Individualized MRI-based stroke PRediction scOre using plaque Vulnerability for symptomatic carotid artEry disease patients (IMPROVE).

Objective: In TIA and stroke patients with carotid stenosis, estimations of future ipsilateral ischemic stroke risk and treatment decisions are currently primarily based on the degree of stenosis. Intraplaque hemorrhage (IPH), which can be readily visualized on carotid MRI, is increasingly established as an easy to assess and a very strong and independent predictor for ipsilateral stroke risk, stronger than any clinical risk factor. We developed a clinical prediction model (IMPROVE) incorporating IPH, degree of stenosis, and clinical risk factors to select patients with symptomatic carotid stenosis at high risk for stroke. Methods: IMPROVE was developed on pooled clinical and MRI data from five cohort studies of 760 recent TIA or minor stroke patients with carotid plaque who received optimal medical treatment. We used Cox proportional hazards models to determine the coefficients of IMPROVE. IMPROVE was internally validated using bootstrapping and converted to one- and three-year ipsilateral ischemic stroke risk. Results: The development dataset contained 65 ipsilateral incident ischemic strokes that occurred during a median follow-up of 1.2 years (IQR: 0.5-4.1). The IMPROVE model includes five predictors, which are in order of importance: degree of stenosis, presence of IPH on MRI, classification of last event (cerebral vs ocular), sex, and age. Internal validation revealed a good accuracy (C-statistic: 0.82; 95% CI: 0.77-0.87) and no evidence for miscalibration (calibration slope: 0.93). Interpretation: Using presence of IPH on MRI and only four conventional parameters, the IMPROVE model provides accurate individual stroke risk estimates, which may facilitate stratification for revascularization.

Retinal imaging with hand-held optical coherence tomography in older people with or without postoperative delirium after hip fracture surgery: A feasibility study.

INTRODUCTION: Postoperative delirium in older people may result from the interaction between intrinsic brain vulnerability (e.g. neurodegeneration) and precipitating factors (e.g. surgery induced cytokines). Intrinsic brain vulnerability may be overt (e.g. Alzheimer's disease) or preclinical. In cognitively intact older people presenting for surgery, identification of preclinical neurodegeneration using bedside tools could aid postoperative delirium risk stratification. Thinning of the circumpapillary retinal nerve fibre layer thickness is associated with neurodegenerative disorders e.g. Alzheimer's disease. We propose that thinning of the retinal nerve fibre layer may be present some older people with postoperative delirium due to preclinical neurodegeneration, albeit to a lesser extent than in overt dementia. OBJECTIVES: The primary objective: Feasibility of acquiring usable retinal images with the hand-held optical coherence device, at the bedside of older, hip fracture surgery patients. Secondary objective: Comparison of the circumpapillary retinal nerve fibre layer thickness between people who did/did not have postoperative delirium. Proportion of exclusions due to retinal pathology. METHOD: Feasibility study involving 30, cognitively intact, older people recovering from hip fracture surgery. Retinal images were obtained using the hand-held optical coherence tomography device at the participants' bedside. Imaging was deferred in participants who had postoperative delirium. RESULTS: Retinal images that could be assessed for circumpapillary retinal nerve fibre layer thickness were obtained in 26 participants (22 no postoperative delirium, 4 postoperative delirium). The mean circumpapillary retinal nerve fibre layer thickness was lower in the participants who had postoperative delirium compared to those who did not experience postoperative delirium (Mean (95% CI) of 76.50 (62.60-90.40) vs 89.19 (85.41-92.97) respectively). CONCLUSION: Retinal imaging at the patient's bedside, using hand-held OCT is feasible. Our data suggests that the circumpapillary retinal nerve fibre layer may be thinner in older people who experience postoperative delirium compared to those who do not. Further studies are required.

Primary central nervous system lymphomas: EHA-ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up.

This EHA-ESMO Clinical Practice Guideline provides key recommendations for managing primary DLBCL of the CNS.The guideline covers clinical, imaging and pathological diagnosis, staging and risk assessment, treatment and follow-up.Algorithms for first-line and salvage treatments are provided.The author group encompasses a multidisciplinary group of experts from different institutions and countries in Europe.Recommendations are based on available scientific data and the authors' collective expert opinion.

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification.

Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.

Characterisation of paediatric brain tumours by their MRS metabolite profiles.

