Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Socially-Assistive Robots Using Empathy to Reduce Pain and Distress during Peripheral IV Placement in Children

Objectives. Socially-assistive robots (SAR) have been used to reduce pain and distress in children in medical settings. Patients who perceive empathic treatment have increased satisfaction and improved outcomes. We sought to determine if an empathic SAR could be developed and used to decrease pain and fear associated with peripheral IV placement in children. Methods. We conducted a pilot study of children receiving IV placement. Participating children were randomized to interact with (1) no robot, or a commercially available 3D printed humanoid SAR robot programmed with (2) empathy or (3) distraction conditions. Children and parents completed demographic surveys, and children used an adapted validated questionnaire to rate the robot’s empathy on an 8-point Likert scale. Survey scores were compared by the t-test or chi-square test. Pain and fear were measured by self-report using the FACES and FEAR scales, and video tapes were coded using the CHEOPS and FLACC. Scores were compared using repeated measures 2-way ANOVA. This trial is registered with NCT02840942. Results. Thirty-one children with an average age of 9.6 years completed the study. For all measures, mean pain and fear scores were lowest in the empathy group immediately before and after IV placement. Children were more likely to attribute characteristics of empathy to the empathic condition (Likert score 7.24 v. 4.70; p=0.012) and to report that having the empathic vs. distraction robot made the IV hurt less (7.45 vs. 4.88; p=0.026). Conclusions. Children were able to identify SAR designed to display empathic characteristics and reported it helped with IV insertion pain and fear. Mean scores of self-reported or objective pain and fear scales were the lowest in the empathy group and the highest in the distraction condition before and after IV insertion. This result suggests empathy improves SAR functionality when used for painful medical procedures and informs future research into SAR for pain management

Comprehensive lipidomics reveals phenotypic differences in hepatic lipid turnover in ALD and NAFLD during alcohol intoxication

BACKGROUND & AIMS: In experimental models, alcohol induces acute changes in lipid metabolism that cause hepatocyte lipoapoptosis and inflammation. Here we study human hepatic lipid turnover during controlled alcohol intoxication. METHODS: We studied 39 participants with 3 distinct hepatic phenotypes: alcohol-related liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), and healthy controls. Alcohol was administrated via nasogastric tube over 30 min. Hepatic and systemic venous blood was sampled simultaneously at 3 time points: baseline, 60, and 180 min after alcohol intervention. Liver biopsies were sampled 240 min after alcohol intervention. We used ultra-high performance liquid chromatography mass spectrometry to measure levels of more than 250 lipid species from the blood and liver samples. RESULTS: After alcohol intervention, the levels of blood free fatty acid (FFA) and lysophosphatidylcholine (LPC) decreased, while triglyceride (TG) increased. FFA was the only lipid class to decrease in NAFLD after alcohol intervention, whereas LPC and FFA decreased and TG increased after intervention in ALD and healthy controls. Fatty acid chain uptake preference in FFAs and LPCs were oleic acid, linoleic acid, arachidonic acid, and docosahexaenoic acid. Hepatic venous blood FFA and LPC levels were lower when compared with systemic venous blood levels throughout the intervention. After alcohol intoxication, liver lipidome in ALD was similar to that in NAFLD. CONCLUSIONS: Alcohol intoxication induces rapid changes in circulating lipids including hepatic turnaround from FFA and LPC, potentially leading to lipoapoptosis and steatohepatitis. TG clearance was suppressed in NAFLD, possibly explaining why alcohol and NAFLD are synergistic risk factors for disease progression. These effects may be central to the pathogenesis of ALD. CLINICAL TRIALS REGISTRATION: The study is registered at Clinicaltrials.gov (NCT03018990). LAY SUMMARY: We report that alcohol induces hepatic extraction of free unsaturated fatty acids and lysophosphatidylcholines, hepatotoxic lipids which have not been previously associated with alcohol-induced liver injury. We also found that individuals with non-alcoholic fatty liver disease have reduced lipid turnover during alcohol intoxication when compared with people with alcohol-related fatty liver disease. This may explain why alcohol is particularly more harmful in people with non-alcoholic fatty liver and why elevated BMI and alcohol have a synergistic effect on the risk of liver-related death

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. MATERIALS AND METHODS Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. RESULTS The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. CONCLUSION Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus

Two Prevalent ∼100-kb GYPB Deletions Causative of the GPB-Deficient Blood Group MNS Phenotype S-s-U-in Black Africans

The U antigen (MNS5) is one of 49 antigens belonging to the MNS blood group system (ISBT002) carried on glycophorins A (GPA) and B (GPB). U is present on the red blood cells in almost all Europeans and Asians but absent in approximately 1.0% of Black Africans. U negativity coincides with negativity for S (MNS3) and s (MNS4) on GPB, thus be called S-s-U-, and is thought to arise from homozygous deletion of GYPB. Little is known about the molecular background of these deletions. Bioinformatic analysis of the 1000 Genomes Project data revealed several candidate regions with apparent deletions in GYPB. Highly specific Gap-PCRs, only resulting in positive amplification from DNAs with deletions present, allowed for the exact genetic localization of 3 different breakpoints; 110.24- A nd 103.26-kb deletions were proven to be the most frequent in Black Americans and Africans. Among 157 CEPH DNAs, deletions in 6 out of 8 African ethnicities were present. Allele frequencies of the deletions within African ethnicities varied greatly and reached a cumulative 23.3% among the Mbuti Pygmy people from the Congo. Similar observations were made for U+var alleles, known to cause strongly reduced GPB expression. The 110- A nd 103-kb deletional GYPB haplotypes were found to represent the most prevalent hereditary factors causative of the MNS blood group phenotype S-s-U-. Respective GYPB deletions are now accessible by molecular detection of homo- A nd hemizygous transmission

