Nicola Trotta

Non-Gaussian space-variant resolution modelling for list-mode reconstruction

Partial volume effect is an important source of bias in PET images that can be lowered by accounting for the point spread function (PSF) of the scanner. We measured such a PSF in various points of a clinical PET scanner and modelled it as a product of matrices acting in image space, taking the asymmetrical, shift-varying and non-Gaussian character of the PSF into account (AMP modelling), and we integrated this accurate image space modelling into a conventional list-mode OSEM algorithm (EM-AMP reconstruction). We showed on the one hand that when a sufficiently high number of iterations are considered, the AMP modelling lead to better recovery coefficients at reduced background noise compared to reconstruction where no or only partial resolution modelling is performed, and on the other hand that for a small number of iterations, a Gaussian modelling gave the best recovery coefficients. Moreover, we have demonstrated that a deconvolution based on the AMP system response model leads to the same recovery coefficients as the corresponding EM-AMP reconstruction, but at the expense of an increased background noise.

¹⁸F-FDG PET/CT and MRI in the follow-up of head and neck squamous cell carcinoma.

We evaluated the diagnostic performance of (18)F-FDG PET/CT and MRI for the assessment of head and neck squamous cell carcinoma (HNSCC) relapse. Since early treatment might prevent inoperable relapse, we also evaluated THE performance of early unenhanced (18)F-FDG PET/CT in residual tumor detection. The study was prospectively performed on 32 patients who underwent (18)F-FDG PET/CT and MRI before treatment and at 4 and 12 months after treatment. (18)F-FDG PET/CT was also performed 2 weeks after the end of radiotherapy. Histopathology or a minimum of 18 months follow-up were used as gold standard. Before treatment (18)F-FDG PET/CT and MRI detected all primary tumors except for two limited vocal fold lesions (sensitivity 94%). MRI was more sensitive than (18)F-FDG PET/CT for the detection of local extension sites (sensitivity 75 vs 58%), but at the cost of a higher rate of false positive results (positive predictive value 74 vs 86%). For relapse detection at 4 months, sensitivity was significantly higher for (18)F-FDG PET/CT (92%) than for MRI (70%), but the diagnostic performances were not significantly different at 12 months. For the detection of residual malignant tissue 2 weeks post-radiotherapy, sensitivity and specificity of (18)F-FDG PET/CT were respectively 86 and 85% (SUV cut-off value 5.8). (18)F-FDG PET/CT is effective in the differentiation between residual tumor and radiation-induced changes, as early as 2 weeks after treatment of a primary HNSCC. For follow-up, performance of (18)F-FDG PET/CT and MRI are similar except for a higher sensitivity of (18)F-FDG PET/CT at 4 months.

Neurophysiological activity underlying altered brain metabolism in epileptic encephalopathies with CSWS

We investigated the neurophysiological correlate of altered regional cerebral glucose metabolism observed in children with epileptic encephalopathy with continuous spike-waves during sleep (CSWS) by using a multimodal approach combining time-sensitive magnetic source imaging (MSI) and positron emission tomography with [(18)F]-fluorodeoxyglucose (FDG-PET). Six patients (4 boys and 2 girls, age range: 4-8 years, 3 patients with Landau-Kleffner syndrome (LKS), 3 patients with atypical rolandic epilepsy (ARE)) were investigated by FDG-PET and MSI at the acute phase of CSWS. In all patients, the onset(s) of spike-waves discharges were associated with significant focal hypermetabolism. The propagation of epileptic discharges to other brain areas was associated with focal hypermetabolism (five patients), hypometabolism (one patient) or the absence of any significant metabolic change (one patient). Interestingly, most of the hypometabolic areas were not involved in the epileptic network per se. This study shows that focal hypermetabolism observed at the acute phase of CSWS are related to the onset or propagation sites of spike-wave discharges. Spike-wave discharges propagation can be associated to other types of metabolic changes, suggesting the occurrence of various neurophysiological mechanisms at the cellular level. Most of the hypometabolic areas are not involved in the epileptic network as such and are probably related to a mechanism of remote inhibition. These findings highlight the critical value of combining FDG-PET with time-sensitive functional neuroimaging approaches such as MSI to assess CSWS epileptic network when surgery is considered as a therapeutic approach.

Metabolic evidence for episodic memory plasticity in the nonepileptic temporal lobe of patients with mesial temporal epilepsy.