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

A preliminary study of dynamic neurochemical changes in the dorsolateral prefrontal cortex during working memory.

Working memory (WM) is one of the fundamental cognitive functions associated with the dorsolateral prefrontal cortex (DLPFC). However, the neurochemical mechanisms of WM, including the dynamic changes in neurometabolites such as glutamate and GABA in the DLPFC, remain unclear. Here, we investigated WM-related glutamate and GABA changes, alongside hemodynamic responses in the DLPFC, using a combination of functional magnetic resonance spectroscopy (fMRS) and functional magnetic resonance imaging (fMRI). During a WM task, we measured Glx (glutamate + glutamine) and GABA levels using GABA editing MEscher-GArwood Point REsolved Spectroscopy (MEGA-PRESS) sequence and blood-oxygen-level-dependent (BOLD) signal changes. In the DLPFC, we observed elevated Glx levels and increased BOLD signal changes during a 2-back task. Specifically, the Glx levels in the DLPFC were significantly higher during the 2-back task compared with fixation, although this difference was not significant when compared with a 0-back task. However, Glx levels during the 0-back task were higher than during fixation. Furthermore, there was a positive correlation between Glx levels in the DLPFC during the 2-back task and the corresponding BOLD signal changes. Notably, higher Glx increases were associated with increased DLPFC activation and lower WM task performance in individuals. No notable changes in DLPFC GABA levels were observed during WM processing. These findings suggest that the modulation of glutamatergic activity in the DLPFC may play a crucial role in both working memory processing and its associated performance outcomes.

Connectivity-guided intermittent theta burst versus repetitive transcranial magnetic stimulation for treatment-resistant depression: a randomized controlled trial.

Disruption in reciprocal connectivity between the right anterior insula and the left dorsolateral prefrontal cortex is associated with depression and may be a target for neuromodulation. In a five-center, parallel, double-blind, randomized controlled trial we personalized resting-state functional magnetic resonance imaging neuronavigated connectivity-guided intermittent theta burst stimulation (cgiTBS) at a site based on effective connectivity from the right anterior insula to the left dorsolateral prefrontal cortex. We tested its efficacy in reducing the primary outcome depression symptoms measured by the GRID Hamilton Depression Rating Scale 17-item over 8, 16 and 26 weeks, compared with structural magnetic resonance imaging (MRI) neuronavigated repetitive transcranial magnetic stimulation (rTMS) delivered at the standard stimulation site (F3) in patients with 'treatment-resistant depression'. Participants were randomly assigned to 20 sessions over 4-6 weeks of either cgiTBS (n = 128) or rTMS (n = 127) with resting-state functional MRI at baseline and 16 weeks. Persistent decreases in depressive symptoms were seen over 26 weeks, with no differences between arms on the primary outcome GRID Hamilton Depression Rating Scale 17-item score (intention-to-treat adjusted mean, -0.31, 95% confidence interval (CI) -1.87, 1.24, P = 0.689). Two serious adverse events were possibly related to TMS (mania and psychosis). MRI-neuronavigated cgiTBS and rTMS were equally effective in patients with treatment-resistant depression over 26 weeks (trial registration no. ISRCTN19674644).

Carotid Plaque-RADS: A Novel Stroke Risk Classification System.

BACKGROUND: Carotid artery atherosclerosis is highly prevalent in the general population and is a well-established risk factor for acute ischemic stroke. Although the morphological characteristics of vulnerable plaques are well recognized, there is a lack of consensus in reporting and interpreting carotid plaque features. OBJECTIVES: The aim of this paper is to establish a consistent and comprehensive approach for imaging and reporting carotid plaque by introducing the Plaque-RADS (Reporting and Data System) score. METHODS: A panel of experts recognized the necessity to develop a classification system for carotid plaque and its defining characteristics. Using a multimodality analysis approach, the Plaque-RADS categories were established through consensus, drawing on existing published reports. RESULTS: The authors present a universal classification that is applicable to both researchers and clinicians. The Plaque-RADS score offers a morphological assessment in addition to the prevailing quantitative parameter of "stenosis." The Plaque-RADS score spans from grade 1 (indicating complete absence of plaque) to grade 4 (representing complicated plaque). Accompanying visual examples are included to facilitate a clear understanding of the Plaque-RADS categories. CONCLUSIONS: Plaque-RADS is a standardized and reliable system of reporting carotid plaque composition and morphology via different imaging modalities, such as ultrasound, computed tomography, and magnetic resonance imaging. This scoring system has the potential to help in the precise identification of patients who may benefit from exclusive medical intervention and those who require alternative treatments, thereby enhancing patient care. A standardized lexicon and structured reporting promise to enhance communication between radiologists, referring clinicians, and scientists.

