Rate-distortion optimization (RDO) plays an essential role in substantially enhancing the coding efficiency. Currently, rate-distortion optimized mode decision is widely used in scalable video coding (SVC). Among all the possible coding modes, it aims to select the one which has the best trade-off between bitrate and compression distortion. Specifically, this tradeoff is tuned through the choice of the Lagrange multiplier. Despite the prevalence of conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to 3-D wavelet-based SVC where the explicit values of the quantization step are not available, with on consideration of the content features of input signal. In this paper, an efficient content adaptive Lagrange multiplier selection algorithm is proposed in the context of RDO for 3-D wavelet-based SVC targeting quality scalability. Our contributions are two-fold. First, we introduce a novel weighting method, which takes account of the mutual information, gradient per pixel, and texture homogeneity to measure the temporal subband characteristics after applying the motion-compensated temporal filtering (MCTF) technique. Second, based on the proposed subband weighting factor model, we derive the optimal Lagrange multiplier. Experimental results demonstrate that the proposed algorithm enables more satisfactory video quality with negligible additional computational complexity. With the rapid development of video services in recent years, how to efficiently compress video sequences has been considered as a very challenging task for transmitting video data over heterogeneous networks. To meet this demand, scalable video coding (SVC) has become a simple and flexible solution to enable seamless delivery by offering three different kinds of scalabilities, namely, temporal scalability, spatial scalability, and quality scalability ,2,3,4. GOwing to the intrinsic localization and multiresolution features of the discrete wavelet transform (DWT), video codecs based on motion-compensated three-dimensional (3-D) DWT have been studied extensively for use in SVC ,6,7,8. 3As in hybrid video coding-based SVC, rate-distortion optimization (RDO) plays a significant role in SVC in choosing the most suitable coding parameters under communication bandwidth constraints. A critical problem residing in RDO is the selection of the Lagrange multiplier that controls the RD trade-off ,21,22,23The Lagrange multiplier selection technique used to solve the RDO problem is an important feature that contributes to the success of current video coding. However, solving the optimal Lagrange optimization problem in 3-D wavelet coding is more complicated because the energy difference between the pixel and wavelet domains is not conserved after applying the bi-orthogonal wavelet transform and MCTF technique. The latter leads to the intrinsic subband coupling across various temporal subbands during motion aligned temporal filtering. Moreover, the open-loop prediction structure employed in 3-D wavelet-based SVC further complicates the issue of Lagrange multiplier selection. This issue has been solved by assigning different weighting factors to the subbands, resulting in degrading unpleasant quality fluctuations ,26,27,28Inspired by the methods mentioned above, we propose a content adaptive Lagrange multiplier selection algorithm for RDO in 3-D wavelet-based SVC. During the RDO process, the wavelet filter types, subband coupling phenomenon, and temporal subband content information are all taken into account to adaptively compute the Lagrange multiplier. Our strategy aims to not only accurately select the Lagrange multiplier of each temporal subband in the MCTF decomposition level, but also yield better video quality. By performing the proposed algorithm, our codec clearly outperforms the existing well-known 3-D wavelet-based SVC coding schemes with higher PSNR gains and much lower video quality variations through the whole reconstructed video sequence.The rest of this paper is organized as follows: In this section, we review relevant background information related to the Lagrange multiplier selection in the wavelet-based SVC. The reasons for the failure of the conventional Lagrange multiplier selection in 3-D wavelet-based SVC are also addressed.The representative motion compensated embedded zero block coding (MC-EZBC) scalable video coder  is a higIn our work, the proposed algorithm is designed for the popular ENH-MC-EZBC considering quality scalability. Our choice of this codec system is motivated by the fact that ENH-MC-EZBC incorporates all the advanced encoding tools found in the state-of-the-art video coding schemes and obtains the excellent coding performance both at low and high bitrates. The ENH-MC-EZBC codec system model contains three parts: encoder, bitstream extractor, and decoder, which is shown in The Lagrange optimization technique provides a systematic way to solve the constrained RDO problem, which aims at selecting the optimal coding parameter that minimizes the overall distortion measure subject to a given target bitrate restriction. More details on Lagrange optimization technique have been discussed in ,20,36. TLet In view of the Lagrange optimization, the above constrained optimization problem (1) can be converted into an unconstrained form as:n search ,39. In RRather than empirically solving the problem of Lagrange multiplier selection as in ,39, the chniques ,40. For Note that the R-D curve is convex and non-increasing in video coding, and if we assume that both In 3-D wavelet-based SVC, the temporal decomposition is efficiently refined by adopting the MCTF technique. The ENH-MC-EZBC scalable video coder utilizes an adaptive lifting-based MCTF framework with switching between LeGall and Tabatabai (LGT) 5/3 and Haar 2/2 filters to exploit the temporal redundancies between successive frames . During ion tree . TherefoIn this section, we introduce the proposed content adaptive Lagrange multiplier selection algorithm for the 3-D wavelet-based SVC codec.Lagrange multiplier based mode decision is one of the most important technologies in SVC . In the Accurate R-D model is crucial in the determination of the Lagrange multiplier. It is worthwhile to mention that the R-D relationships can be quite different for various temporal subbands (T-bands). Unfortunately, it cannot to maximize coding efficiency through utilizing a fixed Lagrange multiplier at each temporal layer. Consequently, the conventional Lagrange multiplier selection method is not optimal as it does not consider the content characteristics of the T-bands.The distortion fluctuation exhibited by the 3-D wavelet-based SVC codecs can be better understood by analyzing the distortion propagation during MCTF. In the ENH-MC-EZBC codec, the adaptive MCTF framework has been implemented using either the Haar or the 5/3 filter to improve the coding performance. As shown in In the Haar MCTF, the original frames are filtered temporally with a two-tap Haar filter along the motion trajectory. For the connected pixels, the low-frequency subbands and high-frequency subbands are implemented by the following lifting structure:For the 5/3-based MCTF, the temporal analysis can be written as:For mathematical convenience, we only consider the case in the lifting structure of one-level 5/3 inverse MCTF in the following. Thus, according to the Equations (10) and (11), the errors in the reconstructed The distortion associated with different T-bands will contribute differently to distortion in the reconstructed frames. Typically, this difference is quantified by analyzing the weighting factor for inverse MCTF. The weighting factor indicates how much a unit distortion in a specified subband contributes to the overall distortion in the reconstructed video. The derivation of its weighting factors is given as:This results in distortion fluctuation across the reconstructed frames after one-level inverse MCTF. When multi-level MCTF is involved, the weighting factor of each temporal subband is modeled as:Therefore, the distortion relationship between the T-bands and the reconstructed frame can be derived in the same way but with an iterative calculation. That is, the distortion for the original video sequences  is a measurement of similarity between frames which can detect the differences among successive frames . In thisLet The joint entropy of discrete random variables The MI between random variable The relation between MI and joint entropy is given by:In a YUV formatted video sequence, let us consider a gray level video sequence with intensity value ranging from The gradient based subband content complexity measure has been considered in our Lagrange multiplier selection scheme. Here, the