Whole-body metabolic connectivity framework with functional PET

[1]  S. Cherry,et al.  Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence , 2022, The Journal of Nuclear Medicine.

[2]  Tao Sun,et al.  Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging , 2022, European Journal of Nuclear Medicine and Molecular Imaging.

[3]  Benjamin Chiao,et al.  The heart-brain axis: A proteomics study of meditation on the cardiovascular system of Tibetan Monks , 2022, EBioMedicine.

[4]  S. Jamadar,et al.  Resting-State FDG-PET Connectivity: Covariance, Ergodicity, and Biomarkers. Response to Commentary by Sala et al.; Static versus Functional PET: Making Sense of Metabolic Connectivity. , 2021, Cerebral cortex.

[5]  T. Freeman,et al.  A Systems-Level Analysis of Total-Body PET Data Reveals Complex Skeletal Metabolism Networks in vivo , 2021, Frontiers in Medicine.

[6]  P. Cumming,et al.  Static versus Functional PET: Making Sense of Metabolic Connectivity. , 2021, Cerebral cortex.

[7]  Phillip G. D. Ward,et al.  Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study. , 2021, Cerebral cortex.

[8]  Phoebe G. Spetsieris,et al.  Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson’s disease , 2020, NeuroImage.

[9]  Ruijin Liu,et al.  Mechanisms of Renal-Splenic Axis Involvement in Acute Kidney Injury Mediated by the α7nAChR-NF-κB Signaling Pathway , 2020, Inflammation.

[10]  Phillip G. D. Ward,et al.  Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain , 2020, Scientific data.

[11]  S. Montagnese,et al.  Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure , 2020, Frontiers in Physiology.

[12]  Ronan M. T. Fleming,et al.  Personalized whole‐body models integrate metabolism, physiology, and the gut microbiome , 2020, Molecular systems biology.

[13]  Michael Breakspear,et al.  Reconfiguration of functional brain networks and metabolic cost converge during task performance , 2020, eLife.

[14]  Jennifer L. L. Major,et al.  The Brain-Heart Axis: Alzheimer's, Diabetes, and Hypertension. , 2019, ACS pharmacology & translational science.

[15]  Sang‐wook Lee,et al.  Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network , 2019, Scientific Reports.

[16]  G. M. Godbersen,et al.  Functional dynamics of dopamine synthesis during monetary reward and punishment processing , 2019, bioRxiv.

[17]  Ping Liu,et al.  Crosstalk Between the Gut Microbiota and the Brain: An Update on Neuroimaging Findings , 2019, Front. Neurol..

[18]  Craig H Meyer,et al.  Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images. , 2019, Medical physics.

[19]  Andreas Hahn,et al.  Making Sense of Connectivity , 2018, The international journal of neuropsychopharmacology.

[20]  C. McNorgan,et al.  Dyslexia on a continuum: A complex network approach , 2018, PloS one.

[21]  Siegfried Kasper,et al.  Reduced task durations in functional PET imaging with [18F]FDG approaching that of functional MRI , 2018, NeuroImage.

[22]  Peyman Golshani,et al.  Reducing Astrocyte Calcium Signaling In Vivo Alters Striatal Microcircuits and Causes Repetitive Behavior , 2018, Neuron.

[23]  J. Appleton The Gut-Brain Axis: Influence of Microbiota on Mood and Mental Health. , 2018, Integrative medicine.

[24]  C. Pepine,et al.  The gut microbiota and the brain–gut–kidney axis in hypertension and chronic kidney disease , 2018, Nature Reviews Nephrology.

[25]  Yong-Ku Kim,et al.  The Microbiota-Gut-Brain Axis in Neuropsychiatric Disorders: Patho-physiological Mechanisms and Novel Treatments , 2018, Current neuropharmacology.

[26]  Igor Yakushev,et al.  Metabolic connectivity: methods and applications , 2017, Current opinion in neurology.

[27]  Rupert Lanzenberger,et al.  Task-relevant brain networks identified with simultaneous PET/MR imaging of metabolism and connectivity , 2017, Brain Structure and Function.

[28]  R. Wahl,et al.  Simplifying volumes‐of‐interest (VOIs) definition in quantitative SPECT: Beyond manual definition of 3D whole‐organ VOIs , 2017, Medical physics.

[29]  R. Geocadin,et al.  Heart-Brain Axis: Effects of Neurologic Injury on Cardiovascular Function. , 2017, Circulation research.

[30]  Rupert Lanzenberger,et al.  Quantification of Task-Specific Glucose Metabolism with Constant Infusion of 18F-FDG , 2016, The Journal of Nuclear Medicine.

[31]  G. Pearlson,et al.  Examining Functional Resting-State Connectivity in Psychosis and Its Subgroups in the Bipolar-Schizophrenia Network on Intermediate Phenotypes Cohort. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[32]  Kang K. L. Liu,et al.  Focus on the emerging new fields of network physiology and network medicine , 2016, New journal of physics.

[33]  A. Murray,et al.  Kidney–brain crosstalk in the acute and chronic setting , 2015, Nature Reviews Nephrology.

[34]  Kang K. L. Liu,et al.  Network Physiology: How Organ Systems Dynamically Interact , 2015, PLoS ONE.

[35]  R. Freeman,et al.  Neurometabolic coupling between neural activity, glucose, and lactate in activated visual cortex , 2015, Journal of neurochemistry.

[36]  M. Carabotti,et al.  The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems , 2015, Annals of gastroenterology.

[37]  Tomasz Arodz,et al.  Identifying influential nodes in a wound healing-related network of biological processes using mean first-passage time , 2015 .

[38]  Bruce R. Rosen,et al.  Dynamic functional imaging of brain glucose utilization using fPET-FDG , 2014, NeuroImage.

[39]  Alan C. Evans,et al.  Hierarchical multivariate covariance analysis of metabolic connectivity , 2014, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[40]  F. Lammert,et al.  Bacterial infections in cirrhosis: a position statement based on the EASL Special Conference 2013. , 2014, Journal of hepatology.

[41]  Jie Xiang,et al.  Resting-state functional connectivity abnormalities in first-onset unmedicated depression , 2014, Neural regeneration research.

[42]  G. Tarantino,et al.  Liver-spleen axis: intersection between immunity, infections and metabolism. , 2013, World journal of gastroenterology.

[43]  D. Attwell,et al.  Synaptic Energy Use and Supply , 2012, Neuron.

[44]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[45]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.