Individual Subject Data Research Articles

PD16-11 COMPARISON OF PROSTATE-SPECIFIC MEMBRANE ANTIGEN (PSMA)-TARGETED RADIONUCLIDE THERAPY (TRT) WITH LUTETIUM-177 ( 177 LU) VIA ANTIBODY J591 VS SMALL MOLECULE LIGAND PSMA-617

You have accessJournal of UrologyProstate Cancer: Advanced (including Drug Therapy) II (PD16)1 Apr 2020PD16-11 COMPARISON OF PROSTATE-SPECIFIC MEMBRANE ANTIGEN (PSMA)-TARGETED RADIONUCLIDE THERAPY (TRT) WITH LUTETIUM-177 (177LU) VIA ANTIBODY J591 VS SMALL MOLECULE LIGAND PSMA-617 Muhammad Junaid Niaz*, Myrto Skafida, Joseph Osborne, David Nanus, Anna Molina, Charlene Thomas, Shankar Vallabhajosula, Paul Christos, Neil Bander, and Scott Tagawa Muhammad Junaid Niaz*Muhammad Junaid Niaz* More articles by this author , Myrto SkafidaMyrto Skafida More articles by this author , Joseph OsborneJoseph Osborne More articles by this author , David NanusDavid Nanus More articles by this author , Anna MolinaAnna Molina More articles by this author , Charlene ThomasCharlene Thomas More articles by this author , Shankar VallabhajosulaShankar Vallabhajosula More articles by this author , Paul ChristosPaul Christos More articles by this author , Neil BanderNeil Bander More articles by this author , and Scott TagawaScott Tagawa More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000859.011AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: PSMA is established for TRT with either monoclonal antibodies (mAb) or small molecule ligands. The molecular weight, pharmacokinetics, and biodistribution of mAb vs ligands are different: mAb has long circulating blood pool time and ligands target areas of normal PSMA expression with limited accessibility to large mAbs (e.g., salivary/lacrimal glands, small bowel, and kidney). METHODS: To compare outcomes from PSMA-TRT using J591 and PSMA-617, we analyzed individual subject data elements from prospective clinical trials in mCRPC. All-grade treatment-emergent adverse events (TEAEs) and PSA changes were extracted with updated survival. Chi-squared or Fisher’s exact test was used to assess TEAEs and treatment type; multivariable logistic regression used to assess treatment and PSA response. Kaplan-Meier overall survival (OS) analysis utilized with comparisons using log-rank test. Multivariable cox proportional hazards regression analysis used to evaluate OS controlling for variables of interest. RESULTS: 131 men treated with 177Lu-J591, 50 treated with 177Lu-PSMA-617. In TEAE analysis, heme TEAEs more common with 177Lu-J591 mAb: neutropenia in 102 (77.9%, 21% Gr 4) vs. 2 (4%), p<0.001; anemia in 101 (77.1%, 11% Gr 3) vs. 8 (16%), p< 0.001, and thrombocytopenia in 118 (90.1%, 37% Gr 4) vs. 10 (20%), p< 0.001. Non-heme TEAE’s more common with ligand: xerostomia in 29 (58%) with PSMA-617 vs. 0 with J591, p<0.001 and nausea in 21 (42%) vs. 19 (14.5%), p<0.001. As dose-response effect is known, after controlling for dose, still more heme TEAEs with mAb targeting (p<0.001) and non-heme more common with ligand (p<0.001). In exploratory analysis, PSMA-617 ligand had more >30% PSA decline, adjusted odds ratio = 6.37 [95% CI 2.97-14.46], adjusting for dose and CALGB prognostic group. Median OS with PSMA-617 was shorter than J591 (14.2 vs. 19.63 months, respectively), but on multivariable analysis only higher dose (hazard ratio [HR] = 0.66, 95% CI 0.46-0.94) and subsequent receipt of FDA-approved life prolonging therapies (HR=0.65, 95% CI 0.46-0.94) significant for OS. CONCLUSIONS: As predicted based upon physical properties leading to different biodistribution, PSMA-TRT via mAb or small molecule ligand is associated with different toxicity profiles. While ligand may be associated with more PSA decline, there is no difference in OS and administering a higher dose of radionuclide is associated with longer OS regardless of type of PSMA-targeting used. Source of Funding: Prostate Cancer Foundation, National Institutes of Health (ULI RR024996, 1-KL2-RR024997-01, PTBF5405), Department of Defense (W81XWH-04-1-0267), H. Koch Foundation, Peter M. Sacerdote Foundation, Robert Dow Foundation, Larry and Carol Zicklin Philanthropic Fund, Endocyte, Weill Cornell Medical College. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e367-e367 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Muhammad Junaid Niaz* More articles by this author Myrto Skafida More articles by this author Joseph Osborne More articles by this author David Nanus More articles by this author Anna Molina More articles by this author Charlene Thomas More articles by this author Shankar Vallabhajosula More articles by this author Paul Christos More articles by this author Neil Bander More articles by this author Scott Tagawa More articles by this author Expand All Advertisement PDF downloadLoading ...

