The aim of this study was to examine the development of the associations between elementary school students’ mindsets and the attentional neural processing of positive and negative feedback in math. For this, we analyzed data collected twice from 100 Finnish elementary school students. During the autumn semesters of their 3rd and 4th grade, the participants’ general intelligence mindset and math ability mindset were measured with a questionnaire, and their brain responses elicited by performance-relevant feedback were recorded during an arithmetic task. We found that students’ fixed mindsets about general intelligence and math ability were associated with greater attention allocated to positive feedback as indicated by a larger P300. These associations were driven by the effects of mindsets on attention allocation to positive feedback in grade 4. Additionally, 4th graders’ more fixed general intelligence mindset was marginally associated with greater attention allocated to negative feedback. In addition, the effects of both mindsets on attention allocation to feedback were marginally stronger when the children were older. The present results, although marginal in the case of negative feedback and mainly driven by effects in grade 4, are possibly a reflection of the greater self-relevance of feedback stimuli for students with a more fixed mindset. It is also possible that these findings reflect the fact that, in evaluative situations, mindset could influence stimulus processing in general. The marginal increase in the effects of mindsets as children mature may reflect the development of coherent mindset meaning systems during elementary school years. Encountering feedback—information about one’s performance or understanding—is part of students’ daily experience in school. The effect of feedback on student outcomes, though, is far from homogeneous  and seemMindsets refer to core beliefs that people hold about the nature and malleability of certain human abilities and attributes, such as intelligence or personality . These bIt has been suggested that, during elementary school years, students’ mindset meaning systems develop towards greater coherence, as indicated by an increase in associations between mindsets and theoretically relevant constructs, such as goal-orientation . MoreoveIn line with the theory of mindsets as meaning systems that perform an organizing function regarding the interpretation of experiences , researcOne of the most frequently examined ERP components elicited by feedback is P300. P300 is a positive-going waveform reflecting the processing of attention-demanding stimulus in general; thus, it is not limited to feedback-stimulus processing . Pe is a positive-going ERP that is elicited on error commission in reaction time tasks peaking, in general, between 200 and 400 ms after an erroneous button press. Pe indicates awareness of committing an error and is assumed to reflect the motivational significance of that error . Pe and In addition to the aforementioned studies on mindsets and error monitoring, to our knowledge there are two studies that have explored associations between mindsets and the attentional neural processing of feedback . Error mThe current study is part of the project Copernicus—Changing Mindsets about Learning: Connecting Psychological, Educational and Neuroscientific Evidence. This project uses a multidisciplinary approach to investigate the mindsets of elementary school students  and theiRQ1. How are children’s mindsets  associated with their attention allocation to performance-relevant positive and negative feedback in an arithmetic task? We hypothesized that:(H1a) mindsets are associated with the attentional neural processing of negative, but not positive, performance-relevant feedback ,(H1b) the associations between math ability mindset and feedback-related P300 during an arithmetic task are stronger than the associations between general intelligence mindset and feedback-related P300 during an arithmetic task .RQ2. How do associations between children’s mindsets  and attention allocation to performance-relevant feedback develop? We hypothesized that:(H2) the association between mindset and feedback-related P300 strengthens as children age .age at the beginning of the study = 8.94 years, SDage = 0.42 years, Minage = 8 years, Maxage = 11 years). Reasons for missing data included withdrawing from the study, absence from school on the data collection days, or a technical issue during the EEG-recording.A total of 104 participants from two Finnish public elementary schools in the Helsinki metropolitan area were initially recruited for this study. One of the schools was situated in a low socioeconomic status (SES) area and the other in a medium SES area . Two outGeneral Intelligence Mindset (GIM), four Entity Theory statements from the Implicit Theories of Intelligence Scale (Math Ability Mindset (MAM), the same four Entity Theory statements from the Implicit Theories of Intelligence Scale used for assessing GIM were adapted to be math-specific. Participants indicated the degree to which they agreed with each of the statements by marking one of six circles, which varied in size  and were mapped to a 6-point scale . Four average scores were used; GIM in grade 3 and 4 and MAM in grade 3 and 4. Higher scores indicate a greater endorsement of a growth mindset. The internal reliabilities of the mindset instruments from both years were good (Cronbach’s α ranged from 0.76 for GIM in grade 3 to 0.86 for MAM in grade 4).1For assessing participants’ ce Scale  were useP300 responses elicited by feedback were recorded during the completion of an age-appropriate two-alternative choice arithmetic task  or a more difficult version  of the actual task in order to ensure that the calculations in the task were sufficiently, but