Fluorodeoxyglucose Positron Emission Tomography Data Research Articles

Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

280O Pharmacokinetics and pharmacodynamics of paxalisib in newly diagnosed glioblastoma patients with unmethylated MGMT promoter status: Final phase II study results

Paxalisib is being developed for the treatment of glioblastoma multiforme (GBM). Post hoc analysis of phase 1 fluorodeoxyglucose-positron emission tomography (FDG-PET) scans suggested that paxalisib crosses the blood-brain barrier with a uniform distribution throughout the brain. The recently completed phase 2 trial (NCT03522298) enrolled patients with newly diagnosed GBM with unmethylated MGMT promoter status following surgical resection and chemotherapy (Stupp Regimen). Study outcomes comprised maximum tolerated dose (MTD), safety, preliminary efficacy, pharmacokinetics (PK) at the MTD under fed and fasted conditions and change in FDG-PET uptake in patients with measurable disease. Patients (n=30; 70.0% males, mean age 58.5 years) received between 1 and 29 treatment cycles. At the MTD (60 mg/day), paxalisib prolonged median progression free survival (8.4 months [RANO], 8.6 months [mRANO]) and improved median overall survival (15.7 months). Paxalisib was steadily absorbed (median Tmax 2.5 and 4 hours postdose) and declined with a mean elimination half-life ranging from 20.2 to 29.0 hours. Mean half-life (20.2 to 29.0 hours) was similar across dose levels and under fed and fasted conditions. Systemic exposure to paxalisib appeared slightly higher for the 60 mg fed status compared to 60 mg fasted status. Comparison of fed versus fasted treatment was statistically significant for Cmax at the 90% level (geometric least squares mean ratios: AUC0-last 1.33, AUC0-inf 1.06, and Cmax 1.52). Ten patients underwent FDG-PET imaging, 8 (80%) had a decrease in FDG uptake on Day 3 and/or Day 7 in Cycle 1 and 4 (40%) had a metabolic partial response. PK parameters were consistent with prior clinical experience and there was no evidence of a deviation from dose-proportionality. At the MTD FDG-PET data provide evidence that paxalisib has the ability to exert biological effect in tumour tissue, irrespective of fed or fasted status. Further evaluation is ongoing in GBM AGILE (NCT03970447).

Vascular risk factors affect different brain regions in people with Alzheimer’s disease

IntroductionVascular risk factors including hypertension, diabetes and dyslipidaemia promote diverse pathological mechanisms in the brain leading to cerebral hypoperfusion and ultimately cognitive decline in people. Medial temporal, medial frontal and anterior cingulate atrophy has been closely associated with diabetes and medial temporal lobe atrophy is associated with hypertension in people with Alzheimer’s disease (AD).ObjectivesTo assess if hypertension, diabetes and dyslipidaemia have differential effects on different brain locations using brain imaging in people with AD.MethodsThe current study is based on [18F] fluorodeoxyglucose- positron emission tomography (FDG-PET) data of 970 participants from two large Phase III multi-centre clinical trials of a novel tau aggregation inhibitor drug Leuco-Methylthioninium (LMTX)meeting research criteria for mild to moderate AD. Vascular risk factor data including hypertension, diabetes and dyslipidaemia were collected and quantification of FDG PET hypo-metabolism was done by calculating Standardized Uptake Value Ratio(SUVR).ResultsHypertension, diabetes and dyslipidaemia were found to have differential effects on brain locations in people with AD. When people with hypertension, diabetes and dyslipidaemia were compared to those without, mean SUVR was increased significantly in both left and right parietal and occipital lobes and decreased in left and right anterior cingulate gyri in hypertensives. SUVR was significantly higher in both left and right temporal lobes in diabetics andlower in both left and right anterior cingulate gyri in people with dyslipidaemia.ConclusionsVascular risk factors including hypertension, diabetes and dyslipidaemia have differential effects on different brain regions, measured using SUVR analysis of FDG-PET.DisclosureThe FDG-PET data was taken from participants of two large phase III clinical trials sponsored by TauRx Therapeutics (Singapore). TauRx Therapeutics has contributed towards my studentship during my PhD but the data related to drug used in the clinical tria

Crossed cerebellar diaschisis in Alzheimer's disease.