Empirical analysis of deep learning networks for affective video tagging

This paper presents, a thorough evaluation of popular deep learning models to analyze and classify electroencephalogram (EEG) data for characterizing human affective states for video content tagging and retrieval. We use two pre-trained convolutional neural network (CNN) models AlexNet and GoogLeNet, and a Long Short Term Memory (LSTM) model to classify EEG data into appropriate affect categories using trans-domain learning. The purpose behind the use of pre-trained networks or trans-domain learning is twofold – to establish the versatility of pre-trained networks by testing their ability to classify EEG data for emotion recognition and the other is to reduce over cost of computation while training the networks. Our work tries to establish the answer of a simple question: Are pre-trained deep models versatile enough for classifying not only similar type of problems but are also effective for classifying problems pertaining to completely different domains? Also, using pre-trained models saves considerable computation time required for training a new model from scratch and fine tuning it. We use DEAP dataset for training and evaluation of these networks over a single modality ‘valence’ to simplify the comparison among these networks. Experiments are carried out by training the networks on EEG recordings obtained from single as well as multiple subjects to show the effects of subject-specific and generalized data on classification accuracy. Experimental results suggest the superiority of GoogLeNet for individual subject data while AlexNet outperforms other networks and has shown its capability of generalizing well. We compare the performance of these networks with state-of-art classifiers handcrafted by other authors for classifying EEG data and find that the performance of pre-trained CNNs used in our work are comparable or even better than the other handcrafted classifiers used by many authors.

Decomposing Simon task BOLD activation using a drift-diffusion model framework

The Simon effect is observed in spatial conflict tasks where the response time of subjects is increased if stimuli are presented in a lateralized manner so that they are incongruous with the response information that they represent symbolically. Previous studies have used fMRI to investigate this phenomenon, and while some have been driven by considerations of an underlying model, none have attempted to directly tie model and BOLD response together. It is likely that this is due to Simon models having been predominantly descriptive of the phenomenon rather than capturing the full spectrum of behavior at the level of individual subjects. Sequential sampling models (SSM) which capture full response distributions for correct and incorrect responses have recently been extended to capture conflict tasks. In this study we use our freely available framework for fitting and comparing non-standard SSMs to fit the Simon effect SSM (SE-SSM) to behavioral data. This model extension includes specific estimates of automatic response bias and a conflict counteraction parameter to individual subject behavioral data. We apply this approach in order to investigate whether our task specific model parameters have a correlate in BOLD response. Under the assumption that the SE-SSM reflects aspects of neural processing in this task, we go on to examine the BOLD correlates with the within trial expected decision-variable. We find that the SE-SSM captures the behavioral data and that our two conflict specific model parameters have clear across subject BOLD correlates, while other model parameters, as well as more standard behavioral measures do not. We also find that examining BOLD in terms of the expected decision-variable leads to a specific pattern of activation that would not be otherwise possible to extract.

Sensory Modality-Independent Activation of the Brain Network for Language.