Ultra-high-field 7T MRI in Parkinson's disease: ready for clinical use?-a narrative review.

Background and Objective: The maturation of ultra-high-field magnetic resonance imaging (MRI) [≥7 Tesla (7T)] has improved our capability to depict and characterise brain structures efficiently, with better signal-to-noise ratio (SNR) and spatial resolution. We evaluated whether these improvements benefit the clinical detection and management of Parkinson's disease (PD). Methods: We performed a literature search in March 2023 in PubMed (MEDLINE), EMBASE and Google Scholar for articles on "7T MRI" AND "Parkinson*", written in English, published between inception and 1st March, 2023, which we synthesised in narrative form. Key Content and Findings: In deep-brain stimulation (DBS) surgical planning, early studies show that 7T MRI can distinguish anatomical substructures, and that this results in reduced adverse effects. In other areas, while there is strong evidence for improved accuracy and precision of 7T MRI-based measurements for PD, there is limited evidence for meaningful clinical translation. In particular, neuromelanin-iron complex quantification and visualisation in midbrain nuclei is enhanced, enabling depiction of nigrosomes 1-5, improved morphometry and vastly improved radiological assessments; however, studies on the related clinical outcomes, diagnosis, subtyping, differentiation of atypical parkinsonisms, and monitoring of treatment response using 7T MRI are lacking. Moreover, improvements in clinical utility must be great enough to justify the additional costs. Conclusions: Together, current evidence supports feasible future clinical implementation of 7T MRI for PD. Future impacts to clinical decision making for diagnosis, differentiation, and monitoring of progression or treatment response are likely; however, to achieve this, further longitudinal studies using 7T MRI are needed in prodromal, early-stage PD and parkinsonism cohorts focusing on clinical translational potential.

The Nottingham Ischaemic Cardiovascular Magnetic Resonance resource (NotIs CMR): a prospective paired clinical and imaging scar database-protocol.

INTRODUCTION: Research utilising artificial intelligence (AI) and cardiovascular magnetic resonance (CMR) is rapidly evolving with various objectives, however AI model development, generalisation and performance may be hindered by availability of robust training datasets including contrast enhanced images. METHODS: NotIs CMR is a large UK, prospective, multicentre, observational cohort study to guide the development of a biventricular AI scar model. Patients with ischaemic heart disease undergoing clinically indicated contrast-enhanced cardiac magnetic resonance imaging will be recruited at Nottingham University Hospitals NHS Trust and Mid-Yorkshire Hospital NHS Trust. Baseline assessment will include cardiac magnetic resonance imaging, demographic data, medical history, electrocardiographic and serum biomarkers. Participants will undergo monitoring for a minimum of 5 years to document any major cardiovascular adverse events. The main objectives include (1) AI training, validation and testing to improve the performance, applicability and adaptability of an AI biventricular scar segmentation model being developed by the authors and (2) develop a curated, disease-specific imaging database to support future research and collaborations and, (3) to explore associations in clinical outcome for future risk prediction modelling studies. CONCLUSION: NotIs CMR will collect and curate disease-specific, paired imaging and clinical datasets to develop an AI biventricular scar model whilst providing a database to support future research and collaboration in Artificial Intelligence and ischaemic heart disease.

Theta Burst Stimulation of the Human Motor Cortex Modulates Secondary Hyperalgesia to Punctate Mechanical Stimuli.

