Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and Alzheimer's disease

Cerebral Metabolic Signature of Chronic Benzodiazepine Use in Nondemented Older Adults: An FDG-PET Study in the MEMENTO Cohort

OBJECTIVE

We sought to examine the association between chronic Benzodiazepine (BZD) use and brain metabolism obtained from 2-deoxy-2-fluoro-D-glucose (FDG) positron emission tomography (PET) in the MEMENTO clinical cohort of nondemented older adults with an isolated memory complaint or mild cognitive impairment at baseline.

METHODS

Our analysis focused on 3 levels: (1) the global mean brain standardized uptake value (SUVR), (2) the Alzheimer's disease (AD)-specific regions of interest (ROIs), and (3) the ratio of total SUVR on the brain and different anatomical ROIs. Cerebral metabolism was obtained from 2-deoxy-2-fluoro-D-glucose-FDG-PET and compared between chronic BZD users and nonusers using multiple linear regressions adjusted for age, sex, education, APOE ε 4 copy number, cognitive and neuropsychiatric assessments, history of major depressive episodes and antidepressant use.

RESULTS

We found that the SUVR was significantly higher in chronic BZD users (n = 192) than in nonusers (n = 1,122) in the whole brain (beta = 0.03; p = 0.038) and in the right amygdala (beta = 0.32; p = 0.012). Trends were observed for the half-lives of BZDs (short- and long-acting BZDs) (p = 0.051) and Z-drug hypnotic treatments (p = 0.060) on the SUVR of the right amygdala. We found no significant association in the other ROIs.

CONCLUSION

Our study is the first to find a greater global metabolism in chronic BZD users and a specific greater metabolism in the right amygdala. Because the acute administration of BZDs tends to reduce brain metabolism, these findings may correspond to a compensatory mechanism while the brain adapts with global metabolism upregulation, with a specific focus on the right amygdala.

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.

Positron Emission Tomography with [18F]ROStrace Reveals Progressive Elevations in Oxidative Stress in a Mouse Model of Alpha-Synucleinopathy

The synucleinopathies are a diverse group of neurodegenerative disorders characterized by the accumulation of aggregated alpha-synuclein (aSyn) in vulnerable populations of brain cells. Oxidative stress is both a cause and a consequence of aSyn aggregation in the synucleinopathies; however, noninvasive methods for detecting oxidative stress in living animals have proven elusive. In this study, we used the reactive oxygen species (ROS)-sensitive positron emission tomography (PET) radiotracer [18F]ROStrace to detect increases in oxidative stress in the widely-used A53T mouse model of synucleinopathy. A53T-specific elevations in [18F]ROStrace signal emerged at a relatively early age (6-8 months) and became more widespread within the brain over time, a pattern which paralleled the progressive development of aSyn pathology and oxidative damage in A53T brain tissue. Systemic administration of lipopolysaccharide (LPS) also caused rapid and long-lasting elevations in [18F]ROStrace signal in A53T mice, suggesting that chronic, aSyn-associated oxidative stress may render these animals more vulnerable to further inflammatory insult. Collectively, these results provide novel evidence that oxidative stress is an early and chronic process during the development of synucleinopathy and suggest that PET imaging with [18F]ROStrace holds promise as a means of detecting aSyn-associated oxidative stress noninvasively.

Comparison of 18F-FDG PET and arterial spin labeling MRI in evaluating Alzheimer's disease and amnestic mild cognitive impairment using integrated PET/MR

BACKGROUND

Developing biomarkers for early stage AD patients is crucial. Glucose metabolism measured by 18F-FDG PET is the most common biomarker for evaluating cellular energy metabolism to diagnose AD. Arterial spin labeling (ASL) MRI can potentially provide comparable diagnostic information to 18F-FDG PET in patients with neurodegenerative disorders. However, the conclusions about the diagnostic performance of AD are still controversial between 18F-FDG PET and ASL. This study aims to compare quantitative cerebral blood flow (CBF) and glucose metabolism measured by 18F-FDG PET diagnostic values in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) using integrated PET/MR.

