Cerebral cortical thickness in chronic pain due to knee osteoarthritis: the effect of pain duration and pain densitization

Objective This study investigates associations between cortical thickness and pain duration, and central sensitization as markers of pain progression in painful knee osteoarthritis. Methods Whole brain cortical thickness and pressure pain thresholds were assessed in 70 participants; 40 patients with chronic painful knee osteoarthritis (age = 66.1± 8.5 years, 21 females, mean duration of pain = 8.5 years), and 30 healthy controls (age = 62.7± 7.4, 17 females). Results Cortical thickness negatively correlated with pain duration mainly in fronto-temporal areas outside of classical pain processing areas (p<0.05, age-controlled, FDR corrected). Pain sensitivity was unrelated to cortical thickness. Patients showed lower cortical thickness in the right anterior insula (p<0.001, uncorrected) with no changes surviving multiple test correction. Conclusion With increasing number of years of suffering from chronic arthritis pain we found increasing cortical thinning in extended cerebral cortical regions beyond recognised pain-processing areas. While the mechanisms of cortical thinning remain to be elucidated, we show that pain progression indexed by central sensitization does not play a major role

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

SPARC 2021 - Against all odds : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2021 SPARC conference. Our conference is called “Against All Odds” as we celebrate the achievements of our PGRs and their supervisors, who have continued to excel despite the most challenging circumstances. For this reason, we showcase the work of our PGRs alongside the outstanding supervision that they receive, with our Doctoral School Best Supervisor awards. We also focus on developing resilience and maintaining good mental health in the research environment, supported by exceptional keynote speakers, including our very own Dr Michelle Howarth and Ruby Wax OBE, which makes this year’s conference extra special

Structural brain changes in medically refractory focal epilepsy resemble premature brain aging

Objective: We used whole brain T1-weighted MRIto estimate the age of individuals with medically refractory focal epilepsy, and compared with individuals with newly diagnosed focal epilepsy and healthy controls. The difference between neuroanatomical age and chronological age was compared between the three groups. Methods: Neuroanatomical age was estimated using a machine learning-based method that was trained using structural MRI scans from a large independent healthy control sample (N = 2001). The prediction model was then used to estimate age from MRI scans obtained from newly diagnosed focal epilepsy patients (N = 42), medically refractory focal epilepsy patients (N = 94) and healthy controls (N = 74). Results: Individuals with medically refractory epilepsy had a difference between predicted brain age and chronological age that was on average 4.5 years older than healthy controls (p = 4.6 × 10−5). No significant differences were observed in newly diagnosed focal epilepsy. Earlier age of onset was associated with an increased brain age difference in the medically refractory group (p = 0.034). Significance: Medically refractory focal epilepsy is associated with structural brain changes that resemble premature brain aging

Structural MRI markers of brain aging early after ischemic stroke

Objective: To examine associations between ischemic stroke, vascular risk factors, and MRI markers of brain aging. Methods: Eighty-one patients (mean age 67.5 ± 13.1 years, 31 left-sided, 61 men) with confirmed first-ever (n = 66) or recurrent (n = 15) ischemic stroke underwent 3T MRI scanning within 6 weeks of symptom onset (mean 26 ± 9 days). Age-matched controls (n = 40) completed identical testing. Multivariate regression analyses examined associations between group membership and MRI markers of brain aging (cortical thickness, total brain volume, white matter hyperintensity [WMH] volume, hippocampal volume), normalized against intracranial volume, and the effects of vascular risk factors on these relationships. Results: First-ever stroke was associated with smaller hippocampal volume (p = 0.025) and greater WMH volume (p = 0.004) relative to controls. Recurrent stroke was in turn associated with smaller hippocampal volume relative to both first-ever stroke (p = 0.017) and controls (p = 0.001). These associations remained significant after adjustment for age, sex, education, and, in stroke patients, infarct volume. Total brain volume was not significantly smaller in first-ever stroke patients than in controls (p = 0.056), but the association became significant after further adjustment for atrial fibrillation (p = 0.036). Cortical thickness and brain volumes did not differ as a function of stroke type, infarct volume, or etiology. Conclusions: Brain structure is likely to be compromised before ischemic stroke by vascular risk factors. Smaller hippocampal and total brain volumes and increased WMH load represent proxies for underlying vascular brain injury.</br