gradient per pixel (GPP) of the temporal subband is defined by:With the statistical analysis, we observe that, if the subband belongs to the homogeneous regions, the number of bits will be a smaller value, and it will relevantly increase with the complex of the texture. Hence, the texture homogeneity of each subband is measured by calculating the ratio between variance and mean of the subband, which is given by:Let d out in , Wi→i+1 Similar to JPEG2000 , we assuHowever, in practice, it is very difficult to determine the variance for a given coefficient, as required by Equation (27). Based on the observation the variance of each coefficient can be estimated by:Moreover, the variance of the residual pixel value before wavelet transform According to Equations (26)–(30), it is easy to compute the Lagrange multiplier Correspondingly, Equation (31) can be written as:Based on Equations (30)–(32), a more practical Lagrange multiplier expression is then derived as:By means of Lagrange multiplier The detailed procedure of the proposed Lagrange multiplier selection algorithm is outlined in the following Algorithm 1. Algorithm 1 An efficient algorithm for content adaptive Lagrange multiplier selectionStep 1Initialization: Input a video sequence with Step 2Modeling the distortion relationship between the temporal subbands and reconstructed frames.Step 3(3-1)Calculate MI value (3-2)(3-3)Texture homogeneity (3-4)Apply Temporal subband weighting factor calculation –(33).Step 5Model parameters update: Parameters update using multiple linear regression analysis.Step 6(6-1)Calculate the Lagrange cost (6-2)Output the best frame mode Temporal subband frame mode selection:Step 7Loop until all frames are encoded:Step 2 until the end of encoding, else report the related encoding parameters and exit.Set In order to access the accuracy of the proposed en model :(35)R2=1In this section, extensive experiments have been conducted to verify the effectiveness of the proposed content adaptive Lagrange multiplier selection algorithm for 3-D wavelet-based SVC.The experimental results over all video coding schemes are reported for fourteen standard test sequences based on YUV color format with 4:2:0 color sampling and 8 bits of precision per sample, including five different resolutions: CIF  computer equipped with RAM 8 GB that is also used to measure the computational complexity of our method. In the simulation, each group of pictures (GOP) contains 16 frames. During the MCTF process, motion estimation is implemented using a full-search with quarter–pixel accuracy on the dyadic wavelet coefficients. The block size varies from 4 MC-EZBC: the original MC-EZBC without employing proper RDO scheme.RPI-MC-EZBC: the bidirectional MC-EZBC from Rensselaer Polytechnic Institute which uses Haar filters for the conventional MCTF framework with the default Lagrange multiplier value for all the temporal decomposition levels.RWTH-MC-EZBC: the improved version of MC-EZBC from RWTH Aachen University, which uses longer filters instead of Haar filters for the conventional MCTF framework with the corresponding fixed Lagrange multiplier for each temporal decomposition level.ENH-MC-EZBC: the enhanced MC-EZBC using an adaptive MCTF framework with the corresponding fixed Lagrange multiplier for each temporal decomposition level.Proposed method: our codec with the proposed content adaptive Lagrange multiplier selection method.To evaluate the effectiveness of our algorithm, we have implemented it on the 3-D wavelet-based SVC reference software ENH-MC-EZBC configured with the common test conditions as suggested in configure file. In the simulation, we have performed the following five representative codecs with the same configuration. Recently, these codecs deliver the best coding performance for scalable coding of video datasets :MC-EZBC:In the experiments, only the luminance component is taken into consideration since human visual system is less sensitive to color than to luminance. For reasons of brevity, the average peak signal-to-noise ratios (PSNR) (dB) and the standard deviation of PSNR (PSNR STD) on luminance component have been used as quality metric, which are defined as follows:To verify the overall rate-distortion (R-D) performance of the proposed Lagrange multiplier selection approach, we compare it with the successful coding schemes on the framework of motion-compensated subband coding (MCSBC): ENH-MC-EZBC, RWTH-MC-EZBC, RPI-MC-EZBC, and MC-EZBC. Let “Scheme 1”, “Scheme 2”, “Scheme 3”and “Scheme 4” denote our method compared to the ENH-MC-EZBC, RWTH-MC-EZBC, RPI-MC-EZBC, and MC-EZBC, respectively.Average PSNR gains for all the test sequences are also displayed in In addition, we also investigate PSNR variations during video reconstruction. Since high fluctuation of frame PSNR values may cause perceptual annoying to viewers, the fluctuation of PSNR values is one of the vital factors for video coding applications. The standard deviation of PSNR (PSNR STD) is utilized to measure for the smoothness of video quality. The smaller PSNR STD value result in the smoother PSNR variation and hence more consistent video quality over the video frames. To obtain the subjective inspection of the reconstructed frames, we show the 8th reconstructed frame of the “City” sequence at 896 kbps in To measure the computational complexity, we define the encoding speed as the number of frames which can be encoded in one second on the hardware platform with processor Intel Core i5 4-core CPU 3.10GHz. The computational complexity is considered as inverse value of the encoding speed, which is measured without any use of assemblers, threads, or other program optimization techniques ,7,15,28.Since the ENH-MC-EZBC reference software is non-optimized C++ implementation, development of an efficient low-complexity 3-D wavelet-based scalable video coding scheme is an important practical problem, which is necessary to be considered in our future work.In this paper, we present an efficient content adaptive Lagrange multiplier selection algorithm for RDO in 3-D wavelet-based SVC. The wavelet filter types, subband coupling in the MCTF process, and temporal subband content characteristics have been incorporated into our algorithm to select the Lagrange multiplier adaptively. The simulation results demonstrate that the proposed algorithm turns out to be much better than the reference methods in terms of both accuracy and effectiveness.In a future work, we are going to extend our algorithm to other scalabilities, not merely quality scalability. The overall video quality can be further improved by employing human visual system-based perceptual features. In addition, development of a scalable low-complexity video codec based on 3-D DWT is our main concern as well. Therefore, we will experiment in these directions to obtain a more compelling result.


The terms emerging and re-emerging infectious diseases have been related to a group of diseases that have appeared in a population in the recent past or that have existed but are rapidly increasing in incidence or changing their geographic range. Among the causative agents, parasitic protozoa are emerging as a global health issue worldwide. This special issue is focused on all aspects of parasitic protozoa diseases, from diagnostics to treatment and prevalence, and will have a focalized section on free living amoebae.Diseases caused by emerging parasitic protozoa present in common the need of standardized diagnostic tools and the lack of fully effective therapeutic agents .Withania aristata were highlighted as new potential agents against these protozoa [Entamoeba infections [Acanthamoeba agents [Giardia spp. infections. The state of the art by scientists working in this field was observed in two of the published papers, one of them dedicated to the development and validation of nanoparticles against Acanthamoeba infections [Naegleria fowleri [In this special issue, supporting the need of therapeutic agents, therapy approaches were described, such as novel natural sources, including leishmanicidal agents. In this work, isolated compounds from a plant called protozoa . Moreovefections , derivata agents , and curfections  and the  fowleri . From a Leishmania in donkeys, as well as the occurrence of parasite DNA in blood [Leishmania epidemiology in endemic areas. A paper included in this issue showed a high seroprevalence for in blood . AdditioAltogether, this special issue presents the current work being carried out by experts on the field of emerging parasitic protozoa. As the guest editors of this issue, we would like to thank and congratulate authors involved in it and also to encourage their research in this field.


