Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Predictive Value of \u3csup\u3e18\u3c/sup\u3eF-Florbetapir and \u3csup\u3e18\u3c/sup\u3eF-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia

© 2020 by the Society of Nuclear Medicine and Molecular Imaging. The present study examined the predictive values of amyloid PET, 18F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18F-florbetapir (pattern expression score of an amyloid-β AD conversion-related pattern, constructed by principle-components analysis); 18F-FDG PET (pattern expression score of a previously defined 18F-FDG-based AD conversion-related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18F-FDG PET + nonimaging; and amyloid PET + 18F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion:18F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials

Reproducible metabolic topographies associated with multiple system atrophy: Network and regional analyses in Chinese and American patient cohorts

© 2020 The Authors Purpose: Multiple system atrophy (MSA) is an atypical parkinsonian syndrome and often difficult to discriminate clinically from progressive supranuclear palsy (PSP) and Parkinson\u27s disease (PD) in early stages. Although a characteristic metabolic brain network has been reported for MSA, it is unknown whether this network can provide a clinically useful biomarker in different centers. This study was aimed to identify and cross-validate MSA-related brain network and assess its ability for differential diagnosis and clinical correlations in Chinese and American patient cohorts. Methods: We included 18F-FDG PET scans retrospectively from 128 clinically diagnosed parkinsonian patients (34 MSA, 34 PSP and 60 PD) and 40 normal subjects in China and in the USA. Using PET images from 20 moderate-stage MSA patients of parkinsonian subtype and 20 normal subjects in both centers, we reproduced MSA-related pattern (MSAPRP) of spatial covariance and estimated its reliability. MSAPRP scores were evaluated in assessing differential diagnosis among moderate- and early-stage MSA, PSP or PD patients and clinical correlations with disease severity. Regional metabolic differences were detected using statistical parameter mapping analysis. MSA-related network and regional topographies of metabolic abnormality were cross-validated between the Chinese and American cohorts. Results: We generated a highly reliable MSAPRP characterized by decreased loading in inferior frontal cortex, striatum and cerebellum, and increased loading in sensorimotor, parietal and occipital cortices. MSAPRP scores discriminated between normal, MSA, PSP and PD subjects and correlated with standardized ratings of clinical stages and motor symptoms in MSA. High similarities in MSAPRPs, network scores and corresponding maps of metabolic abnormality were observed between two different cohorts. Conclusion: We have demonstrated reproducible metabolic topographies associated with MSA at both network and regional levels in two independent patient cohorts. Moreover, MSAPRP scores are sensitive for evaluating disease discrimination and clinical correlates. This study supports differential diagnosis of MSA regardless of different patient populations, PET scanners and imaging protocols

Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder

Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson\u27s disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic network. F-18-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0 +/- 5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5 +/- 7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6 +/- 5.0 years) and 16 moderate parkinsonian patients (age 56.9 +/- 12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P \u3c 0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson\u27s disease-related network activity was also elevated (P \u3c 0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism

Reproducibility of a Parkinsonism-Related Metabolic Brain Network in Non-Human Primates: A Descriptive Pilot Study With FDG PET

Background: We have previously defined a parkinsonism-related metabolic brain network in rhesus macaques using a high-resolution research positron emission tomography camera. This brief article reports a descriptive pilot study to assess the reproducibility of network activity and regional glucose metabolism in independent parkinsonian macaques using a clinical positron emission tomography/CT camera. Methods: [F-18]fluorodeoxyglucose PET scans were acquired longitudinally over 3 months in three drug-naive parkinsonian and three healthy control cynomolgus macaques. Group difference and test-retest stability in network activity and regional glucose metabolism were evaluated graphically, using all brain images from these macaques. Results: Comparing the parkinsonian macaques with the controls, network activity was elevated and remained stable over 3 months. Normalized glucose metabolism increased in putarnen/globus pallidus and sensorirnotor regions but decreased in posterior parietal cortices. Conclusions: Parkinsonism-related network activity can be reliably quantified in different macaques with a clinical positron emission tomography/CT scanner and is reproducible over a period typically employed in preclinical intervention studies. This measure can be a useful biomarker of disease process or drug effects in primate models of Parkinson\u27s disease. (C) 2015 International Parkinson and Movement Disorder Societ