OBJECTIVES: Many chronic pain conditions show evidence of dysregulated synaptic plasticity, including the development and maintenance of central sensitization. This provides a strong rationale for neuromodulation therapies for the relief of chronic pain. However, variability in responses and low fidelity across studies remain an issue for both clinical trials and pain management, demonstrating insufficient mechanistic understanding of effective treatment protocols. MATERIALS AND METHODS: In a randomized counterbalanced crossover designed study, we evaluated two forms of patterned repetitive transcranial magnetic stimulation, known as continuous theta burst stimulation (TBS) and intermittent TBS, during normal and central sensitization states. Secondary hyperalgesia (a form of use-dependent central sensitization) was induced using a well-established injury-free pain model and assessed by standardized quantitative sensory testing involving light touch and pinprick pain thresholds in addition to stimulus-response functions. RESULTS: We found that continuous TBS of the human motor cortex has a facilitatory (pronociceptive) effect on the magnitude of perceived pain to secondary hyperalgesia, which may rely on induction and expression of neural plasticity through heterosynaptic long-term potentiation-like mechanisms. CONCLUSIONS: By defining the underlying mechanisms of TBS-driven synaptic plasticity in the nociceptive system, we offer new insight into disease mechanisms and provide targets for promoting functional recovery and repair in chronic pain. For clinical applications, this knowledge is critical for development of more efficacious and mechanisms-based neuromodulation protocols, which are urgently needed to address the chronic pain and opioid epidemics.

Trial protocol: Feasibility of neuromodulation with connectivity-guided intermittent theta-burst stimulation for improving cognition in multiple sclerosis.

Cognitive impairment in multiple sclerosis (MS) can adversely impact participation in employment, activities of daily living, and wider society. It affects 40-70% of people living with MS (pwMS). There are few effective treatments for cognitive impairment in people with MS. Neuromodulation with intermittent theta-burst stimulation (iTBS) has potential for treating cognitive impairment in pwMS. This single-centre mixed-methods feasibility randomised controlled trial (NCT04931953) will assess feasibility, acceptability, and tolerability of procedures used for applying iTBS for improving cognitive performance in pwMS. Participants will be randomised into three intervention groups with varying lengths of iTBS treatment (from 1 to 4 weeks) and a sham-control group. Quantitative data will be collected at three time points (baseline, end of intervention, and 8-week follow-up). End of the intervention semi-structured interviews will explore the views and experiences of the participants receiving the intervention, analysed using framework analysis. Quantitative and qualitative data will be synthesised to explore the impact of the iTBS intervention. Ethical approval has been received from the Health Research Authority (21/LO/0506) and recruitment started in June 2022. The results will inform the design of an RCT of the efficacy of iTBS as a therapeutic intervention for cognitive impairment in pwMS.

Brain connectivity-guided, Optimised theta burst transcranial magnetic stimulation to improve Central Pain Modulation in knee Osteoarthritis Pain (BoostCPM): protocol of a pilot randomised clinical trial in a secondary care setting in the UK.

INTRODUCTION: Chronic pain is a common health problem that is not efficiently managed by standard analgesic treatments. There is evidence that treatment resistance may result from maladaptive brain changes in areas that are fundamental to the perception of pain. Knee osteoarthritis is one of the most prevalent causes of chronic pain and commonly associated with negative affect. Chronic knee osteoarthritis pain is also associated with altered right anterior insula functional connectivity. We posit that reversal of these brain circuit alterations may be critical to alleviate chronic pain and associated negative affect, and that this can be achieved through non-invasive neuromodulation techniques. Despite growing interest in non-invasive neuromodulation for pain relief and proven efficacy in depression, results in chronic pain are mixed with limited high-quality evidence for clinical and mechanistic efficacy. Limitations include patient heterogeneity, imprecision of target selection, uncertain blinding and protocols that may deliver pulses at subclinical efficacy. METHODS AND ANALYSIS: We hence developed an optimised treatment protocol of connectivity-guided intermittent theta-burst stimulation (iTBS) targeting the left dorsolateral prefrontal cortex with accelerated delivery on four consecutive days (allowing 4 days within the same week as protocol variation) with five daily treatment sessions that will be piloted in a sham-controlled design in 45 participants with chronic knee pain. This pilot study protocol will assess feasibility, tolerability and explore mechanistic efficacy through serial functional/structural magnetic resonance imaging (MRI) and quantitative sensory testing. ETHICS AND DISSEMINATION: This pilot trial has been approved by the Ethics Committee Cornwall and Plymouth.Results of the pilot trial will be submitted to peer-reviewed journals, presented at research conferences and may be shared with participants and PPI/E advisors. TRIAL REGISTRATION NUMBER: ISRCTN15404076.

Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.

Introduction: Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections. Methods: In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. Results: On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.

