Dynamic categorization rules alter representations in human visual cortex.

Everyday perceptual tasks require sensory stimuli to be dynamically encoded and analyzed according to changing behavioral goals. For example, when searching for an apple at the supermarket, one might first find the Granny Smith apples by separating all visible apples into the categories "green" and "non-green". However, suddenly remembering that your family actually likes Fuji apples would necessitate reconfiguring the boundary to separate "red" from "red-yellow" objects. This flexible processing enables identical sensory stimuli to elicit varied behaviors based on the current task context. While this phenomenon is ubiquitous in nature, little is known about the neural mechanisms that underlie such flexible computation. Traditionally, sensory regions have been viewed as mainly devoted to processing inputs, with limited involvement in adapting to varying task contexts. However, from the standpoint of efficient computation, it is plausible that sensory regions integrate inputs with current task goals, facilitating more effective information relay to higher-level cortical areas. Here we test this possibility by asking human participants to visually categorize novel shape stimuli based on different linear and non-linear boundaries. Using fMRI and multivariate analyses of retinotopically-defined visual areas, we found that shape representations in visual cortex became more distinct across relevant decision boundaries in a context-dependent manner, with the largest changes in discriminability observed for stimuli near the decision boundary. Importantly, these context-driven modulations were associated with improved categorization performance. Together, these findings demonstrate that codes in visual cortex are adaptively modulated to optimize object separability based on currently relevant decision boundaries.

A Texture Statistics Encoding Model Reveals Hierarchical Feature Selectivity across Human Visual Cortex.

Midlevel features, such as contour and texture, provide a computational link between low- and high-level visual representations. Although the nature of midlevel representations in the brain is not fully understood, past work has suggested a texture statistics model, called the P-S model (Portilla and Simoncelli, 2000), is a candidate for predicting neural responses in areas V1-V4 as well as human behavioral data. However, it is not currently known how well this model accounts for the responses of higher visual cortex to natural scene images. To examine this, we constructed single-voxel encoding models based on P-S statistics and fit the models to fMRI data from human subjects (both sexes) from the Natural Scenes Dataset (Allen et al., 2022). We demonstrate that the texture statistics encoding model can predict the held-out responses of individual voxels in early retinotopic areas and higher-level category-selective areas. The ability of the model to reliably predict signal in higher visual cortex suggests that the representation of texture statistics features is widespread throughout the brain. Furthermore, using variance partitioning analyses, we identify which features are most uniquely predictive of brain responses and show that the contributions of higher-order texture features increase from early areas to higher areas on the ventral and lateral surfaces. We also demonstrate that patterns of sensitivity to texture statistics can be used to recover broad organizational axes within visual cortex, including dimensions that capture semantic image content. These results provide a key step forward in characterizing how midlevel feature representations emerge hierarchically across the visual system.SIGNIFICANCE STATEMENT Intermediate visual features, like texture, play an important role in cortical computations and may contribute to tasks like object and scene recognition. Here, we used a texture model proposed in past work to construct encoding models that predict the responses of neural populations in human visual cortex (measured with fMRI) to natural scene stimuli. We show that responses of neural populations at multiple levels of the visual system can be predicted by this model, and that the model is able to reveal an increase in the complexity of feature representations from early retinotopic cortex to higher areas of ventral and lateral visual cortex. These results support the idea that texture-like representations may play a broad underlying role in visual processing.

Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features.

Representations of visual and semantic information can overlap in human visual cortex, with the same neural populations exhibiting sensitivity to low-level features (orientation, spatial frequency, retinotopic position) and high-level semantic categories (faces, scenes). It has been hypothesized that this relationship between low-level visual and high-level category neural selectivity reflects natural scene statistics, such that neurons in a given category-selective region are tuned for low-level features or spatial positions that are diagnostic of the region's preferred category. To address the generality of this "natural scene statistics" hypothesis, as well as how well it can account for responses to complex naturalistic images across visual cortex, we performed two complementary analyses. First, across a large set of rich natural scene images, we demonstrated reliable associations between low-level (Gabor) features and high-level semantic categories (faces, buildings, animate/inanimate objects, small/large objects, indoor/outdoor scenes), with these relationships varying spatially across the visual field. Second, we used a large-scale functional MRI dataset (the Natural Scenes Dataset) and a voxelwise forward encoding model to estimate the feature and spatial selectivity of neural populations throughout visual cortex. We found that voxels in category-selective visual regions exhibit systematic biases in their feature and spatial selectivity, which are consistent with their hypothesized roles in category processing. We further showed that these low-level tuning biases are not driven by selectivity for categories themselves. Together, our results are consistent with a framework in which low-level feature selectivity contributes to the computation of high-level semantic category information in the brain.

