low and those with a high risk for the development of dementia. At present there are no specific guideline recommendations in Germany for the diagnostic use of biomarkers in predementia detection of AD and for dementia risk assessment in patients with MCI. This article summarizes the current recommendations of a European expert consensus publication and a multidisciplinary working group of the Alzheimer's Association on the clinical application of cerebrospinal fluid (CSF) biomarkers for the diagnostics of AD in patients with MCI. If the clinical diagnostic criteria for MCI are fulfilled according to the medical history and neuropsychological testing, it is recommended to carry out further diagnostics (blood test, brain imaging) in order to more precisely define the differential diagnostic classification. Counseling on the potential benefits, limits and risks of biomarker testing for early AD detection and dementia risk prediction should always precede assessment of CSF biomarkers. Information about the individual risk of developing dementia has potential consequences for the psychological well-being and life planning; therefore, clinical follow-up visits are recommended.

No evidence for amyloid pathology as a key mediator of neurodegeneration post-stroke - a seven-year follow-up study

Background

Cognitive impairment (CI) with mixed vascular and neurodegenerative pathologies after stroke is common. The role of amyloid pathology in post-stroke CI is unclear. We hypothesize that amyloid deposition, measured with Flutemetamol (¹⁸F-Flut) positron emission tomography (PET), is common in seven-year stroke survivors diagnosed with CI and, further, that quantitatively assessed ¹⁸F-Flut-PET uptake after 7 years correlates with amyloid-β peptide (Aβ₄₂) levels in cerebrospinal fluid (CSF) at 1 year, and with measures of neurodegeneration and cognition at 7 years post-stroke.

Methods

208 patients with first-ever stroke or transient Ischemic Attack (TIA) without pre-existing CI were included during 2007 and 2008. At one- and seven-years post-stroke, cognitive status was assessed, and categorized into dementia, mild cognitive impairment or normal. Etiologic sub-classification was based on magnetic resonance imaging (MRI) findings, CSF biomarkers and clinical cognitive profile. At 7 years, patients were offered ¹⁸F-Flut-PET, and amyloid-positivity was assessed visually and semi-quantitatively. The associations between ¹⁸F-Flut-PET standardized uptake value ratios (SUVr) and measures of neurodegeneration (medial temporal lobe atrophy (MTLA), global cortical atrophy (GCA)) and cognition (Mini-Mental State Exam (MMSE), Trail-making test A (TMT-A)) and CSF Aβ₄₂ levels were assessed using linear regression.

Results

In total, 111 patients completed 7-year follow-up, and 26 patients agreed to PET imaging, of whom 13 had CSF biomarkers from 1 year. Thirteen out of 26 patients were diagnosed with CI 7 years post-stroke, but only one had visually assessed amyloid positivity. CSF Aβ₄₂ levels at 1 year, MTA grade, GCA scale, MMSE score or TMT-A at 7 years did not correlate with ¹⁸F-Flut-PET SUVr in this cohort.

Conclusions

Amyloid binding was not common in 7-year stroke survivors diagnosed with CI. Quantitatively assessed, cortical amyloid deposition did not correlate with other measures related to neurodegeneration or cognition. Therefore, amyloid pathology may not be a key mediator of neurodegeneration 7 years post-stroke.

Trial registration

Clinicaltrials.gov (NCT00506818). July 23, 2007. Inclusion from February 2007, randomization and intervention from May 2007 and trial registration in July 2007.

The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer's disease using Alzheimer's Disease Neuroimaging Initiative database

Amyloid beta (Aβ) plaques aggregation is considered as the “start” of the degenerative process that manifests years before the clinical symptoms appear in Alzheimer’s Disease (AD). The aim of this study is to use back propagation neural networks (BPNNs) in ¹⁸F-florbetapir PET data for automated classification of four patient groups including AD, late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI), and significant memory concern (SMC), versus normal control (NC) for early AD detection. Five hundred images for AD, LMCI, EMCI, SMC, and NC, i.e., 100 images for each group, were used from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The results showed that the automated classification of NC/AD produced a high accuracy of 87.9%, while the results for the prodromal stages of the disease were 66.4%, 60.0%, and 52.9% for NC/LCMI, NC/EMCI and NC/SMC, respectively. The proposed method together with the image preparation steps can be used for early AD detection and classification with high accuracy using Aβ PET dataset.

Retinal layer thickness in preclinical Alzheimer's disease

Purpose

There is urgent need for non‐invasive diagnostic biomarkers in the preclinical phase of Alzheimer's Disease (AD). Several studies suggest that retinal thickness is reduced in AD. Here, we aim to test the diagnostic value of retinal thickness in preclinical AD, as defined by cognitively normal individuals with amyloid pathology on PET.

Methods

One hundred and sixty five cognitively healthy monozygotic twins aged ≥ 60 were included from the Netherlands Twin Register taking part in the European Medical Information Framework for Alzheimer's Disease PreclinAD study. Participants underwent [¹⁸F] flutemetamol PET that was visually rated for presence or absence of cortical amyloid beta (Aβ). Binding potential (BP_ND) was calculated as continuous measure for Aβ. Spectral Domain OCT was used to asses total and individual inner retinal layer thickness in the macular region (ETDRS circles) as well as peripapillary retinal nerve fibre layer (pRNFL) thickness. Differences between Aβ+ and Aβ− individuals and associations between BP_ND and retinal thickness were analyzed.

Results

No differences were found in retinal layer thickness in the macula or pRNFL between Aβ+ and Aβ− individuals. A positive associations between BP_ND and macular total retinal thickness was observed in the inner ring (p = 0.018), but this was not statistically significant after correction for multiple testing (p = 0.144). Brain/eye parameters had moderate to high intra‐twin correlations (p < 0.001) except visual rating score of Aβ, which did not correlate (r = 0.21, p = 0.068).

