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

Sex-related differences in amyotrophic lateral sclerosis: A 2-[18F]FDG-PET study

PURPOSE

We investigated sex-related brain metabolic differences in Amyotrophic Lateral Sclerosis (ALS) and healthy controls (HC).

METHODS

We collected two equal-sized groups of male (m-ALS) and female ALS (f-ALS) patients (n = 130 each), who underwent 2-[18F]FDG-PET at diagnosis, matched for site of onset, cognitive status and King's stage. We included 168 age-matched healthy controls, half female (f-HC) and half male (m-HC). We compared brain metabolism of males and females separately for ALS and HC, including age as covariate. A differential network analysis was performed to evaluate brain connectivity.

RESULTS

M-ALS showed relative hypometabolism of bilateral medial frontal, parietal and occipital cortices, and left temporal cortex, compared to f-ALS. In node-wise comparison, f-ALS showed significantly higher connectivity in right middle cingulate cortex and left superior and medial frontal gyrus. In HC we did not find any sex-related differences.

CONCLUSION

Sex resulted a major determinant of brain metabolism and connectivity in ALS patients.

Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis

INTRODUCTION

This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).

METHODS

This analysis consisted of 150 normal controls (CN), 257 MCI, and 205  AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.

RESULTS

Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.

DISCUSSION

Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.

Sleep Dysregulation Is Associated with 18F-FDG PET and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease

Background

Sleep impairment has been commonly reported in Alzheimer's disease (AD) patients. The association between sleep dysregulation and AD biomarkers has been separately explored in mild cognitive impairment (MCI) and AD patients.

Objective

The present study investigated cerebrospinal-fluid (CSF) and 18F-fluoro-deoxy-glucose positron emission tomography (18F-FDG-PET) biomarkers in MCI and AD patients in order to explore their association with sleep parameters measured with polysomnography (PSG).

Methods

MCI and AD patients underwent PSG, 18F-FDG-PET, and CSF analysis for detecting and correlating these biomarkers with sleep architecture.

Results

Thirty-five patients were included in the study (9 MCI and 26 AD patients). 18F-FDG uptake in left Brodmann area 31 (owing to the posterior cingulate cortex) correlated negatively with REM sleep latency (p = 0.013) and positively with REM sleep (p = 0.033). 18F-FDG uptake in the hippocampus was negatively associated with sleep onset latency (p = 0.041). Higher CSF orexin levels were associated with higher sleep onset latency (p = 0.042), Non-REM stage 1 of sleep (p = 0.031), wake after sleep onset (p = 0.028), and lower sleep efficiency (p = 0.045). CSF levels of Aβ42 correlated negatively with the wake bouts index (p = 0.002). CSF total-tau and phosphorylated tau levels correlated positively with total sleep time (p = 0.045) and time in bed (p = 0.031), respectively.

Conclusion

Sleep impairment, namely sleep fragmentation, REM sleep dysregulation, and difficulty in initiating sleep correlates with AD biomarkers, suggesting an effect of sleep on the pathological processes in different AD stages. Targeting sleep for counteracting the AD pathological processes represents a timely need for clinicians and researchers.

Deep learning signature of brain [18F]FDG PET associated with cognitive outcome of rapid eye movement sleep behavior disorder

An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study aimed to investigate cognitive signature of brain [¹⁸F]FDG PET based on deep learning (DL) for evaluating patients with iRBD. Fifty iRBD patients, 19 with mild cognitive impairment (MCI) (RBD-MCI) and 31 without MCI (RBD-nonMCI), were prospectively enrolled. A DL model for the cognitive signature was trained by using Alzheimer's Disease Neuroimaging Initiative database and transferred to baseline [¹⁸F]FDG PET from the iRBD cohort. The results showed that the DL-based cognitive dysfunction score was significantly higher in RBD-MCI than in RBD-nonMCI. The AUC of ROC curve for differentiating RBD-MCI from RBD-nonMCI was 0.70 (95% CI 0.56-0.82). The baseline DL-based cognitive dysfunction score was significantly higher in iRBD patients who showed a decrease in CERAD scores during 2 years than in those who did not. Brain metabolic features related to cognitive dysfunction-related regions of individual iRBD patients mainly included posterior cortical regions. This work demonstrates that the cognitive signature based on DL could be used to objectively evaluate cognitive function in iRBD. We suggest that this approach could be extended to an objective biomarker predicting cognitive decline and neurodegeneration in iRBD.

Recent advances in pre-clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is the most common dementia with currently no known cures or disease modifying treatments (DMTs), despite much time and effort from the field. Diagnosis and intervention of AD during the early pre-symptomatic phase of the disease is thought to be a more effective strategy. Therefore, the detection of biomarkers has emerged as a critical tool for monitoring the effect of new AD therapies, as well as identifying patients most likely to respond to treatment. The establishment of the amyloid/tau/neurodegeneration (A/T/N) framework in 2018 has codified the contexts of use of AD biomarkers in neuroimaging and bodily fluids for research and diagnostic purposes. Furthermore, a renewed drive for novel AD biomarkers and innovative methods of detection has emerged with the goals of adding additional insight to disease progression and discovery of new therapeutic targets. The use of biomarkers has accelerated the development of AD drugs and will bring new therapies to patients in need. This review highlights recent methods utilized to diagnose antemortem AD.

The Western Australia Olfactory Memory Test: Reliability and Validity in a Sample of Older Adults

Objective

The Western Australia Olfactory Memory Test (WAOMT) is a newly developed test designed to meet a need for a comprehensive measure of olfactory episodic memory (OEM) for clinical and research applications.

Method

This study aimed to establish the psychometric properties of the WAOMT in a sample of 209 community-dwelling older adults. An independent sample of 27 test-naïve participants were recruited to assess test retest reliability (between 7 and 28 days). Scale psychometric properties were examined using item response theory methods, combined samples (final N = 241). Convergent validity was assessed by comparing performance on the WAOMT with a comprehensive neuropsychological battery of domains (verbal and visual episodic memory, and odor identification), as well as other neuropsychological skills. Based on previous literature, it was predicted that the WAOMT would be positively correlated with conceptually similar cognitive domains.

