Brain Positron Emission Tomography Imaging Research Articles

Enhancing Feature Selection in MCI Diagnosis using FDG-PET Images: Leveraging Multiple Simple Autoencoder Architectures

Alzheimer’s Disease (AD) is the most common type of neurodegenerative brain disease in elderly people. Early diagnosis of AD is crucial for providing suitable care. Positron Emission Tomography (PET) images and machine learning can be used to support this purpose. In this paper, we present a method for ranking the effectiveness of brain regions of interest (ROIs) to distinguish between stable mild cognitive impairment (sMCI) from progressive mild cognitive impairment (pMCI) in brain PET images based on AutoEncoder (AE). Experiments on the ADNI dataset show that our proposed method significantly improves classifier performance when compared to other popular feature ranking methods such as Fisher score, t-score, and LASSO. Our results suggest that instead of focusing on designing a complex AE structure, we can also use simple-but-multiple AEs for feature ranking. The proposed method could be easily applied to any image dataset where a feature selection is needed.

Kinetic modeling of the monoamine oxidase-B radioligand [18F]SMBT-1 in human brain with positron emission tomography.

This paper describes pharmacokinetic analyses of the monoamine-oxidase-B (MAO-B) radiotracer [18F](S)-(2-methylpyrid-5-yl)-6-[(3-fluoro-2-hydroxy)propoxy]quinoline ([18F]SMBT-1) for positron emission tomography (PET) brain imaging. Brain MAO-B expression is widespread, predominantly within astrocytes. Reactive astrogliosis in response to neurodegenerative disease pathology is associated with MAO-B overexpression. Fourteen elderly subjects (8 control, 5 mild cognitive impairment, 1 Alzheimer's disease) with amyloid ([11C]PiB) and tau ([18F]flortaucipir) imaging assessments underwent dynamic [18F]SMBT-1 PET imaging with arterial input function determination. [18F]SMBT-1 showed high brain uptake and a retention pattern consistent with the known MAO-B distribution. A two-tissue compartment (2TC) model where the K1/k2 ratio was fixed to a whole brain value best described [18F]SMBT-1 kinetics. The 2TC total volume of distribution (VT) was well identified and highly correlated (r2∼0.8) with post-mortem MAO-B indices. Cerebellar grey matter (CGM) showed the lowest mean VT of any region and is considered the optimal pseudo-reference region. Simplified analysis methods including reference tissue models, non-compartmental models, and standard uptake value ratios (SUVR) agreed with 2TC outcomes (r2 > 0.9) but with varying bias. We found the CGM-normalized 70-90 min SUVR to be highly correlated (r2 = 0.93) with the 2TC distribution volume ratio (DVR) with acceptable bias (∼10%), representing a practical alternative for [18F]SMBT-1 analyses.

Impaired Self-Awareness in Parkinson's and Huntington's Diseases: A Literature Review of Neuroimaging Correlates.

Little is known about the brain correlates of anosognosia or unawareness of disease in Parkinson's Disease (PD) and Huntington's Disease (HD). The presence of unawareness or impaired self-awareness (ISA) of illness has profound implications for patients and their caregivers; therefore, studying awareness and its brain correlates should be considered a key step towards developing effective recognition and management of this symptom as it offers a window into the mechanism of self-awareness and consciousness as critical components of the human cognition. We reviewed research studies adopting MRI or other in vivo neuroimaging technique to assess brain structural and/or functional correlates of unawareness in PD and HD across different cognitive and motor domains. Studies adopting task or resting-state functional magnetic resonance imaging, and/or 18-F fluorodeoxyglucose positron emission tomography brain imaging and/or magnetic resonance imaging structural measures were considered. Only six studies investigating neuroimaging features of unawareness in PD and two in HD were identified; there was great heterogeneity in the clinical characteristics of the study participants, domain of unawareness investigated, method of unawareness assessment, and neuroimaging technique used. Nevertheless, some data converge in identifying regions of the salience and frontoparietal networks to be associated with unawareness in PD patients. In HD, the few data are affected by the variability in the severity of motor symptoms. Further studies are needed to better understand the mechanisms and brain correlates of unawareness in PD and HD; in addition, the use of dopaminergic medications should be carefully considered.

