The past, present, and future of the brain imaging data structure (BIDS).

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning.

Recent work has shown that deep learning is a powerful tool for predicting brain activation patterns evoked through various tasks using resting state features. We replicate and improve upon this recent work to introduce two models, BrainSERF and BrainSurfGCN, that perform at least as well as the state-of-the-art while greatly reducing memory and computational footprints. Our performance analysis observed that low predictability was associated with a possible lack of task engagement derived from behavioral performance. Furthermore, a deficiency in model performance was also observed for closely matched task contrasts, likely due to high individual variability confirmed by low test-retest reliability. Overall, we successfully replicate recently developed deep learning architecture and provide scalable models for further research.

The Power of Preference Signaling: A Monumental Shift in the Orthopaedic Surgery Application Process.

INTRODUCTION: Orthopaedic surgery has consistently been one of the most competitive specialties in the US residency selection process. This is due in part to the steady upward trend in average applications received per program and average applications submitted per applicant, which is of growing concern. With the implementation of the Preference Signaling Program, the total number of applications has now dropped for the first time in many years, indicating signaling may improve the application process. The hypothesis is that signaling has led to a decrease in applications sent by applicants and a decrease in applications received by programs. METHODS: A 7-question survey regarding their interview and match statistics was sent to orthopaedic surgery residency programs that participated in the Electronic Residency Application Service during the 2023-2024 application cycle. A response from the program director/administrator was then recorded. RESULTS: Our program search yielded 159 programs with 106 respondents (66.7%). 82 programs (78.8%) solely interviewed applicants who signaled their program. 92.7% of current interns signaled the program where they matched, and 88 programs (84.6%) matched only applicants who signaled. 95 programs (89.6%) revealed that implementing signaling has improved the application process. CONCLUSION: Most of the programs only interviewed applicants who also signaled, and nearly all matched orthopaedic surgery applicants from the 2022-2023 cycle signaled their matching program. Orthopaedic surgery applicants should consider only applying to 30 programs and using all 30 available signals. Applicants should also be more confident knowing that beyond the 30 signals they use, there is limited support to say that they will receive an interview outside of these 30 applications. Orthopaedic surgery programs will also now have the ability to allocate more time to applicants most interested in their program, given the reduction of applications.

Annotation of epilepsy clinic letters for natural language processing.

BACKGROUND: Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the development and validation of NLP applications is limited. We created synthetic clinical documents to address this, and to validate the Extraction of Epilepsy Clinical Text version 2 (ExECTv2) NLP pipeline. METHODS: We created 200 synthetic clinic letters based on hospital outpatient consultations with epilepsy specialists. The letters were double annotated by trained clinicians and researchers according to agreed guidelines. We used the annotation tool, Markup, with an epilepsy concept list based on the Unified Medical Language System ontology. All annotations were reviewed, and a gold standard set of annotations was agreed and used to validate the performance of ExECTv2. RESULTS: The overall inter-annotator agreement (IAA) between the two sets of annotations produced a per item F1 score of 0.73. Validating ExECTv2 using the gold standard gave an overall F1 score of 0.87 per item, and 0.90 per letter. CONCLUSION: The synthetic letters, annotations, and annotation guidelines have been made freely available. To our knowledge, this is the first publicly available set of annotated epilepsy clinic letters and guidelines that can be used for NLP researchers with minimum epilepsy knowledge. The IAA results show that clinical text annotation tasks are difficult and require a gold standard to be arranged by researcher consensus. The results for ExECTv2, our automated epilepsy NLP pipeline, extracted detailed epilepsy information from unstructured epilepsy letters with more accuracy than human annotators, further confirming the utility of NLP for clinical and research applications.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Moving beyond processing- and analysis-related variation in resting-state functional brain imaging.

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.

Application and expansion of an algorithm predicting attention-deficit/hyperactivity disorder and impairment in a predominantly White sample.