Simultaneous localization and mapping (SLAM), which addresses the problem of constructing a spatial map of an unknown environment while simultaneously determining the mobile robot's position relative to this map, is regarded as one of the key technologies in mobile robot navigation. This data article presents four raw video files, demonstrating the mapping and localization processes of NeuroBayesSLAM, a neurobiologically inspired SLAM system, on two publicly available datasets, namely the St Lucia suburb dataset and the iRat Australia dataset. The cognitive mapping process was recorded by a free screen recorder software on ubuntu Linux system. Neural activities of the head-direction cells and the grid cells, the local view templates of visual scenes, and experience map were included. These data envision the possibility of transferring the multisensory integration mechanism found in the spatial memory circuits of the mammalian brain to develop intelligent cognitive mapping systems for indoor and large outdoor environments as in the research article “NeuroBayesSLAM: Neurobiologically Inspired Bayesian Integration of Multisensory Information for Robot Navigation” Zeng et al., 2020. Specifications TableValue of the Data•The videos presented in this brief demonstrate the performance of a cognitive mapping system, namely NeuroBayesSLAM, on two publicly available datasets.•The conflict between vestibular cue and visual cue is resolved by the competitive dynamics and also explicitly presented in the videos.•The videos provide comparison and benchmark against other brain-inspired SLAM methods.•The dynamic process of neural activities, local view scenes, and experience map over time can help better understand the neural computational mechanisms of head-direction cells and grid cells and their implications for developing intelligent navigation system.1The dataset comprises four raw video files recorded using a free screen recorder software (SimpleScreenRecorder) on ubuntu system when the NeuroBayesSLAM system is demonstrated on two publicly available datasets, namely the St Lucia suburb dataset and the iRat Australia dataset Video S1 shows the mapping process for the St Lucia suburb dataset. Video S2 shows the plastic remapping process when a loop is successfully closed with strong reliability of visual cues. While, weak reliability in visual landmarks fails to complete the loop closure, as shown in Video S3. Video S4 shows the mapping process for the iRat Australia dataset. Supplementary videos are with the article.2The demonstration presented in the video data is based on the NeuroBayesSLAM system In order to demonstrate the competitive dynamics between the integrator cells and the calibration cells of the NeuroBayesSLAM system during the cue conflict resolution process, a loop in the St Lucia suburb dataset is selected to record in finer time resolution. We varied the strength of the injection current from visual cue to show the role of visual cues in plastic remapping process. The injection parameters with smaller energy are for weak/unreliable visual cues. The injection parameters with bigger energy are for strong/reliable visual cues. With strong visual cue, the integration of the visual landmark information generates a loop closure successfully (Video S2). In contrast, weaker visual landmarks fail to create a loop closure (Video S3).The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


Drinking motivations within the UK military have not been studied despite the high prevalence of alcohol misuse in this group.We aimed to characterize drinking motivations and their demographic, military and mental health associations in UK serving and ex-serving personnel.Serving and ex-serving personnel reporting mental health, stress or emotional problems occurring in the last 3 years were selected from an existing cohort study. A semi-structured telephone interview survey examined participants’ mental health, help-seeking, alcohol use and drinking motivations.n = 1279; response rate = 84.6%) yielded 2 factors, labelled ‘drinking to cope’ and ‘social pressure’. Higher drinking to cope motivations were associated with probable anxiety , depression  and post-traumatic stress disorder . Higher social pressure motivations were associated with probable anxiety . Alcohol misuse and binge drinking were associated with reporting higher drinking to cope motivations, drinking at home and drinking alone.Exploratory factor analysis of drinking motivations in military personnel (Amongst military personnel with a stress, emotional or mental health problem, those who drink to cope with mental disorder symptoms or because of social pressure, in addition to those who drink at home or drink alone, are more likely to also drink excessively. Alcohol misuse is more prevalent in UK military personnel than the general population, with rates of help-seeking being lower than for other mental health problems.Alcohol misuse in military personnel may be associated with mental health, with alcohol commonly used as a coping mechanism to alleviate symptoms.Research from US military personnel has identified that drinking to cope motivations predict alcohol problems, yet no study has explored drinking motivations in a UK military sample.This study identified ‘drinking to cope’ and ‘social pressure’ as the key drinking motivations in UK military personnel, with a self-reported stress, emotional or mental health problem.Probable depression, anxiety and post-traumatic stress disorder were associated with higher drinking to cope motivations, with anxiety having a weak relationship with social pressure motivations.Drinking to cope motivations were more common in individuals reporting alcohol misuse and binge drinking, with social pressure motivations also being associated with the latter. Personnel who drink at home and/or alone were also more likely to report harmful drinking behaviours.These findings can inform the development of tailored interventions for this high-risk occupational group, by identifying those at risk of drinking to cope and alcohol misuse, such as those with a mental health problem.Our study identified the need to integrate mental health and substance use services, particularly for military personnel, who may be drinking to cope with an existing mental health problem.We identified the importance of context in relation to alcohol harms, with personnel who drink at home or alone being more likely to report alcohol misuse and binge drinking.Alcohol misuse is common in the UK Armed Forces (AF), at a higher prevalence than the general population . Whilst Alcohol misuse is often co-morbid with mental health problems, particularly with post-traumatic stress disorder (PTSD) in the general population and among military personnel ,7. CommoThroughout history, alcohol has been integral to military culture by creating social bonds between personnel or for ‘de-stressing’ after deployment . Recent No study to date has directly explored the drinking motivations in the UK AF. Therefore, we aimed to (i) identify drinking motivations among UK AF personnel; (ii) assess the association of demographic, military and mental health factors with drinking motivations and (iii) examine the association of drinking motivations and the context of drinking with alcohol misuse.n = 8093) [Participants were identified from the third phase of the health and wellbeing cohort study of UK AF conducted by King’s Centre for Military Health Research between 2014 and 2016, which included data from existing members in previous phases , and a nOf the 2017 participants who self-reported a mental health, stress or emotional problem, 1714 were randomly selected (85%) to participate in a telephone interview, with a total of 1450 interviews completed (response rate 85%) [The Alcohol Use Disorder Identification Test-Consumption (AUDIT-C)  uses thrDrinking motivations were measured using selected items taken from the Hilton Drinking Behaviour Questionnaire (HDBQ)  and DrinAnxiety was measured using the seven-item Generalised Anxiety Disorder assessment (GAD-7) . Scores Military characteristics were measured by asking participants their service, rank, engagement, serving status, if they had been deployed to either Afghanistan or Iraq and if so, which was their last deployment. Participants were asked where they usually drink—the choices being home, military mess hall, public drinking establishments, civilian friends’ homes, military friends’ homes and others. They were asked who they usually drink with—military friends, civilian friends, family, spouse/partner, alone and other; and finally, the context in which, and with whom, they drank the most alcohol in a single session.a priori that items with a factor loading of <0.40 would be excluded. Parallel analysis, a scree plot and Kaiser’s rule were used to determine the number of factors to retain.The descriptive demographic, military and mental health characteristics of participants were outlined using frequencies and percentages. (i) Exploratory factor analysis (EFA) examined the factor structure of the drinking motivations, using the principal factors method as the data violates the assumption of multivariate normality, with Promax (oblique) rotation to allow the factors to correlate. It was decided P < 0.05) with the outcome, as well as sex and age.The distribution and dispersion of the drinking motivations  were assessed using a Chi-Square goodness of fit test. (ii) Negative binomial and Poisson regression models explored the association between drinking motivations as the outcome and demographics , military  and mental health factors . (iii) Logistic regressions explored whether drinking motivations or the context of drinking were associated with alcohol misuse, using AUDIT-C caseness, and a subcomponent of the AUDIT-C, representing binge drinking. Adjustment was carried out for demographic, military and mental health variables which were significantly associated  commands to account for weighting, using the computer software Stata/SE 15 .Response weights were applied to account for non-response, based on variables associated with responding  . Unweighn = 1279) included 707 serving (55%) and 572 ex-serving (45%) military personnel. Most were male (84%), married or in a long-term relationship (80%), in the Army (66%), and serving or had served as a regular (80%). The weighted mean (±SD) age was 41.1 (±9.6), ranging from 20.6 to 68.3 years. A total of 25% had not been deployed to either Iraq or Afghanistan, 20% reported deployment to Afghanistan, 25% to Iraq and 31% had deployed to both operations. About 26% were commissioned officers, 60% were non- commissioned officers and 14% held other ranks . About 18% scored above the validated cut-offs for probable anxiety, 8% for probable depression and 8% for probable PTSD and 18% met the criteria for alcohol misuse . Participants who reported abstinence (n = 91) were excluded from all analyses .The sample (The findings of the EFA are summarized in α = 0.84) indicated good internal consistency and for social pressures (α = 0.69), approached the cut-off for acceptability (i.e. 0.70). Drinking to cope and social pressures were significantly correlated .Factor 1 was labelled ‘drinking to cope’ and included seven items, all of which had positive loadings. Factor 2 was labelled ‘social pressures’ and included five items, all with positive loadings. The Kaiser-Meyer-Olkin measure of sampling adequacy (0.86) indicates a low proportion of common variance among the set of variables, suggesting that the data were well-suited for factor analysis. Cronbach’s alpha for drinking to cope  and drinking alone (relative to drinking with military friends) . PersonnThe principal findings of this study were that UK military personnel who met the criteria for alcohol misuse and engaged in frequent binge drinking reported higher drinking to cope motivations. Those with mental disorder symptoms were more likely to report higher drinking to cope. There was a weak association between anxiety and higher social pressure motivations. Those of a younger age reported higher drinking to cope and social pressure motivations. Personnel holding lower ranks and not in an intimate relationship were more likely to report drinking to cope. Still serving in the military was also a risk for drinking due to social pressure. Drinking at home and/or alone was associated with meeting the criteria for alcohol misuse and frequent binge drinking and drinking with a partner was associated with the latter.One strength of this study is the large sample of UK personnel, with a high response rate (85%). We controlled for a wide range of covariates to determine the influence of possible confounders and used well-validated measures of mental health and alcohol misuse ,19–21. WThe identified drinking motivations, drinking to cope and social pressure—and their associations with alcohol problems—are similar to those observed in studies of both US AF and the UK general population ,14. In UOne explanation for the relationship between coping motivations and alcohol misuse is the self-medication hypothesis , wherebyIn our study, certain sociodemographic characteristics were associated with reporting higher drinking to cope motives, such as younger age, holding a lower rank and being single. Previous US and UK AF research studies confirm that these are important associates of alcohol misuse ,25. We oDrinking alone is a predictor of alcohol problems in the general population . We exteOur study shows the significance of context in relation to alcohol harm, identifying that personnel who drink at home, alone or with a partner, may be particularly at risk. Interventions that assist with self-monitoring of alcohol consumption, such as smartphone applications , may be The research was funded by the Medical Research Council (MR/N028244/2).S.W. and S.S. are affiliated to the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. S.W. is a trustee of the charity Combat Stress. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. N.J. is a serving member of the British Army seconded to King’s College London. The remaining authors declare no declarations of interests.kqaa003_suppl_Supplementary-Table-1Click here for additional data file.


