Early-life adversity shapes anomalous spontaneous behavior in mice heterozygous for Cntnap2

Early-life adversity shapes anomalous spontaneous behavior in mice heterozygous for Cntnap2 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Early-life adversity shapes anomalous spontaneous behavior in mice heterozygous for Cntnap2 Gabriele Chelini, Viviana Canicatti, Vanes Cibin, Nicoletta Berardi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7417825/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The influence of early-life experiences is widely acknowledged as a crafting tool that sculpts complex behavioral patterns and well-being of living organisms. The use of preclinical models can provide invaluable insight into how a negative environmental push interplays with genetic make-up in shaping psychiatric vulnerability. Recently, the development of automated tools to classify spontaneous behavior in mice has opened the possibility to investigate the impact of experimental manipulation onto the composition of naturalistic behavior. Methods : In this work, we applied a tool for digitalized ethological screening to identify spontaneous hallmarks of altered neurobehavioral functioning in a dual-hit mouse model. To do so, mice carrying heterozygous deletion of the gene coding for contactin-associated protein-like 2 ( Cntnap2 +/- ) and their wild-type (WT) littermates were raised with limited bedding and nesting (LBN) and compared to both WT and Cntnap2 +/- mice raised in standard conditions, mapping their spontaneous behavior during freely-moving exploration. Results: Our data show no differences in locomotor activity or anxiety indicators across the four groups. By contrast, automated segmentation of the body-language revealed a significant impact of both genotype and early-life experience in shaping the spontaneous behavioral program. Thus, using unsupervised clustering, we unveiled two alternative behavioral profiles within our dataset. We found that one of the identified profiles largely overlapped with Cntnap2 +/- mice raised with LBN, while the other was equally shared among controls. Conclusions: We conclude that the coincidence of early-life adversity and Cntnap2 haploinsufficiency drastically alters behavioral structure in rodents. Biological sciences/Neuroscience Health sciences/Diseases/Psychiatric disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Genetic factors and adversities during early-postnatal life (ELA) are universally recognized as major contributors to psychiatric vulnerability( 1 ). The use of animal models is an ideal way to stratify the relative contribution of genetic and environmental vulnerabilities that shape core behavioral hallmarks of psychiatric disorders( 1 ). Currently, the majority of validated empirical tools to identify psychiatric-like traits in rodents consist in challenging naïve mice with forced-choice tasks, oscillating between safe options and anxiogenic alternatives( 2 – 4 ). Alternatively, the use of bodily indicators (i.e. body language) as a proxy for the animal affective state is a traditional approach, primarily used by ethologists to obtain an ecological interpretation of the animal affective state ( 5 , 6 ). In recent years, novel computational tools were developed to isolate and quantify spontaneous behavioral expression in mice ( 7 – 11 ). These tools can help recognize and quantify emotion expression within the naturalistic behavioral flow, providing valuable insights about the impact of experimental manipulations on spontaneous behavior( 8 – 10 , 12 ). SEB3R (stimulus-evoked behavioral tracking in 3D for rodents) is a MATLAB pipeline that was recently developed to this purpose ( 8 ). The method uses the key-point detection obtained in DeepLabCut to perform a two-step cluster analysis on the body elongation, and achieving sub-second fragmentation of the naturalistic behavior in freely moving mice ( 8 ). Empirical evidence show that SEB3R successfully identifies discrete behavioral modules (BMs) associated with either positive (explorative rearing) or negative (freezing and fleeing) emotions, when mice are challenged with and invasive, but not painful, whisker stimulation( 8 ). Furthermore, the somatic expression of fear, quantified by SEB3R, was shown to strongly correlate with the expression of immediate early gene ARC in the basolateral amygdala, suggesting that behavioral modules (BMs) recognized with this tool can be directly mapped within the brain emotional network. In this work we applied SEB3R behavioral parcellation to a mouse model with both genetic and environmental vulnerability to multiple psychiatric disorders( 13 – 15 ). To do so, we focused on mice with heterozygous mutation of the gene coding for the Contactin-associated protein-like 2 (CNTNAP2). Haploinsufficiency of CNTNAP2, in humans, is linked to multiple psychiatric diagnosis (schizophrenia, bipolar disorder, autism spectrum, major depressive disorder), but also detectable in neurotypical individuals( 13 – 17 ). Previous work showed that mice carrying this mutation ( Cntnapt2 +/− mice) are virtually indistinguishable from wild-types ( 18 , 19 ). However, when challenged with an environmental stressor, Cntnap2 +/− mice present phenotypic hallmarks relevant to CNTNAP2 haploinsufficiency-related conditions. Following prenatal maternal immune activation, Cntnapt2 +/− mice display significantly reduced social behavior and increased acoustic startle, signatures of an autistic-like phenotype( 20 ). Conversely, we recently showed that when raised in a condition of scarcity-adversity, these mice engage in excessive risk-taking behavior, potentially indicating a loss in self-preservation boundaries similar to bipolar patients during a manic phase( 21 ). Furthermore, using a dimensional approach encompassing data from several behavioral tests, we found a powerful impact of the gene-environment interaction in globally altering behavioral outcomes( 21 ). This evidence suggests that the interplay of Cntnap2 haploinsufficiency and ELA may shape an alternative neurobehavioral functioning in rodents. To test this hypothesis, we replicated this model by raising Cntnap2 +/− mice with limited bedding and nesting (LBN), a widely used paradigm to induce early-life stress in rodents( 22 – 24 ). Litters composed of both Cntnap2 +/+ and Cntnap2 +/− bred in LBN (from this point onward defined as Cntnap2 +/+ LBN or Cntnap2 +/− LBN , respectively) were compared with parallel litters of standard-reared Cntnap2 +/+ and Cntnap2 +/− (defined as Cntnap2 +/+ SR or Cntnap2 +/− SR, respectively) and evaluated using SEB3R during freely-moving exploration of an open field. We observed that, while having limited effect on independent behavioral components (BMs), both Cntnap2 haploinsufficiency and LBN play a role in altering the overall composition of spontaneous behavior. Then, we used unsupervised clustering to stratify the study cohort, discovering that Cntnap2 +/− LBN mice exhibit an alternative - and nearly exclusive - behavioral structure compared to the other groups. Moreover, we found that this atypical behavioral expression was characterized by a remarkable inter-individual homogeneity, suggesting that the combined effect of Cntnap2 haploinsufficiency and early-life stress constrains behavioral strategies into highly stereotyped programs. These findings suggest that Cntnap2 haploinsufficiency alters the expression of naturalistic behavior in mice, and contributes to shape an alternative neurobehavioral functioning when paired with an early-life adverse experience. METHODS AND MATERIALS Animals and housing All mice were generated from our inbred Cntnap2 colony with C57BL/6 background. Animals were housed in a 12h light/dark cycle with unrestricted access to food and water. A total of 38 age-matched adults (6 months old) mice (weight 25–35 g) were used in the study (8 Cntnap2 +/+ SR (3 males, 5 females), 9 Cntnap2 +/+ LBN (3 males, 6 females), 8 Cntnap2 +/− SR (4 males, 4 females) and 13 Cntnap2 +/− LBN (10 males, 3 females), obtained from 3 litters raised in SR and 4 litters raised in LBN). All efforts were made to minimize suffering of the animals. All experimental procedures were performed in accordance with Italian and European directives (DL 26/2014, EU 63/2010) and were reviewed and approved by the University of Trento animal care committee and Italian Ministry of Health (# 935/2021-PR). Breeding strategy . To minimize the effect of confounding factors on the variables of interest (i.e. early-life stress and genotype), we adopted a standardize breeding strategy. Age-matched (postnatal age P = 150–160 days) WT females were crossed with heterozygous males, eliminating the effect of maternal age and genotype on parental care. The first litter was discarded as to exclude the consequences of first experience in maternal care. Only second-born litters were included in the study. Limited bedding and nesting The LBN paradigm was adapted from Walker et al.