Results

The WAOMT is a psychometrically sound test with adequate reliability properties and demonstrated convergent validity with tests of verbal and episodic memory and smell identification. Patterns of performance highlight learning and memory characteristics unique to OEM (e.g., learning curves, cued and free recall).

Conclusion

Clinical and research implications include streamlining future versions of the WAOMT to ease patient and administrative burden, and the potential to reliably detect early neuropathological changes in healthy older adults with nonimpaired OEM abilities.

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks

Background: In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer's disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Added value of semiquantitative analysis of brain FDG-PET for the differentiation between MCI-Lewy bodies and MCI due to Alzheimer's disease

PURPOSE

FDG-PET is an established supportive biomarker in dementia with Lewy bodies (DLB), but its diagnostic accuracy is unknown at the mild cognitive impairment (MCI-LB) stage when the typical metabolic pattern may be difficultly recognized at the individual level. Semiquantitative analysis of scans could enhance accuracy especially in less skilled readers, but its added role with respect to visual assessment in MCI-LB is still unknown.

METHODS

We assessed the diagnostic accuracy of visual assessment of FDG-PET by six expert readers, blind to diagnosis, in discriminating two matched groups of patients (40 with prodromal AD (MCI-AD) and 39 with MCI-LB), both confirmed by in vivo biomarkers. Readers were provided in a stepwise fashion with (i) maps obtained by the univariate single-subject voxel-based analysis (VBA) with respect to a control group of 40 age- and sex-matched healthy subjects, and (ii) individual odds ratio (OR) plots obtained by the volumetric regions of interest (VROI) semiquantitative analysis of the two main hypometabolic clusters deriving from the comparison of MCI-AD and MCI-LB groups in the two directions, respectively.

RESULTS

Mean diagnostic accuracy of visual assessment was 76.8 ± 5.0% and did not significantly benefit from adding the univariate VBA map reading (77.4 ± 8.3%) whereas VROI-derived OR plot reading significantly increased both accuracy (89.7 ± 2.3%) and inter-rater reliability (ICC 0.97 [0.96-0.98]), regardless of the readers' expertise.

CONCLUSION

Conventional visual reading of FDG-PET is moderately accurate in distinguishing between MCI-LB and MCI-AD, and is not significantly improved by univariate single-subject VBA but by a VROI analysis built on macro-regions, allowing for high accuracy independent of reader skills.

Diet Effects on Cerebrospinal Fluid Amino Acids Levels in Adults with Normal Cognition and Mild Cognitive Impairment

Background:

Exploration of cerebrospinal fluid (CSF) amino acids and the impact of dietary intake on central levels may provide a comprehensive understanding of the metabolic component of Alzheimer’s disease.

Objective:

The objective of this exploratory study was to investigate the effects of two diets with varied nutrient compositions on change in CSF amino acids levels in adults with mild cognitive impairment (MCI) and normal cognition (NC). Secondary objectives were to assess the correlations between the change in CSF amino acids and change in Alzheimer’s disease biomarkers.

Methods:

In a randomized, parallel, controlled feeding trial, adults (NC, n = 20; MCI, n = 29) consumed a high saturated fat (SFA)/glycemic index (GI) diet [HIGH] or a low SFA/GI diet [LOW] for 4 weeks. Lumbar punctures were performed at baseline and 4 weeks.

Results:

CSF valine increased and arginine decreased after the HIGH compared to the LOW diet in MCI (ps = 0.03 and 0.04). This pattern was more prominent in MCI versus NC (diet by diagnosis interaction ps = 0.05 and 0.09), as was an increase in isoleucine after the HIGH diet (p = 0.05). Changes in CSF amino acids were correlated with changes in Alzheimer’s disease CSF biomarkers Aβ₄₂, total tau, and p-Tau 181, with distinct patterns in the relationships by diet intervention and cognitive status.

Conclusion:

Dietary intake affects CSF amino acid levels and the response to diet is differentially affected by cognitive status.

F-18 ML-104 tau PET imaging in mild cognitive impairment

OBJECTIVE

This study was undertaken to evaluate the tau distribution patterns in patients with amnestic mild cognitive impairment (aMCI) using PET radiotracer F-18 ML-104.

MATERIALS AND METHODS

Thirty patients, clinically diagnosed as aMCI [mini mental state evaluation ≥24] in the neurology or geriatric memory clinics, were included in the study. Each aMCI patient underwent F-18 fluorodeoxyglucose and F-18 ML-104 tau PET. Standardized uptake value ratios for cortical gray matter regions were evaluated for F-18 ML-104 tau PET and compared with normal controls and with early Alzheimer's disease (AD) patients (used from a previous study).

RESULTS

aMCI revealed significantly higher standardized uptake value ratios in both medial temporal cortices, precuneus and posterior cingulate cortices in comparison to normal controls and a significantly lesser binding in bilateral medial and lateral temporal, precuneus and posterior cingulate cortices in comparison to early AD. A negative correlation was noted between F-18 fluorodeoxyglucose uptake and F-18 ML-104 retention in the precuneus and posterior cingulate cortices in aMCI, while F-18 ML-104 retention and mini mental state evaluation scores revealed a moderate negative correlation in the posterior cingulate cortices.

CONCLUSION

We could demonstrate a significant increase in cortical tau deposition in aMCI patients in comparison to normal controls, thus providing in vivo evidence of the underlying pathological process in this subgroup of patients with high probability of conversion to AD.

High Correlation of Static First-Minute-Frame (FMF) PET Imaging after 18F-Labeled Amyloid Tracer Injection with [18F]FDG PET Imaging

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aβ) have shown to correlate with [18F]fluorodeoxyglucose (FDG) PET images and provide perfusion-like information. Perfusion information of static PET scans acquired during the first minute after radiotracer injection (FMF, first-minute-frame) is compared to [18F]FDG PET images. FMFs of 60 patients acquired with [18F]florbetapir (FBP), [18F]flutemetamol (FMM), and [18F]florbetaben (FBB) are compared to [18F]FDG PET images. Regional standardized uptake value ratios (SUVR) are directly compared and intrapatient Pearson's correlation coefficients are calculated to evaluate the correlation of FMFs to their corresponding [18F]FDG PET images. Additionally, regional interpatient correlations are calculated. The intensity profiles of mean SUVRs among the study cohort (r = 0.98, p < 0.001) and intrapatient analyses show strong correlations between FMFs and [18F]FDG PET images (r = 0.93 ± 0.05). Regional VOI-based analyses also result in high correlation coefficients. The FMF shows similar information to the cerebral metabolic patterns obtained by [18F]FDG PET imaging. Therefore, it could be an alternative to the dynamic imaging of early phase amyloid PET and be used as an additional neurodegeneration biomarker in amyloid PET studies in routine clinical practice while being acquired at the same time as amyloid PET images.

