Dense Sample Deep Learning.

Deep learning (DL), a variant of the neural network algorithms originally proposed in the 1980s (Rumelhart et al., 1986), has made surprising progress in artificial intelligence (AI), ranging from language translation, protein folding (Jumper et al., 2021), autonomous cars, and, more recently, human-like language models (chatbots). All that seemed intractable until very recently. Despite the growing use of DL networks, little is understood about the learning mechanisms and representations that make these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and, of course, the large scale of the data, since not much has changed since 1986. But the nature of deep learned representations remains largely unknown. Unfortunately, training sets with millions or billions of tokens have unknown combinatorics, and networks with millions or billions of hidden units can't easily be visualized and their mechanisms can't be easily revealed. In this letter, we explore these challenges with a large (1.24 million weights VGG) DL in a novel high-density sample task (five unique tokens with more than 500 exemplars per token), which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping. From these results, we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results.

Application of Novosorb biodegradable temporising matrix in wounds of different aetiologies: a case series.

DECLARATION OF INTEREST: The authors have no conflicts of interest to declare.

Experimental evidence for a child-to-adolescent switch in human amygdala-prefrontal cortex communication: A cross-sectional pilot study.

Interactions between the amygdala and prefrontal cortex are fundamental to human emotion. Despite the central role of frontoamygdala communication in adult emotional learning and regulation, little is known about how top-down control emerges during human development. In the present cross-sectional pilot study, we experimentally manipulated prefrontal engagement to test its effects on the amygdala during development. Inducing dorsal anterior cingulate cortex (dACC) activation resulted in developmentally-opposite effects on amygdala reactivity during childhood versus adolescence, such that dACC activation was followed by increased amygdala reactivity in childhood but reduced amygdala reactivity in adolescence. Bayesian network analyses revealed an age-related switch between childhood and adolescence in the nature of amygdala connectivity with the dACC and ventromedial PFC (vmPFC). Whereas adolescence was marked by information flow from dACC and vmPFC to amygdala (consistent with that observed in adults), the reverse information flow, from the amygdala to dACC and vmPFC, was dominant in childhood. The age-related switch in information flow suggests a potential shift from bottom-up co-excitatory to top-down regulatory frontoamygdala connectivity and may indicate a profound change in the circuitry supporting maturation of emotional behavior. These findings provide novel insight into the developmental construction of amygdala-cortical connections and implications for the ways in which childhood experiences may influence subsequent prefrontal function.

A Patient-Centered Environmental Scan of Inpatient Visitor Policies During the COVID-19 Pandemic.

Researchers and patients conducted an environmental scan of policy documents and public-facing websites and abstracted data to describe COVID-19 adult inpatient visitor restrictions at 70 academic medical centers. We identified variations in how centers described and operationalized visitor policies. Then, we used the nominal group technique process to identify patient-centered information gaps in visitor policies and provide key recommendations for improvement. Recommendations were categorized into the following domains: 1) provision of comprehensive, consistent, and clear information; 2) accessible information for patients with limited English proficiency and health literacy; 3) COVID-19 related considerations; and 4) care team member methods of communication.

Effect of a Multifactorial Fall Injury Prevention Intervention on Patient Well-Being: The STRIDE Study.

BACKGROUND/OBJECTIVES: In the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, a multifactorial intervention was associated with a nonsignificant 8% reduction in time to first serious fall injury but a significant 10% reduction in time to first self-reported fall injury relative to enhanced usual care. The effect of the intervention on other outcomes important to patients has not yet been reported. We aimed to evaluate the effect of the intervention on patient well-being including concern about falling, anxiety, depression, physical function, and disability. DESIGN: Pragmatic cluster-randomized trial of 5,451 community-living persons at high risk for serious fall injuries. SETTING: A total of 86 primary care practices within 10 U.S. healthcare systems. PARTICIPANTS: A random subsample of 743 persons aged 75 and older. MEASUREMENTS: The well-being measures, assessed at baseline, 12 months, and 24 months, included a modified version of the Fall Efficacy Scale, Patient-Reported Outcomes Measurement Information System (PROMIS) anxiety and depression scales, and Late-Life Function and Disability Instrument. RESULTS: Participants in the intervention (n = 384) and control groups (n = 359) were comparable in age: mean (standard deviation) of 81.9 (4.7) versus 81.8 (5.0) years. Mean scores were similar between groups at 12 and 24 months for concern about falling, physical function, and disability, whereas the intervention group's mean scores on anxiety and depression were .7 points lower (i.e., better) at 12 months and .6 to .8 points lower at 24 months. For each of these outcomes, differences between the groups' adjusted least square mean changes from baseline to 12 and 24 months, respectively, were quantitatively small. The overall difference in means between groups over 2 years was statistically significant only for depression, favoring the intervention: -1.19 (99% confidence interval, -2.36 to -.02), with 3.5 points representing a minimally important difference. CONCLUSIONS: STRIDE's multifactorial intervention to reduce fall injuries was not associated with clinically meaningful improvements in patient well-being.