Conclusion

Variation in retinal thickness likely reflects genetic differences between individuals, but cannot discriminate between healthy and preclinical AD cases, making its use as biomarker in these early stages limited.

Assessment of the appropriate use criteria for amyloid PET in an unselected memory clinic cohort: The ABIDE project

INTRODUCTION

The objective of this study was to assess the usefulness of the appropriate use criteria (AUC) for amyloid imaging in an unselected cohort.

METHODS

We calculated sensitivity and specificity of appropriate use (increased confidence and management change), as defined by Amyloid Imaging Taskforce in the AUC, and other clinical utility outcomes. Furthermore, we compared differences in post-positron emission tomography diagnosis and management change between "AUC-consistent" and "AUC-inconsistent" patients.

RESULTS

Almost half (250/507) of patients were AUC-consistent. In both AUC-consistent and AUC-inconsistent patients, post-positron emission tomography diagnosis (28%-21%) and management (32%-17%) change was substantial. The Amyloid Imaging Taskforce's definition of appropriate use occurred in 55/507 (13%) patients, detected by the AUC with a sensitivity of 93%, and a specificity of 56%. Diagnostic changes occurred independently of AUC status (sensitivity: 57%, specificity: 53%).

DISCUSSION

The current AUC are not sufficiently able to discriminate between patients who will benefit from amyloid positron emission tomography and those who will not.

Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease

Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD. In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network. Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease.

Protein misassembly and aggregation as potential convergence points for non-genetic causes of chronic mental illness

Chronic mental illnesses (CMI), such as schizophrenia or recurrent affective disorders, are complex conditions with both genetic and non-genetic elements. In many other chronic brain conditions, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and frontotemporal dementia, sporadic instances of the disease are more common than gene-driven familial cases. Yet, the pathology of these conditions can be characterized by the presence of aberrant protein homeostasis, proteostasis, resulting in misfolded or aggregated proteins in the brains of patients that predominantly do not derive from genetic mutations. While visible deposits of aggregated protein have not yet been detected in CMI patients, we propose the existence of more subtle protein misassembly in these conditions, which form a continuum with the psychiatric phenotypes found in the early stages of many neurodegenerative conditions. Such proteinopathies need not rely on genetic variation. In a similar manner to the established aberrant neurotransmitter homeostasis in CMI, aberrant homeostasis of proteins is a functional statement that can only partially be explained by, but is certainly complementary to, genetic approaches. Here, we review evidence for aberrant proteostasis signatures from post mortem human cases, in vivo animal work, and in vitro analysis of candidate proteins misassembled in CMI. The five best-characterized proteins in this respect are currently DISC1, dysbindin-1, CRMP1, TRIOBP-1, and NPAS3. Misassembly of these proteins with inherently unstructured domains is triggered by extracellular stressors and thus provides a converging point for non-genetic causes of CMI.

Fluorescence Chemicals To Detect Insoluble and Soluble Amyloid-β Aggregates

Misfolded amyloid-β (Aβ) is the key biomarker of Alzheimer's disease (AD), and discoveries of fluorescence chemicals visualizing such Aβ aggregates in the brain have made major contributions in postmortem and antemortem diagnosis of the disorder. Insoluble senile plaques of Aβ in brain tissues are commonly stained with thioflavin and congo red dyes and observed through microscopy, while those in living patient brains are detected via radioisotope-labeled fluorescence chemicals for positron emission tomography. Clinical evidence strongly supports the view that plaques are well-associated with the onset but not with the progression of AD. Plaques could accumulate while cognitive functions of at-risk individuals are still intact, and thus, another biomarker is needed to monitor neurodegeneration. Soluble Aβ oligomers are considered to have strong correlation with neuronal loss and brain atrophy as they are the most neurotoxic forms of misfolded Aβ. However, oligomer-targeting probes encounter several major difficulties in development. There is a significant structural distinction between two Aβ species-plaques are β-sheet-rich while oligomers are unordered-and it is still difficult to isolate and stabilize the oligomeric forms of Aβ. Due to these challenges, soluble oligomer-detecting imaging probes are relatively rare compared to the plaque-targeting chemical probes. This Review describes biochemical and optical characteristics of up-to-date fluorescence chemicals targeting insoluble plaques and soluble oligomers of Aβ. We also highlight the contributions of Aβ fluorescence chemicals to the clinical diagnosis of AD and technical challenges in searching for enhanced imaging probes.

Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

Forty years of structural brain imaging in mental disorders: is it clinically useful or not?

Structural brain imaging was introduced into routine clinical practice more than 40 years ago with the hope that it would support the diagnosis and treatment of mental disorders. It is now widely used to exclude organic brain disease (eg, brain tumors, cardiovascular, and inflammatory processes) in mental disorders. However, questions have been raised about whether structural brain imaging is still needed today and whether it could also be clinically useful to apply new biostatistical methods, such as machine learning. Therefore, the current paper not only reviews structural findings in Alzheimer disease, depression, bipolar disorder, and schizophrenia but also discusses the role of structural imaging in supporting diagnostic, prognostic, and therapeutic processes in mental disorders. Thus, it attempts to answer the questions whether, after four decades of use, structural brain imaging is clinically useful in mental disorders or whether it will become so in the future.

T2 Relaxometry and Diffusion Tensor Indices of the Hippocampus and Entorhinal Cortex Improve Sensitivity and Specificity of MRI to Detect Amnestic Mild Cognitive Impairment and Alzheimer's Disease Dementia

BACKGROUND

Quantitative T2 and diffusion MRI indices inform about tissue state and microstructure, both of which may be affected by pathology before tissue atrophy.

PURPOSE

To evaluate the capability of both volumetric and quantitative MRI (qMRI) of the hippocampus and entorhinal cortex (EC) for classification of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease dementia (ADD).

STUDY TYPE

Retrospective cross-sectional study.

POPULATION

Consecutive cohorts of healthy age-matched controls (n = 62), aMCI patients (n = 25), and ADD patients (n = 14).

