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

BACKGROUND: ¹⁸F-FDFG uptake by brain tissue as measured by positron emission tomography (PET) is a well-established method for assessment of brain function in people with dementia. Certain findings on brain PET scans can potentially predict the decline of mild cognitive Impairment (MCI) to Alzheimer's disease dementia or other dementias. OBJECTIVES: To determine the diagnostic accuracy of the ¹⁸F-FDG PET index test 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: We searched the Cochrane Register of Diagnostic Test Accuracy Studies, MEDLINE, EMBASE, Science Citation Index, PsycINFO, BIOSIS previews, LILACS, MEDION, (Meta-analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C-EBLM (International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence-based Laboratory Medicine) databases to January 2013. We checked the reference lists of any relevant studies and systematic reviews for additional studies. SELECTION CRITERIA: We included studies that evaluated the diagnostic accuracy of ¹⁸F-FDG PET to determine the conversion from MCI to Alzheimer's disease dementia or to other forms of dementia, i.e. any or all of vascular dementia, dementia with Lewy bodies, and fronto-temporal dementia. These studies necessarily employ delayed verification of conversion to dementia and are sometimes labelled as 'delayed verification cross-sectional studies'. DATA COLLECTION AND ANALYSIS: Two blinded review authors independently extracted data, resolving disagreement by discussion, with the option to involve a third review author as arbiter if necessary. We extracted and summarised graphically the data for two-by-two tables. We conducted exploratory analyses by plotting estimates of sensitivity and specificity from each study on forest plots and in receiver operating characteristic (ROC) space. When studies had mixed thresholds, we derived estimates of sensitivity and likelihood ratios at fixed values (lower quartile, median and upper quartile) of specificity from the hierarchical summary ROC (HSROC) models. MAIN RESULTS: We included 14 studies (421 participants) in the analysis. The sensitivities for conversion from MCI to Alzheimer's disease dementia were between 25% and 100% while the specificities were between 15% and 100%. From the summary ROC curve we fitted we estimated that the sensitivity was 76% (95% confidence interval (CI): 53.8 to 89.7) at the included study median specificity of 82%. This equates to a positive likelihood ratio of 4.03 (95% CI: 2.97 to 5.47), and a negative likelihood ratio of 0.34 (95% CI: 0.15 to 0.75). Three studies recruited participants from the same Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort but only the largest ADNI study (Herholz 2011) is included in the meta-analysis. In order to demonstrate whether the choice of ADNI study or discriminating brain region (Chételat 2003) or reader assessment (Pardo 2010) make a difference to the pooled estimate, we performed five additional analyses. At the median specificity of 82%, the estimated sensitivity was between 74% and 76%. There was no impact on our findings. In addition to evaluating Alzheimer's disease dementia, five studies evaluated the accuracy of ¹⁸F-FDG PET for all types of dementia. The sensitivities were between 46% and 95% while the specificities were between 29% and 100%; however, we did not conduct a meta-analysis because of too few studies, and those studies which we had found recruited small numbers of participants. Our findings are based on studies with poor reporting, and the majority of included studies had an unclear risk of bias, mainly for the reference standard and participant selection domains. According to the assessment of Index test domain, more than 50% of studies were of poor methodological quality. AUTHORS' CONCLUSIONS: It is difficult to determine to what extent the findings from the meta-analysis can be applied to clinical practice. Given the considerable variability of specificity values and lack of defined thresholds for determination of test positivity in the included studies, the current evidence does not support the routine use of ¹⁸F-FDG PET scans in clinical practice in people with MCI. The ¹⁸F-FDG PET scan is a high-cost investigation, and it is therefore important to clearly demonstrate its accuracy and to standardise the process of ¹⁸F-FDG PET diagnostic modality prior to its being widely used. Future studies with more uniform approaches to thresholds, analysis and study conduct may provide a more homogeneous estimate than the one available from the included studies we have identified

Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-586-UGR20MCIN/AEI P20-00525FEDER \Una manerade hacer Europa RYC2021-030875-IJunta de AndaluciaEuropean Union (EU) Spanish Government RTI2018-098913-B100, CV20-45250, A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades FPU18/0490

My colorful sketches from scratch: molecular imaging

Have you heard the story about the tortoise? Only through perseverance did it manage to get to the boat. It is up to you how much you are willing to sacrifice and how long you want to fight before you obtain your goals. If you decide to pursue a road, then make sure you stick with it until the very end. I believe that these life lessons are the key to fulfillment. PET, CT, and MR are molecular imaging tools. Combined PET-CT has evolved into an established clinical tool in diagnostic imaging. The facility need to be optimized and standardized to help improving the clinical management of patients. "MY COLORFUL SKETCHES FROM SCRATCH" Molecular Imaging is about the struggle on establishing the specialized field of Molecular Imaging, PET-CT in particular, through academia and international networking. I hope my story will inspire those out there looking for their own niche be it academic or non-academic encounter