(21, 22). At postnatal day 4 (P4), the dam with the entire litter was moved to an experimental cage. The cage featured an aluminum mesh placed at 2.5 cm distance from the base and nestled material reduced to ¼ with respect to a standard cage. The mother with the full litter was returned to a standard breeding cage once the pups reached P11. Control litters were moved from standard cages to standard cages at the same time-points, as to exclude the effect of experimental manipulation as a confounding factor. Both LBN mice and controls were weaned at postnatal day 21/22, housed in couples with one of their same-sex littermates in standard cages, with a single nestled square provided as an enrichment. Behavioral Testing Open Field. Each mouse was acclimated with the experimental room for 20 minutes. After habituation, each mouse was placed in the center of a large, squared arena (40 x 40 x 40 cm) and let free to navigate the open space for a total of 20 minutes. The experimental sessions were video recorded from above, using a camera interfaced with the Ethovision software (Noldus, Wageningen, the Netherlands) for the extraction of traditional behavioral descriptors. Two synchronized cameras in stereo-configuration were used for sidewise videorecording using the open-source software OBS studio and the resulting video analyzed using DeepLabCut 3D(25). Classical assessment of the open field test . Distance traveled, velocity, time moving and time immobile were used as descriptors of locomotor activity. The ratio between the time spent in the center of the arena versus the borders (thigmotaxis) and the ratio between the time spent in the center of the arena versus the corners (safety preference) were used as a proxy for emotional state, as previously described. Stimulus-evoked behavioral tracking in 3D for rodents (SEB3R). SEB3R is a MATLAB pipeline for digitalized behavioral screening that was previously described in detail(8). The code and extensive instructions for SEB3R pipeline are available at the link: https://github.com/gchelini87/SEB3R. Briefly, 6 key-points along the mouse spine were tracked in DeepLabCut. Using the relative distances between key-points, SEB3R identifies a subset of statistically meaningful body-postures, for each animal. Then, similar postures are allocated within discrete behavioral categories that are shared across all (or most) subjects, and labeled with a progressive enumeration (BMs). BMs labels are finally assigned to each frame of each video, matching the label of the original postures with the final label of the BMs. Statistical Analysis The statistical analysis was computed using R-studio package in R software ( Integrated Development for R. RStudio, PBC, Boston, MA, USA . http://www.rstudio.com/) or JMPpro17 software (SAS Institute Inc., Cary, NC, 2023). Graphical visualization was complemented by GraphPad Prism (GraphPad Software, Boston, MA, USA. www.graphpad.com) and Matlab 2021 (The MathWorks Inc., Natick, MA, USA. https://it.mathworks.com/products/matlab.html). No compelling effect of the variable sex was detected by the statistical analysis (supplementary Fig. 1), in agreement with previous findings (26); thus, the variable sex was excluded from the analysis. A two-way ANOVA was applied to test the main effect of Genotype (Gen), Early Life Experience (ELE) and their interactions for traditional descriptors and individual BMs. Tuckey’s correction for multiple comparisons was applied to assess group differences. Multivariate analysis of variance (MANOVA) was used to evaluate the impact of independent variables onto the global behavioral pattern. Multiple hypothesis testing indicators were considered to evaluate the MANOVA model significance (Wilks’ λ, Pillai’s Trace, Hotelling-Lawley, Roy’s Max Root; Fig. 2). The suitability of the data for multivariate testing was confirmed by inspecting the QQ plots of residuals. K-mean clustering. To stratify the study cohort into distinct behavioral profiles we ran an iterative k-clustering algorithm testing from 2 to n (= 38) possible combination of data repartition. The cubic clustering criterion was used to determine the best ft to the data and the classification reliability was confirmed using a two-way ANOVA of the distance from centroids. Network Analysis : Network structure and analysis of centrality was performed using the igraph library package (https://github.com/igraph/igraphdata) contained in R-studio software. For each behavioral profile (P1 and P2), we calculated the pairwise Spearman correlation ( ρ ) of BMs expression between each subject, obtaining a squared correlation matrix (Fig. 4A). The raw correlation values were then transformed into binary entries (0 or 1) using a value of ρ = 0.8 as a cutoff; every correlation above 0.8 was converted to 1, while the others were set to 0, generating an adjacency matrix. Next, undirected networks were designed starting from the adjacency matrices. Each node within the networks represents a single mouse and the connecting line the degree of similarity between connected subjects. Then, the following centrality measures were extracted for each subject: barycenter centrality, betweenness, clustering coefficient and mean path distance. Using the “proper centralities” function from the R-studio pipeline Central Informative Nodes in Network Analysis (CINNA), we selected the most informative centrality indicators identified after dimensional reduction (https://cran.r-project.org/web/packages/CINNA/index.html). RESULTS Neither Cntnap2 haploinsufficiency nor LBN yield significant impact on locomotor activity or generalized anxiety tested in the open field. First, we evaluated the four experimental groups ( Cntnap2 +/+ and Cntnap2 +/− LBN with their respective SR controls) using the traditional metrics of a classical open field test (Fig. 1 A), looking into the mice preferential choice for the borders and corners (safe option) against the center (anxiogenic option) of the arena. Our data show that none of the group display significant changes in the preference for the corners (Fig. 1 B) or the borders of the apparatus (Fig. 1 C), indicating a lack of obvious hallmarks of anxiety. Moreover, we found no differences among the four groups in the locomotor activity (Fig. 1 D-F). These findings suggest that neither Cntnap2 haploinsufficiency nor LBN exerts a significant effect on directly quantifiable level of stress and locomotion quantified in the open field. Both Cntnap2 heterozygous mutation and ELA alter naturalistic behavior in mice To capture the nuances of behavioral expression we analyzed the same experimental sessions using SEB3R (Fig. 2 A, B). With this tool, the mouse body-language was deconstructed in a total of nine distinct BMs (Fig. 2 C). First, we focused on quantifying difference in individual BMs (supplementary Fig. 2). Our analysis only detected a limited effect of the variable early-life experience (ELE, p = 0.014) and a nearly significant effect of the gene x environment interaction (ELE X GEN, p = 0.051) on the rearing-associated BM2. The difference was mostly driven by significant reduction of BM2 in Cntnap2 +/− LBN with respect to SR genotype-matched controls (p = 0.009). Furthermore, a main effect of the variable genotype (Gen) was found for BM5 (p = 0.001), driven by significant reduction in both Cntnap2 +/− groups (SR and LBN) compared to Cntnap2 +/+ SR (p = 0.02 and p = 0.009 respectively, supplementary Fig. 2E). More importantly, using a MANOVA, we identified a significant effect of both genotype (p = 0.004) and LBN (p = 0.049) onto the global BMs expression, but no contribution of the gene x environment interaction (p = 0.43) (Fig. 2 D, E). These findings indicate that Cntnap2 haploinsufficiency plays a significant role in shaping the global behavioral structure in rodents, rather than isolated instances, and suggest that ELA may differentially impact subjects carrying this mutation. Data-driven Stratification of the study cohort revealed a nearly-exclusive behavioral profile in Cntnap2 +/− LBN Then we asked whether the discrepancies in BM expression may determine alternative neurobehavioral functioning in our study cohort. To test this hypothesis, we used BMs expression as classifiers for unsupervised k-mean clustering and categorized our study cohort into two distinct behavioral profiles, P1 ( n = 20) and P2 ( n = 18) (Fig. 3 A, B). The successful distinction between the two profiles was confirmed using a two-way ANOVA on the Euclidean distances from centroids (Fig. 3 C). Then we asked what was the prevalence of the two behavioral profiles within the four experimental groups. Strikingly, we found that profile P2 was vastly over-represented in the Cntnap2 +/− LBN group (84.62% of the total), while P1 was similarly shared among other conditions (87.5% Cntnap2 +/+ SR/ 66.67 Cntnap2 +/+ LBN / 62.5 Cntnap2 +/− SR). Significant differences in the frequency distribution were confirmed by χ 2 test (Fig. 3 D, p = .004). Then, we looked at discrepancies in BMs expression to elucidate the nature of behavioral hallmarks characterizing the separation of the two profiles. We found that P2 displayed significantly reduced