FIELD STRENGTH/SEQUENCE

3T using T1-weighted imaging, T2-weighted imaging, T2 relaxometry and diffusion tensor imaging (DTI).

ASSESSMENT

Montreal Cognitive Assessment and paired associate learning tests for cognitive state. Hippocampal subfield volumes by the automated segmentation of hippocampal subfields system from structural brain images. T2 relaxation time and DTI indices quantified for hippocampal subfields. The fraction of voxels with high T2 values (>20 ms above subfield median) was calculated and regionalized for hippocampus and EC.

STATISTICAL TESTS

Support vector machine and receiver operating characteristic analyses from cognitive and MRI data.

RESULTS

qMRI classified aMCI and ADD with excellent sensitivity (79.0% and 94.5%, respectively) and specificity (85.6% and 86.1%, respectively), superior to volumes alone (70.0% and 84.5% for respective sensitivities; 82.2 and 91.1 for respective specificities) and similar to cognitive tests (61.7% and 87.5% for respective sensitivities; 88.2% and 90.7% for respective specificities). Regions of high T2 are dispersed throughout each hippocampal subfield in aMCI and ADD with higher concentration than controls, and was most pronounced in the EC. No other individual qMRI marker than EC volume can separate aMCI from ADD, however. DATA CONCLUSION: qMRI markers of hippocampal and entorhinal tissue states are sensitive and specific classifiers of aMCI and ADD. They may serve as markers of a neurodegenerative state preceding volume loss.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:445-455.

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

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

Validity of the QUADAS-2 in Assessing Risk of Bias in Alzheimer's Disease Diagnostic Accuracy Studies

Accurate detection of Alzheimer's disease (AD) is of considerable clinical importance. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) is the current research standard for evaluating the quality of studies that validate diagnostic tests; however, its own construct validity has not yet been evaluated empirically. Our aim was to evaluate how well the proposed QUADAS-2 items and its domains converge to indicate the study quality criteria. This study applies confirmatory factor analysis to determine whether a measurement model would be consistent with meta-analytic data. Cochrane meta-analyses assessing the accuracy of AD diagnostic tests were identified. The seven ordinal QUADAS-2 items, intended to inform study quality based on risk of bias and applicability concerns, were extracted for each of the included studies. The QUADAS-2 pre-specified factor structure (i.e., four domains assessed in terms of risk of bias and applicability concerns) was not testable. An alternative model based on two correlated factors (i.e., risk of bias and applicability concerns) returned a poor fit model. Poor factor loadings were obtained, indicating that we cannot provide evidence that the indicators convergent validity markers in the context of AD diagnostic accuracy metanalyses, where normally the sample size is low (around 60 primary included studies). A Monte Carlo simulation suggested that such a model would require at least 90 primary studies to estimate these parameters with 80% power. The reliability of the QUADAS-2 items to inform a measurement model for study quality remains unconfirmed. Considerations for conceptualizing such a tool are discussed.

Clinical imaging in dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a common neurodegenerative dementia in older people; however, the clinical features, particularly cognitive fluctuations and rapid eye movement sleep disorder, are often hard to elicit, leading to difficulty in making the diagnosis clinically. Here we examine the literature for the evidence behind imaging modalities that could assist in making the diagnosis. Dopamine transporter (DAT) imaging remains the best modality for differentiation from dementia of Alzheimer's type with high sensitivity and specificity reported based on pathological diagnoses. 123Iodine-metaiodobenzylguanidine myocardial scintigraphy (MIBG) however is rapidly becoming an alternative imaging modality for the diagnosis of DLB, though studies assessing its accuracy with postmortem verification are still awaited. However, there are suggestions that MIBG may be better in the differentiation of vascular parkinsonism from DLB than DAT scans but may have lower sensitivity for detecting DLB compared with the 80% sensitivity seen in DAT imaging. Structural MRI scans have long been used for the diagnosis of dementia; however, their utility in DLB is limited to revealing the presence of coexisting Alzheimer's disease. Fluorodeoxyglucose (FDG) PET is an alternative biomarker that can also differentiate Alzheimer's disease and DLB but lacks the evidence base of both DAT and MIBG scans.

An Algorithm for Preclinical Diagnosis of Alzheimer's Disease

Almost all Alzheimer's disease (AD) therapeutic trials have failed in recent years. One of the main reasons for failure is due to designing the disease-modifying clinical trials at the advanced stage of the disease when irreversible brain damage has already occurred. Diagnosis of the preclinical stage of AD and therapeutic intervention at this phase, with a perfect target, are key points to slowing the progression of the disease. Various AD biomarkers hold enormous promise for identifying individuals with preclinical AD and predicting the development of AD dementia in the future, but no single AD biomarker has the capability to distinguish the AD preclinical stage. A combination of complimentary AD biomarkers in cerebrospinal fluid (Aβ₄₂, tau, and phosphor-tau), non-invasive neuroimaging, and genetic evidence of AD can detect preclinical AD in the in-vivo ante mortem brain. Neuroimaging studies have examined region-specific cerebral blood flow (CBF) and microstructural changes in the preclinical AD brain. Functional MRI (fMRI), diffusion tensor imaging (DTI) MRI, arterial spin labeling (ASL) MRI, and advanced PET have potential application in preclinical AD diagnosis. A well-validated simple framework for diagnosis of preclinical AD is urgently needed. This article proposes a comprehensive preclinical AD diagnostic algorithm based on neuroimaging, CSF biomarkers, and genetic markers.