Effect of Scan Time on Neuro 18F-Fluorodeoxyglucose Positron Emission Tomography Image Generated Using Deep Learning

The purpose of this study was to generate the PET images with high signal-to-noise ratio (SNR) acquired for typical scan durations (H-PET) from short scan time PET images with low SNR (L-PET) using deep learning and to evaluate the effect of scan time on the quality of predicted PET image. A convolutional neural network (CNN) with a concatenated connection and residual learning framework was implemented. PET data from 27 patients were acquired for 900 s, starting 60 minutes after the intravenous administration of FDG using a commercial PET/CT scanner. To investigate the effect of scan time on the quality of the predicted H-PETs, 10 s, 30 s, 60 s, and 120 s PET data were generated by sorting the 900 s LMF data into the LMF data acquired for each scan time. Twenty-three of the 27 patient images were used for training of the proposed CNN and the remaining four patient images were used for test of the CNN. The predicted H-PETs generated by the CNN were compared to ground-truth H-PETs, L-PETs, and filtered L-PETs processed with four commonly used denoising algorithms. The peak signal-to-noise ratios (PSNRs), normalized root mean square errors (NRMSEs), and average regionof- interest (ROI) differences as a function of scan time were calculated. The quality of the predicted H-PETs generated by the CNN was superior to that of the L-PETs and filtered L-PETs. Lower NRMSEs and higher PSNRs were also obtained from predicted H-PETs compared to the L-PETs and filtered L-PETs. ROI differences in the predicted H-PETs were smaller than those of the L-PETs. The quality of the predicted H-PETs gradually improved with increasing scan times. The lowest NRMSEs, highest PSNRs, and smallest ROI differences were obtained using the predicted H-PETs for 120 s. Various performance test results for the proposed CNN indicate that it is possible to generate H-PETs from neuro FDG L-PETs using the proposed CNN method, which might allow reductions in both scan time and injection dose.

The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia

INTRODUCTION

The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI).

METHODS

From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined.

RESULTS

Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve.

CONCLUSION

We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.

Positron Emission Tomography (PET) and Neuroimaging in the Personalized Approach to Neurodegenerative Causes of Dementia

Generally, dementia should be considered an acquired syndrome, with multiple possible causes, rather than a specific disease in itself. The leading causes of dementia are neurodegenerative and non-neurodegenerative alterations. Nevertheless, the neurodegenerative group of diseases that lead to cognitive impairment and dementia includes multiple possibilities or mixed pathologies with personalized treatment management for each cause, even if Alzheimer's disease is the most common pathology. Therefore, an accurate differential diagnosis is mandatory in order to select the most appropriate therapy approach. The role of personalized assessment in the treatment of dementia is rapidly growing. Neuroimaging is an essential tool for differential diagnosis of multiple causes of dementia and allows a personalized diagnostic and therapeutic protocol based on risk factors that may improve treatment management, especially in early diagnosis during the prodromal stage. The utility of structural and functional imaging could be increased by standardization of acquisition and analysis methods and by the development of algorithms for automated assessment. The aim of this review is to focus on the most commonly used tracers for differential diagnosis in the dementia field. Particularly, we aim to explore ¹⁸F Fluorodeoxyglucose (FDG) and amyloid positron emission tomography (PET) imaging in Alzheimer's disease and in other neurodegenerative causes of dementia.

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.

Brain Glucose Metabolism Heterogeneity in Idiopathic REM Sleep Behavior Disorder and in Parkinson's Disease

BACKGROUND/OBJECTIVE

Idiopathic REM sleep behavior disorder (iRBD) often precedes Parkinson's disease (PD) and other alpha-synucleinopathies. The aim of the study is to investigate brain glucose metabolism of patients with RBD and PD by means of a multidimensional scaling approach, using18F-FDG-PET as a biomarker of synaptic function.

METHODS

Thirty-six iRBD patients (64.1±6.5 y, 32 M), 72 PD patients, and 79 controls (65.6±9.4 y, 53 M) underwent brain 18F-FDG-PET. PD patients were divided according to the absence (PD, 32 subjects; 68.4±8.5 y, 15 M) or presence (PDRBD, 40 subjects; 71.8±6.6 y, 29 M) of RBD. 18F-FDG-PET scans were used to independently discriminate subjects belonging to four categories: controls (RBD no, PD no), iRBD (RBD yes, PD no), PD (RBD no, PD yes) and PDRBD (RBD yes, PD yes).

RESULTS

The discriminant analysis was moderately accurate in identifying the correct category. This is because the model mostly confounds iRBD and PD, thus the intermediate classes. Indeed, iRBD, PD and PDRBD were progressively located at increasing distance from controls and are ordered along a single dimension (principal coordinate analysis) indicating the presence of a single flux of variation encompassing both RBD and PD conditions.

CONCLUSION

Data-driven approach to brain 18F-FDG-PET showed only moderate discrimination between iRBD and PD patients, highlighting brain glucose metabolism heterogeneity among such patients. iRBD should be considered as a marker of an ongoing condition that may be picked-up in different stages across patients and thus express different brain imaging features and likely different clinical trajectories.

Relevance of quantification in brain PET studies with 18F-FDG

The inclusion of ¹⁸F-FDG PET as a biomarker in the diagnostic criteria of neurodegenerative diseases and its indication in the presurgical assessment for drug-resistant epilepsies allow to improve specificity of these diagnosis. The traditional interpretation of neurological PET studies has been performed qualitatively, although in the last decade, several quantitative evaluation methods have emerged. This technical development has become relevant in clinical practice, improving specificity, reproducibility and reducing the interrater reliability derived from visual analysis. In this article we update/review the main imaging processing techniques currently used. This may allow the Nuclear Medicine physician to know their advantages and disadvantages when including these procedures in daily clinical practice.

Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study

BACKGROUND

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Studies on MCI progression are important for Alzheimer's disease (AD) prevention. 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) has been proven to be a powerful tool for measuring cerebral glucose metabolism. In this study, we proposed a classification framework for MCI prediction with both baseline and multiple follow-up FDG-PET scans as well as cognitive scores of 33 progressive MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHOD

First, PET images were normalized using the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA). The average metabolic intensities of brain regions were defined as static features. Dynamic features were the intensity variation between baseline and the other three time points and change ratios with the intensity obtained at baseline considered as reference. Mini-mental State Examination (MMSE) scores and Alzheimer's disease Assessment Scale-Cognitive section (ADAS-cog) scores of each time point were collected as cognitive features. And F-score was applied for feature selection. Finally, support vector machine (SVM) with radial basis function (RBF) kernel was used for the three above features.

RESULTS

Dynamic features showed the best classification performance in accuracy of 88.61% than static features (accuracy of 78.48%). And the combination of cognitive features and dynamic features improved the classification performance in specificity of 95.65% and Area Under Curve (AUC) of 0.9308.

CONCLUSION

Our results reported that dynamic features are more representative in longitudinal research for MCI prediction work. And dynamic features and cognitive scores complementarily enhance the classification performance in specificity and AUC. These findings may predict the disease course and clinical changes in individuals with mild cognitive impairment.

Neuroimaging and analytical methods for studying the pathways from mild cognitive impairment to Alzheimer's disease: protocol for a rapid systematic review

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder commonly associated with deficits of cognition and changes in behavior. Mild cognitive impairment (MCI) is the prodromal stage of AD that is defined by slight cognitive decline. Not all with MCI progress to AD dementia. Thus, the accurate prediction of progression to Alzheimer's, particularly in the stage of MCI could potentially offer developing treatments to delay or prevent the transition process. The objective of the present study is to investigate the most recent neuroimaging procedures in the domain of prediction of transition from MCI to AD dementia for clinical applications and to systematically discuss the machine learning techniques used for the prediction of MCI conversion.

METHODS

Electronic databases including PubMed, SCOPUS, and Web of Science will be searched from January 1, 2017, to the date of search commencement to provide a rapid review of the most recent studies that have investigated the prediction of conversion from MCI to Alzheimer's using neuroimaging modalities in randomized trial or observational studies. Two reviewers will screen full texts of included papers using predefined eligibility criteria. Studies will be included if addressed research on AD dementia and MCI, explained the results in a way that would be able to report the performance measures such as the accuracy, sensitivity, and specificity. Only studies addressed Alzheimer's type of dementia and its early-stage MCI using neuroimaging modalities will be included. We will exclude other forms of dementia such as vascular dementia, frontotemporal dementia, and Parkinson's disease. The risk of bias in individual studies will be appraised using an appropriate tool. If feasible, we will conduct a random effects meta-analysis. Sensitivity analyses will be conducted to explore the potential sources of heterogeneity.

DISCUSSION

The information gathered in our study will establish the extent of the evidence underlying the prediction of conversion to AD dementia from its early stage and will provide a rigorous and updated synthesis of neuroimaging modalities allied with the data analysis techniques used to measure the brain changes during the conversion process.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO,CRD42019133402.

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.

The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer's disease, using automatic quantitative clinical tools

The hippocampus is one of the earliest sites involved in the pathology of Alzheimer's disease (AD). Therefore, we specifically investigated the sensitivity and specificity of hippocampal volume and glucose metabolism in patients being evaluated for AD, using automated quantitative tools (NeuroQuant - magnetic resonance imaging [MRI] and Scenium - positron emission tomography [PET]) and clinical evaluation.This retrospective study included adult patients over the age of 45 years with suspected AD, who had undergone fluorodeoxyglucose positron emission tomography-computed tomography (FDG-PET-CT) and MRI. FDG-PET-CT images were analyzed both qualitatively and quantitatively. In quantitative volumetric MRI analysis, the percentage of the total intracranial volume of each brain region, as well as the total hippocampal volume, were considered in comparison to an age-adjusted percentile. The remaining brain regions were compared between groups according to the final diagnosis.Thirty-eight patients were included in this study. After a mean follow-up period of 23 ± 11 months, the final diagnosis for 16 patients was AD or high-risk mild cognitive impairment (MCI). Out of the 16 patients, 8 patients were women, and the average age of all patients was 69.38 ± 10.98 years. Among the remaining 22 patients enrolled in the study, 14 were women, and the average age was 67.50 ± 11.60 years; a diagnosis of AD was initially excluded, but the patients may have low-risk MCI. Qualitative FDG-PET-CT analysis showed greater accuracy (0.87), sensitivity (0.76), and negative predictive value (0.77), when compared to quantitative PET analysis, hippocampal MRI volumetry, and specificity. The positive predictive value of FDG-PET-CT was similar to the MRI value.The performance of FDG-PET-CT qualitative analysis was significantly more effective compared to MRI volumetry. At least in part, this observation could corroborate the sequential hypothesis of AD pathophysiology, which posits that functional changes (synaptic dysfunction) precede structural changes (atrophy).

Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer's disease

BACKGROUND AND OBJECTIVE

¹⁸F-FluoroDeoxyGlucose Positron Emission Tomography (¹⁸F-FDG PET) is one of the imaging biomarkers to diagnose Alzheimer's Disease (AD). In ¹⁸F-FDG PET images, the changes of voxels' intensities reflect the differences of glucose rates, therefore voxel intensity is usually used as a feature to distinguish AD from Normal Control (NC), or at earlier stage to distinguish between progressive and stable Mild Cognitive Impairment (pMCI and sMCI). In this paper, ¹⁸F-FDG PET images are characterized in an alternative way-the spatial gradient, which is motivated by the observation that the changes of ¹⁸F-FDG rates also cause gradient changes.

METHODS

We improve Histogram of Oriented Gradient (HOG) descriptor to quantify spatial gradients, thereby achieving the goal of diagnosing AD. First, the spatial gradient of ¹⁸F-FDG PET image is computed, and then each subject is segmented into different regions by using an anatomical atlas. Second, two types of improved HOG features are extracted from each region, namely Small Scale HOG and Large Scale HOG, then some relevant regions are selected based on a classifier fed with spatial gradient features. Last, an ensemble classification framework is designed to make a decision, which considers the performance of both individual and concatenated selected regions.

