Improving Spatial and Elemental Associations in Analytical Field Ion Microscopy.

Chemically resolved atomic resolution imaging can give fundamental information about material properties. However, even today, a technique capable of such achievement is still only an ambition. Here, we take further steps in developing the analytical field ion microscopy (aFIM), which combines the atomic spatial resolution of field ion microscopy (FIM) with the time-of-flight spectrometry of atom probe tomography (APT). To improve the performance of aFIM that are limited in part by a high level of background, we implement bespoke flight path time-of-flight corrections normalized by the ion flight distances traversed in electrostatic simulations modeled explicitly for an atom probe chamber. We demonstrate effective filtering in the field evaporation events upon spatially and temporally correlated multiples, increasing the mass spectrum's signal-to-background. In an analysis of pure tungsten, mass peaks pertaining to individual W isotopes can be distinguished and identified, with the signal-to-background improving by three orders of magnitude over the raw data. We also use these algorithms for the analysis of a CoTaB amorphous film to demonstrate application of aFIM beyond pure metals and binary alloys. These approaches facilitate elemental identification of the FIM-imaged surface atoms, making analytical FIM more precise and reliable.

Analytical Three-Dimensional Field Ion Microscopy of an Amorphous Glass FeBSi.

Three-dimensional field ion microscopy is a powerful technique to analyze material at a truly atomic scale. Most previous studies have been made on pure, crystalline materials such as tungsten or iron. In this article, we study more complex materials, and we present the first images of an amorphous sample, showing the capability to visualize the compositional fluctuations compatible with theoretical medium order in a metallic glass (FeBSi), which is extremely challenging to observe directly using other microscopy techniques. The intensity of the spots of the atoms at the moment of field evaporation in a field ion micrograph can be used as a proxy for identifying the elemental identity of the imaged atoms. By exploiting the elemental identification and positioning information from field ion images, we show the capability of this technique to provide imaging of recrystallized phases in the annealed sample with a superior spatial resolution compared with atom probe tomography.

Reflections on the Spatial Performance of Atom Probe Tomography in the Analysis of Atomic Neighborhoods.

Atom probe tomography (APT) is often introduced as providing "atomic-scale" mapping of the composition of materials and as such is often exploited to analyze atomic neighborhoods within a material. Yet quantifying the actual spatial performance of the technique in a general case remains challenging, as it depends on the material system being investigated as well as on the specimen's geometry. Here, by using comparisons with field-ion microscopy experiments, field-ion imaging and field evaporation simulations, we provide the basis for a critical reflection on the spatial performance of APT in the analysis of pure metals, low alloyed systems and concentrated solid solutions (i.e., akin to high-entropy alloys). The spatial resolution imposes strong limitations on the possible interpretation of measured atomic neighborhoods, and directional neighborhood analyses restricted to the depth are expected to be more robust. We hope this work gets the community to reflect on its practices, in the same way, it got us to reflect on our work.

Assessing the feasibility of hyperpolarized 129 Xe multiple-breath washout MRI in pediatric cystic fibrosis.

PURPOSE: To assess the feasibility of hyperpolarized 129 Xe multiple-breath washout MRI in pediatric cystic fibrosis (CF) participants with preserved lung function. Fractional ventilation (r), defined as the fractional gas replacement per breath, was mapped using 2 signal models: (1) constant T1 and (2) variable T1 as a function of the hyperpolarized gas washout. METHODS: A total of 17 pediatric participants were recruited (mean age 11.7 ± 2.8 years), including 7 children with clinically stable CF and 10 aged-matched healthy controls. Pulmonary function tests were performed, including spirometry, to measure the forced expiratory volume in 1 second and nitrogen multiple-breath washout to measure the lung clearance index. Hyperpolarized 129 Xe MRI was performed during consecutive breaths of air following a single 129 Xe inhalation, and fractional ventilation maps were calculated. RESULTS: The forced expiratory volume in 1 second was similar in both groups (P = .32), but there was a statistically significant difference in lung clearance index between healthy and CF participants (P = .001). With variable T1 modeling, CF participants had a mean r of 0.44 ± 0.08 and healthy participants had a mean r of 0.37 ± 0.12 (P = .20). With constant T1 modeling, CF participants had a mean r' of 0.48 ± 0.08, and healthy participants had a mean r' of 0.43 ± 0.12 (P = .32). Therefore, assuming a constant T1 leads to a relative bias in r of 15.1% ± 6.4% and 20.8% ± 7.4% for CF and healthy participants, respectively (P = .12). CONCLUSION: This study demonstrates that hyperpolarized 129 Xe multiple-breath washout imaging is feasible in pediatric participants with CF, and inclusion of variable T1 modeling reduces bias in the fractional ventilation measurements.