The current study investigates the theory of planned behavior with important additional predictors from the social identity approach. The study explores whether social identity might function as a driver of the theory of planned behavior and help explain how abstract group processes might impact student binge drinking behavior. Adopting a controlled statistical analysis, the hypothesized model expands the theory of planned behavior’s current conceptualization of group norms and considers how the behavioral content of a specific group, with group identification, impacts binge drinking behavior (N = 551 university students). A path analysis that simultaneously mapped all the hypothesized relationships supported a reconceptualization of social identity as a predictor within the theory of planned behavior. The interaction between group identification and the importance of drinking to the group’s identity significantly predicted an individual’s attitudes towards binge drinking and perceived social binge drinking norms , which in turn predicted intentions to binge drink. Intentions to binge drink predicted self-reported binge drinking behavior two weeks later, above and beyond relevant covariates. The implications of these findings are discussed, with recommendations for future research. Despite considerable effort, researchers are still striving to understand binge drinking behavior, as alcohol remains a leading contributor to burden of disease. Harmful alcohol use is responsible for 1 in 20 deaths worldwide, which is an estimated 3 million people a year . The popMany researchers have utilized the theory of planned behavior TPB; , 1991 toThe TPB also recognizes a range of possible background variables that might explain why individuals hold certain beliefs, which inform their attitudes, norm perceptions and PBC in a given context. Depending on the target behavior, different salient background variables, such as education, media exposure and neighborhood quality, might indirectly impact intentions and behavior or affect the relationship between attitudes, subjective norms and PBC with intentions . Many paIn the current study, a possible extension of the TPB is proposed with respect to further emphasis on the social identity approach and group norms. A central idea is that people belong to a number of different groups which can shape their self-concept. Shifts in self-identities may impact on attitudes and inform which ‘others’ and standards will be proponent in shaping one’s own behavior. As a result, when explaining behaviors such as binge drinking, it may be helpful to look at group-level processes in more detail. More specifically, it might be important to consider people’s group memberships, whether drinking is central to what it means to be a group member and the level of connection the individual has to the group . As is explained in more detail below, these social identity predictors have rarely been explored in relation to the TPB.The current research proposes that in some instances, social identity might function as a driving background variable in the TPB and therefore make a significant contribution to explaining behavior. Being oriented to a situation as a group member may help explain variability in attitudes, norms and PBC. These key TPB constructs, in part, could be viewed as outcomes of different self-concepts becoming salient in any given context. Furthermore, if a group identity is made salient, specific group norms might have a direct impact on intentions and become highly relevant to behavioral decisions.The TPB has received extensive support, but there is still scope to improve the current model’s predictive power. One area of improvement that warrants attention is the model’s conceptualization of normative influence. The only direct variable in the original TPB that considers social pressure is subjective norms. Yet, subjective norms fail to significantly predict health intentions across a number of studies . ArmitagWhile the concept of subjective norms intends to consider all kinds of important social expectations, the way it has been traditionally measured, both directly and indirectly, might limit this process. Participants are usually asked whether most people who are important to them approve or disapprove of the target behavior. Particularly in the context of drinking, this additive approach raises the issue that in an individual’s social network some would typically approve of drinking, such as peers, whereas others would typically disapprove of drinking, such as parents. Similarly, as items do not specify who participants should think about, some participants might answer questions with individuals in mind, such as their grandmother, and others with larger groups, such as their extended family, making the results hard to interpret.To understand more about which ‘others’ participants are thinking about when perceiving subjective norms, Additionally, as participants are asked questions about ‘important people’, this can implicitly lead to a focus on specific individuals, such as a sister or brother . Other gA common extension of the TPB, which has been recommended by Indeed, the addition of both descriptive and injunctive norms as direct predictors of intentions often has increased the predictive power of the TPB , signifiOne influential approach called the social identity approach (SIA) might offer some further insights. The SIA incorporates both social identity theory  and selfThe SIA argues that people’s behaviors are influenced by how much they identify as a member of a group. A social identity refers to a person’s knowledge that they belong to a group and that group membership has some emotional and valued significance . It is aOnce the group characteristics have been internalized, the group members will have an intrinsic motivation to act in line with the group norms. The group norms impose an internal sanction on the individual to uphold the prototype of the group, which become self-defining and self-reinforcing, as the individual tries to conform to their own self-concept . TherefoBuilding on these ideas, there is a growing body of research that has incorporated group identification with a specific reference group in the TPB model. In the context of the TPB, most studies have analyzed social identification as a moderator of the relationship between group norms and intentions . For exaAlthough social identification has been shown to moderate the relationship between perceived norms and behaviour, some researchers have found little support for this relationship. For example, Perhaps following As stated by A study by Past research has predominantly focused on measuring a form of group identification, such as “how much do you feel you identify with (insert group)?” . Howeverits’ raison d'être) needs to be taking into consideration in conjunction with identification levels when predicting student drinking.Along these lines, By exploring group processes and social norms from a SIA perspective, we can begin to understand how the social context shapes binge drinking behaviour in the context of the TPB. There are three major gaps in the literature that the current research seeks to address. First, in addition to the subjective norm variable, group specific injunctive and descriptive norms will be added to the model to understand how larger social group norms directly impact intentions to binge drink. Second, instead of exploring social identification as a moderator of the norm – intention relationship, it is proposed that social identity processes act as a background variable within the TPB. Third, instead of just focusing on whether one identifies with a group in order to predict behavior, we will explore how the perceived importance of the behavior relative to what it means to be a group member will impact one’s decision to engage in binge drinking.The current project also adopts more controlled statistical analyses to understand the TPB. The majority of studies in the TPB drinking literature used multiple hierarchical regressions for analysis  and intentions (H1b) will directly predict drinking behavior. As PBC decreases and binge drinking intentions increase, binge drinking behavior will increase after all covariates are considered. Furthermore, attitudes towards drinking (H2a), subjective norms (H2b) and PBC (H2c) will significantly predict binge drinking intentions. As positive attitudes towards binge drinking increase, subjective drinking norms increase and PBC decreases, one’s intentions to engage in binge drinking will increase.In addition to the traditional TPB model, it is hypothesized that as group specific descriptive norms increase (H3a) and group specific injunctive norms increase (H3b), binge drinking intentions will also increase. It is further predicted that the interaction between group identification and the importance of drinking to group identity will significantly predict drinking attitudes (H4a), subjective norms (H4b), descriptive norm (H4c), injunctive norm (H4d) and PBC (H4e). The importance of drinking to group identity will be more strongly related to drinking attitudes, drinking norms  and PBC for high group identifiers than for low group identifiers.MAge = 21.1 years, MTime spent at university = 5.8 semesters. Participants were asked open-ended questions about their drinking behavior, such as which individuals or groups would approve or disapprove of their drinking.ii Two markers reviewed and categorized the responses.An online study was conducted prior to the main study to confirm whether the traditional method of gathering normative beliefs lead participants to neglect larger groups. The pilot consisted of 39 university students; 25 females (64.1%), When listing people or groups who approved