Choice of reference area in studies of Alzheimer's disease using positron emission tomography with fluorodeoxyglucose-F18

Regional Brain Metabolism across the Alzheimer's Disease Continuum in Down Syndrome

OBJECTIVE

The goal was to examine the effect of sociodemographic variables, Alzheimer's disease (AD) clinical stages and pathology on brain metabolism in Down syndrome (DS).

METHODS

We included 71 euploid healthy controls (HC) and 105 adults with DS (67 asymptomatic, 12 prodromal, and 26 with dementia) from the Down-Alzheimer Barcelona Neuroimaging Initiative. Participants underwent [18F]fluorodeoxyglucose positron emission tomography, 3 Tmagnetic resonance imaging, and lumbar puncture to measure cerebrospinal fluid (CSF) biomarkers (ratio beween amyloid β peptide 42 and 40, phosphorylated tau 181, and neurofilament light chain [NfL]). Voxel-wise analyses in SPM12 examined the effects of age, sex, intellectual disability, Alzheimer's clinical stage, and CSF biomarkers on brain metabolism.

RESULTS

In HC, brain metabolism decreased with age primarily in the frontal lobe. By contrast, a more distributed pattern of metabolic loss was observed in DS with age, predominating in temporoparietal regions. Compared to asymptomatic DS participants, those at the prodromal stage exhibited medial parietal hypometabolism, which later extended to other temporoparietal and frontal regions at the dementia stage. In asymptomatic individuals, we observed a widespread hypometabolism compared to HC, mainly in medial frontal and parietal regions. All CSF biomarkers were closely associated with hypometabolism in regions affected by the disease, with the strongest association observed for NfL in medial parietal structures.

INTERPRETATION

The brain metabolic decline in DS with age reflects Alzheimer's pathological processes and involves temporoparietal regions in a similar pattern to that found in other forms of AD. Hypometabolism is more tightly related to CSF NfL levels than to core AD biomarkers. ANN NEUROL 2025.

SNMMI Procedure Standard/EANM Practice Guideline for Brain [18F]FDG PET Imaging, Version 2.0

PREAMBLEThe Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional nonprofit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The EANM was founded in 1985. SNMMI and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine.The SNMMI and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated.Each practice guideline, representing a policy statement by the SNMMI/EANM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized.These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, both the SNMMI and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question.The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines.The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment.Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.

Cerebral glucose metabolism in Alzheimer's disease

18F-fluoro-deoxy-glucose positron emission tomography (FDG-PET) is a useful paraclinical exam for the diagnosis of Alzheimer's disease (AD). In this narrative review, we report seminal studies in clinically probable AD that have shown the importance of posterior brain metabolic decrease and the paradoxical variability of the hippocampal metabolism. The FDG-PET pattern was a sensitive indicator of AD in pathologically confirmed cases and it was used for differential diagnosis of dementia conditions. In prodromal AD, the AD FDG-PET pattern was observed in converters and predicted conversion. Automated data analysis techniques provided variable accuracy according to the reported indices and machine learning methods showed variable reliability of results. FDG-PET could confirm AD clinical heterogeneity and image data driven analyses identified hypometabolic subtypes with variable involvement of the hippocampus, reminiscent if the paradoxical FDG uptake. In studies dedicated to clinical and metabolic correlations, episodic memory was related to metabolism in the default mode network (and Papez's circuit) in prodromal and mild AD stages, and specific cognitive processes were associated to precisely distributed brain metabolism. Cerebral metabolic correlates of anosognosia could also be related to current neuropsychological models. AD FDG-PET pattern was reported in preclinical AD stages and related to cognition or to conversion to mild cognitive impairment (MCI). Using other biomarkers, the AD FDG-PET pattern was confirmed in AD participants with positive PET-amyloid. Intriguing observations reported increased metabolism related to brain amyloid and/or tau deposition. Preserved glucose metabolism sometimes appear as a compensation, but it was frequently detrimental and the nature of such a preservation of glucose metabolism remains an open question. Limbic metabolic involvement was frequently related to non-AD biomarkers profile and clinical stability, and it was reported in non-AD pathologies, such as the limbic predominant age-related encephalopathy (LATE). FDG-PET abnormalities observed in the absence of classical AD proteinopathies can be useful to search for pathological mechanisms and differential diagnosis of AD.

3T sodium-MRI as predictor of neurocognition in nondemented older adults: a cross sectional study

Dementia is a burgeoning global problem. Novel magnetic resonance imaging (MRI) metrics beyond volumetry may bring new insight and aid clinical trial evaluation of interventions early in the Alzheimer's disease course to complement existing imaging and clinical metrics. To determine whether: (i) normalized regional sodium-MRI values (Na-SI) are better predictors of neurocognitive status than volumetry (ii) cerebral amyloid PET status improves modelling. Nondemented older adult (>60 years) volunteers of known Alzheimer's Disease Assessment Scale (ADAS-Cog11), Mini-Mental State Examination (MMSE) and Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neurocognitive test scores, ApolipoproteinE (APOE) e4 +/- cerebral amyloid PET status were prospectively recruited for 3T sodium-MRI brain scans. Left and right hippocampal, entorhinal and precuneus volumes and Na-SI (using the proportional intensity scaling normalization method with field inhomogeneity and partial volume corrections) were obtained after segmentation and co-registration of 3D-T1-weighted proton images. Descriptive statistics, correlation and best-subset regression analyses were performed. In our 76 nondemented participants (mean(standard deviation) age 75(5) years; woman 47(62%); cognitively unimpaired 54/76(71%), mildly cognitively impaired 22/76(29%)), left hippocampal Na-SI, not volume, was preferentially in the best models for predicting MMSE (Odds Ratio (OR) = 0.19(Confidence Interval (CI) = 0.07,0.53), P-value = 0.001) and ADAS-Cog11 (Beta(B) = 1.2(CI = 0.28,2.1), P-value = 0.01) scores. In the entorhinal analysis, right entorhinal Na-SI, not volume, was preferentially selected in the best model for predicting ADAS-Cog11 (B = 0.94(CI = 0.11,1.8), P-value = 0.03). While right entorhinal Na-SI and volume were both selected for MMSE modelling (Na-SI OR = 0.23(CI = 0.09,0.6), P-value = 0.003; volume OR = 2.6(CI = 1.0,6.6), P-value = 0.04), independently, Na-SI explained more of the variance (Na-SI R 2 = 10.3; volume R 2 = 7.5). No imaging variable was selected in the best CERAD models. Adding cerebral amyloid status improved model fit (Akaike Information Criterion increased 2.0 for all models, P-value < 0.001-0.045). Regional Na-SI were more predictive of MMSE and ADAS-Cog11 scores in our nondemented older adult cohort than volume, hippocampal more robust than entorhinal region of interest. Positive amyloid status slightly further improved model fit.

Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Formula: see text] ( [Formula: see text]. In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

Quantitative brain 18F-FDG PET/CT analysis in seronegative autoimmune encephalitis

OBJECTIVE

Brain 18F-FDG PET/CT is a useful diagnostic in evaluating patients with suspected autoimmune encephalitis (AE). Specific patterns of brain dysmetabolism have been reported in anti-NMDAR and anti-LGI1 AE, and the degree of dysmetabolism may correlate with clinical functional status.18FDG-PET/CT abnormalities have not yet been described in seronegative AE.