rearing-associated BMs (BM7, p = 1.07e-05. BM8, p = .0004. BM2, p = .0002) as well as BM4 ( p = .005), compared to P1. Conversely, immobility-associated BM3 was significantly reduced in P1 compared to P2 ( p = .007) (Fig. 3 E, F). These data point to a loss of “proactive” behaviors (BM2,4,7,8) in P2, replaced by the stationary BM3. Finally, we assessed whether the discrepancies emerging from the analysis of the body language were also reflected in differential expression of classical indicators of stress, anxiety, and locomotor activity. We applied an identical clustering strategy, replacing BMs with traditional parameters of the open field (shown in Fig. 1 ). Once again, two predominant behavioral profiles emerged (P T 1 and P T 2; Fig. 3 G), but none of the two showed any privileged association with neither of the experimental groups ( χ 2 p = 0.61 , Fig. 3 H ) , nor overlap with the previous classification. These data show that SEB3R analysis captures discrepancies in the spontaneous behavior where classical OF fails to do so, indicating that the combined effect of genetic and environmental vulnerability operates by shaping alternative behavioral programs, rather than linearly increasing or decreasing the levels of stress and anxiety. DISCUSSION The use of novel computational tools provides the opportunity of integrating traditional behavioral tests with advanced methods for detection and analysis of spontaneous behavior in rodents ( 7 , 8 , 12 , 25 , 29 – 32 ). Our previous work shows that using the mouse body elongation to extract subtle differences in spine curvature allows the identification of behavioral readouts (BMs) that work as a proxy for emotional response to a stimulus ( 8 ). Using the same methodology, here we asked whether BMs (identified using SEB3R) were able to describe alterations of the spontaneous behavior in mice affected by manipulations related to multiple psychiatric conditions. We show that both Cntnap2 haploinsufficiency and early-life adverse experience determine atypical expression of naturalistic behavior during freely-moving exploration. Previous work from our laboratory suggests that, while showing limited anomalies in specific behavioral domains, the interplay between Cntnap2 haploinsufficiency and ELA exert a powerful effect when looking at the wholesome behavioral expression ( 21 ). The present study corroborates this interpretation by picking up only marginal changes in specific BM, but evidencing significant abnormalities in the global behavioral structure (Fig. 2 ). The same study further suggested that the combination of Cntnap2 haploinsufficiency and ELA may not directly impact the quantifiable outcome of behavioral tests, but rather qualitatively produces alternative behavioral strategies. For instance, no impairment in social preference was found using a 3-chamber test. However, by classifying the study cohort based on their social-behavioral pattern, we found that Cntnap2 +/− LBN mice were more likely to express extreme level of hyper- or hypo- sociability( 21 ). Here, we report a similar scenario. While no direct changes emerged in locomotor activity or anxiety measurements, the expression of naturalistic behavior was significantly affected by both Cntnap2 haploinsufficiency and ELA, giving rise to a peculiar behavioral profile in mice affected by both manipulations (Fig. 3 ). We also report that, among the two behavioral profiles that emerged, the one more strongly associated with Cntnap 2 +/− LBN group (P2) was characterized by a dramatic increase of inter-individual similarity (Fig. 4 ). Reduced cognitive-behavioral flexibility is a common trait in psychiatric disorders, including in conditions associated with Cntnap2 haploinsufficiency, such as schizophrenia and autisms( 33 , 34 ). Cntnap 2 +/− LBN mice were previously shown to fail in adjusting their risk-taking strategies in the elevated plus maze, putatively indicating deficient flexibility in emotion regulation ( 21 ). Based on these findings, we speculate that genetic and environmental vulnerabilities act by limiting the complexity of behavioral options in favor of stereotyped programs (Fig. 5 ). This evidence also suggests that stratifying the population based on the spontaneous behavioral expression may implicate similarities in the underlying etiology, corroborating novel interpretative models in biological psychiatric research and speaking to the need of a dimensional classification of mental health conditions ( 27 , 28 ). One aspect that our work does not address is the putative impact of sex-differences. A body of evidence shows that paradigms mimicking early-life adversities in rodents result in major sex-dependent effects in WT animals ( 23 , 35 – 38 ). Using linear modeling, our work was unable to identify a meaningful effect of sex within our data (supplementary Fig. 1), possibly due to the low number of subjects underpowering statistical analysis. Nevertheless, this data support previous findings showing that sex has no significant impact in several behavioral domains tested in the Cntnap2 +/− LBN model ( 21 ). Taken together, these findings may indicate that Cntnap2 mutation overrides the role of sex in determining behavioral trajectories after exposure to an adverse juvenile experience. Another limitation of this study consists in the fact that the unbiased stratification of the dataset into two subpopulations does not entirely match the original experimental groups. Moreover, neither of the two profiles perfectly corresponds to any of the experimental conditions, showing a maximum overlap of approximately 85%. This may be a consequence of the limitations of measuring tools, flaws in the available statistical models, or undetected noise within the data. However, we think that this percentage still provides enough evidence of the fundamental behavioral differences driven by genetic and environment vulnerabilities accounted in the study. Furthermore, we cannot exclude that the classification of Cntnap2 +/− LBN mice within P1 may have some biological explanation. A minority of vulnerable subjects that express a behavioral profile similar to WT animals could be explained as statistically infrequent resiliency. As known, the adverse life experience resulting from scarcity of resources is mediated by maternal behavior( 22 – 24 ). Thus, it is reasonable to speculate that, despite the adverse environment, pups who received sufficient level of maternal care may escape the negative long-lasting effect of the mutation. On the other hand, mice raised in standard condition may not receive enough maternal care as the dam unevenly splits her attention across the whole litter, mimicking the effect of LBN and accounting for the ~ 30% of subject in P2 that do not share the double-hit vulnerability. A third point of discussion is that, while our method excels at eliminating subjective bias by digitalizing ethological observation, the interpretability of BMs, such as the different expressions of rearing behavior (BM2-7-8), would benefit from further contextualization. Future work could consider combining SEB3R high-density analysis with classical tests targeting specific domains (e.g., fear, anxiety, memory) to obtain a more comprehensive and nuanced understanding of mice behavioral profiles. A similar interpretation can be obtained here by retrospectively relying on the results of previously published data ( 21 ), with the obvious limitation of data coming from different study cohort. Recently, the growing field of computational neuroethology is paving the way to a renewed understanding of how the brain generates behavior, in health and disease ( 11 ). The use of high-dimensional tools to deconstruct and classify rodents naturalistic behavior can provide information about the animal individuality and the emergence of redundant behavioral profiles across individuals; in addition, it allows to glance at the innate expression of affective state, and unveil the effect of experimental manipulations onto the mouse spontaneous behavior ( 6 , 7 , 9 – 12 , 29 , 30 , 39 ). This study aligns with this field by showing that both genetic and environmental vulnerabilities to psychiatric disorders alter the composition of behavior in mice. We conclude by stressing the importance of an ethological approach to enrich our current understanding of mental health. The long lasting bet of ethology is to use naturalistic behavior to understand brain function ( 11 , 40 – 42 ). Our work and others show that both experimental or biological variations impact dramatically on the composition of naturalistic behavior ( 12 , 29 , 30 ), possibly exposing new hypothesis on how the brain generates and supports alternative neurobehavioral programs underlying psychiatric disorders. Declarations Acknowledgments. We thank all the administrative and technical staff of CIMeC for support. A special thank goes to Mrs. Michela Maffei, animal caretaker of the CIMeC animal facility, for her endless care and support provided in managing the animal colony used in the study and Dr. Tommaso Pecchia for helping with the technical implementations of some of the behavioral apparatuses. Funding . GC effort was covered by the ‘CARITRO postdoctoral fellowship’, funded by Fondazione Cassa di Risparmio di Trento e Rovereto. AS acknowledges funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8 – Project Age-It: “Ageing Well in an Ageing Society” [DM 1557 11.10.2022]. This resource was co-financed by the Next Generation EU. Author Contributions. Conceptualization: GC, AS, YB, NB Methodology: GC Investigation: GC, VCA, VCI Visualization: GC, VCA Supervision: GC Writing—original draft: VCA, GC Writing—review & editing: GC, AS, YB, NB Conflict of interest: authors declare no conflict of interest Data and materials availability: The code to use the MATLAB pipeline SEB3R is freely available at: https://github.com/gchelini87/SEB3R. The data used in the study are entirely shown in the manuscript, and will be made available for reproducibility purposes upon request, after the completion of the publication process. References Caspi A, Moffitt TE (2006): Gene–environment interactions in psychiatry: joining forces with neuroscience. Nat Rev Neurosci 7: 583–590. 