Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia

Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.

Brain FDG PET for Short- to Medium-Term Prediction of Further Cognitive Decline and Need for Assisted Living in Acutely Hospitalized Geriatric Patients With Newly Detected Clinically Uncertain Cognitive Impairment

PURPOSE

The aim of this study was to evaluate brain FDG PET for short- to medium-term prediction of cognitive decline, need for assisted living, and survival in acutely hospitalized geriatric patients with newly detected clinically uncertain cognitive impairment (CUCI).

MATERIALS AND METHODS

The study included 96 patients (62 females, 81.4 ± 5.4 years) hospitalized due to (sub)acute admission indications with newly detected CUCI (German Clinical Trials Register DRKS00005041). FDG PET was categorized as "neurodegenerative" (DEG+) or "nonneurodegenerative" (DEG-) based on visual inspection by 2 independent readers. In addition, each individual PET was tested voxel-wise against healthy controls (P < 0.001 uncorrected). The resulting total hypometabolic volume (THV) served as reader-independent measure of the spatial extent of neuronal dysfunction/degeneration. FDG PET findings at baseline were tested for association with the change in living situation and change in vital status 12 to 24 months after PET. The association with the annual change of the CDR-SB (Clinical Dementia Rating Sum of Boxes) after PET was tested in a subsample of 72 patients.

RESULTS

The mean time between PET and follow-up did not differ between DEG+ and DEG- patients (1.37 ± 0.27 vs 1.41 ± 0.27 years, P = 0.539). Annual change of CDR-SB was higher in DEG+ compared with DEG- patients (2.78 ± 2.44 vs 0.99 ± 1.81, P = 0.001), and it was positively correlated with THV (age-corrected Spearman ρ = 0.392, P = 0.001). DEG+ patients moved from at home to assisted living significantly earlier than DEG- patients (P = 0.050). Survival was not associated with DEG status or with THV.

CONCLUSIONS

In acutely hospitalized geriatric patients with newly detected CUCI, the brain FDG PET can contribute to the prediction of further cognitive/functional decline and the need for assisted living within 1 to 2 years.

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

Background: Neuroscience lacks a reliable method of screening the early stages of dementia. Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains. Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes. Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age. Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

Clinical impact of 18F-FDG-PET among memory clinic patients with uncertain diagnosis

Purpose

To assess the clinical impact and incremental diagnostic value of ¹⁸F-fluorodeoxyglucose (FDG-PET) among memory clinic patients with uncertain diagnosis.

Methods

The study population consisted of 277 patients who, despite extensive baseline cognitive assessment, MRI, and CSF analyses, had an uncertain diagnosis of mild cognitive impairment (MCI) (n = 177) or dementia (n = 100). After baseline diagnosis, each patient underwent an FDG-PET, followed by a post-FDG-PET diagnosis formulation. We evaluated (i) the change in diagnosis (baseline vs. post-FDG-PET), (ii) the change in diagnostic accuracy when comparing each baseline and post-FDG-PET diagnosis to a long-term follow-up (3.6 ± 1.8 years) diagnosis used as reference, and (iii) comparative FDG-PET performance testing in MCI and dementia conditions.

Results

FDG-PET led to a change in diagnosis in 86 of 277 (31%) patients, in particular in 57 of 177 (32%) MCI and in 29 of 100 (29%) dementia patients. Diagnostic change was greater than two-fold in the sub-sample of cases with dementia “of unclear etiology” (change in diagnosis in 20 of 32 (63%) patients). In the dementia group, after results of FDG-PET, diagnostic accuracy improved from 77 to 90% in Alzheimer’s disease (AD) and from 85 to 94% in frontotemporal lobar degeneration (FTLD) patients (p < 0.01). FDG-PET performed better in dementia than in MCI (positive likelihood ratios >5 and < 5, respectively).

Conclusion

Within a selected clinical population, FDG-PET has a significant clinical impact, both in early and differential diagnosis of uncertain dementia. FDG-PET provides significant incremental value to detect AD and FTLD over a clinical diagnosis of uncertain dementia.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04969-7) contains supplementary material, which is available to authorized users.

Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer's Disease

BACKGROUND

Several automatic tools have been implemented for semi-quantitative assessment of brain [18]F-FDG-PET.

OBJECTIVE

We aimed to head-to-head compare the diagnostic performance among three statistical parametric mapping (SPM)-based approaches, another voxel-based tool (i.e., PALZ), and a volumetric region of interest (VROI-SVM)-based approach, in distinguishing patients with prodromal Alzheimer's disease (pAD) from controls.

METHODS

Sixty-two pAD patients (MMSE score = 27.0±1.6) and one hundred-nine healthy subjects (CTR) (MMSE score = 29.2±1.2) were enrolled in five centers of the European Alzheimer's Disease Consortium. The three SPM-based methods, based on different rationales, included 1) a cluster identified through the correlation analysis between [18]F-FDG-PET and a verbal memory test (VROI-1), 2) a VROI derived from the comparison between pAD and CTR (VROI-2), and 3) visual analysis of individual maps obtained by the comparison between each subject and CTR (SPM-Maps). The VROI-SVM approach was based on 6 VROI plus 6 VROI asymmetry values derived from the pAD versus CTR comparison thanks to support vector machine (SVM).

RESULTS

The areas under the ROC curves between pAD and CTR were 0.84 for VROI-1, 0.83 for VROI-2, 0.79 for SPM maps, 0.87 for PALZ, and 0.95 for VROI-SVM. Pairwise comparisons of Youden index did not show statistically significant differences in diagnostic performance between VROI-1, VROI-2, SPM-Maps, and PALZ score whereas VROI-SVM performed significantly (p < 0.005) better than any of the other methods.

CONCLUSION

The study confirms the good accuracy of [18]F-FDG-PET in discriminating healthy subjects from pAD and highlights that a non-linear, automatic VROI classifier based on SVM performs better than the voxel-based methods.

Biomarker-Based Prediction of Progression to Dementia: F-18 FDG-PET in Amnestic MCI

Background

Metabolic patterns on brain F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) can predict the decline in amnestic mild cognitive impairment (aMCI) to Alzheimer's disease dementia (AD) or other dementias.

Objective

This study was undertaken to evaluate the diagnostic accuracy of baseline F-18 FDG-PET in aMCI for predicting conversion to AD or other dementias on follow-up.

Patients and Methods

A total of 87 patients with aMCI were enrolled in the study. Each patient underwent a detailed clinical and neuropsychological examination and FDG-PET at baseline. Each PET scan was visually classified based on predefined dementia patterns. Automated analysis of FDG PET was performed using Cortex ID (GE Healthcare). The mean follow-up duration was 30.4 ± 9.3 months (range: 18-48 months). Diagnosis of dementia at follow-up (obtained using clinical diagnostic criteria) constituted the reference standard, and all the included aMCI patients were divided into two groups: the aMCI converters (MCI-C) and MCI nonconverters (MCI-NC). Diagnostic accuracy of FDG PET was calculated using this reference standard.

Results

There were 23 MCI-C and 64 MCI-NC. Of the 23 MCI-C, 19 were diagnosed as probable AD, 1 as frontotemporal demetia (FTD), and 3 as vascular dementia (VD). Of the 64 MCI-NC, 9 had subjective improvement in cognition, and 55 remained stable. The conversion rate for all types of dementia in our series was 26.4% (23/87) and for Alzheimer's type dementia was 21.8% (19/87). The of PET-based visual interpretation was 91.9%. Sensitivity, specificity, positive predictive value, and negative predictive value for FDG-PET-based prediction of dementia conversion were 86.9% [confidence interval (CI) 66.4%-97.2%)], 93.7% (CI 84.7%-98.2%), 83.3% (CI 65.6%-92.9%), and 95.2% (CI 87.4%-98.9%), respectively. Kappa for agreement between visual and Cortex ID was 0.94 indicating excellent agreement. In the three aMCI patients progressing to VD, no specific abnormality in metabolic pattern was noted; however, there was marked cortical atrophy on computed tomography.

Conclusion

FDG-PET-based visual and cortex ID classification has a good accuracy in predicting progression to dementia including AD in the prodromal aMCI phase. Absence of typical metabolic patterns on FDG-PET can play an important exclusionary role for progression to dementia. Vascular cognitive impairment with cerebral atrophy needs further studies to confirm and uncover potential mechanisms.

Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score

18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) enables in-vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG-PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross-validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0-3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.

Plasma Aβ42/40 ratio alone or combined with FDG-PET can accurately predict amyloid-PET positivity: a cross-sectional analysis from the AB255 Study

BACKGROUND

To facilitate population screening and clinical trials of disease-modifying therapies for Alzheimer's disease, supportive biomarker information is necessary. This study was aimed to investigate the association of plasma amyloid-beta (Aβ) levels with the presence of pathological accumulation of Aβ in the brain measured by amyloid-PET. Both plasma Aβ42/40 ratio alone or combined with an FDG-PET-based biomarker of neurodegeneration were assessed as potential AD biomarkers.

METHODS

We included 39 cognitively normal subjects and 20 patients with mild cognitive impairment from the AB255 Study who had undergone PiB-PET scans. Total Aβ40 and Aβ42 levels in plasma (TP42/40) were quantified using ABtest kits. Subjects were dichotomized as Aβ-PET positive or negative, and the ability of TP42/40 to detect Aβ-PET positivity was assessed by logistic regression and receiver operating characteristic analyses. Combination of plasma Aβ biomarkers and FDG-PET was further assessed as an improvement for brain amyloidosis detection and diagnosis classification.

RESULTS

Eighteen (30.5%) subjects were Aβ-PET positive. TP42/40 ratio alone identified Aβ-PET status with an area under the curve (AUC) of 0.881 (95% confidence interval [CI] = 0.779-0.982). Discriminating performance of TP42/40 to detect Aβ-PET-positive subjects yielded sensitivity and specificity values at Youden's cutoff of 77.8% and 87.5%, respectively, with a positive predictive value of 0.732 and negative predictive value of 0.900. All these parameters improved after adjusting the model for significant covariates. Applying TP42/40 as the first screening tool in a sequential diagnostic work-up would reduce the number of Aβ-PET scans by 64%. Combination of both FDG-PET scores and plasma Aβ biomarkers was found to be the most accurate Aβ-PET predictor, with an AUC of 0.965 (95% CI = 0.913-0.100).

CONCLUSIONS

Plasma TP42/40 ratio showed a relevant and significant potential as a screening tool to identify brain Aβ positivity in preclinical and prodromal stages of Alzheimer's disease.

18F-FDG PET for Prediction of Conversion to Alzheimer's Disease Dementia in People with Mild Cognitive Impairment: An Updated Systematic Review of Test Accuracy

Background:

A previous Cochrane systematic review concluded there is insufficient evidence to support the routine use of ¹⁸F-FDG PET in clinical practice in people with mild cognitive impairment (MCI).

Objectives:

To update the evidence and reassess the accuracy of ¹⁸F-FDG-PET for detecting people with MCI at baseline who would clinically convert to Alzheimer’s disease (AD) dementia at follow-up.

Methods:

A systematic review including comprehensive search of electronic databases from January 2013 to July 2017, to update original searches (1999 to 2013). All key review steps, including quality assessment using QUADAS 2, were performed independently and blindly by two review authors. Meta-analysis could not be conducted due to heterogeneity across studies.

Results:

When all included studies were examined across all semi-quantitative and quantitative metrics, exploratory analysis for conversion of MCI to AD dementia (n = 24) showed highly variable accuracy; half the studies failed to meet four or more of the seven sets of QUADAS 2 criteria. Variable accuracy for all metrics was also found across eleven newly included studies published in the last 5 years (range: sensitivity 56–100%, specificity 24–100%). The most consistently high sensitivity and specificity values (approximately ≥80%) were reported for the sc-SPM (single case statistical parametric mapping) metric in 6 out of 8 studies.

Conclusion:

Systematic and comprehensive assessment of studies of ¹⁸FDG-PET for prediction of conversion from MCI to AD dementia reveals many studies have methodological limitations according to Cochrane diagnostic test accuracy gold standards, and shows accuracy remains highly variable, including in the most recent studies. There is some evidence, however, of higher and more consistent accuracy in studies using computer aided metrics, such as sc-SPM, in specialized clinical settings. Robust, methodologically sound prospective longitudinal cohort studies with long (≥5 years) follow-up, larger consecutive samples, and defined baseline threshold(s) are needed to test these promising results. Further evidence of the clinical validity and utility of ¹⁸F-FDG PET in people with MCI is needed.