Selectivity for food in human ventral visual cortex.

Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI "localizer" using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.

Early experience with low-pass filtered images facilitates visual category learning in a neural network model.

Humans are born with very low contrast sensitivity, meaning that inputs to the infant visual system are both blurry and low contrast. Is this solely a byproduct of maturational processes or is there a functional advantage for beginning life with poor visual acuity? We addressed the impact of poor vision during early learning by exploring whether reduced visual acuity facilitated the acquisition of basic-level categories in a convolutional neural network model (CNN), as well as whether any such benefit transferred to subordinate-level category learning. Using the ecoset dataset to simulate basic-level category learning, we manipulated model training curricula along three dimensions: presence of blurred inputs early in training, rate of blur reduction over time, and grayscale versus color inputs. First, a training regime where blur was initially high and was gradually reduced over time-as in human development-improved basic-level categorization performance in a CNN relative to a regime in which non-blurred inputs were used throughout training. Second, when basic-level models were fine-tuned on a task including both basic-level and subordinate-level categories (using the ImageNet dataset), models initially trained with blurred inputs showed a greater performance benefit as compared to models trained exclusively on non-blurred inputs, suggesting that the benefit of blurring generalized from basic-level to subordinate-level categorization. Third, analogous to the low sensitivity to color that infants experience during the first 4-6 months of development, these advantages were observed only when grayscale images were used as inputs. We conclude that poor visual acuity in human newborns may confer functional advantages, including, as demonstrated here, more rapid and accurate acquisition of visual object categories at multiple levels.

Flexible utilization of spatial- and motor-based codes for the storage of visuo-spatial information.

Working memory provides flexible storage of information in service of upcoming behavioral goals. Some models propose specific fixed loci and mechanisms for the storage of visual information in working memory, such as sustained spiking in parietal and prefrontal cortex during working memory maintenance. An alternative view is that information can be remembered in a flexible format that best suits current behavioral goals. For example, remembered visual information might be stored in sensory areas for easier comparison to future sensory inputs, or might be re-coded into a more abstract action-oriented format and stored in motor areas. Here, we tested this hypothesis using a visuo-spatial working memory task where the required behavioral response was either known or unknown during the memory delay period. Using functional magnetic resonance imaging (fMRI) and multivariate decoding, we found that there was less information about remembered spatial position in early visual and parietal regions when the required response was known versus unknown. Furthermore, a representation of the planned motor action emerged in primary somatosensory, primary motor, and premotor cortex during the same task condition where spatial information was reduced in early visual cortex. These results suggest that the neural networks supporting working memory can be strategically reconfigured depending on specific behavioral requirements during a canonical visual working memory paradigm.

A crisis is also an opportunity: GP teachers' views on continuing clinical placements in general practice during the evolving COVID-19 pandemic.

BACKGROUND AND OBJECTIVES: Early in the COVID-19 pandemic, some universities suspended student placements in general practice. The aim of this study was to explore the views of general practitioners (GPs) teaching in clinical practice ('GP teachers'). METHOD: Semi-structured interviews were held with 15 GP teachers in southeast Queensland from June to August 2020. RESULTS: Challenges included lack of masks and space, and concerns about infection control, medico-legal liability and student learning. Telehealth created more time for reflection and accessing information, increased student access to sensitive consultations, and provided opportunities to think-through rather than rote-do physical examinations. Students could be an extra pair of hands, also accessing and implementing the latest public health advice. GP teachers wanted clear communication and guidance from universities and for students to be trained in infection control and telehealth skills. DISCUSSION: Findings suggest that many GP teachers can be supported to continue clinical placements during an evolving crisis. The pandemic presented many challenges for general practice but also new opportunities for students to learn and contribute in practice.

Creating a more inclusive journal: the Journal of the Medical Library Association's evolving process for selecting editorial board members.

The Journal of the Medical Library Association (JMLA) selects new editorial board members every year. In the spring of 2021, JMLA used a new process for reviewing and selecting applicants for the limited number of open editorial board positions. This reevaluation of the selection process was spurred by a desire to create a more diverse and representative board. Changes to the procedures for selecting new editorial board members included having an open call for editorial board members, creating an application form, creating a selection committee to screen applicants, creating a form for the selection committee to extract data from applications, and creating a two-step process for screening and then selecting board members. As part of construction of this new process, areas for continued improvement were also identified, such as refining the application form to allow more specific answers to areas of interest to the selection committee. The newly created selection process for editorial board members constitutes a significant change in JMLA processes; however, more can be done to build on this work by further refining the selection process and ensuring that new members are selected in a transparent and streamlined manner.