METHODS

We conducted a cross-sectional analysis of brain18FDG-PET/CT data in people with seronegative AE treated at the Johns Hopkins Hospital. Utilizing NeuroQ™ software, the Z-scores of 47 brain regions were calculated relative to healthy controls, then visually and statistically compared for probable and possible AE per clinical consensus diagnostic criteria to previous data from anti-NMDAR and anti-LGI1 cohorts.

RESULTS

Eight probable seronegative AE and nine possible seronegative AE were identified. The group only differed in frequency of abnormal brain MRI, which was seen in all of the probable seronegative AE patients. Both seronegative groups had similar overall patterns of brain dysmetabolism. A common pattern of frontal lobe hypometabolism and medial temporal lobe hypermetabolism was observed in patients with probable and possible seronegative AE, as well as anti-NMDAR and anti-LGI1 AE as part of their respective characteristic patterns of dysmetabolism. Four patients had multiple brain18FDG-PET/CT scans, with changes in number and severity of abnormal brain regions mirroring clinical status.

CONCLUSIONS

A18FDG-PET/CT pattern of frontal lobe hypometabolism and medial temporal lobe hypermetabolism could represent a common potential biomarker of AE, which along with additional clinical data may facilitate earlier diagnosis and treatment.

In Vivo Measurement of Tau Depositions in Anti-IgLON5 Disease Using [18F]PI-2620 PET

OBJECTIVES

Anti-IgLON5 disease is a recently discovered neurologic disorder combining autoimmunity and neurodegeneration. Core manifestations include sleep disorders, bulbar symptoms, gait abnormalities, and cognitive dysfunction, but other presentations have been reported. Hallmarks are autoantibodies targeting the neuronal surface protein IgLON5, a strong human leukocyte antigen system Class II association, and brainstem and hypothalamus-dominant tau deposits. The purpose of this cohort study was to visualize tau deposition in vivo with the second-generation tau-PET tracer.

METHODS

A cohort of 4 patients with anti-IgLON5 disease underwent a dynamic PET scan with [18F]PI-2620. One patient received a follow-up scan. Z-deviation maps and a 2-sample t test in comparison with healthy controls (n = 10) were performed. Antibody titers, neurofilament light chain, and disease duration were correlated with brainstem binding potentials.

RESULTS

Patients demonstrated increased [18F]PI2620 tau binding potentials in the pons, dorsal medulla, and cerebellum. The longitudinal scan after 28 months showed an increase of tracer uptake in the medulla despite immunotherapy. Higher antibody titers and neurofilament light chain correlated with higher tracer retention.

DISCUSSION

The results indicate that tau depositions in anti-IgLON5 disease can be visualized with [18F]PI-2620 and might correlate with the extent of disease. For validation, a larger longitudinal study is necessary.

Psychological stress is associated with arterial inflammation in people living with treated HIV infection

Stress and depression are increasingly recognized as cerebrovascular risk factors, including among high stress populations such as people living with HIV infection (PLWH). Stress may contribute to stroke risk through activation of neural inflammatory pathways. In this cross-sectional study, we examined the relationships between stress, systemic and arterial inflammation, and metabolic activity in stress-related brain regions on 18F-fluorodeoxyglucose (FDG)-PET in PLWH. Participants were recruited from a parent trial evaluating the impact of alirocumab on radiologic markers of cardiovascular risk in people with treated HIV infection. We administered a stress battery to assess different forms of psychological stress, specifying the Perceived Stress Scale as the primary stress measure, and quantified plasma markers of inflammation and immune activation. Participants underwent FDG-PET of the brain, neck, and chest. Age- and sex-matched control participants without HIV infection were selected for brain FDG-PET comparisons. Among PLWH, we used nonparametric pairwise correlations, partial correlations, and linear regression to investigate the association between stress and 1) systemic inflammation; 2) atherosclerotic inflammation on FDG-PET; and metabolic activity in 3) brain regions in which glucose metabolism differed significantly by HIV serostatus; and 4) in a priori defined stress-responsive regions of interest (ROI) and stress-related neural network activity (i.e., ratio of amygdala to ventromedial prefrontal cortex or temporal lobe activity). We studied 37 PLWH (mean age 60 years, 97% men) and 29 control participants without HIV (mean age 62 years, 97% men). Among PLWH, stress was significantly correlated with systemic inflammation (r = 0.33, p = 0.041) and arterial inflammation in the carotid (r = 0.41, p = 0.023) independent of age, race/ethnicity, traditional vascular risk factors and health-related behaviors. In voxel-wise analyses, metabolic activity in a cluster corresponding to the anterior medial temporal lobes, including the bilateral amygdalae, was significantly lower in PLWH compared with controls. However, we did not find a significant positive relationship between stress and this cluster of decreased metabolic activity in PLWH, a priori defined stress-responsive ROI, or stress-related neural network activity. In conclusion, psychological stress was associated with systemic and carotid arterial inflammation in this group of PLWH with treated infection. These data provide preliminary evidence for a link between psychological stress, inflammation, and atherosclerosis as potential drivers of excess cerebrovascular risk among PLWH.

Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging

Alzheimer's disease (AD) is a degenerative disorder involving significant memory loss and other cognitive deficits, manifesting as a progression from normal cognitive functioning to mild cognitive impairment to AD. The sooner an accurate diagnosis of probable AD is made, the easier it is to manage symptoms and plan for future therapy. Functional neuroimaging stands to be a useful tool in achieving early diagnosis. Among the many neuroimaging modalities, positron emission tomography (PET) provides direct regional assessment of, among others, brain metabolism, cerebral blood flow, amyloid deposition-all quantities of interest in the characterization of AD. However, there are analytic challenges in identifying early indicators of AD from these high-dimensional imaging data sets, and it is unclear whether early indicators of AD are more likely to emerge in localized patterns of brain activity or in patterns of correlation between distinct brain regions. Early PET-based analyses of AD focused on alterations in metabolic activity at the voxel-level or in anatomically defined regions of interest. Other approaches, including seed-voxel and multivariate techniques, seek to characterize metabolic connectivity by identifying other regions in the brain with similar patterns of activity across subjects. We briefly review various neuroimaging statistical approaches applied to determine changes in metabolic activity or metabolic connectivity associated with AD. We then present an approach that provides a unified statistical framework for addressing both metabolic activity and connectivity. Specifically, we apply a Bayesian spatial hierarchical framework to longitudinal metabolic PET scans from the Alzheimer's Disease Neuroimaging Initiative.

Regional brain glucose metabolism is differentially affected by ketogenic diet: a human semiquantitative positron emission tomography

PURPOSE

Ketogenic diet (KD) is recommended to avoid intense [18F]FDG myocardial physiologic uptake in PET imaging. Neuroprotective and anti-seizure effects of KD have been suggested, but their mechanisms remain to be elucidated. This [18F]FDG PET study aims to evaluate the effect of KD on glucose brain metabolism.

METHOD

Subjects who underwent KD prior to whole-body and brain [18F]FDG PET between January 2019 and December 2020 in our department for suspected endocarditis were retrospectively included. Myocardial glucose suppression (MGS) on whole-body PET was analyzed. Patients with brain abnormalities were excluded. Thirty-four subjects with MGS (mean age: 61.8 ± 17.2 years) were included in the KD population, and 14 subjects without MGS were considered for a partial KD group (mean age: 62.3 ± 15.1 years). Brain SUVmax was first compared between these two KD groups to determine possible global uptake difference. Semiquantitative voxel-based intergroup analyses were secondarily performed to determine possible inter-regional differences by comparing KD groups with and without MGS, separately, to 27 healthy subjects fasting for at least 6 h (mean age of 62.4 ± 10.9 years), and KD groups between them (p-voxel < 0.001, and p-cluster < 0.05, FWE-corrected).