FDG-PET as an independent biomarker for Alzheimer's biological diagnosis: a longitudinal study

BACKGROUND

Reduced ¹⁸F-fluorodeoxyglucose-positron emission tomography (FDG-PET) brain metabolism was recognized as a biomarker of neurodegeneration in the recently proposed ATN framework for Alzheimer's disease (AD) biological definition. However, accumulating evidence suggested it is an independent biomarker, which is denoted as "F" in the very study.

METHODS

A total of 551 A+T+ individuals from the Alzheimer's Disease Neuroimaging Initiative database were recruited and then further divided into four groups based on the biomarker positivity as 132 A+T+N-F-, 102 A+T+N-F+, 113 A+T+N+F-, and 204 A+T+N+F+. Frequency distributions of the groups were compared, as well as the clinical progression [measured by the longitudinal changes in cognition and brain structure, and mild cognitive impairment (MCI) to AD dementia conversion] between every pair of F+ and F- groups.

RESULTS

The prevalence of A+T+N+F+ profile was 66.24% in clinically diagnosed AD dementia patients; similarly, the majority of individuals with reduced FDG-PET were AD dementia subjects. Among the 551 individuals that included, 537 had at least one follow-up (varied from 1 to 8 years). Individuals in F+ groups performed worse and dropped faster in Mini-Mental State Examination scale and had faster shrinking middle temporal lobe than those in F- groups (all p < 0.05). Moreover, in MCI patients, reduced FDG-PET exerted 2.47 to 4.08-fold risk of AD dementia progression compared with those without significantly impaired FDG-PET (both p < 0.001).

CONCLUSIONS

Based on the analyses, separating FDG-PET from "N" biomarker to build the ATN(F) system is necessary and well-founded. The analysis from this study could be a complement to the original ATN framework for AD's biological definition.

Dementia spectrum disorders: lessons learnt from decades with PET research

The dementia spectrum encompasses a range of disorders with complex diagnosis, pathophysiology and limited treatment options. Positron emission tomography (PET) imaging provides insights into specific neurodegenerative processes underlying dementia disorders in vivo. Here we focus on some of the most common dementias: Alzheimer's disease, Parkinsonism dementias including Parkinson's disease with dementia, dementia with Lewy bodies, progressive supranuclear palsy and corticobasal syndrome, and frontotemporal lobe degeneration. PET tracers have been developed to target specific proteinopathies (amyloid, tau and α-synuclein), glucose metabolism, cholinergic system and neuroinflammation. Studies have shown distinct imaging abnormalities can be detected early, in some cases prior to symptom onset, allowing disease progression to be monitored and providing the potential to predict symptom onset. Furthermore, advances in PET imaging have identified potential therapeutic targets and novel methods to accurately discriminate between different types of dementias in vivo. There are promising imaging markers with a clinical application on the horizon, however, further studies are required before they can be implantation into clinical practice.

Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease

Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and ¹⁸F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell’s C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Genome-wide association study identifies RBFOX1 locus influencing brain glucose metabolism

BACKGROUND

Fluorodeoxyglucose f18 positron emission tomography (18F-FDG PET) is regarded as the only functional neuroimaging biomarker for degeneration which can be used to increase the certainty of Alzheimer's disease (AD) pathophysiological process in research settings or as an optional clinical tool where available. Although a decline in FDG metabolism was confirmed in some regions known to be associated with AD, there was little known about the genetic association of FDG metabolism in AD cohorts. In this study, we present the first genome-wide association study (GWAS) analysis of brain FDG metabolism.

METHODS

A total of 222 individuals were included from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) cohort. All subjects were restricted to non-Hispanic Caucasians and met all quality control (QC) criteria. Associations of 18F-FDG with the genetic variants were assessed using PLINK 1.07 under the additive genetic model. Genome-wide associations were visualized using a software program R 3.2.3.

