Uneventful salvage for partial deployment of a coil via endoscopic ultrasound in the treatment of rebleeding gastric varices.

A 72-year-old female who had received emergent endoscopic cyanoacrylate (CYA) injection for bleeding gastric varices (GV) two month before was readmitted due to recurrence of melena. Current gastroscopy verified the type-2 GV (GOV-2) according to Sarin's classification with stigmata of recent bleeding. Endoscopic ultrasound (EUS) identified the largest varix of 8.7mm in diameter, which prompted us to consider EUS-guided coiling combined with CYA injection as an alternative therapeutic strategy, considering the short interval between prior injection and rebleeding. Via trans-esophageal route, the abovementioned varix was punctured using a 19-gauge FNA needle preloaded with a 0.035-inch coil with diameter of 10mm and length of 14cm (Nester, Cook Medical, Bloomington, IN). Initially, the stylet used as a pusher was advanced smoothly and part of the coil was visualized to have been pushed out of the needle tip. However, the stylet could not be fully advanced to place the entire coil into the varix due to substantial resistance, which, regardless of the endeavor to adjust the needle, was not diminished.

Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis.

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson's disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 - 0.95) for presynaptic DA, 0.79 (95% CI: 0.75-0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96-0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.

Head-to-head comparison of plasma and PET imaging ATN markers in subjects with cognitive complaints.

BACKGROUND: Gaining more information about the reciprocal associations between different biomarkers within the ATN (Amyloid/Tau/Neurodegeneration) framework across the Alzheimer's disease (AD) spectrum is clinically relevant. We aimed to conduct a comprehensive head-to-head comparison of plasma and positron emission tomography (PET) ATN biomarkers in subjects with cognitive complaints. METHODS: A hospital-based cohort of subjects with cognitive complaints with a concurrent blood draw and ATN PET imaging (18F-florbetapir for A, 18F-Florzolotau for T, and 18F-fluorodeoxyglucose [18F-FDG] for N) was enrolled (n = 137). The ß-amyloid (Aß) status (positive versus negative) and the severity of cognitive impairment served as the main outcome measures for assessing biomarker performances. RESULTS: Plasma phosphorylated tau 181 (p-tau181) level was found to be associated with PET imaging of ATN biomarkers in the entire cohort. Plasma p-tau181 level and PET standardized uptake value ratios of AT biomarkers showed a similarly excellent diagnostic performance for distinguishing between Aß+ and Aß- subjects. An increased tau burden and glucose hypometabolism were significantly associated with the severity of cognitive impairment in Aß+ subjects. Additionally, glucose hypometabolism - along with elevated plasma neurofilament light chain level - was related to more severe cognitive impairment in Aß- subjects. CONCLUSION: Plasma p-tau181, as well as 18F-florbetapir and 18F-Florzolotau PET imaging can be considered as interchangeable biomarkers in the assessment of Aß status in symptomatic stages of AD. 18F-Florzolotau and 18F-FDG PET imaging could serve as biomarkers for the severity of cognitive impairment. Our findings have implications for establishing a roadmap to identifying the most suitable ATN biomarkers for clinical use.

Metabolic Asymmetry Relates to Clinical Characteristics and Brain Network Abnormalities in Alzheimer's Disease.

BACKGROUND: Metabolic asymmetry has been observed in Alzheimer's disease (AD), but different studies have inconsistent viewpoints. OBJECTIVE: To analyze the asymmetry of cerebral glucose metabolism in AD and investigate its clinical significance and potential metabolic network abnormalities. METHODS: Standardized uptake value ratios (SUVRs) were obtained from 18F-FDG positron emission tomography (PET) images of all participants, and the asymmetry indices (AIs) were calculated according to the SUVRs. AD group was divided into left/right-dominant or bilateral symmetric hypometabolism (AD-L/AD-R or AD-BI) when more than half of the AIs of the 20 regions of interest (ROIs) were < -2SD, >2SD, or between±1SD. Differences in clinical features among the three AD groups were compared, and the abnormal network characteristics underlying metabolic asymmetry were explored. RESULTS: In AD group, the proportions of AD-L, AD-R, and AD-BI were 28.4%, 17.9%, and 18.5%, respectively. AD-L/AD-R groups had younger age of onset and faster rate of cognitive decline than AD-BI group (p < 0.05). The absolute values of AIs in half of the 20 ROIs became higher at follow-up than at baseline (p < 0.05). Compared with those in AD-BI group, metabolic connection strength of network, global efficiency, cluster coefficient, degree centrality and local efficiency were lower, but shortest path length was longer in AD-L and AD-R groups (p < 0.05). CONCLUSION: Asymmetric and symmetric hypometabolism may represent different clinical subtypes of AD, which may provide a clue for future studies on the heterogeneity of AD and help to optimize the design of clinical trials.

