Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Publisher Copyright: © 2022, The Author(s).Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior–posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.Peer reviewe

Primary prevention of diabetic retinopathy with fibrates: a retrospective, matched cohort study

Objectives To compare the progression of diabetic retinopathy (DR) in people with type 2 diabetes treated with fibrates with that of non-exposed controls. Design Retrospective, matched cohort study. Setting UK Clinical Practice Research Datalink (CPRD). Participants 5038 people with type 2 diabetes with a history of fibrate exposure but without evidence of DR were identified. Three thousand one hundred and seventy-six (63%) people could be randomly matched to one non-exposed control; of these, 2599 (81.8%) were matched without any missing blood pressure or glycated haemoglobin (HbA1c) values. Main outcome measures The primary endpoint was first recorded DR with a secondary endpoint of all-cause mortality or first DR. Time to clinical endpoints was compared using Cox proportional hazards models. Results Mean follow-up was 5.1 and 5.0 years for fibrate-exposed and non-exposed patients, respectively. For fibrate-exposed participants, there was a reduction in DR: 33.4 events/1000 person-years vs 40.4 (p=0.002), and in death or DR: 50.6 vs 60.2 (p<0.001). For those matched with full systolic blood pressure and HbA1c data, crude event rates were 34.3 versus 43.9 for DR (p<0.001) and 51.2 vs 63.4 (p<0.001) for death or DR. Following adjustment, DR was significantly delayed for those treated with fibrates, with an adjusted HR (aHR) of 0.785 (p<0.001) for participants with complete data and an aHR of 0.802 (p<0.001) for all participants. Conclusions The treatment with fibrates in people with type 2 diabetes was independently associated with reduced progression to a first diagnosis of DR

Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (IMS). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for IMS). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans

CT-and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements