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BACKGROUND: Little is known about the characteristics and occurrence frequencies of rapid eye movements (REMs) during REM sleep in movement disorders. OBJECTIVES: The aim of this study was to detect and characterize REMs during polysomnographically defined REM sleep as recorded by electro-oculography (EOG) in 12 patients with progressive supranuclear palsy (PSP), 13 patients with Parkinson's disease (PD) and 12 healthy controls. METHODS: Using a modified EOG montage, we developed an algorithm that automatically detects and characterizes REMs during REM sleep based on their presumptive saccadic kinematics. RESULTS: Compared to PD and healthy controls, REM densities and REM peak velocities were significantly reduced in PSP. These effects were most pronounced in vertical REMs. CONCLUSION: Ocular motor dysfunction, one of the cardinal features of PSP, seems to be equally at play during REM sleep and wakefulness. For future studies, we provide a novel tool for the unbiased analysis of REMs during REM sleep in movement disorders.

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Making causal inferences regarding human behaviour is difficult given the complex interplay between countless contributors to behaviour, including factors in the external world and our internal states. We provide a non-technical conceptual overview of challenges and opportunities for causal inference on human behaviour. The challenges include our ambiguous causal language and thinking, statistical under- or over-control, effect heterogeneity, interference, timescales of effects and complex treatments. We explain how methods optimized for addressing one of these challenges frequently exacerbate other problems. We thus argue that clearly specified research questions are key to improving causal inference from data. We suggest a triangulation approach that compares causal estimates from (quasi-)experimental research with causal estimates generated from observational data and theoretical assumptions. This approach allows a systematic investigation of theoretical and methodological factors that might lead estimates to converge or diverge across studies.

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Objective.To experimentally validate two online adaptive proton therapy (APT) workflows using Gafchromic EBT3 films and optically stimulated luminescent dosimeters (OSLDs) in an anthropomorphic head-and-neck phantom.Approach.A three-field proton plan was optimized on the planning CT of the head-and-neck phantom with 2.0 Gy(RBE) per fraction prescribed to the clinical target volume. Four fractions were simulated by varying the internal anatomy of the phantom. Three distinct methods were delivered: daily APT researched by the Paul Scherrer Institute (DAPTPSI), online adaptation researched by the Massachusetts General Hospital (OAMGH), and a non-adaptive (NA) workflow. All methods were implemented and measured at PSI. DAPTPSIperformed full online replanning based on analytical dose calculation, optimizing to the same objectives as the initial treatment plan. OAMGHperformed Monte-Carlo-based online plan adaptation by only changing the fluences of a subset of proton beamlets, mimicking the planned dose distribution. NA delivered the initial plan with a couch-shift correction based on in-room imaging. For all 12 deliveries, two films and two sets of OSLDs were placed at different locations in the phantom.Main results.Both adaptive methods showed improved dosimetric results compared to NA. For film measurements in the presence of anatomical variations, the [min-max] gamma pass rates (3%/3 mm) between measured and clinically approved doses were [91.5%-96.1%], [94.0%-95.8%], and [67.2%-93.1%] for DAPTPSI, OAMGH, and NA, respectively. The OSLDs confirmed the dose calculations in terms of absolute dosimetry. Between the two adaptive workflows, OAMGHshowed improved target coverage, while DAPTPSIshowed improved normal tissue sparing, particularly relevant for the brainstem.Significance.This is the first multi-institutional study to experimentally validate two different concepts with respect to online APT workflows. It highlights their respective dosimetric advantages, particularly in managing interfractional variations in patient anatomy that cannot be addressed by non-adaptive methods, such as internal anatomy changes.

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There is a rising interest in developing and utilizing arc delivery techniques with charged particle beams, e.g., proton, carbon or other ions, for clinical implementation. In this work, perspectives from the European Society for Radiotherapy and Oncology (ESTRO) 2022 physics workshop on particle arc therapy are reported. This outlook provides an outline and prospective vision for the path forward to clinically deliverable proton, carbon, and other ion arc treatments. Through the collaboration among industry, academic, and clinical research and development, the scientific landscape and outlook for particle arc therapy are presented here to help our community understand the physics, radiobiology, and clinical principles. The work is presented in three main sections: (i) treatment planning, (ii) treatment delivery, and (iii) clinical outlook.