The heterogeneity of asymmetric tau distribution is associated with an early age at onset and poor prognosis in Alzheimer's disease.

PURPOSE: Left-right asymmetry, an important feature of brain development, has been implicated in neurodegenerative diseases, although it's less discussed in typical Alzheimer's disease (AD). We sought to investigate whether asymmetric tau deposition plays a potential role in AD heterogeneity. METHODS: Two independent cohorts consisting of patients with mild cognitive impairment due to AD and AD dementia with tau PET imaging were enrolled [the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort with 18F-Flortaucipir, the Shanghai Memory Study (SMS) cohort with 18F-Florzolotau]. Based on the absolute global tau interhemispheric differences, each cohort was divided into two groups (asymmetric versus symmetric tau distribution). The two groups were cross-sectionally compared in terms of demographic, cognitive characteristics, and pathological burden. The cognitive decline trajectories were analyzed longitudinally. RESULTS: Fourteen (23.3%) and 42 (48.3%) patients in the ADNI and SMS cohorts showed an asymmetric tau distribution, respectively. An asymmetric tau distribution was associated with an earlier age at disease onset (proportion of early-onset AD: ADNI/SMS/combined cohorts, p = 0.093/0.026/0.001) and more severe pathological burden (i.e., global tau burden: ADNI/SMS cohorts, p < 0.001/= 0.007). And patients with an asymmetric tau distribution were characterized by a steeper cognitive decline longitudinally (i.e., the annual decline of Mini-Mental Status Examination score: ADNI/SMS/combined cohorts, p = 0.053 / 0.035 / < 0.001). CONCLUSIONS: Asymmetry in tau deposition, which may be associated with an earlier age at onset, more severe pathological burden, and a steeper cognitive decline, is potentially an important characteristic of AD heterogeneity.

Feasibility of 18F-florzolotau quantification in patients with Alzheimer's disease based on an MRI-free tau PET template.

OBJECTIVES: Quantification of tau accumulation using positron emission tomography (PET) is critical for the diagnosis of Alzheimer's disease (AD). This study aimed to evaluate the feasibility of 18F-florzolotau quantification in patients with AD using a magnetic resonance imaging (MRI)-free tau PET template, since individual high-resolution MRI is costly and not always available in practice. METHODS: 18F-florzolotau PET and MRI scans were obtained in a discovery cohort including (1) patients within the AD continuum (n = 87), (2) cognitively impaired patients with non-AD (n = 32), and (3) cognitively unimpaired subjects (n = 26). The validation cohort comprised 24 patients with AD. Following MRI-dependent spatial normalization (standard approach) in randomly selected subjects (n = 40) to cover the entire spectrum of cognitive function, selected PET images were averaged to create the 18F-florzolotau-specific template. Standardized uptake value ratios (SUVRs) were calculated in five predefined regions of interest (ROIs). MRI-free and MRI-dependent methods were compared in terms of continuous and dichotomous agreement, diagnostic performances, and associations with specific cognitive domains. RESULTS: MRI-free SUVRs had a high continuous and dichotomous agreement with MRI-dependent measures for all ROIs (intraclass correlation coefficient ≥ 0.980; agreement ≥ 94.5%). Similar findings were observed for AD-related effect sizes, diagnostic performances with respect to categorization across the cognitive spectrum, and associations with cognitive domains. The robustness of the MRI-free approach was confirmed in the validation cohort. CONCLUSIONS: The use of an 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer. KEY POINTS: • Regional 18F-florzolotau SUVRs reflecting tau accumulation in the living brains are reliable biomarkers for the diagnosis, differential diagnosis, and assessment of disease severity in patients with AD. • The 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer.

Adjustment for the Age- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism.

Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration, and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). We aimed to explore the potential of the adjustment of age- and gender-related confounding factors on [18F]FDG PET images in differentiation of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supra-nuclear palsy (PSP). Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included. An age- and gender-adjusted Z (AGAZ) score was established based on the gender-specific longitudinal metabolic changes on healthy subjects. AGAZ scores and standardized uptake value ratio (SUVR) values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification. Additional references of the classifications based on metabolic pattern scores were included. The feature-based AGAZ score showed the best performance in classification (accuracy for PD, MSA, PSP: 93.1%, 96.3%, 94.8%). In both genders, the AGAZ score consistently achieved the best efficiency, and the improvements compared to the conventional SUVR value for PD, MSA, and PSP mainly laid in specificity (Male: 5.7%; Female: 11.1%), sensitivity (Male: 7.2%; Female: 7.3%), and sensitivity (Male: 7.3%; Female: 17.2%). Female patients benefited more from the adjustment on [18F]FDG PET in MSA and PSP groups (absolute net reclassification index, p < 0.001). Collectively, the adjustment of age- and gender-related confounding factors may improve the differential diagnosis of Parkinsonism. Particularly, the diagnosis of female Parkinsonian population has the best improvement from this correction. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00079-6.

In Vivo Tau Burden Is Associated with Abnormal Brain Functional Connectivity in Alzheimer's Disease: A 18F-Florzolotau Study.

PURPOSE: 18F-Florzolotau is a novel second-generation tau radiotracer that shows higher binding affinity and selectivity and no off-target binding. The proportion loss of functional connectivity strength (PLFCS) is a new indicator for representing brain functional connectivity (FC) alteration. This study aims to estimate the relationship between the regional tau accumulation and brain FC abnormality in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients based on Florzolotau PET and fMRI. METHODS: 22 NC (normal control), 31 MCI and 42 AD patients who have already been scanned with 18F-Florzolotau PET were recruited in this study. (We calculated the PLFCS and standardized uptake value ratio (SUVR) of each node based on the Brainnetome atlas (BNA) template. The SUVR of 246 brain regions was calculated with the cerebellum as the reference region. Further functional connection strength (FCs), PLFCS and SUVR of each brain region were obtained in three groups for comparison.) For each patient, PLFCS and standardized uptake value ratio (SUVR) were calculated based on the Brainnetome atlas (BNA) template. These results, as well as functional connection strength (FCs), were then compared between different groups. Multiple permutation tests were used to determine the target nodes between NC and cognitive impairment (CI) groups (MCI and AD). The relationship between PLFCS and neuropsychological scores or cortical tau deposit was investigated via Pearson correlation analysis. RESULTS: Higher PLFCS and FCs in AD and MCI groups were found compared to the NC group. The PLFCS of 129 brain regions were found to be different between NC and CI groups, and 8 of them were correlated with tau SUVR, including superior parietal lobule (MCI: r = 0.4360, p = 0.0260, AD: r = -0.3663, p = 0.0280), middle frontal gyrus (AD: MFG_R_7_2: r = 0.4106, p = 0.0129; MFG_R_7_5: r = 0.4239, p = 0.0100), inferior frontal gyrus (AD: IFG_R_6_2: r = 0.3589, p = 0.0316), precentral gyrus (AD: PrG_R_6_6: r = 0.3493, p = 0.0368), insular gyrus (AD: INS_R_6_3: r = 0.3496, p = 0.0366) and lateral occipital cortex (AD: LOcC _L_4_3: r = -0.3433, p = 0.0404). Noteworthily, the opposing relationship was found in the superior parietal lobule in the MCI and AD groups. CONCLUSIONS: Brain functional connectivity abnormality is correlated with tau pathology in AD and MCI.

Effects of Bilateral Subthalamic Nucleus Stimulation on Depressive Symptoms and Cerebral Glucose Metabolism in Parkinson's Disease: A 18F-Fluorodeoxyglucose Positron Emission Tomography/Computerized Tomography Study.

