MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms

PURPOSE: Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. METHODS: American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse’s ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. RESULTS: Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse’s ratio). CONCLUSIONS: Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers

Egalitarian despots: hierarchy steepness, reciprocity and the grooming-trade model in wild chimpanzees (Pan troglodytes)

Biological-markets theory models the action of natural selection as a marketplace in which animals are viewed as traders with commodities to offer and exchange. Studies of female Old World monkeys have suggested that grooming might be employed as a commodity to be reciprocated or traded for alternative services, yet previous tests of this grooming-trade model in wild adult male chimpanzees (Pan troglodytes) have yielded mixed results. Here we provide the strongest test of the model to date for male chimpanzees: we use data drawn from two social groups (communities) of chimpanzees from different populations, and give explicit consideration to variation in dominance hierarchy steepness as such variation results in differing conditions for biological markets. First, analysis of data from published accounts of other chimpanzee communities, together with our own data, showed that hierarchy steepness varied considerably within and across communities and that the number of adult males in a community aged 20-30 years predicted hierarchy steepness. The two communities in which we tested predictions of the grooming-trade model lay at opposite extremes of this distribution. Second, in accord with the grooming-trade model, we found evidence that male chimpanzees trade grooming for agonistic support where hierarchies are steep (despotic) and consequent effective support is a rank-related commodity, but not where hierarchies are shallow (egalitarian). However, we also found that grooming was reciprocated regardless of hierarchy steepness. Our findings also hint at the possibility of agonistic competition, or at least exclusion, in relation to grooming opportunities compromising the free market envisioned by Biological Markets theory. Our results build on previous findings across chimpanzee communities to emphasise the importance of reciprocal grooming exchanges among adult male chimpanzees, which can be understood in a biological markets framework if grooming by or with particular individuals is a valuable commodity

Cooperation in wild Barbary macaques: factors affecting free partner choice

A key aspect of cooperation is partner choice: choosing the best available partner improves the chances of a successful cooperative interaction and decreases the likelihood of being exploited. However, in studies on cooperation subjects are rarely allowed to freely choose their partners. Group-living animals live in a complex social environment where they can choose among several social partners differing in, for example, sex, age, temperament, or dominance status. Our study investigated whether wild Barbary macaques succeed to cooperate using an experimental apparatus, and whether individual and social factors affect their choice of partners and the degree of cooperation. We used the string pulling task that requires two monkeys to manipulate simultaneously a rope in order to receive a food reward. The monkeys were free to interact with the apparatus or not and to choose their partner. The results showed that Barbary macaques are able to pair up with a partner to cooperate using the apparatus. High level of tolerance between monkeys was necessary for the initiation of successful cooperation, while strong social bond positively affected the maintenance of cooperative interactions. Dominance status, sex, age, and temperament of the subjects also affected their choice and performance. These factors thus need to be taken into account in cooperative experiment on animals. Tolerance between social partners is likely to be a prerequisite for the evolution of cooperation

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures

Self-expanding metal stents in malignant colonic obstruction: experiences from Sweden

<p/> <p>Background</p> <p>Acute surgery in the management of malignant colonic obstruction is associated with high morbidity and mortality. The use of self-expanding metal stents (SEMS) is an alternative method of decompressing colonic obstruction. SEMS may allow time to optimize the patient and to perform preoperative staging, converting acute surgery into elective. SEMS is also proposed as palliative treatment in patients with contraindications to open surgery. Aim: To review our experience of SEMS focusing on clinical outcome and complications. The method used was a review of 75 consecutive trials at SEMS on 71 patients based on stent-protocols and patient charts.</p> <p>Findings</p> <p>SEMS was used for palliation in 64 (85%) cases and as a bridge to surgery in 11 (15%) cases. The majority of obstructions, 53 (71%) cases, were located in the recto-sigmoid. Technical success was achieved in 65 (87%) cases and clinical decompression was achieved in 60 (80%) cases. Reasons for technical failure were inability to cannulate the stricture in 5 (7%) cases and suboptimal SEMS placement in 3 (4%) cases. Complications included 4 (5%) procedure-related bowel perforations of which 2 (3%) patients died in junction to post operative complications. Three cases of bleeding after SEMS occurred, none of which needed invasive treatment. Five of the SEMS occluded. Two cases of stent erosion were diagnosed at the time of surgery. Average survival after palliative SEMS treatment was 6 months.</p> <p>Conclusion</p> <p>Our results correspond well to previously published data and we conclude that SEMS is a relatively safe and effective method of treating malignant colonic obstruction although the risk of SEMS-related perforations has to be taken into account.</p

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Eswl con Dornier Lithotripter S: puntamento radiologico o ecografico?

Eswl con Dornier Lithotripter S: puntamento radiologico o ecografico