RESULTS

One significant SNP rs12444565 in RNA-binding Fox1 (RBFOX1) was found to have a strong association with 18F-FDG (P=6.06×10-8). Rs235141, rs79037, rs12526331 and rs12529764 were identified as four suggestive loci associated with 18F-FDG.

CONCLUSIONS

Our study results suggest that a genome-wide significant SNP (rs12444565) in the RBFOX1, and four suggestive loci (rs235141, rs79037, rs12526331 and rs12529764) are associated with 18F-FDG.

European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND AND PURPOSE

Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured.

METHODS

Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion.

RESULTS

Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline.

CONCLUSIONS

Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.

Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.

FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort

Background/aims

In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias.

Methods

We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as "typical-AD", "atypical-AD" (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), "non-AD" (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or "negative" patterns. To perform the statistical analyses, the individual patterns were grouped either as "AD dementia vs. non-AD dementia (all diseases)" or as "FTD vs. non-FTD (all diseases)". Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated.

Results

The multivariate logistic model identified FDG-PET "AD" SPM classification (Expβ = 19.35, 95% C.I. 4.8-77.8, p < 0.001) and CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64-25.43, p < 0.05) as the best predictors of conversion from MCI to AD dementia. The "FTD" SPM pattern significantly predicted conversion to FTD dementias at follow-up (Expβ = 14, 95% C.I. 3.1-63, p < 0.001). Overall, FDG-PET-SPM classification was the most accurate biomarker, able to correctly differentiate either the MCI subjects who converted to AD or FTD dementias, and those who remained stable or reverted to normal cognition (Expβ = 17.9, 95% C.I. 4.55-70.46, p < 0.001).

Conclusions

Our results support the relevant role of FDG-PET-SPM classification in predicting progression to different dementia conditions in prodromal MCI phase, and in the exclusion of progression, outperforming CSF biomarkers.

Clinical validity of brain fluorodeoxyglucose positron emission tomography as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

The use of Alzheimer's disease (AD) biomarkers is supported in diagnostic criteria, but their maturity for clinical routine is still debated. Here, we evaluate brain fluorodeoxyglucose positron emission tomography (FDG PET), a measure of cerebral glucose metabolism, as a biomarker to identify clinical and prodromal AD according to the framework suggested for biomarkers in oncology, using homogenous criteria with other biomarkers addressed in parallel reviews. FDG PET has fully achieved phase 1 (rational for use) and most of phase 2 (ability to discriminate AD subjects from healthy controls or other forms of dementia) aims. Phase 3 aims (early detection ability) are partly achieved. Phase 4 studies (routine use in prodromal patients) are ongoing, and only preliminary results can be extrapolated from retrospective observations. Phase 5 studies (quantify impact and costs) have not been performed. The results of this study show that specific efforts are needed to complete phase 3 evidence, in particular comparing and combining FDG PET with other biomarkers, and to properly design phase 4 prospective studies as a basis for phase 5 evaluations.

Early identification of MCI converting to AD: a FDG PET study

PURPOSE

Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer's disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not.

METHODS

FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy.

RESULTS

The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients.

CONCLUSION

In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data

Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of ¹⁸F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of ¹⁸F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.

Optimization of Statistical Single Subject Analysis of Brain FDG PET for the Prognosis of Mild Cognitive Impairment-to-Alzheimer's Disease Conversion

BACKGROUND

Positron emission tomography (PET) with the glucose analog F-18-fluorodeoxyglucose (FDG) is widely used in the diagnosis of neurodegenerative diseases. Guidelines recommend voxel-based statistical testing to support visual evaluation of the PET images. However, the performance of voxel-based testing strongly depends on each single preprocessing step involved.

OBJECTIVE

To optimize the processing pipeline of voxel-based testing for the prognosis of dementia in subjects with amnestic mild cognitive impairment (MCI).