Down Syndrome, Obesity, Alzheimer's Disease, and Cancer: A Brief Review and Hypothesis

Down syndrome (trisomy 21), a complex mix of physical, mental, and biochemical issues, includes an increased risk of Alzheimer’s disease and childhood leukemia, a decreased risk of other tumors, and a high frequency of overweight/obesity. Certain features related to the third copy of chromosome 21 (which carries the APP gene and several anti-angiogenesis genes) create an environment favorable for Alzheimer’s disease and unfavorable for cancer. This environment may be enhanced by two bioactive compounds from fat cells, leptin, and adiponectin. This paper outlines these fat-related disease mechanisms and suggests new avenues of research to reduce disease risk in Down syndrome.

Validation of Immunoassay-Based Tools for the Comprehensive Quantification of Aβ40 and Aβ42 Peptides in Plasma

Recent advances in neuroimaging and cerebrospinal fluid (CSF) biomarker assays have provided evidence of a long preclinical stage of Alzheimer's disease (AD). This period is being increasingly targeted for secondary prevention trials of new therapies. In this context, the interest of a noninvasive, cost-effective amyloid-β (Aβ) blood-based test does not need to be overstated. Nevertheless, a thorough validation of these bioanalytical methods should be performed as a prerequisite for confident interpretation of clinical results. The aim of this study was to validate ELISA sandwich colorimetric ABtest40 and ABtest42 for the quantification of Aβ40 and Aβ42 in human plasma. The validation parameters assessed included precision, accuracy, sensitivity, specificity, recovery, and dilution linearity. ABtest40 and ABtest42 proved to be specific for their target peptide using Aβ peptides with sequence similar to the target. Mean relative error in the quantification was found to be below 7.5% for both assays, with high intra-assay, inter-assay, and inter-batch precision (CV <9.0% on average). Sensitivity was assessed by determination of the limit of quantification fulfilling precision and accuracy criteria; it was established at 7.60 pg/ml and 3.60 pg/ml for ABtest40 and ABtest42, respectively. Plasma dilution linearity was demonstrated in PBS; however, dilution in a proprietary formulated buffer significantly increased the recovery of both Aβ40 and Aβ42 masked by matrix interactions, allowing a more comprehensive assessment of the free and total peptide levels in the plasma. In conclusion, both assays were successfully validated as tools for the quantification Aβ40 and Aβ42 in plasma.

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning

Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.

Fluid and imaging biomarkers for Alzheimer's disease: Where we stand and where to head to

There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.

Understanding the roles of mutations in the amyloid precursor protein in Alzheimer disease

Many models of disease progression in Alzheimer's disease (AD) have been proposed to help guide experimental design and aid the interpretation of results. Models focussing on the genetic evidence include the amyloid cascade (ACH) and presenilin (PSH) hypotheses and the amyloid precursor protein (APP) matrix approach (AMA), of which the ACH has held a dominant position for over two decades. However, the ACH has never been fully accepted and has not yet delivered on its therapeutic promise. We review the ACH, PSH and AMA in relation to levels of APP proteolytic fragments reported from AD-associated mutations in APP. Different APP mutations have diverse effects on the levels of APP proteolytic fragments. This evidence is consistent with at least three disease pathways that can differ between familial and sporadic AD and two pathways associated with cerebral amyloid angiopathy. We cannot fully evaluate the ACH, PSH and AMA in relation to the effects of mutations in APP as the APP proteolytic system has not been investigated systematically. The confounding effects of sequence homology, complexity of competing cleavages and antibody cross reactivities all illustrate limitations in our understanding of the roles these fragments and the APP proteolytic system as a whole in normal aging and disease play. Current experimental design should be refined to generate clearer evidence, addressing both aging and complex disorders with standardised reporting formats. A more flexible theoretical framework capable of accommodating the complexity of the APP proteolytic system is required to integrate available evidence.

A Systematic Review and Aggregated Analysis on the Impact of Amyloid PET Brain Imaging on the Diagnosis, Diagnostic Confidence, and Management of Patients being Evaluated for Alzheimer's Disease

BACKGROUND

Amyloid PET (aPET) imaging could improve patient outcomes in clinical practice, but the extent of impact needs quantification.

OBJECTIVE

To provide an aggregated quantitative analysis of the value added by aPET in cognitively impaired subjects.

METHODS

Systematic literature searches were performed in Embase and Medline until January 2017. 1,531 cases over 12 studies were included (1,142 cases over seven studies in the primary analysis where aPET was the key biomarker; the remaining cases included as defined groups in the secondary analysis). Data was abstracted by consensus among two observers and assessed for bias. Clinical utility was measured by diagnostic change, diagnostic confidence, and patient management before and after aPET. Three groups were further analyzed: control patients for whom feedback of aPET scan results was delayed; aPET Appropriate Use Criteria (AUC+) cases; and patients undergoing additional FDG/CSF testing.

RESULTS

For 1,142 cases with only aPET, 31.3% of diagnoses were revised, whereas 3.2% of diagnoses changed in the delayed aPET control group (p < 0.0001). Increased diagnostic confidence following aPET was found for 62.1% of 870 patients. Management changes with aPET were found in 72.2% of 740 cases and in 55.5% of 299 cases in the control group (p < 0.0001). The diagnostic value of aPET in AUC+ patients or when FDG/CSF were additionally available did not substantially differ from the value of aPET alone in the wider population.

CONCLUSIONS

Amyloid PET contributed to diagnostic revision in almost a third of cases and demonstrated value in increasing diagnostic confidence and refining management plans.

Negative 11C-PIB PET Predicts Lack of Alzheimer's Disease Pathology in Postmortem Examination

Our aim was to assess whether in vivo11C-PIB negative memory-impaired subjects may nonetheless exhibit brain Alzheimer's disease (AD) pathology. We re-evaluated the PET images and systematically characterized the postmortem neuropathology of six individuals who had undergone clinically indicated amyloid PET. The single case with negligible amyloid-β (Aβ) pathology had Lewy body disease, where concomitant AD changes are often seen. Further, the subject's plaques were predominantly diffuse. The predictive value of a negative 11C-PIB scan appears to be good, even in memory-impaired populations. Our results suggest that considerable neuritic Aβ plaque pathology in the absence of specific/cortical 11C-PIB binding upon PET is unlikely.