RESULTS

the evaluation is done on ADNI dataset. The proposed method outperforms other state-of-the-art ¹⁸F-FDG PET-based algorithms for AD vs. NC with an accuracy, a sensitivity and a specificity values of 93.65%, 91.22% and 96.25%, respectively. For the case of pMCI vs. sMCI, the three metrics are 75.38%, 74.84% and 77.11%, which is significantly better than most existing methods. Besides, promising results are also achieved for multiple classifications under ¹⁸F-FDG PET modality.

CONCLUSIONS

¹⁸F-FDG PET images can be characterized by spatial gradient features for diagnosing AD and its early stage, and the proposed ensemble framework can enhance the classification performance.

Spatial distributions of cholinergic impairment and neuronal hypometabolism differ in MCI due to AD

Elucidating the relationship between neuronal metabolism and the integrity of the cholinergic system is prerequisite for a profound understanding of cholinergic dysfunction in Alzheimer's disease. The cholinergic system can be investigated specifically using positron emission tomography (PET) with [¹¹C]N-methyl-4-piperidyl-acetate (MP4A), while neuronal metabolism is often assessed with 2-deoxy-2-[¹⁸F]fluoro-d-glucose-(FDG) PET. We hypothesised a close correlation between MP4A-perfusion and FDG-uptake, permitting inferences about metabolism from MP4A-perfusion, and investigated the patterns of neuronal hypometabolism and cholinergic impairment in non-demented AD patients. MP4A-PET was performed in 18 cognitively normal adults and 19 patients with mild cognitive impairment (MCI) and positive AD biomarkers. In nine patients with additional FDG-PET, the sum images of every combination of consecutive early MP4A-frames were correlated with FDG-scans to determine the optimal time window for assessing MP4A-perfusion. Acetylcholinesterase (AChE) activity was estimated using a 3-compartmental model. Group comparisons of MP4A-perfusion and AChE-activity were performed using the entire sample. The highest correlation between MP4A-perfusion and FDG-uptake across the cerebral cortex was observed 60-450 s after injection (r = 0.867). The patterns of hypometabolism (FDG-PET) and hypoperfusion (MP4A-PET) in MCI covered areas known to be hypometabolic early in AD, while AChE activity was mainly reduced in the lateral temporal cortex and the occipital lobe, sparing posterior midline structures. Data indicate that patterns of cholinergic impairment and neuronal hypometabolism differ significantly at the stage of MCI in AD, implying distinct underlying pathologies, and suggesting potential predictors of the response to cholinergic pharmacotherapy.

Altered Brain Glucose Metabolism Assessed by 18F-FDG PET Imaging Is Associated with the Cognitive Impairment of CADASIL

Recurrent stroke and cognitive impairment are the primary features of patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). The cognitive deficits in these patients are known to be correlated with structural brain changes, such as white matter lesions and lacunae, and resting-state functional connectivity in brain networks. However, the associations between changes in brain glucose metabolism based on ¹⁸F-2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission tomography (PET) imaging and cognitive scores in CADASIL patients remain unclear. In the present study, 24 CADASIL patients and 24 matched healthy controls underwent ¹⁸F-FDG PET imaging. Brain glucose metabolism was measured in all subjects and Pearson's correlation analyses were performed to evaluate relationships between abnormal glucose metabolism in various brain areas and cognitive scores. Compared to controls, CADASIL patients exhibited significantly lower metabolism in the right cerebellar posterior lobe, left cerebellar anterior lobe, bilateral thalamus and left limbic lobe. Additionally, hypermetabolism was observed in the left precentral and postcentral gyri. Importantly, glucose metabolism in the left limbic lobe was positively associated with cognitive scores on the Mini-Mental State Examination (MMSE). Furthermore, glucose metabolism in the left precentral gyri was negatively correlated with cognitive scores on the Montreal Cognitive Assessment (MoCA). The present findings provide strong support for the presence of altered brain glucose metabolism in CADASIL patients as well as the associations between abnormal metabolism and cognitive scales in this population. The present findings suggest that patterns of brain glucose metabolism may become useful markers of cognitive impairment in CADASIL patients.

Activated peripheral blood mononuclear cell mediators trigger astrocyte reactivity

Sepsis is characterized by a severe and disseminated inflammation. In the central nervous system, sepsis promotes synaptic dysfunction and permanent cognitive impairment. Besides sepsis-induced neuronal dysfunction, glial cell response has been gaining considerable attention with microglial activation as a key player. By contrast, astrocytes' role during acute sepsis is still underexplored. Astrocytes are specialized immunocompetent cells involved in brain surveillance. In this context, the potential communication between the peripheral immune system and astrocytes during acute sepsis still remains unclear. We hypothesized that peripheral blood mononuclear cell (PBMC) mediators are able to affect the brain during an episode of acute sepsis. With this in mind, we first performed a data-driven transcriptome analysis of blood from septic patients to identify common features among independent clinical studies. Our findings evidenced pronounced impairment in energy-related signaling pathways in the blood of septic patients. Since astrocytes are key for brain energy homeostasis, we decided to investigate the communication between PBMC mediators and astrocytes in a rat model of acute sepsis, induced by cecal ligation and perforation (CLP). In the CLP animals, we identified widespread in vivo brain glucose hypometabolism. Ex vivo analyses demonstrated astrocyte reactivity along with reduced glutamate uptake capacity during sepsis. Also, by exposing cultured astrocytes to mediators released by PBMCs from CLP animals, we reproduced the energetic failure observed in vivo. Finally, by pharmacologically inhibiting phosphoinositide 3-kinase (PI3K), a central metabolic pathway downregulated in the blood of septic patients and reduced in the CLP rat brain, we mimicked the PBMC mediators effect on glutamate uptake but not on glucose metabolism. These results suggest that PBMC mediators are capable of directly mediating astrocyte reactivity and contribute to the brain energetic failure observed in acute sepsis. Moreover, the evidence of PI3K participation in this process indicates a potential target for therapeutic modulation.