The meaning of a sentence can be understood, whether presented in written or spoken form. Therefore, it is highly probable that brain processes supporting language comprehension are at least partly independent of sensory modality. To identify where and when in the brain language processing is independent of sensory modality, we directly compared neuromagnetic brain signals of 200 human subjects (102 males) either reading or listening to sentences. We used multiset canonical correlation analysis to align individual subject data in a way that boosts those aspects of the signal that are common to all, allowing us to capture word-by-word signal variations, consistent across subjects and at a fine temporal scale. Quantifying this consistency in activation across both reading and listening tasks revealed a mostly left-hemispheric cortical network. Areas showing consistent activity patterns included not only areas previously implicated in higher-level language processing, such as left prefrontal, superior and middle temporal areas, and anterior temporal lobe, but also parts of the control network as well as subcentral and more posterior temporal-parietal areas. Activity in this supramodal sentence-processing network starts in temporal areas and rapidly spreads to the other regions involved. The findings indicate not only the involvement of a large network of brain areas in supramodal language processing but also that the linguistic information contained in the unfolding sentences modulates brain activity in a word-specific manner across subjects.SIGNIFICANCE STATEMENT The brain can extract meaning from written and spoken messages alike. This requires activity of both brain circuits capable of processing sensory modality-specific aspects of the input signals as well as coordinated brain activity to extract modality-independent meaning from the input. Using traditional methods, it is difficult to disentangle modality-specific activation from modality-independent activation. In this work, we developed and applied a multivariate methodology that allows for a direct quantification of sensory modality-independent brain activity, revealing fast activation of a wide network of brain areas, both including and extending beyond the core network for language.

Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks.

Mental tasking and rotary Chair-Induced vestibular nystagmus utilizing Video-Oculography

Objective: To investigate whether the use of mental tasking, when compared to no mental task, affects measurement of nystagmus response with regard to gain, phase & symmetry, and artefact when utilising video-oculography (VOG) as the measurement technique in rotary chair testing (RCT).Design: A within-subject repeated-measures design was utilised.Study samples: Seventeen (17) healthy adults were evaluated (age 22–25 years). Each participant underwent slow harmonic acceleration (SHA) testing for 0.01, 0.02, 0.04, 0.08, and 0.16 Hz using RCT at two separate counterbalanced visits. At one visit mental tasking was utilised while the other visit did not utilise mental tasking. The following outcomes were measured for each visit: gain, phase, symmetry, and artefact.Results: No significant difference between the tasking conditions with regard to gain, phase, symmetry, or artefact. Significant frequency affects were noted, as expected, for gain, phase, and artefact. Analysis of individual subject data did, however, describe significant effects of tasking with regard to gain, phase, symmetry, and artefact.Conclusion: These results suggest that the use of mental tasking during RCT using VOG had no significant group effect on SHA gain, phase, symmetry, and artefact. However, individual subject effects were observed indicating variability in the effects of mental tasking during RCT.

Auditory Perceptual History Is Propagated through Alpha Oscillations.

SummaryPerception is a proactive, “predictive” process, in which the brain relies, at least in part, on accumulated experience to make best guesses about the world to test against sensory data, updating the guesses as new experience is acquired. Using novel behavioral methods, the present study demonstrates the role of alpha rhythms in communicating past perceptual experience. Participants were required to discriminate the ear of origin of brief sinusoidal tones that were presented monaurally at random times within a burst of uncorrelated dichotic white noise masks. Performance was not constant but varied with delay after noise onset in an oscillatory manner at about 9 Hz (alpha rhythm). Importantly, oscillations occurred only for trials preceded by a target tone to the same ear, either on the previous trial or two trials back. These results suggest that communication of perceptual history generates neural oscillations within specific perceptual circuits, strongly implicating behavioral oscillations in predictive perception and with formation of working memory.

Statistical Model of Motor-Evoked Potentials.

Motor-evoked potentials (MEPs) are widely used for biomarkers and dose individualization in transcranial stimulation. The large variability of MEPs requires sophisticated methods of analysis to extract information fast and correctly. Development and testing of such methods relies on the availability for realistic models of MEP generation, which are presently lacking. This paper presents a statistical model that can simulate long sequences of individualized MEP amplitude data with properties matching experimental observations. The MEP model includes three sources of trial-to-trial variability: excitability fluctuations, variability in the neural and muscular pathways, and physiological and measurement noise. It also generates virtual human subject data from statistics of population variability. All parameters are extracted as statistical distributions from experimental data from the literature. The model exhibits previously described features, such as stimulus-intensity-dependent MEP amplitude distributions, including bimodal ones. The model can generate long sequences of test data for individual subjects with specified parameters or for subjects from a virtual population. The presented MEP model is the most detailed to date and can be used for the development and implementation of dosing and biomarker estimation algorithms for transcranial stimulation.