Crossed cerebellar diaschisis (CCD) is characterized by hypometabolism and hypoperfusion on molecular imaging in the cerebellum due to a supratentorial lesion on the contralateral side. CCD is a well-established phenomenon in acute or subacute conditions such as infarction but it has been less well described in chronic conditions such as neurodegenerative dementias. Here, we investigate CCD in a large sample of 830 people meeting research criteria for Alzheimer's disease (AD) using [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET). This study is based on FDG-PET data collected at baseline as part of two large-scale Phase III clinical trials of a novel tau aggregation inhibitor medication, methylthioninium in mild to moderate AD participants. Quantification of FDG-PET hypometabolism was carried out using standardized uptake value ratio (SUVR), with the pons as the comparison region. SUVR was compared in different regions of interest between the right and left hemispheres of the brain and cerebellum in people with mild AD (Mini-Mental State Examination score ≥ 20). Comparison of SUVR in different brain regions demonstrated significant differences in the temporal, occipital and cerebellar cortices. Right and left asymmetry was noted with lower SUVR in the left temporal and occipital regions, whereas SUVR was lower in the right side of the cerebellum. Here, we found robust evidence of CCD in a large sample of people with AD, a chronic neurodegenerative condition. The presence of this phenomenon in AD opens up a new avenue of research in AD pathogenesis and has the potential to change future diagnostic and therapeutic strategies.

Differential Alterations to the Metabolic Connectivity of the Cortical and Subcortical Regions in Rat Brain During Ketamine-Induced Unconsciousness.

Ketamine anesthesia increased glucose metabolism in most brain regions compared to another intravenous anesthetic propofol. However, whether the changes in cerebral metabolic networks induced by ketamine share the same mechanism with propofol remains to be explored. The purpose of the present study was to identify specific features of metabolic network in rat brains during ketamine-induced subanesthesia state and anesthesia state compared to awake state. We acquired fluorodeoxyglucose positron emission tomography (FDG-PET) images in 20 healthy adult Sprague-Dawley rats that were intravenously administrated saline and ketamine to achieve different conscious states: awake (normal saline), subanesthesia (30 mg kg -1 h -1 ), and anesthesia (160 mg kg -1 h -1 ). Based on the FDG-PET data, the alterations in cerebral glucose metabolism and metabolic topography were investigated by graph-theory analysis. The baseline metabolism in rat brains was found significantly increased during ketamine-induced subanesthesia and anesthesia. The graph-theory analysis manifested a reduction in metabolism connectivity and network global/local efficiency across cortical regions and an increase across subcortical regions during ketamine-induced anesthesia (nonparametric permutation test: global efficiency between awake and anesthesia, cortex: P = .016, subcortex: P = .015; global efficiency between subanesthesia and anesthesia, subcortex: P = .012). Ketamine broadly increased brain metabolism alongside decreased metabolic connectivity and network efficiency of cortex network. Modulation of these cortical metabolic networks may be a candidate mechanism underlying general anesthesia-induced loss of consciousness.

Assessing different approaches to estimate single-subject metabolic connectivity from dynamic [18F]fluorodeoxyglucose Positron Emission Tomography data.

Metabolic connectivity is conventionally calculated in terms of correlation of static positron emission tomography (PET) measurements across subjects. There is increasing interest in deriving metabolic connectivity at the single-subject level from dynamic PET data, in a similar way to functional magnetic resonance imaging. However, the strong multicollinearity among region-wise PET time-activity curves (TACs), their non-Gaussian distribution, and the choice of the best strategy for TAC standardization before metabolic connectivity estimation, are non-trivial methodological issues to be tackled.In this work we test four different approaches to estimate sparse inverse covariance matrices, as well as three similarity-based methods to derive adjacency matrices. These approaches, combined with three different TAC standardization strategies, are employed to quantify metabolic connectivity from dynamic [18F]fluorodeoxyglucose ([18F]FDG) PET data in four healthy subjects.