Imaging a putative marker of brain cortisol regulation in alcohol use disorder

BackgroundStress is a potent activator of the hypothalamic-pituitary-adrenal (HPA) axis, initiating the release of glucocorticoid hormones, such as cortisol. Alcohol consumption can lead to HPA axis dysfunction, including altered cortisol levels. Until recently, research has only been able to examine peripheral cortisol associated with alcohol use disorder (AUD) in humans. We used positron emission tomography (PET) brain imaging with the radiotracer [18F]AS2471907 to measure 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), a cortisol-regenerating enzyme, in people with AUD compared to healthy controls. MethodsWe imaged 9 individuals with moderate to severe AUD (5 men, 4 women; mean age = 38 years) and 12 healthy controls (8 men, 4 women; mean age = 29 years). Participants received 93.5 ± 15.6 MBq of the 11β-HSD1 inhibitor radiotracer [18F]AS2471907 as a bolus injection and were imaged for 150–180 min on the High-Resolution Research Tomograph. 11β-HSD1 availability was quantified by [18F]AS2471907 volume of distribution (VT; mL/cm3). A priori regions of interest included amygdala, anterior cingulate cortex (ACC), hippocampus, ventromedial PFC (vmPFC) and caudate. ResultsIndividuals with AUD consumed 52.4 drinks/week with 5.8 drinking days/week. Healthy controls consumed 2.8 drinks/week with 1.3 drinking days/week. Preliminary findings suggest that [18F]AS2471907 VT was higher in amygdala, ACC, hippocampus, vmPFC, and caudate of those with AUD compared to healthy controls (p < 0.05). In AUD, vmPFC [18F]AS2471907 VT was associated with drinks per week (r = 0.81, p = 0.01) and quantity per drinking episode (r = 0.75, p = 0.02). ConclusionsThis is the first in vivo examination of 11β-HSD1 availability in individuals with AUD. Our data suggest higher brain availability of the cortisol-regenerating enzyme 11β-HSD1 in people with AUD (vs. controls), and that higher vmPFC 11β-HSD1 availability is related to greater alcohol consumption. Thus, in addition to the literature suggesting that people with AUD have elevated peripheral cortisol, our findings suggest there may also be heightened central HPA activity. These findings set the foundation for future hypotheses on mechanisms related to HPA axis function in this population.

Development and evaluation of a new high-TOF-resolution all-digital brain PET system

Objective. Time-of-flight (TOF) capability and high sensitivity are essential for brain-dedicated positron emission tomography (PET) imaging, as they improve the contrast and the signal-to-noise ratio (SNR) enabling a precise localization of functional mechanisms in the different brain regions. Approach. We present a new brain PET system with transverse and axial field-of-view (FOV) of 320 mm and 255 mm, respectively. The system head is an array of 6 × 6 detection elements, each consisting of a 3.9 × 3.9 × 20 mm3 lutetium–yttrium oxyorthosilicate crystal coupled with a 3.93 × 3.93 mm2 SiPM. The SiPMs analog signals are individually digitized using the multi-voltage threshold (MVT) technology, employing a 1:1:1 coupling configuration. Main results. The brain PET system exhibits a TOF resolution of 249 ps at 5.3 kBq ml−1, an average sensitivity of 22.1 cps kBq−1, and a noise equivalent count rate (NECR) peak of 150.9 kcps at 8.36 kBq ml−1. Furthermore, the mini-Derenzo phantom study demonstrated the system’s ability to distinguish rods with a diameter of 2.0 mm. Moreover, incorporating the TOF reconstruction algorithm in an image quality phantom study optimizes the background variability, resulting in reductions ranging from 44% (37 mm) to 75% (10 mm) with comparable contrast. In the human brain imaging study, the SNR improved by a factor of 1.7 with the inclusion of TOF, increasing from 27.07 to 46.05. Time-dynamic human brain imaging was performed, showing the distinctive traits of cortex and thalamus uptake, as well as of the arterial and venous flow with 2 s per time frame. Significance. The system exhibited a good TOF capability, which is coupled with the high sensitivity and count rate performance based on the MVT digital sampling technique. The developed TOF-enabled brain PET system opens the possibility of precise kinetic brain PET imaging, towards new quantitative predictive brain diagnostics.

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.