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Science 185: 20–27. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryFigures.pdf Supplementary Material Contains: Supplementary figure 1: Graphical depiction of the linear modeling used to weight the effect of the variable early-life experience, genotype, and sex. Supplementary figure 2: Moderate variation in single BMs expression observed across experimental groups Supplementary figure 3: Additional centrality measure showing marked differences in the network homogeneity Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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open field (OF) test. \u003cstrong\u003eD-E) \u003c/strong\u003eSimilarly, no effect of environmental and genetic manipulations was found on locomotor activity.\u003c/p\u003e\n\u003cp\u003eEach dot represents an individual subject. Data are expressed as mean +/-\u003csup\u003e \u003c/sup\u003eSEM. Statistical analysis was performed by two-way ANOVA (B, C). \u003cstrong\u003ens:\u003c/strong\u003e not significant.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7417825/v1/87542f29d90e78202ef2b786.png"},{"id":93430888,"identity":"0c5c77ae-3c82-401c-9c2d-93dce3cd75da","added_by":"auto","created_at":"2025-10-13 18:02:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228127,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7417825/v1/2c451cdbc5e62d6bc4af9ef5.png"},{"id":93430889,"identity":"58ab6daf-cbf1-4c78-936c-f9367d69e41d","added_by":"auto","created_at":"2025-10-13 18:02:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnsupervised clustering using SEB3R output evidence a distinctive behavioral profile in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCntnap2\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003e+/-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mice raised with LBN.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Graphical depiction of the cubic clustering criterion (CCC) for unsupervised \u003cem\u003ek-mean\u003c/em\u003e cluster selection using SEB3R output, suggesting the presence of two distinct behavioral profiles [P1 and P2]. \u003cstrong\u003eB)\u003c/strong\u003e Scatter plot showing the separation of clusters identified using SEB3R output. \u003cstrong\u003eC)\u003c/strong\u003e Betweenness and Withiness of clusters confirms the efficacy of \u003cem\u003ek-mean\u003c/em\u003e in separating distinct behavioral profiles (2-way ANOVA, P1-P1 vs P1-P2 p=0.005; P1-P1 vs P2-P1 p=0.012; P2-P1 vs P2-P2 p=0.009; P1-P2 vs P2-P2 p=0.003). \u003cstrong\u003eD) \u003c/strong\u003eBehavioral profiles are differentially represented among \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/-\u003c/sup\u003eLBN with respect to any other group (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p= 0.004**). \u003c/em\u003e\u003cstrong\u003eE)\u003c/strong\u003e Raster plot depicting the density of each BM for each subject. BMs are arranged (left to right) according to their effect size in driving the differences between behavioral profiles (graphically represented in the red gradient map below the plot). Note the drastic shift in BM density between P1 and P2, beginning at BM3. \u003cstrong\u003eF) \u003c/strong\u003ePolar plot displaying the differences in BMs expression of the two behavioral profiles; BM2,4,7,8 are significantly reduced in P2 compared to P1. BM3 is increased in P2 respect to P1. \u003cstrong\u003eG) \u003c/strong\u003eGraphical depiction of the CCC method for unsupervised \u003cem\u003ek-mean\u003c/em\u003e cluster selection using traditional behavioral descriptors suggests the presence of two distinct behavioral profiles [P\u003csup\u003eT\u003c/sup\u003e1 and P\u003csup\u003eT\u003c/sup\u003e2. \u003cstrong\u003eH) \u003c/strong\u003eBehavioral profiles identified using traditional metrics (P\u003csup\u003eT\u003c/sup\u003e) are equally distributed across experimental groups (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p= 0.61)\u003c/em\u003e. *p\u0026lt;0,05 / **p\u0026lt;0,001 / ***p\u0026lt;0,0001.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7417825/v1/1b92ab57e52b73d6d78354db.png"},{"id":93430926,"identity":"566c3093-137a-4cb6-969c-30e56b92efef","added_by":"auto","created_at":"2025-10-13 18:02:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe artificial group P2 exhibits a remarkable intrinsic homogeneity compared to P1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor both groups, the correlation matrix of BM expression \u003cstrong\u003e(A)\u003c/strong\u003ewas transformed into a network\u003cstrong\u003e (B) \u003c/strong\u003ewhere each node is a subject and the connecting lines are the pairwise degree of similarity. Then, the centrality measures of the network \u003cstrong\u003e(C)\u003c/strong\u003e were used as a measure of the group homogeneity. \u003cstrong\u003e(D)\u003c/strong\u003e Network barycenter centrality is significantly higher in P2 (Mann-Whitney test p\u0026gt;0.0001). ***p\u0026lt;0,0001.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7417825/v1/d204069f2bfdef61411f04d8.png"},{"id":93430892,"identity":"14b8ba0d-1de7-4a65-a8c8-9901cc5c684e","added_by":"auto","created_at":"2025-10-13 18:02:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe combination of psychiatric vulnerabilities constrains behavioral complexity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results suggest that the convergence of multiple developmental hits limits the complexity of behavioral trajectories, constraining the individuals into stereotyped programs.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7417825/v1/c85b845e26ba298f5ae68630.png"},{"id":98434999,"identity":"b561ab14-b61d-4191-a7b7-7b8e2f3a2918","added_by":"auto","created_at":"2025-12-17 16:52:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7417825/v1/6c75778a-0bfa-48e9-9fbc-57b1dbc19be7.pdf"},{"id":93431581,"identity":"9fa63913-911d-49a8-8506-c62acd694d7c","added_by":"auto","created_at":"2025-10-13 18:10:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":451374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Material Contains:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary figure 1:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGraphical depiction of the linear modeling used to weight the effect of the variable early-life experience, genotype, and sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary figure 2:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModerate variation in single BMs expression observed across experimental groups\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary figure 3:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional centrality measure showing marked differences in the network homogeneity\u003c/p\u003e","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7417825/v1/74f767d5cd579314e4ced5e2.