Task-evoked simultaneous FDG-PET and fMRI data for measurement of neural metabolism in the human visual cortex

Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18–49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics.

Correlation of active contact location with weight gain after subthalamic nucleus deep brain stimulation: a case series

BackgroundWeight gain (WG) is a frequently reported side effect of subthalamic deep brain stimulation; however, the underlying mechanisms remain unclear. The active contact locations influence the clinical outcomes of subthalamic deep brain stimulation, but it is unclear whether WG is directly associated with the active contact locations. We aimed to determine whether WG is associated with the subthalamic deep brain stimulation active contact locations.MethodsWe enrolled 14 patients with Parkinson’s disease who underwent bilateral subthalamic deep brain stimulation between 2013 and 2019. Bodyweight and body mass index were measured before and one year following the surgery. The Lead-DBS Matlab toolbox was used to determine the active contact locations based on magnetic resonance imaging and computed tomography. We also created sweet spot maps for WG using voxel-wise statistics, based on volume of tissue activation and the WG of each patient. Fluorodeoxyglucose-positron emission tomography data were also acquired before and one year following surgery, and statistical parametric mapping was used to evaluate changes in brain metabolism. We examined which brain regions’ metabolism fluctuation significantly correlated with increased body mass index scores and positron emission tomography data.ResultsOne year after surgery, the body mass index increase was 2.03 kg/m2. The sweet spots for WG were bilateral, mainly located dorsally outside of the subthalamic nucleus (STN). Furthermore, WG was correlated with increased metabolism in the left limbic and associative regions, including the middle temporal gyrus, inferior frontal gyrus, and orbital gyrus.ConclusionsAlthough the mechanisms underlying WG following subthalamic deep brain stimulation are possibly multifactorial, our findings suggest that dorsal stimulation outside of STN may lead to WG. The metabolic changes in limbic and associative cortical regions after STN-DBS may also be one of the mechanisms underlying WG. Further studies are warranted to confirm whether dorsal stimulation outside of STN changes the activities of these cortical regions.

The default mode network and cognition in Parkinson's disease: A multimodal resting‐state network approach

Involvement of the default mode network (DMN) in cognitive symptoms of Parkinson's disease (PD) has been reported by resting‐state functional MRI (rsfMRI) studies. However, the relation to metabolic measures obtained by [18F]‐fluorodeoxyglucose positron emission tomography (FDG‐PET) is largely unknown. We applied multimodal resting‐state network analysis to clarify the association between intrinsic metabolic and functional connectivity abnormalities within the DMN and their significance for cognitive symptoms in PD. PD patients were classified into normal cognition (n = 36) and mild cognitive impairment (MCI; n = 12). The DMN was identified by applying an independent component analysis to FDG‐PET and rsfMRI data of a matched subset (16 controls and 16 PD patients) of the total cohort. Besides metabolic activity, metabolic and functional connectivity within the DMN were compared between the patients' groups and healthy controls (n = 16). Glucose metabolism was significantly reduced in all DMN nodes in both patient groups compared to controls, with the lowest uptake in PD‐MCI (p < .05). Increased metabolic and functional connectivity along fronto‐parietal connections was identified in PD‐MCI patients compared to controls and unimpaired patients. Functional connectivity negatively correlated with cognitive composite z‐scores in patients (r = −.43, p = .005). The current study clarifies the commonalities of metabolic and hemodynamic measures of brain network activity and their individual significance for cognitive symptoms in PD, highlighting the added value of multimodal resting‐state network approaches for identifying prospective biomarkers.

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ).Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists.Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients.Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully.