of binge drinking behavior, only six participants (15%) mentioned a wider social group . Similarly, only three participants (8%) mentioned a wider social group  who disapproved of drinking. The remaining participants listed people who were specific to them, such as their parents, older sibling or significant other. It appears that if researchers want to explore the influence of social groups, which might be more relevant when designing large-scale interventions, the group may needs to be explicitly named when measuring social norms in the TPB.SD = 2.75). The sample consisted of 158 (28.7%) males, 325 (59%) females, and 6 (1.1%) people who identified their gender as ‘other’, with the mean age of participants being 20.5 years . Of the 551 people who completed the initial survey, 274 (50%) completed the follow-up behavioral survey after two weeks. Of those respondents, 90% could be linked to their initial survey response via research codes, which resulted in 246 (45%) participants with data from both the initial survey and the follow-up. Following missing value analysis and attrition analysis, measures for participants who did not respond to the follow-up were imputed using multiple imputation in Mplus (Version 8.3), as is recommended for statistical rigor over listwise deletion  were living at university residencies  and 270 (49%) had other housing types, such as the family home or rented share houses. The mean length of time spent enrolled at the university was 4.18 semesters  to 7 (strongly agree).At the start of the questionnaire a visual representation of Australian standard drinks was presented. The surveys were customized to reflect a relevant reference group for the participant. If the participant was a member of a university residency, their reference group became ‘other (college) students’, with their residential college name inserted in each relevant item. If a participant was not a remember of a residency, their reference group became ‘other (university) students’, with the university’s name inserted in each relevant item. Measures were constructed in line with iii. Past research has indicated that these control variables are associated with student drinking behavior , adapted from The importance of drinking to the in-group was measured on a 7-item scale (α = .88) adapted from Attitude was measured using a 7-point semantic differential scale with seven differential adjectives (α = .92). Participants responded to the statement “Drinking more than 7 standard alcoholic drinks on a single occasion over the next two weeks would be…” anchored with semantics such as Unenjoyable-Enjoyable, Good-Bad, Foolish-Wise. Higher scores indicate pro-drinking attitudes.No control at all (1) – Complete control (7).PBC was measured with two items (α = .60). The items measured were “I feel in complete control of whether I drink more than 7 standard alcoholic drinks in a single occasion over the next two weeks” and “How much personal control do you have over whether you drink 7 or more standard alcoholic drinks on a single occasion over the next two weeks?” from should not (1) - should (7) drink 7 or more standard alcoholic drinks on a single occasion over the next two weeks”.Subjective norms for pro-drinking were measured using three items (α = .83). All items referred to ‘most people who are important to me’, with items such as “Most people who are important to me expect me to drink 7 or more standard alcoholic drinks on a single occasion over the next two weeks” and “Most people who are important to me think I should not (1) - should (7) drink 7 or more standard alcoholic drinks on a single occasion over the next two weeks” and “If I drank 7 or more standard alcoholic drinks on a single occasion over the next two weeks, most students at [insert group] would approve (7) - disapprove (1)”.Injunctive norms for pro-drinking were measured using two items (α = .69) on a 7-point scale. The items included “Most student at [insert group] think I The Drinking Norms Rating Form DNRF;  was usedI intend/I want/I expect to drink more than 7 standard alcoholic drinks on a single occasion over the next two weeks”. Descriptive statistic results showed that intentions were over-dispersed and polarized, with people either having extremely low or extremely high intentions to drink. Therefore, based on the distribution of the data, a binary variable of intentions was created. If participants’ intentions fell on or below the midpoint of 4, they were coded as ‘0’ for low intentions and if their intentions fell above the midpoint of 4, they were coded as ‘1’ for high intentions.Intentions were measured on a three-item scale of generalized intentions to drink (α = .97). Participants responded to the items “viBinge drinking behavior was measured during a follow-up, two weeks after the initial questionnaire. Participants were asked to recall their drinking behavior since the initial survey, recording the information using the DDQ . If the Descriptive statistics analysis was conducted in SPSS (Version 22). The nested nature of the data was considered, as students from the same residential college could be more correlated compared to students from other residential colleges, or non-residents. Therefore, Mplus (Version 8.3) was used to calculate interclass correlation coefficients (ICC) and design effects to determine whether the assumption of independent observations had been violated . As all 2 = 159.79, df = 141, p = .133) indicating that missingness was completely at random. Therefore, as recommended with MCAR and MAR data, multiple imputation with 40 iterations in Mplus was used to impute the missing data  test was non-significant (χing data  and Mplup < .001), with visual inspection of histograms and Q-Q plots suggesting that the data was non-normal, as many of the variables were skewed. Therefore, the maximum likelihood estimator with robust standard errors (MLR) in Mplus was used to account for the non-normality violations. Construct validity of the measures was examined with the measurement models in Mplus, with modification indices applied. CFA confirmed that all items adequately loaded onto their respective latent factor, ranging from .56 (good) to .98 (excellent), except for item 4 in the ‘importance of drinking to the group’ measure, which had a poor loading of .36  recorded that they had participated in binge drinking within a typical week and 302 (55%) participated in binge drinking during a heavy week of drinking. Overall, 250 (45.5%) participants were coded as intending to binge drink during the two weeks following the questionnaire. Most students perceived that the descriptive norm involved binge drinking, as 308 (58.4%) students indicated that the typical student in their group would binge drink at least once in a typical week. Of the 245 participants who completed the follow-up, 82 (33.5%) self-reported binge drinking behavior within the two-week period. The number of drinks on a given day ranged from 0 to 25 drinks. Correlations among the study variables are presented in M = 6.87 compared to 9.74) and a heavy drinking week (M = 14.2 compared to 17.3), lower perceptions of subjective norms for drinking (M = 3.27 compared to 3.63), higher PBC (M = 6.07 compared to 5.67), more negative attitudes towards drinking (M = 3.49 compared to 3.9) and were more likely to be female. All other variables were not significantly different between the two groups. Multiple imputation was used so that a more representative sample would be analyzed, but the results should be interpreted with caution considering the attrition results.Attrition analysis was conducted in SPSS (Version 22) before the missing data imputation, as seen in df = 27, N = 551) = 6516.68, p < .001, with McFadden’s adjusted R2 = .58, showing excellent model fit , as the odds ratio of binge drinking behavior increases by a factor of 2.95 for every unit increase in past drinking behavior. If a participant self-reported binge drinking behavior anytime in the month prior to the survey, they were more likely to report binge drinking in the follow-up. All other demographic variables were nonsignificant in predicting binge drinking behavior. It also appeared that PBC did not directly predict binge drinking behavior . Contrary to predictions, a decrease in PBC did not predict an increase in drinking behavior (H1a). Intentions positively influenced binge drinking behavior , as the odds ratio of binge drinking behavior increases by a factor of 4.52 for every unit increase in drinking intentions. As hypothesized, the more an individual intended to binge drink, the more likely they were to participate in binge drinking within the two-week follow-up (H1b).Past drinking behavior was the only covariate to significantly predict binge drinking behavior . The odds ratio of binge drinking intentions increases by a factor of 2.89 for every unit increase in positive attitudes towards drinking. As positive attitudes towards binge drinking increased, intentions to engage in binge drinking increased significantly, as was originally hypothesized (H2a). Subjective norms positively determined intentions , with the odds ratio of binge drinking intentions increasing by a factor of 1.65 for every unit increase in subjective norms. In line with predictions, if an individual perceived that most people who were important to them approved of binge drinking, they were more likely to intend to binge drink themselves (H2b). PBC negatively predicts intentions , as the odds ratio of binge drinking intentions is 0.66 times as likely for every unit increase in PBC. As feelings of PBC decreases, intentions to binge drink increase, as was hypothesized (H2c).In support of the original TPB, drinking attitudes, subjective norms and PBC all emerged as significant