A Picture Is Worth a Thousand Dollars

<it>David Linden read medicine, classics and philosophy in Germany. He obtained a DPhil from the University of Oxford for his work on medical ethics in antiquity in 2000 and a Dr. med. in neuroscience at the Max Planck Institute for Brain Research in 1999. He trained in psychiatry at Frankfurt University and has a special interest in clinical neuropsychiatry and neurodevelopmental genetic syndromes. Since 2011 he has been Professor of Translational Neuroscience at Cardiff University and Head of the Neuroimaging theme group of the MRC Centre for Neuropsychiatric Genetics and Genomics. He is also the director for clinical research at the Cardiff University Brain Research Imaging Centre. In his research, he applies structural and functional magnetic resonance imaging (MRI), neurophysiological techniques and genetics in order to understand the function of the brain in health and disease. Current research interests include functional imaging of psychopathology, neural substrates of social cognition and decision making, genetic imaging, treatment and training effects on the brain, and neurofeedback. David is the author of “The Biology of Psychological Disorders” (2012), “Brain Control” (2014) and “Neuroimaging and Neurophysiology in Psychiatry” (2016) and over 180 scientific papers in the fields of biological psychiatry, functional brain imaging, visual and social cognitive neuroscience, and neurofeedback. He has coordinated trials of functional MRI-based neurofeedback in depression and Parkinson’s disease. He is also the coordinator of the European Consortium “BRAINTRAIN” (www.braintrainproject.eu), funded by the European Commission under the 7th Framework Programme, which develops and evaluates imaging-based neurofeedback methods for a range of mental disorders. He is a consultant psychiatrist with the Cardiff and Vale University Health Board and provides clinics at the Welsh Neuropsychiatric Service, the Cardiff University Psychiatric Service (specialising in neurodevelopmental genetic syndromes) and the Welsh Huntington’s Disease Service. He has been a member of the board of directors of the BNPA since 2014.</it> The techniques of brain imaging and neurophysiology (both invasive and non-invasive) hold considerable promise for the understanding of human mental processes. One branch of neurophysiology and cognitive neuroscience research is concerned with the inference of particular mental states from neural activation patterns. One of the aims of this research is to identify intentions in patients with severe communication disorders (for example the “locked-in” state). Another proposed application is in the distinction between disorders of consciousness and disorders of communication. Recent advances in the analysis of functional brain images that allow for the classification of mental states also raise the question whether these techniques can be used to underpin psychiatric diagnoses, possibly even support or refute patients’ claim about their own mental states. This talk discusses the clinical promise and also the technical and ethical limitations of these approaches and puts them in the context of the history and future of neuropsychiatry. <sec><st>References</st> <l type="ord"><li> . David Linden. Brain Control – Developments in Therapy and Implications for Society. Palgrave Macmillan 2014. </li><li> . Sarah Richmond, Geraint Rees, Sarah JL, Edwards (eds). I know what you’re thinking – Brain imaging and mental privacy. Oxford University Press 2012. </li></l> </sec>

Limited sample size hinders the application of deep learning in brain image analysis, and transfer learning is a possible solution. However, most pretrained models are 2D based and cannot be applied directly to 3D brain images. In this study, we propose a novel framework to apply 2D pretrained models to 3D brain images by projecting surface-based cortical morphometry into planar images using computational geometry mapping. Firstly, 3D cortical meshes are reconstructed from magnetic resonance imaging (MRI) using FreeSurfer and projected into 2D planar meshes with topological preservation based on area-preserving geometry mapping. Then, 2D deep models pretrained on ImageNet are adopted and fine-tuned for cortical image classification on morphometric shape metrics. We apply the framework to sex classification on the Human Connectome Project dataset and autism spectrum disorder (ASD) classification on the Autism Brain Imaging Data Exchange dataset. Moreover, a 2-stage transfer learning strategy is suggested to boost the ASD classification performance by using the sex classification as an intermediate task. Our framework brings significant improvement in sex classification and ASD classification with transfer learning. In summary, the proposed framework builds a bridge between 3D cortical data and 2D models, making 2D pretrained models available for brain image analysis in cognitive and psychiatric neuroscience.

PurposeTo systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI.MethodsArticles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool.ResultsIn all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02.ConclusionsMachine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

Afterglow luminescence imaging ingeniously circumvents the need for real‐time excitation, thereby substantially eliminating background interference. Nevertheless, its application in brain imaging has been hindered by low afterglow brightness under aqueous conditions. Here, we present naked‐eye‐visible afterglow nanoprobes excited by low‐power light for high‐contrast imaging of brain inflammation. By strategically integrating highly efficient donor–acceptor–donor (D–A–D) luminescent molecules into photochemical afterglow systems, we developed a series of ultrabright afterglow materials emitting in the yellow, orange, and red spectral regions. The resulting afterglow nanoparticles remain naked‐eye detectable even under ultralow excitation power (0.73 mW cm −2 ). Their afterglow brightness is over 1300 times higher than that of commonly used afterglow nanoparticles, and they still maintain a 3‐fold advantage compared to previously developed blue‐emitting nanoparticles based on molecular fusion strategies. Leveraging this exceptional performance, we accomplished real‐time naked‐eye observation of freely moving mice. Moreover, macrophage‐encapsulated nanoparticles enabled blood–brain barrier (BBB) penetration and high‐contrast imaging of brain inflammation. This work introduces a new paradigm for constructing high‐brightness afterglow materials and opens transformative avenues for real‐time visualization of brain disorders.