Dynamic Effects in Voltage Pulsed Atom Probe.

Atom probe tomography (APT) is particularly suited for the analysis of nanoscale microstructural features in metallic alloys. APT has become important in the quantitative assessment at high spatial resolution of light elements, which are notoriously difficult to analyze by electron- or X-ray-based techniques. These control the physical properties of high-strength materials and semiconductors. However, the mass spectrometer of state-of-the-art commercial atom probes with the highest spatial precision and detection efficiency are optimized for elements with mass-to-charge ratios corresponding to Fe and neighboring elements. Little is known on the theoretical performances for light elements. Here, we discuss the theoretical instrumental performance of one such instrument using accurate three-dimensional transient electrostatic simulations in a time-varying field approach. We compare the simulations to experimental measurements obtained on an FeBSi bulk-metallic glass. Dynamics effects during the ion's flight are revealed when examining multi-hit mass-to-charge correlations, and we demonstrate their influence on the mass resolution. The model reveals significant differences in ion projection as a function of the mass. We discuss how these chromatic aberrations affect the spatial precision. This approach shows that by tuning the shape of the voltage pulses used to trigger field evaporation, minimizing the influence of these detrimental dynamic effects is possible.

Effect of T1 relaxation on ventilation mapping using hyperpolarized 129 Xe multiple breath wash-out imaging.

PURPOSE: To investigate the effect of incorporating T1 as a function of wash-out breath number (T1 (n)) on estimation of fractional ventilation (r) using hyperpolarized 129 Xe multiple breath wash-out (MBWO) imaging in rats. METHODS: MBWO imaging was performed in 8 healthy mechanically ventilated rats at several inter-image delay times (τ) and tidal volumes (TV). r maps were calculated from the imaging data using a model of T1 (n) (assuming that the longitudinal relaxation rate of 129 Xe in the lung is directly proportional to pA O2 ) and compared to r maps obtained by assuming a fixed T1 measured before wash-out breaths (r'). RESULTS: Fractional ventilation was overestimated by up to 19.3% when T1 was fixed. An inverse relationship between bias (Δr) and ventilation was observed at all τ and TV. Additionally, Δr significantly increased when TV was decreased (F statistic F(2,7) = 48.97, P < 10-4 ). Histograms from r' maps were significantly more skewed toward lower values as compared to r histograms at all τ and TV (P < 0.05) except TV = Vdose - 1 mL. CONCLUSION: Analysis of hyperpolarized 129 Xe MBWO imaging using a model incorporating T1 (n) corrects for an overestimating bias in the mapping of fractional ventilation in mechanically ventilated rats introduced by assuming a fixed T1 .

Behaviour-correlated profiles of cerebellar-cerebral functional connectivity observed in independent neurodevelopmental disorder cohorts.