Regional brain metabolism in a murine systemic lupus erythematosus model

Systemic lupus erythematosus (SLE) is characterized by multiorgan inflammation, neuropsychiatric disorders (NPSLE), and antinuclear antibodies. We previously identified a subset of anti-DNA antibodies (DNRAb) cross-reactive with the N-methyl-D-aspartate receptor, present in 30% to 40% of patients, able to enhance excitatory post-synaptic potentials and trigger neuronal apoptosis. DNRAb + mice exhibit memory impairment or altered fear response, depending on whether the antibody penetrates the hippocampus or amygdala. Here, we used 18F-fluorodeoxyglucose (FDG) microPET to plot changes in brain metabolism after regional blood-brain barrier (BBB) breach. In DNRAb + mice, metabolism declined at the site of BBB breach in the first 2 weeks and increased over the next 2 weeks. In contrast, DNRAb mice exhibited metabolic increases in these regions over the 4 weeks after the insult. Memory impairment was present in DNRAb + animals with hippocampal BBB breach and altered fear conditioning in DNRAb + mice with amygdala BBB breach. In DNRAb + mice, we observed an inverse relationship between neuron number and regional metabolism, while a positive correlation was observed in DNRAb mice. These findings suggest that local metabolic alterations in this model take place through different mechanisms with distinct time courses, with important implications for the interpretation of imaging data in SLE subjects

The Age-Related Perfusion Pattern Measured With Arterial Spin Labeling MRI in Healthy Subjects

Aim: To analyze age-related cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI in healthy subjects with multivariate principal component analysis (PCA).Methods: 50 healthy subjects (mean age 45.8 ± 18.5 years, range 21–85) had 3D structural MRI and pseudo-continuous ASL MRI at resting state. The relationship between CBF and age was examined with voxel-based univariate analysis using multiple regression and two-sample t-test (median age 41.8 years as a cut-off). An age-related CBF pattern was identified using multivariate PCA.Results: Age correlated negatively with CBF especially anteriorly and in the cerebellum. After adjusting by global value, CBF was relatively decreased with aging in certain regions and relatively increased in others. The age-related CBF pattern showed relative reductions in frontal and parietal areas and cerebellum, and covarying increases in temporal and occipital areas. Subject scores of this pattern correlated negatively with age (R2 = 0.588; P < 0.001) and discriminated between the older and younger subgroups (P < 0.001).Conclusion: A distinct age-related CBF pattern can be identified with multivariate PCA using ASL MRI

Atypical disengagement from faces and its modulation by the control of eye fixation in children with Autism Spectrum Disorder

By using the gap overlap task, we investigated disengagement from faces and objects in children (9–17 years old) with and without autism spectrum disorder (ASD) and its neurophysiological correlates. In typically developing (TD) children, faces elicited larger gap effect, an index of attentional engagement, and larger saccade-related event-related potentials (ERPs), compared to objects. In children with ASD, by contrast, neither gap effect nor ERPs differ between faces and objects. Follow-up experiments demonstrated that instructed fixation on the eyes induces larger gap effect for faces in children with ASD, whereas instructed fixation on the mouth can disrupt larger gap effect in TD children. These results suggest a critical role of eye fixation on attentional engagement to faces in both groups

Cerebral activations related to ballistic, stepwise interrupted and gradually modulated movements in parkinson patients

Patients with Parkinson's disease (PD) experience impaired initiation and inhibition of movements such as difficulty to start/stop walking. At single-joint level this is accompanied by reduced inhibition of antagonist muscle activity. While normal basal ganglia (BG) contributions to motor control include selecting appropriate muscles by inhibiting others, it is unclear how PD-related changes in BG function cause impaired movement initiation and inhibition at single-joint level. To further elucidate these changes we studied 4 right-hand movement tasks with fMRI, by dissociating activations related to abrupt movement initiation, inhibition and gradual movement modulation. Initiation and inhibition were inferred from ballistic and stepwise interrupted movement, respectively, while smooth wrist circumduction enabled the assessment of gradually modulated movement. Task-related activations were compared between PD patients (N = 12) and healthy subjects (N = 18). In healthy subjects, movement initiation was characterized by antero-ventral striatum, substantia nigra (SN) and premotor activations while inhibition was dominated by subthalamic nucleus (STN) and pallidal activations, in line with the known role of these areas in simple movement. Gradual movement mainly involved antero-dorsal putamen and pallidum. Compared to healthy subjects, patients showed reduced striatal/SN and increased pallidal activation for initiation, whereas for inhibition STN activation was reduced and striatal-thalamo-cortical activation increased. For gradual movement patients showed reduced pallidal and increased thalamo-cortical activation. We conclude that PD-related changes during movement initiation fit the (rather static) model of alterations in direct and indirect BG pathways. Reduced STN activation and regional cortical increased activation in PD during inhibition and gradual movement modulation are better explained by a dynamic model that also takes into account enhanced responsiveness to external stimuli in this disease and the effects of hyper-fluctuating cortical inputs to the striatum and STN in particular