METHODS

The study included 108 ADNI MCI subjects grouped as 'stable MCI' (n = 77) or 'MCI-to-AD converter' according to their diagnostic trajectory over 3 years. Thirty-two ADNI normals served as controls. Voxel-based testing was performed with the statistical parametric mapping software (SPM8) starting with default settings. The following modifications were added step-by-step: (i) motion correction, (ii) custom-made FDG template, (iii) different reference regions for intensity scaling, and (iv) smoothing was varied between 8 and 18 mm. The t-sum score for hypometabolism within a predefined AD mask was compared between the different settings using receiver operating characteristic (ROC) analysis with respect to differentiation between 'stable MCI' and 'MCI-to-AD converter'. The area (AUC) under the ROC curve was used as performance measure.

RESULTS

The default setting provided an AUC of 0.728. The modifications of the processing pipeline improved the AUC up to 0.832 (p = 0.046). Improvement of the AUC was confirmed in an independent validation sample of 241 ADNI MCI subjects (p = 0.048).

CONCLUSION

The prognostic value of voxel-based single subject analysis of brain FDG PET in MCI subjects can be improved considerably by optimizing the processing pipeline.

Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging

BACKGROUND

Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer's disease (AD) in its early stages.

OBJECTIVE

However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method.

METHODS

In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student's t-test, and Welch's t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis.

RESULTS

The results showed that "3D-DF+WTT+TSVM" achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications.

CONCLUSIONS

The 3D-DF is effective in AD subject and related region detection.

The Utilization of Retinal Nerve Fiber Layer Thickness to Predict Cognitive Deterioration

Our previous studies have shown that longitudinal reduction in retinal nerve fiber layer (RNFL) thickness is associated with cognitive deterioration. However, whether the combination of longitudinal reduction in RNFL thickness with baseline episodic memory performance can better predict cognitive deterioration remains unknown. Therefore, we set out to re-analyze the data obtained from our previous studies with 78 elderly adults (mean age 74.4 ± 3.83 years, 48.7% male) in the community over a 25-month period. The participants were categorized as either stable participants whose cognitive status did not change (n = 60) or converted participants whose cognitive status deteriorated (n = 18). A logistic regression analysis was applied to determine a conversion score for predicting the cognitive deterioration in the participants. We found that the area under the receiver operating characteristic curve (AUC) for the multivariable model was 0.854 (95% CI 0.762-0.947) using baseline story recall as a predictor, but the AUC increased to 0.915 (95% CI 0.849-0.981) with the addition of the longitudinal reduction of RNFL thickness in the inferior quadrant. The conversion score was significantly higher for the converted participants than the stable participants (0.59 ± 0.30 versus 0.12 ± 0.19, p <  0.001). Finally, the optimal cutoff value of the conversion score (0.134) was determined by the analysis of receiver operating characteristic curve, and this conversion score generated a sensitivity of 0.944 and a specificity of 0.767 in predicting the cognitive deterioration. These findings have established a system to perform a larger scale study to further test whether the longitudinal reduction in RNFL thickness could serve as a biomarker of Alzheimer's disease.

Brain fluorodeoxyglucose (FDG) PET in dementia

The purpose of this article is to present a selective and concise summary of fluorodeoxyglucose (FDG) positron emission tomography (PET) in dementia imaging. FDG PET is used to visualize a downstream topographical marker that indicates the distribution of neural injury or synaptic dysfunction, and can identify distinct phenotypes of dementia due to Alzheimer's disease (AD), Lewy bodies, and frontotemporal lobar degeneration. AD dementia shows hypometabolism in the parietotemporal association area, posterior cingulate, and precuneus. Hypometabolism in the inferior parietal lobe and posterior cingulate/precuneus is a predictor of cognitive decline from mild cognitive impairment (MCI) to AD dementia. FDG PET may also predict conversion of cognitively normal individuals to those with MCI. Age-related hypometabolism is observed mainly in the anterior cingulate and anterior temporal lobe, along with regional atrophy. Voxel-based statistical analyses, such as statistical parametric mapping or three-dimensional stereotactic surface projection, improve the diagnostic performance of imaging of dementias. The potential of FDG PET in future clinical and methodological studies should be exploited further.