Epidemiology of Dementia: The Burden on Society, the Challenges for Research

Dementia is among the leading causes of death and disability. Due to the ageing population, its prevalence is expected to nearly triple worldwide by 2050, urging the development of preventive and curative interventions. Various modifiable risk factors have been identified in community-based cohort studies, but insight into the underlying pathophysiological mechanisms is lacking. Clinical trials have thus far failed in the development of disease-modifying therapy in patients with dementia, thereby triggering a shift of focus toward the presymptomatic phase of disease. The extensive preclinical disease course of Alzheimer's disease warrants reliable, easily obtainable biomarkers to aid in timely application of preventive strategies, selecting participants for neuroprotective trials, and disease monitoring in trials and clinical practice. Biomarker and drug discovery may yield the fruits from technology-driven developments in the field of genomics, epigenetics, metabolomics, and brain imaging. In that context, bridging the gap between translational and population research may well prove a giant leap toward development of successful preventive and curative interventions against dementia.

Regional association of pCASL-MRI with FDG-PET and PiB-PET in people at risk for autosomal dominant Alzheimer's disease

Autosomal dominant Alzheimer's disease (ADAD) is a small subset of Alzheimer's disease that is genetically determined with 100% penetrance. It provides a valuable window into studying the course of pathologic processes that leads to dementia. Arterial spin labeling (ASL) MRI is a potential AD imaging marker that non-invasively measures cerebral perfusion. In this study, we investigated the relationship of cerebral blood flow measured by pseudo-continuous ASL (pCASL) MRI with measures of cerebral metabolism (FDG PET) and amyloid deposition (Pittsburgh Compound B (PiB) PET). Thirty-one participants at risk for ADAD (age 39 ± 13 years, 19 females) were recruited into this study, and 21 of them received both MRI and FDG and PiB PET scans. Considerable variability was observed in regional correlations between ASL-CBF and FDG across subjects. Both regional hypo-perfusion and hypo-metabolism were associated with amyloid deposition. Cross-sectional analyses of each biomarker as a function of the estimated years to expected dementia diagnosis indicated an inverse relationship of both perfusion and glucose metabolism with amyloid deposition during AD development. These findings indicate that neurovascular dysfunction is associated with amyloid pathology, and also indicate that ASL CBF may serve as a sensitive early biomarker for AD. The direct comparison among the three biomarkers provides complementary information for understanding the pathophysiological process of AD.

•

Regional associations of three imaging biomarkers (pCASL-MRI, FDG-PET and PiB-PET) are studied in persons at risk for ADAD.

•

PiB-PET shows different spatial pattern compared to pCASL-MRI and FDG-PET.

•

There is considerable variability among regional correlations between pCASL and FDG.

•

Both regional hypo-perfusion and hypo-metabolism are associated with amyloid deposition.

Diagnostic Accuracy of Memory Measures in Alzheimer's Dementia and Mild Cognitive Impairment: a Systematic Review and Meta-Analysis

With an increasing focus on biomarkers in dementia research, illustrating the role of neuropsychological assessment in detecting mild cognitive impairment (MCI) and Alzheimer's dementia (AD) is important. This systematic review and meta-analysis, conducted in accordance with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) standards, summarizes the sensitivity and specificity of memory measures in individuals with MCI and AD. Both meta-analytic and qualitative examination of AD versus healthy control (HC) studies (n = 47) revealed generally high sensitivity and specificity (≥ 80% for AD comparisons) for measures of immediate (sensitivity = 87%, specificity = 88%) and delayed memory (sensitivity = 89%, specificity = 89%), especially those involving word-list recall. Examination of MCI versus HC studies (n = 38) revealed generally lower diagnostic accuracy for both immediate (sensitivity = 72%, specificity = 81%) and delayed memory (sensitivity = 75%, specificity = 81%). Measures that differentiated AD from other conditions (n = 10 studies) yielded mixed results, with generally high sensitivity in the context of low or variable specificity. Results confirm that memory measures have high diagnostic accuracy for identification of AD, are promising but require further refinement for identification of MCI, and provide support for ongoing investigation of neuropsychological assessment as a cognitive biomarker of preclinical AD. Emphasizing diagnostic test accuracy statistics over null hypothesis testing in future studies will promote the ongoing use of neuropsychological tests as Alzheimer's disease research and clinical criteria increasingly rely upon cerebrospinal fluid (CSF) and neuroimaging biomarkers.

18F PET with florbetapir for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

¹⁸F-florbetapir uptake by brain tissue measured by positron emission tomography (PET) is accepted by regulatory agencies like the Food and Drug Administration (FDA) and the European Medicine Agencies (EMA) for assessing amyloid load in people with dementia. Its added value is mainly demonstrated by excluding Alzheimer's pathology in an established dementia diagnosis. However, the National Institute on Aging and Alzheimer's Association (NIA-AA) revised the diagnostic criteria for Alzheimer's disease and confidence in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease may be increased when using amyloid biomarkers tests like ¹⁸F-florbetapir. These tests, added to the MCI core clinical criteria, might increase the diagnostic test accuracy (DTA) of a testing strategy. However, the DTA of ¹⁸F-florbetapir to predict the progression from MCI to Alzheimer's disease dementia (ADD) or other dementias has not yet been systematically evaluated.

OBJECTIVES

To determine the DTA of the ¹⁸F-florbetapir PET scan for detecting people with MCI at time of performing the test who will clinically progress to ADD, other forms of dementia (non-ADD), or any form of dementia at follow-up.