Subthalamic nucleus (STN) deep brain stimulation (DBS) can improve motor symptoms in Parkinson's disease (PD), as well as potentially improving otherwise intractable comorbid depressive symptoms. To address the latter issue, we evaluated the severity of depressive symptoms along with the severity of motor symptoms in 18 PD patients (mean age, 58.4 ± 5.4 years; 9 males, 9 females; mean PD duration, 9.4 ± 4.4 years) with treatment-resistant depression (TRD) before and after approximately 1 year of STN-DBS treatment. Moreover, to gain more insight into the brain mechanism mediating the therapeutic action of STN-DBS, we utilized 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) to assess cerebral regional glucose metabolism in the patients at baseline and 1-year follow-up. Additionally, the baseline PET data from patients were compared with PET data from an age- and sex-matched control group of 16 healthy volunteers. Among them, 12 PD patients underwent post-operative follow-up PET scans. Results showed that the severity of both motor and depressive symptoms in patients with PD-TRD was reduced significantly at 1-year follow-up. Also, patients used significantly less antiparkinsonian medications and antidepressants at 1-year follow-up, as well as experiencing improved daily functioning and a better quality of life. Moreover, relative to the PET data from healthy controls, PD-TRD patients displayed widespread abnormalities in cerebral regional glucose metabolism before STN-DBS treatment, which were partially recovered at 1-year follow-up. Additionally, significant correlations were observed between the patients' improvements in depressive symptoms following STN-DBS and post-operative changes in glucose metabolism in brain regions implicated in emotion regulation. These results support the view that STN-DBS provides a promising treatment option for managing both motor and depressive symptoms in patients who suffer from PD with TRD. However, the results should be interpreted with caution due to the observational nature of the study, small sample size, and relatively short follow-up.

Neuropeptide Y Plays an Important Role in the Relationship Between Brain Glucose Metabolism and Brown Adipose Tissue Activity in Healthy Adults: A PET/CT Study.

Introduction: Brown adipose tissue (BAT) becomes the favorite target for preventing and treating metabolic diseases because the activated BAT can produce heat and consume energy. The brain, especially the hypothalamus, which secretes Neuropeptide Y (NPY), is speculated to regulate BAT activity. However, whether NPY is involved in BAT activity's central regulation in humans remains unclear. Thus, it's essential to explore the relationship between brain glucose metabolism and human BAT activity. Methods: A controlled study with a large sample of healthy adults used Positron emission tomography/computed tomography (PET/CT) to noninvasively investigate BAT's activity and brain glucose metabolism in vivo. Eighty healthy adults with activated BAT according to the PET/CT scan volunteered to be the BAT positive group, while 80 healthy adults without activated BAT but with the same gender, similar age, and BMI, scanning on the same day, were recruited as the control (BAT negative). We use Statistical parametric mapping (SPM) to analyze the brain image data, Picture Archiving & Communication System (PACS), and PET/CT Viewer software to calculate the semi-quantitative values of brain glucose metabolism and BAT activity. ELISA tested the levels of fasting plasma NPY. The multiple linear regression models were used to analyze the correlation between brain glucose metabolism, the level of NPY, and the BAT activity in the BAT positive group. Results: (1) Compared with controls, BAT positive group showed significant metabolic decreases mainly in the right Insula (BA13a, BA13b) and the right claustrum (uncorrected P <0.01, adjusted BMI). (2) The three brain regions' semi-quantitative values in the BAT positive group were significantly lower than the negative group (all P values < 0.05). (3) After adjusting for age, gender, BMI, and outside temperature, there was a negative correlation between brain metabolic values and BAT activity (all P values < 0.05). However, after further adjusting for NPY level, there were no significant differences between the BA13b metabolic values and BAT activity (P>0.05), while the correlation between the BA13a metabolic values and BAT activity still was significant (P< 0.05). Conclusions: Regional brain glucose metabolism is closely related to healthy adults' BAT activity, which may be mediated by NPY.

Parametric Estimation of Reference Signal Intensity for Semi-Quantification of Tau Deposition: A Flortaucipir and [18F]-APN-1607 Study.