New ways of estimating excess mortality of chronic diseases from aggregated data: insights from the illness-death model

BackgroundRecently, we have shown that the age-specific prevalence of a disease can be related to the transition rates in the illness-death model via a partial differential equation (PDE). The transition rates are the incidence rate, the remission rate and mortality rates from the ‘Healthy’ and ‘Ill’ states. In case of a chronic disease, we now demonstrate that the PDE can be used to estimate the excess mortality from age-specific prevalence and incidence data. For the prevalence and incidence, aggregated data are sufficient - no individual subject data are needed, which allows application of the methods in contexts of strong data protection or where data from individual subjects is not accessible.MethodsAfter developing novel estimators for the excess mortality derived from the PDE, we apply them to simulated data and compare the findings with the input values of the simulation aiming to evaluate the new approach. In a practical application to claims data from 35 million men insured by the German public health insurance funds, we estimate the population-wide excess mortality of men with diagnosed type 2 diabetes.ResultsIn the simulation study, we find that the estimation of the excess mortality is feasible from prevalence and incidence data if the prevalence is given at two points in time. The accuracy of the method decreases as the temporal difference between these two points in time increases. In our setting, the relative error was 5% and below if the temporal difference was three years or less. Application of the new method to the claims data yields plausible findings for the excess mortality of type 2 diabetes in German men.ConclusionsThe described approach is useful to estimate the excess mortality of a chronic condition from aggregated age-specific incidence and prevalence data.Trial registrationThe article does not report the results of any health care intervention.

Tranexamic acid through intravenous, intramuscular and oral routes: an individual participant data meta-analysis of pharmacokinetic studies in healthy volunteers.

Tranexamic acid (TXA) is an antifibrinolytic drug that reduces surgical blood loss and death due to bleeding after trauma and post-partum haemorrhage. One key issue for treatment success is early administration. While usually given intravenously, oral and intramuscular use would be useful in specific circumstances. Therefore, an understanding of TXA pharmacokinetics when given via different routes is valuable. The aim of this study was to perform an individual participant data meta-analysis of pharmacokinetic studies with TXA given to healthy volunteers via different routes. We searched the following databases: PubMed, Web of Science, Wiley Online Library, Elsevier Science Direct and J-STAGE. Individual subject data were extracted when available, otherwise arithmetic means were used. A population pharmacokinetic model was developed using nonlinear mixed effect modelling. Seven studies were included in the analysis with data from 10 patients for the IV route, six patients for the IM route and 114 patients for the oral route. The pharmacokinetics was ascribed to a two-compartment model, and the main covariate was allometrically scaled bodyweight. Oral and IM bioavailabilities were 46 and 105%, respectively. For a 70kg bodyweight, the population estimates were 7.6L/h for clearance, 17.9L for the volume of the central compartment, 2.5L/h for the diffusional clearance and 16.6L for the peripheral volume of distribution. Larger well-designed studies are needed to describe the pharmacokinetics of TXA when given IM or as an oral solution before these can be recommended as alternatives to IV.

Authors' Response.