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Beam position uncertainties along the beam trajectory arise from the accelerator, beamline, and scanning magnets (SMs). They can be monitored in real time, e.g., through strip ionization chambers (ICs), and treatments can be paused if needed. Delivery is more reliable and accurate if the beam position is projected from monitored nozzle parameters to the isocenter, allowing for accurate online corrections to be performed. Beam position projection algorithms are also used in post-delivery log file analyses. In this paper, we investigate the four potential algorithms that can be applied to all pencil beam scanning (PBS) nozzles. For some combinations of nozzle configurations and algorithms, however, the projection uses beam properties determined offline (e.g., through beam tuning or technical commissioning). The best algorithm minimizes either the total uncertainty (i.e., offline and online) or the total offline uncertainty in the projection. Four beam position algorithms are analyzed (A1-A4). Two nozzle lengths are used as examples: a large nozzle (1.5 m length) and a small nozzle (0.4 m length). Three nozzle configurations are considered: IC after SM, IC before SM, and ICs on both sides. Default uncertainties are selected for ion chamber measurements, nozzle entrance beam position and angle, and scanning magnet angle. The results for other uncertainties can be determined by scaling these results or repeating the error propagation. We show the propagation of errors from two locations and the SM angle to the isocenter for all the algorithms. The best choice of algorithm depends on the nozzle length and is A1 and A3 for the large and small nozzles, respectively. If the total offline uncertainty is to be minimized (a better choice if the offline uncertainty is not stable), the best choice of algorithm changes to A1 for the small nozzle for some hardware configurations. Reducing the nozzle length can help to reduce the gantry size and make proton therapy more accessible. This work is important for designing smaller nozzles and, consequently, smaller gantries. This work is also important for log file analyses.

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BACKGROUND AND OBJECTIVES: We developed repetitive ocular vestibular-evoked myogenic potentials (roVEMP) as an electrophysiologic test that allows us to elicit the characteristic decrement of extraocular muscles in patients with ocular myasthenia gravis (OMG). Case-control studies demonstrated that roVEMP reliably differentiates patients with OMG from healthy controls. We now aimed to evaluate the diagnostic accuracy of roVEMP for OMG diagnosis in patients with ptosis and/or diplopia. METHODS: In this blinded prospective diagnostic accuracy trial, we compared roVEMP in 89 consecutive patients presenting with ptosis and/or diplopia suspicious of OMG with a multimodal diagnostic approach, including clinical examination, antibodies, edrophonium testing, repetitive nerve stimulation of accessory and facial nerves, and single-fiber EMG (SFEMG). We calculated the roVEMP decrement as the ratio between the mean of the first 2 responses compared with the mean of the sixth-ninth responses in the train and used cutoff of >9% (unilateral decrement) in a 30 Hz stimulation paradigm. RESULTS: Following a complete diagnostic work-up, 39 patients (44%) were diagnosed with ocular MG, while 50 patients (56%) had various other neuro-ophthalmologic conditions, but not MG (non-MG). roVEMP yielded 88.2% sensitivity, 30.2% specificity, 50% positive predictive value (PPV), and 76.5% negative predictive value (NPV). For comparison, SFEMG resulted in 75% sensitivity, 56% specificity, 55.1% PPV, and 75.7% NPV. All other diagnostic tests (except for the ice pack test) also yielded significantly higher positive results in patients with MG compared with non-MG. DISCUSSION: The study revealed a high sensitivity of 88.2% for roVEMP in OMG, but specificity and PPV were too low to allow for the OMG diagnosis as a single test. Thus, differentiating ocular MG from other neuro-ophthalmologic conditions remains challenging, and the highest diagnostic accuracy is still obtained by a multimodal approach. In this study, roVEMP can complement the diagnostic armamentarium for the diagnosis of MG. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that in patients with diplopia and ptosis, roVEMP alone does not accurately distinguish MG from non-MG disorders. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov: NCT03049956.