RESULTS

Analyses revealed overlapping between decreased regional rCBF and 18F-FDG PET SUVR in patients with AD compared with NC participants in the bilateral parietotemporal regions, frontal cortex, and cingulate cortex. Compared with NC participants, patients with aMCI exclusively demonstrated lower 18F-FDG PET SUVR in the bilateral temporal cortex, insula cortex, and inferior frontal cortex. Comparison of the rCBF in patients with aMCI and NC participants revealed no significant difference (P > 0.05). The ROC analysis of rCBF in the meta-ROI could diagnose patients with AD (AUC, 0.87) but not aMCI (AUC, 0.61). The specificity of diagnosing aMCI has been improved to 75.56% when combining rCBF and 18F-FDG PET SUVR.

CONCLUSION

ASL could detect similar aberrant patterns of abnormalities compared to 18F-FDG PET in patients with AD compared with NC participants but not in aMCI. The diagnostic efficiency of 18F-FDG-PET for AD and aMCI patients remained higher to ASL. Our findings support that applying 18F-FDG PET may be preferable for diagnosing AD and aMCI.

Challenges and innovations in brain PET analysis of neurodegenerative disorders: a mini-review on partial volume effects, small brain region studies, and reference region selection

Positron Emission Tomography (PET) brain imaging is increasingly utilized in clinical and research settings due to its unique ability to study biological processes and subtle changes in living subjects. However, PET imaging is not without its limitations. Currently, bias introduced by partial volume effect (PVE) and poor signal-to-noise ratios of some radiotracers can hamper accurate quantification. Technological advancements like ultra-high-resolution scanners and improvements in radiochemistry are on the horizon to address these challenges. This will enable the study of smaller brain regions and may require more sophisticated methods (e.g., data-driven approaches like unsupervised clustering) for reference region selection and to improve quantification accuracy. This review delves into some of these critical aspects of PET molecular imaging and offers suggested strategies for improvement. This will be illustrated by showing examples for dopaminergic and cholinergic nerve terminal ligands.

Aiming for [18F]FDG-PET acquisition time reduction in clinical practice for neurological patients

PURPOSE

Positron emission tomography (PET) imaging with [18F]FDG provides valuable information regarding the underlying pathological processes in neurodegenerative disorders. PET imaging for these populations should be as short as possible to limit head movements and improve comfort. This study aimed to validate an optimized [18F]FDG-PET image reconstruction protocol aiming to reduce acquisition time while maintaining adequate quantification accuracy and image quality.

METHODS

A time-reduced reconstruction protocol (5 min) was evaluated in [18F]FDG-PET retrospective data from healthy individuals and Alzheimer's disease (AD) patients. Standard (8 min) and time-reduced protocols were compared by means of image quality and quantification accuracy metrics, as well as standardized uptake value ratio (SUVR) and Z-scores (pons was used as reference). Images were randomly and blindly presented to experienced physicians and scored in terms of image quality.

RESULTS

No differences between protocols were identified during the visual assessment. Small differences (p < 0.01) in the pons SUVR were observed between the standard and time-reduced protocols for healthy individuals (-0.002 ± 0.011) and AD patients (-0.007 ± 0.013). Likewise, incorporating the PSF correction in the reconstruction algorithm resulted in small differences (p < 0.01) in SUVR between protocols (healthy individuals: -0.003 ± 0.011; AD patients: -0.007 ± 0.014).

CONCLUSION

Quality metrics were similar between time-reduced and standard protocols. In the visual assessment of the images, the physicians did not consider the use of PSF adequate, as it degraded the quality image. Shortening the acquisition time is possible by optimizing the image reconstruction parameters while maintaining adequate quantification accuracy and image quality.