BACKGROUND: Tau positron emission tomography (PET) imaging can reveal the pathophysiology and neurodegeneration that occurs in Alzheimer's disease (AD) in vivo. The standardized uptake value ratio (SUVR) is widely used for semi-quantification of tau deposition but is susceptible to disturbance from the reference region and the partial volume effect (PVE). To overcome this problem, we applied the parametric estimation of reference signal intensity (PERSI) method-which was previously evaluated for flortaucipir imaging-to two tau tracers, flortaucipir and [18F]-APN-1607. METHODS: Two cohorts underwent tau PET scanning. Flortaucipir PET imaging data for cohort I (65 healthy controls [HCs], 60 patients with mild cognitive impairment [MCI], and 12 AD patients) were from the AD Neuroimaging Initiative database. [18F]-APN-1607 ([18F]-PM-PBB3) PET imaging data were for Cohort II, which included 21 patients with a clinical diagnosis of amyloid PET-positive AD and 15 HCs recruited at Huashan Hospital. We used white matter (WM) postprocessed by PERSI (PERSI-WM) as the reference region and compared this with the traditional semi-quantification method that uses the whole cerebellum as the reference. SUVRs were calculated for regions of interest including the frontal, parietal, temporal, and occipital lobes; anterior and posterior cingulate; precuneus; and Braak I/II (entorhinal cortex and hippocampus). Receiver operating characteristic (ROC) curve analysis and effect sizes were used to compare the two methods in terms of ability to discriminate between different clinical groups. RESULTS: In both cohorts, regional SUVR determined using the PERSI-WM method was superior to using the cerebellum as reference region for measuring tau retention in AD patients (e.g., SUVR of the temporal lobe: flortaucipir, 1.08 ± 0.17 and [18F]-APN-1607, 1.57 ± 0.34); and estimates of the effect size and areas under the ROC curve (AUC) indicated that it also increased between-group differences (e.g., AUC of the temporal lobe for HC vs AD: flortaucipir, 0.893 and [18F]-APN-1607: 0.949). CONCLUSION: The PERSI-WM method significantly improves diagnostic discrimination compared to conventional approach of using the cerebellum as a reference region and can mitigate the PVE; it can thus enhance the efficacy of semi-quantification of multiple tau tracers in PET scanning, making it suitable for large-scale clinical application.

A disease-specific metabolic imaging marker for diagnosis and progression evaluation of semantic variant primary progressive aphasia.

BACKGROUND AND PURPOSE: The diagnosis and monitoring of semantic variant primary progressive aphasia (sv-PPA) are clinically challenging. We aimed to establish a distinctive metabolic pattern in sv-PPA for diagnosis and severity evaluation. METHODS: Fifteen sv-PPA patients and 15 controls were enrolled to identify sv-PPA-related pattern (sv-PPARP) by principal component analysis of 18 F-fluorodeoxyglucose positron emission tomography. Eighteen Alzheimer disease dementia (AD) and 14 behavioral variant frontotemporal dementia (bv-FTD) patients were enrolled to test the discriminatory power. Correspondingly, regional metabolic activities extracted from the voxelwise analysis were evaluated for the discriminatory power. RESULTS: The sv-PPARP was characterized as decreased metabolic activity mainly in the bilateral temporal lobe (left predominance), middle orbitofrontal gyrus, left hippocampus/parahippocampus gyrus, fusiform gyrus, insula, inferior orbitofrontal gyrus, and striatum, with increased activity in the bilateral lingual gyrus, cuneus, calcarine gyrus, and right precentral and postcentral gyrus. The pattern expression had significant discriminatory power (area under the curve [AUC] = 0.98, sensitivity = 100%, specificity = 94.4%) in distinguishing sv-PPA from AD, and the asymmetry index offered complementary discriminatory power (AUC = 0.91, sensitivity = 86.7%, specificity = 92.9%) in distinguishing sv-PPA from bv-FTD. In sv-PPA patients, the pattern expression correlated with Boston Naming Test scores at baseline and showed significant increase in the subset of patients with follow-up. The voxelwise analysis showed similar topography, and the regional metabolic activities had equivalent or better discriminatory power and clinical correlations with Boston Naming Test scores. The ability to reflect disease progression in longitudinal follow-up seemed to be inferior to the pattern expression. CONCLUSIONS: The sv-PPARP might serve as an objective biomarker for diagnosis and progression evaluation.

Parametric estimation of reference signal intensity in the quantification of amyloid-beta deposition: an 18F-AV-45 study.

