Brain Volume Changes after COVID-19 Compared to Healthy Controls by Artificial Intelligence-Based MRI Volumetry.

peer reviewedCohort studies that quantify volumetric brain data among individuals with different levels of COVID-19 severity are presently limited. It is still uncertain whether there exists a potential correlation between disease severity and the effects of COVID-19 on brain integrity. Our objective was to assess the potential impact of COVID-19 on measured brain volume in patients with asymptomatic/mild and severe disease after recovery from infection, compared with healthy controls, using artificial intelligence (AI)-based MRI volumetry. A total of 155 participants were prospectively enrolled in this IRB-approved analysis of three cohorts with a mild course of COVID-19 (n = 51, MILD), a severe hospitalised course (n = 48, SEV), and healthy controls (n = 56, CTL) all undergoing a standardised MRI protocol of the brain. Automated AI-based determination of various brain volumes in mL and calculation of normalised percentiles of brain volume was performed with mdbrain software, using a 3D T1-weighted magnetisation-prepared rapid gradient echo (MPRAGE) sequence. The automatically measured brain volumes and percentiles were analysed for differences between groups. The estimated influence of COVID-19 and demographic/clinical variables on brain volume was determined using multivariate analysis. There were statistically significant differences in measured brain volumes and percentiles of various brain regions among groups, even after the exclusion of patients undergoing intensive care, with significant volume reductions in COVID-19 patients, which increased with disease severity (SEV > MILD > CTL) and mainly affected the supratentorial grey matter, frontal and parietal lobes, and right thalamus. Severe COVID-19 infection, in addition to established demographic parameters such as age and sex, was a significant predictor of brain volume loss upon multivariate analysis. In conclusion, neocortical brain degeneration was detected in patients who had recovered from SARS-CoV-2 infection compared to healthy controls, worsening with greater initial COVID-19 severity and mainly affecting the fronto-parietal brain and right thalamus, regardless of ICU treatment. This suggests a direct link between COVID-19 infection and subsequent brain atrophy, which may have major implications for clinical management and future cognitive rehabilitation strategies

Biologically Inspired Semantic Lateral Connectivity for Convolutional Neural Networks

Lateral connections play an important role for sensory processing in visual cortex by supporting discriminable neuronal responses even to highly similar features. In the present work, we show that establishing a biologically inspired Mexican hat lateral connectivity profile along the filter domain can significantly improve the classification accuracy of a variety of lightweight convolutional neural networks without the addition of trainable network parameters. Moreover, we demonstrate that it is possible to analytically determine the stationary distribution of modulated filter activations and thereby avoid using recurrence for modeling temporal dynamics. We furthermore reveal that the Mexican hat connectivity profile has the effect of ordering filters in a sequence resembling the topographic organization of feature selectivity in early visual cortex. In an ordered filter sequence, this profile then sharpens the filters' tuning curves

Effective high-throughput isolation of fully human antibodies targeting infectious pathogens

As exemplified by the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is a strong demand for rapid high-throughput isolation pipelines to identify potent neutralizing antibodies for prevention and therapy of infectious diseases. However, despite substantial progress and extensive efforts, the identification and production of antigen-specific antibodies remains labor- and cost-intensive. We have advanced existing concepts to develop a highly efficient high-throughput protocol with proven application for the isolation of potent antigen-specific antibodies against human immunodeficiency virus 1, hepatitis C virus, human cytomegalovirus, Middle East respiratory syndrome coronavirus, SARS-CoV-2 and Ebola virus. It is based on computationally optimized multiplex primer sets (openPrimeR), which guarantee high coverage of even highly mutated immunoglobulin gene segments as well as on optimized antibody cloning and production strategies. Here, we provide the detailed protocol, which covers all critical steps from sample collection to antibody production within 12-14 d. The authors provide a highly efficient high-throughput protocol for the isolation of potent pathogen-specific antibodies

Proton Density Fat Fraction Spine MRI for Differentiation of Erosive Vertebral Endplate Degeneration and Infectious Spondylitis

Vertebral Modic type 1 (MT1) degeneration may mimic infectious disease on conventional spine magnetic resonance imaging (MRI), potentially leading to additional costly and invasive investigations. This study evaluated the diagnostic performance of the proton density fat fraction (PDFF) for distinguishing MT1 degenerative endplate changes from infectious spondylitis. A total of 31 and 22 patients with equivocal diagnosis of MT1 degeneration and infectious spondylitis, respectively, were retrospectively enrolled in this IRB-approved retrospective study and examined with a chemical-shift encoding (CSE)-based water-fat 3D six-echo modified Dixon sequence in addition to routine clinical spine MRI. Diagnostic reference standard was established according to histopathology or clinical and imaging follow-up. Intravertebral PDFF [%] and PDFFratio (i.e., vertebral endplate PDFF/normal vertebrae PDFF) were calculated voxel-wise within the single most prominent edematous bone marrow lesion per patient and examined for differences between MT1 degeneration and infectious spondylitis. Mean PDFF and PDFFratio of infectious spondylitis were significantly lower compared to MT1 degenerative changes (mean PDFF, 4.28 ± 3.12% vs. 35.29 ± 17.15% [p p p p < 0.001) and 98.1% (cut-off at 0.27) for PDFFratio. Our data suggest that quantitative evaluation of vertebral PDFF can provide a high diagnostic accuracy for differentiating erosive MT1 endplate changes from infectious spondylitis

Diagnostic Accuracy of Quantitative Imaging Biomarkers in the Differentiation of Benign and Malignant Vertebral Lesions

Purpose!#!To compare and combine the diagnostic performance of the apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) and proton density fat fraction (PDFF) derived from chemical-shift encoding (CSE)-based water-fat magnetic resonance imaging (MRI) for distinguishing benign and malignant vertebral bone marrow lesions (VBML).!##!Methods!#!A total of 55 consecutive patients with 53 benign (traumatic, inflammatory and primary) and 36 malignant (metastatic and hematologic) previously untreated VBMLs were prospectively enrolled in this IRB-approved study and underwent sagittal DWI (single-shot spin-echo echo-planar with multi-slice short TI inversion recovery fat suppression) and CSE-based MRI (gradient-echo 6‑point modified Dixon) in addition to routine clinical spine MRI at 1.5 T or 3.0 T. Diagnostic reference standard was established according to histopathology or imaging follow-up. The ADC = ADC (0, 800) and PDFF = fat / (water + fat) were calculated voxel-wise and examined for differences between benign and malignant lesions.!##!Results!#!The ADC and PDFF values of malignant lesions were significantly lower compared to benign lesions (mean ADC 861 × 10!##!Conclusion!#!Quantitative evaluation of both ADC and PDFF was useful in differentiating benign VBMLs from malignancy. The combination of ADC and PDFF improved the diagnostic performance and yielded high diagnostic accuracy for the differentiation of benign and malignant VBMLs