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Early-life adversity shapes anomalous spontaneous behavior in mice heterozygous for Cntnap2","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGenetic factors and adversities during early-postnatal life (ELA) are universally recognized as major contributors to psychiatric vulnerability(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The use of animal models is an ideal way to stratify the relative contribution of genetic and environmental vulnerabilities that shape core behavioral hallmarks of psychiatric disorders(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Currently, the majority of validated empirical tools to identify psychiatric-like traits in rodents consist in challenging na\u0026iuml;ve mice with forced-choice tasks, oscillating between safe options and anxiogenic alternatives(\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Alternatively, the use of bodily indicators (i.e. body language) as a proxy for the animal affective state is a traditional approach, primarily used by ethologists to obtain an ecological interpretation of the animal affective state (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In recent years, novel computational tools were developed to isolate and quantify spontaneous behavioral expression in mice (\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These tools can help recognize and quantify emotion expression within the naturalistic behavioral flow, providing valuable insights about the impact of experimental manipulations on spontaneous behavior(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). SEB3R (stimulus-evoked behavioral tracking in 3D for rodents) is a MATLAB pipeline that was recently developed to this purpose (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The method uses the key-point detection obtained in DeepLabCut to perform a two-step cluster analysis on the body elongation, and achieving sub-second fragmentation of the naturalistic behavior in freely moving mice (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Empirical evidence show that SEB3R successfully identifies discrete behavioral modules (BMs) associated with either positive (explorative rearing) or negative (freezing and fleeing) emotions, when mice are challenged with and invasive, but not painful, whisker stimulation(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Furthermore, the somatic expression of fear, quantified by SEB3R, was shown to strongly correlate with the expression of immediate early gene ARC in the basolateral amygdala, suggesting that behavioral modules (BMs) recognized with this tool can be directly mapped within the brain emotional network.\u003c/p\u003e\u003cp\u003eIn this work we applied SEB3R behavioral parcellation to a mouse model with both genetic and environmental vulnerability to multiple psychiatric disorders(\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To do so, we focused on mice with heterozygous mutation of the gene coding for the Contactin-associated protein-like 2 (CNTNAP2). Haploinsufficiency of CNTNAP2, in humans, is linked to multiple psychiatric diagnosis (schizophrenia, bipolar disorder, autism spectrum, major depressive disorder), but also detectable in neurotypical individuals(\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Previous work showed that mice carrying this mutation (\u003cem\u003eCntnapt2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e mice) are virtually indistinguishable from wild-types (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, when challenged with an environmental stressor, \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e mice present phenotypic hallmarks relevant to CNTNAP2 haploinsufficiency-related conditions. Following prenatal maternal immune activation, \u003cem\u003eCntnapt2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e mice display significantly reduced social behavior and increased acoustic startle, signatures of an autistic-like phenotype(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Conversely, we recently showed that when raised in a condition of scarcity-adversity, these mice engage in excessive risk-taking behavior, potentially indicating a loss in self-preservation boundaries similar to bipolar patients during a manic phase(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Furthermore, using a dimensional approach encompassing data from several behavioral tests, we found a powerful impact of the gene-environment interaction in globally altering behavioral outcomes(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This evidence suggests that the interplay of \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency and ELA may shape an alternative neurobehavioral functioning in rodents. To test this hypothesis, we replicated this model by raising \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e mice with limited bedding and nesting (LBN), a widely used paradigm to induce early-life stress in rodents(\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Litters composed of both \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e and \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/\u0026minus;\u003c/sup\u003e bred in LBN (from this point onward defined as \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eLBN\u003c/em\u003e or \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eLBN\u003c/em\u003e, respectively) were compared with parallel litters of standard-reared \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e and \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/\u0026minus;\u003c/sup\u003e (defined as \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003eSR or \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003eSR, respectively) and evaluated using SEB3R during freely-moving exploration of an open field. We observed that, while having limited effect on independent behavioral components (BMs), both \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency and LBN play a role in altering the overall composition of spontaneous behavior. Then, we used unsupervised clustering to stratify the study cohort, discovering that \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003eLBN mice exhibit an alternative - and nearly exclusive - behavioral structure compared to the other groups. Moreover, we found that this atypical behavioral expression was characterized by a remarkable inter-individual homogeneity, suggesting that the combined effect of \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency and early-life stress constrains behavioral strategies into highly stereotyped programs. These findings suggest that \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency alters the expression of naturalistic behavior in mice, and contributes to shape an alternative neurobehavioral functioning when paired with an early-life adverse experience.\u003c/p\u003e"},{"header":"METHODS AND MATERIALS","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eAnimals and housing\u003c/h2\u003e\n \u003cp\u003eAll mice were generated from our inbred \u003cem\u003eCntnap2\u003c/em\u003e colony with C57BL/6 background. Animals were housed in a 12h light/dark cycle with unrestricted access to food and water. A total of 38 age-matched adults (6 months old) mice (weight 25–35 g) were used in the study (8 \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003eSR (3 males, 5 females), 9 \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003eLBN (3 males, 6 females), 8 \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/−\u003c/sup\u003eSR (4 males, 4 females) and 13 \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e+/−\u003c/sup\u003eLBN (10 males, 3 females), obtained from 3 litters raised in SR and 4 litters raised in LBN). All efforts were made to minimize suffering of the animals. All experimental procedures were performed in accordance with Italian and European directives (DL 26/2014, EU 63/2010) and were reviewed and approved by the University of Trento animal care committee and Italian Ministry of Health (# 935/2021-PR).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBreeding strategy\u003c/em\u003e. To minimize the effect of confounding factors on the variables of interest (i.e. early-life stress and genotype), we adopted a standardize breeding strategy. Age-matched (postnatal age P = 150–160 days) WT females were crossed with heterozygous males, eliminating the effect of maternal age and genotype on parental care. The first litter was discarded as to exclude the consequences of first experience in maternal care. Only second-born litters were included in the study.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLimited bedding and nesting\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe LBN paradigm was adapted from Walker et al.(21, 22). At postnatal day 4 (P4), the dam with the entire litter was moved to an experimental cage. The cage featured an aluminum mesh placed at 2.5 cm distance from the base and nestled material reduced to ¼ with respect to a standard cage. The mother with the full litter was returned to a standard breeding cage once the pups reached P11. Control litters were moved from standard cages to standard cages at the same time-points, as to exclude the effect of experimental manipulation as a confounding factor. Both LBN mice and controls were weaned at postnatal day 21/22, housed in couples with one of their same-sex littermates in standard cages, with a single nestled square provided as an enrichment.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eBehavioral Testing\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eOpen Field.\u003c/em\u003e Each mouse was acclimated with the experimental room for 20 minutes. After habituation, each mouse was placed in the center of a large, squared arena (40 x 40 x 40 cm) and let free to navigate the open space for a total of 20 minutes. The experimental sessions were video recorded from above, using a camera interfaced with the Ethovision software (Noldus, Wageningen, the Netherlands) for the extraction of traditional behavioral descriptors. Two synchronized cameras in stereo-configuration were used for sidewise videorecording using the open-source software OBS studio and the resulting video analyzed using DeepLabCut 3D(25).