SEARCH METHODS

This review is current to May 2017. We searched MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), BIOSIS Citation Index (Thomson Reuters Web of Science), Web of Science Core Collection, including the Science Citation Index (Thomson Reuters Web of Science) and the Conference Proceedings Citation Index (Thomson Reuters Web of Science), LILACS (BIREME), CINAHL (EBSCOhost), ClinicalTrials.gov (https://clinicaltrials.gov), and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (http://www.who.int/ictrp/search/en/). We also searched ALOIS, the Cochrane Dementia & Cognitive Improvement Group's specialised register of dementia studies (http://www.medicine.ox.ac.uk/alois/). We checked the reference lists of any relevant studies and systematic reviews, and performed citation tracking using the Science Citation Index to identify any additional relevant studies. No language or date restrictions were applied to the electronic searches.

SELECTION CRITERIA

We included studies that had prospectively defined cohorts with any accepted definition of MCI at time of performing the test and the use of ¹⁸F-florbetapir scan to evaluate the DTA of the progression from MCI to ADD or other forms of dementia. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis, for example, National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts identified in electronic-database searches. Two review authors independently selected studies for inclusion and extracted data to create two-by-two tables, showing the binary test results cross-classified with the binary reference standard. We used these data to calculate sensitivities, specificities, and their 95% confidence intervals. Two independent assessors performed quality assessment using the QUADAS-2 tool plus some additional items to assess the methodological quality of the included studies.

MAIN RESULTS

We included three studies, two of which evaluated the progression from MCI to ADD, and one evaluated the progression from MCI to any form of dementia.Progression from MCI to ADD was evaluated in 448 participants. The studies reported data on 401 participants with 1.6 years of follow-up and in 47 participants with three years of follow-up. Sixty-one (15.2%) participants converted at 1.6 years follow-up; nine (19.1%) participants converted at three years of follow-up.Progression from MCI to any form of dementia was evaluated in five participants with 1.5 years of follow-up, with three (60%) participants converting to any form of dementia.There were concerns regarding applicability in the reference standard in all three studies. Regarding the domain of flow and timing, two studies were considered at high risk of bias. MCI to ADD;Progression from MCI to ADD in those with a follow-up between two to less than four years had a sensitivity of 67% (95% CI 30 to 93) and a specificity of 71% (95% CI 54 to 85) by visual assessment (n = 47, 1 study).Progression from MCI to ADD in those with a follow-up between one to less than two years had a sensitivity of 89% (95% CI 78 to 95) and a specificity of 58% (95% CI 53 to 64) by visual assessment, and a sensitivity of 87% (95% CI 76 to 94) and a specificity of 51% (95% CI 45 to 56) by quantitative assessment by the standardised uptake value ratio (SUVR)(n = 401, 1 study). MCI to any form of dementia;Progression from MCI to any form of dementia in those with a follow-up between one to less than two years had a sensitivity of 67% (95% CI 9 to 99) and a specificity of 50% (95% CI 1 to 99) by visual assessment (n = 5, 1 study). MCI to any other forms of dementia (non-ADD);There was no information regarding the progression from MCI to any other form of dementia (non-ADD).

AUTHORS' CONCLUSIONS

Although sensitivity was good in one included study, considering the poor specificity and the limited data available in the literature, we cannot recommend routine use of ¹⁸F-florbetapir PET in clinical practice to predict the progression from MCI to ADD.Because of the poor sensitivity and specificity, limited number of included participants, and the limited data available in the literature, we cannot recommend its routine use in clinical practice to predict the progression from MCI to any form of dementia.Because of the high financial costs of ¹⁸F-florbetapir, clearly demonstrating the DTA and standardising the process of this modality are important prior to its wider use.

18F PET with flutemetamol for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

¹⁸F-flutemetamol uptake by brain tissue, measured by positron emission tomography (PET), is accepted by regulatory agencies like the Food and Drug Administration (FDA) and the European Medicine Agencies (EMA) for assessing amyloid load in people with dementia. Its added value is mainly demonstrated by excluding Alzheimer's pathology in an established dementia diagnosis. However, the National Institute on Aging and Alzheimer's Association (NIA-AA) revised the diagnostic criteria for Alzheimer's disease and the confidence in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease may be increased when using some amyloid biomarkers tests like ¹⁸F-flutemetamol. These tests, added to the MCI core clinical criteria, might increase the diagnostic test accuracy (DTA) of a testing strategy. However, the DTA of ¹⁸F-flutemetamol to predict the progression from MCI to Alzheimer's disease dementia (ADD) or other dementias has not yet been systematically evaluated.

OBJECTIVES

To determine the DTA of the ¹⁸F-flutemetamol PET scan for detecting people with MCI at time of performing the test who will clinically progress to ADD, other forms of dementia (non-ADD) or any form of dementia at follow-up.

SEARCH METHODS

The most recent search for this review was performed in May 2017. We searched MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), BIOSIS Citation Index (Thomson Reuters Web of Science), Web of Science Core Collection, including the Science Citation Index (Thomson Reuters Web of Science) and the Conference Proceedings Citation Index (Thomson Reuters Web of Science), LILACS (BIREME), CINAHL (EBSCOhost), ClinicalTrials.gov (https://clinicaltrials.gov), and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (http://www.who.int/ictrp/search/en/). We also searched ALOIS, the Cochrane Dementia & Cognitive Improvement Group's specialised register of dementia studies (http://www.medicine.ox.ac.uk/alois/). We checked the reference lists of any relevant studies and systematic reviews, and performed citation tracking using the Science Citation Index to identify any additional relevant studies. No language or date restrictions were applied to the electronic searches.

SELECTION CRITERIA

We included studies that had prospectively defined cohorts with any accepted definition of MCI at time of performing the test and the use of ¹⁸F-flutemetamol scan to evaluate the DTA of the progression from MCI to ADD or other forms of dementia. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis, for example, National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts identified in electronic-database searches. Two review authors independently selected studies for inclusion and extracted data to create two-by-two tables, showing the binary test results cross-classified with the binary reference standard. We used these data to calculate sensitivities, specificities, and their 95% confidence intervals. Two independent assessors performed quality assessment using the QUADAS-2 tool plus some additional items to assess the methodological quality of the included studies.