not overly, challenging for the participants.The actual task consisted of two blocks  with a total of 93 trials. The 93 trials  were presented in random order to each participant. Between the blocks, the children were permitted a 5- to 10-min refreshment pause. In order to avoid possible motor response confounds in the aggregated data, the positions of the two buttons on the response box were alternated every second experimental day . The difThe children’s participation in the study was voluntary. Written parental consent for the study was obtained. The children and their parents were informed about the research procedures and their right to withdraw their participation at any moment of the study. The University of Helsinki Ethical Review Board reviewed and approved the research project for the study.The students completed a questionnaire including the mindset scales as part of a longer questionnaire during their regular school activities in the autumn semester of their 3rd and 4th grade. The questionnaires were administered by a researcher who explained the questionnaire procedure with examples of statements with response options. For data collection in grade 3, the researcher read each question and response option aloud as the participants completed the electronic questionnaire on laptops or tablets provided by the school. In grade 4, the participants completed the electronic questionnaire individually during a school lesson. The procedure lasted approximately 40 min.The psychophysiological recordings were performed by the experimenter(s) in a separate room at the school premises during regular school hours. Before the recordings, the children were briefed about how the experiment would proceed and reminded of their right to withdraw their participation at any moment. After the task and the recording, the children were compensated for their participation with sweets and stickers. The entire procedure lasted for 60-75 min per participant, with the duration of the EEG-recording being approximately 20 min per participant.Continuous electroencephalographic activity was recorded with portable equipment (BrainVision QuickAmp amplifier) using 32 Ag-AgCl active electrodes . Electrolyte gel  was used at each electrode. The data were recorded with a BrainVision Recorder at a 250 Hz sampling rate. The Recording reference was Fpz or FCz, depending on the size of the cap used. Afterwards, the EEG data were processed with MATLAB R2019a software  with the EEGLAB 19.0 toolbox. The signal was band-pass filtered with cutoffs of 0.1 and 30 Hz and segmented into epochs beginning 200 ms before each button press and continuing for 750 ms following each button press. In addition to visual inspection, artifactual epochs were rejected by detecting abnormal trends and abnormal spectra, and eye movement artefacts were removed using independent component analysis . SubsequFeedback-locked P300 amplitudes were calculated relative to a -150 to -50 ms baseline window, which was also approximately 150–50 ms prior the response (button press), as the time difference between the button press and the feedback stimulus onset was only a few milliseconds. In order to obtain feedback-related ERPs regarding participants’ authentic decisions about the accuracy of the math calculations and in order to exclude trials involving accidental button presses, all trials where the reaction time was less than 300 ms after the appearance of the pre-response-stimulus (the answer appearing in place of the missing number of the equation on the screen) were omitted from the analyses . In addilme4 was used, and p-values were computed with the lmerTest package , the effectsize package was used. The continuous variables were standardized for modeling, and thus standardized coefficients are reported.The descriptive statistics of mindsets, behavioral variables, and P300 were calculated and the distribution of data inspected visually. None of the variables severely violated the normality assumption. Therefore, Pearson correlation was used to inspect the relationships between the study variables. The research questions were answered by using linear mixed modelling (LMM). R package  package , using SBefore conducting the main analysis, a model without any fixed predictors was first specified to estimate how much variance in P300 amplitude could be attributed to individual differences between participants within classrooms and between different classes . As this model demonstrated a low variance of P300 to be explained by the nestedness of participants in classrooms, subsequent analyses were conducted without random effects of class. Second, the effects of background variables  and the behavioral measure of task accuracy on P300 were inspected using LMM  and grade 4 .A range of mindset endorsements was observed, with participants’ GIM and MAM scores falling between fixed and growth extremes . Positivn = 38) being 57.4% (SD = 8.2%) and the accuracy for the more difficult version (n = 62) being 62.4% (SD = 11.4%). In the 4th grade, the average task accuracy was 64.8% (SD = 9.5%), with the average accuracy in the group of students completing the easier version of the task (n = 34) being 64.0% (SD = 8.4%) and the accuracy for the more difficult version (n = 63) being 65.3% (SD = 10.0%). Regarding associations with mindset, a higher MAM was associated with greater task accuracy in grade 4 , while this association was not significant in grade 3  of the trials , with the average accuracy in the group of students completing the easier version of the task . More specifically, a stronger growth mindset was associated with a smaller P300 in the case of positive feedback, while based on 95% confidence intervals a similar trend for the effect of GIM on negative-feedback P300 in grade 4 was observed , in grade 4, P300 was