RESULTS

A 20% lower brain SUVmax was found in subjects under KD with MGS in comparison to those without MGS (Student's t-test, p = 0.02). Whole-brain voxel-based intergroup analysis revealed that patients under KD with and without MGS had relative hypermetabolism of limbic regions including medial temporal cortices and cerebellum lobes and relative hypometabolism of bilateral posterior regions (occipital), without significant difference between them.

CONCLUSION

KD globally reduces brain glucose metabolism but with regional differences, requiring special attention to clinical interpretation. On a pathophysiological perspective, these findings could help understand underlying neurological effects of KD through possible decrease of oxidative stress in posterior regions and functional compensation in the limbic regions.

Metabolic connectivity is associated with seizure outcome in surgically treated temporal lobe epilepsies: A 18F-FDG PET seed correlation analysis

¹⁸F-FDG PET provides high sensitivity for the pre-surgical assessment of drug-resistant temporal lobe epilepsy (TLE). However, little is known about the metabolic connectivity of epileptogenic networks involved. This study therefore aimed to evaluate the association between metabolic connectivity and seizure outcome in surgically treated TLE.

METHODS

The study included 107 right-handed patients that had undergone a presurgical interictal ¹⁸F-FDG PET assessment followed by an anterior temporal lobectomy and were classified according to seizure outcome 2 years after surgery. Metabolic connectivity was evaluated by seed correlation analysis in left and right epilepsy patients with a Class Engel IA or > IA outcome and compared to age-, sex- and handedness-matched healthy controls.

RESULTS

Increased metabolic connectivity was observed in the >IA compared to the IA group within the operated temporal lobe (respective clusters of 7.5 vs 3.3 cm³ and 2.6 cm³ vs 2.2 cm³ in left and right TLE), and to a lower extent with the contralateral temporal lobe (1.2 vs 0.7 cm³ and 1.7 cm³ vs 0.7 cm³ in left and right TLE). Seed correlations provided added value for the estimated individual performance of seizure outcome over the group comparisons in left TLE (AUC of 0.74 vs 0.67).

CONCLUSION

Metabolic connectivity is associated with outcome in surgically treated TLE with a strengthened epileptogenic connectome in patients with non-free-seizure outcomes. The added value of seed correlation analysis in left TLE underlines the importance of evaluating metabolic connectivity in network related diseases.

Brain laterality evaluated by F-18 fluorodeoxyglucose positron emission computed tomography in autism spectrum disorders

Background and rationale

Autism spectrum disorder (ASD) is a neuropsychiatric disorder that has no curative treatment. Little is known about the brain laterality in patients with ASD. F-18 fluorodeoxyglucose positron emission computed tomography (F-18 FDG PET/CT) is a neuroimaging technique that is suitable for ASD owing to its ability to detect whole brain functional abnormalities in a short time and is feasible in ASD patients. The purpose of this study was to evaluate brain laterality using F-18 FDG PET/CT in patients with high-functioning ASD.

Materials and methods

This case-control study recruited eight ASD patients who met the DSM-5 criteria, the recorded data of eight controls matched for age, sex, and handedness were also enrolled. The resting state of brain glucose metabolism in the regions of interest (ROIs) was analyzed using the Q.Brain software. Brain glucose metabolism and laterality index in each ROI of ASD patients were compared with those of the controls. The pattern of brain metabolism was analyzed using visual analysis and is reported in the data description.

Results

The ASD group’s overall brain glucose metabolism was lower than that of the control group in both the left and right hemispheres, with mean differences of 1.54 and 1.21, respectively. We found statistically lower mean glucose metabolism for ASD patients than controls in the left prefrontal lateral (Z = 1.96, p = 0.049). The left laterality index was found in nine ROIs for ASD and 11 ROIs for the control. The left laterality index in the ASD group was significantly lower than that in the control group in the prefrontal lateral (Z = 2.52, p = 0.012), precuneus (Z = 2.10, p = 0.036), and parietal inferior (Z = 1.96, p = 0.049) regions.

Conclusion

Individuals with ASD have lower brain glucose metabolism than control. In addition, the number of ROIs for left laterality index in the ASD group was lower than control. Left laterality defects may be one of the causes of ASD. This knowledge can be useful in the treatment of ASD by increasing the left-brain metabolism. This trial was registered in the Thai Clinical Trials Registry (TCTR20210705005).

Tracer-specific reference tissues selection improves detection of 18 F-FDG, 18 F-florbetapir, and 18 F-flortaucipir PET SUVR changes in Alzheimer's disease

This study sought to identify a reference tissue‐based quantification approach for improving the statistical power in detecting changes in brain glucose metabolism, amyloid, and tau deposition in Alzheimer's disease studies. A total of 794, 906, and 903 scans were included for ¹⁸F‐FDG, ¹⁸F‐florbetapir, and ¹⁸F‐flortaucipir, respectively. Positron emission tomography (PET) and T1‐weighted images of participants were collected from the Alzheimer's disease Neuroimaging Initiative database, followed by partial volume correction. The standardized uptake value ratios (SUVRs) calculated from the cerebellum gray matter, centrum semiovale, and pons were evaluated at both region of interest (ROI) and voxelwise levels. The statistical power of reference tissues in detecting longitudinal SUVR changes was assessed via paired t‐test. In cross‐sectional analysis, the impact of reference tissue‐based SUVR differences between cognitively normal and cognitively impaired groups was evaluated by effect sizes Cohen's d and two sample t‐test adjusted by age, sex, and education levels. The average ROI t values of pons were 86.62 and 38.40% higher than that of centrum semiovale and cerebellum gray matter in detecting glucose metabolism decreases, while the centrum semiovale reference tissue‐based SUVR provided higher t values for the detection of amyloid and tau deposition increases. The three reference tissues generated comparable d images for ¹⁸F‐FDG, ¹⁸F‐florbetapir, and ¹⁸F‐flortaucipir and comparable t maps for ¹⁸F‐florbetapir and ¹⁸F‐flortaucipir, but pons‐based t map showed superior performance in ¹⁸F‐FDG. In conclusion, the tracer‐specific reference tissue improved the detection of ¹⁸F‐FDG, ¹⁸F‐florbetapir, and ¹⁸F‐flortaucipir PET SUVR changes, which helps the early diagnosis, monitoring of disease progression, and therapeutic response in Alzheimer's disease.

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [¹⁸F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [¹⁸F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [¹⁸F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.

Voxel-Based Analysis of [18F]-FDG Brain PET in Rats Using Data-Driven Normalization

Introduction: [18F]-FDG PET is a widely used imaging modality that visualizes cellular glucose uptake and provides functional information on the metabolic state of different tissues in vivo. Various quantification methods can be used to evaluate glucose metabolism in the brain, including the cerebral metabolic rate of glucose (CMR_glc) and standard uptake values (SUVs). Especially in the brain, these (semi-)quantitative measures can be affected by several physiological factors, such as blood glucose level, age, gender, and stress. Next to this inter- and intra-subject variability, the use of different PET acquisition protocols across studies has created a need for the standardization and harmonization of brain PET evaluation. In this study we present a framework for statistical voxel-based analysis of glucose uptake in the rat brain using histogram-based intensity normalization.

Methods: [18F]-FDG PET images of 28 normal rat brains were coregistered and voxel-wisely averaged. Ratio images were generated by voxel-wisely dividing each of these images with the group average. The most prevalent value in the ratio image was used as normalization factor. The normalized PET images were voxel-wisely averaged to generate a normal rat brain atlas. The variability of voxel intensities across the normalized PET images was compared to images that were either normalized by whole brain normalization, or not normalized.