Authors' Response: We thank Kendall and Haff for their interest in this study. Carry-over effects are a well-recognized limitation of the otherwise-efficient crossover design. These limitations are not insurmountable. Wash out, random assignment to intervention sequence, and statistical control of order effects mitigate carry-over bias. Mehrotra (3) and others have proposed crossover design analysis methods, including the use of postrandomization baseline measures that may be superior to change from baseline analyses. But change from common baseline analyses is not inappropriate. For example, Ebbeling et al. (2) used 3 dietary phases in a counterbalanced crossover design with a common baseline and statistical control for diet sequence to explore effects on resting energy expenditure. Is a 2-week wash out sufficient? Diet, body composition, and training adaptations after a 12-week intervention probably cannot be entirely reversed by a wash out of any reasonable duration, that is, reasonable within the constraints of a diet intervention study, including potential loss to follow-up and the ongoing training and competition schedule of the subjects. Importantly, carry-over bias alone cannot account for the differences observed in the 2 dietary phases. The hypothetical example provided is unnecessary. By displaying individual subject data in Figure 2, we have made it transparent for the readership to deduce these effects. With regard to absolute lifting performance, one can easily determine that most subjects improved from baseline regardless of diet phase, 7 subjects performed better on UD than LCKD, 3 subjects performed better on LCKD than UD, performance was unaltered by diet in 2 subjects, and there were no differences at the group level. The same types of conclusions can be extracted from the rest of Figure 2. Compliance and contact intervals are clearly described in the methods. Wilson et al. (4) used a serum β-Hydroxybutyrate threshold of <0.3 mmol/L to monitor compliance and adjust dietary carbohydrates. Three subjects in our study averaged β-hydroxybutyrate levels <0.3 mmol/L. However, this threshold is somewhat arbitrary, and there is no convention for using ketones deterministically for compliance. Ketones are circadian, affected by production and clearance rates, and altered by various hormones (1) and are thus, at best, a biomarker of LCKD metabolic alterations. To be precise, Powerlifters can compete in single lifts. Nine subjects were PL and 3 were OL, and 6 subjects performed single lift types. We are not convinced distinguishing PL and OL, and their respective lift type will provide any additional insight into the study findings.

Host Mesh Fitting of a Generic Musculoskeletal Model of the Lower Limbs to Subject-Specific Body Surface Data: A Validation Study.

Challenges remain in accurately capturing the musculoskeletal geometry of individual subjects for clinical and biomechanical gait analysis. The aim of this study was to use and validate the Host Mesh Fitting (HMF) technique for fitting a generic anatomically based musculoskeletal model to 3D body surface data of individual subjects. The HMF technique is based on the free-form idea of deforming geometrically complex structures according to the deformation of a surrounding volumetric mesh. Using the HMF technique, an anatomically based model of the lower limbs of an adult female subject (29 years) was customized to subject-specific skin surface data of five typically developing children (mean age 10.2 years) and six children with Cerebral Palsy (CP) (mean age 9.6 years). The fitted lengths and volumes of six muscle-tendon structures were compared against measures from Magnetic Resonance (MR) images for validation purposes. The HMF technique resulted in accurate approximations of the lower limb shapes of all subjects in both study groups. The average error between the MR data and the fitted muscle-tendon lengths from HMF was 4 ± 4% in children without CP and 7 ± 5% in children with CP, respectively. The average error between the MR data and the fitted muscle volumes from HMF was 28 ± 19% in children without CP and 27 ± 28% in children with CP, respectively. This study presents a crucial step towards personalized musculoskeletal modelling for gait analysis by demonstrating the feasibility of fitting a generic anatomically based lower limb model to 3D body surface data of children with and without CP using the HMF technique. Additional improvements in the quality of fit are expected to be gained by developing age-matched generic models for different study groups, accounting for subject-specific variations in subcutaneous body fat, as well as considering supplementary data from ultrasound imaging to better capture physiological muscle tissue properties.

Neurodegenerative Dementias: Improving Brain Health to Decrease Risk.

Neurodegenerative Dementias: Improving Brain Health to Decrease Risk.

Multimodal Data Registration for Brain Structural Association Networks.

We present a method for multimodal brain data registration that aligns shapes of nodal network configurations in an invertible manner. We use ideas from shape analysis to represent an individual subject data configuration as an element on a hypersphere, where geodesics have closed form solutions. The method not only performs inter-subject data registration, but also allows for the construction of a population data template to which all subject data configurations can be registered. Results show compression of data measures and significant reduction in variance after registration. We also observe increased predictive power of regions of interest (ROI) node identification, significant increases in pairwise network connectivity measures, as well as significant increases in canonical correlations with age after registration.

A Pooled Analysis of Growth and Tolerance of Infants Exclusively Fed Partially Hydrolyzed Whey or Intact Protein-Based Infant Formulas.