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClassical assessment of the open field test\u003c/em\u003e. Distance traveled, velocity, time moving and time immobile were used as descriptors of locomotor activity. The ratio between the time spent in the center of the arena versus the borders (thigmotaxis) and the ratio between the time spent in the center of the arena versus the corners (safety preference) were used as a proxy for emotional state, as previously described.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStimulus-evoked behavioral tracking in 3D for rodents (SEB3R).\u003c/em\u003e SEB3R is a MATLAB pipeline for digitalized behavioral screening that was previously described in detail(8). The code and extensive instructions for SEB3R pipeline are available at the link: https://github.com/gchelini87/SEB3R. Briefly, 6 key-points along the mouse spine were tracked in DeepLabCut. Using the relative distances between key-points, SEB3R identifies a subset of statistically meaningful body-postures, for each animal. Then, similar postures are allocated within discrete behavioral categories that are shared across all (or most) subjects, and labeled with a progressive enumeration (BMs). BMs labels are finally assigned to each frame of each video, matching the label of the original postures with the final label of the BMs.\u003c/p\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe statistical analysis was computed using R-studio package in R software (\u003cem\u003eIntegrated Development for R. RStudio, PBC, Boston, MA, USA\u003c/em\u003e. http://www.rstudio.com/) or JMPpro17 software (SAS Institute Inc., Cary, NC, 2023). Graphical visualization was complemented by GraphPad Prism (GraphPad Software, Boston, MA, USA. www.graphpad.com) and Matlab 2021 (The MathWorks Inc., Natick, MA, USA. https://it.mathworks.com/products/matlab.html). No compelling effect of the variable sex was detected by the statistical analysis (supplementary Fig. 1), in agreement with previous findings (26); thus, the variable sex was excluded from the analysis. A two-way ANOVA was applied to test the main effect of \u003cem\u003eGenotype\u003c/em\u003e (Gen), Early Life Experience (ELE) and their interactions for traditional descriptors and individual BMs. Tuckey’s correction for multiple comparisons was applied to assess group differences. Multivariate analysis of variance (MANOVA) was used to evaluate the impact of independent variables onto the global behavioral pattern. Multiple hypothesis testing indicators were considered to evaluate the MANOVA model significance (Wilks’ λ, Pillai’s Trace, Hotelling-Lawley, Roy’s Max Root; Fig.\u0026nbsp;2). The suitability of the data for multivariate testing was confirmed by inspecting the QQ plots of residuals.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eK-mean clustering.\u003c/em\u003e To stratify the study cohort into distinct behavioral profiles we ran an iterative \u003cem\u003ek-clustering\u003c/em\u003e algorithm testing from 2 to n (= 38) possible combination of data repartition. The cubic clustering criterion was used to determine the best ft to the data and the classification reliability was confirmed using a two-way ANOVA of the distance from centroids.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNetwork Analysis\u003c/em\u003e: Network structure and analysis of centrality was performed using the igraph library package (https://github.com/igraph/igraphdata) contained in R-studio software. For each behavioral profile (P1 and P2), we calculated the pairwise Spearman correlation (\u003cem\u003eρ\u003c/em\u003e) of BMs expression between each subject, obtaining a squared correlation matrix (Fig.\u0026nbsp;4A). The raw correlation values were then transformed into binary entries (0 or 1) using a value of \u003cem\u003eρ = 0.8\u003c/em\u003e as a cutoff; every correlation above 0.8 was converted to 1, while the others were set to 0, generating an adjacency matrix. Next, undirected networks were designed starting from the adjacency matrices. Each node within the networks represents a single mouse and the connecting line the degree of similarity between connected subjects. Then, the following centrality measures were extracted for each subject: barycenter centrality, betweenness, clustering coefficient and mean path distance. Using the “proper centralities” function from the R-studio pipeline Central Informative Nodes in Network Analysis (CINNA), we selected the most informative centrality indicators identified after dimensional reduction (https://cran.r-project.org/web/packages/CINNA/index.html).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eNeither\u003c/b\u003e \u003cb\u003eCntnap2\u003c/b\u003e \u003cb\u003ehaploinsufficiency nor LBN yield significant impact on locomotor activity or generalized anxiety tested in the open field.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirst, we evaluated the four experimental groups (\u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e LBN with their respective SR controls) using the traditional metrics of a classical open field test (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), looking into the mice preferential choice for the borders and corners (safe option) against the center (anxiogenic option) of the arena. Our data show that none of the group display significant changes in the preference for the corners (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) or the borders of the apparatus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), indicating a lack of obvious hallmarks of anxiety. Moreover, we found no differences among the four groups in the locomotor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-F). These findings suggest that neither \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency nor LBN exerts a significant effect on directly quantifiable level of stress and locomotion quantified in the open field.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e \u003cb\u003eCntnap2\u003c/b\u003e \u003cb\u003eheterozygous mutation and ELA alter naturalistic behavior in mice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo capture the nuances of behavioral expression we analyzed the same experimental sessions using SEB3R (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). With this tool, the mouse body-language was deconstructed in a total of nine distinct BMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). First, we focused on quantifying difference in individual BMs (supplementary Fig.\u0026nbsp;2). Our analysis only detected a limited effect of the variable early-life experience (ELE, p\u0026thinsp;=\u0026thinsp;0.014) and a nearly significant effect of the gene x environment interaction (ELE\u003csup\u003eX\u003c/sup\u003eGEN, p\u0026thinsp;=\u0026thinsp;0.051) on the rearing-associated BM2. The difference was mostly driven by significant reduction of BM2 in \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003eLBN with respect to SR genotype-matched controls (p\u0026thinsp;=\u0026thinsp;0.009). Furthermore, a main effect of the variable genotype (Gen) was found for BM5 (p\u0026thinsp;=\u0026thinsp;0.001), driven by significant reduction in both \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e groups (SR and LBN) compared to \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003eSR (p\u0026thinsp;=\u0026thinsp;0.02 and p\u0026thinsp;=\u0026thinsp;0.009 respectively, supplementary Fig.\u0026nbsp;2E). More importantly, using a MANOVA, we identified a significant effect of both genotype (p\u0026thinsp;=\u0026thinsp;0.004) and LBN (p\u0026thinsp;=\u0026thinsp;0.049) onto the global BMs expression, but no contribution of the gene x environment interaction (p\u0026thinsp;=\u0026thinsp;0.43) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E). These findings indicate that \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency plays a significant role in shaping the global behavioral structure in rodents, rather than isolated instances, and suggest that ELA may differentially impact subjects carrying this mutation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData-driven Stratification of the study cohort revealed a nearly-exclusive behavioral profile in\u003c/b\u003e \u003cb\u003eCntnap2\u003c/b\u003e\u003csup\u003e\u003cb\u003e+/\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eLBN\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThen we asked whether the discrepancies in BM expression may determine alternative neurobehavioral functioning in our study cohort. To test this hypothesis, we used BMs expression as classifiers for unsupervised \u003cem\u003ek-mean\u003c/em\u003e clustering and categorized our study cohort into two distinct behavioral profiles, P1 (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;20) and P2 (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;18) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). The successful distinction between the two profiles was confirmed using a two-way ANOVA on the Euclidean distances from centroids (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Then we asked what was the prevalence of the two behavioral profiles within the four experimental groups. Strikingly, we found that profile P2 was vastly over-represented in the \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003eLBN group (84.62% of the total), while P1 was similarly shared among other conditions (87.5% \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003eSR/ 66.67 \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003eLBN / 62.5 \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003eSR). Significant differences in the frequency distribution were confirmed by \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, p\u0026thinsp;=\u0026thinsp;.004).