Is there a difference in the analgesic response to intra-articular bupivacaine injection in people with knee osteoarthritis pain with or without central sensitisation? Protocol of a feasibility randomised controlled trial.

INTRODUCTION: Pain is the main symptom of osteoarthritis (OA) with approximately 50% of patients reporting moderate-to-severe pain. Total knee replacement (TKR) is the ultimate treatment option to alleviate pain in knee OA. Nevertheless, TKR does not provide complete relief for all as approximately 20% of patients experience chronic postoperative pain. Painful peripheral stimuli may alter the central nociceptive pathways leading to central sensitisation that can influence treatment response in patients with OA. Currently, there is no objective protocol for detecting whether a patient will respond to a given treatment. Therefore, there is a need for a better mechanistic understanding of individual factors affecting pain relief, consequently informing personalised treatment guidelines. The purpose of this research is to examine the feasibility of conducting a full-scale mechanistic clinical trial in painful knee OA investigating the analgesic response to intra-articular bupivacaine between those with or without evidence of central sensitisation. METHODS AND ANALYSIS: The Understanding Pain mechanisms in KNEE osteoarthritis (UP-KNEE) study is a feasibility, double-blinded, placebo-controlled randomised parallel study in participants with radiographically defined knee OA and with self-reported chronic knee pain. The study involves the following assessments: (1) a suite of psychometric questionnaires; (2) quantitative sensory testing; (3) magnetic resonance imaging (MRI) scan of the knee and brain; (4) a 6-minute walk test; and (5) an intra-articular injection of bupivacaine or placebo (sodium chloride 0.9%) into the index knee. Assessments will be repeated post intra-articular injection apart from the MRI scan of the knee. Our aim is to provide proof of concept and descriptive statistics to power a future mechanistic trial. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Health Research Authority (HRA) (REC: 20/EM/0287). Results will be disseminated via peer-reviewed journals and scientific conferences. The results will also be shared with lay audiences through relevant channels, such as Pain Centre Versus Arthritis website and patient advocacy groups. TRIAL REGISTRATION NUMBER: NCT05561010.

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.

OBJECTIVE: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. METHODS: 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. RESULTS: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. CONCLUSION: The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. ADVANCES IN KNOWLEDGE: A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Deuterium brain imaging at 7T during D2 O dosing.

PURPOSE: To characterize the (2 H) deuterium MR signal measured from human brain at 7T in participants loading with D2 O to ˜1.5% enrichment over a six-week period. METHODS: 2 H spectroscopy and imaging measurements were used to track the time-course of 2 H enrichment within the brain during the initial eight-hour loading period in two participants. Multi-echo gradient echo (MEGE) images were acquired at a range of TR values from four participants during the steady-state loading period and used for mapping 2 H T1 and T2 * relaxation times. Co-registration to higher resolution 1 H images allowed T1 and T2 * relaxation times of deuterium in HDO in cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) to be estimated. RESULTS: 2 H concentrations measured during the eight-hour loading were consistent with values estimated from cumulative D2 O dose and body mass. Signal changes measured from three different regions of the brain during loading showed similar time-courses. After summing over echoes, gradient echo brain images acquired in 7.5 minutes with a voxel volume of 0.36 ml showed an SNR of ˜16 in subjects loaded to 1.5%. T1 -values for deuterium in HDO were significantly shorter than corresponding values for 1 H in H2 O, while T2 * values were similar. 2 H relaxation times in CSF were significantly longer than in GM or WM. CONCLUSION: Deuterium MR Measurements at 7T were used to track the increase in concentration of 2 H in brain during heavy water loading. 2 H T1 and T2 * relaxation times from water in GM, WM, and CSF are reported.

Medio-dorsal thalamic dysconnectivity in chronic knee pain: A possible mechanism for negative affect and pain comorbidity.