Multimodal analysis in normal aging, mild cognitive impairment, and Alzheimer's disease: group differentiation, baseline cognition, and prediction of future cognitive decline

Thesis (Ph.D.)--Boston UniversityAlzheimer's disease (AD) is a progressive neurodegenerative disease with an insidious onset that makes it difficult to distinguish from normal aging. It begins with an impairment of memory that develops into amnestic mild cognitive impairment (aMCI) and later to dementia as deficits become apparent in other cognitive domains. Effective biomarkers that differentiate normal aging, MCI, and AD and predict future cognitive decline are needed. Potential biomarkers have been studied in isolation, but their impact when combined is not understood. The goal of this project is to determine the optimal combination of CSF biomarkers, MRI morphometry, FDG PET metabolism, and neuropsychological test scores to differentiate between normal aging subjects and those with MCI and AD. This study addresses: 1) the optimal normalization region and partial volume correction method to quantify FDG PET analysis, 2) the effects of adjusting MRI-based cortical thickness measures for differences in gray/white matter tissue contrast in normal aging and disease, 3) whether multimodal multivariate stepwise logistic regression models can predict group membership, and 4) whether multimodal multivariate stepwise linear regression models can determine which imaging and CSF biomarker variables best predict future cognitive decline. The results indicate that normalizing FDG PET to the cerebellum along with using a gray matter mask for partial volume correction provides optimal prediction. In contrast, age-associated changes in gray/white matter intensity ratio did not differentiate between the groups and only slightly improved the efficacy of cortical thickness as a biomarker. MRI morphometry of the gray matter and neuropsychological test scores were better able to discriminate between the groups than FDG PET or CSF biomarker concentrations. Combining all modalities significantly improved the index of discrimination, especially at the earliest stages of the disease. MRI gray matter morphometry variables were more highly associated with baseline cognitive function and best predicted future cognitive decline compared to other variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores, and CSF biomarkers provides significantly better discrimination than any modality alone. Hence, the variables important for discriminating between the groups may be candidates for biomarkers in human clinical interventional trials

Secondary prevention of Alzheimer's dementia: neuroimaging contributions

BACKGROUND: In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations. MAIN BODY: Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation. CONCLUSION: We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI

The Alzheimer\u27s Biomarker Consortium-Down Syndrome: Rationale and methodology

Introduction: Adults with Down syndrome (DS) are at exceptionally high risk for Alzheimer\u27s disease (AD), with virtually all individuals developing key neuropathological features by age 40. Identifying biomarkers of AD progression in DS can provide valuable insights into pathogenesis and suggest targets for disease modifying treatments. Methods: We describe the development of a multi-center, longitudinal study of biomarkers of AD in DS. The protocol includes longitudinal examination of clinical, cognitive, blood and cerebrospinal fluid-based biomarkers, magnetic resonance imaging and positron emission tomography measures (at 16-month intervals), as well as genetic modifiers of AD risk and progression. Results: Approximately 400 individuals will be enrolled in the study (more than 370 to date). The methodological approach from the administrative, clinical, neuroimaging, omics, neuropathology, and statistical cores is provided. Discussion: This represents the largest U.S.-based, multi-site, biomarker initiative of AD in DS. Findings can inform other multidisciplinary networks studying AD in the general population

Amyloid positron emission tomography in sporadic cerebral amyloid angiopathy: A systematic critical update

