Comparison of Pittsburgh compound B and florbetapir in cross-sectional and longitudinal studies.

IntroductionQuantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B-based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design.MethodsPittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally.ResultsGlobal amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers.DiscussionAlthough the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. Methods: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. Results: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). Conclusion: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery

Mapping of EEG to Brain MRI for Epilepsy Localization

Accurate localization of epilepsy foci is essential prior to resection of epileptogenic zones of the brain. EEG signatures indicative of epileptic seizures can be mapped to their location on a brain MRI by acquiring the MRI before implanting the electrodes in the brain, and localizing the electrodes within the head with a CT after implantation. However, distortion resulting from the craniotomy and the implantation procedure change the brain geometry causing the frame of reference to shift and this model to be inaccurate. Using B-Spline transformations and an Advanced Mattes Mutual Information metric, a nonrigid registration was performed between CT scans before and after the implantation procedure. This transformation was subsequently applied to the MRI in order to correct for the mismatch in registration in 5 subjects. After the application of this nonrigid transformation there was an average increase in accuracy of 12.5% ± 2.3% (p<0.001) in the ability of depth electrodes to localize gray matter. On average, the grid electrodes were 3mm ± 3mm (p= 0.06) closer to their locations given by an intraoperative photograph in one patient when the transformation was applied as well. This has been incorporated into a completely automated tool that can be easily implemented into a lab or clinical environment

Automated and Nonbiased Regional Quantification of Functional Neuroimaging Data

Purpose: In the quantification of functional neuroimaging data, region-of-interest (ROI) analysis can be used to assess a variety of properties of the activation signal, but taken alone these properties are susceptible to noise and may fail to accurately describe overall regional involvement. Here, the authors present and evaluate an automated method for quantification and localization of functional neuroimaging data that combines multiple properties of the activation signal to generate rank-order lists of regional activation results. Methods: The proposed automated quantification method, referred to as neuroimaging results decomposition (NIRD), begins by decomposing an activation map into a hierarchical list of ROIs using a digital atlas. In an intermediate step, the ROIs are rank-ordered according to extent, mean intensity, and total intensity. A final rank-order list (NIRD average rank) is created by sorting the ROIs according to the average of their ranks from the intermediate step. The authors hypothesized that NIRD average rank would have improved regional quantification accuracy compared to all other quantitative metrics, including methods based on properties of statistical clusters. To test their hypothesis, NIRD rankings were directly compared to three common cluster-based methods using simulated fMRI data both with and without realistic head motion. Results: For both the no-motion and motion datasets, an analysis of variance found that significant differences between the quantification methods existed (F = 64.8, p \u3c 0.0001 for no motion; F = 55.2, p \u3c 0.0001 for motion), and a post-hoc test found that NIRD average rank was the most accurate quantification method tested (p \u3c 0.05 for both datasets). Furthermore, all variants of the NIRD method were found to be significantly more accurate than the cluster-based methods in all cases. Conclusions: These results confirm their hypothesis and demonstrate that the proposed NIRD methodology provides improved regional quantification accuracy compared to cluster-based methods

Increase in foreign body and harmful substance ingestion and associated complications in children: a retrospective study of 1199 cases from 2005 to 2017

Background!#!Children with a history of caustic or foreign body ingestion (FBI) seem to be presenting more frequently to emergency departments. This study aims to elucidate the clinical presentation, diagnostic procedures, and complications associated with the ingestion of different object categories over a 13-year time period.!##!Methods!#!A structured retrospective data analysis of patients who presented between January 2005 and December 2017 to the University Medical Centre Ulm was performed. Patients up to 17 years of age with food impaction or foreign body or harmful substance ingestion were included by selection of the corresponding International Statistical Classification of Diseases and Related Health Problems (ICD10-GM) codes. Descriptive statistics, parametric or non-parametric tests, and linear regression analysis were performed.!##!Result!#!In total, 1199 patients were analysed; the mean age was 3.3 years (SD 3.12; range 7 days to 16 years), the male to female ratio was 1.15:1, and 194 (16.2%) were hospitalized. The number of patients seen annually increased from 66 in 2005 to 119 in 2017, with a rise in percentage of all emergency patients from 0.82% in 2010 to 1.34% in 2017. The majority of patients (n = 619) had no symptoms, and 244 out of 580 symptomatic patients complained of retching or vomiting. Most frequently, ingested objects were coins (18.8%). Radiopaque objects accounted for 47.6%, and sharp objects accounted for 10.5% of the ingested foreign bodies, both of which were significantly more often ingested by girls (p < 0.001 for both). Button battery ingestion was recorded for 63 patients with a significant annual increase (R2 = 0.57; β = 0.753; p = 0.003). The annual rate of complications also increased significantly (R2 = 0.42; β = 0.647; p = 0.017).!##!Conclusion!#!We found an alarming increase in the number of children who presented to our emergency department with FBI and associated complications. A standardized diagnostic and therapeutic approach may reduce and prevent serious complications. Further preventive measures within the home environment are needed to stop this trend

Effects of anterior thalamic nuclei stimulation on hippocampal activity: Chronic recording in a patient with drug-resistant focal epilepsy.

Implanted neurostimulation devices are gaining traction as palliative treatment options for certain forms of drug-resistant epilepsy, but clinical utility of these devices is hindered by incomplete mechanistic understanding of their therapeutic effects. Approved devices for anterior thalamic nuclei deep brain stimulation (ANT DBS) are thought to work at a network level, but limited sensing capability precludes characterization of neurophysiological effects outside the thalamus. Here, we describe a patient with drug-resistant temporal lobe epilepsy who was implanted with a responsive neurostimulation device (RNS System), involving hippocampal and ipsilateral temporal neocortical leads, and subsequently received ANT DBS. Over 1.5 years, RNS System electrocorticography enabled multiscale characterization of neurophysiological effects of thalamic stimulation. In brain regions sampled by the RNS System, ANT DBS produced acute, phasic, frequency-dependent responses, including suppression of hippocampal low frequency local field potentials. ANT DBS modulated functional connectivity between hippocampus and neocortex. Finally, ANT DBS progressively suppressed hippocampal epileptiform activity in relation to the extent of hippocampal theta suppression, which informs stimulation parameter selection for ANT DBS. Taken together, this unique clinical scenario, involving hippocampal recordings of unprecedented chronicity alongside ANT DBS, sheds light on the therapeutic mechanism of thalamic stimulation and highlights capabilities needed in next-generation devices