The reciprocal interaction between pain and negative affect is acknowledged but pain-related alterations in brain circuits involved in this interaction, such as the mediodorsal thalamus (MDThal), still require a better understanding. We sought to investigate the relationship between MDThal circuitry, negative affect and pain severity in chronic musculoskeletal pain. For these analyses, participants with chronic knee pain (CKP, n = 74) and without (n = 36) completed magnetic resonance imaging scans and questionnaires. Seed-based MDThal functional connectivity (FC) was compared between groups. Within CKP group, we assessed the interdependence of MDThal FC with negative affect. Finally, post hoc moderation analysis explored whether burden of pain influences affect-related MDThal FC. The CKP group showed altered MDThal FC to hippocampus, ventromedial prefrontal cortex and subgenual anterior cingulate. Furthermore, in CKP group, MDThal connectivity correlated significantly with negative affect in several brain regions, most notably the medial prefrontal cortex, and this association was stronger with increasing pain burden and absent in pain-free controls. In conclusion, we demonstrate mediodorsal thalamo-cortical dysconnectivity in chronic pain with areas linked to mood disorders and associations of MDThal FC with negative affect. Moreover, burden of pain seems to enhance affect sensitivity of MDThal FC. These findings suggest mediodorsal thalamic network changes as possible drivers of the detrimental interplay between chronic pain and negative affect.

Mapping brain endophenotypes associated with idiopathic pulmonary fibrosis genetic risk.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a serious disease of the lung parenchyma. It has a known polygenetic risk, with at least seventeen regions of the genome implicated to date. Growing evidence suggests linked multimorbidity of IPF with neurodegenerative or affective disorders. However, no study so far has explicitly explored links between IPF, associated genetic risk profiles, and specific brain features. METHODS: We exploited imaging and genetic data from more than 32,000 participants available through the UK Biobank population-level resource to explore links between IPF genetic risk and imaging-derived brain endophenotypes. We performed a brain-wide imaging-genetics association study between the presence of 17 known IPF risk variants and 1248 multi-modal imaging-derived features, which characterise brain structure and function. FINDINGS: We identified strong associations between cortical morphological features, white matter microstructure and IPF risk loci in chromosomes 17 (17q21.31) and 8 (DEPTOR). Through co-localisation analysis, we confirmed that cortical thickness in the anterior cingulate and more widespread white matter microstructure changes share a single causal variant with IPF at the chromosome 8 locus. Post-hoc preliminary analysis suggested that forced vital capacity may partially mediate the association between the DEPTOR variant and white matter microstructure, but not between the DEPTOR risk variant and cortical thickness. INTERPRETATION: Our results reveal the associations between IPF genetic risk and differences in brain structure, for both cortex and white matter. Differences in tissue-specific imaging signatures suggest distinct underlying mechanisms with focal cortical thinning in regions with known high DEPTOR expression, unrelated to lung function, and more widespread microstructural white matter changes consistent with hypoxia or neuroinflammation with potential mediation by lung function. FUNDING: This study was supported by the NIHR Nottingham Biomedical Research Centre and the UK Medical Research Council.

Trait related aberrant connectivity in clinically stable patients with schizophrenia: A seed based resting state fMRI study.

Aberrant resting-state connectivity within and between the Default Mode Network, the Executive Control Network, and the Salience Network is well-established in schizophrenia. Meta-analyses have identified that bilateral lingual gyrus is as the only region showing hyperactivity in schizophrenia and there are reports of increased connectivity between the lingual gyrus and other brain regions in schizophrenia. It is not clear whether these abnormalities represent state or trait markers of the illness, i.e., if they are only present during the acute phase of the illness (state) or if they reflect a predisposition to schizophrenia (trait). In this study, we used a seed-based functional connectivity analysis to investigate brain networks in schizophrenia patients who are in the stable phase of their illness and assess functional connectivity using seeds in the lingual gyrus, the posterior cingulate, the right dorsolateral prefrontal cortex (dlPFC), the right anterior insula (rAI) and the right orbital frontoinsula. Twenty patients with schizophrenia in a stable phase of their illness (as defined by the course of illness and Signs and Symptoms of Psychotic Illness (SSPI) scores) and 20 age and sex-matched healthy controls underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI). Data was analysed using the Data Processing Assistant for Resting-State fMRI Advanced Edition (DPARSFA) V3.1 ( http://rfmri.org/DPARSF ) and the statistical parametric mapping software 8 (SPM8). Compared with healthy controls, patients with schizophrenia showed increased connectivity between the left lingual gyrus and the middle frontal gyrus, and the cingulate cortex. Lingual gyrus hyper-connectivity may be a stable trait neuroimaging marker for schizophrenia. Our findings suggest that aberrant connectivity in major resting-state networks may not be present after the acute illness has stabilised.