Computations and Connectivity Underlying Aversive Counterfactuals.

Background: Mentally simulating counterfactuals (scenarios that have not actually occurred) is a sophisticated human cognitive function underlying creativity, planning, and daydreaming. One example is the "would you rather" game, in which forced choices are made between outlandish negative counterfactuals. Materials and Methods: We measured behavioral and neural correlates while participants made "would you rather" choices framed as approaching or avoiding aversive counterfactual scenarios (e.g., illnesses, car accidents). Results: We found in two independent cohorts that participants were highly susceptible to framing effects when making these decisions, taking significantly longer to respond to approach frames compared with avoidance. Brain imaging showed that choices to approach and avoid resulted in a pattern of activation consistent with a network associated with responding to aversive stimuli, identified via a coordinate-based meta-analysis of 238 studies. Bayesian graph connectivity analysis showed that network connectivity differed by choice frame, with significantly stronger connectivity for approach choices compared with avoidance choices among primarily limbic nodes (putamen, insula, caudate, and amygdala). Computational modeling of behavior revealed that approach frames led to significantly longer nondecision times, increased evidence required to make decisions, and faster evidence accumulation than avoidance frames. Stronger network connectivity between corticostriatal and limbic regions was associated with rate of evidence accumulation and length of nondecision time during approach choices. For avoidance choices, prefrontal connectivity was related to nondecision time. Conclusions: These results suggest that "would you rather" decisions about aversive counterfactuals differentially recruit limbic circuit connectivity based on choice frame. Impact statement We measured brain connectivity and latent cognitive variables underlying aversive counterfactual choices. We found a replicable reaction time effect whereby approach decisions were slower than avoidance decisions. Computational modeling identified that the latent cognitive variable of evidence accumulation was related to strength of connectivity between corticostriatal and limbic nodes during approach decisions. Multidimensional scaling (MDS) and clustering revealed a three-dimensional choice structure that differed between individuals, and between approach and avoidance choices within individuals. Our results suggest that cognitive evaluations of aversive counterfactuals involve flexible representations that can be altered by choice framing. These findings have broad implications for prospective decision making.

Patient and Family Advisory Councils for Research: Recruiting and Supporting Members From Diverse and Hard-to-Reach Communities.

OBJECTIVE: To describe strategies to recruit and support members from hard-to-reach groups on research-focused Patient and Family Advisory Councils (PFACs). BACKGROUND: Ensuring diverse representation of members of research PFACs is challenging, and few studies have given attention to addressing this problem. METHODS: A qualitative study was conducted using 8 focus groups and 19 interviews with 80 PFAC members and leaders, hospital leaders, and researchers. RESULTS: Recruitment recommendations were: 1) utilizing existing networks; 2) going out to the community; 3) accessing outpatient clinics; and 4) using social media. Strategies to support inclusion were: 1) culturally appropriate communication methods; 2) building a sense of community between PFAC members; 3) equalizing roles between community members/leaders; 4) having a diverse PFAC leadership team; and 5) setting transparent expectations for PFAC membership. CONCLUSION: Increasing the diversity of research PFACs is a priority, and it is important to determine how best to engage groups that have been traditionally underrepresented.

Patient stakeholder engagement in research: A narrative review to describe foundational principles and best practice activities.