Brain Metabolism but Not Gray Matter Volume Underlies the Presence of Language Function in the Minimally Conscious State (MCS): MCS+ Versus MCS− Neuroimaging Differences

Background. The minimally conscious state (MCS) is subcategorized into MCS− and MCS+, depending on the absence or presence, respectively, of high-level behavioral responses such as command-following. Objective. We aim to investigate the functional and structural neuroanatomy underlying the presence of these responses in MCS− and MCS+ patients. Methods. In this cross-sectional retrospective study, chronic MCS patients were diagnosed using repeated Coma Recovery Scale–Revised assessments. Fluorodeoxyglucose-positron emission tomography data were acquired on 57 patients (16 MCS−; 41 MCS+) and magnetic resonance imaging with voxel-based morphometry analysis was performed on 66 patients (17 MCS−; 49 MCS+). Brain glucose metabolism and gray matter integrity were compared between patient groups and control groups. A metabolic functional connectivity analysis testing the hypothesis of preserved language network in MCS+ compared with MCS− was also done. Results. Patients in MCS+ presented higher metabolism mainly in the left middle temporal cortex, known to be important for semantic processing, compared with the MCS− group. The left angular gyrus was also functionally disconnected from the left prefrontal cortex in MCS− compared with MCS+ group. No significant differences were found in gray matter volume between patient groups. Conclusions. The clinical subcategorization of MCS is supported by differences in brain metabolism but not in gray matter structure, suggesting that brain function in the language network is the main support for recovery of command-following, intelligible verbalization and/or intentional communication in the MCS. Better characterizing the neural correlates of residual cognitive abilities of MCS patients contributes to reduce their misdiagnosis and to adapt therapeutic approaches.

Metabolic Network Topology of Alzheimer's Disease and Dementia with Lewy Bodies Generated Using Fluorodeoxyglucose Positron Emission Tomography.

Background:Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are often misdiagnosed with each other because of similar symptoms including progressive memory loss. The metabolic network topology that describes inter-regional metabolic connections can be generated using fluorodeoxyglucose positron emission tomography (FDG-PET) data with the graph-theoretical method. We hypothesized that different metabolic connectivity underlies the symptoms of AD patients, DLB patients, and cognitively normal (CN) individuals.Objective:This study aimed to generate metabolic connectivity using FDG-PET data and assess the network topology to differentiate AD patients, DLB patients, and CN individuals.Methods:This study included 45 AD patients, 18 DLB patients, and 142 CN controls. We analyzed FDG-PET data using the graph-theoretical method and generated the network topology in AD patients, DLB patients, and CN individuals. We statistically assessed the topology with global and nodal parameters.Results:The whole metabolic network was preserved in CN; however, diffusely decreased connection was found in AD and partially but more deeply decreased connection was observed in DLB. The metabolic topology revealed that the right posterior cingulate and the left transverse temporal gyrus were significantly different between AD and DLB.Conclusion:The present findings indicate that metabolic connectivity decreased in both AD and DLB, compared with CN. DLB was characterized restricted but deeper stereotyped network disruption compared with AD. The right posterior cingulate and the left transverse temporal gyrus are significant regions in the metabolic connectivity for differentiating AD from DLB.

Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme.

The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. In this paper, we present an efficacious and plain bias-adjustment scheme using chronological age as a covariate through the training set for downgrading the prediction bias in a Brain-age estimation framework. We applied proposed bias-adjustment scheme coupled by a machine learning-based brain age framework on a large set of metabolic brain features acquired from 675 cognitively unimpaired adults through fluorodeoxyglucose positron emission tomography data as the training set to build a robust Brain-age estimation framework. Then, we tested the reliability of proposed bias-adjustment scheme on 75 cognitively unimpaired adults, 561 mild cognitive impairment patients as well as 362 Alzheimer's disease patients as independent test sets. Using the proposed method, we gained a strong R2 of 0.81 between the chronological age and brain estimated age, as well as an excellent mean absolute error of 2.66 years on 75 cognitively unimpaired adults as an independent set; whereas an R2 of 0.24 and a mean absolute error of 4.71 years was achieved without bias-adjustment. The simulation results demonstrated that the proposed bias-adjustment scheme has a strong capability to diminish prediction error in brain age estimation frameworks for clinical settings.

Comparison of Count Normalization Methods for Statistical Parametric Mapping Analysis Using a Digital Brain Phantom Obtained from Fluorodeoxyglucose-positron Emission Tomography.