Visualizing reactive astrocyte-neuron interaction in Alzheimer's disease using 11C-acetate and 18F-FDG

Reactive astrogliosis is a hallmark of Alzheimer's disease (AD). However, a clinically validated neuroimaging probe to visualize the reactive astrogliosis is yet to be discovered. Here, we show that PET imaging with 11C-acetate and 18F-fluorodeoxyglucose (18F-FDG) functionally visualizes the reactive astrocyte-mediated neuronal hypometabolism in the brains with neuroinflammation and AD. To investigate the alterations of acetate and glucose metabolism in the diseased brains and their impact on the AD pathology, we adopted multifaceted approaches including microPET imaging, autoradiography, immunohistochemistry, metabolomics, and electrophysiology. Two AD rodent models, APP/PS1 and 5xFAD transgenic mice, one adenovirus-induced rat model of reactive astrogliosis, and post-mortem human brain tissues were used in this study. We further curated a proof-of-concept human study that included 11C-acetate and 18F-FDG PET imaging analyses along with neuropsychological assessments from 11 AD patients and 10 healthy control subjects. We demonstrate that reactive astrocytes excessively absorb acetate through elevated monocarboxylate transporter-1 (MCT1) in rodent models of both reactive astrogliosis and AD. The elevated acetate uptake is associated with reactive astrogliosis and boosts the aberrant astrocytic GABA synthesis when amyloid-β is present. The excessive astrocytic GABA subsequently suppresses neuronal activity, which could lead to glucose uptake through decreased glucose transporter-3 in the diseased brains. We further demonstrate that 11C-acetate uptake was significantly increased in the entorhinal cortex, hippocampus and temporo-parietal neocortex of the AD patients compared to the healthy controls, while 18F-FDG uptake was significantly reduced in the same regions. Additionally, we discover a strong correlation between the patients' cognitive function and the PET signals of both 11C-acetate and 18F-FDG. We demonstrate the potential value of PET imaging with 11C-acetate and 18F-FDG by visualizing reactive astrogliosis and the associated neuronal glucose hypometablosim for AD patients. Our findings further suggest that the acetate-boosted reactive astrocyte-neuron interaction could contribute to the cognitive decline in AD.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

3T sodium MR imaging in Alzheimer's disease shows stage-dependent sodium increase influenced by age and local brain volume

INTRODUCTION

Application of MRI in clinical routine mainly addresses structural alterations. However, pathological changes at a cellular level are expected to precede the occurrence of brain atrophy clusters and of clinical symptoms. In this context, ²³Na-MRI examines sodium changes in the brain as a potential metabolic parameter. Recently, we have shown that ²³Na-MRI at ultra-high-field (7 T) was able to detect increased tissue sodium concentration (TSC) in Alzheimer's disease (AD). In this work, we aimed at assessing AD-pathology with ²³Na-MRI in a larger cohort and on a clinical 3T MR scanner.

METHODS

We used a multimodal MRI protocol on 52 prodromal to mild AD patients and 34 cognitively healthy control subjects on a clinical 3T MR scanner. We examined the TSC, brain volume, and cortical thickness in association with clinical parameters. We further compared TSC with intra-individual normalized TSC for the reduction of inter-individual TSC variability resulting from physiological as well as experimental conditions. Normalized TSC maps were created by normalizing each voxel to the mean TSC inside the brain stem.

RESULTS

We found increased normalized TSC in the AD cohort compared to elderly control subjects both on global as well as on a region-of-interest-based level. We further confirmed a significant association of local brain volume as well as age with TSC. TSC increase in the left temporal lobe was further associated with the cognitive state, evaluated via the Montreal cognitive assessment (MoCA) screening test. An increase of normalized TSC depending on disease stage reflected by the Clinical Dementia Rating (CDR) was found in our AD patients in temporal lobe regions. In comparison to classical brain volume and cortical thickness assessments, normalized TSC had a higher discriminative power between controls and prodromal AD patients in several regions of the temporal lobe.

DISCUSSION

We confirm the feasibility of ²³Na-MRI at 3T and report an increase of TSC in AD in several regions of the brain, particularly in brain regions of the temporal lobe. Furthermore, to reduce inter-subject variability caused by physiological factors such as circadian rhythms and experimental conditions, we introduced normalized TSC maps. This showed a higher discriminative potential between different clinical groups in comparison to the classical TSC analysis. In conclusion, ²³Na-MRI represents a potential translational imaging marker applicable e.g.for diagnostics and the assessment of intervention outcomes in AD even under clinically available field strengths such as 3T. Implication of ²³Na-MRI in association with other metabolic imaging marker needs to be further elucidated.

