The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness.

Cartilage thickness change is a well-documented biomarker of osteoarthritis pathogenesis. However, there is still much to learn about the spatial and temporal patterns of cartilage thickness change in health and disease. In this study, we develop a novel analysis method for elucidating such patterns using a functional connectivity approach. Descriptive statistics are reported for 1186 knees that did not develop osteoarthritis during the 8 years of observation, which we present as a model of cartilage thickness change related to healthy aging. Within the control population, patterns vary greatly between male and female subjects, while body mass index (BMI) has a more moderate impact. Finally, several differences are shown between knees that did and did not develop osteoarthritis. Some but not all significance appears to be accounted for by differences in sex, BMI, and knee alignment. With this work, we present the connectome as a novel tool for studying spatiotemporal dynamics of tissue change.

Rich-club connectivity and structural connectome organization in youth at clinical high-risk for psychosis and individuals with early illness schizophrenia.

Brain dysconnectivity has been posited as a biological marker of schizophrenia. Emerging schizophrenia connectome research has focused on rich-club organization, a tendency for brain hubs to be highly-interconnected but disproportionately vulnerable to dysconnectivity. However, less is known about rich-club organization in individuals at clinical high-risk for psychosis (CHR-P) and how it compares with abnormalities early in schizophrenia (ESZ). Combining diffusion tensor imaging (DTI) and magnetic resonance imaging (MRI), we examined rich-club and global network organization in CHR-P (n = 41) and ESZ (n = 70) relative to healthy controls (HC; n = 74) after accounting for normal aging. To characterize rich-club regions, we examined rich-club MRI morphometry (thickness, surface area). We also examined connectome metric associations with symptom severity, antipsychotic dosage, and in CHR-P specifically, transition to a full-blown psychotic disorder. ESZ had fewer connections among rich-club regions (ps < .024) relative to HC and CHR-P, with this reduction specific to the rich-club even after accounting for other connections in ESZ relative to HC (ps < .048). There was also cortical thinning of rich-club regions in ESZ (ps < .013). In contrast, there was no strong evidence of global network organization differences among the three groups. Although connectome abnormalities were not present in CHR-P overall, CHR-P converters to psychosis (n = 9) had fewer connections among rich-club regions (ps < .037) and greater modularity (ps < .037) compared to CHR-P non-converters (n = 19). Lastly, symptom severity and antipsychotic dosage were not significantly associated with connectome metrics (ps < .012). Findings suggest that rich-club and connectome organization abnormalities are present early in schizophrenia and in CHR-P individuals who subsequently transition to psychosis.

Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping.

Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.

Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging.

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

Illustrating the value of social work: results of an open pilot trial of the psychosocial acuity scale in a large urban pediatric hospital.

While Electronic Medical Records (EMRs) are an important mechanism used to document patient information and service provision, most interfaces prioritize collection of information required for medical billing purposes, rather than complexities of behavioral and social service needs. An emphasis on encounter data renders it challenging for social workers (SWs) to communicate statistically compelling explanations of contributions to team-based care and overall value to the health system. This paper reports outcomes of feasibility, acceptability, and appropriateness of integrating a validated psychosocial acuity metric into standardized social work (SW) documentation at a large, pediatric quaternary hospital in the northeastern United States. Approximately 20% (N = 42) of departmental SWs participated in an open pilot trial wherein participants were first trained in scale administration, utilized it for a circumscribed period following training, and evaluated implementation outcomes. Across five unique practice settings, results showed that the metric was feasible, acceptable and appropriate for use; 78% of participants were in favor of integrating it into the EMR interface as a required component of SW documentation. Assessing psychosocial acuity in every documented patient encounter facilitates intermittent review of psychosocial acuity at individual, setting, and programmatic levels and opportunities to evaluate how SW interventions address psychosocial acuity.

Ovarian Hormones Mediate Changes in Adaptive Choice and Motivation in Female Rats.