BACKGROUND: Positron emission tomography (PET) with the radiotracer florbetapir (18F-AV-45) allows the pathophysiology of Alzheimer's disease (AD) to be tracked in vivo. The semi-quantification of amyloid-beta (Aß) has been extensively evaluated with the standardized uptake value ratio (SUVR) but is susceptible to disturbance from the candidate reference region and the partial volume effect (PVE). In the present study, we applied the parametric estimation of reference signal intensity (PERSI) method to 18F-AV-45 PET images for intensity normalization. METHODS: We enrolled 479 people with 18F-AV-45 images from the Alzheimer's Disease Neuroimaging Initiative database: 261 healthy controls (HCs), 102 patients with mild cognitive impairment (MCI), and 116 AD patients. We used white matter post-processed by PERSI (PERSI-WM) as the reference region and compared our proposed method with the traditional method for semi-quantification. SUVRs were calculated for eight regions of interest: the frontal lobe, the parietal lobe, the temporal lobe, the occipital lobe, the anterior cingulate cortex, the posterior cingulate cortex, the precuneus, and the global cortex. The SUVRs derived from PERSI-WM and other reference regions were evaluated by effect size and receiver-operator characteristic curve analyses. RESULTS: The SUVRs derived from PERSI-WM showed significantly higher trace retention in the frontal, parietal, temporal, and occipital lobes, as well as in the anterior cingulate, posterior cingulate, precuneus, and global cortex in the AD Aß-positive (+) group (mean: +43.3%±5.4%, P<0.01) and MCI Aß+ group (mean: +29.6%±5.3%, P<0.01). For the global cortex, PERSI-WM had the greatest Cohen's d effect size compared with the HC Aß-negative (-) group (AD Aß+ and MCI Aß+: 3.02, AD Aß+: 3.56, MCI Aß+: 2.34), and the highest area under the curve (AUC) between the HC Aß- and AD Aß+ groups (AUC: 0.983, 95% confidence interval: 0.978-0.998). CONCLUSIONS: PERSI-WM could mitigate the influence of PVE and improve the semi-quantification of 18F-AV-45 images; therefore, it could be used for large-scale clinical application in the nuclear medicine domain.

The Parkinson's Disease Progression Neuroimaging Initiative.

The Parkinson's Disease Progressive Neuroimaging Initiative (PDPNI) is a longitudinal observational clinical study. In PDPNI, the clinical and imaging data of patients diagnosed with Parkinsonian syndromes and Idiopathic rapid eye movement sleep behavior disorder (RBD) were longitudinally followed every two years, aiming to identify progression biomarkers of Parkinsonian syndromes through functional imaging modalities including FDG-PET, DAT-PET imaging, ASL MRI, and fMRI, as well as the treatment conditions, clinical symptoms, and clinical assessment results of patients. From February 2012 to March 2019, 224 subjects (including 48 healthy subjects and 176 patients with confirmed PDS) have been enrolled in PDPNI. The detailed clinical information and clinical assessment scores of all subjects were collected by neurologists from Huashan Hospital, Fudan University. All subjects enrolled in PDPNI were scanned with 18F-FDG PET, 11C-CFT PET, and MRI scan sequence. All data were collected in strict accordance with standardized data collection protocols.

Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson's disease: a longitudinal study.

BACKGROUND: With the advance of subthalamic nucleus (STN) deep brain stimulation (DBS) in the treatment of Parkinson's disease (PD), it is desired to identify objective criteria for the monitoring of the therapy outcome. This paper explores the feasibility of metabolic network derived from positron emission tomography (PET) with 18F-fluorodeoxyglucose in monitoring the STN DBS treatment for PD. METHODS: Age-matched 33 PD patients, 33 healthy controls (HCs), 9 PD patients with bilateral DBS surgery and 9 controls underwent 18F-FDG PET scans. The DBS patients were followed longitudinally to investigate the alternations of the PD-related metabolic covariance pattern (PDRP) expressions. RESULTS: The PDRP expression was abnormally elevated in PD patients compared with HCs (P < 0.001). For DBS patients, a significant decrease in the Unified Parkinson's Disease Rating Scale (UPDRS, P = 0.001) and PDRP expression (P = 0.004) was observed 3 months after STN DBS treatment, while a rollback was observed in both UPDRS and PDRP expressions (both P < 0.01) 12 months after treatment. The changes in PDRP expression mediated by STN DBS were generally in line with UPDRS improvement. The graphical network analysis shows increased connections at 3 months and a return at 12 months confirmed by small-worldness coefficient. CONCLUSIONS: The preliminary results demonstrate the potential of metabolic network expression as complimentary objective biomarker for the assessment and monitoring of STN DBS treatment in PD patients. Clinical Trial Registration ChiCTR-DOC-16008645.  http://www.chictr.org.cn/showproj.aspx?proj=13865 .

