James M. Elliott’s research while affiliated with The University of Sydney and other places

Study Design: Prospective Randomized Controlled Trial. Objectives: To investigate the effect of combined motor control and isolated lumbar strengthening exercise (MC + ILEX) vs general exercise (GE) on upper lumbar paraspinal muscle volume and composition, strength and patient outcomes in individuals with chronic low back pain (LBP). Methods: 50 participants with nonspecific chronic LBP were randomly allocated (1:1) to each group (MC + ILEX or GE) and underwent a 12-week supervised intervention program 2 times per week. Magnetic resonance imaging was performed at baseline, 6-weeks and 12-weeks to examine the impact of each intervention on multifidus (MF) and erector spinae (ES) muscle volume (cm3) and fatty infiltration (%FI) at L1-L2, L2-L3 and L3-L4. Results: Our results revealed no significant between-groups findings for MF and ES %FI and volume, and patient-reported psychosocial measures. However, both groups had significant within-groups decreases in MF %FI at L1-L2, L2-L3 and L3-L4, with concomitant decreases in MF volume at L1-L2 and L2-L3, and at L3-L4 in the GE group. Each group displayed significant improvements in Kinesiophobia, while only MC + ILEX had significant improvements in pain catastrophizing, anxiety, depression and sleep. Lastly, significant correlations were found between change in Kinesiophobia and upper lumbar MF %FI, and between change in strength and lower lumbar MF and ES size. Conclusions: Both exercise interventions may help reduce upper lumbar MF %FI in individuals with chronic LBP, while MC + ILEX could significantly improve important patient outcomes. Our results support the idea that improvements in paraspinal muscle health associate with better patient outcomes. Further high-quality imaging studies are needed to explore these relationships.

Background Maintaining skeletal muscle health (i.e., muscle size and quality) is crucial for preserving mobility. Decreases in lower limb muscle volume and increased intramuscular fat (IMF) are common findings in people with impaired mobility. We developed an automated method to extract markers of leg muscle health, muscle volume and IMF, from MRI. We then explored their associations with age, body mass index (BMI), sex and voluntary force generation. Methods We trained (n = 34) and tested (n = 16) a convolutional neural network (CNN) to segment five muscle groups in both legs from fat‐water MRI to explore muscle volume and IMF. In 95 participants (70 females, 25 males, mean age [standard deviation] = 34.2 (11.2) years, age range = 18–60 years), we explored associations between the CNN measures and age, BMI and sex, and then in a subset of 75 participants, we explored associations between CNN muscle volume, CNN IMF and maximum plantarflexion force after controlling for age, BMI and sex. Results The CNN demonstrated high test accuracy (Sørensen–Dice index ≥ 0.87 for all muscle groups) and reliability (muscle volume ICC2,1 ≥ 0.923 and IMF ICC2,1 ≥ 0.815 for all muscle groups) compared to manual segmentation. CNN muscle volume was positively associated with BMI across all muscle groups (p ≤ 0.001) but not with age (p ≥ 0.406). CNN IMF was positively associated with age for all muscle groups (p ≤ 0.015), and CNN IMF was positively associated with BMI for all muscle groups (p ≤ 0.043) except the right deep posterior compartment (p = 0.130). Males had greater CNN volume of all muscle groups (p < 0.001) except the left and right gastrocnemius (p ≥ 0.067). Gastrocnemius CNN IMF was greater in females (p ≤ 0.043). Plantarflexion force was positively associated with lateral compartment, soleus and gastrocnemius CNN volume (p ≤ 0.025) but not with CNN IMF (p ≥ 0.358). Conclusions Computer‐vision models combined with fat‐water MRI permits the non‐invasive, automatic assessment of leg muscle volume and IMF. Associations with age, BMI and sex are important when interpreting these measures. Markers of leg muscle health may enhance our understanding of the relationship between muscle health, force generation and mobility. Trial Registration ClinicalTrials.gov identifier: NCT02157038

Space agencies plan crewed missions to the Moon and Mars. However, microgravity-induced lumbopelvic deconditioning, characterized by an increased fat fraction (FF) due to reduced physical activity, poses a significant challenge to spine health. This study investigates the spatial distribution of FF in the lumbopelvic muscles to identify the most affected regions by deconditioning, utilizing a computer-vision model and a tile-based approach to assess FF changes. Twenty-four healthy individuals (8F) were recruited, and automatic segmentation of the lumbopelvic muscles was applied before and after 59 days of head-down tilt bed rest (HDTBR+59) and 13 days of reconditioning (R+13). Axial Dixon sequence images were acquired from 3T magnetic resonance imaging. FF in the lumbar multifidus (LM), lumbar erector spinae (LES), quadratus lumborum, psoas major, gluteus maximus (GMax), gluteus medius (GMed) and gluteus minimus (GMin) muscles from the upper margin of L1 vertebra to the inferior border of GMax muscle were automatically derived using a computer-vision model. Lumbar muscles were segmented into eight tiles (superficial and deep, lateral to medial), and gluteal muscles into regions (anterior/superior for GMed and GMin, superior/inferior for GMax). At HDTBR+59, the deep centro-lateral region at L5/S1 for LM (18.7±15.7%, p<0.001; d=0.97) and the deep medial region at Upper L4 for LES (5.4±5.9%, p<0.001; d=0.34) showed the largest increase in FF compared to BDC. These regions did not recover at R+13 (p<0.05; d≥0.25). These findings highlight the need to target deep fascicles of LM and LES in countermeasure strategies to mitigate microgravity-induced lumbopelvic deconditioning, optimizing spine health and performance.

