FLAVOUR Study: FLow profiles And postoperative VasOplegia after continUous-flow left ventriculaR assist device implantation.

This study aims to associate the incidence of postoperative vasoplegia and short-term survival to the implantation of various left ventricular assist devices differing in hemocompatibility and flow profiles. The overall incidence of vasoplegia was 25.3% (73/289 patients) and 30.3% (37/122), 25.0% (18/72), and 18.9% (18/95) in the axial flow (AXF), centrifugal flow (CF), and centrifugal flow with artificial pulse (CFAP) group, respectively. Vasoplegia was associated with longer intensive care (ICU) and hospital length of stay (LOS) and mortality. ICU and in-hospital LOS and 1-year mortality were the lowest in the CFAP group. Post hoc analysis resulted in a p-value of 0.43 between AXF and CF; 0.35 between CF and CFAP; and 0.06 between AXF and CFAP. Although there is a trend in diminished incidence of vasoplegia, pooled logistic regression using flow profile and variables that remained after feature selection showed that flow profile was not an independent predictor for postoperative vasoplegia.

Soluble Suppression of Tumorigenicity-2 Predicts Mortality and Right Heart Failure in Patients With a Left Ventricular Assist Device.

BACKGROUND: Soluble suppression of tumorigenicity-2 (sST2) predicts mortality in patients with heart failure. The predictive value of sST2 in patients with a left ventricular assist device remains unknown. Therefore, we studied the relationship between sST2 and outcome after left ventricular assist device implantation. METHODS AND RESULTS: sST2 levels of patients with a left ventricular assist device implanted between January 2015 and December 2022 were included in this observational study. The median follow-up was 25 months, during which 1573 postoperative sST2 levels were measured in 199 patients, with a median of 29 ng/mL. Survival of patients with normal and elevated preoperative levels was compared using Kaplan-Meier analysis, which did not differ significantly (P=0.22) between both groups. The relationship between postoperative sST2, survival, and right heart failure was evaluated using a joint model, which showed a significant relationship between the absolute sST2 level and mortality, with a hazard ratio (HR) of 1.20 (95% CI, 1.10-1.130; P<0.01) and an HR of 1.22 (95% CI, 1.07-1.39; P=0.01) for right heart failure, both per 10-unit sST2 increase. The sST2 instantaneous change was not predictive for survival or right heart failure (P=0.99 and P=0.94, respectively). Multivariate joint model analysis showed a significant relationship between sST2 with mortality adjusted for NT-proBNP (N-terminal pro-B-type natriuretic peptide), with an HR of 1.19 (95% CI, 1.00-1.42; P=0.05), whereas the HR of right heart failure was not significant (1.22 [95% CI, 0.94-1.59]; P=0.14), both per 10-unit sST2 increase. CONCLUSIONS: Time-dependent postoperative sST2 predicts all-cause mortality after left ventricular assist device implantation after adjustment for NT-proBNP. Future research is warranted into possible target interventions and the optimal monitoring frequency.

Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data.

The multilevel hidden Markov model (MHMM) is a promising method to investigate intense longitudinal data obtained within the social and behavioral sciences. The MHMM quantifies information on the latent dynamics of behavior over time. In addition, heterogeneity between individuals is accommodated with the inclusion of individual-specific random effects, facilitating the study of individual differences in dynamics. However, the performance of the MHMM has not been sufficiently explored. We performed an extensive simulation to assess the effect of the number of dependent variables (1-8), number of individuals (5-90), and number of observations per individual (100-1600) on the estimation performance of a Bayesian MHMM with categorical data including various levels of state distinctiveness and separation. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Moreover, including variables only consisting of random noise was generally not detrimental to model performance. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. However, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the level of state distinctiveness and separation and study objectives of the researcher.

Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure.

Aims: Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD). Methods and results: In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods. Conclusion: The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement.

Monitoring left ventricular assist device parameters to detect flow- and power-impacting complications: a proof of concept.