Background For infants who are partially or exclusively fed infant formula, many options exist with compositional differences between formulas making choices difficult for caregivers and healthcare professionals. The protein in routine infant formulas differs by the source, fraction of cow's milk protein used, and degree of hydrolysis. All commercially available regulated infant formulas support growth and development, but different stool patterns have been observed based on formula composition. A pooled analysis of seven clinical trials was conducted to examine growth, stool consistency, and stool frequency of infants fed an intact cow's milk-based formula (CMF) or a partially hydrolyzed whey formula (PHF-W) from a single manufacturer. Methods. Individual subject data from seven infant formula growth studies (3 CMF, 4 PHF-W) were pooled and analyzed. All studies included healthy, full-term, formula-fed infants enrolled at 14 days of age with outcomes assessed over 4 months. Gains in weight and length to 4 months were analyzed using linear regression accounting for clustering within study. Outcomes of caregiver-reported stool consistency and frequency were analyzed using a longitudinal multinomial model. Results Data from 511 infants were included (197 CMF, 314 PHF-W). There were no differences in weight gain between groups. There was no difference in length gain in girls fed PHF-W while boys fed PHF-W had a significant difference of +0.016 cm/month compared to boys fed CMF. Infants fed PHF-W had a significantly higher probability of soft and lower probability of hard stools as compared to infants fed CMF at each time point (p<0.001). Stool frequency was similar between groups. Conclusions Infants fed CMF and PHF-W exhibit appropriate growth with comparable gains in weight and length through 4 months. More soft and fewer hard stools are observed in infants fed PHF-W compared to CMF. This difference could help to inform decision-making when choosing an infant formula.

The frontoparietal network: function, electrophysiology, and importance of individual precision mapping

The frontoparietal network is critical for our ability to coordinate behavior in a rapid, accurate, and flexible goal-driven manner. In this review, we outline support for the framing of the frontoparietal network as a distinct control network, in part functioning to flexibly interact with and alter other functional brain networks. This network coordination likely occurs in a 4 Hz to 73 Hz θ/α rhythm, both during resting state and task state. Precision mapping of individual human brains has revealed that the functional topography of the frontoparietal network is variable between individuals, underscoring the notion that group-average studies of the frontoparietal network may be obscuring important typical and atypical features. Many forms of psychopathology implicate the frontoparietal network, such as schizophrenia and attention-deficit/hyperactivity disorder. Given the interindividual variability in frontoparietal network organization, clinical studies will likely benefit greatly from acquiring more individual subject data to accurately characterize resting-state networks compromised in psychopathology.

Abstract Memory Representations in the Ventromedial Prefrontal Cortex and Hippocampus Support Concept Generalization.

Memory function involves both the ability to remember details of individual experiences and the ability to link information across events to create new knowledge. Prior research has identified the ventromedial prefrontal cortex (VMPFC) and the hippocampus as important for integrating across events in the service of generalization in episodic memory. The degree to which these memory integration mechanisms contribute to other forms of generalization, such as concept learning, is unclear. The present study used a concept-learning task in humans (both sexes) coupled with model-based fMRI to test whether VMPFC and hippocampus contribute to concept generalization, and whether they do so by maintaining specific category exemplars or abstract category representations. Two formal categorization models were fit to individual subject data: a prototype model that posits abstract category representations and an exemplar model that posits category representations based on individual category members. Latent variables from each of these models were entered into neuroimaging analyses to determine whether VMPFC and the hippocampus track prototype or exemplar information during concept generalization. Behavioral model fits indicated that almost three-quarters of the subjects relied on prototype information when making judgments about new category members. Paralleling prototype dominance in behavior, correlates of the prototype model were identified in VMPFC and the anterior hippocampus with no significant exemplar correlates. These results indicate that the VMPFC and portions of the hippocampus play a broad role in memory generalization and that they do so by representing abstract information integrated from multiple events.SIGNIFICANCE STATEMENT Whether people represent concepts as a set of individual category members or by deriving generalized concept representations abstracted across exemplars has been debated. In episodic memory, generalized memory representations have been shown to arise through integration across events supported by the ventromedial prefrontal cortex (VMPFC) and hippocampus. The current study combined formal categorization models with fMRI data analysis to show that the VMPFC and anterior hippocampus represent abstract prototype information during concept generalization, contributing novel evidence of generalized concept representations in the brain. Results indicate that VMPFC-hippocampal memory integration mechanisms contribute to knowledge generalization across multiple cognitive domains, with the degree of abstraction of memory representations varying along the long axis of the hippocampus.