Afterglow luminescence imaging ingeniously circumvents the need for real-time excitation, thereby substantially eliminating background interference. Nevertheless, its application in brain imaging has been hindered by low afterglow brightness under aqueous conditions. Here, we present naked-eye-visible afterglow nanoprobes excited by low-power light for high-contrast imaging of brain inflammation. By strategically integrating highly efficient donor-acceptor-donor (D-A-D) luminescent molecules into photochemical afterglow systems, we developed a series of ultrabright afterglow materials emitting in the yellow, orange, and red spectral regions. The resulting afterglow nanoparticles remain naked-eye detectable even under ultralow excitation power (0.73mWcm-2). Their afterglow brightness is over 1300 times higher than that of commonly used afterglow nanoparticles, and they still maintain a 3-fold advantage compared to previously developed blue-emitting nanoparticles based on molecular fusion strategies. Leveraging this exceptional performance, we accomplished real-time naked-eye observation of freely moving mice. Moreover, macrophage-encapsulated nanoparticles enabled blood-brain barrier (BBB) penetration and high-contrast imaging of brain inflammation. This work introduces a new paradigm for constructing high-brightness afterglow materials and opens transformative avenues for real-time visualization of brain disorders.

Motivation: Motion during MRI can severely impact image quality, necessitating advanced techniques to accurately sense and compensate for head movements. Goal(s): This study aims to develop a novel algorithm for high-fidelity motion sensing and position estimation for 3D Radial head scan. Approach: By using Region-Optimized Virtual Coils to move the coil profile and Fourier Slice Theorem to calculate center-of-mass from few spokes, the algorithm detects positional changes and estimates rigid-body motion parameters in high-temporally. Results: Experimental validation using simulated and real datasets demonstrated effective motion detection and significant improvements in image quality, showcasing the method's robustness in motion correction. Impact: This research offers a groundbreaking approach to high-temporal motion sensing in MRI, enhancing image quality through accurate position estimation. It opens avenues for future investigations into motion compensation techniques, ultimately benefiting clinical applications in brain imaging and diagnostics.

Misophonia has gained attention in scientific circles that utilise brain imaging to validate diagnoses. The condition is promoted as not merely a symptom of other psychiatric diagnoses but as a discrete clinical entity. We illustrate the social construction of the diagnostic category of misophonia through examining prominent claims in research studies that use brain imaging to substantiate the diagnosis. We show that brain images are insufficient to establish the 'brain basis for misophonia' due to both technical and logical limitations of imaging data. Often misunderstood as providing direct access to the matter of the body, brain images are mediated and manipulated numerical data (Joyce, 2005, Social Studies of Science 35(3), p. 437). Interpretations of brain scans are further shaped by social expectations and attributes considered salient to the data. Causal inferences drawn from these studies are problematic because 'misophonics' are clinically pre-diagnosed before participating. We argue that imaging cannot replace the social process of diagnosis in the case of misophonia, nor validate diagnostic measures or otherwise substantiate the condition. More broadly, we highlight both the cultural authority and inherent limitations of brain imaging in the social construction of contested diagnoses while also illustrating its role in the disaggregation of symptoms into new diagnoses.

Near-infrared (NIR) brain imaging is one of the most promising techniques for brain research in recent years. As a significant supplement to the clinical imaging technique, such as CT and MRI, the NIR technique can achieve a fast, non-invasive, and low cost imaging of the brain, which is widely used for the brain functional imaging and hematoma detection. NIR imaging can achieve an imaging depth up to only several centimeters due to the reduced optical attenuation. The structure of the human brain is so particularly complex, from the perspective of optical detection, the measurement light needs go through the skin, skull, cerebrospinal fluid (CSF), grey matter, and white matter, and then reverses the order reflected by the detector. The more photons from the Depth of Interest (DOI) in brain the detector capture, the better detection accuracy and stability can be obtained. In this study, the Equivalent Signal to Noise Ratio (ESNR) was defined as the proportion of the photons from the DOI to the total photons the detector evaluated the best Source and Detector (SD) separation. The Monte-Carlo (MC) simulation was used to establish a multi brain layer model to analyze the distribution of the ESNR along the radial direction for different DOIs and several basic brain optical and structure parameters. A map between the best detection SD separation, in which distance the ESNR was the highest, and the brain parameters was established for choosing the best detection point in the NIR brain imaging application. The results showed that the ESNR was very sensitivity to the SD separation. So choosing the best SD separation based on the ESNR is very significant for NIR brain imaging application. It provides an important reference and new thinking for the brain imaging in the near infrared.

Small-animal positron emission tomography as a tool for neuropharmacology

Microwave imaging (MWI) has emerged as a promising non-invasive diagnostic technology in medical applications, offering significant advantages over conventional imaging modalities including reduced radiation exposure, cost-effectiveness, and enhanced patient comfort. This comprehensive survey presents a systematic review of MWI through a novel bifurcated taxonomy addressing both methodological aspects (imaging techniques, deep learning approaches, and antenna design) and application domains (breast imaging, brain imaging, and stroke diagnostics). The methodological analysis reveals substantial progress in reconstruction algorithms, with physics-assisted deep learning frameworks demonstrating superior performance in automated thresholding and bias reduction compared to traditional qualitative imaging methods. Advanced techniques like MDLI-Net and DL-MITAT have shown particular promise in handling sparse data scenarios. In antenna technologies, ultra-wideband antennas and miniaturized millimeter-wave sensors represent significant breakthroughs, with some systems capable of detecting malignancies as small as 1mm. Application-wise, brain imaging applications have achieved remarkable accuracy rates exceeding 94% using YOLOv5 models, while breast imaging systems demonstrate effective tumor detection in dense tissues where conventional mammography faces limitations. Despite these advancements in computational methods, particularly deep learning and sophisticated reconstruction algorithms, several challenges remain in translating MWI technology from experimental settings to widespread clinical adoption. Our contributions include structured analysis of recent advances in MWI techniques, critical insights into challenges across medical applications, and identification of research gaps and future directions. Key findings demonstrate that combining qualitative imaging with deep neural networks achieves reliable real-time reconstruction capabilities. While MWI shows considerable potential as a safe, cost-effective alternative to traditional imaging techniques, particularly for breast and brain imaging, further research in computational efficiency, clinical validation, and standardization protocols is necessary for successful clinical implementation across diverse healthcare settings.

Mobile computed tomography (CT) with a cone-beam source is increasingly used in the clinical field. Mobile cone-beam CT (CBCT) has great merits; however, its clinical utility for brain imaging has been limited due to problems including scan time and image quality. The aim of this study was to develop a dedicated mobile volumetric CBCT for obtaining brain images, and to optimize the imaging protocol using a brain phantom. The mobile volumetric CBCT system was evaluated with regards to scan time and image quality, measured as signal-to-noise-ratio (SNR), contrast-to-noise-ratio (CNR), spatial resolution (10% MTF), and effective dose. Brain images were obtained using a CT phantom. The CT scan took 5.14 s at 360 projection views. SNR and CNR were 5.67 and 14.5 at 120 kV/10 mA. SNR and CNR values showed slight improvement as the x-ray voltage and current increased (p < 0.001). Effective dose and 10% MTF were 0.92 mSv and 360 μ m at 120 kV/10 mA. Various intracranial structures were clearly visible in the brain phantom images. Using this CBCT under optimal imaging acquisition conditions, it is possible to obtain human brain images with low radiation dose, reproducible image quality, and fast scan time.

Brain imaging is a persuasive visual rhetoric by which neuroscience is articulated as relevant to the construction and maintenance of desirable selves. In this essay, I describe how "brain-based self-help" literature disseminates neuroscientific vocabularies to public audiences. In this genre, brain images are an authoritative visual resource for translating neuroscience into a comprehensive program for living. I use Foucault's discussion of biopower to describe the ways in which brain-based self-help literature enables self-constitution in a biosocial age where health is a central means of communicating personal worth, social value and political order. The implications of this continuous self-fashioning are not limited to the personal realm but have important political consequences.

ADHD IN OLDER ADULTS: A CASE REPORT LITERATURE AND REVIEW OF THE

Neural consequences of post-exertion malaise in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Functional MRI (fMRI) generally employs gradient-echo echo-planar imaging (GE-EPI) to measure blood oxygen level-dependent (BOLD) signal changes that result from changes in tissue relaxation time T(*) (2) between activation and rest. Since T(*) (2) strongly varies across the brain and BOLD contrast is maximal only where the echo time (TE) equals the local T(*) (2), imaging at a single TE is a compromise in terms of overall sensitivity. Furthermore, the long echo train makes EPI very sensitive to main field inhomogeneities, causing strong image distortion. A method is presented that uses accelerated parallel imaging to reduce image artifacts and acquire images at multiple TEs following a single excitation, with no need to increase TR. Sensitivity gains from the broadened T(*) (2) coverage are optimized by pixelwise weighted echo summation based on local T(*) (2) or contrast-to-noise ratio (CNR) measurements. The method was evaluated using an approach that allows differential BOLD CNR to be calculated without stimulation, as well as with a Stroop experiment. Results obtained at 3 T showed that BOLD sensitivity improved by 11% or more in all brain regions, with larger gains in areas typically affected by strong susceptibility artifacts. The use of parallel imaging markedly reduces image distortion, and hence the method should find widespread application in functional brain imaging.