Aims: The number of patients on left ventricular assist device (LVAD) support increases due to the growing number of patients with end-stage heart failure and the limited number of donor hearts. Despite improving survival rates, patients frequently suffer from adverse events such as cardiac arrhythmia and major bleeding. Telemonitoring is a potentially powerful tool to early detect deteriorations and may further improve outcome after LVAD implantation. Hence, we developed a personalized algorithm to remotely monitor HeartMate3 (HM3) pump parameters aiming to early detect unscheduled admissions due to cardiac arrhythmia or major bleeding. Methods and results: The source code of the algorithm is published in an open repository. The algorithm was optimized and tested retrospectively using HeartMate 3 (HM3) power and flow data of 120 patients, including 29 admissions due to cardiac arrhythmia and 14 admissions due to major bleeding. Using a true alarm window of 14 days prior to the admission date, the algorithm detected 59 and 79% of unscheduled admissions due to cardiac arrhythmia and major bleeding, respectively, with a false alarm rate of 2%. Conclusion: The proposed algorithm showed that the personalized algorithm is a viable approach to early identify cardiac arrhythmia and major bleeding by monitoring HM3 pump parameters. External validation is needed and integration with other clinical parameters could potentially improve the predictive value. In addition, the algorithm can be further enhanced using continuous data.

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex.

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition-specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single-level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

Identifying patients at risk: multi-centre comparison of HeartMate 3 and HeartWare left ventricular assist devices.

AIMS: Since the withdrawal of HeartWare (HVAD) from the global market, there is an ongoing discussion if and which patients require prophylactically exchange for a HeartMate 3 (HM3). Therefore, it is important to study outcome differences between HVAD and HM3 patients. Because centres differ in patient selection and standard of care, we performed a propensity score (PS)-based study including centres that implanted both devices and aimed to identify which HVAD patients are at highest risk. METHODS AND RESULTS: We performed an international multi-centre study (n = 1021) including centres that implanted HVAD and HM3. PS-matching was performed using clinical variables and the implanting centre. Survival and complications were compared. As a sensitivity analysis, PS-adjusted Cox regression was performed. Landmark analysis with conditional survival >2 years was conducted to evaluate long-term survival differences. To identify which HVAD patients may benefit from a HM3 upgrade, Cox regression using pre-operative variables and their interaction with device type was performed. Survival was significantly better for HM3 patients (P < 0.01) in 458 matched patients, with a median follow-up of 23 months. Within the matched cohort, HM3 patients had a median age of 58 years, and 83% were male, 80% of the HVAD patients were male, with a median age of 59 years. PS-adjusted Cox regression confirmed a significantly better survival for HM3 patients when compared with HVAD, with a HR of 1.46 (95% confidence interval 1.14-1.85, P < 0.01). Pump thrombosis (P < 0.01) and ischaemic stroke (P < 0.01) occurred less in HM3 patients. No difference was found for haemorrhagic stroke, right heart failure, driveline infection, and major bleeding. Landmark-analysis confirmed a significant difference in conditional survival >2 years after implantation (P = 0.03). None of the pre-operative variable interactions in the Cox regression were significant. CONCLUSIONS: HM3 patients have a significantly better survival and a lower incidence of ischaemic strokes and pump thrombosis than HVAD patients. This survival difference persisted after 2 years of implantation. Additional research using post-operative variables is warranted to identify which HVAD patients need an upgrade to HM3 or expedited transplantation.

Data-driven monitoring in patients on left ventricular assist device support.

INTRODUCTION: Despite an increasing population of patients supported with a left ventricular assist device (LVAD), it remains a complex therapy, and patients are frequently admitted. Therefore, a strict follow-up including frequent hospital visits, patient self-management and telemonitoring is needed. AREAS COVERED: The current review describes the principles of LVADs, the possibilities of (tele)monitoring using noninvasive and invasive devices. Furthermore, possibilities, challenges, and future perspectives in this emerging field are discussed. EXPERT OPINION: Several studies described initial experiences on telemonitoring in LVAD patients, using mobile phone applications to collect clinical data and pump data. This may replace frequent hospital visits in near future. In addition, algorithms were developed aiming to early detect pump thrombosis or driveline infections. Since not all complications are reflected by pump parameters, data from different sources should be combined to detect a broader spectrum of complications in an early stage. We need to focus on the development of sophisticated but understandable algorithms and infrastructure combining different data sources, while addressing essential aspects such as data safety, privacy, and cost-effectiveness.