In female rodents, sexual receptivity is coordinated with cyclic changes in the release of gonadal hormones. Increases in estradiol (E) and progesterone (P) during proestrus and estrus not only induce ovulation but also modulate behaviors that increase the likelihood that the female will find a mate and reproduce. This includes changes in receptive behaviors, such as lordosis, as well as changes in appetitive or proceptive behaviors, including motivation. Interestingly, the direction of these changes in motivation is dependent on the type of reward that is being pursued. While induction of sexual receptivity by E and P increases motivation for access to a male, motivation for a palatable food reward is decreased. These concurrent changes may facilitate adaptive choice across the estrous cycle; females bias their choice for sex when fertilization is most likely to occur, but for food when copulation is unlikely to result in impregnation. In order to test this hypothesis, we developed a novel paradigm to measure the motivated choice between a palatable food reward and access to a male conspecific. Ovariectomized, hormone primed females were trained to operantly respond for both food and sex on a fixed interval (FI) schedule. After training, unprimed and primed females were tested in a chamber that allows them to choose between food and sex while still requiring responding on the FI schedule for reach reward. From this we can not only determine the impact of hormone priming on female choice for food or sex, but also how this is reflected by changes in motivation for each specific reward, as measured by the average number of responses made during each fixed interval. Induction of sexual receptivity by hormone priming biases choice toward sex over food and this change is accompanied by an increase in motivation for sex but a decrease in motivation for food. This work provides evidence in support of a novel framework for understanding how the release of ovarian hormones over the course of the estrous cycle modulates adaptive behavioral choice in females by directly assessing motivation via operant responding when multiple rewards are available.

Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task.

Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use online neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance. Specifically, we use a brain-computer interface (BCI) that uses information in the EEG to generate a neurofeedback signal that dynamically adjusts an individual's arousal state when they are engaged in a boundary-avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality and which creates cognitive conditions that escalate arousal and quickly results in task failure (e.g., missing or crashing into the boundary). We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart-rate variability show that the neurofeedback indeed reduces arousal. Our work demonstrates a BCI system that uses online neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes-Dodson law.

Oestradiol influences on dopamine release from the nucleus accumbens shell: sex differences and the role of selective oestradiol receptor subtypes.

BACKGROUND AND PURPOSE: Females are more sensitive than males to both the acute and prolonged effects of psychomotor stimulants. In females, this is regulated by oestradiol, which enhances dopamine release in the dorsal striatum. In this study, we tested the acute effect of oestradiol on dopamine release in the nucleus accumbens (NAc) shell after cocaine administration and investigated which oestradiol receptors (ERs) contribute to sex differences in the response to cocaine. EXPERIMENTAL APPROACH: The ability of oestradiol benzoate (EB) to acutely modulate the effect of cocaine on phasic dopamine release in the NAc shell was measured by fast-scan cyclic voltammetry in anaesthetized male and female rats. The roles of ER subtypes, ERα and ERß, was determined with selective agonists. KEY RESULTS: EB acutely enhanced the effect of cocaine on stimulated dopamine release from the NAc shell in females but not in male rats only at levels of stimulation expected to optimally saturate dopamine transporters. Enhanced dopamine release after cocaine administration was also observed in females after selective activation of ERß but not ERα. EB attenuated the effect of cocaine on NAc shell dopamine reuptake in males but not in females. CONCLUSIONS AND IMPLICATIONS: Oestradiol acutely and rapidly regulates dopamine release in females and dopamine reuptake in males. In females, oestradiol rapidly enhances the effect of cocaine on dopamine release, likely via activation of ERß. The effect of oestradiol in males is not seen with selective receptor subtype activation, a topic deserving of further study. LINKED ARTICLES: This article is part of a themed section on The Importance of Sex Differences in Pharmacology Research. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.21/issuetoc.

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials.

OBJECTIVE: Steady-state visual evoked potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. APPROACH: In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration. MAIN RESULTS: The Compact-CNN demonstrates across subject mean accuracy of approximately 80%, out-performing current state-of-the-art, hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, the Compact-CNN approach can reveal the underlying feature representation, revealing that the deep learner extracts additional phase- and amplitude-related features associated with the structure of the dataset. SIGNIFICANCE: We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g. asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.

Bead-extraction and heat-dissociation (BEHD): A novel way to overcome drug and matrix interference in immunogenicity testing.

Biological therapeutics are foreign antigens and can potentially induce immune response resulting in the formation of anti-drug antibodies (ADA), which in turn may lead to a wide range of side effects. Neutralizing Ab (NAb) is a subset of ADA that can bind to the pharmacological activity regions of therapeutic to inhibit or complete neutralize its clinical efficacy. A cell-based functional NAb assay is preferred to characterize its neutralization activity. However, cell-based NAb assays are often vulnerable to drug interference, as well as interference from numerous serum factors, including but not limited to growth factors and disease-related cytokines. Bead Extraction with Acid Dissociation (BEAD) has been successfully applied to remove circulating drug and/or other interfering factors from human serum samples, thereby enriching for ADA/NAb. However, the harsh acid used in the extraction procedure can cause irreversible denaturing of NAb and lead to underestimated NAb measurement. Herein we describe a new approach when acid-dissociation is not optimal for a PEGylated domain antibody (Ab). We further demonstrate that heating at 62 °C can not only dissociate drug/ADA/NAb immune complex but also selectively and irreversibly denature domain Ab drug due to much lower thermal stability of the domain Ab, when compared to that of full antibodies. The irreversible denaturing of the drug favors the formation of an immune complex between ADA/NAb and the added biotinylated drug thus increasing the recovery of ADA/NAb from samples. We call this new procedure Bead Extraction with Heat Dissociation (BEHD), which can potentially be applied to other NAb assays that have poor compatibility with acid dissociation.