To illustrate the added value of this normal rat brain atlas, 9 animals with a striatal hemorrhagic lesion and 9 control animals were intravenously injected with [18F]-FDG and the PET images of these animals were voxel-wisely compared to the normal atlas by group- and individual analyses.

Results: The average coefficient of variation of the voxel intensities in the brain across normal [18F]-FDG PET images was 6.7% for the histogram-based normalized images, 11.6% for whole brain normalized images, and 31.2% when no normalization was applied. Statistical voxel-based analysis, using the normal template, indicated regions of significantly decreased glucose uptake at the site of the ICH lesion in the ICH animals, but not in control animals.

Conclusion: In summary, histogram-based intensity normalization of [18F]-FDG uptake in the brain is a suitable data-driven approach for standardized voxel-based comparison of brain PET images.

Evaluation of Age and Sex-Related Metabolic Changes in Healthy Subjects: An Italian Brain 18F-FDG PET Study

Background: 18F-fluorodeoxyglucose (18F-FDG) positron-emission-tomography (PET) allows detection of cerebral metabolic alterations in neurological diseases vs. normal aging. We assess age- and sex-related brain metabolic changes in healthy subjects, exploring impact of activity normalization methods. Methods: brain scans of Italian Association of Nuclear Medicine normative database (151 subjects, 67 Males, 84 Females, aged 20–84) were selected. Global mean, white matter, and pons activity were explored as normalization reference. We performed voxel-based and ROI analyses using SPM12 and IBM-SPSS software. Results: SPM proved a negative correlation between age and brain glucose metabolism involving frontal lobes, anterior-cingulate and insular cortices bilaterally. Narrower clusters were detected in lateral parietal lobes, precuneus, temporal pole and medial areas bilaterally. Normalizing on pons activity, we found a more significant negative correlation and no positive one. ROIs analysis confirmed SPM results. Moreover, a significant age × sex interaction effect was revealed, with worse metabolic reduction in posterior-cingulate cortices in females than males, especially in post-menopausal age. Conclusions: this study demonstrated an age-related metabolic reduction in frontal lobes and in some parieto-temporal areas more evident in females. Results suggested pons as the most appropriate normalization reference. Knowledge of age- and sex-related cerebral metabolic changes is critical to correctly interpreting brain 18F-FDG PET imaging.

Mapping covariance in brain FDG uptake to structural connectivity

PURPOSE

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDG_cov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDG_cov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDG_cov and structural connectivity networks.

METHODS

We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDG_cov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking.

RESULTS

For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDG_cov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections.

CONCLUSION

Structural connectivity via white matter fiber tracts is a relevant substrate of FDG_cov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDG_cov connections.

CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [18F]FDG PET, T1 and FLAIR MRI, and CT images available for research

We present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent static [18F]FDG PET/CT and MRI, resulting in [18F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants' individual demographics (mean age 38 ± 11.5 years, range 23-65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: (1) DICOM (data not processed), (2) NIFTI (multimodal images coregistered to PET subject space), (3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.

Decrease in the cortex/striatum metabolic ratio on [18F]-FDG PET: a biomarker of autoimmune encephalitis

PURPOSE

The aim of this [¹⁸F]-FDG PET study was to determine the diagnostic value of the cortex/striatum metabolic ratio in a large cohort of patients suffering from autoimmune encephalitis (AE) and to search for correlations with the course of the disease.

METHODS

We retrospectively collected clinical and paraclinical data of patients with AE, including brain ¹⁸F-FDG PET/CT. Whole-brain statistical analysis was performed using SPM8 software after activity parametrization to the striatum in comparison to healthy subjects. The discriminative performance of this metabolic ratio was evaluated in patients with AE using receiver operating characteristic curves against 44 healthy subjects and a control group of 688 patients with MCI. Relationship between cortex/striatum metabolic ratios and clinical/paraclinical data was assessed using univariate and multivariate analysis in patients with AE.

RESULTS

Fifty-six patients with AE were included. In comparison to healthy subjects, voxel-based statistical analysis identified one large cluster (p-cluster < 0.05, FWE corrected) of widespread decreased cortex/striatum ratio in patients with AE. The mean metabolic ratio was significantly lower for AE patients (1.16 ± 0.13) than that for healthy subjects (1.39 ± 0.08; p < 0.001) and than that for MCI patients (1.32 ± 0.11; p < 0.001). A ratio threshold of 1.23 allowed to detect AE patients with a sensitivity of 71% and a specificity of 82% against MCI patients, and 98% against healthy subjects. A lower cortex/striatum metabolic ratio had a trend towards shorter delay before ¹⁸F-FDG PET/CT (p = 0.07) in multivariate analysis.

CONCLUSION

The decrease in the cortex/striatal metabolic ratio has a good early diagnostic performance for the differentiation of AE patients from controls.

Evaluation of the early-phase [18F]AV45 PET as an optimal surrogate of [18F]FDG PET in ageing and Alzheimer's clinical syndrome

Dual-phase [18F]AV45 positron emission tomography (PET) is highly promising in the assessment of neurodegenerative diseases, allowing to obtain information on both neurodegeneration (early-phase; eAV45) and amyloid deposition (late-phase; lAV45) which are highly complementary; yet eAV45 needs further evaluation. This study aims at validating eAV45 as an optimal proxy of [18F]FDG PET in a large mixed-population of healthy ageing and Alzheimer's clinical syndrome participants (n = 191) who had [18F]FDG PET, eAV45 and lAV45 scans. We found early time frame 0-4 min to give maximal correlation with [18F]FDG PET and minimal correlation with lAV45. Moreover, maximal overlap of [18F]FDG PET versus eAV45 associations with clinical diagnosis and cognition was obtained with pons scaling. Across reference regions, classification performance between clinical subgroups was similar for both eAV45 and [18F]FDG PET. These findings highlight the optimal use of eAV45 to assess neurodegeneration as a validated proxy of [18F]FDG PET. On top of this purpose, this study showed that combined [18F]AV45 PET dual-biomarker even outperformed [18F]FDG PET or lAV45 alone.

The pons as reference region for intensity normalization in semi-quantitative analysis of brain 18FDG PET: application to metabolic changes related to ageing in conventional and digital control databases

Background

The objective of the study is to define the most appropriate region for intensity normalization in brain ¹⁸FDG PET semi-quantitative analysis. The best option could be based on previous absolute quantification studies, which showed that the metabolic changes related to ageing affect the quasi-totality of brain regions in healthy subjects. Consequently, brain metabolic changes related to ageing were evaluated in two populations of healthy controls who underwent conventional (n = 56) or digital (n = 78) ¹⁸FDG PET/CT. The median correlation coefficients between age and the metabolism of each 120 atlas brain region were reported for 120 distinct intensity normalizations (according to the 120 regions). SPM linear regression analyses with age were performed on most significant normalizations (FWE, p < 0.05).

Results

The cerebellum and pons were the two sole regions showing median coefficients of correlation with age less than − 0.5. With SPM, the intensity normalization by the pons provided at least 1.7- and 2.5-fold more significant cluster volumes than other normalizations for conventional and digital PET, respectively.

Conclusions

The pons is the most appropriate area for brain ¹⁸FDG PET intensity normalization for examining the metabolic changes through ageing.