Current assessment protocols for attention-deficit/hyperactivity disorder (ADHD) focus heavily on a set of highly overlapping symptoms, with well-validated factors like cognitive disengagement syndrome (CDS), executive function (EF), age, sex, and race and ethnicity generally being ignored. Using machine learning techniques, the current study aimed to validate recent findings proposing a subset of ADHD symptoms that, together, predict ADHD diagnosis, severity, and impairment level better than the full symptom list, while also testing whether the inclusion of the factors listed above could further increase accuracy. Parents of 1,922 children (50.1% male) aged 6-17 years completed rating scales of ADHD, CDS, EF, and impairment. Results suggested nine symptoms as most important in predicting outcomes: (a) has difficulty sustaining attention in tasks or play activities; (b) does not follow through on instructions and fails to finish work; (c) avoids tasks (e.g., schoolwork, homework) that require sustained mental effort; (d) is often easily distracted; (e) has difficulty organizing tasks and activities; (f) is often forgetful in daily activities; (g) fidgets with hands or feet or squirms in seat; (h) interrupts/intrudes on others; and (i) shifts around excessively or feels restless or hemmed in. The abbreviated algorithm achieved accuracy rates that did not significantly differ compared to an algorithm comprising all 18 symptoms in predicting impairment, while also demonstrating excellent discriminative ability in predicting ADHD diagnosis. Adding CDS and EF to the abbreviated algorithm further improved the prediction of global impairment. Continued refinement of screening tools will be key to ensuring access to clinical services for youth at risk for ADHD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Intubation and Lower Saturation in the Delivery Room are Associated with Balloon Atrial Septostomy in Neonates with Transposition of the Great Arteries.

OBJECTIVE: To identify delivery room (DR) characteristics of patients with transposition of the great arteries (TGA) who underwent preoperative balloon atrial septostomy (BAS). STUDY DESIGN: Retrospective cohort study of all patients with prenatally diagnosed TGA delivered at our center between 2013 and 2023 who underwent arterial switch operation during the newborn admission. RESULTS: A total of 168 patients were included (median gestational age 39.5 weeks, 64% male, 33% with ventricular septal defect, 8% with aortic arch hypoplasia). BAS was performed in 84 patients (50%). Patients who underwent BAS had higher proportion of intubation in the DR (87% vs 33%, P < .001), lower maximum oxygen saturation in the first 10 minutes (64% vs 74%, P < .001) and 20 minutes (71% vs 81%, P < .001) of life, and lower maximum oxygen saturation at any point in the DR (79% vs 87%, P < .001). Adjusting for confounders (ventricular septal defect, aortic arch anomaly, 5-minute Apgar, birth weight), intubation in the DR (aOR 9.5, 95% CI 3.9, 25.0) and lower maximum oxygen saturation in the DR (aOR 0.9, 95% CI 0.8, 0.9) were independently associated with BAS. By receiver operating characteristic analysis, a maximum oxygen saturation of less than 86% at any time point in the DR discriminated for BAS with a specificity of 0.88, sensitivity of 0.70, and area under the curve of 0.82. CONCLUSIONS: Intubation and lower oxygen saturation in the DR are independently associated with BAS in patients with TGA born at our center. A maximum DR saturation of less than 86% best discriminates patients who undergo BAS in our population.

Behavioural compatibility, not fear, best predicts the looking patterns of chacma baboons.

Animal vigilance is often investigated under a narrow set of scenarios, but this approach may overestimate its contribution to animal lives. A solution may be to sample all looking behaviours and investigate numerous competing hypotheses in a single analysis. In this study, using a wild group of habituated chacma baboons (Papio ursinus griseipes) as a model system, we implemented a framework for predicting the key drivers of looking by comparing the strength of a full array of biological hypotheses. This included methods for defining individual-specific social threat environments, quantifying individual tolerance to human observers, and incorporating predator resource selection functions. Although we found evidence supporting reactionary and within-group (social) vigilance hypotheses, risk factors did not predict looking with the greatest precision, suggesting vigilance was not a major component of the animals' behavioural patterns generally. Instead, whilst some behaviours constrain opportunities for looking, many shared compatibility with looking, alleviating the pressure to be pre-emptively vigilant for threats. Exploring looking patterns in a thorough multi-hypothesis framework should be feasible across a range of taxa, offering new insights into animal behaviour that could alter our concepts of fear ecology.

Hypermobile Ehlers-Danlos syndrome and spontaneous CSF leaks: the connective tissue conundrum.