Computational neurosurgery is a novel and disruptive field where artificial intelligence and computational modeling are used to improve the diagnosis, treatment, and prognosis of patients affected by diseases of neurosurgical relevance. The field aims to bring new knowledge to clinical neurosciences and inform on the profound questions related to the human brain by applying augmented intelligence, where the power of artificial intelligence and computational inference can enhance human expertise. This transformative field requires the articulation of ethical considerations that will enable scientists, engineers, and clinical neuroscientists, including neurosurgeons, to ensure that the use of such a powerful application is conducted based on the highest moral and ethical standards with a patient-centric approach to predict and prevent mistakes. This declaration is a first attempt to draw a roadmap to guide the application of practical or applied ethics to computational neurosurgery. It is intended for the use of practitioners, ethicists, and scientists using artificial intelligence to understand and treat all the pathophysiological conditions related to the human brain.

Disorders affecting the neurological and musculoskeletal systems represent international health burdens. A significant impediment to progress with interventional trials is the absence of responsive, objective, and valid outcome measures sensitive to early disease or disorder change. A key finding in individuals with spinal disorders is compositional changes to the paraspinal muscle and soft tissue (e.g., intervertebral disc, facet joint capsule, and ligamentous) structure. Quantification of paraspinal muscle composition by MRI has emerged as a sensitive marker for the severity of these conditions; however, little is known about the composition of muscles across the lifespan. Knowledge of what is “typical” age-related muscle composition is essential in order to accurately identify and evaluate “atypical,” with a potential impact being improvements in pre- and postsurgical plan and measurement of surgical implants, exoskeletons, and care on a patient-by-patient basis.

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in individuals with neuromuscular and musculoskeletal disorders is the compositional changes to muscles, evinced by the expression of fatty infiltrates. Quantification of skeletal muscle composition by MRI has emerged as a sensitive marker for the severity of these disorders; however, little is known about the composition of healthy muscles across the lifespan. Knowledge of what is ‘typical’ age-related muscle composition is essential to accurately identify and evaluate what is ‘atypical’. This innovative project, known as the MuscleMap, will achieve the first important steps towards establishing a world-first, normative reference MRI dataset of skeletal muscle composition with the potential to provide valuable insights into various diseases and disorders, ultimately improving patient care and advancing research in the field.

Complex Musculoskeletal (MSK) pain conditions are the leading cause of Years Lived with Disability (YLD) globally [1]. Alarmingly, this has remained the same since 1990 [2] suggesting that research into prevention and rehabilitation of MSK pain over the past 25+ years has had limited effect on its overall global burden. The reasons some fail to report full recovery while others follow a less problematic recovery trajectory are becoming clearer with psychological predictors (anxiety, depression, stress) showing some prognostic value [3]. Effective interventions however have proven elusive. Treatment of chronic MSK pain in many clinical settings tends to focus on the physical modalities such as pharmacologic, surgical, and other physical therapies excluding holistic interventions targeting psychosocial causes [4]. An integrative approach towards assessing and effectively managing a patient’s pain should cover the physical, behavioural, and psychosocial drivers of the patient’s pain experience. Moreover, a better understanding of the myriad of biopsychosocial mechanisms driving the clinical course for each patient seems particularly germane to the acute care encounter, given the current challenges with pharmaceutical dependence and overutilisation of and reliance on diagnostic tests that rarely inform management; or worse, promote ineffective management.

Background Hip‐related pain (HRP) affects young to middle‐aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep‐learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine‐learning segmentations compared with human‐generated segmentation. Methods This cross‐sectional study included sub‐elite/amateur football players (Australian football and soccer) with a greater than 6‐month history of HRP [n = 180, average age 28.32, (standard deviation 5.88) years, 19% female] and a control group of sub‐elite/amateur football players without pain [n = 48, 28.89 (6.22) years, 29% female]. Muscle volume and MFI of gluteus maximus, medius, minimis and tensor fascia latae were assessed using MRI. Associations between muscle volume and group were explored using linear regression models, controlling for body mass index, age, sport and sex. A convolutional neural network (CNN) machine‐learning approach was compared with human‐performed muscle segmentations in a subset of participants (n = 52) using intraclass correlation coefficients and Sorensen–Dice index. Results When considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm³ [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm³ [2440, 13 075]; p = 0.042). No differences were observed between groups for gluteus maximus (18 265 mm³ [−21 209, 50 782]; p = 0.419) or minimus (3893 mm³ [−2209, 9996]; p = 0.21). The CNN was trained for 30 000 iterations and assessed its accuracy and reliability on an independent testing dataset, achieving high segmentation accuracy (mean Sorenson–Dice index >0.900) and excellent muscle volume and MFI reliability (ICC2,1 > 0.900). The CNN outperformed manual raters, who had slightly lower interrater accuracy (Sorensen–Dice index >0.800) and reliability (ICC2,1 > 0.800). Conclusions The increased muscle volumes in the symptomatic group compared with controls could be associated with increased myofibrillar size, sarcoplasmic hypertrophy or both. These changes may facilitate greater muscular efficiency for a given load, enabling the athlete to maintain their normal level of function. In addition, the CNNs for muscle segmentation was more efficient and demonstrated excellent reliability in comparison to manual segmentations.