The cerebellum, through its connectivity with the cerebral cortex, plays an integral role in regulating cognitive and affective processes, and its dysregulation can result in neurodevelopmental disorder (NDD)-related behavioural deficits. Identifying cerebellar-cerebral functional connectivity (FC) profiles in children with NDDs can provide insight into common connectivity profiles and their correlation to NDD-related behaviours. 479 participants from the Province of Ontario Neurodevelopmental Disorders (POND) network (typically developing = 93, Autism Spectrum Disorder = 172, Attention Deficit/Hyperactivity Disorder = 161, Obsessive-Compulsive Disorder = 53, mean age = 12.2) underwent resting-state functional magnetic resonance imaging and behaviour testing (Social Communication Questionnaire, Toronto Obsessive-Compulsive Scale, and Child Behaviour Checklist - Attentional Problems Subscale). FC components maximally correlated to behaviour were identified using canonical correlation analysis. Results were then validated by repeating the investigation in 556 participants from an independent NDD cohort provided from a separate consortium (Healthy Brain Network (HBN)). Replication of canonical components was quantified by correlating the feature vectors between the two cohorts. The two cerebellar-cerebral FC components that replicated to the greatest extent were correlated to, respectively, obsessive-compulsive behaviour (behaviour feature vectors, rPOND-HBN = -0.97; FC feature vectors, rPOND-HBN = -0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, rPOND-HBN = -0.99; FC feature vectors, rPOND-HBN = -0.78). The statistically stable (|z| > 1.96) features of the FC feature vectors, measured via bootstrap re-sampling, predominantly comprised of correlations between cerebellar attentional and control network regions and cerebral attentional, default mode, and control network regions. In both cohorts, spectral clustering on FC loading values resulted in subject clusters mixed across diagnostic categories, but no cluster was significantly enriched for any given diagnosis as measured via chi-squared test (p > 0.05). Overall, two behaviour-correlated components of cerebellar-cerebral functional connectivity were observed in two independent cohorts. This suggests the existence of generalizable cerebellar network differences that span across NDD diagnostic boundaries.

Postoperative cerebellar mutism syndrome is an acquired Autism-like network disturbance.

BACKGROUND: Cerebellar mutism syndrome (CMS) is a common and debilitating complication of posterior fossa tumour surgery in children. Affected children exhibit communication and social impairments that overlap phenomenologically with subsets of deficits exhibited by children with Autism spectrum disorder (ASD). Although both CMS and ASD are thought to involve disrupted cerebro-cerebellar circuitry, they are considered independent conditions due to an incomplete understanding of their shared neural substrates. METHODS: In this study, we analyzed post-operative cerebellar lesions from 90 children undergoing posterior fossa resection of medulloblastoma, 30 of whom developed CMS. Lesion locations were mapped to a standard atlas, and the networks functionally connected to each lesion were computed in normative adult and paediatric datasets. Generalizability to ASD was assessed using an independent cohort of children with ASD and matched controls (n=427). RESULTS: Lesions in children who developed CMS involved the vermis and inferomedial cerebellar lobules. They engaged large-scale cerebellothalamocortical circuits with a preponderance for the prefrontal and parietal cortices in the paediatric and adult connectomes, respectively. Moreover, with increasing connectomic age, CMS-associated lesions demonstrated stronger connectivity to the midbrain/red nuclei, thalami and inferior parietal lobules and weaker connectivity to prefrontal cortex. Importantly, the CMS-associated lesion network was independently reproduced in ASD and correlated with communication and social deficits, but not repetitive behaviours. CONCLUSIONS: Our findings indicate that CMS-associated lesions result in an ASD-like network disturbance that occurs during sensitive windows of brain development. A common network disturbance between CMS and ASD may inform improved treatment strategies for affected children.

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.

Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.

Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty.

Estimates in a 1989 study indicated that physicians in the United States were unable to reach a diagnosis that accounted for their patient's symptoms in up to 90% of outpatient patient encounters. Many proponents of artificial intelligence (AI) see the current process of moving from clinical data gathering to medical diagnosis as being limited by human analytic capability and expect AI to be a valuable tool to refine this process. The use of AI fundamentally calls into question the extent to which uncertainty in medical decision making is tolerated. Uncertainty is perceived by some as fundamentally undesirable and thus, for them, optimal decision making should be based on minimizing uncertainty. However, uncertainty cannot be reduced to zero; thus, relative uncertainty can be used as a metric to weigh the likelihood of various diagnoses being correct and the appropriateness of treatments. Here, the authors make the argument, using as examples the experiences of 2 AI systems, IBM Watson on Jeopardy and Watson for Oncology, that medical decision making based on relative uncertainty provides a better lens for understanding the application of AI to medicine than one that minimizes uncertainty. This approach to uncertainty has significant implications for how health care leaders consider the benefits and trade-offs of AI-assisted and AI-driven decision tools and ultimately integrate AI into medical practice.

What do medical students actually need to know about artificial intelligence?