Data-driven FDG-PET subtypes of Alzheimer's disease-related neurodegeneration

BACKGROUND

Previous research has described distinct subtypes of Alzheimer's disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes.

METHODS

Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment ("prodromal AD") according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months.

RESULTS

Three main hypometabolic subtypes were identified: (i) "typical" (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) "limbic-predominant" (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare "cortical-predominant" subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline.

CONCLUSIONS

These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.

Anosognosia in Mild Cognitive Impairment: Lack of Awareness of Memory Difficulties Characterizes Prodromal Alzheimer's Disease

While anosognosia is often present in Alzheimer's disease, the degree of awareness of cognitive difficulties in the earlier stages, such as Mild Cognitive Impairment (MCI), is less clear. Using a questionnaire and Feeling-of-Knowing tasks, the aims of this study were (1) to test the hypothesis that anosognosia is present specifically in prodromal AD stage in patients that, owing to a more severe AD neuropathology, will rapidly progress to overt dementia and (2) to assess the neural bases of self-awareness for memory functioning. A group of 44 patients with amnestic MCI and a group of 29 healthy older participants (CTRL) performed two Feeling-of-Knowing tasks (episodic and semantic FOK) and responded to the Functional Memory Scale (MARS), also completed by one of their relatives. They underwent FDG-PET and structural MRI. The participants were followed clinically for 4 years. At the end of follow-up, 23 patients with MCI developed Alzheimer's disease (converters) and 21 patients still presented symptoms of MCI without progression (non-converters). The analyses focused on the data from inclusion stratified according to clinical status 4 years later (converters, non-converters, CTRL). On the episodic FOK task, converters patients overestimated their ability to later recognize unrecalled words and they showed prediction accuracy (Hamann coefficient) at the level of chance. No difficulty was observed in any group with the semantic FOK task. On the MARS, converters patients had a higher anosognosia score than non-converters patients and CTRL, which did not differ from each other. Correlations between self-awareness scores and neuroimaging data using small volume correction analyses in a priori regions of interest in converters indicated that inaccurate episodic FOK judgments was related to changes in brain areas that might support interpretation of retrieved content for judging the likelihood of recognition. For the MARS, the association between anosognosia and decreased gray matter density of the left inferior prefrontal cortex in converters might indicate poor inhibition over outdated personal knowledge. In amnestic MCI, anosognosia could be an early sign of neurodegeneration in brain areas that would support control mechanisms over memory representations.

Cognitive reserve hypothesis in frontotemporal dementia: A FDG-PET study

•

The cognitive reserve hypothesis is also applicable for FTD.

•

The educational level predicts left-temporal hypometabolism in FTD.

•

Residualized cognitive performance is positively correlated with education.

•

Residualized cognitive performance is negatively correlated with hypometabolism.

Background and objective

Reserve is defined as the ability to maintain cognitive functions relatively well at a given level of pathology. Early life experiences such as education are associated with lower dementia risk in general. However, whether more years of education guards against the impact of brain alterations also in frontotemporal dementia (FTD) has not been shown in a large patient collective. Therefore, we assessed whether education is associated with relatively high cognitive performance despite the presence of [¹⁸F]-fluorodeoxyglucose positron-emission-tomography (FDG-PET) hypometabolism in FTD.

Methods

Sixty-six FTD subjects (age 67 ± 8 years) and twenty-four cognitively healthy controls (HC) were evaluated. Brain regions with FTD-related glucose hypometabolism in the contrast against HC and brain regions that correlate with the cognitive function were defined by a voxel-based analysis and individual FDG-PET values were extracted from all frontotemporal brain areas. Linear regression analysis served to test if education is associated with residualized cognitive performance and regional FDG-PET hypometabolism after controlling for global cognition.

Results

Compared to healthy controls, patients with FTD showed glucose hypometabolism in bilateral frontal and temporal brain areas whereas cognition was only associated with deteriorated glucose metabolism in the left temporal lobe. The education level was significantly correlated with the residualized cognitive performance (residuals from regression analysis between hypometabolism and cognitive function as a quantitative index of reserve) and also negatively correlated with left temporal FDG-PET hypometabolism after controlling for cognition.

Conclusions

In patients with FTD, the education level predicts the existing left temporal FDG-PET hypometabolism at the same cognition level, supporting the cognitive reserve hypothesis in FTD.

Clinical impact of digital and conventional PET control databases for semi-quantitative analysis of brain 18F-FDG digital PET scans

PURPOSE

Digital PET cameras markedly improve sensitivity and spatial resolution of brain ¹⁸F-FDG PET images compared to conventional cameras. Our study aimed to assess whether specific control databases are required to improve the diagnostic performance of these recent advances.

METHODS

We retrospectively selected two groups of subjects, twenty-seven Alzheimer's Disease (AD) patients and twenty-two healthy control (HC) subjects. All subjects underwent a brain ¹⁸F-FDG PET on a digital camera (Vereos, Philips®). These two group (AD and HC) are compared, using a Semi-Quantitative Analysis (SQA), to two age and sex matched controls acquired with a digital PET/CT (Vereos, Philips®) or a conventional PET/CT (Biograph 6, Siemens®) camera, at group and individual levels. Moreover, individual visual interpretation of SPM T-maps was provided for the positive diagnosis of AD by 3 experienced raters.

RESULTS

At group level, SQA using digital controls detected more marked hypometabolic areas in AD (+ 116 cm³ at p < 0.001 uncorrected for the voxel, corrected for the cluster) than SQA using conventional controls. At the individual level, the accuracy of SQA for discriminating AD using digital controls was higher than SQA using conventional controls (86% vs. 80%, p < 0.01, at p < 0.005 uncorrected for the voxel, corrected for the cluster), with higher sensitivity (89% vs. 78%) and similar specificity (82% vs. 82%). These results were confirmed by visual analysis (accuracies of 84% and 82% for digital and conventional controls respectively, p = 0.01).

CONCLUSION

There is an urgent need to establish specific digital PET control databases for SQA of brain ¹⁸F-FDG PET images as such databases improve the accuracy of AD diagnosis.

Longitudinal [18F]FDG-PET/CT analysis of the glucose metabolism in ApoE-deficient mice

Background

Strong line of evidence suggests that the increased risk to develop AD may at least be partly mediated by cholesterol metabolism. A key regulator of cholesterol transport is the Apolipoprotein E4 (ApoE4), which plays a fundamental role in neuronal maintenance and repair. Impaired function of ApoE4 may contribute to altered cerebral metabolism leading to higher susceptibility to neurodegeneration.

Methods

To determine a possible link between ApoE function and alterations in AD in the brain of Apolipoprotein E-deficient mice (ApoE−/−) in a longitudinal manner metabolic and neurochemical parameters were analyzed. Cortical metabolism was measured by 2-deoxy-2-[¹⁸F]fluoroglucose ([¹⁸F]FDG)-PET/CT and proton magnetic resonance spectroscopy (¹H-MRS) served to record neurochemical status.

Results

By using [¹⁸F]FDG-PET/CT, we showed that brain metabolism declined significantly stronger with age in ApoE−/− versus wild type (wt) mice. This difference was particularly evident at the age of 41 weeks in almost each analyzed brain region. In contrast, the ¹H-MRS-measured N-acetylaspartate to creatine ratio, a marker of neuronal viability, did not decline with age and did not differ between ApoE−/− and wt mice.

Conclusion

In summary, this longitudinal in vivo study shows for the first time that ApoE−/− mice depict cerebral hypometabolism without neurochemical alterations.

Intensity normalization methods in brain FDG-PET quantification

BACKGROUND

The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output.