Synthesis and evaluation of a novel PET ligand, a GSK’963 analog, aiming at autoradiography and imaging of the receptor interacting protein kinase 1 in the brain

BackgroundReceptor interacting protein kinase 1 (RIPK1) is a serine/threonine kinase, which regulates programmed cell death and inflammation. Recently, the involvement of RIPK1 in the pathophysiology of Alzheimer’s disease (AD) has been reported; RIPK1 is involved in microglia’s phenotypic transition to their dysfunctional states, and it is highly expressed in the neurons and microglia in the postmortem brains in AD patients. They prompt neurodegeneration leading to accumulations of pathological proteins in AD. Therefore, regulation of RIPK1 could be a potential therapeutic target for the treatment of AD, and in vivo imaging of RIPK1 may become a useful modality in studies of drug discovery and pathophysiology of AD. The purpose of this study was to develop a suitable radioligand for positron emission tomography (PET) imaging of RIPK1.Results(S)-2,2-dimethyl-1-(5-phenyl-4,5-dihydro-1H-pyrazol-1-yl)propan-1-one (GSK’963) has a high affinity, selectivity for RIPK1, and favorable physiochemical properties based on its chemical structure. In this study, since 11C-labeling (half-life: 20.4 min) GSK’963 retaining its structure requiring the Grignard reaction of tert-butylmagnesium halides and [11C]carbon dioxide was anticipated to give a low yield, we decided instead to 11C-label a GSK’963 analog ((S)-2,2-dimethyl-1-(5-(m-tolyl)-4,5-dihydro-1H-pyrazol-1-yl)propan-1-one, GG502), which has a high RIPK1 inhibitory activity equivalent to that of the original compound GSK’963. Thus, we successfully 11C-labeled GG502 using a Pd-mediated cross-coupling reaction in favorable yields (3.6 ± 1.9%) and radiochemical purities (> 96%), and molar activity (47–115 GBq/μmol). On autoradiography, radioactivity accumulation was observed for [11C]GG502 and decreased by non-radioactive GG502 in the mouse spleen and human brain, indicating the possibility of specific binding of this ligand to RIPK1. On brain PET imaging in a rhesus monkey, [11C]GG502 showed a good brain permeability (peak standardized uptake value (SUV) ~3.0), although there was no clear evidence of specific binding of [11C]GG502. On brain PET imaging in acute inflammation model rats, [11C]GG502 also showed a good brain permeability, and no significant increased uptake was observed in the lipopolysaccharide-treated side of striatum. On metabolite analysis in rats at 30 min after administration of [11C]GG502, ~55% and ~10% of radioactivity was from unmetabolized [11C]GG502 in the brain and the plasma, respectively.ConclusionsWe synthesized and evaluated a 11C-labeled PET ligand based on the methylated analog of GSK’963 for imaging of RIPK1 in the brain. Although in autoradiography of the resulting [11C]GG502 indicated the possibility of specific binding, the actual PET imaging failed to detect any evidence of specific binding to RIPK1 despite its good brain permeability. Further development of radioligands with a higher binding affinity for RIPK1 in vivo and more stable metabolite profiles compared with the current compound may be required.

Forecasting the Pharmacokinetics With Limited Early Frames in Dynamic Brain PET Imaging Using Neural Ordinary Differential Equation

In dynamic brain positron emission tomography (PET) studies, acquiring a time series of images, typically lasting more than an hour, is necessary to derive pharmacokinetic parameters. Analytically, these parameters are estimated by establishing kinetic models such as compartment models that consist of sets of ordinary differential equations (ODE), and by fitting the sparse time-activity curve (TAC) of the tracer. Yet, these models are simplified approximations of highly complex underlying processes, and sufficient samples of TAC are required throughout the entire acquisition, which is not only impractical but also hindered by patient involuntary motion and intrinsic noise. Therefore, recovering samples in missing timeframes is often required, which, in practice, is achieved by interpolation or extrapolation. Here, we introduce a novel deep-learning-based method that utilizes neural ODE (N-ODE) to predict TAC in the extended timeframes by mimicking analytical method in a data-driven manner. By training N-ODE to solve and fit sets of ODE such that the solution replicates the observed TAC, the N-ODE converges to the functional shapes that best describe the underlying pharmacokinetic processes. We customized N-ODE to predict the full-dynamic images (12 frames, 60min), hence pharmacokinetic parameters, given limited early-frame images (7 to 9 frames, 20 to 30min). For proof of concept, the proposed N-ODE was applied to simulated and clinical 18F-PI-2620 brain PET. We demonstrated that the proposed N-ODE delivered promising performance, indicated by bias, variance, and mean absolute error as well as pharmacokinetic parameters such as rate constants, standardized uptake value ratio (SUVr), and binding potential (BPND).