Collagen, the most abundant protein in the body, is a key component of the extracellular matrix (ECM), which plays a crucial role in the structure and support of connective tissues. Abnormalities in collagen associated with connective tissue disorders (CTD) can lead to neuroinflammation and weaken the integrity of the blood-brain barrier (BBB), a semi-permeable membrane that separates the brain's extracellular fluid from the bloodstream. This compromise in the BBB can result from disruptions in ECM components, leading to neuroinflammatory responses, neuronal damage, and increased risks of neurological disorders. These changes impact central nervous system homeostasis and may exacerbate neurological conditions linked to CTD, manifesting as cognitive impairment, sensory disturbances, headaches, sleep issues, and psychiatric symptoms. The Ehlers-Danlos syndromes (EDS) are a group of heritable CTDs that result from varying defects in collagen and the ECM. The most prevalent subtype, hypermobile EDS (hEDS), involves clinical manifestations that include joint hypermobility, skin hyperextensibility, autonomic dysfunction, mast cell activation, chronic pain, as well as neurological manifestations like chronic headaches and cerebrospinal fluid (CSF) leaks. Understanding the connections between collagen, CSF, inflammation, and the BBB could provide insights into neurological diseases associated with connective tissue abnormalities and guide future research.

Co-occurrence of tethered cord syndrome and cervical spine instability in hypermobile Ehlers-Danlos syndrome.

The Ehlers-Danlos Syndromes (EDS) represent a group of hereditary connective tissue disorders, with the hypermobile subtype (hEDS) being the most prevalent. hEDS manifests with a diverse array of clinical symptoms and associated comorbidities spanning the musculoskeletal, neurological, gastrointestinal, cardiovascular, and immunological systems. hEDS patients may experience spinal neurological complications, including cervico-medullary symptoms arising from cranio-cervical and/or cervical instability/hypermobility, as well as tethered cord syndrome (TCS). TCS is often radiographically occult in nature, not always detectable on standard imaging and presents with lower back pain, balance issues, weakness in the lower extremities, sensory loss, and bowel or bladder dysfunction. Cervical instability due to ligament laxity can lead to headaches, vertigo, tinnitus, vision changes, syncope, radiculopathy, pain, and dysphagia. TCS and cervical instability not only share clinical features but can also co-occur in hEDS patients, posing challenges in diagnostics and clinical management. We present a review of the literature and a case study of a 20-year-old female with hEDS, who underwent surgical interventions for these conditions, highlighting the challenges in diagnosing and managing these complexities and underscoring the importance of tailored treatment strategies to improve patient outcomes.

Distinct neural mechanisms for heading retrieval and context recognition in the hippocampus during spatial reorientation.

Reorientation, the process of regaining one's bearings after becoming lost, requires identification of a spatial context (context recognition) and recovery of facing direction within that context (heading retrieval). We previously showed that these processes rely on the use of features and geometry, respectively. Here, we examine reorientation behavior in a task that creates contextual ambiguity over a long timescale to demonstrate that male mice learn to combine both featural and geometric cues to recover heading. At the neural level, most CA1 neurons persistently align to geometry, and this alignment predicts heading behavior. However, a small subset of cells remaps coherently in a context-sensitive manner, which serves to predict context. Efficient heading retrieval and context recognition correlate with rate changes reflecting integration of featural and geometric information in the active ensemble. These data illustrate how context recognition and heading retrieval are coded in CA1 and how these processes change with experience.

Keystone individuals - linking predator traits to community ecology.

Individual behavioral plasticity enables animals to adjust to different scenarios. Yet, personality traits limit this flexibility, leading to consistent interindividual differences in behavior. These individual behavioral traits have the potential to govern community interactions, although testing this is difficult in complex natural systems. For large predators who often exert strong effects on ecosystem functioning, this behavioral diversity may be especially important and lead to individualized ecosystem roles. We present a framework for quantifying individual behavioral plasticity and personality traits of large wild predators, revealing the extent to which certain natural behaviors are governed by these latent traits. The outcomes will reveal how the innate characteristics of wildlife can scale up to affect community interactions.

Wastewater Surveillance to Confirm Differences in Influenza A Infection between Michigan, USA, and Ontario, Canada, September 2022-March 2023.