Accurate differentiation between physiological and pathological ripples recorded with scalp-EEG.

OBJECTIVE: To compare scalp-EEG recorded physiological ripples co-occurring with vertex waves to pathological ripples co-occurring with interictal epileptiform discharges (IEDs). METHODS: We marked ripples in sleep EEGs of children. We compared the start of ripples to vertex wave- or IED-start, and duration, frequency, and root mean square (RMS) amplitude of physiological and pathological ripples using multilevel modeling. Ripples were classified as physiological or pathological using linear discriminant analysis. RESULTS: We included 40 children with and without epilepsy. Ripples started (χ2(1) = 38.59, p < 0.001) later if they co-occurred with vertex waves (108.2 ms after vertex wave-start) than if they co-occurred with IEDs (4.3 ms after IED-start). Physiological ripples had longer durations (75.7 ms vs 53.0 ms), lower frequencies (98.3 Hz vs 130.6 Hz), and lower RMS amplitudes (0.9 µV vs 1.8 µV, all p < 0.001) than pathological ripples. Ripples could be classified as physiological or pathological with 98 % accuracy. Ripples recorded in children with idiopathic or symptomatic epilepsy seemed to form two subgroups of pathological ripples. CONCLUSIONS: Ripples co-occurring with vertex waves or IEDs have different characteristics and can be differentiated as physiological or pathological with high accuracy. SIGNIFICANCE: This is the first study that compares physiological and pathological ripples recorded with scalp EEG.

Relationships between educational achievement, intelligence, and perfectionism in adolescents with eating disorders.

OBJECTIVE: High performance at school is associated with the risk of eating disorders (EDs), and perfectionism is proposed as an explanatory factor for this association. This study aims to evaluate (a) potential discrepancies between the measured IQ of adolescents with EDs and the IQ that is expected given their educational track, and (b) to what extent perfectionism was associated with educational achievement independent from IQ. METHOD: WISC-III Verbal IQ, Performance IQ, and Full Scale IQ of 386 adolescent ED patients were compared with population norms for their educational track, using one-sample t tests. The association between self-oriented perfectionism (Eating Disorder Inventory-2) and educational achievement, adjusted for IQ, was determined with sequential ordinal regression analyses. RESULTS: Over 50% of the patients received pre-university education, the most complex educational track. For patients receiving education in the second most complex track, IQ-scores were lower than normative data for that track. For patients receiving pre-university education, the verbal IQ was lower than the norm for that track. Self-oriented perfectionism was associated with educational achievement independent from intelligence. DISCUSSION: This study suggests that high educational achievement is common in adolescents with EDs. Particularly for patients who receive education in the most complex tracks the demands at school may be higher than they can handle, based on their IQ. Self-oriented perfectionism contributed to educational achievement independent from IQ. Our results indicate that treatment for EDs should include awareness for the possibility of a too high target level of patients at school and perfectionism.

Propensity score-based analysis of long-term outcome of patients on HeartWare and HeartMate 3 left ventricular assist device support.