Neurocognitive Profile and 18 F-Fluorodeoxyglucose Positron Emission Tomography Brain Imaging Correlation in Children with Electrical Status Epilepticus during Sleep.

Objective Electrical status epilepticus in sleep (ESES) is defined by near-continuous epileptiform discharges during sleep along with cognitive, behavioral, and/or imaging abnormalities. We studied the neurocognitive profile and their correlation with 18 F fluorodeoxyglucose positron emission tomography (FDG PET) brain abnormalities in children with ESES. Methods Fourteen children with ESES with normal magnetic resonance imaging (MRI) from March to December 2019 were included. The intelligence quotient (IQ) and child behavior checklist (CBCL) scores were estimated using validated scales, and FDG PET brain was done at the same point of time to look for cerebral metabolic defects which was compared with a control group. Results Fourteen patients with a mean age of 8.2 ± 2.7 years were analyzed. The average duration of epilepsy was 6 ± 2.8 years. The mean IQ was 72.4 ± 18.2 and mean CBCL score was 37.3 ± 11.8. There was negative correlation between IQ and CBCL ( r = -0.55, p < 0.001). The duration of epilepsy also showed negative correlation with IQ ( r = -4.75, p < 0.001). FDG PET scan showed predominant thalamic hypometabolism in 12 of 14 patients (85.7%) on visual analysis with multiple other hypometabolic cortical and subcortical regions in the brain. The quantitative analysis showed significant difference in metabolism of basal ganglion when compared with control group. The total number of hypometabolic regions seen in the brain showed moderate positive correlation with CBCL score but no significant correlation with the IQ of cases. Conclusion This study demonstrates functional impairment of cerebral cortical, basal ganglia, and thalamic hypometabolism in a cohort of ESES patients with normal structural MRI brain study. There was a moderate correlation of extent and pattern of cerebral hypometabolism with the neuropsychological status of the child and duration of epilepsy.

Permutation-based inference for function-on-scalar regression with an application in PET brain imaging

The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data analysis is most commonly performed on data that are aggregated into several discrete a priori regions of interest, in this study, primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterise the binding patterns along this tract and also to determine how such patterns differ between control subjects and depressed patients. Due to the nature of our data, we utilise function-on-scalar regression modelling to make optimal use of our data. Inference on both main effects (position along the tract; diagnostic group) and their interactions are made using permutation testing strategies that do not require distributional assumptions. Also, to investigate the question of homogeneity we implement a permutation testing strategy, which adapts a ‘block bootstrapping’ approach from time series analysis to the functional data setting.

MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.

Head motion occurring during brain positron emission tomography images acquisition leads to a decrease in image quality and induces quantification errors. We have previously introduced a Deep Learning Head Motion Correction (DL-HMC) method based on supervised learning of gold-standard Polaris Vicra motion tracking device and showed the potential of this method. In this study, we upgrade our network to a multi-task architecture in order to include image appearance prediction in the learning process. This multi-task Deep Learning Head Motion Correction (mtDL-HMC) model was trained on 21 subjects and showed enhanced motion prediction performance compared to our previous DL-HMC method on both quantitative and qualitative results for 5 testing subjects. We also evaluate the trustworthiness of network predictions by performing Monte Carlo Dropout at inference on testing subjects. We discard the data associated with a great motion prediction uncertainty and show that this does not harm the quality of reconstructed images, and can even improve it.

Cholinergic system adaptations are associated with cognitive function in people recently abstinent from smoking: a (-)-[18F]flubatine PET study.