Purpose:  Metabolic changes have been described in the nonepileptic temporal lobe of patients with unilateral mesiotemporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS). To better understand the functional correlate of this metabolic finding, we have sought to characterize brain regions in patients with MTLE that show correlation between unilateral episodic memory performances, as assessed by intracarotid amobarbital test (IAT), and interictal regional cerebral metabolism measured by [18F]‐fluorodeoxyglucose positron emission tomography (FDG‐PET).

Default mode network hypometabolism in epileptic encephalopathies with CSWS.

Previous studies investigating cerebral metabolic changes associated with continuous spike-waves during sleep (CSWS) compared the metabolism of children with CSWS with that of healthy adults, precluding any assessment in brain areas showing physiologic age-related metabolic changes. Here, we investigated the metabolic and connectivity changes characterizing the acute phase of CSWS activity by comparing awake brain metabolism of children with CSWS with that of pediatric pseudo-controls. Positron emission tomography using [18F]-fluorodeoxyglucose (FDG-PET) was performed in 17 awake children with cryptogenic CSWS (5 girls, age: 5-11 years). Voxel-based analyses identified significant metabolic changes in CSWS patients compared with 18 pediatric pseudo-controls (12 girls, age: 6-11 years, non-CSWS focal cryptogenic epilepsy with normal FDG-PET). CSWS-induced changes in the contribution of brain areas displaying metabolic changes to the level of metabolic activity in other brain areas were investigated using pathophysiological interaction. Hypermetabolism in perisylvian regions bilaterally and hypometabolism in lateral and mesial prefrontal cortex, precuneus, posterior cingulate cortex and parahippocampal gyri characterized the acute phase of CSWS (p<0.05 FWE). No change in thalamic metabolism was disclosed. Altered functional connectivity was found between hyper- and hypometabolic regions in CSWS patients compared with pediatric pseudo-controls. This study demonstrates hypometabolism in key nodes of the default mode network (DMN) in awake patients with CSWS, in relation with a possible phenomenon of sustained remote inhibition from the epileptic foci. This hypometabolism might account for some of the acquired cognitive or behavioral features of CSWS epileptic encephalopathies. This study failed to find any evidence of thalamic metabolic changes, which supports the primary involvement of the cortex in CSWS genesis.

Functional integration changes in regional brain glucose metabolism from childhood to adulthood

The aim of this study was to investigate the age‐related changes in resting‐state neurometabolic connectivity from childhood to adulthood (6–50 years old). Fifty‐four healthy adult subjects and twenty‐three pseudo‐healthy children underwent [18F]‐fluorodeoxyglucose positron emission tomography at rest. Using statistical parametric mapping (SPM8), age and age squared were first used as covariate of interest to identify linear and non‐linear age effects on the regional distribution of glucose metabolism throughout the brain. Then, by selecting voxels of interest (VOI) within the regions showing significant age‐related metabolic changes, a psychophysiological interaction (PPI) analysis was used to search for age‐induced changes in the contribution of VOIs to the metabolic activity in other brain areas. Significant linear or non‐linear age‐related changes in regional glucose metabolism were found in prefrontal cortices (DMPFC/ACC), cerebellar lobules, and thalamo‐hippocampal areas bilaterally. Decreases were found in the contribution of thalamic, hippocampal, and cerebellar regions to DMPFC/ACC metabolic activity as well as in the contribution of hippocampi to preSMA and right IFG metabolic activities. Increases were found in the contribution of the right hippocampus to insular cortex and of the cerebellar lobule IX to superior parietal cortex metabolic activities. This study evidences significant linear or non‐linear age‐related changes in regional glucose metabolism of mesial prefrontal, thalamic, mesiotemporal, and cerebellar areas, associated with significant modifications in neurometabolic connectivity involving fronto‐thalamic, fronto‐hippocampal, and fronto‐cerebellar networks. These changes in functional brain integration likely represent a metabolic correlate of age‐dependent effects on sensory, motor, and high‐level cognitive functional networks. Hum Brain Mapp 37:3017–3030, 2016.