METHODS

Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings.

RESULTS

Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm³) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering.

CONCLUSIONS

Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.

AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size

INTRODUCTION

Robust and reliable attenuation correction (AC) is a prerequisite for accurate quantification of activity concentration. In combined PET/MRI, AC is challenged by the lack of bone signal in the MRI from which the AC maps has to be derived. Deep learning-based image-to-image translation networks present itself as an optimal solution for MRI-derived AC (MR-AC). High robustness and generalizability of these networks are expected to be achieved through large training cohorts. In this study, we implemented an MR-AC method based on deep learning, and investigated how training cohort size, transfer learning, and MR input affected robustness, and subsequently evaluated the method in a clinical setup, with the overall aim to explore if this method could be implemented in clinical routine for PET/MRI examinations.

METHODS

A total cohort of 1037 adult subjects from the Siemens Biograph mMR with two different software versions (VB20P and VE11P) was used. The software upgrade included updates to all MRI sequences. The impact of training group size was investigated by training a convolutional neural network (CNN) on an increasing training group size from 10 to 403. The ability to adapt to changes in the input images between software versions were evaluated using transfer learning from a large cohort to a smaller cohort, by varying training group size from 5 to 91 subjects. The impact of MRI sequence was evaluated by training three networks based on the Dixon VIBE sequence (DeepDixon), T1-weighted MPRAGE (DeepT1), and ultra-short echo time (UTE) sequence (DeepUTE). Blinded clinical evaluation relative to the reference low-dose CT (CT-AC) was performed for DeepDixon in 104 independent 2-[¹⁸F]fluoro-2-deoxy-d-glucose ([¹⁸F]FDG) PET patient studies performed for suspected neurodegenerative disorder using statistical surface projections.

RESULTS

Robustness increased with group size in the training data set: 100 subjects were required to reduce the number of outliers compared to a state-of-the-art segmentation-based method, and a cohort >400 subjects further increased robustness in terms of reduced variation and number of outliers. When using transfer learning to adapt to changes in the MRI input, as few as five subjects were sufficient to minimize outliers. Full robustness was achieved at 20 subjects. Comparable robust and accurate results were obtained using all three types of MRI input with a bias below 1% relative to CT-AC in any brain region. The clinical PET evaluation using DeepDixon showed no clinically relevant differences compared to CT-AC.

CONCLUSION

Deep learning based AC requires a large training cohort to achieve accurate and robust performance. Using transfer learning, only five subjects were needed to fine-tune the method to large changes to the input images. No clinically relevant differences were found compared to CT-AC, indicating that clinical implementation of our deep learning-based MR-AC method will be feasible across MRI system types using transfer learning and a limited number of subjects.

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.

Quantitative and clinical impact of MRI-based attenuation correction methods in [18F]FDG evaluation of dementia

Background

Positron emission tomography/magnetic resonance imaging (PET/MRI) is a promising diagnostic imaging tool for the diagnosis of dementia, as PET can add complementary information to the routine imaging examination with MRI. The purpose of this study was to evaluate the influence of MRI-based attenuation correction (MRAC) on diagnostic assessment of dementia with [¹⁸F]FDG PET. Quantitative differences in both [¹⁸F]FDG uptake and z-scores were calculated for three clinically available (DixonNoBone, DixonBone, UTE) and two research MRAC methods (UCL, DeepUTE) compared to CT-based AC (CTAC). Furthermore, diagnoses based on visual evaluations were made by three nuclear medicine physicians and one neuroradiologist (PET_CT, PET_DeepUTE, PET_DixonBone, PET_UTE, PET_CT + MRI, PET_DixonBone + MRI). In addition, pons and cerebellum were compared as reference regions for normalization.

Results

The mean absolute difference in z-scores were smallest between MRAC and CTAC with cerebellum as reference region: 0.15 ± 0.11 σ (DeepUTE), 0.15 ± 0.12 σ (UCL), 0.23 ± 0.20 σ (DixonBone), 0.32 ± 0.28 σ (DixonNoBone), and 0.54 ± 0.40 σ (UTE). In the visual evaluation, the diagnoses agreed with PET_CT in 74% (PET_DeepUTE), 67% (PET_DixonBone), and 70% (PET_UTE) of the patients, while PET_CT + MRI agreed with PET_DixonBone + MRI in 89% of the patients.

Conclusion

The MRAC research methods performed close to that of CTAC in the quantitative evaluation of [¹⁸F]FDG uptake and z-scores. Among the clinically implemented MRAC methods, Dixon_Bone should be preferred for diagnostic assessment of dementia with [¹⁸F]FDG PET/MRI. However, as artifacts occur in Dixon_Bone attenuation maps, they must be visually inspected to assure proper quantification.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0553-2) contains supplementary material, which is available to authorized users.

[Characteristics of cerebral glucose metabolism in patients with cognitive impairment in Parkinson's disease]

AIM

To study the relationship between early cognitive impairment symptoms and cerebral glucose metabolism in different brain regions (according to the positron emission tomography (PET) data) in Parkinson's disease (PD) in order to increase the diagnostic and treatment efficacy.

MATERIAL AND METHODS

Two groups of patients with PD (stage I-III), including 11 patients without cognitive disorders and 13 with mild cognitive impairment (MCI), were examined. The control group included 10 age-matched people with normal cognition. To evaluate cognitive state, the Mini mental state examination (MMSE), the Frontal assessment battery (FAB) and the 'clock drawing test' were used. The regional cerebral glucose metabolism rate (CMRglu) was assessed using PET with 18F-fluorodeoxyglucose (FDG).

RESULTS AND CONCLUSION

In PD patients, CMRglu were decreased in the frontal (Brodmann areas (BA) 9, 10, 11, 46, 47), occipital (BA 19) and parietal (BA 39), temporal (BA 20, 37), and cingulate cortex (BA 32) compared to the control group. Cerebral glucose metabolism was decreased in the frontal (BA 8, 9, 10, 45, 46, 47), parietal (BA 7, 39, 40) and cingulate cortex (BA 23, 24, 31, 32) in the group of PD patients with MCI compared to PD patients with normal cognition. Hypometabolism in BA 7, 8, 23, 24, 31, 40 was revealed only in comparison of PD and PD-MCI groups, and did not appear in case of comparison of cognitively normal PD patients with the control group. It is possible to suggest that the mentioned above brain areas were associated with cognitive impairment. The revealed glucose hypometabolism pattern possibly has the diagnostic value for the early and preclinical diagnosis of MCI in PD and control of treatment efficacy.