Metabolic Patterns across core features in Dementia with Lewy Bodies (DLB)

OBJECTIVE: To identify brain regions whose metabolic impairment contributes to DLB clinical core features expression and to assess the influence of severity of global cognitive impairment on the DLB-hypometabolic-pattern. METHODS: Brain FDG-PET and information on core features were available in 171 patients belonging to the imaging repository of the European DLB-consortium. Principal component analysis was applied to identify brain regions relevant to the local data variance. A linear regression model was applied to generate core feature-specific patterns controlling for the main confounding variables (MMSE, Age, Education, Gender, and Center). Regression analysis to the locally-normalized intensities was performed to generate a MMSE score-sensitive map. RESULTS: Parkinsonism negatively covaried with bilateral parietal, precuneus and anterior cingulate metabolism, visual-hallucinations with bilateral dorsolateral-frontal cortex, posterior cingulate and parietal metabolism and RBD with bilateral parieto-occipital cortex, precuneus and ventrolateral-frontal metabolism. VH and RBD shared a positive covariance with metabolism in medial temporal lobe, cerebellum, brainstem, basal ganglia, thalami, orbitofrontal and sensorimotor cortex. Cognitive fluctuations negatively covaried with occipital metabolism and positively with parietal lobes metabolism. MMSE positively covaried with metabolism in left superior frontal gyrus, bilateral-parietal cortex, and left precuneus, and negatively with metabolism in insula, medial frontal gyrus, hippocampus in the left hemisphere and in right cerebellum. INTERPRETATION: Regions of more preserved metabolism are relatively consistent across the variegate DLB spectrum. By contrast, core features were associated to more prominent hypometabolism in specific regions thus suggesting a close clinical-imaging correlation, reflecting the interplay between topography of neurodegeneration and clinical presentation in DLB patients. This article is protected by copyright. All rights reserved

EuroInf 2 : Subthalamic stimulation, apomorphine, and levodopa infusion in Parkinson's disease

Objective: Real-life observational report of clinical efficacy of bilateral subthalamic stimulation (STN-DBS), apomorphine (APO), and intrajejunal levodopa infusion (IJLI) on quality of life, motor, and nonmotor symptoms (NMS) in Parkinson's disease (PD). Methods: In this prospective, multicenter, international, real-life cohort observation study of 173 PD patients undergoing STN-DBS (n = 101), IJLI (n = 33), or APO (n = 39) were followed-up using PDQuestionnaire-8, NMSScale (NMSS), Unified PD Rating Scale (UPDRS)-III, UPDRS-IV, and levodopa equivalent daily dose (LEDD) before and 6 months after intervention. Outcome changes were analyzed with Wilcoxon signed-rank or paired t test when parametric tests were applicable. Multiple comparisons were corrected (multiple treatments/scales). Effect strengths were quantified with relative changes, effect size, and number needed to treat. Analyses were computed before and after propensity score matching, balancing demographic and clinical characteristics. Results: In all groups, PDQuestionnaire-8, UPDRS-IV, and NMSS total scores improved significantly at follow-up. Levodopa equivalent daily dose was significantly reduced after STN-DBS. Explorative NMSS domain analyses resulted in distinct profiles: STN-DBS improved urinary/sexual functions, mood/cognition, sleep/fatigue, and the miscellaneous domain. IJLI improved the 3 latter domains and gastrointestinal symptoms. APO improved mood/cognition, perceptual problems/hallucinations, attention/memory, and the miscellaneous domain. Overall, STN-DBS and IJLI seemed favorable for NMSS total score, and APO favorable for neuropsychological/neuropsychiatric NMS and PDQuestionnaire-8 outcome. Conclusions: This is the first comparison of quality of life, nonmotor. and motor outcomes in PD patients undergoing STN-DBS, IJLI, and APO in a real-life cohort. Distinct effect profiles were identified for each treatment option. Our results highlight the importance of holistic nonmotor and motor symptoms assessments to personalize treatment choices

Associations among education, age, and the dementia with Lewy bodies (DLB) metabolic pattern:A European-DLB consortium project

Introduction: We assessed the influence of education as a proxy of cognitive reserve and age on the dementia with Lewy bodies (DLB) metabolic pattern. Methods: Brain 18F-fluorodeoxyglucose positron emission tomography and clinical/demographic information were available in 169 probable DLB patients included in the European DLB-consortium database. Principal component analysis identified brain regions relevant to local data variance. A linear regression model was applied to generate age- and education-sensitive maps corrected for Mini-Mental State Examination score, sex (and either education or age). Results: Age negatively covaried with metabolism in bilateral middle and superior frontal cortex, anterior and posterior cingulate, reducing the expression of the DLB-typical cingulate island sign (CIS). Education negatively covaried with metabolism in the left inferior parietal cortex and precuneus (making the CIS more prominent). Discussion: These findings point out the importance of tailoring interpretation of DLB biomarkers considering the concomitant effect of individual, non–disease-related variables such as age and cognitive reserve

Adopting transfer learning for neuroimaging : a comparative analysis with a custom 3D convolution neural network model

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones