Objective: Visual snow (VS) is a distressing, life-impacting condition with persistent visual phenomena. VS patients show cerebral hypermetabolism within the visual cortex, resulting in altered neuronal excitability. We hypothesized to see disease-dependent alterations in functional connectivity and gray matter volume (GMV) in regions associated with visual perception. Methods: Nineteen patients with VS and 16 sex-and age-matched controls were recruited. Functional magnetic resonance imaging (fMRI) was applied to examine resting-state functional connectivity (rsFC). Volume changes were assessed by means of voxel-based morphometry (VBM). Finally, we assessed associations between MRI indices and clinical parameters. Results: Patients with VS showed hyperconnectivity between extrastriate visual and inferior temporal brain regions and also between prefrontal and parietal (angular cortex) brain regions (p < 0.05, corrected for age and migraine occurrence). In addition, patients showed increased GMV in the right lingual gyrus (p < 0.05 corrected). Symptom duration positively correlated with GMV in both lingual gyri (p < 0.01 corrected). Conclusion: This study found VS to be associated with both functional and structural changes in the early and higher visual cortex, as well as the temporal cortex. These brain regions are involved in visual processing, memory, spatial attention, and cognitive control. We conclude that VS is not just confined to the visual system and that both functional and structural changes arise in VS patients, be it as an epiphenomenon or a direct contributor to the pathomechanism of VS. These in vivo neuroimaging biomarkers may hold potential as objective outcome measures of this so far purely subjective condition.

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

[F]FCh uptake of parathyroid adenomas is strongly correlated with preoperative PTH serum concentration. Therefore, the preoperative PTH level might potentially be able to predict success of [F]FCh-PET imaging in hyperparathyroidism, with higher lesion-to-background ratios being expected in patients with high PTH. PET/MR is accurate in estimating the volume of parathyroid adenomas.

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