Lower brain glucose metabolism in normal ageing is predominantly frontal and temporal: A systematic review and pooled effect size and activation likelihood estimates meta-analyses

This review provides a qualitative and quantitative analysis of cerebral glucose metabolism in ageing. We undertook a systematic literature review followed by pooled effect size and activation likelihood estimates (ALE) meta-analyses. Studies were retrieved from PubMed following the PRISMA guidelines. After reviewing 635 records, 21 studies with 22 independent samples (n = 911 participants) were included in the pooled effect size analyses. Eight studies with eleven separate samples (n = 713 participants) were included in the ALE analyses. Pooled effect sizes showed significantly lower cerebral metabolic rates of glucose for older versus younger adults for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes. Among the sub-cortical structures, the caudate showed a lower metabolic rate among older adults. In sub-group analyses controlling for changes in brain volume or partial volume effects, the lower glucose metabolism among older adults in the frontal lobe remained significant, whereas confidence intervals crossed zero for the other lobes and structures. The ALE identified nine clusters of lower glucose metabolism among older adults, ranging from 200 to 2640 mm³ . The two largest clusters were in the left and right inferior frontal and superior temporal gyri and the insula. Clusters were also found in the inferior temporal junction, the anterior cingulate and caudate. Taken together, the results are consistent with research showing less efficient glucose metabolism in the ageing brain. The findings are discussed in the context of theories of cognitive ageing and are compared to those found in neurodegenerative disease.

PET Imaging of Dementia: Update 2022

ABSTRACT

PET imaging plays an essential role in achieving earlier and more specific diagnoses of dementia syndromes, important for clinical prognostication and optimal medical management. This has become especially vital with the recent development of pathology-specific disease-modifying therapy for Alzheimer disease, which will continue to evolve and require methods to select appropriate treatment candidates. Techniques that began as research tools such as amyloid and tau PET have now entered clinical use, making nuclear medicine physicians and radiologists essential members of the care team. This review discusses recent changes in the understanding of dementia and examines the roles of nuclear medicine imaging in clinical practice. Within this framework, multiple cases will be shown to illustrate a systematic approach of FDG PET interpretation and integration of PET imaging of specific molecular pathology including dopamine transporters, amyloid, and tau. The approach presented here incorporates contemporary understanding of both common and uncommon dementia syndromes, intended as an updated practical guide to assist with the sophisticated interpretation of nuclear medicine examinations in the context of this rapidly and continually developing area of imaging.

Face recognition from research brain PET: An unexpected PET problem

It is well known that de-identified research brain images from MRI and CT can potentially be re-identified using face recognition; however, this has not been examined for PET images. We generated face reconstruction images of 182 volunteers using amyloid, tau, and FDG PET scans, and we measured how accurately commercial face recognition software (Microsoft Azure’s Face API) automatically matched them with the individual participants’ face photographs. We then compared this accuracy with the same experiments using participants’ CT and MRI. Face reconstructions from PET images from PET/CT scanners were correctly matched at rates of 42% (FDG), 35% (tau), and 32% (amyloid), while CT were matched at 78% and MRI at 97–98%. We propose that these recognition rates are high enough that research studies should consider using face de-identification (“de-facing”) software on PET images, in addition to CT and structural MRI, before data sharing. We also updated our mri_reface de-identification software with extended functionality to replace face imagery in PET and CT images. Rates of face recognition on de-faced images were reduced to 0–4% for PET, 5% for CT, and 8% for MRI. We measured the effects of de-facing on regional amyloid PET measurements from two different measurement pipelines (PETSurfer/FreeSurfer 6.0, and one in-house method based on SPM12 and ANTs), and these effects were small: ICC values between de-faced and original images were > 0.98, biases were <2%, and median relative errors were <2%. Effects on global amyloid PET SUVR measurements were even smaller: ICC values were 1.00, biases were <0.5%, and median relative errors were also <0.5%.

Spatial normalization and quantification approaches of PET imaging for neurological disorders

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for ¹⁸F-FDG, ¹¹C-PIB, ¹⁸F-Florbetapir, ¹⁸F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features

Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.

Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults

Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.

Decoupling of regional neural activity and inter-regional functional connectivity in Alzheimer's disease: a simultaneous PET/MR study

PURPOSE

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are characterized by both aberrant regional neural activity and disrupted inter-regional functional connectivity (FC). However, the effect of AD/MCI on the coupling between regional neural activity (measured by regional fluorodeoxyglucose imaging (rFDG)) and inter-regional FC (measured by resting-state functional magnetic resonance imaging (rs-fMRI)) is poorly understood.

METHODS

We scanned 19 patients with MCI, 33 patients with AD, and 26 healthy individuals by simultaneous FDG-PET/rs-fMRI and assessed rFDG and inter-regional FC metrics (i.e., clustering coefficient and degree centrality). Next, we examined the potential moderating effect of disease status (MCI or AD) on the link between rFDG and inter-regional FC metrics using hierarchical moderated multiple regression analysis. We also tested this effect by considering interaction between disease status and inter-regional FC metrics, as well as interaction between disease status and rFDG.

RESULTS

Our findings revealed that both rFDG and inter-regional FC metrics were disrupted in MCI and AD. Moreover, AD altered the relationship between rFDG and inter-regional FC metrics. In particular, we found that AD moderated the effect of inter-regional FC metrics of the caudate, parahippocampal gyrus, angular gyrus, supramarginal gyrus, frontal pole, inferior temporal gyrus, middle frontal, lateral occipital, supramarginal gyrus, precuneus, and thalamus on predicting their rFDG. On the other hand, AD moderated the effect of rFDG of the parietal operculum on predicting its inter-regional FC metric.

CONCLUSION

Our findings demonstrated that AD decoupled the link between regional neural activity and functional segregation and global connectivity across particular brain regions.

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.

Self-normalized Classification of Parkinson's Disease DaTscan Images

Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region. The choice of the normalization region is not standard, and using different normalization regions introduces normalization region-dependent variability. This paper mathematically analyzes the effect of the normalization region to show that normalized-classification is exactly equivalent to a subspace separation of the half rays of the images under multiplicative equivalence. Using this geometry, a new self-normalized classification strategy is proposed. This strategy eliminates the normalizing region altogether. The theory is used to classify DaTscan images of 365 Parkinson's disease (PD) subjects and 208 healthy control (HC) subjects from the Parkinson's Progression Marker Initiative (PPMI). The theory is also used to understand PD progression from baseline to year 4.

Alzheimer's disease alters oligodendrocytic glycolytic and ketolytic gene expression

INTRODUCTION

Sporadic Alzheimer's disease (AD) is strongly correlated with impaired brain glucose metabolism, which may affect AD onset and progression. Ketolysis has been suggested as an alternative pathway to fuel the brain.

METHODS

RNA-seq profiles of post mortem AD brains were used to determine whether dysfunctional AD brain metabolism can be determined by impairments in glycolytic and ketolytic gene expression. Data were obtained from the Knight Alzheimer's Disease Research Center (62 cases; 13 controls), Mount Sinai Brain Bank (110 cases; 44 controls), and the Mayo Clinic Brain Bank (80 cases; 76 controls), and were normalized to cell type: astrocytes, microglia, neurons, oligodendrocytes.

RESULTS

In oligodendrocytes, both glycolytic and ketolytic pathways were significantly impaired in AD brains. Ketolytic gene expression was not significantly altered in neurons, astrocytes, and microglia.

DISCUSSION

Oligodendrocytes may contribute to brain hypometabolism observed in AD. These results are suggestive of a potential link between hypometabolism and dysmyelination in disease physiology. Additionally, ketones may be therapeutic in AD due to their ability to fuel neurons despite impaired glycolytic metabolism.

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.