larger in case of negative feedback when compared to positive feedback  and at Pz . Positive-feedback P300 was larger at Fz and Cz when compared to Pz  resulted in significant main effects of feedback and electrode site and significant interaction effects between feedback type and grade level as well as between feedback type and electrode site . Post-hot ≤ -3.06, p ≤ 0.007, for all). More specifically, higher accuracy was associated with a smaller positive-feedback P300 , with higher accuracy associated with a greater negative-feedback P300 only in grade 4 , but, in the case of positive feedback, the effect of accuracy emerged at Pz , we found that students with a more fixed mindset about general intelligence as well as math ability allocated greater attention to positive feedback, as indicated by a larger P300 elicited by positive feedback. These associations were driven by the effects of mindsets on positive-feedback P300 in grade 4. Regarding the participants’ general intelligence mindset, a similar trend was observed in the case of negative feedback when the children were older. The direction of these associations between mindsets and feedback-related P300 found in the present study are in line with the single earlier study on mindsets and feedback processing analyzing positive- and negative-feedback P300 separately . MangelsAnother possible explanation concerns more general stimulus processing in stressful situations. Namely, a recent study conducted by rd grade cross-sectional data from the same sample analyzed in the present study. However, they demonstrated a marginal unique effect of a math ability mindset on the difference between P300 elicited by positive and negative feedback when controlling for the effect of general intelligence mindset. Nevertheless, the researchers focused on examining the relative effects of a general intelligence and math ability mindset and did not control for the potentially confounding effects of task accuracy. The results of the present study indicate that when it comes to attention allocated to feedback in math, it is the individual’s general growth or fixed mindset tendency, rather than the domain-specific aspects of their math ability mindset, that is associated with their brain responses . It was also in grade 4 that participants’ math ability mindset was positively associated with their accuracy in the arithmetic task. Nevertheless, no stronger effect of a math ability mindset on attention allocation, when compared to a general intelligence mindset, emerged in the present study (H1b). The study by esponses .Regarding both general intelligence mindset and math ability mindset, we observed a marginal increase in the association between mindsets and attention allocation to feedback as the students aged (H2). Researchers have suggested that, during the first years of elementary school, students’ mindset meaning systems are still developing towards a greater coherence . More spWhen analyzing the development of the amplitude of P300 responses, we found no overall differences between 3rd and 4th grade students. Nonetheless, a large-scale study by When investigating the neural processing of positive and negative feedback, we found that the P300 responses elicited by such feedback did not differ among students in grade 3, but, in grade 4, greater P300 amplitudes were elicited by negative feedback than by positive feedback. Earlier findings on the effects of outcome valence on P300 have been mixed  and corrective feedback (information on the correct response) were presented simultaneously. In future, these two feedback types should be presented sequentially, enabling the inspection of the effects of mindsets on attention allocation to both performance-relevant as well as corrective feedback.Fourth, we did not assess participants’ subjective experiences during the experiment. Future studies could ask participants to report, for example, their level of enjoyment or anxiety during the task, as inspecting associations between these self-reports, mindsets, and P300 would enable more informative inferences to be made regarding the associations between mindsets and attention allocation.Finally, students’ mindsets were assessed using self-report measures. However, research has shown that the use of self-report questionnaires among young children might be problematic, as such children may experience difficulties understanding the questions and engaging in the self-reflection necessary to answer the questions . NeverthAs to the practical implications of our results, the present study is rather limited, as it only explored the associations between mindsets and performance-relevant feedback. The present findings and earlier research demonstrating the role of mindsets regarding perceptions of feedback  suggest In sum, the findings of the present study demonstrated the association between elementary school students’ fixed mindsets about general intelligence and math ability and greater attention allocated to positive feedback in an arithmetic task, with a similar trend identified for the effect of a general intelligence mindset in the case of negative feedback among older children. These results, although tentative and with small effect sizes, suggest that the perspective should be widened to explore the effects of students’ mindsets on their experiences more generally rather than focusing primarily on situations involving setbacks.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the University of Helsinki Ethical Review Board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.IP, MH, TK, EK, SL, and KT planned the research design. IP collected the data and wrote the manuscript. IP, TL, and TT pre-processed the data and conducted the analyses. All authors contributed to the article and approved the submitted version.


Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people’s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics.This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics.This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated.P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays.Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner. Suicide is the 17th leading cause of death worldwide . DespiteThere is a growing body of literature using social media data and advanced techniques  to identify suicide risks and improve suicide prevention ,9. HowevThis study aimed to corroborate whether suicide risks identified on social media align with actual suicidal behaviors by tracking suicide risks detected from 62 million tweets posted in Japan from January 2013 to December 2022 and compare such suicide risks with actual suicide statistics in terms of their timing and location. We took Twitter as the data source to retrieve text-based contents given that Twitter is one of the most popular social media platforms with the largest number of users in Japan, where people express their ideas, opinions, and perceptions . TwitterWe retrieved tweets via Twitter academic application programming interface (API) based on the following criteria: (1) the tweets were posted in Japan in the Japanese language; (2) the time of posting was between January 1, 2013, and December 31, 2022; (3) the tweets contained an array of keywords that include suicide risk information  retrieved using suicide-related keywords would inevitably contain tweets that might not obtain actual suicide ideation and thoughts. For example, a tweet of “suicide is not good” contains “suicide” but is not a suicide-risk tweet. This manual scanning procedure took 3 months from October 2022 to January 2023 by 2 research assistants who are native in the Japanese language. Through training workshops and cross annotation and calibration, these 2 research assistants largely achieved the same standard to judge suicide-risk tweets. Second, we randomly selected 30,000 tweets from the original keyword-filtered data set  to forge an initial training data set. The research assistants manually scanned these 30,000 tweets and annotated suicide-related tweets as positive samples if they indicated actual suicide risk or as negative samples if not. As a result, 7.52%  were identified as “positive”—tweets with actual suicide risks—whereas the rest were “negative,” non–suicide-risk tweets. This ratio of actual suicide-risk tweets over the total seems to be low but is reasonable and consistent with existing studies [k tweets . After tWe compared between Twitter-identified suicide risks and actual suicide statistics in both the temporal and spatial dimensions via a Pearson correlation as the baseline, a smooth-window correlation, and a time lagged cross correlation. In the temporal dimension, we created a range of measures for Twitter-identified suicide risks to compare with the actual suicide records measured as the proportion of suicide deaths over the total population. Such measures included (1) the proportion of positive suicide-risk tweets over total tweets (including and excluding retweets separately); (2) the same proportion as (1) but with 2020 data excluded given that the outbreak of COVID-19 in 2020 may have brought a systematic interruption to the human society as a whole and influenced the behavior of suicide to some degree ; (3) theIn the spatial dimension, we commenced with mapping geotweets based on their latitude and longitude or the name of a city  to generate a point data layer containing the locations of all geotweets posted in the 10-year period. We then overlayed this point data layer with the polygon layer containing the boundaries of 1848 Japanese cities and 47 prefectures to generate the total number of geotweets within each city and prefecture. These data were then used to assess its correlation with the number of actual suicide records at the city and prefecture levels. We visualized the spatial pattern of geotweets and actual suicide records using the quintile classification in ArcGIS Pro 2.8 (Esri), with cities in the top quintile  being identified as high suicide-risk areas. Herein, we compared the raw counts of geotweets and actual suicide records rather than the proportional measures as we did previously because (1) the total number of population and total tweets posted by city and by month is not available and (2) visualizing spatial patterns by the quintile classification is a relative approach by comparing values within one data set to identify hot spots (high-risk areas), and thus, it is less sensitive to the measure of proportions using the total population and total tweets.This study does not involve human subjects at the individual level; thus, it is not subject to ethnical clearance. Tweets data retrieved via the Twitter API were deidentified, and we removed users’ ID and any identifiable information during the data-scanning process. All analyses were conducted at the aggregated population level based on sampling counts. All data saved in the project repository can be only accessed by