Region-specific association between basal blood insulin and cerebral glucose metabolism in older adults

Background

Although previous studies have suggested that insulin plays a role in brain function, it still remains unclear whether or not insulin has a region-specific association with neuronal and synaptic activity in the living human brain. We investigated the regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism (CMglu), a proxy for neuronal and synaptic activity, in older adults.

Method

A total of 234 nondiabetic, cognitively normal (CN) older adults underwent comprehensive clinical assessment, resting-state 18F-fluodeoxyglucose (FDG)-positron emission tomography (PET) and blood sampling to determine overnight fasting blood insulin and glucose levels, as well as apolipoprotein E (APOE) genotyping.

Results

An exploratory voxel-wise analysis of FDG-PET without a priori hypothesis demonstrated a positive association between basal blood insulin levels and resting-state CMglu in specific cerebral cortices and hippocampus, rather than in non-specific overall cerebral regions, even after controlling for the effects of APOE e4 carrier status, vascular risk factor score, body mass index, fasting blood glucose, and demographic variables. Particularly, a positive association of basal blood insulin with CMglu in the right posterior hippocampus and adjacent parahippocampal region as well as in the right inferior parietal region remained significant after multiple comparison correction. Conversely, no region showed negative association between basal blood insulin and CMglu.

Conclusions

Our finding suggests that basal fasting blood insulin may have association with neuronal and synaptic activity in specific cerebral regions, particularly in the hippocampal/parahippocampal and inferior parietal regions.

•

We investigated regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism.

•

Significant clusters with positive associations were found mainly in the hippocampal and inferior parietal regions.

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Our finding suggests a region-specific association of basal blood insulin with resting-state cerebral glucose metabolism.

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Further studies to elucidate underlying mechanism and implication of this region-specific association will be necessary.

Links Between Metabolic and Structural Changes in the Brain of Cognitively Normal Older Adults: A 4-Year Longitudinal Follow-Up

We aimed to longitudinally assess the relationship between changing brain energy metabolism (glucose and acetoacetate) and cognition during healthy aging. Participants aged 71 ± 5 year underwent cognitive evaluation and quantitative positron emission tomography (PET) and magnetic resonance imaging (MRI) scans at baseline (N = 25) and two (N = 25) and four (N = 16) years later. During the follow-up, the rate constant for brain extraction of glucose (K_glc) declined by 6%–12% mainly in the temporo-parietal lobes and cingulate gyri (p ≤ 0.05), whereas brain acetoacetate extraction (Kacac) and utilization remained unchanged in all brain regions (p ≥ 0.06). Over the 4 years, cognitive results remained within the normal age range but an age-related decline was observed in processing speed. K_glc in the caudate was directly related to performance on several cognitive tests (r = +0.41 to +0.43, allp ≤ 0.04). Peripheral insulin resistance assessed by the homeostasis model assessment of insulin resistance (HOMA-IR) was significantly inversely related to K_glc in the thalamus (r = −0.44, p = 0.04) and in the caudate (r = −0.43, p = 0.05), and also inversely related to executive function, attention and processing speed (r = −0.45 to −0.53, all p ≤ 0.03). We confirm in a longitudinal setting that the age-related decline in K_glc is directly associated with declining performance on some tests of cognition but does not significantly affect Kacac.

Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease

Using a single imaging modality to diagnose Alzheimer's disease (AD) or mild cognitive impairment (MCI) is a challenging task. FluoroDeoxyGlucose Positron Emission Tomography (FDG-PET) is an important and effective modality used for that purpose. In this paper, we develop a novel method by using single modality (FDG-PET) but multilevel feature, which considers both region properties and connectivities between regions to classify AD or MCI from normal control. First, three levels of features are extracted: statistical, connectivity, and graph-based features. Then, the connectivity features are decomposed into three different sets of features according to a proposed similarity-driven ranking method, which can not only reduce the feature dimension but also increase the classifier's diversity. Last, after feeding the three levels of features to different classifiers, a new classifier selection strategy, maximum Mean squared Error (mMsE), is developed to select a pair of classifiers with high diversity. In order to do the majority voting, a decision-making scheme, a nested cross validation technique is applied to choose another classifier according to the accuracy. Experiments on Alzheimer's Disease Neuroimaging Initiative database show that the proposed method outperforms most FDG-PET-based classification algorithms, especially for classifying progressive MCI (pMCI) from stable MCI (sMCI).

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.

Automated assessment of FDG-PET for differential diagnosis in patients with neurodegenerative disorders

PURPOSE

To review literature until November 2015 and reach a consensus on whether automatic semi-quantification of brain FDG-PET is useful in the clinical setting for neurodegenerative disorders.

METHODS

A literature search was conducted in Medline, Embase, and Google Scholar. Papers were selected with a lower limit of 30 patients (no limits with autopsy confirmation). Consensus recommendations were developed through a Delphi procedure, based on the expertise of panelists, who were also informed about the availability and quality of evidence, assessed by an independent methodology team.

RESULTS

Critical outcomes were available in nine among the 17 papers initially selected. Only three papers performed a direct comparison between visual and automated assessment and quantified the incremental value provided by the latter. Sensitivity between visual and automatic analysis is similar but automatic assessment generally improves specificity and marginally accuracy. Also, automated assessment increases diagnostic confidence. As expected, performance of visual analysis is reported to depend on the expertise of readers.

CONCLUSIONS

Tools for semi-quantitative evaluation are recommended to assist the nuclear medicine physician in reporting brain FDG-PET pattern in neurodegenerative conditions. However, heterogeneity, complexity, and drawbacks of these tools should be known by users to avoid misinterpretation. Head-to-head comparisons and an effort to harmonize procedures are encouraged.

[Recommendations of Swiss Memory Clinics for the Diagnosis of Dementia]

Recommendations of Swiss Memory Clinics for the Diagnosis of Dementia Abstract. The early diagnosis of subjectively perceived or externally anamnestically observed cognitive impairments is essential for proving neurodegenerative diseases or excluding treatable causes such as internal, neurological or psychiatric disorders. Only in this way is early treatment made possible. As part of the project 3.1 of the National Dementia Strategy 2014-2019 ('Development and expansion of regional and networked centres of competence for diagnostics'), the association Swiss Memory Clinics (SMC) set itself the goal of developing quality standards for dementia clarification and improving the community-based care in this field. In these recommendations, general guidelines of diagnostics and individual examination possibilities are presented, and standards for the related processes are suggested. Individual areas such as anamnesis, clinical examination, laboratory examination, neuropsychological testing and neuroradiological procedures are discussed in detail as part of standard diagnostics, and supplementary examination methods for differential diagnosis considerations are portrayed. The most important goals of the SMC recommendations for the diagnosis of dementia are to give all those affected access to high-quality diagnostics, if possible, to improve early diagnosis of dementia and to offer the basic service providers and the employees of Memory Clinics a useful instrument for the clarification.