Objective(s):Alternative normalization methods were proposed to solve the biased information of SPM in the study of neurodegenerative disease. The objective of this study was to determine the most suitable count normalization method for SPM analysis of a neurodegenerative disease based on the results of different count normalization methods applied on a prepared digital phantom similar to one obtained using fluorodeoxyglucose-positron emission tomography (FDG-PET) data of a brain with a known neurodegenerative condition. Methods:Digital brain phantoms, mimicking mild and intermediate neurodegenerative disease conditions, were prepared from the FDG-PET data of 11 healthy subjects. SPM analysis was performed on these simulations using different count normalization methods.Results: In the slight-decrease phantom simulation, the Yakushev method correctly visualized wider areas of slightly decreased metabolism with the smallest artifacts of increased metabolism. Other count normalization methods were unable to identify this slightly decreases and produced more artifacts. The intermediate-decreased areas were well visualized by all methods. The areas surrounding the grey matter with the slight decreases were not visualized with the GM and VOI count normalization methods but with the Andersson. The Yakushev method well visualized these areas. Artifacts were present in all methods. When the number of reference area extraction was increased, the Andersson method better-captured the areas with decreased metabolism and reduced the artifacts of increased metabolism. In the Yakushev method, increasing the threshold for the reference area extraction reduced such artifacts.Conclusion:The Yakushev method is the most suitable count normalization method for the SPM analysis of neurodegenerative disease.

Diagnostic Value of Diffusion Tensor Imaging and Positron Emission Tomography in Early Stages of Frontotemporal Dementia.

Due to suboptimal sensitivity and specificity of structural and molecular neuroimaging tools, the diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging. Investigation of the sensitivity of diffusion tensor imaging (DTI) and fluorodeoxyglucose positron emission tomography (FDG-PET) to detect cerebral alterations in early stages of bvFTD despite inconspicuous conventional MRI. Thirty patients with early stages of bvFTD underwent a detailed neuropsychological examination, cerebral 3T MRI with DTI analysis, and FDG-PET. After 12 months of follow-up, all patients finally fulfilled the diagnosis of bvFTD. Individual FDG-PET data analyses showed that 20 patients exhibited a "typical" pattern for bvFTD with bifrontal and/or temporal hypometabolism (bvFTD/PET+), and that 10 patients showed a "non-typical"/normal pattern (bvFTD/PET-). DTI data were compared with 42 healthy controls in an individual and voxel-based group analysis. To examine the clinical relevance of the findings, associations between pathologically altered voxels of DTI or FDG-PET results and behavioral symptoms were estimated by linear regression analyses. DTI voxel-based group analyses revealed microstructural degeneration in bifrontal and bitemporal areas in bvFTD/PET+ and bvFTD/PET- groups. However, when comparing the sensitivity of individual DTI data analysis with FDG-PET, DTI appeared to be less sensitive. Neuropsychological symptoms were considerably related to neurodegeneration within frontotemporal areas identified by DTI and FDG-PET. DTI seems to be an interesting tool for detection of functionally relevant neurodegenerative alterations in early stages of bvFTD, even in bvFTD/PET- patients. However, at a single subject level, it seems to be less sensitive than FDG-PET. Thus, improvement of individual DTI analysis is necessary.

MR-assisted PET motion correction in simultaneous PET/MRI studies of dementia subjects.