Brain Functional and Structural Signatures in Parkinson's Disease.

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson's disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal-occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia.

PURPOSE: Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual's risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual's risk of conversion from MCI to AD. METHODS: FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual's metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell's concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics. RESULTS: The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77-4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model). CONCLUSION: The KLSE indicator identifies abnormal brain networks predicting an individual's risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.

Combination of Magnetic Resonance Spectroscopy and ¹¹C-Methionine Positron Emission Tomography for the Accurate Diagnosis of Non-Enhancing Supratentorial Glioma.

OBJECTIVE: To evaluate whether the combination of magnetic resonance spectroscopy (MRS) and ¹¹C-methionine positron emission tomography (¹¹C-MET PET) could increase accurate diagnostic sensitivity for non-enhancing supratentorial gliomas. MATERIALS AND METHODS: Between February 2012 and December 2017, 109 patients with non-enhanced supratentorial lesions on contrast-enhanced MRI were enrolled. Each patient underwent MRS and ¹¹C-MET PET before treatment. A lesion was considered to be a glioma when either the MRS or ¹¹C-MET PET results reached the diagnostic threshold. The radiological diagnosis was compared with the pathological diagnosis or medical diagnostic criteria. RESULTS: The sensitivity and specificity were 60.0% and 50.0% for MRS and 75.8% and 50.0% for ¹¹C-MET PET, respectively. Upon combining the two modalities, the sensitivity and specificity of the imaging-based diagnosis prior to surgery reached 89.5% and 42.9%, respectively. Statistically significant differences in the sensitivities were observed between the combined and individual approaches (MRS alone, 89.5% vs. 60.0%, p < 0.001; ¹¹C-MET PET alone, 89.5% vs. 75.8%, p = 0.001). However, no significant differences in specificity were observed between the combined and individual modalities. CONCLUSION: The combination of MRS and ¹¹C-MET PET findings significantly increases accurate diagnostic sensitivity for non-enhancing supratentorial gliomas without significantly lowering the specificity. This finding suggests the potential of the combined MRS and ¹¹C-MET PET approach in clinical applications.

Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Alzheimer's disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. METHODS: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student's t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach's alpha coefficient for radiomic feature stability analyses. Pearson's correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer's disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. RESULTS: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. CONCLUSION: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.

Brain Network and Abnormal Hemispheric Asymmetry Analyses to Explore the Marginal Differences in Glucose Metabolic Distributions Among Alzheimer's Disease, Parkinson's Disease Dementia, and Lewy Body Dementia.

Facilitating accurate diagnosis and ensuring appropriate treatment of dementia subtypes, including Alzheimer's disease (AD), Parkinson's disease dementia (PDD), and Lewy body dementia (DLB), is clinically important. However, the differences in glucose metabolic distribution among these three dementia subtypes are minor, which can result in difficulties in diagnosis by visual assessment or traditional quantification methods. Here, we explored this issue using novel approaches, including brain network and abnormal hemispheric asymmetry analyses. We generated 18F-labeled fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images from patients with AD, PDD, and DLB, and healthy control (HC) subjects (n = 22, 18, 22, and 22, respectively) from Huashan hospital, Shanghai, China. Brain network properties were measured and between-group differences evaluated using graph theory. We also calculated and explored asymmetry indices for the cerebral hemispheres in the four groups, to explore whether differences between the two hemispheres were characteristic of each group. Our study revealed significant differences in the network properties of the HC and AD groups (small-world coefficient, 1.36 vs. 1.28; clustering coefficient, 1.48 vs. 1.59; characteristic path length, 1.57 vs. 1.64). In addition, differing hub regions were identified in the different dementias. We also identified rightward asymmetry in the hemispheric brain networks of patients with AD and DLB, and leftward asymmetry in the hemispheric brain networks of patients with PDD, which were attributable to aberrant topological properties in the corresponding hemispheres.