The cholinergic system is a critical mediator of cognition in animals. People who smoke cigarettes exhibit cognitive deficits, especially during quit attempts. Few studies jointly examine the cholinergic system and cognition in people while trying to quit smoking. We used positron emission tomography (PET) brain imaging with the β2-subunit containing nicotinic acetylcholine receptor (β2*-nAChR) partial agonist radioligand (-)-[18F]flubatine and the acetylcholinesterase inhibitor physostigmine to jointly examine the cholinergic system, smoking status, and cognition. (-)-[18F]Flubatine scans and cognitive data were acquired from twenty people who recently stopped smoking cigarettes (aged 38 ± 11 years; 6 female, 14 male; abstinent 7 ± 1 days) and 27 people who never smoked cigarettes (aged 29 ± 8 years; 11 female, 16 male). A subset of fifteen recently abstinent smokers and 21 never smokers received a mid-scan physostigmine challenge to increase acetylcholine levels. Regional volume of distribution (VT) was estimated with equilibrium analysis at "baseline" and post-physostigmine. Participants completed a cognitive battery prior to (-)-[18F]flubatine injection and physostigmine administration assessing executive function (Groton Maze Learning test), verbal learning (International Shopping List test), and working memory (One Back test). Physostigmine significantly decreased cortical (-)-[18F]flubatine VT, consistent with increased cortical acetylcholine levels reducing the number of β2*-nAChR sites available for (-)-[18F]flubatine binding, at comparable magnitudes across groups (p values < 0.05). A larger magnitude of physostigmine-induced decrease in (-)-[18F]flubatine VT was significantly associated with worse executive function in people who recently stopped smoking (p values < 0.05). These findings underscore the role of the cholinergic system in early smoking cessation and highlight the importance of neuroscience-informed treatment strategies.

Developing a PET normal brain template using diffusion tensor imaging images: A proof of concept

Background: Registered Positron emission tomography (PET) brain images to the standard normal PET brain templates can be performed to diagnosis dementia by using a vendor software, in which the brain template is based on T1-Weighted (T1W) images. However, the imperfection of an overlap between PET images and the PET-T1W based brain template could be observed. Objectives: This pilot study aimed to develop a new PET brain template and compare the accuracy of image registration between a conventional PET-T1W based brain template and our proposed PET-DTI based brain template. Materials and methods: The new PET-DTI based brain template was developed from twenty-four normal volunteers (age ranged 42-79 years old) who underwent 11C-Pittsburgh compound B PET scans and both T1W and diffusion tensor image (DTI) magnetic resonance imaging brain scans. The correction of Eddy-Current distortions and related artifact removing in DTI images were performed using the open-source FMRIB Software Library (FSL) to generate whole-brain probabilistic tractography maps (MRI-Probtract). MRI-Probtract map was then deformably registered and normalized to PET images, which were used for brain boundary guidance. The accuracy of image registration was assessed by applying the newly developed PET-DTI brain template to PET images of four mild cognitive impairment patients who underwent the same brain-scanning protocols. The accuracy of image registrations using the conventional PET-T1 and PET-DTI templates was evaluated qualitatively by three nuclear medicine physicians. Wilcoxon Signed Ranks test was used to compare registration scores of the two methods. Additionally, the dice similarly coefficient was obtained to quantitatively evaluate the accuracy of image registration. Results: The registration scores of the PET images registered with the PET-DTI template were significantly higher than the PET-T1 template at p-value &lt; 0.05. This result is consistent with the dice similarly coefficient where the value of PET-DTI template was higher. Conclusion: Result of this pilot study showed that new PET-DTI brain template provides higher registration quality, suggesting the feasibility of using PET-DTI template in a clinical PET study of the brain.

Is psilocybin an effective antidepressant and what is its Mechanism of action?

Goodwin etal.1 report a single 25mg dose of psilocybin has an antidepressant effect short-term in medication-resistant depression. Unanswered questions include drug blood level as a guide to dose, psychedelic effects relationship to antidepressant benefit, and potential suicide risk of psilocybin.

Cross-Attention for Improved Motion Correction in Brain PET.

Head movement during long scan sessions degrades the quality of reconstruction in positron emission tomography (PET) and introduces artifacts, which limits clinical diagnosis and treatment. Recent deep learning-based motion correction work utilized raw PET list-mode data and hardware motion tracking (HMT) to learn head motion in a supervised manner. However, motion prediction results were not robust to testing subjects outside the training data domain. In this paper, we integrate a cross-attention mechanism into the supervised deep learning network to improve motion correction across test subjects. Specifically, cross-attention learns the spatial correspondence between the reference images and moving images to explicitly focus the model on the most correlative inherent information - the head region the motion correction. We validate our approach on brain PET data from two different scanners: HRRT without time of flight (ToF) and mCT with ToF. Compared with traditional and deep learning benchmarks, our network improved the performance of motion correction by 58% and 26% in translation and rotation, respectively, in multi-subject testing in HRRT studies. In mCT studies, our approach improved performance by 66% and 64% for translation and rotation, respectively. Our results demonstrate that cross-attention has the potential to improve the quality of brain PET image reconstruction without the dependence on HMT. All code will be released on GitHub: https://github.com/OnofreyLab/dl_hmc_attention_mlcn2023.