Implication of metabolic and dopamine transporter PET in dementia with Lewy bodies

To evaluate the implication of ¹⁸F-fluorodeoxyglucose (FDG)- and dopamine transporter (DAT)-positron emission tomography (PET) in the diagnosis and clinical symptoms of dementia with Lewy bodies (DLB), 55 DLB patients and 49 controls underwent neuropsychological evaluation and FDG-, DAT-, and ¹⁸F-Florbetaben (FBB) PET. DAT- and FDG-uptake and FDG/DAT ratio were measured in the anterior and posterior striatum. The first principal component (PC1) of FDG subject residual profiles was identified for each subject. Receiver operating characteristic curve analyses for the diagnosis of DLB were performed using FDG- and DAT-PET biomarkers as predictors, and general linear models for motor severity and cognitive scores were performed adding FBB standardized uptake value ratio as a predictor. Increased metabolism in the bilateral putamen, vermis, and somato-motor cortices, which characterized PC1, was observed in the DLB group, compared to the control group. A combination of posterior putamen FDG/DAT ratio and PC1 showed the highest diagnostic accuracy (91.8% sensitivity and 96.4% specificity), which was significantly greater than that obtained by DAT uptake alone. Striatal DAT uptake and PC1 independently contributed to motor severity and language, memory, frontal/executive, and general cognitive dysfunction in DLB patients, while only PC1 contributed to attention and visuospatial dysfunction.

Differential trajectories of hypometabolism across cognitively-defined Alzheimer's disease subgroups

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Cognitive-subgroups can be identified among individuals with AD dementia.

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Subgroup-specific patterns and longitudinal trajectories of hypometabolism observed.

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Regional hypometabolism matched respective cognitive profiles of subgroups.

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Cognitive-classification yields biologically distinct subgroups.

Disentangling biologically distinct subgroups of Alzheimer’s disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [¹⁸F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.6 ± 1.8 years). These participants were categorized into subgroups on the basis of substantial impairment at time of dementia diagnosis in a specific cognitive domain relative to the average across domains. This approach resulted in groups of AD-Memory (n = 135), AD-Executive (n = 8), AD-Language (n = 22), AD-Visuospatial (n = 44), AD-Multiple Domains (n = 15) and AD-No Domains (for whom no domain showed substantial relative impairment; n = 160). Voxelwise contrasts against controls revealed that all AD-subgroups showed progressive hypometabolism compared to controls across temporoparietal regions at time of AD diagnosis. Voxelwise and regions-of-interest (ROI)-based linear mixed model analyses revealed there were also subgroup-specific hypometabolism patterns and trajectories. The AD-Memory group had more pronounced hypometabolism compared to all other groups in the medial temporal lobe and posterior cingulate, and faster decline in metabolism in the medial temporal lobe compared to AD-Visuospatial. The AD-Language group had pronounced lateral temporal hypometabolism compared to all other groups, and the pattern of metabolism was also more asymmetrical (left < right) than all other groups. The AD-Visuospatial group had faster decline in metabolism in parietal regions compared to all other groups, as well as faster decline in the precuneus compared to AD-Memory and AD-No Domains. Taken together, in addition to a common pattern, cognitively-defined subgroups of people with AD dementia show subgroup-specific hypometabolism patterns, as well as differences in trajectories of metabolism over time. These findings provide support to the notion that cognitively-defined subgroups are biologically distinct.

Deep learning-based amyloid PET positivity classification model in the Alzheimer's disease continuum by using 2-[18F]FDG PET

BACKGROUND

Considering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[18F]FDG).

METHODS

We used 2-[18F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules.

RESULTS

There were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803-0.819) and 0.798 (95% CI, 0.789-0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values.

CONCLUSION

The proposed model based on the 2-[18F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.

Evidence of Age-Related Hemodynamic and Functional Connectivity Impairment: A Resting State fMRI Study