Biased orientation representations can be explained by experience with nonuniform training set statistics.

Visual acuity is better for vertical and horizontal compared to other orientations. This cross-species phenomenon is often explained by "efficient coding," whereby more neurons show sharper tuning for the orientations most common in natural vision. However, it is unclear if experience alone can account for such biases. Here, we measured orientation representations in a convolutional neural network, VGG-16, trained on modified versions of ImageNet (rotated by 0°, 22.5°, or 45° counterclockwise of upright). Discriminability for each model was highest near the orientations that were most common in the network's training set. Furthermore, there was an overrepresentation of narrowly tuned units selective for the most common orientations. These effects emerged in middle layers and increased with depth in the network, though this layer-wise pattern may depend on properties of the evaluation stimuli used. Biases emerged early in training, consistent with the possibility that nonuniform representations may play a functional role in the network's task performance. Together, our results suggest that biased orientation representations can emerge through experience with a nonuniform distribution of orientations, supporting the efficient coding hypothesis.

The Medical Library Association Data Services Competency: a framework for data science and open science skills development.

Increasingly, users of health and biomedical libraries need assistance with challenges they face in working with their own and others' data. Librarians have a unique opportunity to provide valuable support and assistance in data science and open science but may need to add to their expertise and skill set to have the most impact. This article describes the rationale for and development of the Medical Library Association Data Services Competency, which outlines a set of five key skills for data services and provides a course of study for gaining these skills.

NCBI's Virus Discovery Hackathon: Engaging Research Communities to Identify Cloud Infrastructure Requirements.

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.

Multivariate Analysis of BOLD Activation Patterns Recovers Graded Depth Representations in Human Visual and Parietal Cortex.

Navigating through natural environments requires localizing objects along three distinct spatial axes. Information about position along the horizontal and vertical axes is available from an object's position on the retina, while position along the depth axis must be inferred based on second-order cues such as the disparity between the images cast on the two retinae. Past work has revealed that object position in two-dimensional (2D) retinotopic space is robustly represented in visual cortex and can be robustly predicted using a multivariate encoding model, in which an explicit axis is modeled for each spatial dimension. However, no study to date has used an encoding model to estimate a representation of stimulus position in depth. Here, we recorded BOLD fMRI while human subjects viewed a stereoscopic random-dot sphere at various positions along the depth (z) and the horizontal (x) axes, and the stimuli were presented across a wider range of disparities (out to ∼40 arcmin) compared to previous neuroimaging studies. In addition to performing decoding analyses for comparison to previous work, we built encoding models for depth position and for horizontal position, allowing us to directly compare encoding between these dimensions. Our results validate this method of recovering depth representations from retinotopic cortex. Furthermore, we find convergent evidence that depth is encoded most strongly in dorsal area V3A.

A systematic review of the approaches to multi-level learning in the general practice context, using a realist synthesis approach: BEME Guide No. 55.

Background: Recent global increases in medical student numbers and shifts in medical education from teaching hospitals to community settings call for effective strategies to meet the demand for general practice teaching placements. It has been proposed that "multi-level learning" (MLL), in which learning and teaching are shared across different levels of learners, may provide teaching efficiencies and valuable experiences for learners and teachers. Aims: To identify, evaluate and synthesize the evidence related to the types, benefits, challenges, and facilitators of MLL in community-based general practice, and the underlying mechanisms and associated contexts to explain the reported outcomes. Method: A realist synthesis approach guided the systematic review. Results: Fifteen papers were identified, providing primary evaluation data predominantly from interviews with or surveys of key stakeholders. Generally, all levels of learners reported overall satisfaction with their MLL experiences. Medical students appreciated learning from prevocational doctors and registrars due to social and cognitive congruence. Mechanisms and contexts that supported our hypotheses regarding successful MLL outcomes were identified, with "a strong teaching culture" being a major mechanism. Conclusions: The findings can help inform practices considering the implementation or enhancement of MLL initiatives in general practice. Further research should include measuring defined learning outcomes.

Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features.