predictors of binge drinking intentions. Attitudes positively determined intentions , with the odds ratio of binge drinking intentions increasing by a factor of 2.42 for every unit increase in descriptive norms. In accordance with the hypothesis, the more an individual perceived that a typical member of their group would engage in binge drinking behavior in a typical week, the greater their own binge drinking intentions (H3a). Unexpectedly, injunctive norms did not significantly predict intentions . Hypothesis H3b was not supported, as the approval or disapproval of the specified student group did not appear to impact individual intentions to binge drink.Group-specific descriptive drinking norms and group-specific injunctive drinking norms were predicted to have a significant relationship with drinking intentions. Perceived descriptive norms positively predicted intentions , subjective norms , descriptive norms  and injunctive norms . As was hypothesized, drinking attitudes (H4a), subjective norms (H4b), descriptive norms (H4c) and injunctive norms (H4d) were all influenced by whether one identified with the relevant group and whether drinking was important to the group’s identity. However, identification × importance did not significantly predict PBC . Contrary to the hypothesis, there was no moderated relationship between importance of drinking to the group, group identification and PBC (H4e). Yet, the main effects of identification  and importance of drinking to the group  were significant negative predictors of PBC. Although they were not predicted effects, it appears that the higher one’s identification with the group, the lower their PBC. Additionally, as one’s perception of the importance of drinking to the group identity increases, their PBC decreases.viiIt was also predicted that the group identification and importance of drinking to the group identity interaction would directly predict attitudes, perceived social norms and PBC. The interaction between identification × importance did significantly predicted attitudes  does not have a cluster analysis option, therefore the following results include confounding from intraclass correlations within residency groups and should be interpreted with caution.A second set of analyses plotted the significant group identification × importance of drinking to the group identity effects, in order to visualize the relationships. The significant interaction effects were explored in SPSS  could be reconceptualized to act as a driver of the TPB when predicting binge drinking in university students. Overall, there appears to be support for exploring social identity as an additional predictor in the TPB, with group processes predicting individual perceptions, including group specific norms, that affect drinking behavior.With regards to Hypothesis 1b, intentions did significantly predict binge drinking behavior, as was expected and consistent with past research. If a student intended to participate in binge drinking, they were more likely to self-report binge drinking behavior two weeks later. In line with the original TPB, it was also hypothesized that PBC would directly predict behavior (H1a), which was not supported. However, numerous studies have found the relationship between PBC and drinking behavior to be non-significant . A meta-The other hypotheses regarding the original TPB model were supported, as attitudes (H2a), subjective norms (H2b) and PBC (H2c) all predicted drinking intentions. As attitudes towards drinking became more positive and perceptions of pro-drinking subjective norms increased, overall intentions to engage in binge drinking increased, as is consistent with the TPB. Furthermore, the less PBC one felt they had over the decision to binge drink, the more likely that they intended to drink. Although the relationship between PBC and intentions is sometimes theorized to be positive, in relation to drinking behavior, the significant negative relationship is in line with past research . It is pHypothesis 3 was partly supported. Binge drinking descriptive norms did significantly predict intentions (H3a), but injunctive norms did not (H3b). As is consistent with past research, the more one perceived everyone else in the group was binge drinking, the greater their intentions to engage in binge drinking themselves . AlthougOf the research that does specifically measure injunctive group norms and drinking intentions, the results are mixed. Some researchers have concluded that the main effect of injunctive norms on intentions and/or drinking behavior is only significant when the reference group is in close proximity to the individual . For exaThe interaction between group identification and the importance of drinking to the group did significantly predicted attitudes (H4a), subjective norms (H4b), the descriptive norm (H4c) and the injunctive norm (H4d). Although this interaction has never been directly tested, the results are in line with past theoretical observations that social context influences individual attitudes and how normative information is perceived . HoweverOverall, the current study has been able to both replicate and extend the TPB. Whilst taking into account all the TPB variables simultaneously with advanced statistical analysis, the current study was able to confirm the findings of past research and concluded that attitudes, subjective norms and PBC predict binge drinking intentions, which in turn predict binge drinking behavior. The current study also helped to clarify the mixed evidence with regards to normative influence within the TPB. It appears beneficial to differentiate between the different types of social norms  as all three appear to have distinct effects on intentions. It is advised that when descriptive and injunctive norms are added into the TPB, they should not be measured as one construct, nor should they replace subjective norms, as the concepts are not interchangeable. Similarly, the results also demonstrate that measuring the descriptive and injunctive norms of the group is not the same as measuring the importance of the behavior to the group’s identity. It appears that one’s experience of the group and what it means to be a part of that group affects their perception of associated social norms.Most notably, the current study demonstrates the SIA has much to contribute to understanding behavior in the context of the TPB. The present study revealed that it is not enough just to measure whether one identifies with a group or not when trying to predict behavior. It is beneficial to understand what behaviors are central to the salient group’s unique identity, in conjunction with how much an individual psychologically identifies with the group, as both will affect an individual’s perception of reality and ultimately their behavior. As four of the five interaction effects that were tested between group identification and the importance of the behavior to the group were significant predictors within the TPB, there is compelling evidence to argue that researchers could pay more attention to group processes when trying to predict one’s attitudes, PBC and perceptions of social norms in relation to drinking. Furthermore, being oriented to a situation as a group member may help explain the source of TPB constructs and variability across social contexts. In relation to binge drinking behavior, the TPB constructs can be viewed as outcomes of particular self-concepts becoming salient in a particular situation. Therefore, the current study has highlighted that a more comprehensive understanding of how the SIA can contribute to the TPB is highly valuable, as it advances our understanding of how to predict and potentially influence behavior.Despite the current study’s important implications, there are some limitations that should be noted and rectified in future research. First, the current study only explores correlations between cross sectional variables, meaning that the causal direction of the relationships could be misinterpreted. For example, instead of one’s group membership predicting attitudes, it is possible that one’s attitudes will determine which group they chose to be a part of, as they seek out similar individuals . SimilarSecond, some of the measurements used in the current study could be improved upon. Injunctive norms and PBC were only measured on two-item scales, which have been recognized as problematic . Future Similarly, it is possible that when students are physically engaging in binge drinking within a social setting (such as a party or in a club), different and potentially conflicting social identities could be salient and affect behavior in a currently unknown way. It is necessary to see whether the current results can be replicated when other reference groups are made salient. Furthermore, as the current study was focused on specifically understanding binge drinking in university students, it would also be beneficial to replicate the proposed model with other health behaviors that range in social motivation  and with other samples in the wider population, to further generalize the results.In sum, the findings from the current study appear to offer promising extensions to the TPB when predicting binge drinking. It appears that viewing social identity processes as a background variable in the TPB offers insight into how the social environment can play a role in one’s drinking behavior. More specifically, group membership, including group identification and what behaviors are central to a group’s identity, seems to shape an individual’s perception of related attitudes, social norms and PBC. Therefore, perhaps a greater focus on social processes in line with the TPB should be encouraged in order to advance our ability to predict binge drinking behavior.


Advances in the ability to monitor freely-moving mice may prove valuable for the study of behavior and its neural correlates. Here we present a head-mounted multi-camera system comprised of inexpensive miniature analog camera modules, and illustrate its use for investigating natural behaviors such as prey capture, courtship, sleep, jumping, and exploration. With a four-camera headset, monitoring the eyes, ears, whiskers, rhinarium, and binocular visual field can all be achieved simultaneously with high-density electrophysiology. With appropriate focus and positioning, all eye movements can be captured, including cyclotorsion. For studies of vision and eye movements, cyclotorsion provides the final degree of freedom required to reconstruct the visual scene in retinotopic coordinates or to investigate the vestibulo-ocular reflex in mice. Altogether, this system allows for comprehensive measurement of freely-moving mouse behavior, enabling a more holistic, and multimodal approach to investigate ethological behaviors and other processes of active perception. Mice move. Whiskers whisk, eyes saccade, nostrils sniff, and ears pivot Preyer, . These aThere are many inherent technical difficulties when designing attached devices for freely-moving behaviors in small animals. Every component comes with an associated cost of size, weight, positioning, balance, and cabling. Each of these factors necessarily constrains the free movement of the animal, and the magnitude of their effects scale inversely with the animal's size and strength. The first rodent head-mounted camera system was developed to monitor the behavior of freely-moving rats, with each attached camera weighing 800 mg . The common ground introduces interference in the video signals caused by high-frequency crosstalk, but this interference can be removed through the use of simple low-pass filters . One camera can remain unfiltered, yielding a higher quality video signal. We chose the overhead camera in this case, to achieve finer detail of the eyes, whiskers, and rhinarium.This headset design has some drawbacks compared to the two-camera headset described below. Once assembled, the camera positioning is not adjustable, requiring iterations of building, and testing to optimize camera angles for each use-case. Additionally, while this headset arrangement allows for a large view of multimodal behavior, the overhead perspective may miss a portion of the pupil in rare cases of extreme ventral gaze. Lastly, because the headset is fully integrated, if one component were to fail, it may be difficult to individually replace it without rebuilding the entire headset .Next, we describe an adjustable two-camera headset as shown in While this headset is not necessarily integrated with an electrophysiology headstage, it can be easily paired with one. Example waveforms and rasters of unit activity recorded in anterior lateral motor cortex (ALM) during prey capture behavior are shown in With the camera module's factory-installed lens fully extended and focused on the surface of the iris, these cameras can detect cyclotorsion of the mouse eye. Examples of this behavior can be seen in We tracked cyclotorsion using the unique pattern of natural serrations of the mouse eye in a manner similar to iris-registration software used to track torsional movements in humans during laser eye surgery , the headset could be adapted to use a 3D printed carrier, similar to the four-camera headset described above. Even during rapid behaviors such as jumping, we did not observe any motion artifacts in recorded video  and inexpensive (US$23) analog camera modules with a number of advantages that increase the flexibility of implementation compared to previously published alternatives. Their size, weight, and cabling advantages allow multi-camera headsets to monitor more sensory structures and capture more of the visual field, while their low impact on behavior enables chronic monitoring under natural conditions across the sleep-wake cycle. Additionally, the system can provide high-precision tracking of eye movements, such as cyclotorsion, and allows the absolute position of the eyes to be translated into true retinal coordinates via visualization of the fundus and optic disc. Together, these features allow the alignment of neuronal activity with both motor events and retinotopically mapped visual stimuli in freely-moving animals.Although we provide examples of specific configurations for monitoring certain body parts and views , we anticipate that this system will be adapted to best suit specific experimental goals using the strategies and considerations presented below.Size and weight are perhaps the most crucial aspects of head-mounted devices, and together are significant advantages of this system compared to previously published alternatives. Because analog camera modules forgo on-chip digitization of the video signal, ADC hardware components are eliminated from the design at the level of the animal, resulting in a significant reduction of both size and weight. At 5 × 5 × 5 mm and 180 mg, the size and weight of these camera modules serve to minimize their effects on mouse behavior. The primary benefit of this is to reduce the load carried by the animal, which in turn leads to a number of corollary benefits. First, cameras can be much more readily incorporated with larger and heavier hardware, such as electrophysiology headstages, and positioned in much closer quarters due to their size and flexible cabling options. Second, more cameras can be used to monitor additional body parts and classes of movements. Using more cameras is likely to be important, as recently described brainwide correlates of high-dimensional motor variables show the importance of capturing as much behavioral information as possible  presents a trade-off that depends on individual experimental requirements.Natural cyclotorsion of the eye has been observed in pigeons , but other miniature analog camera modules with similar specifications are available from a range of manufacturers. Before modifying the cameras in any way, we ensured that they produced a clean and stable video signal out of the box by powering the camera's attached barrel cable with a 12V DC power supply and connecting its RCA composite video connector to a display. We then removed the infrared filter as described in All 3D-printed components  were desWe 3D-printed a camera carrier (Part #5) as shown in To create the headset, we first prepared a wiring harness with measured segments of wire needed to power the IR LEDs and camera modules on the headset . We thenWe terminated the ends of the signal, power, and ground tethers with male jumpers for easy connection/disconnection. We used a +3.3V terminal on an RHD2000 USB interface board to power the cameras for use with or without accompanying electrophysiology, but any +3.3V power supply should work. The cameras could optionally be powered directly by the headstage as well, reducing the number of wires in the tether and still allowing accelerometer signals to be acquired, but at the cost of introducing noise on the electrophysiology channels.The 4 cameras can share power and ground connections, thereby using only 6 wires, but the common ground introduces interference caused by high-frequency crosstalk (producing chrominance and dot-crawl noise). If additional wiring is not a consideration, this interference can be avoided by using independent power and ground connections for each camera . Alternatively, the interference can be removed with simple low-pass filters . We constructed these filters on a separate breadboard for the camera signals, and integrated it into the system as shown in The filtering eliminates the crosstalk between the cameras produced by the common ground loop, but comes at the cost of losing the chrominance signal and the high frequency portion of the luminance signal. Therefore, color information is lost and a reduction in the luminance of the signal causes a dimming of the video. The drop in luminance can be compensated for by increasing the intensity of LED illumination . We found that filters using a 560 Ω resistor effectively removed crosstalk without too much dimming of the video signal, but this filtering could be affected by stray capacitance in the system and the optimal resistance value may therefore differ across implementations.Finally, we pass the filtered signal and ground for each camera through individual video baluns  before being connected to USB video capture cards (DVRs) for digitization of the video signal. The video baluns were used to isolate a second ground loop that would otherwise normally occur at this point in this system, produced by having two paths to ground: one to the ground of the camera power supply and one to the ground of the computer.We first applied a small amount of super glue (Part #7) for strain relief between the wires at the base of the prepared camera modules so they were secured for long-term use. We then soldered an independent signal, power, and ground tether (Part #8) to each camera module. A 560 Ω resistor (Part #9) was soldered at this location to the power wire, as well as an additional wire to the ground tether, for powering an illumination LED (see below). Shrink tubing was then applied to these three junctions (Part #10). For some experiments, a 6 mm collimating lens (Part #11) was placed onto the front of each camera module using tape (Part #12), but this is unnecessary and not recommended due to the additional 125 mg weight imposed. We then wrapped the camera module with a layer of micropore surgical tape (Part #13) and applied a thin coat of super glue