Simultaneous resting-state FDG-PET/fMRI in Alzheimer Disease: Relationship between glucose metabolism and intrinsic activity

Simultaneously evaluating resting-state brain glucose metabolism and intrinsic functional activity has potential to impact the clinical neurosciences of Alzheimer Disease (AD). Indeed, integrating such combined information obtained in the same physiological setting may clarify how impairments in neuroenergetic and neuronal function interact and contribute to the mechanisms underlying AD. The present study used this multimodality approach to investigate, by means of a hybrid PET/MR scanner, the coupling between glucose consumption and intrinsic functional activity in 23 patients with AD-related cognitive impairment ranging from amnestic mild cognitive impairment (MCI) to mild-moderate AD (aMCI/AD), in comparison with a group of 23 healthy elderly controls. Between-group (Controls > Patients) comparisons were conducted on data from both imaging modalities using voxelwise 2-sample t-tests, corrected for partial-volume effects, head motion, age, gender and multiple tests. FDG-PET/fMRI relationships were assessed within and across subjects using Spearman partial correlations for three different resting-state fMRI (rs-fMRI) metrics sensitive to AD: fractional amplitude of low frequency fluctuations (fALFF), regional homogeneity (ReHo) and group independent component analysis with dual regression (gICA-DR). FDG and rs-fMRI metrics distinguished aMCI/AD from controls according to spatial patterns analogous to those found in stand-alone studies. Within-subject correlations were comparable across the three rs-fMRI metrics. Correlations were overall high in healthy controls (ρ = 0.80 ± 0.04), but showed a significant 17% reduction (p < 0.05) in aMCI/AD patients (ρ = 0.67 ± 0.05). Positive across-subject correlations were overall moderate (ρ = 0.33 ± 0.07) and consistent across rs-fMRI metrics. These were confined around AD-target posterior regions for metrics of functional connectivity (ReHo and gICA-DR). In contrast, FDG/fALFF correlations were distributed in the frontal gyrus, thalami and caudate nuclei. Taken together, these results support the presence of bioenergetic coupling between glucose utilization and rapid transmission of neural information in healthy ageing, which is substantially reduced in aMCI/AD, suggesting that abnormal glucose utilization is in some way linked to communication breakdown among brain regions impacted by the underlying pathological process.

Multimodal Discrimination between Normal Aging, Mild Cognitive Impairment and Alzheimer's Disease and Prediction of Cognitive Decline

Alzheimer’s Disease (AD) and mild cognitive impairment (MCI) are associated with widespread changes in brain structure and function, as indicated by magnetic resonance imaging (MRI) morphometry and 18-fluorodeoxyglucose position emission tomography (FDG PET) metabolism. Nevertheless, the ability to differentiate between AD, MCI and normal aging groups can be difficult. Thus, the goal of this study was to identify the combination of cerebrospinal fluid (CSF) biomarkers, MRI morphometry, FDG PET metabolism and neuropsychological test scores to that best differentiate between a sample of normal aging subjects and those with MCI and AD from the Alzheimer’s Disease Neuroimaging Initiative. The secondary goal was to determine the neuroimaging variables from MRI, FDG PET and CSF biomarkers that can predict future cognitive decline within each group. To achieve these aims, a series of multivariate stepwise logistic and linear regression models were generated. Combining all neuroimaging modalities and cognitive test scores significantly improved the index of discrimination, especially at the earliest stages of the disease, whereas MRI gray matter morphometry variables best predicted future cognitive decline compared to other neuroimaging variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores and CSF biomarkers may provide significantly better discrimination than any modality alone.

Longitudinal Positron Emission Tomography in Preventive Alzheimer's Disease Drug Trials, Critical Barriers from Imaging Science Perspective

Recent Alzheimer's trials have recruited cognitively normal people at risk for Alzheimer's dementia. Due to the lack of clinical symptoms in normal population, conventional clinical outcome measures are not suitable for these early trials. While several groups are developing new composite cognitive tests that could serve as potential outcome measures by detecting subtle cognitive changes in normal people, there is a need for longitudinal brain imaging techniques that can correlate with temporal changes in these new tests and provide additional objective measures of neuropathological changes in brain. Positron emission tomography (PET) is a nuclear medicine imaging procedure based on the measurement of annihilation photons after positron emission from radiolabeled molecules that allow tracking of biological processes in body, including the brain. PET is a well-established in vivo imaging modality in Alzheimer's disease diagnosis and research due to its capability of detecting abnormalities in three major hallmarks of this disease. These include (1) amyloid beta plaques; (2) neurofibrillary tau tangles and (3) decrease in neuronal activity due to loss of nerve cell connection and death. While semiquantitative PET imaging techniques are commonly used to set discrete cut-points to stratify abnormal levels of amyloid accumulation and neurodegeneration, they are suboptimal for detecting subtle longitudinal changes. In this study, we have identified and discussed four critical barriers in conventional longitudinal PET imaging that may be particularly relevant for early Alzheimer's disease studies. These include within and across subject heterogeneity of AD-affected brain regions, PET intensity normalization, neuronal compensations in early disease stages and cerebrovascular amyloid deposition.

Data Mining and Machine Learning Methods for Dementia Research

Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, and demographic variables. Given the various sources of information, we can develop computerized methods that can be a great help to clinicians to discover hidden patterns in the data. The computerized methods often employ data mining and machine learning algorithms, lending themselves as the computer-aided diagnosis (CAD) tool that assists clinicians in making diagnostic decisions. In this chapter, we review state-of-the-art methods used in dementia research, and briefly introduce some recently proposed algorithms subsequently.

Nuclear medicine of the cerebellum

Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) with different radiotracers enable regional evaluation of blood flow and glucose metabolism, of receptors and transporters of several molecules, and of abnormal deposition of peptides and proteins in the brain. The cerebellum has been used as a reference region for different radiotracers in several disease conditions. Whole-brain voxel-wise analysis is not affected by a priori knowledge bias and should be preferred. SPECT and PET have contributed to establishing the cerebellum role in motion, cognition, and emotion control in physiologic and pathophysiologic conditions. The basic abnormal imaging findings include decreased or increased uptake of flow or metabolism tracers in the cerebellum alone or as part of a network. Decreased uptake is generally observed in primary structural damage of the cerebellum, but can also represent a distant effect of cerebral damage (crossed diaschisis). Increased uptake can be observed in Freidreich ataxia, inflammatory or immune-mediated diseases of the cerebellum, and in status epilepticus. The possibility is also recognized that primary structural damage of the cerebellum might determine distance effects on other brain structures (reversed diaschisis). So far, SPECT and PET have been predominantly used in clinical studies to investigate cerebellar changes in neurologic and psychiatric diseases and in connection with pharmacologic, transcranial magnetic stimulation, deep-brain stimulation, or surgical treatments.

Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals

Objective:

To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer’s disease (AD).

Method:

Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation.

Results:

The accuracy obtained using PET and SPECT data were similar. PET accuracy was 68∼71% and area under curve (AUC) 0.77∼0.81; SPECT accuracy was 68∼74% and AUC 0.75∼0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67). The addition of PET or SPECT to MRI produced higher accuracy indices (68∼74%; AUC: 0.74∼0.82) than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities.

Conclusion:

In line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis.

Different subregional metabolism patterns in patients with cerebellar ataxia by 18F-fluorodeoxyglucose positron emission tomography

We evaluated cerebellar subregional metabolic alterations in patients with cerebellar ataxia, a representative disease involving the spinocerebellum. We retrospectively analyzed ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) images in 44 patients with multiple system atrophy of the cerebellar type (MSA-C), 9 patients with spinocerebellar ataxia (SCA) type 2, and 14 patients with SCA type 6 and compared with 15 patients with crossed cerebellar diaschisis (CCD) and 89 normal controls. Cerebellar subregional metabolism was assessed using 13 cerebellar subregions (bilateral anterior lobes [ANT], superior/mid/inferior posterior lobes [SUPP/MIDP/INFP], dentate nucleus [DN], anterior vermis [ANTV], and superior/inferior posterior vermis [SUPV/INFV]) to determine FDG uptake ratios. MSA-C and SCA type 2 showed severely decreased metabolic ratios in all cerebellar subregions compared to normal controls (ANT, 0.58 ± 0.08 and 0.50 ± 0.06 vs. 0.82 ± 0.07, respectively, p < 0.001). SCA type 6 showed lower metabolic ratios in almost all cerebellar subregions (ANT, 0.57 ± 0.06, p < 0.001) except INFV. Anterior-posterior lobe ratio measurements revealed that SCA type 2 (Right, 0.81 ± 0.05 vs. 0.88 ± 0.04, p < 0.001; Left, 0.83 ± 0.05 vs. 0.88 ± 0.04, p = 0.003) and SCA type 6 (Right, 0.72 ± 0.05 vs. 0.88 ± 0.04, p < 0.001; Left, 0.72 ± 0.05 vs. 0.88 ± 0.04, p < 0.001) showed preferential hypometabolism in the anterior lobe compared to normal controls, which was not observed in CCD and MSA-C. Asymmetric indices were higher in CCD and MSA-C than in normal controls (p < 0.001), whereas such differences were not found in SCA types 2 and 6. In summary, quantitative analysis of cerebellar subregional metabolism ratios revealed preferential involvement of the anterior lobe, corresponding to the spinocerebellum, in patients with cerebellar ataxia, whereas patients with CCD and MSA-C exhibited more asymmetric hypometabolism in the posterior lobe.