BACKGROUND: Health research is evolving to include patient stakeholders (patients, families and caregivers) as active members of research teams. Frameworks describing the conceptual foundations underlying this engagement and strategies detailing best practice activities to facilitate engagement have been published to guide these efforts. OBJECTIVE: The aims of this narrative review are to identify, quantify and summarize (a) the conceptual foundational principles of patient stakeholder engagement in research and (b) best practice activities to support these efforts. SEARCH STRATEGY, INCLUSION CRITERIA, DATA EXTRACTION AND SYNTHESIS: We accessed a publicly available repository of systematically identified literature related to patient engagement in research. Two reviewers independently screened articles to identify relevant articles and abstracted data. MAIN RESULTS: We identified 990 potentially relevant articles of which 935 (94.4%) were excluded and 55 (5.6%) relevant. The most commonly reported foundational principles were "respect" (n = 25, 45%) and "equitable power between all team members" (n = 21, 38%). Creating "trust between patient stakeholders and researchers" was described in 17 (31%) articles. Twenty-seven (49%) articles emphasized the importance of providing training and education for both patient stakeholder and researchers. Providing financial compensation for patient stakeholders' time and expertise was noted in 19 (35%) articles. Twenty articles (36%) emphasized regular, bidirectional dialogue between patient partners and researchers as important for successful engagement. DISCUSSION AND CONCLUSIONS: Engaging patient stakeholders in research as partners presents an opportunity to design, implement and disseminate patient-centred research. This review creates an overarching foundational framework for authentic and sustainable partnerships between patient stakeholders and researchers.

Screening, Recruitment, and Baseline Characteristics for the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) Study.

Background: We describe the recruitment of participants for Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE), a large pragmatic cluster randomized trial that is testing the effectiveness of a multifactorial intervention to prevent serious fall injuries. Eligible persons were 70 years or older, community-living, and at increased risk for serious fall injuries. The modified goal was to recruit 5,322 participants over 20 months from 86 primary care practices within 10 diverse health care systems across the United States. Methods: The at-risk population was identified using two distinct but complementary screening strategies that included three questions administered centrally via the mail (nine sites) or in the clinic (one site), while recruitment was completed centrally by staff at Yale. Results: For central screening, 226,603 letters mailed to 135,118 patients yielded 28,719 positive screens (12.7% of those mailed and 46.5% of the 61,729 returned). In the clinic, 22,537 screens were completed, leading to 5,732 positive screens (25.4%). Of the 34,451 patients who screened positive for high risk of serious fall injuries, 31,872 were sent a recruitment packet and, of these, 5,451 (17.1%) were enrolled over 20 months (mean age: 80 years; 62% female). The participation rate was 34.0% among eligible patients. The enrollment yields were 3.6% (vs 5% projected) for each patient screened centrally, despite multiple screens, and 10.5% (vs 33.9% projected) for each positive clinic screen. Conclusions: Despite lower-than-expected yields, the STRIDE Study exceeded its modified recruitment goal. If the STRIDE intervention is found to be effective, the two distinct strategies for identifying a high-risk population of older persons could be implemented by most health care systems.

Attentional Bias in Human Category Learning: The Case of Deep Learning.

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

Differences in atypical resting-state effective connectivity distinguish autism from schizophrenia.

Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure and common cognitive deficits. A number of studies using resting state fMRI have shown that machine learning algorithms can distinguish between healthy controls and individuals diagnosed with either autism spectrum disorder or schizophrenia. However, it has not yet been determined whether machine learning algorithms can be used to distinguish between the two disorders. Using a linear support vector machine, we identify features that are most diagnostic for each disorder and successfully use them to classify an independent cohort of subjects. We find both common and divergent connectivity differences largely in the default mode network as well as in salience, and motor networks. Using divergent connectivity differences, we are able to distinguish autistic subjects from those with schizophrenia. Understanding the common and divergent connectivity changes associated with these disorders may provide a framework for understanding their shared cognitive deficits.

Patient and Family Advisory Councils (PFACs): Identifying Challenges and Solutions to Support Engagement in Research.