Subject motion in positron emission tomography (PET) studies leads to image blurring and artifacts; simultaneously acquired magnetic resonance imaging (MRI) data provides a means for motion correction (MC) in integrated PET/MRI scanners. To assess the effect of realistic head motion and MR-based MC on static [18 F]-fluorodeoxyglucose (FDG) PET images in dementia patients. Observational study. Thirty dementia subjects were recruited. 3T hybrid PET/MR scanner where EPI-based and T1 -weighted sequences were acquired simultaneously with the PET data. Head motion parameters estimated from high temporal resolution MR volumes were used for PET MC. The MR-based MC method was compared to PET frame-based MC methods in which motion parameters were estimated by coregistering 5-minute frames before and after accounting for the attenuation-emission mismatch. The relative changes in standardized uptake value ratios (SUVRs) between the PET volumes processed with the various MC methods, without MC, and the PET volumes with simulated motion were compared in relevant brain regions. The absolute value of the regional SUVR relative change was assessed with pairwise paired t-tests testing at the P = 0.05 level, comparing the values obtained through different MR-based MC processing methods as well as across different motion groups. The intraregion voxelwise variability of regional SUVRs obtained through different MR-based MC processing methods was also assessed with pairwise paired t-tests testing at the P = 0.05 level. MC had a greater impact on PET data quantification in subjects with larger amplitude motion (higher than 18% in the medial orbitofrontal cortex) and greater changes were generally observed for the MR-based MC method compared to the frame-based methods. Furthermore, a mean relative change of ∼4% was observed after MC even at the group level, suggesting the importance of routinely applying this correction. The intraregion voxelwise variability of regional SUVRs was also decreased using MR-based MC. All comparisons were significant at the P = 0.05 level. Incorporating temporally correlated MR data to account for intraframe motion has a positive impact on the FDG PET image quality and data quantification in dementia patients. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1288-1296.

Overlapping and distinct neural metabolic patterns related to impulsivity and hypomania in Parkinson's disease.

Impulsivity and hypomania are common non-motor features in Parkinson's disease (PD). The aim of this study was to find the overlapping and distinct neural correlates of these symptoms in PD. Symptoms of impulsivity and hypomania were assessed in 24 PD patients using the Barratt Impulsiveness Scale (BIS-11) and Self-Report Manic Inventory (SRMI), respectively. In addition, fluorodeoxyglucose positron emission tomography (FDG-PET) imaging for each individual was performed. We conducted two separate multiple regression analyses for BIS-11 and SRMI scores with FDG-PET data to identify the brain regions that are associated with both impulsivity and hypomania scores, as well as those exclusive to each symptom. Then, seed-based functional connectivity analyses on healthy subjects identified the areas connected to each of the exclusive regions and the overlapping region, used as seeds. We observed a positive association between BIS-11 and SRMI scores and neural metabolism only in the prefrontal areas. Conjunction analysis revealed an overlapping region in the middle frontal gyrus. Regions exclusive to impulsivity were found in the medial part of the right superior frontal gyrus and regions exclusive to hypomania were in the right superior frontal gyrus, right precentral gyrus and right paracentral lobule. Connectivity patterns of seeds exclusively related to impulsivity were different from those for hypomania in healthy brains. These results provide evidence of both overlapping and distinct regions linked with impulsivity and hypomania scores in PD. The exclusive regions for each characteristic are connected to specific intrinsic functional networks.

Functional neuroanatomical associations of working memory in early-onset Alzheimer's disease.

To characterize metabolic correlates of working memory impairment in clinically defined subtypes of early-onset Alzheimer's disease. Established models of working memory suggest a key role for frontal lobe function, yet the association in Alzheimer's disease between working memory impairment and visuospatial and language symptoms suggests that temporoparietal neocortical dysfunction may be responsible. Twenty-four patients with predominantly early-onset Alzheimer's disease were clinically classified into groups with predominantly amnestic, multidomain or visual deficits. Patients underwent neuropsychological evaluation focused on the domains of episodic and working memory, T1-weighted magnetic resonance imaging and brain fluorodeoxyglucose positron emission tomography. Fluorodeoxyglucose positron emission tomography data were analysed by using a region-of-interest approach. Patients with multidomain and visual presentations performed more poorly on tests of working memory compared with amnestic Alzheimer's disease. Working memory performance correlated with glucose metabolism in left-sided temporoparietal, but not frontal neocortex. Carriers of the apolipoprotein E4 gene showed poorer episodic memory and better working memory performance compared with noncarriers. Our findings support the hypothesis that working memory changes in early-onset Alzheimer's disease are related to temporoparietal rather than frontal hypometabolism and show dissociation from episodic memory performance. They further support the concept of subtypes of Alzheimer's disease with distinct cognitive profiles due to prominent neocortical dysfunction early in the disease course. Copyright © 2017 John Wiley & Sons, Ltd.