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OBJECTIVES: To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). METHODS: This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. RESULTS: A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001). CONCLUSION: Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. CLINICAL RELEVANCE STATEMENT: Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. KEY POINTS: • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

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Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neural networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.

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OBJECTIVES: The goal of this analysis was to assess the prognostic value of the post-treatment serum CA 125 level in each member of a group of advanced endometrial cancer (aEC) patients in comparison to other clinical and pathological parameters. MATERIAL AND METHODS: Records of 266 patients treated at the Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology, Cracow Branch between the years 2006 and 2018 were included in the study. Follow-up ranged from 1 to 138 months. Progression free survival (PFS) and overall survival (OS) were set as the endpoints. The tests chi-squared, Fisher, log-rank, Mann-Whitney, Kruskal-Wallis and Cox proportional hazard ratio were used for statistical analyses. RESULTS: In the analysed group, there was a significant association between an elevated serum CA 125 level following adjuvant treatment and shorter PFS and OS. After setting a cut-off value for CA 125 there was a statistically significant correlation between the marker and PFS and OS. Multivariate analysis indicated that the post-treatment serum CA 125 level is an independent prognostic factor of the course of aEC. CONCLUSIONS: The post-treatment serum CA 125 level correlates significantly with both PFS and OS in each patient with aEC. The marker is an independent prognostic factor in this group. A low post-treatment level of the marker is a strong indicator of good 5-year survival, with 82% of patients reaching 5-year OS.

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The brain learns representations of sensory information from experience, but the algorithms by which it does so remain unknown. One popular theory formalizes representations as inferred factors in a generative model of sensory stimuli, meaning that learning must improve this generative model and inference procedure. This framework underlies many classic computational theories of sensory learning, such as Boltzmann machines, the Wake/Sleep algorithm, and a more recent proposal that the brain learns with an adversarial algorithm that compares waking and dreaming activity. However, in order for such theories to provide insights into the cellular mechanisms of sensory learning, they must be first linked to the cell types in the brain that mediate them. In this study, we examine whether a subtype of cortical interneurons might mediate sensory learning by serving as discriminators, a crucial component in an adversarial algorithm for representation learning. We describe how such interneurons would be characterized by a plasticity rule that switches from Hebbian plasticity during waking states to anti-Hebbian plasticity in dreaming states. Evaluating the computational advantages and disadvantages of this algorithm, we find that it excels at learning representations in networks with recurrent connections but scales poorly with network size. This limitation can be partially addressed if the network also oscillates between evoked activity and generative samples on faster timescales. Consequently, we propose that an adversarial algorithm with interneurons as discriminators is a plausible and testable strategy for sensory learning in biological systems.