Searching for items that are useful given current goals, or "target" recognition, requires observers to flexibly attend to certain object properties at the expense of others. This could involve focusing on the identity of an object while ignoring identity-preserving transformations such as changes in viewpoint or focusing on its current viewpoint while ignoring its identity. To effectively filter out variation due to the irrelevant dimension, performing either type of task is likely to require high-level, abstract search templates. Past work has found target recognition signals in areas of ventral visual cortex and in subregions of parietal and frontal cortex. However, target status in these tasks is typically associated with the identity of an object, rather than identity-orthogonal properties such as object viewpoint. In this study, we used a task that required subjects to identify novel object stimuli as targets according to either identity or viewpoint, each of which was not predictable from low-level properties such as shape. We performed functional MRI in human subjects of both sexes and measured the strength of target-match signals in areas of visual, parietal, and frontal cortex. Our multivariate analyses suggest that the multiple-demand (MD) network, including subregions of parietal and frontal cortex, encodes information about an object's status as a target in the relevant dimension only, across changes in the irrelevant dimension. Furthermore, there was more target-related information in MD regions on correct compared with incorrect trials, suggesting a strong link between MD target signals and behavior. NEW & NOTEWORTHY Real-world target detection tasks, such as searching for a car in a crowded parking lot, require both flexibility and abstraction. We investigated the neural basis of these abilities using a task that required invariant representations of either object identity or viewpoint. Multivariate decoding analyses of our whole brain functional MRI data reveal that invariant target representations are most pronounced in frontal and parietal regions, and the strength of these representations is associated with behavioral performance.

Medical students, early general practice placements and positive supervisor experiences.

Introduction Community-based longitudinal clinical placements for medical students are becoming more common globally. The perspective of supervising clinicians about their experiences and processes involved in maximising these training experiences has received less attention than that of students. Aims This paper explores the general practitioner (GP) supervisor perspective of positive training experiences with medical students undertaking urban community-based, longitudinal clinical placements in the early years of medical training. Methods Year 2 medical students spent a half-day per week in general practice for either 13 or 26 weeks. Transcribed semi-structured interviews from a convenience sample of participating GPs were thematically analysed by two researchers, using a general inductive approach. Results Identified themes related to the attributes of participating persons and organisations: GPs, students, patients, practices and their supporting institution; GPs' perceptions of student development; and triggers enhancing the experience. A model was developed to reflect these themes. Conclusions Training experiences were enhanced for GPs supervising medical students in early longitudinal clinical placements by the synergy of motivated students and keen teachers with support from patients, practice staff and academic institutions. We developed an explanatory model to better understand the mechanism of positive experiences. Understanding the interaction of factors enhancing teaching satisfaction is important for clinical disciplines wishing to maintain sustainable, high quality teaching.

Clinical outcomes of HIV care delivery models in the US: a systematic review.

With over 1 million people living with HIV, the US faces national challenges in HIV care delivery due to an inadequate HIV specialist workforce and the increasing role of non-communicable chronic diseases in driving morbidity and mortality in HIV-infected patients. Alternative HIV care delivery models, which include substantial roles for advanced practitioners and/or coordination between specialty and primary care settings in managing HIV-infected patients, may address these needs. We aimed to systematically review the evidence on patient-level HIV-specific and primary care health outcomes for HIV-infected adults receiving outpatient care across HIV care delivery models. We identified randomized trials and observational studies from bibliographic and other databases through March 2016. Eligible studies met pre-specified eligibility criteria including on care delivery models and patient-level health outcomes. We considered all available evidence, including non-experimental studies, and evaluated studies for risk of bias. We identified 3605 studies, of which 13 met eligibility criteria. Of the 13 eligible studies, the majority evaluated specialty-based care (9 studies). Across all studies and care delivery models, eligible studies primarily reported mortality and antiretroviral use, with specialty-based care associated with mortality reductions at the clinician and practice levels and with increased antiretroviral initiation or use at the clinician level but not the practice level. Limited and heterogeneous outcomes were reported for other patient-level HIV-specific outcomes (e.g., viral suppression) as well as for primary care health outcomes across all care delivery models. No studies addressed chronic care outcomes related to aging. Limited evidence was available across geographic settings and key populations. As re-design of care delivery in the US continues to evolve, better understanding of patient-level HIV-related and primary care health outcomes, especially across different staffing models and among different patient populations and geographic locations, is urgently needed to improve HIV disease management.

A review of antidepressant-induced urinary hesitancy: a focus on levomilnacipran ER including two case presentations(5633).