Automatic lesion detection and segmentation in 18F-flutemetamol positron emission tomography images using deep learning

BackgroundBeta amyloid in the brain, which was originally confirmed by post-mortem examinations, can now be confirmed in living patients using amyloid positron emission tomography (PET) tracers, and the accuracy of diagnosis can be improved by beta amyloid plaque confirmation in patients. Amyloid deposition in the brain is often associated with the expression of dementia. Hence, it is important to identify the anatomically and functionally meaningful areas of the human brain cortex surface using PET to diagnose the possibility of developing dementia. In this study, we demonstrated the validity of automated 18F-flutemetamol PET lesion detection and segmentation based on a complete 2D U-Net convolutional neural network via masking treatment strategies.MethodsPET data were first normalized by volume and divided into five amyloid accumulation zones through axial, coronary, and thalamic slices. A single U-Net was trained using a divided dataset for one of these zones. Ground truth segmentations were obtained by manual delineation and thresholding (1.5 × background).ResultsThe following intersection over union values were obtained for the various slices in the verification dataset: frontal lobe axial/sagittal: 0.733/0.804; posterior cingulate cortex and precuneus coronal/sagittal: 0.661/0.726; lateral temporal lobe axial/coronal: 0.864/0.892; parietal lobe axial/coronal: 0.542/0.759; and striatum axial/sagittal: 0.679/0.752. The U-Net convolutional neural network architecture allowed fully automated 2D division of the 18F-flutemetamol PET brain images of Alzheimer's patients.ConclusionsAs dementia should be tested and evaluated in various ways, there is a need for artificial intelligence programs. This study can serve as a reference for future studies using auxiliary roles and research in Alzheimer's diagnosis.

Blood protein kinase activity regulating genes are associated with brain glucose metabolism

AbstractBackgroundThe diagnosis of Alzheimer’s disease (AD) has been greatly improved due to the fundamental role of positron emission tomography (PET) imaging. Also, predicting PET brain imaging alterations using blood‐based biomarkers is of high interest. This way, integrating PET and omics data can provide new insights into AD pathophysiology. Here, we aimed to develop a framework that combines blood transcriptomics with PET data. We hypothesized that integrating omics and PET data will help advance our understanding of AD neurobiology and may reveal relevant new peripheral biomarkers.Method[18F]Fluorodeoxyglucose ([18F]FDG)‐PET imaging and transcriptomics data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microarray gene expression profiling from blood samples of 99 Cognitively Unimpaired (CU) and 218 Cognitively Impaired (CI) individuals were submitted to differential expression (DE) analysis. Differentially expressed genes (DEGs) were submitted to Gene Ontology (GO) analysis. The GO terms were clustered by semantic similarity using the GOSemSim method for biological processes ontology. All computations analyses were performed in the R statistical environment. Gene clusters obtained in the previous step were selected to undergo integration with [18F]FDG‐PET images using voxel‐wise generalized linear regression (GLR) models adjusted for age, gender, years of education, and APOEε4 (RMINC package).ResultThe GO semantic similarity resulted in 16 GO clusters. The voxel‐wise correlation between [18F]FDG‐PET and GO clusters resulted in t‐statistical maps. Afterwards, only statistically significant correlated voxels (uncorrected t‐value &gt; 2.0) were retained (Fig. 1A). A cluster related to the regulation of protein serine/threonine kinase activity showed a strong correlation with the [18F]FDG‐PET signal in the brain. At the voxel level, this cluster has a positive correlation in the precuneus’ gray matter (95.7% left, 81.1% right) as well in the medial frontal gyrus (79.6% left, 44.66% right) and cingulate region (76.9% left, 56.4% right) (Fig. 1B).ConclusionWe identified several peripheral biological processes associated with [18F]FDG‐PET metabolism in the brain of CU and CI individuals. Furthermore, the prominent enrichment of protein serine/threonine kinase activity‐related genes highlights its potential as novel AD biomarkers and as a proxy of [18F]FDG‐PET metabolism.