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Importance: The current clinical criterion standard for measuring abnormal eyeball protrusion is still the historic Hertel exophthalmometer, which is prone to reading errors. Therefore, a smartphone application has been developed to measure exophthalmos. Objective: To evaluate a relatively simple noninvasive measurement method for exophthalmos using a smartphone. Design, Setting, and Participants: This cross-sectional study compared smartphone exophthalmometry with the existing reference standard, the Hertel exophthalmometer, or a professional high-resolution 3-dimensional scanner. Participants were patients with exophthalmos due to Graves orbitopathy and other intraorbital conditions and healthy volunteers who were recruited between June 2019 and January 2022 from the Department of Ophthalmology, University Hospital Zurich. Interventions: All participants were examined twice by 3 different operators using 3 different methods (smartphone, high-resolution scanner, or Hertel exophthalmometer) at an interval of a minimum of 2 weeks or after exophthalmos-changing treatment. Main Outcome Measures: Accuracy and precision, test-retest reliability, and interoperator reliability of eyeball protrusion measurements obtained with the smartphone compared to the Hertel exophthalmometer and the high-resolution face scanner. Results: Of 39 participants, 23 patients (median [IQR] age, 54 [44-59] years; 15 [65%] female and 8 [35%] male) showed a mean difference in eyeball protrusion of 3.3 mm and 16 healthy volunteers (median [IQR] age, 32 [30-37] years; 11 [69%] female and 5 [31%] male) of 0.8 mm without any significant difference between the 3 methods. Accuracy and precision agreement between exophthalmos measures with the smartphone and the Hertel exophthalmometer showed an intraclass correlation coefficient (ICC) of 0.89 (95% CI, 0.80-0.94) and 0.93 (95% CI, 0.83-0.97) for the high-resolution scanner. Interoperator agreement was highest for the high-resolution scanner (ICC, 0.99 [95% CI, 0.98-0.99]), followed by the smartphone (ICC, 0.95 [95% CI, 0.92-0.97]) and the Hertel exophthalmometer (ICC, 0.91 [95% CI, 0.85-0.95]). Test retest reliability was similarly high for the smartphone (ICC, 0.93 [95% CI, 0.82-0.95]), the Hertel exophthalmometer (ICC, 0.92 [95% CI, 0.83-0.96]), and the high-resolution scanner (ICC, 0.95 [95% CI, 0.89-0.97]). Conclusions and Relevance: The findings of this study demonstrate relatively high accuracy and precision, interoperator reliability, and test-retest reliability for smartphone exophthalmometry. These data support the use of a smartphone in place of a Hertel exophthalmometer for measuring exophthalmos in the future.

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PURPOSE: The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data. METHOD: A 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets. RESULTS: The model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types. CONCLUSIONS: The herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows.

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Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neuronal networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.

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This brief overview highlights the global crisis of perinatal psychiatric illness (PPI). PPI is a major contributor to many adverse pregnancy, childbirth, and childhood development outcomes. It contributes to billions of dollars in spending worldwide each year and has a significant impact on the individual, their family, and their community. It is also highly preventable. Current recommendations for intervention and management of PPI are limited and vary considerably from country to country. Furthermore, there are several significant challenges asso-ciated with implementation of these recommendations. These challenges are magnified in number and consequence among women of color and/or minority populations, who experience persistent and negative health disparities during pregnancy and the postpartum period. This paper aims to provide a broad overview of the current state of recommendations and implementation challenges for PPI and layout a framework for overcoming these challenges. An equity-informed model of care that provides universal intervention for pregnant women may be one solution to address the preventable consequences of PPI on child and maternal health. Uniquely, this model emphasizes the importance of managing and eliminating known barriers to traditional health care models. Culturally and contextually specific challenges must be overcome to fully realize the impact of improved management of PPI.

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Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.

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Purpose: This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods: IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results: Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion: Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.

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BACKGROUND: Video-oculography (VOG) is used to quantify functional deficits in internuclear ophthalmoplegia (INO), whereas MRI can detect the corresponding structural lesions in the medial longitudinal fasciculus (MLF). This study investigates the diagnostic agreement of MRI compared to VOG measurements. METHODS: We prospectively compared structural MRI findings and functional VOG measures of 63 MS patients to assess their diagnostic agreement for INO. RESULTS: MRI detected 12 true-positive and 92 true-negative MLF lesions for INO compared to VOG (12 true-positive and 38 true-negative patients) but identified one-third of the MLF lesions on the wrong side. MRI ratings were specific (92.0%) to detect MLF lesions but not sensitive (46.2%) for diagnosing INO (86.4% and 63.2% by patient). Accordingly, MRI has a high positive likelihood ratio of 5.77 but a modest negative likelihood ratio of 0.59 for the probability of INO (4.63 and 0.43) with an accuracy of 82.5% (79.4%). CONCLUSION: MRI assessments are highly specific but not sensitive for detecting INO compared to VOG. While MRI identifies MLF lesions in INO, VOG quantifies the deficit. As a simple, quick, and non-invasive test for diagnosing and tracking functional INO deficits, it will hopefully find its place in the diagnostic and therapeutic pathways of MS.

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