Yes they can! If you want to know how, check our latest publication in Clinical and Translational Imaging journal
Special thanks to NIHR Maudsley Biomedical Research Centre (BRC) and the Service User Advisory Group
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CHINESE TRANSLATION OF EXPERIMENTAL DESIGN AND PRACTICAL DATA ANALYSIS IN PET
本书内容提供 PET 研究的理论与务实概念，包含实验设计、数据采集与处理、依据动力学模型的资料量化以及数据分析。
本书中文译本始于学界同仁与学生的需求。当今中国的 PET 临床应用与研发规模已超过英国十倍有余，我们向如此蓬勃发展的PET学术与临床研究同仁致敬！并且期盼我们在此所尽的绵薄之力会是有意义的贡献。
我们诚挚感谢范甄博士、蔡之正先生、艾明医师以及王少楠博士，没有他们的帮助，本书的中文版将无法面世。
How does chronic pain shapes the anatomy of the brain? What biological pathways might explain the vulnerability of certain brain regions to these changes? Here, we examined alterations in morphometric similarity and applied an integrative imaging transcriptomics approach to identify transcriptional and cellular correlates of these MS changes, in three independent small cohorts of patients with distinct chronic pain syndromes (knee osteoarthritis, low back pain and fibromyalgia) and age and sex-matched pain-free controls.
When dealing with very large imaging datasets, it becomes hard to perform quality control (QC) of the data collected. Several solutions have been proposed in literature including the use of Artificial Intelligence (AI) to perform automated QC of MRI and EEG on both single-site and multi-site datasets.
Given the successful studies using Convolutional Neural Networks (CNNs) when working with medical images, we trained a CNN – DenseNet – to test whether an automated QC pipeline for brain [18F]-FDOPA PET imaging as a biomarker for the dopamine system could be implemented to return the expected output classification with a minimal error.
Particularly, we trained DenseNet for two different classification problems: the assessment of the image alignment to a standard template and the assessment of the SNR of the images compared to those that were manually-assessed. Both CNNs returned a high accuracy on the training and the independent test datasets. The method is far from clinical use, but these encouraging results support the application of deep learning for automated quality control of medical imaging data.
We are currently carrying some renovation work on our website but we will come back soon full of new content and exciting updates. Apologies for the inconvenience. We will do our best to minimise the disruption.
We are creating a group repository at https://github.com/molecular-neuroimaging for sharing our codes and programs with the rest of the brain imaging community.
And if you need assistance with anything please do not hesitate to get in touch!
“Vincent van Gogh was one of the most influential artists of the Western world, having shaped the post-impressionist art movement by shifting its boundaries forward into abstract expressionism. His distinctive style, which was not valued by the art-buying public during his lifetime, is nowadays one of the most sought after. However, despite the great deal of attention from academic and artistic circles, one important question remains open: was van Gogh’s original style a visual manifestation distinct from his troubled mind, or was it in fact a by-product of an impairment that resulted from the psychiatric illness that marred his entire life?”
In this paper, a team of scientists from our Neuroimaging and Computational modelling groups quantitatively analyze the brushwork of his large production of self-portraits using the image autocorrelation and demonstrate a strong association between the contrasts in the paintings, the occurrence of psychiatric symptoms, and his simultaneous use of absinthe—a strong liquor known to affect gamma aminobutyric acid (GABA) alpha receptors. They propose that van Gogh suffered from a defective function of parvalbumin interneurons, which seems likely given his family history of schizophrenia and his addiction to substances associated with GABA action. This could explain the need for the artist to increasingly amplify the contrasts in his brushwork as his disease progressed, as well as his tendency to merge esthetic and personal experiences into a new form of abstraction.
Viribus Unitis (Lit. “Union is strength”) is becoming more and more important to tackle the challenges of modern experimental medicine research. For this reason, in collaboration with Cardiff University and other academic institutions across the world we launched the PET Partnership UK (PPUK). PPUK is a platform built to share recourses and expertise for PET imaging. It aims to be an open and democratic community to help scientists working in PET when no local support is available.
For further information visit PPUK website or stay in touch via Twitter
Research on brain glymphatic system and its function continues in Multiple Sclerosis and Depression. Our group has been invited to present the latest findings at the ARSEP annual meeting on “Imaging (G?)lymphatic Drainage in Multiple Sclerosis” (ICM, Paris) and at the NIMA consortium face2face workshop (Academy of Medical Sciences, Wellcome Trust, London). A couple of methodological publications are on their way, with the hope to develop a sensitive and effective imaging method for measuring this system in the living human brain.