MAIN RESULTS

Progression from MCI to ADD was evaluated in 243 participants from two studies. The studies reported data on 19 participants with two years of follow-up and on 224 participants with three years of follow-up. Nine (47.4%) participants converted at two years follow-up and 81 (36.2%) converted at three years of follow-up.There were concerns about participant selection and sampling in both studies. The index test domain in one study was considered unclear and in the second study it was considered at low risk of bias. For the reference standard domain, one study was considered at low risk and the second study was considered to have an unclear risk of bias. Regarding the domains of flow and timing, both studies were considered at high risk of bias. MCI to ADD;Progression from MCI to ADD at two years of follow-up had a sensitivity of 89% (95% CI 52 to 100) and a specificity of 80% (95% CI 44 to 97) by quantitative assessment by SUVR (n = 19, 1 study).Progression from MCI to ADD at three years of follow-up had a sensitivity of 64% (95% CI 53 to 75) and a specificity of 69% (95% CI 60 to 76) by visual assessment (n = 224, 1 study).There was no information regarding the other two objectives in this systematic review (SR): progression from MCI to other forms of dementia and progression to any form of dementia at follow-up.

AUTHORS' CONCLUSIONS

Due to the varying sensitivity and specificity for predicting the progression from MCI to ADD and the limited data available, we cannot recommend routine use of ¹⁸F-flutemetamol in clinical practice. ¹⁸F-flutemetamol has high financial costs; therefore, clearly demonstrating its DTA and standardising the process of the ¹⁸F-flutemetamol modality is important prior to its wider use.

18F PET with florbetaben for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

18F-florbetaben uptake by brain tissue, measured by positron emission tomography (PET), is accepted by regulatory agencies like the Food and Drug Administration (FDA) and the European Medicine Agencies (EMA) for assessing amyloid load in people with dementia. Its added value is mainly demonstrated by excluding Alzheimer's pathology in an established dementia diagnosis. However, the National Institute on Aging and Alzheimer's Association (NIA-AA) revised the diagnostic criteria for Alzheimer's disease and confidence in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease may be increased when using some amyloid biomarkers tests like 18F-florbetaben. These tests, added to the MCI core clinical criteria, might increase the diagnostic test accuracy (DTA) of a testing strategy. However, the DTA of 18F-florbetaben to predict the progression from MCI to Alzheimer's disease dementia (ADD) or other dementias has not yet been systematically evaluated.

OBJECTIVES

To determine the DTA of the 18F-florbetaben PET scan for detecting people with MCI at time of performing the test who will clinically progress to ADD, other forms of dementia (non-ADD), or any form of dementia at follow-up.

SEARCH METHODS

The most recent search for this review was performed in May 2017. We searched MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), BIOSIS Citation Index (Thomson Reuters Web of Science), Web of Science Core Collection, including the Science Citation Index (Thomson Reuters Web of Science) and the Conference Proceedings Citation Index (Thomson Reuters Web of Science), LILACS (BIREME), CINAHL (EBSCOhost), ClinicalTrials.gov (https://clinicaltrials.gov), and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (http://www.who.int/ictrp/search/en/). We also searched ALOIS, the Cochrane Dementia & Cognitive Improvement Group's specialised register of dementia studies (http://www.medicine.ox.ac.uk/alois/). We checked the reference lists of any relevant studies and systematic reviews, and performed citation tracking using the Science Citation Index to identify any additional relevant studies. No language or date restrictions were applied to electronic searches.

SELECTION CRITERIA

We included studies that had prospectively defined cohorts with any accepted definition of MCI at time of performing the test and the use of 18F-florbetaben scan to evaluate the DTA of the progression from MCI to ADD or other forms of dementia. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis, for example, the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts identified in electronic-database searches. Two review authors independently selected studies for inclusion and extracted data to create two-by-two tables, showing the binary test results cross-classified with the binary reference standard. We used these data to calculate sensitivities, specificities, and their 95% confidence intervals. Two independent assessors performed quality assessment using the QUADAS-2 tool plus some additional items to assess the methodological quality of the included studies.

MAIN RESULTS

Progression from MCI to ADD, any other form of dementia, and any form of dementia was evaluated in one study (Ong 2015). It reported data on 45 participants at four years of follow-up; 21 participants met NINCDS-ADRDA criteria for Alzheimer's disease dementia at four years of follow-up, the proportion converting to ADD was 47% of the 45 participants, and 11% of the 45 participants met criteria for other types of dementias (three cases of FrontoTemporal Dementia (FTD), one of Dementia with Lewy body (DLB), and one of Progressive Supranuclear Palsy (PSP)). We considered the study to be at high risk of bias in the domains of the reference standard, flow, and timing (QUADAS-2). MCI to ADD; 18F-florbetaben PET scan analysed visually: the sensitivity was 100% (95% confidence interval (CI) 84% to 100%) and the specificity was 83% (95% CI 63% to 98%) (n = 45, 1 study). Analysed quantitatively: the sensitivity was 100% (95% CI 84% to 100%) and the specificity was 88% (95% CI 68% to 97%) for the diagnosis of ADD at follow-up (n = 45, 1 study). MCI to any other form of dementia (non-ADD); 18F-florbetaben PET scan analysed visually: the sensitivity was 0% (95% CI 0% to 52%) and the specificity was 38% (95% CI 23% to 54%) (n = 45, 1 study). Analysed quantitatively: the sensitivity was 0% (95% CI 0% to 52%) and the specificity was 40% (95% CI 25% to 57%) for the diagnosis of any other form of dementia at follow-up (n = 45, 1 study). MCI to any form of dementia;18F-florbetaben PET scan analysed visually: the sensitivity was 81% (95% CI 61% to 93%) and the specificity was 79% (95% CI 54% to 94%) (n = 45, 1 study). Analysed quantitatively: the sensitivity was 81% (95% CI 61% to 93%) and the specificity was 84% (95% CI 60% to 97%) for the diagnosis of any form of dementia at follow-up (n = 45, 1 study).