Purpose: To assess age-related changes in intrinsic functional brain connectivity and hemodynamics during adulthood in the context of the retrogenesis hypothesis, which states that the rate of age-related changes is higher in late-myelinating (prefrontal, lateral-posterior temporal) cerebrocortical areas as compared to early myelinating (parietal, occipital) regions. In addition, to examine the dependence of age-related changes upon concurrent subclinical depression symptoms which are common even in healthy aging. Methods: Sixty-four healthy adults (28 men) aged 23-79 years (mean 45.0, SD = 18.8 years) were examined. Resting-state functional MRI (rs-fMRI) time series were used to compute voxel-wise intrinsic connectivity contrast (ICC) maps reflecting the strength of functional connectivity between each voxel and the rest of the brain. We further used Time Shift Analysis (TSA) to estimate voxel-wise hemodynamic lead or lag for each of 22 ROIs from the automated anatomical atlas (AAL). Results: Adjusted for depression symptoms, gender and education level, reduced ICC with age was found primarily in frontal, temporal regions, and putamen, whereas the opposite trend was noted in inferior occipital cortices (p < 0.002). With the same covariates, increased hemodynamic lead with advancing age was found in superior frontal cortex and thalamus, with the opposite trend in inferior occipital cortex (p < 0.002). There was also evidence of reduced coupling between voxel-wise intrinsic connectivity and hemodynamics in the inferior parietal cortex. Conclusion: Age-related intrinsic connectivity reductions and hemodynamic changes were demonstrated in several regions-most of them part of DMN and salience networks-while impaired neurovascular coupling was, also, found in parietal regions. Age-related reductions in intrinsic connectivity were greater in anterior as compared to posterior cortices, in line with implications derived from the retrogenesis hypothesis. These effects were affected by self-reported depression symptoms, which also increased with age.

Impact of prophylactic cranial irradiation and hippocampal sparing on 18F-FDG brain metabolism in small cell lung cancer patients

BACKGROUND AND PURPOSE

Prophylactic cranial irradiation (PCI) in small-cell lung cancer (SCLC) patients improves survival. However, it is also associated with cognitive impairment, although the underlying mechanisms remain poorly understood. Our study aims to evaluate the impact of PCI and potential benefit of hippocampal sparing (HS) on brain metabolism assessed by ¹⁸F-Fluoro-Deoxy-Glucose Positron Emission Tomography/Computed Tomography (¹⁸F-FDG PET/CT).

MATERIALS AND METHODS

We retrospectively included 22 SCLC patients. 50% had hippocampal-sparing (HS) PCI. ¹⁸F-FDG PET/CT was performed 144.5 ± 73 days before and 383 ± 451 days after PCI. Brain ¹⁸F-FDG PET scans were automatically segmented in 12 regions using Combined-AAL Atlas from MI-Neurology Software (Syngo.Via, Siemens Healthineers). For all atlas regions, we computed SUV Ratio using brainstem as a reference region (SUVR = SUVmean/Brainstem SUVmean) and compared SUVR before and after PCI, using a Wilcoxon test, with a level of significance of p < 0.05.

RESULTS

We found significant decreases in ¹⁸F-FDG brain metabolism after PCI in the basal ganglia (p = 0.004), central regions (p = 0.001), cingulate cortex (p < 0.001), corpus striata (p = 0.003), frontal cortex (p < 0.001), parietal cortex (p = 0.001), the occipital cortex (p = 0.002), precuneus (p = 0.001), lateral temporal cortex (p = 0.001) and cerebellum (p < 0.001). Conversely, there were no significant changes in the mesial temporal cortex (MTC) which includes the hippocampi (p = 0.089). The subgroup who received standard PCI showed a significant decrease in metabolism of the hippocampi (p = 0.033). Contrastingly, the subgroup of patients who underwent HS-PCI showed no significant variation in metabolism of the hippocampi (p = 0.783).

CONCLUSION

PCI induced a diffuse decrease in ¹⁸F-FDG brain metabolism. HS-PCI preserves metabolic activity of the hippocampi.

Neuropsychiatric symptoms are early indicators of an upcoming metabolic decline in Alzheimer's disease

Background

Neuropsychiatric symptoms (NPS) are increasingly recognized as early non-cognitive manifestations in the Alzheimer’s disease (AD) continuum. However, the role of NPS as an early marker of pathophysiological progression in AD remains unclear. Dominantly inherited AD (DIAD) mutation carriers are young individuals who are destined to develop AD in future due to the full penetrance of the genetic mutation. Hence, the study of DIAD mutation carriers enables the evaluation of the associations between pure AD pathophysiology and metabolic correlates of NPS without the confounding effects of co-existing pathologies. In this longitudinal study, we aimed to identify regional brain metabolic dysfunctions associated with NPS in cognitively intact DIAD mutation carriers.