AIMS: Left ventricular assist device therapy has become the cornerstone in the treatment of end-stage heart failure and is increasingly used as destination therapy next to bridge to transplant or recovery. HeartMate 3 (HM3) and HeartWare (HVAD) are centrifugal continuous flow devices implanted intrapericardially and most commonly used worldwide. No randomized controlled trials have been performed yet. Analysis based on large registries may be considered as the best alternative but has the disadvantage of different standard of care between centres and missing data. Bias is introduced, because the decision which device to use was not random, even more so because many centres use only one type of left ventricular assist device. Therefore, we performed a propensity score (PS)-based analysis of long-term clinical outcome of patients that received HM3 or HVAD in a single centre. METHODS AND RESULTS: Between December 2010 and December 2019, 100 patients received HVAD and 81 patients HM3 as primary implantation at the University Medical Centre Utrecht. We performed PS matching with an extensive set of covariates, resulting in 112 matched patients with a median follow-up of 28 months. After PS matching, survival was not significantly different (P = 0.21) but was better for HM3. The cumulative incidences for haemorrhagic stroke (P = 0.01) and pump thrombosis (P = 0.02) were significantly higher for HVAD patients. The cumulative incidences for major bleeding, ischaemic stroke, right heart failure, and driveline infection were not different between the groups. We found no interaction between the surgeon who performed the implantation and survival (P = 0.59, P = 0.78, and P = 0.89). Sensitivity analysis was performed, by PS matching without patients on preoperative temporary support resulting in 74 matched patients. This also resulted in a non-significant difference in survival (P = 0.07). The PS-adjusted Cox regression showed a worse but non-significant (P = 0.10) survival for HVAD patients with hazard ratio 1.71 (95% confidence interval 0.91-3.24). CONCLUSIONS: Survival was not significantly different between both groups after PS matching, but was better for HM3, with a significantly lower incidence of haemorrhagic stroke and pump thrombosis for HM3. These results need to be interpreted carefully, because matching may have introduced greater imbalance on unmeasured covariates. A multicentre approach of carefully selected centres is recommended to enlarge the number of matched patients.

Cognitive Remediation Therapy Does Not Enhance Treatment Effect in Obsessive-Compulsive Disorder and Anorexia Nervosa: A Randomized Controlled Trial.

BACKGROUND: Guideline-recommended therapies are moderately successful in the treatment of obsessive-compulsive disorder (OCD) and anorexia nervosa (AN), leaving room for improvement. Cognitive inflexibility, a common trait in both disorders, is likely to prevent patients from engaging in treatment and from fully benefiting from existing therapies. Cognitive remediation therapy (CRT) is a practical augmentation intervention aimed at ameliorating this impairing cognitive style prior to disorder-specific therapy. OBJECTIVE: To compare the effectiveness of CRT and a control treatment that was not aimed at enhancing flexibility, named specialized attention therapy (SAT), as add-ons to treatment as usual (TAU). METHODS: In a randomized controlled multicenter clinical trial, 71 adult patients with OCD and 61 with AN were randomized to ten twice-weekly sessions with either CRT or SAT, followed by TAU. Patients were evaluated at baseline, post-CRT/SAT, and after 6 and 12 months, with outcomes being quantified using the Yale-Brown Obsessive Compulsive Scale for OCD and the Eating Disorder Examination Questionnaire for AN. RESULTS: Across study groups, most importantly CRT+TAU was not superior to control treatment (SAT)+TAU in reducing OCD and AN pathology. Contrary to expectations, SAT+TAU may have been more effective than CRT+TAU in patients being treated for OCD. CONCLUSIONS: CRT did not enhance the effect of TAU for OCD and AN more than SAT. Unexpectedly, SAT, the control condition, may have had an augmentation effect on TAU in OCD patients. Although this latter finding may have been due to chance, the effect of SAT delivered as a pretreatment add-on intervention for adults with OCD and AN merits future efforts at replication.

Insensitivity to Losses: A Core Feature in Patients With Anorexia Nervosa?

BACKGROUND: Patients with anorexia nervosa (AN) demonstrate aberrations in choice behavior, including impairments in laboratory measures of decision making. Although a wealth of studies suggest that these aberrations arise from alterations in value processing, it remains unclear by which core component of value processing this is mediated. METHODS: We fit trial-by-trial data of patients with AN (n = 60 first cohort, n = 216 second cohort) and healthy control participants (n = 55) performing the Iowa Gambling Task to a computational model based on prospect utility theory. We determined, per participant, the best-fit model parameters and compared these between the groups. RESULTS: Analyses revealed a decreased estimate of model parameter λ in patients with AN, indicative of an attenuation of loss-aversive behavior in the Iowa Gambling Task. In comparison, measures of reward sensitivity, value-based learning, and exploration versus exploitation were unaltered in patients with AN. A measurement in a second independent cohort replicated the finding that loss aversion, typically observed in healthy individuals, is reduced in patients with AN. CONCLUSIONS: We show that patients with AN, in contrast to healthy control participants, demonstrate reduced loss-aversive behavior. This finding provides important fundamental insights into the decision-making capacity of patients with AN, suggesting alterations in the mechanisms involved in value processing related to negative feedback.