INTRODUCTION: Levomilnacipran ER was recently FDA approved as Fetzima® for the treatment of MDD. Urinary hesitancy can be an adverse event associated with levomilnacipran treatment. AREAS COVERED: This manuscript details the longitudinal course of levomilnacipran-induced urinary hesitancy in 2 cases that were in a pivotal clinical trial, examining possible predisposing factors and treatment issues. This manuscript also reviews the literature comparing urinary hesitancy associated with levomilnacipran versus other antidepressants. Antidepressants that are potent norepinephrine reuptake inhibitors like levomilnacipran, may have increased rates of associated urinary hesitancy. The latter can cause significant discomfort and a compromised quality of life. Occasionally, it can progress to urinary retention necessitating an emergency medical intervention. EXPERT OPINION: All patients being treated with antidepressants should be carefully monitored for this side effect. Discontinuation of treatment or reduction of the dose of antidepressant frequently relieves urinary hesitancy; alternatively, treatment with an alpha1A antagonist, e.g., tamsulosin may relieve antidepressant-induced urinary hesitancy within hours to days; such strategies allow for continued antidepressant treatment without urinary hesitancy recurring. Thus, with appropriate clinical care, the benefits using levomilnacipran outweigh its risks.

Levomilnacipran for the treatment of major depressive disorder: a review.

Levomilnacipran (LVM, Fetzima(®)) was recently approved by the US Food and Drug Administration for the treatment of major depressive disorder. It is a unique dual neurotransmitter reuptake inhibitor. In contrast with other selective serotonin norepinephrine reuptake inhibitors, including duloxetine, venlafaxine, and desvenlafaxine, it has greater selectivity for inhibiting norepinephrine reuptake than serotonin reuptake. Our review focuses on the efficacy, safety, and tolerability data for five double-blind, placebo-controlled, short-term studies and two long-term studies. In the short-term studies, LVM was found to be more effective than placebo in reducing depression (Montgomery-Åsberg Depression Rating Scale) scores as well as improving functional impairment (Sheehan Disability Scale) scores. Long-term studies found LVM to be similarly effective but in the only placebo-controlled long-term study, LVM was not significantly superior to placebo. LVM is fairly well tolerated, with the most common adverse events being nausea, headache, dry mouth, hyperhidrosis, and constipation. Discontinuation rates were mildly increased in those being treated with LVM (9%) versus placebo (3%). Adverse events were not dose-related except for urinary hesitancy and erectile dysfunction. LVM was weight neutral, was not toxic to the liver, and did not cause clinically significant QTc prolongation. Consistent with being a predominant potentiator of norepinephrine, pulse and blood pressure were significantly elevated by LVM but rarely induced tachycardia or hypertension. LVM is a relatively safe alternative antidepressant treatment with minimal drug-drug interactions. It is the only antidepressant that has in its labeling that it is not only effective in improving depression but also effective in improving impaired functioning. Whether this important effect on functioning is unique to LVM must be researched. In addition, whether LVM might be effective in norepinephrine-deficit depression, refractory depression, atypical depression, or seasonal depression is yet to be evaluated. Ultimately, head-to-head studies comparing LVM with other antidepressants will determine the place of LM in antidepressant treatment.

Starting a research data management program based in a university library.

As the need for research data management grows, many libraries are considering adding data services to help with the research mission of their institution. The Virginia Commonwealth University (VCU) Libraries created a position and hired a director of research data management in September 2013. The position was new to the libraries and the university. With the backing of the library administration, a plan for building relationships with VCU faculty, researchers, students, service and resource providers, including grant administrators, was developed to educate and engage the community in data management plan writing and research data management training.

Pharmacotherapy Treatment Options for Insomnia: A Primer for Clinicians.

Insomnia is a prevalent disorder with deleterious effects such as decreased quality of life, and a predisposition to a number of psychiatric disorders. Fortunately, numerous approved hypnotic treatments are available. This report reviews the state of the art of pharmacotherapy with a reference to cognitive behavioral therapy for insomnia (CBT-I) as well. It provides the clinician with a guide to all the Food and Drug Administration (FDA) approved hypnotics (benzodiazepines, nonbenzodiazepines, ramelteon, low dose sinequan, and suvorexant) including potential side effects. Frequently, chronic insomnia lasts longer than 2 years. Cognizant of this and as a result of longer-term studies, the FDA has approved all hypnotics since 2005 without restricting the duration of use. Our manuscript also reviews off-label hypnotics (sedating antidepressants, atypical antipsychotics, anticonvulsants and antihistamines) which in reality, are more often prescribed than approved hypnotics. The choice of which hypnotic to choose is discussed partially being based on which segment of sleep is disturbed and whether co-morbid illnesses exist. Lastly, we discuss recent label changes required by the FDA inserting a warning about "sleep-related complex behaviors", e.g., sleep-driving for all hypnotics. In addition, we discuss FDA mandated dose reductions for most zolpidem preparations in women due to high zolpidem levels in the morning hours potentially causing daytime carry-over effects.