across the surface of the tape.For adjustable headsets that mounted independently of the headstage, we prepared two 15G hypodermic tubes to a length of 5 mm, and a 20G stainless steel wire (Part #14) to a length of 3 cm. The hypodermic tubes were inserted onto the front two screws of an assembled implant 3 in i, and thFor epi-illumination of the eyes, we then cemented 3 mm IR LEDs (Part #15) to the sides of each camera module in a coaxial fashion, and soldered the leads to the power (through the current-limiting resistor) and ground tethers. The distal ends of the camera signal, power, and ground tethers were then soldered back to their original connections on the factory-provided wiring harness with the power and video connectors. The two cameras were then each powered with an independent 12V DC power supply and connected with RCA cables to USB video capture cards  for digitization of the video signal. The factory-provided power connector includes a step-down from 12 to 3.3 V.Electrophysiology and accelerometer signals were acquired from an attached Intan headstage amplifier (Part #17) with an RHD2000 USB interface board (Part #18) using OpenEphys software (Part #19). The Intan headstage contains 3 integrated accelerometers. We identified single neurons offline using Kilosort spike sorting software (Part #20).For either headset design, DVRs were connected to a recording PC, and video signals were acquired with Bonsai software (Part #21) at 30 Hz and a resolution of 1,280 × 960. Video from additional webcams  were also simultaneously acquired with Bonsai at 30 Hz and a resolution of 1,280 × 960 or 1,920 × 1,080. We increased the brightness of the ear-facing and overhead camera videos using the color balance function in Bonsai. Video can also be flipped or rotated at this point; for example, we rotated the signal from the forward camera video to compensate for the installation orientation. We also used Bonsai to record timestamps for all captured frames and log them to individual csv files for each camera. To synchronize the video signals with electrophysiology and accelerometer signals, we positioned an IR LED in view of the webcam, and drove it with TTL pulses also recorded by the RHD2000 USB interface board. The LED pulses were detected online during data acquisition with Bonsai in real time. The analog camera modules are not directly triggerable. We note that the DVRs are susceptible to electrical interference, e.g., from nearby power supplies or equipment that can introduce artifacts into the digitized video signal if too close to the DVRs.The RS-306 camera produces an NTSC analog waveform encoding 4:3 aspect ratio interlaced frames with an optical resolution of 800 × 480 at 30 Hz. We deinterlaced the video offline to remove combing artifacts and recover the full 60 Hz field rate. Analog video is interlaced such that each 30 Hz video frame consists of two 60 Hz fields taken in sequence: the first containing the odd lines of the image, and the second containing the even lines. We used a line-doubling deinterlacing algorithm to separate each frame into two consecutive deinterlaced images consisting of the odd or even field lines, and doubled line width to preserve image dimensions, yielding deinterlaced video at 60 Hz, with an effective single-frame optical resolution around 800 × 240. The horizontal and vertical angles of view are 60° and 45°.Stereo audio recordings for vocalization experiments were obtained with two Brüel and Kjær 1/4-inch microphones (Part #24) and acquired with a Lynx 22 sound card (Part #25) and Audacity software (Part #26). The transducers of the microphones were positioned at the ends of the cage and angled at 45 degrees to point at the base of the interaction barrier. We delivered white noise bursts through a free-field speaker at the beginning and end of each experiment along with a TTL pulse to an IR LED and the RHD 2000 interface board to synchronize audio and video recordings with electrophysiology and accelerometer signals. For the spectrograms in We tapped the three holes at the front of the implant base i with a For tetrode implants, we then inserted two 6 mm 18G hypodermic tubes into the center of the implant base  to electrically connect individual stainless steel wires to the ground and reference channels on a Neuralynx EIB (Part #32). An EIB (electrode interface board) is a miniature break-out board with a headstage connector (such as an Omnetics connector) and connection points for implanted microwires, to provide a robust interface between a headstage and electrode or tetrode channels. The remaining channels on the EIB were similarly connected to prepared tetrodes (Part #33) or stainless steel wires for tetrode and wire-array implants, respectively. Once connected, we coated these sites with silicone sealant (Part #34). The reference and ground wires were either routed through the drive posts for tetrode implants, or two 19G hypodermic tubes in the case of wire-array implants. The tetrodes or remaining stainless-steel wires were then routed through their respective hypodermic tubes, and the EIB was lowered and flushly screwed into place on the surface of the implant base with two screws (Part #35). Stainless steel wires routed through the same tubes were cut to slightly different lengths so that we could properly identify them during implantation and note their respective channels on the EIB. The implants were then sterilized in 70% EtOH before implantation. Protective caps  and made craniotomies as described below; these can be customized to the specific needs of different experiments. Mice were anesthetized with isoflurane (1–2%). A craniotomy was created over the right hemisphere, where a skull screw (Part #37) was fastened into place and cemented with dental acrylic. Two additional craniotomies were then created at −1.5 AP, −2 ML, 0 DV, and −2 AP, −2 ML, 0 DV for the ground and reference wires. For implants utilizing infrared back-illumination pupillometry [iBip;  at 4 AP, 0 ML, 0 DV relative to bregma, and ensuring the total height of the implant was 1.3 cm from the bottom of the cuff (or the IR LED in the case of iBip implants) to the top of the drive screw. This positions the EIB centrally over the head and the camera carrier such that the cameras are over the ears and the front of the head.For tetrode implants, the tetrodes were trimmed at this point so they would pass just beyond the level of the dura when the implant was lowered into its final position. Once at the proper length, we applied a thin coating of antibacterial ointment to the tetrodes, made a small incision in the dura, and slowly lowered the implant and tetrodes into place. We then coated the remaining exposed portion of the tetrodes in vaseline, and cemented the vaseline, cuff, and surrounding surface of the skull with dental acrylic. We then positioned the ground and reference wires just below the dura in their respective craniotomies, and cemented them with dental acrylic along with the drive posts so that no portion of the wires were left exposed.For wire-array implants, the stainless steel wires were splayed out laterally so the implant could be lowered into its proper position. We then raised the implant 1.5 mm dorsally, and positioned the hippocampal wire vertically just above the dura, and cemented it to the hypodermic tube in this position with dental acrylic. This process was then repeated for the remaining electrodes targeting non-superficial structures. We then made small incisions in the dura at these locations, and slowly lowered the implant into position. We applied dental acrylic to these two sites and the cuff to cement them in place. Once the acrylic sufficiently stabilized the implant, the remaining wires were placed just below the dura in their respective craniotomies, and cemented with dental acrylic so that no portion of the wires were left exposed.We administered Ketoprofen (5 mg/kg) to reduce post-operative inflammation. The bottom surface of a protective cap (Part #36) was then coated with silicone sealant (Part #34) and snapped over the EIBs of the implant to cure in place. Mice were then given 7 days of post-operative recovery.All experiments occurred within an electrically shielded sound-attenuating chamber. Sleep experiments occurred in the nest of the home cage, with no external sources of illumination. Vocalization experiments occurred in the home cage of individually housed male mice. A webcam (Part #22) recorded video directly above the arena (referred to as the “SkyCam”), with illumination from an IR LED light source (Part #38) and 6000K LED Floodlight (Part #39). We placed a perforated clear acrylic barrier (Part #40) between the resident male, who was unimplanted, and an implanted female. Vocalizations were recorded with two overhead Brüel and Kjær 1/4-inch microphones (Part #24).Prey capture experiments occurred in a 24 inch diameter circular arena with a clean paper floor. SkyCam video of the arena was recorded as described above. Illumination was provided by an IR LED light source (Part #38) and 5,500 K lightbulb (Part #41). An implanted mouse waited in the arena until a cricket was remotely dropped into the arena, and then the mouse chased and captured the cricket  to record from one side of the arena, and a dome camera (Part #23) to record from above, with illumination provided by an IR LED light source (Part #42) and 6000K LED Floodlight (Part #39).The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by the University of Oregon Institutional Animal Care and Use Committee.NJS developed and tested the system. NJS and MW wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.