The experienced temperature sensitivity and regulation survey.

Individuals differ in thermosensitivity, thermoregulation, and zones of thermoneutrality and thermal comfort. Whereas temperature sensing and -effectuating processes occur in part unconsciously and autonomic, awareness of temperature and thermal preferences can affect thermoregulatory behavior as well. Quantification of trait-like individual differences of thermal preferences and experienced temperature sensitivity and regulation is therefore relevant to obtain a complete understanding of human thermophysiology. Whereas several scales have been developed to assess instantaneous appreciation of heat and cold exposure, a comprehensive scale dedicated to assess subjectively experienced autonomic or behavioral thermoregulatory activity has been lacking so far. We constructed a survey that specifically approaches these domains from a trait-like perspective, sampled 240 volunteers across a wide age range, and analyzed the emergent component structure. Participants were asked to report their thermal experiences, captured in 102 questions, on a 7-point bi-directional Likert scale. In a second set of 32 questions, participants were asked to indicate the relative strength of experiences across different body locations. Principal component analyses extracted 21 meaningful dimensions, which were sensitive to sex-differences and age-related changes. The questions were also assessed in a matched sample of 240 people with probable insomnia to evaluate the sensitivity of these dimensions to detect group differences in a case-control design. The dimensions showed marked mean differences between cases and controls. The survey thus has discriminatory value. It can freely be used by anyone interested in studying individual or group differences in thermosensitivity and thermoregulation.

Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives.

BACKGROUND: In neuroscience, experimental designs in which multiple measurements are collected in the same research object or treatment facility are common. Such designs result in clustered or nested data. When clusters include measurements from different experimental conditions, both the mean of the dependent variable and the effect of the experimental manipulation may vary over clusters. In practice, this type of cluster-related variation is often overlooked. Not accommodating cluster-related variation can result in inferential errors concerning the overall experimental effect. RESULTS: The exact effect of ignoring the clustered nature of the data depends on the effect of clustering. Using simulation studies we show that cluster-related variation in the experimental effect, if ignored, results in a false positive rate (i.e., Type I error rate) that is appreciably higher (up to ~20-~50 %) than the chosen [Formula: see text]-level (e.g., [Formula: see text] = 0.05). If the effect of clustering is limited to the intercept, the failure to accommodate clustering can result in a loss of statistical power to detect the overall experimental effect. This effect is most pronounced when both the magnitude of the experimental effect and the sample size are small (e.g., ~25 % less power given an experimental effect with effect size d of 0.20, and a sample size of 10 clusters and 5 observations per experimental condition per cluster). CONCLUSIONS: When data is collected from a research design in which observations from the same cluster are obtained in different experimental conditions, multilevel analysis should be used to analyze the data. The use of multilevel analysis not only ensures correct statistical interpretation of the overall experimental effect, but also provides a valuable test of the generalizability of the experimental effect over (intrinsically) varying settings, and a means to reveal the cause of cluster-related variation in experimental effect.

Within-strain variation in behavior differs consistently between common inbred strains of mice.

Genetic and environmental factors interact throughout life and give rise to individual differences, i.e., individuality. The diversifying effect of environmental factors is counteracted by genetic mechanisms to yield persistence of specific features (robustness). Here, we compared robustness between cohorts of isogenic mice of eight different commonly used strains by analyzing to what extent environmental variation contributed to individuality in each of the eight genotypes, using a previously published dataset. Behavior was assessed in the home-cage, providing control over environmental factors, to reveal within-strain variability in numerous spontaneous behaviors. Indeed, despite standardization and in line with previous studies, substantial variability among mice of the same inbred strain was observed. Strikingly, across a multidimensional set of 115 behavioral parameters, several strains consistently ranked high in within-strain variability (DBA/2J, 129S1/Sv A/J and NOD/LtJ), whereas other strains ranked low (C57BL/6J and BALB/c). Strain rankings of within-strain variability in behavior were confirmed in an independent, previously published behavioral dataset using conventional behavioral tests administered to different mice from the same breeding colonies. Together, these show that genetically inbred mouse strains consistently differ in phenotypic robustness against environmental variation, suggesting that genetic factors contribute to variation in robustness.

