The Posterior Dominant Rhythm Remains Within Normal Limits in the Microgravity Environment.

Background: Electroencephalogram (EEG) biomarkers with adequate sensitivity and specificity to reflect the brain's health status can become indispensable for health monitoring during prolonged missions in space. The objective of our study was to assess whether the basic features of the posterior dominant rhythm (PDR) change under microgravity conditions compared to earth-based scalp EEG recordings.

Methods: Three crew members during the 16-day AXIOM-1 mission to the International Space Station (ISS), underwent scalp EEG recordings before, during, and after the mission by means of a dry-electrode self-donning headgear designed to support long-term EEG recordings in space.

Fat beyond muscle: Assessing epimuscular fat of the lumbar spine and its association with vertebral level, demographics, BMI, and low back pain.

Introduction: Epimuscular fat (EF) has rarely been studied in the context of low back pain (LBP).

Research Question: This study aims to assess the presence and extent of EF in the lumbar muscles and its association with vertebral level in patients with low back disorders and to explore correlations between EF, demographics, BMI, and LBP.

Material And Methods: T2 axial MRIs from L1 to L5 were manually segmented to analyze the cross-sectional area (CSA) of EF (mm), and fat infiltration (FI,%) of 40 patients (23 females, 17 males; mean age:65.

Methodological considerations in calculating the minimal clinically important change score for the core outcome measures index (COMI): insights from a large single-centre spine surgery registry.

Introduction: The Minimal Clinically Important Change (MCIC) is used in conjunction with Patient-Reported Outcome Measures (PROMs) to determine the clinical relevance of changes in health status. MCIC measures a change within the same person or group over time. This study aims to evaluate the variability in computing MCIC for the Core Outcome Measure Index (COMI) using different methods.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

Purpose: This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol.

Methods: A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.