Modeling Individual Differences in the Go/No-go Task with a Diffusion Model.

The go/no-go task is one in which there are two choices, but the subject responds only to one of them, waiting out a time-out for the other choice. The task has a long history in psychology and modern applications in the clinical/neuropsychological domain. In this article we fit a diffusion model to both experimental and simulated data. The model is the same as the two-choice model and assumes that there are two decision boundaries and termination at one of them produces a response and at the other, the subject waits out the trial. In prior modeling, both two-choice and go/no-go data were fit simultaneously and only group data were fit. Here the model is fit to just go/no-go data for individual subjects. This allows analyses of individual differences which is important for clinical applications. First, we fit the standard two-choice model to two-choice data and fit the go/no-go model to RTs from one of the choices and accuracy from the two-choice data. Parameter values were similar between the models and had high correlations. The go/no-go model was also fit to data from a go/no-go version of the task with the same subjects as the two-choice task. A simulation study with ranges of parameter values that are obtained in practice showed similar parameter recovery between the two-choice and go/no-go models. Results show that a diffusion model with an implicit (no response) boundary can be fit to data with almost the same accuracy as fitting the two-choice model to two-choice data.

Dose, duration, and animal sex predict vancomycin-associated acute kidney injury in preclinical studies

BackgroundAlthough the exposure-dependent efficacy thresholds of vancomycin have been probed, less is known about acute kidney injury (AKI) thresholds for this drug. Sensitive urinary biomarkers, such as kidney injury molecule 1 (KIM-1), have shown high sensitivity and specificity for vancomycin-associated AKI. The aims of the study were to determine if there were dose–response curves with urinary KIM-1, and to evaluate the impact of therapy duration and sex on observed relationships. MethodsA systematic review was conducted via PubMed/MEDLINE. Data were compiled from preclinical studies that reported individual subject data for urinary KIM-1 concentrations, vancomycin dose (mg/kg), duration of treatment, and sex. Sigmoidal Hill-type models were fit to the individual dose-response data. ResultsA total of 15 studies were identified, 6 of which reported vancomycin dose and KIM-1 data. Of these, three included individual animal-level data suitable for analysis. For all pooled rats, increasing total daily vancomycin doses displayed a dose-response curve with urinary KIM-1 concentrations (50% maximal toxic response=130.4 mg/kg/day). Dose-response curves were shifted left for females vs. males (P = 0.05) and for long (i.e. ≥7 days) vs. short (i.e. <4 days) duration of vancomycin therapy (P=0.02). ConclusionsThe collective findings demonstrate a clear dose–response relationship between vancomycin dose and AKI. As these analyses focused exclusively on dose-response relationships, additional preclinical data are needed to more clearly define vancomycin exposures that predict the onset of AKI.

Inter-subject alignment of MEG datasets in a common representational space.

Pooling neural imaging data across subjects requires aligning recordings from different subjects. In magnetoencephalography (MEG) recordings, sensors across subjects are poorly correlated both because of differences in the exact location of the sensors, and structural and functional differences in the brains. It is possible to achieve alignment by assuming that the same regions of different brains correspond across subjects. However, this relies on both the assumption that brain anatomy and function are well correlated, and the strong assumptions that go into solving the under-determined inverse problem given the high-dimensional source space. In this article, we investigated an alternative method that bypasses source-localization. Instead, it analyzes the sensor recordings themselves and aligns their temporal signatures across subjects. We used a multivariate approach, multiset canonical correlation analysis (M-CCA), to transform individual subject data to a low-dimensional common representational space. We evaluated the robustness of this approach over a synthetic dataset, by examining the effect of different factors that add to the noise and individual differences in the data. On an MEG dataset, we demonstrated that M-CCA performs better than a method that assumes perfect sensor correspondence and a method that applies source localization. Last, we described how the standard M-CCA algorithm could be further improved with a regularization term that incorporates spatial sensor information. Hum Brain Mapp 38:4287-4301, 2017. © 2017 Wiley Periodicals, Inc.