The light spot test: Measuring anxiety in mice in an automated home-cage environment.

Behavioral tests of animals in a controlled experimental setting provide a valuable tool to advance understanding of genotype-phenotype relations, and to study the effects of genetic and environmental manipulations. To optimally benefit from the increasing numbers of genetically engineered mice, reliable high-throughput methods for comprehensive behavioral phenotyping of mice lines have become a necessity. Here, we describe the development and validation of an anxiety test, the light spot test, that allows for unsupervised, automated, high-throughput testing of mice in a home-cage system. This automated behavioral test circumvents bias introduced by pretest handling, and enables recording both baseline behavior and the behavioral test response over a prolonged period of time. We demonstrate that the light spot test induces a behavioral response in C57BL/6J mice. This behavior reverts to baseline when the aversive stimulus is switched off, and is blunted by treatment with the anxiolytic drug Diazepam, demonstrating predictive validity of the assay, and indicating that the observed behavioral response has a significant anxiety component. Also, we investigated the effectiveness of the light spot test as part of sequential testing for different behavioral aspects in the home-cage. Two learning tests, administered prior to the light spot test, affected the light spot test parameters. The light spot test is a novel, automated assay for anxiety-related high-throughput testing of mice in an automated home-cage environment, allowing for both comprehensive behavioral phenotyping of mice, and rapid screening of pharmacological compounds.

A 1-night operant learning task without food-restriction differentiates among mouse strains in an automated home-cage environment.

Individuals are able to change their behavior based on its consequences, a process involving instrumental learning. Studying instrumental learning in mice can provide new insights in this elementary aspect of cognition. Conventional appetitive operant learning tasks that facilitate the study of this form of learning in mice, as well as more complex operant paradigms, require labor-intensive handling and food deprivation to motivate the animals. Here, we describe a 1-night operant learning protocol that exploits the advantages of automated home-cage testing and circumvents the interfering effects of food restriction. The task builds on behavior that is part of the spontaneous exploratory repertoire during the days before the task. We compared the behavior of C57BL/6J, BALB/cJ and DBA/2J mice and found various differences in behavior during this task, but no differences in learning curves. BALB/cJ mice showed the largest instrumental learning response, providing a superior dynamic range and statistical power to study instrumental learning by using this protocol. Insights gained with this home-cage-based learning protocol without food restriction will be valuable for the development of other, more complex, cognitive tasks in automated home-cages.

Sheltering behavior and locomotor activity in 11 genetically diverse common inbred mouse strains using home-cage monitoring.

Functional genetic analyses in mice rely on efficient and in-depth characterization of the behavioral spectrum. Automated home-cage observation can provide a systematic and efficient screening method to detect unexplored, novel behavioral phenotypes. Here, we analyzed high-throughput automated home-cage data using existing and novel concepts, to detect a plethora of genetic differences in spontaneous behavior in a panel of commonly used inbred strains (129S1/SvImJ, A/J, C3H/HeJ, C57BL/6J, BALB/cJ, DBA/2J, NOD/LtJ, FVB/NJ, WSB/EiJ, PWK/PhJ and CAST/EiJ). Continuous video-tracking observations of sheltering behavior and locomotor activity were segmented into distinguishable behavioral elements, and studied at different time scales, yielding a set of 115 behavioral parameters of which 105 showed highly significant strain differences. This set of 115 parameters was highly dimensional; principal component analysis identified 26 orthogonal components with eigenvalues above one. Especially novel parameters of sheltering behavior and parameters describing aspects of motion of the mouse in the home-cage showed high genetic effect sizes. Multi-day habituation curves and patterns of behavior surrounding dark/light phase transitions showed striking strain differences, albeit with lower genetic effect sizes. This spontaneous home-cage behavior study demonstrates high dimensionality, with a strong genetic contribution to specific sets of behavioral measures. Importantly, spontaneous home-cage behavior analysis detects genetic effects that cannot be studied in conventional behavioral tests, showing that the inclusion of a few days of undisturbed, labor extensive home-cage assessment may greatly aid gene function analyses and drug target discovery.