Wastewater surveillance is an effective way to track the prevalence of infectious agents within a community and, potentially, the spread of pathogens between jurisdictions. We conducted a retrospective wastewater surveillance study of the 2022-23 influenza season in 2 communities, Detroit, Michigan, USA, and Windsor-Essex, Ontario, Canada, that form North America's largest cross-border conurbation. We observed a positive relationship between influenza-related hospitalizations and the influenza A virus (IAV) wastewater signal in Windsor-Essex (ρ = 0.785; p<0.001) and an association between influenza-related hospitalizations in Michigan and the IAV wastewater signal for Detroit (ρ = 0.769; p<0.001). Time-lagged cross correlation and qualitative examination of wastewater signal in the monitored sewersheds showed the peak of the IAV season in Detroit was delayed behind Windsor-Essex by 3 weeks. Wastewater surveillance for IAV reflects regional differences in infection dynamics which may be influenced by many factors, including the timing of vaccine administration between jurisdictions.

Challenging the 'acceptable option': Public health's advocacy for continued care in the case of pediatric vaccine refusal.

BACKGROUND: In the United States, nearly half of pediatricians dismiss or refuse to accept families that withhold consent from the administration of childhood vaccines. Since 2016, the American Academy of Pediatrics has called patient dismissal in these cases "an acceptable option." Clinician dismissal and non-acceptance pose a problem to public health because they cluster under-vaccinated children in the practices that remain willing to treat such children, and they decrease access to routine care for children who cannot find practices willing to accept or retain them. This paper reports the emergence of a new consciousness about dismissal and non-acceptance policies in the leadership of a local health department (LHD) of a populous metropolitan county. OBJECTIVES: To understand the prevalence and diversity of patient dismissal within Oakland County, Michigan and to measure shifts in clinicians' attitudes about dismissal following an educational intervention. METHODS: A preliminary community survey was distributed to immunizing providers during April 2023 with 61 responses measuring the frequency and reasoning for dismissal policies. The results of the survey were used to inform a brief, evidence-backed educational intervention which was delivered in June 2023 to 82 participants from local pediatric medical offices. RESULTS: The initial survey was completed by 61 immunizing providers, representing an estimated 37% of vaccinating practices in the county. Half said their practice "always" or "sometimes" dismisses patients due to vaccine refusal. After the educational intervention, the proportion of participants who agreed/strongly agreed with the statement "I believe patient dismissal for vaccine refusal is a good choice for public health" decreased from 36% to 18%. CONCLUSION: The changes that we observed between the pre- and post-intervention surveys demonstrate the opportunity that exists for LHD leaders to enter the conversation around patient dismissal and nonacceptance and shed new light on this issue.

Uncovering the phonon spectra and lattice dynamics of plastically deformable InSe van der Waals crystals.

Stacking two-dimensional (2D) van der Waals (vdW) materials in a layered bulk structure provides an appealing platform for the emergence of exotic physical properties. As a vdW crystal with exceptional plasticity, InSe offers the opportunity to explore various effects arising from the coupling of its peculiar mechanical behaviors and other physical properties. Here, we employ neutron scattering techniques to investigate the correlations of plastic interlayer slip, lattice anharmonicity, and thermal transport in InSe crystals. Not only are the interlayer slip direction and magnitude well captured by shifts in the Bragg reflections, but we also observe a deviation from the expected Debye behaviour in the heat capacity and lattice thermal conductivity. Combining the experimental data with first-principles calculations, we tentatively attribute the observed evidence of strong phonon-phonon interactions to a combination of a large acoustic-optical frequency resonance and a nesting effect. These findings correlate the macroscopic plastic slip and the microscopic lattice dynamics, providing insights into the mechano-thermo coupling and modulation in 2D vdW materials.

Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation.

We describe the following shared data from N = 103 healthy adults who completed a broad set of cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting state fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90-minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

A systematic review of social-validity assessments in the Journal of Applied Behavior Analysis: 2010-2020.

We conducted a systematic review of studies published in the Journal of Applied Behavior Analysis between 2010 and 2020 to identify reports of social validity. A total of 160 studies (17.60%) published during this time included a measure of social validity. For each study, we extracted data on (a) the dimensions of social validity, (b) the methods used for collecting social-validity data, (c) the respondents, and (d) when social-validity data were collected. Most social-validity assessments measured the acceptability of intervention procedures and outcomes, with fewer evaluating goals. The most common method for collecting social validity data was Likert-type rating scales, followed by non-Likert-type questionnaires. In most studies, the direct recipients of the intervention provided feedback on social validity. Social-validity assessment data were often collected at the conclusion of the study. We provide examples of social-validity measurement methods, discuss their strengths and limitations, and provide recommendations for improving the future collection and reporting of social-validity data.