AUTHORS' CONCLUSIONS

Although we were able to calculate one estimation of DTA in, especially, the prediction of progression from MCI to ADD at four years follow-up, the small number of participants implies imprecision of sensitivity and specificity estimates. We cannot make any recommendation regarding the routine use of 18F-florbetaben in clinical practice based on one single study with 45 participants. 18F-florbetaben has high financial costs, therefore, clearly demonstrating its DTA and standardising the process of the 18F-florbetaben modality are important prior to its wider use.

Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease

Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.

CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

Research suggests that measurable change in cerebrospinal fluid (CSF) biomarkers occurs years in advance of the onset of clinical symptoms (Beckett 2010). In this review, we aimed to assess the ability of CSF tau biomarkers (t-tau and p-tau) and the CSF tau (t-tau or p-tau)/ABeta ratio to enable the detection of Alzheimer's disease pathology in patients with mild cognitive impairment (MCI). These biomarkers have been proposed as important in new criteria for Alzheimer's disease dementia that incorporate biomarker abnormalities.

OBJECTIVES

To determine the diagnostic accuracy of 1) CSF t-tau, 2) CSF p-tau, 3) the CSF t-tau/ABeta ratio and 4) the CSF p-tau/ABeta ratio index tests for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease dementia or other forms of dementia at follow-up.

SEARCH METHODS

The most recent search for this review was performed in January 2013. We searched MEDLINE (OvidSP), Embase (OvidSP), BIOSIS Previews (Thomson Reuters Web of Science), Web of Science Core Collection, including Conference Proceedings Citation Index (Thomson Reuters Web of Science), PsycINFO (OvidSP), and LILACS (BIREME). We searched specialized sources of diagnostic test accuracy studies and reviews. We checked reference lists of relevant studies and reviews for additional studies. We contacted researchers for possible relevant but unpublished data. We did not apply any language or data restriction to the electronic searches. We did not use any methodological filters as a method to restrict the search overall.

SELECTION CRITERIA

We selected those studies that had prospectively well-defined cohorts with any accepted definition of MCI and with CSF t-tau or p-tau and CSF tau (t-tau or p-tau)/ABeta ratio values, documented at or around the time the MCI diagnosis was made. We also included studies which looked at data from those cohorts retrospectively, and which contained sufficient data to construct two by two tables expressing those biomarker results by disease status. Moreover, studies were only selected if they applied a reference standard for Alzheimer's disease dementia diagnosis, for example, the NINCDS-ADRDA or Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies, and the full papers for eligibility. Two independent assessors performed data extraction and quality assessment. Where data allowed, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic (ROC) curve.

MAIN RESULTS

In total, 1282 participants with MCI at baseline were identified in the 15 included studies of which 1172 had analysable data; 430 participants converted to Alzheimer's disease dementia and 130 participants to other forms of dementia. Follow-up ranged from less than one year to over four years for some participants, but in the majority of studies was in the range one to three years. Conversion to Alzheimer's disease dementia The accuracy of the CSF t-tau was evaluated in seven studies (291 cases and 418 non-cases).The sensitivity values ranged from 51% to 90% while the specificity values ranged from 48% to 88%. At the median specificity of 72%, the estimated sensitivity was 75% (95% CI 67 to 85), the positive likelihood ratio was 2.72 (95% CI 2.43 to 3.04), and the negative likelihood ratio was 0.32 (95% CI 0.22 to 0.47).Six studies (164 cases and 328 non-cases) evaluated the accuracy of the CSF p-tau. The sensitivities were between 40% and 100% while the specificities were between 22% and 86%. At the median specificity of 47.5%, the estimated sensitivity was 81% (95% CI: 64 to 91), the positive likelihood ratio was 1.55 (CI 1.31 to 1.84), and the negative likelihood ratio was 0.39 (CI: 0.19 to 0.82).Five studies (140 cases and 293 non-cases) evaluated the accuracy of the CSF p-tau/ABeta ratio. The sensitivities were between 80% and 96% while the specificities were between 33% and 95%. We did not conduct a meta-analysis because the studies were few and small. Only one study reported the accuracy of CSF t-tau/ABeta ratio.Our findings are based on studies with poor reporting. A significant number of studies had unclear risk of bias for the reference standard, participant selection and flow and timing domains. According to the assessment of index test domain, eight of 15 studies were of poor methodological quality.The accuracy of these CSF biomarkers for 'other dementias' had not been investigated in the included primary studies. Investigation of heterogeneity The main sources of heterogeneity were thought likely to be reference standards used for the target disorders, sources of recruitment, participant sampling, index test methodology and aspects of study quality (particularly, inadequate blinding).We were not able to formally assess the effect of each potential source of heterogeneity as planned, due to the small number of studies available to be included.

AUTHORS' CONCLUSIONS

The insufficiency and heterogeneity of research to date primarily leads to a state of uncertainty regarding the value of CSF testing of t-tau, p-tau or p-tau/ABeta ratio for the diagnosis of Alzheimer's disease in current clinical practice. Particular attention should be paid to the risk of misdiagnosis and overdiagnosis of dementia (and therefore over-treatment) in clinical practice. These tests, like other biomarker tests which have been subject to Cochrane DTA reviews, appear to have better sensitivity than specificity and therefore might have greater utility in ruling out Alzheimer's disease as the aetiology to the individual's evident cognitive impairment, as opposed to ruling it in. The heterogeneity observed in the few studies awaiting classification suggests our initial summary will remain valid. However, these tests may have limited clinical value until uncertainties have been addressed. Future studies with more uniformed approaches to thresholds, analysis and study conduct may provide a more homogenous estimate than the one that has been available from the included studies we have identified.