With emerging innovations in artificial intelligence (AI) poised to substantially impact medical practice, interest in training current and future physicians about the technology is growing. Alongside comes the question of what, precisely, should medical students be taught. While competencies for the clinical usage of AI are broadly similar to those for any other novel technology, there are qualitative differences of critical importance to concerns regarding explainability, health equity, and data security. Drawing on experiences at the University of Toronto Faculty of Medicine and MIT Critical Data's "datathons", the authors advocate for a dual-focused approach: combining robust data science-focused additions to baseline health research curricula and extracurricular programs to cultivate leadership in this space.

From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics.

Purpose of review: Machine learning (ML), a branch of artificial intelligence, is influencing all fields in medicine, with an abundance of work describing its application to adult practice. ML in pediatrics is distinctly unique with clinical, technical, and ethical nuances limiting the direct translation of ML tools developed for adults to pediatric populations. To our knowledge, no work has yet focused on outlining the unique considerations that need to be taken into account when designing and implementing ML in pediatrics. Recent findings: The nature of varying developmental stages and the prominence of family-centered care lead to vastly different data-generating processes in pediatrics. Data heterogeneity and a lack of high-quality pediatric databases further complicate ML research. In order to address some of these nuances, we provide a common pipeline for clinicians and computer scientists to use as a foundation for structuring ML projects, and a framework for the translation of a developed model into clinical practice in pediatrics. Throughout these pathways, we also highlight ethical and legal considerations that must be taken into account when working with pediatric populations and data. Summary: Here, we describe a comprehensive outline of special considerations required of ML in pediatrics from project ideation to implementation. We hope this review can serve as a high-level guideline for ML scientists and clinicians alike to identify applications in the pediatric setting, generate effective ML solutions, and subsequently deliver them to patients, families, and providers.

Regulation of autism-relevant behaviors by cerebellar-prefrontal cortical circuits.

Cerebellar dysfunction has been demonstrated in autism spectrum disorders (ASDs); however, the circuits underlying cerebellar contributions to ASD-relevant behaviors remain unknown. In this study, we demonstrated functional connectivity between the cerebellum and the medial prefrontal cortex (mPFC) in mice; showed that the mPFC mediates cerebellum-regulated social and repetitive/inflexible behaviors; and showed disruptions in connectivity between these regions in multiple mouse models of ASD-linked genes and in individuals with ASD. We delineated a circuit from cerebellar cortical areas Right crus 1 (Rcrus1) and posterior vermis through the cerebellar nuclei and ventromedial thalamus and culminating in the mPFC. Modulation of this circuit induced social deficits and repetitive behaviors, whereas activation of Purkinje cells (PCs) in Rcrus1 and posterior vermis improved social preference impairments and repetitive/inflexible behaviors, respectively, in male PC-Tsc1 mutant mice. These data raise the possibility that these circuits might provide neuromodulatory targets for the treatment of ASD.

Hyperpolarized Gas Magnetic Resonance Imaging of Pediatric Cystic Fibrosis Lung Disease.

Conventional pulmonary function tests appear normal in early cystic fibrosis (CF) lung disease. Therefore, new diagnostic approaches are required that can detect CF lung disease in children and monitor treatment response. Hyperpolarized (HP) gas (129Xe and 3He) magnetic resonance imaging (MRI) is a powerful, emergent tool for mapping regional lung function and may be well suited for studying pediatric CF. HP gas MRI is well tolerated, reproducible, and it can be performed longitudinally without the need for ionizing radiation. In particular, quantification of the distribution of ventilation, or ventilation defect percent (VDP), has been shown to be a sensitive indicator of CF lung disease and correlates well with pulmonary function tests. This article presents the current state of CF diagnosis and treatment and describes the potential role of HP gas MRI for detection of early CF lung disease and following the effects of interventions. The typical HP gas imaging workflow is described, along with a discussion of image analysis to calculate VDP, dosing considerations, and the reproducibility of VDP. The potential use of VDP as an outcome measure in CF is discussed, by considering the correlation with pulmonary function measures, preliminary interventional studies, and case studies involving longitudinal imaging and pulmonary exacerbations. Finally, emerging HP gas imaging techniques such as multiple breath washout imaging are introduced, followed by a discussion of future directions. Overall, HP gas MRI biomarkers are expected to provide sensitive outcome measures that can be used in disease surveillance as well as interventional studies involving novel CF therapies.