Metabolic connectivity for differential diagnosis of dementing disorders

Presently, visual and quantitative approaches for image-supported diagnosis of dementing disorders rely on regional intensity rather than on connectivity measurements. Here, we test metabolic connectivity for differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Positron emission tomography with 18F-fluorodeoxyglucose was conducted in 47 patients with mild Alzheimer's disease, 52 patients with mild frontotemporal lobar degeneration, and 45 healthy elderly subjects. Sparse inverse covariance estimation and selection were used to identify patterns of metabolic, inter-subject covariance on the basis of 60 regional values. Relative to healthy subjects, significantly more pathological within-lobe connections were found in the parietal lobe of patients with Alzheimer's disease, and in the frontal and temporal lobes of subjects with frontotemporal lobar degeneration. Relative to the frontotemporal lobar degeneration group, more pathological connections between the parietal and temporal lobe were found in the Alzheimer's disease group. The obtained connectivity patterns differentiated between two patients groups with an overall accuracy of 83%. Linear discriminant analysis and univariate methods provided an accuracy of 74% and 69%, respectively. There are characteristic patterns of abnormal metabolic connectivity in mild Alzheimer's disease and frontotemporal lobar degeneration. Such patterns can be utilized for single-subject analyses and might be more accurate in the differential diagnosis of dementing disorders than traditional intensity-based analyses.

Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers

A variety of imaging, neuropsychological, and genetic biomarkers have been suggested as potential biomarkers for the identification of mild cognitive impairment (MCI) in patients who later develop Alzheimer's disease (AD). Here, we systematically evaluated the most promising combinations of these biomarkers regarding discrimination between stable and converter MCI and reflection of disease staging. Alzheimer's Disease Neuroimaging Initiative data of AD (n = 144), controls (n = 112), stable (n = 265) and converter (n = 177) MCI, for which apolipoprotein E status, neuropsychological evaluation, and structural, glucose, and amyloid imaging were available, were included in this study. Naïve Bayes classifiers were built on AD and controls data for all possible combinations of these biomarkers, with and without stratification by amyloid status. All classifiers were then applied to the MCI cohorts. We obtained an accuracy of 76% for discrimination between converter and stable MCI with glucose positron emission tomography as a single biomarker. This accuracy increased to about 87% when including further imaging modalities and genetic information. We also identified several biomarker combinations as strong predictors of time to conversion. Use of amyloid validated training data resulted in increased sensitivities and decreased specificities for differentiation between stable and converter MCI when amyloid was included as a biomarker but not for other classifier combinations. Our results indicate that fully independent classifiers built only on AD and controls data and combining imaging, genetic, and/or neuropsychological biomarkers can more reliably discriminate between stable and converter MCI than single modality classifiers. Several biomarker combinations are identified as strongly predictive for the time to conversion to AD.

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.

Data-driven identification of intensity normalization region based on longitudinal coherency of 18F-FDG metabolism in the healthy brain

OBJECTIVES

In brain ¹⁸F-FDG PET data intensity normalization is usually applied to control for unwanted factors confounding brain metabolism. However, it can be difficult to determine a proper intensity normalization region as a reference for the identification of abnormal metabolism in diseased brains. In neurodegenerative disorders, differentiating disease-related changes in brain metabolism from age-associated natural changes remains challenging. This study proposes a new data-driven method to identify proper intensity normalization regions in order to improve separation of age-associated natural changes from disease related changes in brain metabolism.

METHODS

127 female and 128 male healthy subjects (age: 20 to 79) with brain¹⁸F-FDG PET/CT in the course of a whole body cancer screening were included. Brain PET images were processed using SPM8 and were parcellated into 116 anatomical regions according to the AAL template. It is assumed that normal brain ¹⁸F-FDG metabolism has longitudinal coherency and this coherency leads to better model fitting. The coefficient of determination R² was proposed as the coherence coefficient, and the total coherence coefficient (overall fitting quality) was employed as an index to assess proper intensity normalization strategies on single subjects and age-cohort averaged data. Age-associated longitudinal changes of normal subjects were derived using the identified intensity normalization method correspondingly. In addition, 15 subjects with clinically diagnosed Parkinson's disease were assessed to evaluate the clinical potential of the proposed new method.

RESULTS

Intensity normalizations by paracentral lobule and cerebellar tonsil, both regions derived from the new data-driven coherency method, showed significantly better coherence coefficients than other intensity normalization regions, and especially better than the most widely used global mean normalization. Intensity normalization by paracentral lobule was the most consistent method within both analysis strategies (subject-based and age-cohort averaging). In addition, the proposed new intensity normalization method using the paracentral lobule generates significantly higher differentiation from the age-associated changes than other intensity normalization methods.

CONCLUSION

Proper intensity normalization can enhance the longitudinal coherency of normal brain glucose metabolism. The paracentral lobule followed by the cerebellar tonsil are shown to be the two most stable intensity normalization regions concerning age-dependent brain metabolism. This may provide the potential to better differentiate disease-related changes from age-related changes in brain metabolism, which is of relevance in the diagnosis of neurodegenerative disorders.

Direct voxel-based comparisons between grey matter shrinkage and glucose hypometabolism in chronic alcoholism

Alcoholism is associated with widespread brain structural abnormalities affecting mainly the frontocerebellar and the Papez's circuits. Brain glucose metabolism has received limited attention, and few studies used regions of interest approach and showed reduced global brain metabolism predominantly in the frontal and parietal lobes. Even though these studies have examined the relationship between grey matter shrinkage and hypometabolism, none has performed a direct voxel-by-voxel comparison between the degrees of structural and metabolic abnormalities. Seventeen alcoholic patients and 16 control subjects underwent both structural magnetic resonance imaging and (18)F-2-fluoro-deoxy-glucose-positron emission tomography examinations. Structural abnormalities and hypometabolism were examined in alcoholic patients compared with control subjects using two-sample t-tests. Then, these two patterns of brain damage were directly compared with a paired t-test. Compared to controls, alcoholic patients had grey matter shrinkage and hypometabolism in the fronto-cerebellar circuit and several nodes of Papez's circuit. The direct comparison revealed greater shrinkage than hypometabolism in the cerebellum, cingulate cortex, thalamus and hippocampus and parahippocampal gyrus. Conversely, hypometabolism was more severe than shrinkage in the dorsolateral, premotor and parietal cortices. The distinct profiles of abnormalities found within the Papez's circuit, the fronto-cerebellar circuit and the parietal gyrus in chronic alcoholism suggest the involvement of different pathological mechanisms.