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThen, we looked at discrepancies in BMs expression to elucidate the nature of behavioral hallmarks characterizing the separation of the two profiles. We found that P2 displayed significantly reduced rearing-associated BMs (BM7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.07e-05. BM8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0004. BM2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.0002) as well as BM4 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005), compared to P1. Conversely, immobility-associated BM3 was significantly reduced in P1 compared to P2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F). These data point to a loss of \u0026ldquo;proactive\u0026rdquo; behaviors (BM2,4,7,8) in P2, replaced by the stationary BM3.\u003c/p\u003e\u003cp\u003eFinally, we assessed whether the discrepancies emerging from the analysis of the body language were also reflected in differential expression of classical indicators of stress, anxiety, and locomotor activity. We applied an identical clustering strategy, replacing BMs with traditional parameters of the open field (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Once again, two predominant behavioral profiles emerged (P\u003csup\u003eT\u003c/sup\u003e1 and P\u003csup\u003eT\u003c/sup\u003e2; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eG), but none of the two showed any privileged association with neither of the experimental groups (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.61\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eH\u003cem\u003e)\u003c/em\u003e, nor overlap with the previous classification.\u003c/p\u003e\u003cp\u003eThese data show that SEB3R analysis captures discrepancies in the spontaneous behavior where classical OF fails to do so, indicating that the combined effect of genetic and environmental vulnerability operates by shaping alternative behavioral programs, rather than linearly increasing or decreasing the levels of stress and anxiety.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe use of novel computational tools provides the opportunity of integrating traditional behavioral tests with advanced methods for detection and analysis of spontaneous behavior in rodents (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Our previous work shows that using the mouse body elongation to extract subtle differences in spine curvature allows the identification of behavioral readouts (BMs) that work as a proxy for emotional response to a stimulus (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Using the same methodology, here we asked whether BMs (identified using SEB3R) were able to describe alterations of the spontaneous behavior in mice affected by manipulations related to multiple psychiatric conditions. We show that both \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency and early-life adverse experience determine atypical expression of naturalistic behavior during freely-moving exploration. Previous work from our laboratory suggests that, while showing limited anomalies in specific behavioral domains, the interplay between \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency and ELA exert a powerful effect when looking at the wholesome behavioral expression (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The present study corroborates this interpretation by picking up only marginal changes in specific BM, but evidencing significant abnormalities in the global behavioral structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe same study further suggested that the combination of \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency and ELA may not directly impact the quantifiable outcome of behavioral tests, but rather qualitatively produces alternative behavioral strategies. For instance, no impairment in social preference was found using a 3-chamber test. However, by classifying the study cohort based on their social-behavioral pattern, we found that \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eLBN\u003c/em\u003e mice were more likely to express extreme level of hyper- or hypo- sociability(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Here, we report a similar scenario. While no direct changes emerged in locomotor activity or anxiety measurements, the expression of naturalistic behavior was significantly affected by both \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency and ELA, giving rise to a peculiar behavioral profile in mice affected by both manipulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe also report that, among the two behavioral profiles that emerged, the one more strongly associated with \u003cem\u003eCntnap\u003c/em\u003e2\u003csup\u003e+/\u0026minus;\u003c/sup\u003eLBN group (P2) was characterized by a dramatic increase of inter-individual similarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Reduced cognitive-behavioral flexibility is a common trait in psychiatric disorders, including in conditions associated with \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency, such as schizophrenia and autisms(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). \u003cem\u003eCntnap\u003c/em\u003e2\u003csup\u003e+/\u0026minus;\u003c/sup\u003eLBN mice were previously shown to fail in adjusting their risk-taking strategies in the elevated plus maze, putatively indicating deficient flexibility in emotion regulation (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Based on these findings, we speculate that genetic and environmental vulnerabilities act by limiting the complexity of behavioral options in favor of stereotyped programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This evidence also suggests that stratifying the population based on the spontaneous behavioral expression may implicate similarities in the underlying etiology, corroborating novel interpretative models in biological psychiatric research and speaking to the need of a dimensional classification of mental health conditions (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOne aspect that our work does not address is the putative impact of sex-differences. A body of evidence shows that paradigms mimicking early-life adversities in rodents result in major sex-dependent effects in WT animals (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Using linear modeling, our work was unable to identify a meaningful effect of sex within our data (supplementary Fig.\u0026nbsp;1), possibly due to the low number of subjects underpowering statistical analysis. Nevertheless, this data support previous findings showing that sex has no significant impact in several behavioral domains tested in the \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eLBN\u003c/em\u003e model (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Taken together, these findings may indicate that \u003cem\u003eCntnap2\u003c/em\u003e mutation overrides the role of sex in determining behavioral trajectories after exposure to an adverse juvenile experience.\u003c/p\u003e\u003cp\u003eAnother limitation of this study consists in the fact that the unbiased stratification of the dataset into two subpopulations does not entirely match the original experimental groups. Moreover, neither of the two profiles perfectly corresponds to any of the experimental conditions, showing a maximum overlap of approximately 85%. This may be a consequence of the limitations of measuring tools, flaws in the available statistical models, or undetected noise within the data. However, we think that this percentage still provides enough evidence of the fundamental behavioral differences driven by genetic and environment vulnerabilities accounted in the study. Furthermore, we cannot exclude that the classification of \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/\u0026minus;\u003c/em\u003e\u003c/sup\u003eLBN mice within P1 may have some biological explanation. A minority of vulnerable subjects that express a behavioral profile similar to WT animals could be explained as statistically infrequent resiliency. As known, the adverse life experience resulting from scarcity of resources is mediated by maternal behavior(\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Thus, it is reasonable to speculate that, despite the adverse environment, pups who received sufficient level of maternal care may escape the negative long-lasting effect of the mutation. On the other hand, mice raised in standard condition may not receive enough maternal care as the dam unevenly splits her attention across the whole litter, mimicking the effect of LBN and accounting for the ~\u0026thinsp;30% of subject in P2 that do not share the double-hit vulnerability.