Development and validation of a functional cell-based neutralizing antibody assay for ipilimumab.

Ipilimumab is the first US FDA-approved immune checkpoint-blocking antibody drug to harness the patient's own immune cells. One of the postmarketing requirements is to develop a cell-based neutralizing antibody assay. Here, we share some of the most challenging aspects encountered during the assay development: new cell line construction; an unexpected inhibition of T-cell activation by low concentrations of ipilimumab; and two issues caused by sample pretreatment with acid dissociation to overcome drug interference: instability of neutralizing antibody positive control at low pH, and incompatibility of commonly used acid dissociation buffers in the cell assay. After troubleshooting and optimization, we successfully validated the assay and used the assay to test clinical samples to date.

Identification and Ablation of Dormant Conduction in Atrial Fibrillation Using Adenosine.

BACKGROUND: Ablation is used for treatment of atrial fibrillation (AF) but recurrence is common. Dormant conduction is hypothesized to be responsible for these recurrences, and the role of adenosine in identification and ablation of these pathways is controversial with conflicting results on AF recurrence. MATERIALS AND METHODS: We conducted a meta-analysis for studies evaluating AF ablation and adenosine use. Included in the meta-analysis were human studies that compared ablation using adenosine or adenosine triphosphate (ATP) and reported freedom from AF in patients beyond a minimum follow-up of 6 months. RESULTS: Our analysis suggests that the use of adenosine leads to a decrease in recurrence of AF compared to the cohort which did not utilize adenosine. Subgroup analysis showed no difference in the recurrence of AF with the modality used for ablation (cryoablation vs. radiofrequency ablation) or with the preparation of adenosine used (ATP vs. adenosine). There was a significant benefit in delayed administration of ATP over early administration. Pooling results of only randomized control trials did not show any significant difference in AF recurrence. CONCLUSIONS: Adenosine-guided identification and ablation of dormant pathways may lead to a decrease in recurrence of AF.

Singlicate Ligand Binding Assay Using an Automated Microfluidic System: a Clinical Case Study.

The bioanalytical strategy for monoclonal antibody therapeutics, intended for multiple oncology indications, includes multiple integrated measurements of pharmacologically relevant therapeutics from discovery through development. Three ligand binding assays were cohesively developed and validated, as applicable, using the Gyrolab microfluidic system for the measurement of a free monoclonal antibody BMS-986207. Accuracy and precision demonstrate %bias from -6.3 to 4.4%, percent coefficient of variation (%CV) from 2.6 to 9.8%, and total error from 4.2 to 13.4% in the nonclinical assay; %bias from -0.3 to 3.3%, %CV from 3.5 to 18.2%, and total error from 6.1 to 19.7% in the clinical assay; and >97% of the sample meeting incurred sample reanalysis criteria. The clinical assay was validated using singlicate wells after gaining significant data in the early phase studies to support this cost-effective and efficient strategy. Each assay met fit-for-purpose and/or regulated bioanalytical method validation criteria including stability, selectivity, dilutional linearity, carryover, and specificity criteria with no interference from co-administered monoclonal antibody.

Barriers to Pelvic Floor Physical Therapy Regarding Treatment of High-Tone Pelvic Floor Dysfunction.

OBJECTIVE: Chronic pelvic pain is a prevalent and debilitating condition with a wide range of etiologies. An estimated 30% to 70% of chronic pelvic cases involve musculoskeletal component pain including high-tone pelvic floor dysfunction (HTPFD). Pelvic floor physical therapy has been shown to be a beneficial treatment for HTPFD, yet many patients do not have access to this treatment. The objective of this study was to identify the barriers preventing patients from following through with the first-line management, physical therapy. METHODS: Participants with a diagnosis of HTPFD (n = 154) were identified from the list of referrals sent from the obstetrics and gynecology department to an affiliated PFPT center. Participants were contacted and asked to complete a phone survey addressing demographics and perceived barriers to care. Responses were collected in REDCap. Univariate and bivariate analyses were performed using a statistical analysis software. RESULTS: Seventy surveys were completed. The top barriers identified by participants were financial constraints (51.4%), perceived lack of utility (37.1%), time constraints (30.0%), and travel issues (18.6%); 84.4% of participants had 1 or more comorbid pain condition. Whereas 51.4% expressed some level of anxiety regarding the PFPT option, only 9.6% of participants did not start treatment because of fear of treatment. CONCLUSIONS: The majority of treatment barriers identified were concrete restraints, with insurance noncoverage and time constraints being the top issues. A fair number of participants expressed anxiety about the treatment or felt they received unclear explanations of the treatment. These are areas in which providers can potentially alleviate some barriers to care.

Validation of an integrated series of ligand-binding assays for the quantitative determination of antibody-drug conjugates in biological matrices.

BACKGROUND: The bioanalytical strategy for antibody-drug conjugates (ADC) includes multiple integrated measurements of pharmacologically relevant ADC. METHODS & RESULTS: Three ligand-binding assays were validated for the measurement of total antibody, active ADC and total ADC. Accuracy and precision demonstrate %bias from -8 to 14%, %CV from 3 to 11% and total error from 3 to 21%, with >98% samples meeting incurred sample reanalysis criteria. Each assay met stability, selectivity, dilutional integrity, carry over and specificity criteria with no interference from associated metabolite/impurity. Given the active ADC assay sensitivity to payload, active ADC was used to assess drug to antibody ratio. DISCUSSION & CONCLUSION: Implementation of a microfluidic automated platform enabled high throughput sample analysis of multiple analytes with minimal sample processing.

An integrated multiplatform bioanalytical strategy for antibody-drug conjugates: a novel case study.

BACKGROUND: The bioanalytical strategy for antibody-drug conjugates (ADC) includes numerous measurements integrally designed to provide comprehensive characterization of PK, PD and immunogenicity. This manuscript describes the utilization of reagents specifically tailored to an ADC with a microtubule polymerization inhibitor payload and cathepsin B cleavable linker. METHODS: The PK strategy includes the evaluation of physiological levels of total antibody, active ADC, total ADC, antibody-conjugated payload and unconjugated payload. These data are evaluated in the context of target and antidrug antibody levels to elucidate bioactive ADC. RESULTS & CONCLUSION: Herein, we discuss how this strategy has been applied and present our preliminary observations. Continuously evolving to meet pipeline demands, the integrated bioanalytical data will provide critical insights into the exposure-response relationship.

Structuring Health in Colorectal Cancer Screening Conversations: An Analysis of Intersecting Activity Systems.

This study used structurating activity theory to analyze 21 conversations between genetic counselors and individuals at increased risk for familial colorectal cancer (CRC). The qualitative analysis revealed ways elements of family, primary healthcare, cancer prevention and treatment, and other systems emerged in intervention conversations as shaping CRC screening attitudes and behaviors. Results indicate that family stories, norms, and roles are resources for enacting health practices in families and that the authority of healthcare providers is a resource for making screening decisions. Conclusions include practical implications for using findings in clinical applications as well as future research directions to build on this exploratory study.

Estradiol, dopamine and motivation.

The gonadal hormone estradiol modulates mesolimbic dopamine systems in the female rat. This modulatory effect is thought to be responsible for the observed effects of estradiol on motivated behaviors. Dopamine acting in the nucleus accumbens is thought to be important for the attribution of incentive motivational properties to cues that predict reward delivery, while dopamine in the striatum is associated with the expression of repetitive or stereotyped behaviors. Elevated concentrations of estradiol are associated with increased motivation for sex or cues associated with access to a mate, while simultaneously attenuating motivation for food. This shift in motivational salience is important for adaptive choice behavior in the natural environment. Additionally, estradiol's adaptive effects on motivation can be maladaptive when increasing motivation for non-natural reinforcers, such as drugs of abuse. Here we discuss the effect of estradiol on mesotelencephalic dopamine transmission and subsequent effects on motivated behaviors.

Substrate deconstruction and the nonadditivity of enzyme recognition.

Predicting substrates for enzymes of unknown function is a major postgenomic challenge. Substrate discovery, like inhibitor discovery, is constrained by our ability to explore chemotypes; it would be expanded by orders of magnitude if reactive sites could be probed with fragments rather than fully elaborated substrates, as is done for inhibitor discovery. To explore the feasibility of this approach, substrates of six enzymes from three different superfamilies were deconstructed into 41 overlapping fragments that were tested for activity or binding. Surprisingly, even those fragments containing the key reactive group had little activity, and most fragments did not bind measurably, until they captured most of the substrate features. Removing a single atom from a recognized substrate could often reduce catalytic recognition by 6 log-orders. To explore recognition at atomic resolution, the structures of three fragment complexes of the ß-lactamase substrate cephalothin were determined by X-ray crystallography. Substrate discovery may be difficult to reduce to the fragment level, with implications for function discovery and for the tolerance of enzymes to metabolite promiscuity. Pragmatically, this study supports the development of libraries of fully elaborated metabolites as probes for enzyme function, which currently do not exist.