Neurometabolic Resting-State Networks Derived from Seed-Based Functional Connectivity Analysis

TO THE EDITOR: We read with great interest the paper by Savio et al. (1) on the imaging of resting-state networks (RSNs) from simultaneous 18F-FDG PET/functional MRI (fMRI) data. The authors applied an independent component analysis (ICA) commonly used in fMRI and reported fair cross-modality agreement for several RSNs. In view of the distinct nature of the neurovascular and neurometabolic couplings, this tends to confirm the neural basis of RSNs, in line with recent magnetoencephalographic studies. Interestingly, some networks were only identified in one modality or the other, for example, the 18F-FDG PET ICA reported by Savio et al. (1) failed at detecting the salience/insular or temporopolar RSNs disclosed in the corresponding fMRI ICA. This leaves the question of their neural underpinning pending. Methodologically, this study focused on the ICA technique. A complementary approach is seed-based functional connectivity (sbFC) whereby a seed location is selected a priori as part of the sought network, and correlation maps are estimated between the seed and all other voxel activities. Compared with ICA, sbFC is straightforward to interpret and avoids the issues of selecting the number of components (which affects ICA decompositions) and visually discriminating between physiologic and noise components. Furthermore, sbFC can be subjected to the rigorous statistical framework of random field theory (RFT) (2), henceforth eliminating the usage of somewhat arbitrary thresholds. Supplementing ICAwith sbFC thus appears necessary for robust inferences about RSNs. We hereby report that sbFC analysis of 18F-FDG PET data do allow statistical mapping of most RSNs, including those unidentified from the 18F-FDG PET ICA (1). Specifically, we considered a resting-state (eyes closed) 18FFDG PET dataset of 50 healthy adults (27 women; age range, 18–43 y) whose acquisition and preprocessing procedures have been detailed in a previous publication (3). We used SPM8 (http://www.fil.ion.ucl.ac.uk/spm/, Wellcome Trust Centre for Neuroimaging) to design voxelwise general linear models of the 18F-FDG PET images with metabolism at the seed location as covariate of interest. One-sided t tests were then applied to identify significantly positive sbFC, both at P, 0.05 with the whole-brain familywise error rate controlled by RFT and at P , 0.001 uncorrected. Figure 1 illustrates the statistically masked sbFC t-maps obtained from selected seeds. Several RSNs emerged at RFT significance, from low-level (e.g., sensorimotor, visual) to cognitive (e.g., language, executive) and subcortical (e.g., cerebellar, basal ganglia) networks. Other RSNs were identified only partially but recovered at P , 0.001 uncorrected (e.g., auditory, default-mode, and frontoparietal networks). In line with the discussion in Savio et al. (1), this may be due to the nondynamic nature of our 18F-FDG PET data. Indeed, compared with fMRI, the loss of temporal samples strongly limits the sensitivity of correlation estimates. Importantly, the salience/insular and temporopolar RSNs missing in the 18F-FDG PET ICA (1) were identified as well. The reason may simply be that our dataset contains approximately 2.3 times more subjects, leading to approximately 33% less correlation noise. Indeed, repeating our sbFC analyses on half our population failed at revealing these 2 RSNs. Besides, this increase in sampling size would enable computing extra 18F-FDG PET ICA components possibly disclosing these RSNs. Together with previous seminal studies that had some shortcomings limiting the interpretations of their results (4,5), Savio et al.’s study (1) and our 18F-FDG PET data obtained by statistical sbFC mapping bring novel evidence that the field of RSNs— up to now exclusive to fMRI and to a lesser extent extracranial electrophysiology—can be expanded to the realm of neurometabolism and thus pervades all functional neuroimaging. In particular, the combination of sbFC with ICA applied to resting-state 18F-FDG

No evidence of thalamic metabolic abnormality associated with continuous spike-and-wave during sleep

Lombardia, Italy; Emory+Children’s Pediatric Research Center, Atlanta, Georgia, U.S.A.; Children’s Memorial Hospital, Chicago, Illinois, U.S.A.; Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, U.S.A.; Hospital de Santa Maria, Lisbon, Portugal; University of Colorado Anschutz Medical Campus, Aurora, Colorado, U.S.A.; The Children’s Hospital, Aurora, Colorado, U.S.A.; Texas Children’s Hospital, Houston, Texas, U.S.A.; Baylor College of Medicine, Houston, Texas, U.S.A.; University of Oklahoma College of Medicine, Oklahoma City, Oklahoma, U.S.A.; Boston Children’s Hospital, Boston, Massachusetts, U.S.A.; Harvard Medical School, Boston, Massachusetts, U.S.A.; Washington University School of Medicine, St. Louis, Missouri,U.S.A.; Cleveland Clinic, Cleveland, Ohio, U.S.A.; The Royal Children’s Hospital Melbourne, Parkville, Victoria, Australia; Bruce Lefroy Centre for Genetic Health Research, Murdoch Children’s Research Institute, Parkville, Victoria, Australia; University of Melbourne, Melbourne, Victoria, Australia; and Medical College of Georgia at Augusta University, Augusta, Georgia, U.S.A.