[Recommendations of Swiss Memory Clinics for the Diagnosis of Dementia]

The early diagnosis of subjectively perceived or externally anamnestically observed cognitive impairments is essential for proving neurodegenerative diseases or excluding treatable causes such as internal, neurological or psychiatric disorders. Only in this way is early treatment made possible. As part of the project 3.1 of the National Dementia Strategy 2014–2019 («Development and expansion of regional and networked centres of competence for diagnostics»), the association Swiss Memory Clinics (SMC) set itself the goal of developing quality standards for dementia clarification and improving the community-based care in this field. In these recommendations, general guidelines of diagnostics and individual examination possibilities are presented, and standards for the related processes are suggested. Individual areas such as anamnesis, clinical examination, laboratory examination, neuropsychological testing and neuroradiological procedures are discussed in detail as part of standard diagnostics, and supplementary examination methods for differential diagnosis considerations are portrayed. The most important goals of the SMC recommendations for the diagnosis of dementia are to give all those affected access to high-quality diagnostics, if possible, to improve early diagnosis of dementia and to offer the basic service providers and the employees of Memory Clinics a useful instrument for the clarification.

Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification

Positron emission tomography (PET) is a molecular medical imaging modality which is commonly used for neurodegenerative diseases diagnosis. Computer-aided diagnosis, based on medical image analysis, could help quantitative evaluation of brain diseases such as Alzheimer's disease (AD). A novel method of ranking the effectiveness of brain volume of interest (VOI) to separate healthy control from AD brains PET images is presented in this paper. Brain images are first mapped into anatomical VOIs using an atlas. Histogram-based features are then extracted and used to select and rank VOIs according to the area under curve (AUC) parameter, which produces a hierarchy of the ability of VOIs to separate between groups of subjects. The top-ranked VOIs are then input into a support vector machine classifier. The developed method is evaluated on a local database image and compared to the known selection feature methods. Results show that using AUC outperforms classification results in the case of a two group separation.

Patients with Amyloid-Negative Mild Cognitive Impairment have Cortical Hypometabolism but the Hippocampus is Preserved

BACKGROUND

Patients with mild cognitive impairment (MCI) are at risk for Alzheimer's dementia but the presence of amyloid (Aβ) strongly increases this risk. In clinical settings, when Aβ status is not available, different neurodegenerative markers are used to characterize MCI. The accuracy of these markers to discriminate between Aβ-and Aβ+ MCI is not yet determined.

OBJECTIVE

To compare different markers of neurodegeneration in Aβ-and Aβ+ MCI, with an Aβ-elderly control (EC) group.

METHODS

Patients with MCI (n = 39) and EC (n = 28) underwent MRI, 18F-FDG PET, and Aβ PET (18F-flutemetamol). We compared FDG and MRI biomarker values in cortical and hippocampal regions of interest, and using voxel-wise surface maps. We computed ROC curves discriminating between the three groups for each biomarker.

RESULTS

All biomarker values were reduced in Aβ+ MCI compared to EC (p < 0.001). Aβ-MCI had low cortical metabolism (p = 0.002), but hippocampal volume, cortical thickness, and hippocampal metabolism were not significantly different between Aβ-MCI and EC (p > 0.40). Cortical metabolism best discriminated between MCI and EC (AUC = 0.92/0.86, Aβ+/Aβ-) while hippocampal volume best discriminated between Aβ-MCI and Aβ+ MCI (AUC = 0.79).

CONCLUSIONS

Cortical hypometabolism was observed in both Aβ-MCI and Aβ+ MCI whereas hippocampal atrophy was mostly found in Aβ+ MCI. For MCI patients without available Aβ information, hippocampal atrophy is thus more informative about Aβ status than cortical hypometabolism.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

BACKGROUND

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL).

METHODS

We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients.

RESULTS

Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei.

CONCLUSION

The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

Brain metabolic correlates of CSF Tau protein in a large cohort of Alzheimer's disease patients: A CSF and FDG PET study

AIMS

Physiopathological mechanisms of Alzheimer's disease (AD) are still matter of debate. Especially the role of amyloid β and tau pathology in the development of the disease are still matter of debate. Changes in tau and amyloid β peptide concentration in cerebrospinal fluid (CSF) and hypometabolic patterns at fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) PET scanning are considered as biomarkers of AD. The present study was aimed to evaluate the relationships between the concentrations of CSF total Tau (t-Tau), phosphorilated Tau (p-Tau) and Aβ_1-42 amyloid peptide with ¹⁸F-FDG brain distribution in a group of patients with AD.

MATERIALS AND METHODS

We examined 131 newly diagnosed AD patients according to the NINCDS-ADRDA criteria and 20 healthy controls. The mean (±SD) age of the patients was 70 (±7) years; 57 were male and 74 were female. All patients and controls underwent a complete clinical investigation, including medical history, neurological examination, mini-mental state examination (MMSE), a complete blood screening (including routine exams, thyroid hormones and a complete neuropsychological evaluation). Structural MRI was performed not earlier than 1 month before the ¹⁸F-FDG PET/CT. The following patients were excluded: those with isolated deficits and/or unmodified MMSE (=25/30) on revisit (period of follow-up: 6, 12 and 18 months); patients who had had a clinically manifest acute stroke in the last 6 months with a Hachinsky score greater than 4; and patients with radiological evidence of subcortical lesions. All AD patients were taken off cholinesterase inhibitor treatment throughout the study. We performed lumbar puncture and CSF sampling for diagnostic purposes 2 weeks (±2 days) before the PET/CT scan. The relationship between brain F-FDG uptake and CSF biomarkers was analysed using statistical parametric mapping (SPM8; Wellcome Department of Cognitive Neurology, London, UK) implemented in Matlab R2012b using the MMSE score, sex and age, and other CSF biomarkers as covariates.