OBJECTIVE: The aim was to describe barriers to patient and family advisory council (PFAC) member engagement in research and strategies to support engagement in this context. METHODS: We formed a study team comprising patient advisors, researchers, physicians, and nurses. We then undertook a qualitative study using focus groups and interviews. We invited PFAC members, PFAC leaders, hospital leaders, and researchers from nine academic medical centers that are part of a hospital medicine research network to participate. All participants were asked a standard set of questions exploring the study question. We used content analysis to analyze data. RESULTS: Eighty PFAC members and other stakeholders (45 patient/caregiver members of PFACs, 12 PFAC leaders, 12 hospital leaders, 11 researchers) participated in eight focus and 19 individual interviews. We identified ten barriers to PFAC member engagement in research. Codes were organized into three categories: (1) individual PFAC member reluctance; (2) lack of skills and training; and (3) problems connecting with the right person at the right time. We identified ten strategies to support engagement. These were organized into four categories: (1) creating an environment where the PFAC members are making a genuine and unique contribution; (2) building community between PFAC members and researchers; (3) best practice activities for researchers to facilitate engagement; and (4) tools and training. CONCLUSION: Barriers to engaging PFAC members in research include patients' negative perceptions of research and researchers' lack of training. Building community between PFAC members and researchers is a foundation for partnerships. There are shared training opportunities for PFAC members and researchers to build skills about research and research engagement.

Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE): A Cluster-Randomized Pragmatic Trial of a Multifactorial Fall Injury Prevention Strategy: Design and Methods.

Background: Fall injuries are a major cause of morbidity and mortality among older adults. We describe the design of a pragmatic trial to compare the effectiveness of an evidence-based, patient-centered multifactorial fall injury prevention strategy to an enhanced usual care. Methods: Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) is a 40-month cluster-randomized, parallel-group, superiority, pragmatic trial being conducted at 86 primary care practices in 10 health care systems across United States. The 86 practices were randomized to intervention or control group using covariate-based constrained randomization, stratified by health care system. Participants are community-living persons, ≥70 years, at increased risk for serious fall injuries. The intervention is a comanagement model in which a nurse Falls Care Manager performs multifactorial risk assessments, develops individualized care plans, which include surveillance, follow-up evaluation, and intervention strategies. Control group receives enhanced usual care, with clinicians and patients receiving evidence-based information on falls prevention. Primary outcome is serious fall injuries, operationalized as those leading to medical attention (nonvertebral fractures, joint dislocation, head injury, lacerations, and other major sequelae). Secondary outcomes include all fall injuries, all falls, and well-being (concern for falling; anxiety and depressive symptoms; physical function and disability). Target sample size was 5,322 participants to provide 90% power to detect 20% reduction in primary outcome rate relative to control. Results: Trial enrolled 5,451 subjects in 20 months. Intervention and follow-up are ongoing. Conclusions: The findings of the STRIDE study will have important clinical and policy implications for the prevention of fall injuries in older adults.

Modeling Causal Relationships among Brain Areas in the Mesocorticolimbic System during Resting-State in Cocaine Users Utilizing a Graph Theoretic Approach.

OBJECTIVE: While effective connectivity (EC, causal interaction) between brain areas has been investigated in chronic users of cocaine as they view cocaine pictures cues, no study has examined EC while they take part in a resting-state scan. This resting-state fMRI study aims to investigate the causal interaction among brain areas in the mesocorticolimbic system (MCLS), which is involved in reward and motivation, in cocaine users (vs. controls). METHOD: Twenty cocaine users and 17 healthy controls finished a structural and a resting-state scan. Mean voxel-based time series data were obtained from brain regions of interest (ROIs) from the MCLS, and were input into a Bayesian search algorithm called IMaGES. RESULTS: The causal interaction pattern was different between the two groups. The feed-forward pattern found in cocaine smokers, between 7 ROIs of the MCLS during resting-state [ventral tegmental area (VTA)→hippocampus (HIPP)→ventral striatum (VenStri)→orbital frontal cortex (OFC), medial frontal cortex (MFC), anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC)], was absent in controls. That is, the subcortical VenStri area had a causal influence on four cortical brain areas only in cocaine users. CONCLUSIONS: During the resting-state scan, the VTA of cocaine smokers abstinent for at least 72 hours, but not controls, begins causal connections to limbic, midbrain, and frontal regions in the MCLS in a feed-forward manner. Following replication, further studies may assess if changes over time in EC during resting-state predict cocaine treatment efficacy and outcome.