Identification of cholinergic centro-cingulate topography as main contributor to cognitive functioning in Parkinson's disease: Results from a data-driven approach.

Degeneration of the cholinergic system plays an important role in cognitive impairment in Parkinson's disease (PD). Positron emission tomography (PET) imaging using the presynaptic vesicular acetylcholine transporter (VAChT) tracer [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) allows for regional assessment of cholinergic innervation. The purpose of this study was to perform a data-driven analysis to identify co-varying cholinergic regions and to evaluate the relationship of these with cognitive functioning in PD. A total of 87 non-demented PD patients (77% male, mean age 67.9 ± 7.6 years, disease duration 5.8 ± 4.6 years) and 27 healthy control (HC) subjects underwent [18F]FEOBV brain PET imaging and neuropsychological assessment. A volume-of-interest based factor analysis was performed for both groups to identify cholinergic principal components (PCs). Seven main PCs were identified for the PD group: (1) bilateral posterior cortex, (2) bilateral subcortical, (3) bilateral centro-cingulate, (4) bilateral frontal, (5) right-sided fronto-temporal, (6) cerebellum, and (7) predominantly left sided temporal regions. A complementary principal component analysis (PCA) analysis in the control group showed substantially different cholinergic covarying patterns. A multivariate linear regression analyses demonstrated PC3, PC5, and PC7, together with motor impairment score, as significant predictors for cognitive functioning in PD. PC3 showed most robust correlations with cognitive functioning (p < 0.001). A data-driven approach identified covarying regions in the bilateral peri-central and cingulum cortex as a key determinant of cognitive impairment in PD. Cholinergic vulnerability of the centro-cingulate network appears to be disease-specific for PD rather than being age-related. The cholinergic system may be an important contributor to regional and large scale neural networks involved in cognitive functioning.

18F]NOS PET Brain Imaging Suggests Elevated Neuroinflammation in Idiopathic Parkinson's Disease.

Neuroinflammation is implicated as a key pathologic mechanism in many neurodegenerative diseases and is thought to be mediated in large part by microglia, native phagocytic immune cells of the CNS. Abnormal aggregation of the protein α-synuclein after phagocytosis by microglia is one possible neuropathophysiological mechanism driving Parkinson’s disease (PD). We conducted a human pilot study to evaluate the feasibility of targeting the inducible isoform of nitric oxide synthase using the [18F]NOS radiotracer to measure neuroinflammation in idiopathic PD. Ten adults consisting of 6 PD patients and 4 healthy controls (HC) underwent one hour of dynamic [18F]NOS positron emission tomography (PET) brain imaging with arterial blood sampling. We observed increased [18F]NOS whole brain distribution volume (VT) in PD patients compared to age-matched healthy controls (p < 0.008) via a 1-tissue compartment (TC) model. The rate constant K1 for transport from blood into tissue did not differ between groups (p = 0.72). These findings suggest elevated oxidative stress, a surrogate marker of inflammation, is present in early-stage idiopathic PD and indicate that [18F]NOS PET imaging is a promising, non-invasive method to measure neuroinflammation.

Novel brain PET imaging agents: Strategies for imaging neuroinflammation in Alzheimer's disease and mild cognitive impairment.

Alzheimer’s disease (AD) is a devastating neurodegenerative disease with a concealed onset and continuous deterioration. Mild cognitive impairment (MCI) is the prodromal stage of AD. Molecule-based imaging with positron emission tomography (PET) is critical in tracking pathophysiological changes among AD and MCI patients. PET with novel targets is a promising approach for diagnostic imaging, particularly in AD patients. Our present review overviews the current status and applications of in vivo molecular imaging toward neuroinflammation. Although radiotracers can remarkably diagnose AD and MCI patients, a variety of limitations prevent the recommendation of a single technique. Recent studies examining neuroinflammation PET imaging suggest an alternative approach to evaluate disease progression. This review concludes that PET imaging towards neuroinflammation is considered a promising approach to deciphering the enigma of the pathophysiological process of AD and MCI.