Regional Lymph Node Uptake of [18F]Fluorodeoxyglucose After Definitive Chemoradiation Therapy Predicts Local-Regional Failure of Locally Advanced Non-Small Cell Lung Cancer: Results of ACRIN 6668/RTOG 0235

The American College of Radiology Imaging Network (ACRIN) 6668/Radiation Therapy Oncology Group (RTOG) 0235 study demonstrated that standardized uptake values (SUV) on post-treatment [(18)F]fluorodeoxyglucose-positron emission tomography (FDG-PET) correlated with survival in locally advanced non-small cell lung cancer (NSCLC). This secondary analysis determined whether SUV of regional lymph nodes (RLNs) on post-treatment FDG-PET correlated with patient outcomes. Included for analysis were patients treated with concurrent chemoradiation therapy, using radiation doses ≥60 Gy, with identifiable FDG-avid RLNs (distinct from primary tumor) on pretreatment FDG-PET, and post-treatment FDG-PET data. ACRIN core laboratory SUV measurements were used. Event time was calculated from the date of post-treatment FDG-PET. Local-regional failure was defined as failure within the treated RT volume and reported by the treating institution. Statistical analyses included Wilcoxon signed rank test, Kaplan-Meier curves (log rank test), and Cox proportional hazards regression modeling. Of 234 trial-eligible patients, 139 (59%) had uptake in both primary tumor and RLNs on pretreatment FDG-PET and had SUV data from post-treatment FDG-PET. Maximum SUV was greater for primary tumor than for RLNs before treatment (P<.001) but not different post-treatment (P=.320). Post-treatment SUV of RLNs was not associated with overall survival. However, elevated post-treatment SUV of RLNs, both the absolute value and the percentage of residual activity compared to the pretreatment SUV were associated with inferior local-regional control (P<.001). High residual metabolic activity in RLNs on post-treatment FDG-PET is associated with worse local-regional control. Based on these data, future trials evaluating a radiation therapy boost should consider inclusion of both primary tumor and FDG-avid RLNs in the boost volume to maximize local-regional control.

FDG PET and Split-Bolus Multi-Detector Row CT Fusion Imaging in Oncologic Patients: Preliminary Results.

To assess the incremental value of split-bolus multidetector computed tomography (CT) combined with fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) for follow-up of oncologic patients. The institutional ethics committee approved the use of this protocol. Thirty-eight oncologic patients who underwent FDG PET/unenhanced multidetector CT and split-bolus multidetector CT for restaging were investigated retrospectively. The split-bolus CT protocol included imaging during the hepatic arterial and portal venous phases in one scan. Software was used for fusion of the independently acquired FDG PET and split-bolus CT data, and fused datasets were compared with FDG PET/unenhanced CT data. The standard of reference for diagnosis of lesions in all patients was a combination of histologic results (if available), clinical results (medical history, physical examination, and laboratory test results), and the results of follow-up imaging (conventional CT, magnetic resonance imaging, and/or ultrasonography) for at least 6 months. Descriptive statistics were used. Fifty-nine true-positive lesions were identified with fused FDG PET/split-bolus CT; 41 were concordant and detected with both split-bolus CT and PET/unenhanced CT, 16 with split-bolus CT only, and two with PET/unenhanced CT. Two different false-positive lesions were identified with PET/unenhanced CT and PET/split-bolus CT. Furthermore, in 20 of 38 (53%) patients, FDG PET/split-bolus CT allowed detection of important additional findings (n = 40) not detected at FDG PET/unenhanced CT. Both the tumor-related findings (n = 13, 32.5%) and the non-tumor-related findings (n = 27, 67.5%) were important to the clinical treatment of these patients. Fused FDG PET/split-bolus multidetector CT provides additional information compared with FDG PET/unenhanced multidetector CT in oncologic patients.