Sporadic cerebral amyloid angiopathy (CAA) is a very common small vessel disease of the brain, showing preferential and progressive amyloid-βdeposition in the wall of small arterioles and capillaries of the leptomeninges and cerebral cortex. CAA now encompasses not only a specific cerebrovascular pathological trait, but also different clinical syndromes - including spontaneous lobar intracerebral haemorrhage (ICH), dementia and ‘amyloid spells’ - an expanding spectrum of brain parenchymal MRI lesions and a set of diagnostic criteria – the Boston criteria, which have resulted in increasingly detecting CAA during life. Although currently available validated diagnostic criteria perform well in multiple lobar ICH, a formal diagnosis is currently lacking unless a brain biopsy is performed. This is partly because in practice CAA MRI biomarkers provide only indirect evidence for the disease. An accurate diagnosis of CAA in different clinical settings would have substantial impact for ICH risk stratification and antithrombotic drug use in elderly people, but also for sample homogeneity in drug trials. It has recently been demonstrated that vascular (in addition to parenchymal) amyloid-βdeposition can be detected and quantified in vivo by positron emission tomography (PET) amyloid tracers. This non-invasive approach has the potential to provide a molecular signature of CAA, and could in turn have major clinical impact. However, several issues around amyloid-PET in CAA remain unsettled and hence its diagnostic utility is limited. In this article we systematically review and critically appraise the published literature on amyloid-PET (PiB and other tracers) in sporadic CAA. We focus on two key areas: (a) the diagnostic utility of amyloid-PET in CAA and (b) the use of amyloid-PET as a window to understand pathophysiological mechanism of the disease. Key issues around amyloid-PET imaging in CAA, including relevant technical aspects are also covered in depth. A total of six small-scale studies have addressed (or reported data useful to address) the diagnostic utility of late-phase amyloid PET imaging in CAA, and one additional study dealt with early PiB images as a proxy of brain perfusion. Across these studies, amyloid PET imaging has definite diagnostic utility (currently tested only in probable CAA): it helps rule out CAA if negative, whether compared to healthy controls or to hypertensive deep ICH controls. If positive, however, differentiation from underlying incipient Alzheimer's disease (AD) can be challenging and so far, no approach (regional values, ratios, visual assessment) seems sufficient and specific enough, although early PiB data seem to hold promise. Based on the available evidence reviewed, we suggest a tentative diagnostic flow algorithm for amyloid-PET use in the clinical setting of suspected CAA, combining early- and late-phase PiB-PET images. We also identified ten mechanistic amyloid-PET studies providing early but promising proof-of-concept data on CAA pathophysiology and its various manifestations including key MRI lesions, cognitive impairment and large scale brain alterations. Key open questions that should be addressed in future studies of amyloid-PET imaging in CAA are identified and highlighted

A Machine Learning Classification Framework for Early Prediction of Alzheimer’s Disease

People today, in addition to their concerns about getting old and having to go through watching themselves grow weak and wrinkly, are facing an increasing fear of dementia. There are around 47 million people affected by dementia worldwide and the cost associated with providing them health and social care support is estimated to reach 2 trillion by 2030 which is almost equivalent to the 18th largest economy in the world. The most common form of dementia with the highest costs in health and social care is Alzheimer’s disease, which gradually kills neurons and causes patients to lose loving memories, the ability to recognise family members, childhood memories, and even the ability to follow simple instructions. Alzheimer’s disease is irreversible, unstoppable and has no known cure. Besides being a calamity to affected patients, it is a great financial burden on health providers. Health care providers also face a challenge in diagnosing the disease as current methods used to diagnose Alzheimer’s disease rely on manual evaluations of a patient’s medical history and mental examinations such as the Mini-Mental State Examination. These diagnostic methods often give a false diagnosis and were designed to identify Alzheimer’s after stage two when the part of all symptoms are evident. The problem is that clinicians are unable to stop or control the progress of Alzheimer’s disease, because of a lack of knowledge on the patterns that triggered the development of the disease. In this thesis, we explored and investigated Alzheimer’s disease from a computational perspective to uncover different risk factors and present a strategic framework called Early Prediction of Alzheimer’s Disease Framework (EPADf) that would give a future prediction of early-onset Alzheimer’s disease. Following extensive background research that resulted in the formalisation of the framework concept, prediction approaches, and the concept of ranking the risk factors based on clinical instinct, knowledge and experience using mathematical reasoning, we carried out experiments to get further insight and investigate the disease further using machine learning models. In this study, we used machine learning models and conducted two classification experiments for early prediction of Alzheimer’s disease, and one ranking experiment to rank its risk factors by importance. Besides these experiments, we also presented two logical approaches to search for patterns in an Alzheimer’s dataset, and a ranking algorithm to rank Alzheimer’s disease risk factors based on clinical evaluation. For the classification experiments we used five different Machine Learning models; Random Forest (RF), Random Oracle Model (ROM), a hybrid model combined of Levenberg-Marquardt neural network and Random Forest, combined using Fischer discriminate analysis (H2), Linear Neural Networks (LNN), and Multi-layer Perceptron Model (MLP). These models were deployed on a de-identified multivariable patient’s data, provided by the ADNI (Alzheimer’s disease Neuroimaging Initiative), to illustrate the effective use of data analysis to investigate Alzheimer’s disease biological and behavioural risk factors. We found that the continues enhancement of patient’s data and the use of combined machine learning models can provide an early cost-effective prediction of Alzheimer’s disease, and help in extracting insightful information on the risk factors of the disease. Based on this work and findings we have developed the strategic framework (EPADf) which is discussed in more depth in this thesis

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

Abstract: The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group’s short-term, intermediate, and long-term goals

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

Abstract: The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group’s short-term, intermediate, and long-term goals