Methods

We stratified 221 cognitively intact participants from the Dominantly Inherited Alzheimer’s Network according to their mutation carrier status. The interactions of NPS measured by the Neuropsychiatric Inventory-Questionnaire (NPI-Q), age, and estimated years to symptom onset (EYO) as a function of metabolism measured by [¹⁸F]flurodeoxyglucose ([¹⁸F]FDG) positron emission tomography, were evaluated by the mixed-effects regression model with family-level random effects in DIAD mutation carriers and non-carriers. Exploratory factor analysis was performed to identify the neuropsychiatric subsyndromes in DIAD mutation carriers using the NPI-Q sub-components. Then the effects of interactions between specific neuropsychiatric subsyndromes and EYO on metabolism were evaluated with the mixed-effects regression model.

Results

A total of 119 mutation carriers and 102 non-carriers were studied. The interaction of higher NPI-Q and shorter EYO was associated with more rapid declines of global and regional [¹⁸F]FDG uptake in the posterior cingulate and ventromedial prefrontal cortices, the bilateral parietal lobes and the right insula in DIAD mutation carriers. The neuropsychiatric subsyndromes of agitation, disinhibition, irritability and depression interacted with the EYO to drive the [¹⁸F]FDG uptake decline in the DIAD mutation carriers. The interaction of NPI and EYO was not associated with [¹⁸F]FDG uptake in DIAD mutation non-carriers.

Conclusions

The NPS in cognitively intact DIAD mutation carriers may be a clinical indicator of subsequent metabolic decline in brain networks vulnerable to AD, which supports the emerging conceptual framework that NPS represent early manifestations of neuronal injury in AD. Further studies using different methodological approaches to identify NPS in preclinical AD are needed to validate our findings.

Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods

BACKGROUND

Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature.

OBJECTIVE

In this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer's type (DAT) and Non-DAT controls.

METHODS

FDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians.

RESULTS

Blinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases.

CONCLUSION

In this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.

Associating Type 2 Diabetes Risk Factor Genes and FDG-PET Brain Metabolism in Normal Aging and Alzheimer's Disease

Background: Several studies have linked type 2 diabetes (T2D) to an increased risk of developing Alzheimer’s disease (AD). This has led to an interest in using antidiabetic treatments for the prevention of AD. However, the underlying mechanisms explaining the relationship between T2D and AD have not been completely elucidated.

Objective: Our objective was to examine cerebral ¹⁸F-fluorodeoxyglucose (FDG) uptake during normal aging and in AD patients in regions associated with diabetes genetic risk factor expression to highlight which genes may serve as potential targets for pharmaceutical intervention.

Methods: We calculated regional glucose metabolism differences in units of standardized uptake values (SUVR) for 386 cognitively healthy adults and 335 clinically probable AD patients. We then proceeded to extract gene-expression data from the publicly available Allen Human Brain Atlas (HBA) database. We used the nearest genes to 46 AD- and T2D-associated SNPs previously identified in the literature, and mapped their expression to the same 34 cortical regions in which we calculated SUVRs. SNPs with a donor consistency of 0.40 or greater were selected for further analysis. We evaluated the associations between SUVR and gene-expression across the brain.

Results: Of the 46 risk-factor genes, 15 were found to be significantly correlated with FDG-PET brain metabolism in healthy adults and probable AD patients after correction for multiple comparisons. Using multiple regression, we found that five genes explained a total of 72.5% of the SUVR variance across the healthy adult group regions, while four genes explained a total of 79.3% of the SUVR variance across the probable AD group regions. There were significant differences in whole-brain SUVR as a function of allele frequencies for two genes.

Conclusions: These results highlight the association between risk factor genes for T2D and regional glucose metabolism during both normal aging and in probable AD. Highlighted genes were associated with mitochondrial stability, vascular maintenance, and glucose intolerance. Pharmacological intervention of these pathways has the potential to improve glucose metabolism during normal again as well as in AD patients.

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.