\u003c/p\u003e\u003cp\u003eA third point of discussion is that, while our method excels at eliminating subjective bias by digitalizing ethological observation, the interpretability of BMs, such as the different expressions of rearing behavior (BM2-7-8), would benefit from further contextualization. Future work could consider combining SEB3R high-density analysis with classical tests targeting specific domains (e.g., fear, anxiety, memory) to obtain a more comprehensive and nuanced understanding of mice behavioral profiles. A similar interpretation can be obtained here by retrospectively relying on the results of previously published data (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), with the obvious limitation of data coming from different study cohort.\u003c/p\u003e\u003cp\u003eRecently, the growing field of computational neuroethology is paving the way to a renewed understanding of how the brain generates behavior, in health and disease (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The use of high-dimensional tools to deconstruct and classify rodents naturalistic behavior can provide information about the animal individuality and the emergence of redundant behavioral profiles across individuals; in addition, it allows to glance at the innate expression of affective state, and unveil the effect of experimental manipulations onto the mouse spontaneous behavior (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This study aligns with this field by showing that both genetic and environmental vulnerabilities to psychiatric disorders alter the composition of behavior in mice. We conclude by stressing the importance of an ethological approach to enrich our current understanding of mental health. The long lasting bet of ethology is to use naturalistic behavior to understand brain function (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Our work and others show that both experimental or biological variations impact dramatically on the composition of naturalistic behavior (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), possibly exposing new hypothesis on how the brain generates and supports alternative neurobehavioral programs underlying psychiatric disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u0026nbsp;\u003c/strong\u003eWe thank all the administrative and technical staff of CIMeC for support. A special thank goes to Mrs. Michela Maffei, animal caretaker of the CIMeC animal facility, for her endless care and support provided in managing the animal colony used in the study and Dr. Tommaso Pecchia for helping with the technical implementations of some of the behavioral apparatuses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e.\u0026nbsp;GC effort was covered by the \u0026lsquo;CARITRO postdoctoral fellowship\u0026rsquo;, funded by Fondazione Cassa di Risparmio di Trento e Rovereto. AS acknowledges funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8 \u0026ndash; Project Age-It: \u0026ldquo;Ageing Well in an Ageing Society\u0026rdquo; [DM 1557 11.10.2022]. This resource was co-financed by the Next Generation EU.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: GC, AS, YB, NB\u003c/p\u003e\n\u003cp\u003eMethodology: GC\u003c/p\u003e\n\u003cp\u003eInvestigation: GC, VCA, VCI\u003c/p\u003e\n\u003cp\u003eVisualization: GC, VCA\u003c/p\u003e\n\u003cp\u003eSupervision: GC\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;original draft: VCA, GC\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;review \u0026amp; editing: GC, AS, YB, NB\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eauthors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability:\u0026nbsp;\u003c/strong\u003eThe code to use the MATLAB pipeline SEB3R is freely available at: https://github.com/gchelini87/SEB3R. The data used in the study are entirely shown in the manuscript, and will be made available for reproducibility purposes upon request, after the completion of the publication process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCaspi A, Moffitt TE (2006): Gene\u0026ndash;environment interactions in psychiatry: joining forces with neuroscience. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e 7: 583\u0026ndash;590.\u003c/li\u003e\n\u003cli\u003eKraeuter A-K, Guest PC, Sarnyai Z (2019): The Open Field Test for Measuring Locomotor Activity and Anxiety-Like Behavior. In: Guest PC, editor. \u003cem\u003ePre-Clinical Models\u003c/em\u003e, vol. 1916. 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Behavior\u003c/em\u003e 159: 105478.\u003c/li\u003e\n\u003cli\u003eDennis EJ, El Hady A, Michaiel A, Clemens A, Tervo DRG, Voigts J, Datta SR (2021): Systems Neuroscience of Natural Behaviors in Rodents. \u003cem\u003eJ Neurosci\u003c/em\u003e 41: 911\u0026ndash;919.\u003c/li\u003e\n\u003cli\u003eTinbergen N (1954): The Study of Instinct. \u003cem\u003eBritish Journal for the Philosophy of Science\u003c/em\u003e 5: 72\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eSimmons P, Young D (2010): \u003cem\u003eNerve Cells and Animal Behaviour, 3rd Ed.\u003c/em\u003e New York, NY, US: Cambridge University Press, pp viii, 284.\u003c/li\u003e\n\u003cli\u003eTinbergen N (1974): Ethology and stress diseases. \u003cem\u003eScience\u003c/em\u003e 185: 20\u0026ndash;27.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7417825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7417825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe influence of early-life experiences is widely acknowledged as a crafting tool that sculpts complex behavioral patterns and well-being of living organisms. The use of preclinical models can provide invaluable insight into how a negative environmental push interplays with genetic make-up in shaping psychiatric vulnerability. Recently, the development of automated tools to classify spontaneous behavior in mice has opened the possibility to investigate the impact of experimental manipulation onto the composition of naturalistic behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: In this work, we applied a tool for digitalized ethological screening to identify spontaneous hallmarks of altered neurobehavioral functioning in a dual-hit mouse model. To do so, mice carrying heterozygous deletion of the gene coding for contactin-associated protein-like 2 (\u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/-\u003c/em\u003e\u003c/sup\u003e)\u003csup\u003e \u003c/sup\u003eand their wild-type (WT) littermates\u003csup\u003e \u003c/sup\u003ewere raised with limited bedding and nesting (LBN) and compared to both WT and \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/-\u003c/em\u003e\u003c/sup\u003e mice raised in standard conditions, mapping their spontaneous behavior during freely-moving exploration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our data show no differences in locomotor activity or anxiety indicators across the four groups. By contrast, automated segmentation of the body-language revealed a significant impact of both genotype and early-life experience in shaping the spontaneous behavioral program. Thus, using unsupervised clustering, we unveiled two alternative behavioral profiles within our dataset. We found that one of the identified profiles largely overlapped with \u003cem\u003eCntnap2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/-\u003c/em\u003e\u003c/sup\u003e mice raised with LBN, while the other was equally shared among controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e We conclude that the coincidence of early-life adversity and \u003cem\u003eCntnap2\u003c/em\u003e haploinsufficiency drastically alters behavioral structure in rodents.\u003c/p\u003e","manuscriptTitle":"Early-life adversity shapes anomalous spontaneous behavior in mice heterozygous for Cntnap2","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 18:01:57","doi":"10.21203/rs.3.rs-7417825/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"286de6ac-221e-4d48-87a0-ceefe47d9d28","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55602562,"name":"Biological sciences/Neuroscience"},{"id":55602563,"name":"Health sciences/Diseases/Psychiatric disorders"}],"tags":[],"updatedAt":"2026-03-18T14:21:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 18:01:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7417825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7417825","identity":"rs-7417825","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}