RESULTS

t-Tau, p-Tau and Aβ(1-42) in CSF resulted 774 ± 345 pg/ml, 98 ± 64 pg/ml and 348.8 ± 111 pg/ml respectively. SPM analysis showed a significant negative correlation between CSF t-Tau and ¹⁸F FDG uptake in right temporal, parietal and frontal lobe (Brodmann areas, BA, 20, 40 and 8; P fdr and few corr < 0.001, ke 19534). We did not find any significant relationships with other CSF biomarkers.

CONCLUSIONS

t-Tau deposition in brain is related to temporal, parietal and frontal hypometabolism in AD.

Hypothalamic dysfunction is related to sleep impairment and CSF biomarkers in Alzheimer's disease

Hypothalamus is a key brain region regulating several essential homeostatic functions, including the sleep-wake cycle. Alzheimer's disease (AD) pathology affects nuclei controlling sleep-wake rhythm sited in this brain area. Since only post-mortem studies documented the relationship between hypothalamic atrophy and sleep-wake cycle impairment, we investigated in AD patients the possible hypothalamic in vivo alteration using 2-deoxy-2-(18F) fluoro-D-glucose ([18F]FDG) positron emission tomography ([18F]FDG PET), and its correlations with sleep impairment and cerebrospinal-fluid (CSF) AD biomarkers (tau proteins and β-amyloid₄₂). We measured sleep by polysomnography, CSF AD biomarkers and orexin levels, and hypothalamic [18F]FDG PET uptake in a population of AD patients compared to age- and sex-matched controls. We documented the significant reduction of hypothalamic [18F]FDG PET uptake in AD patients (n = 18) compared to controls (n = 18) (p < 0.01). Moreover, we found the increase of CSF orexin levels coupled with the marked alteration of nocturnal sleep in AD patients than controls. We observed the significant association linking the reduction of both sleep efficiency and REM sleep to the reduction of hypothalamic [18F]FDG PET uptake in the AD group, which in turn negatively correlated with the total-tau/beta-amyloid₄₂ ratio (index of more marked neurodegeneration). Moreover, controls but not AD patients showed [18F]FDG PET interconnections between hypothalamus and limbic system. We documented the in vivo dysfunction of hypothalamus in AD patients, which lost the physiological connections with limbic system and was correlated with both nocturnal sleep disruption and CSF AD biomarkers.

18F-FDG PET diagnostic and prognostic patterns do not overlap in Alzheimer's disease (AD) patients at the mild cognitive impairment (MCI) stage

PURPOSE

We aimed to identify the cortical regions where hypometabolism can predict the speed of conversion to dementia in mild cognitive impairment due to Alzheimer's disease (MCI-AD).

METHODS

We selected from the clinical database of our tertiary center memory clinic, eighty-two consecutive MCI-AD that underwent 18F-fluorodeoxyglucose (FDG) PET at baseline during the first diagnostic work-up and were followed up at least until their clinical conversion to AD dementia. The whole group of MCI-AD was compared in SPM8 with a group of age-matched healthy controls (CTR) to verify the presence of AD diagnostic-pattern; then the correlation between conversion time and brain metabolism was assessed to identify the prognostic-pattern. Significance threshold was set at p < 0.05 False-Discovery-Rate (FDR) corrected at peak and at cluster level. Each MCI-AD was then compared with CTR by means of a SPM single-subject analysis and grouped according to presence of AD diagnostic-pattern and prognostic-pattern. Kaplan-Meier-analysis was used to evaluate if diagnostic- and/or prognostic-patterns can predict speed of conversion to dementia.

RESULTS

Diagnostic-pattern corresponded to typical posterior hypometabolism (BA 7, 18, 19, 30, 31 and 40) and did not correlate with time to conversion, which was instead correlated with metabolic levels in right middle and inferior temporal gyri as well as in the fusiform gyrus (prognostic-pattern, BA 20, 21 and 38). At Kaplan-Meier analysis, patients with hypometabolism in the prognostic pattern converted to AD-dementia significantly earlier than patients not showing significant hypometabolism in the right middle and inferior temporal cortex (9 versus 19 months; Log rank p < 0.02, Breslow test: p < 0.003, Tarone-Ware test: p < 0.007).

CONCLUSION

The present findings support the role of FDG PET as a robust progression biomarker even in a naturalist population of MCI-AD. However, not the AD-typical diagnostic-pattern in posterior regions but the middle and inferior temporal metabolism captures speed of conversion to dementia in MCI-AD since baseline. The highlighted prognostic pattern is a further, independent source of heterogeneity in MCI-AD and affects a primary-endpoint on interventional clinical trials (time of conversion to dementia).

Functional correlates of TSH, fT3 and fT4 in Alzheimer disease: a F-18 FDG PET/CT study

The present study was aimed to investigate the relationships between thyroid stimulating hormone (TSH), freeT3 (fT3) and freeT4 (fT4) and brain glucose consumption as detectable by means of 2-deoxy-2-(F-18) fluoro-D-glucose (F-18 FDG) Positron Emission Tomography/Computed Tomography (PET/CT) in a selected population with Alzheimer disease (AD). We evaluated 87 subjects (37 males and 50 females, mean age 70 (±6) years old) with AD. All of them were subjected to TSH, fT3 and fT4 assay and to cerebrospinal fluid amyloid (Aβ1-42) and tau [phosphorylated-tau (p-tau) and total-tau (t-tau)] assay prior PET/CT examination. Values for TSH, fT3 and fT4 were in the normal range. The relationships were evaluated by means of statistical parametric mapping (SPM8) using age, sex, MMSE, scholarship and CSF values of amyloid and tau as covariates. We found a significant positive correlation between TSH values and cortical glucose consumption in a wide portion of the anterior cingulate cortex bilaterally (BA32) and left frontal lobe (BA25) (p FWE-corr <0.001; p FDRcorr <0.000; cluster extent 66950). No significant relationships were found between cortical F-18 FDG uptake and T3 and T4 serum levels. The results of our study suggest that a cortical dysfunction in anterior cingulate and frontal lobes may affect serum values of TSH in AD patients.