Detection of Alzheimer's disease by displacement field and machine learning

Aim. Alzheimer's disease (AD) is a chronic neurodegenerative disease. Recently, computer scientists have developed various methods for early detection based on computer vision and machine learning techniques. Method. In this study, we proposed a novel AD detection method by displacement field (DF) estimation between a normal brain and an AD brain. The DF was treated as the AD-related features, reduced by principal component analysis (PCA), and finally fed into three classifiers: support vector machine (SVM), generalized eigenvalue proximal SVM (GEPSVM), and twin SVM (TSVM). The 10-fold cross validation repeated 50 times. Results. The results showed the "DF + PCA + TSVM" achieved the accuracy of 92.75 ± 1.77, sensitivity of 90.56 ± 1.15, specificity of 93.37 ± 2.05, and precision of 79.61 ± 2.21. This result is better than or comparable with not only the other proposed two methods, but also ten state-of-the-art methods. Besides, our method discovers the AD is related to following brain regions disclosed in recent publications: Angular Gyrus, Anterior Cingulate, Cingulate Gyrus, Culmen, Cuneus, Fusiform Gyrus, Inferior Frontal Gyrus, Inferior Occipital Gyrus, Inferior Parietal Lobule, Inferior Semi-Lunar Lobule, Inferior Temporal Gyrus, Insula, Lateral Ventricle, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterior Cingulate, Precentral Gyrus, Precuneus, Sub-Gyral, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, and Uncus. Conclusion. The displacement filed is effective in detection of AD and related brain-regions.

Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.

Recommendations for the use of PET imaging biomarkers in the diagnosis of neurodegenerative conditions associated with dementia: SEMNIM and SEN consensus

The new diagnostic criteria for Alzheimer's disease (AD) acknowledges the interest given to biomarkers to improve the specificity in subjects with dementia and to facilitate an early diagnosis of the pathophysiological process of AD in the prodromal or pre-dementia stage. The current availability of PET imaging biomarkers of synaptic dysfunction (PET-FDG) and beta amyloid deposition using amyloid-PET provides clinicians with the opportunity to apply the new criteria and improve diagnostic accuracy in their clinical practice. Therefore, it seems essential for the scientific societies involved to use the new clinical diagnostic support tools to establish clear, evidence-based and agreed set of recommendations for their appropriate use. The present work includes a systematic review of the literature on the utility of FDG-PET and amyloid-PET for the diagnosis of AD and related neurodegenerative diseases that occur with dementia. Thus, we propose a series of recommendations agreed on by the Spanish Society of Nuclear Medicine and Spanish Society of Neurology as a consensus statement on the appropriate use of PET imaging biomarkers.

Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

PURPOSE

Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.

METHOD

First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.

RESULTS

The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.

CONCLUSION

The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study

An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimer's disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine (SVM) and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed.

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¹⁸F-FDG-PET/CT analysis of metabolic differences between MCI converting to AD and HC

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Large and very well controlled cohorts from EADC-Consortium were investigated.

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Data were analyzed by a friendly-to-use Matlab-based script and Support Vector Machine.

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Excellent discrimination between MCI and HC (sensitivity 92%; specificity 91%)

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Highest accuracy reported so far in MCI and promising implementation in clinical routine

A survey of FDG- and amyloid-PET imaging in dementia and GRADE analysis

PET based tools can improve the early diagnosis of Alzheimer's disease (AD) and differential diagnosis of dementia. The importance of identifying individuals at risk of developing dementia among people with subjective cognitive complaints or mild cognitive impairment has clinical, social, and therapeutic implications. Within the two major classes of AD biomarkers currently identified, that is, markers of pathology and neurodegeneration, amyloid- and FDG-PET imaging represent decisive tools for their measurement. As a consequence, the PET tools have been recognized to be of crucial value in the recent guidelines for the early diagnosis of AD and other dementia conditions. The references based recommendations, however, include large PET imaging literature based on visual methods that greatly reduces sensitivity and specificity and lacks a clear cut-off between normal and pathological findings. PET imaging can be assessed using parametric or voxel-wise analyses by comparing the subject's scan with a normative data set, significantly increasing the diagnostic accuracy. This paper is a survey of the relevant literature on FDG and amyloid-PET imaging aimed at providing the value of quantification for the early and differential diagnosis of AD. This allowed a meta-analysis and GRADE analysis revealing high values for PET imaging that might be useful in considering recommendations.