Prefrontal-limbic connectivity during worry in older adults with generalized anxiety disorder.

OBJECTIVES: Although generalized anxiety disorder (GAD) is one of the most prevalent anxiety disorders in older adults, very little is known about the neurobiology of worry, the hallmark symptom of GAD in adults over the age of 60. This study investigated the neurobiology and neural circuitry of worry in older GAD patients and controls. METHOD: Twenty older GAD patients and 16 age-matched controls (mean age = 67.88) were compared on clinical measures and neural activity during worry using functional magnetic resonance imaging. RESULTS: As expected, worry elicited activation in frontal regions, amygdala, and insula within the GAD group, with a similar but less prominent frontal pattern was observed in controls. Effective connectivity analyses revealed a positive directional circuit in the GAD group extending from ventromedial through dorsolateral prefrontal cortices, converging on the amygdala. A less complex circuit was observed in controls with only dorsolateral prefrontal regions converging on the amygdala; however, a separate circuit passing through the orbitofrontal cortex converged on the insula. CONCLUSION: Results elucidate a different neurobiology of pathological versus normal worry in later life. A limited resource model is implicated wherein worry in GAD competes for the same neural resources (e.g. prefrontal cortical areas) that are involved in the adaptive regulation of emotion through cognitive and behavioral strategies.

Modeling Causal Relationship Between Brain Regions Within the Drug-Cue Processing Network in Chronic Cocaine Smokers.

The cues associated with drugs of abuse have an essential role in perpetuating problematic use, yet effective connectivity or the causal interaction between brain regions mediating the processing of drug cues has not been defined. The aim of this fMRI study was to model the causal interaction between brain regions within the drug-cue processing network in chronic cocaine smokers and matched control participants during a cocaine-cue exposure task. Specifically, cocaine-smoking (15M; 5F) and healthy control (13M; 4F) participants viewed cocaine and neutral cues while in the scanner (a Siemens 3 T magnet). We examined whole brain activation, including activation related to drug-cue processing. Time series data extracted from ROIs determined through our General Linear Model (GLM) analysis and prior publications were used as input to IMaGES, a computationally powerful Bayesian search algorithm. During cocaine-cue exposure, cocaine users showed a particular feed-forward effective connectivity pattern between the ROIs of the drug-cue processing network (amygdala → hippocampus → dorsal striatum → insula → medial frontal cortex, dorsolateral prefrontal cortex, anterior cingulate cortex) that was not present when the controls viewed the cocaine cues. Cocaine craving ratings positively correlated with the strength of the causal influence of the insula on the dorsolateral prefrontal cortex in cocaine users. This study is the first demonstration of a causal interaction between ROIs within the drug-cue processing network in cocaine users. This study provides insight into the mechanism underlying continued substance use and has implications for monitoring treatment response.

Brain network response underlying decisions about abstract reinforcers.

Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames.

Modeling activation and effective connectivity of VWFA in same script bilinguals.

Previous neuroimaging research revealed a small area in the inferior occipito-temporal cortex (VWFA), which seems to be involved in recognition of written words. The specialized response of the VWFA to words could result from repeated exposure to print in the course of functional fine-tuning of the brain. Research with bilingual speakers holds promise in helping to reveal response properties of the VWFA by assessing its sensitivity to language proficiency, word-form similarity, and meaning overlap across two languages. Using fMRI, we compared VWFA activity for cognate and homograph prime-target pairs in a group of fluent Spanish-English speakers. Cognates share form and meaning in two languages, while homographs only share form. Relative to baseline, the VWFA showed repetition suppression to pairs of homographs, but not to pairs of cognates, suggesting that this area is sensitive to word meaning. The different response to cognates and homographs was only observed when English was the prime language and Spanish was the target language. To help explain this result we compared patterns of effective connectivity between the VWFA and other parts of the reading network implicated in semantic and phonological processing. Our neural models showed that English targets engaged a direct ventral route from the VWFA to the frontal lobe and Spanish targets engaged an indirect dorsal route. Considering that frontal cortex has been implicated in semantic processing, a direct connection to this area could signal a fast and automatic access to meaning and would facilitate early semantic influences in visual word recognition.