research members.The temporal comparison between Twitter-identified suicide risks and actual suicide records by year A shows tWe further examined the Twitter-identified suicide risks at a finer level , serving as the baseline for comparison with other scenarios. By excluding the year 2020 with the COVID-19 outbreak as a systematic interruption to human society, the correlation coefficient increased to 0.620 and 0.502 (P<.001). By advancing actual suicide records 1 month earlier, the correlation coefficient further increased to 0.652 and 0.521 (P<.001); however, such an increase was not observed for advancing actual suicide records 2 or 3 months earlier. This finding reflects the potential existence of a 1-month time lag between the time when suicide-risk tweets were posted and the time when suicides were committed. By smoothing the data with averaged values across 3, 5, and 7 months, we observed much-improved correlation coefficients  compared to “all years,” possibly due to the fact that data fluctuations across months are reduced by the smoothing method, revealing that the smoothed temporal trends of suicide risks identified by 2 data sets are highly correlated.The time-lag effect observed previously was tested statistically through the correlation analysis . “All yeP<.001) in 2016, followed by 2018  and 2017 ; meanwhile the correlation coefficient for the 10 years as a whole was 0.699 (P<.001), which is reasonably high, indicating that the cities where suicide-risk geotweets were concentrated are highly correlated with where actual suicides were committed. Moreover, the correlation coefficients between geotweets and actual suicide records at the prefecture level in each single year ranged from 0.725 in 2013 to 0.956 in 2016, and it was 0.945 for the 10 years as a whole ; by advancing actual suicide records 1 month earlier, the correlation coefficient further increases to 0.652—reflecting the 1-month time lag between Twitter-identified suicide risks and actual suicide records. They were also spatially correlated by city with correlation coefficients of 0.699 and 0.945 (P<.001) for the 10 years as a whole at the city and prefecture levels, respectively. In all, 196 cities overlapped in the risk identification by tweets and actual suicide records, accounting for 73.5% of the cities in the top quintile of suicide risks classified at the national level.Our study contributes to a 10-year investigation of suicide risks identified through social media in Japan, via a human-in-the-loop approach and an advanced deep learning model for natural language processing and tweet classification. The procedure of data collection (tweets over 10 years posted from January 2013 to December 2022), human scanning, data analysis, and modelling occurred from October 2022 to February 2023. We corroborated that the Twitter-identified suicide risks largely align with actual suicidal facts regarding their timing and location. More specifically, Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022  and geographic locations. Second, the involved suicide-related keywords might not be comprehensive due to the variance of suicide-related expressions on social media. We acknowledge that tweets with suicidal risks expressed in different expressions might be excluded from our study. However, we are confident that the keywords used in this study were able to capture the majority of suicide-related tweets. Third, the definition of positive samples, which refers to tweets that indicate a risk of suicide, was manually defined and, therefore, is subject to a certain level of ambiguity. Thus, the repetitive model training process  might propagate such ambiguity toward the final classification. Fourth, we acknowledge that tweets with suicidal risks might not reflect the actual suicidal intention of users. In this study, we aimed to align suicide risks detected on social media with actual suicidal behaviors in a statistical manner, whereas linking suicidal expressions with suicidal intentions is beyond the scope of our study.In conclusion, our study provides new and valuable insights into the temporal and spatial patterns of suicide risks identified through social media data in Japan and corroborates the alignment between social media–identified suicide risks and actual suicide behaviors through the 10-year evidence. Our findings have important public health implications for suicide prevention and intervention efforts and highlight the potential of advanced natural language processing techniques for suicide surveillance on social media. By using newly emerging techniques and data sources in conjunction with traditional surveillance systems, we can work toward a comprehensive and effective approach to suicide prevention in Japan and beyond, especially in countries without reliable suicide statistics.


Medical internet interventions such as asynchronous apps and synchronous digital live seminars can be effective behavior change interventions. The research question of this study was whether digital interventions based on the Health Action Process Approach can improve pregnant women’s safe communication and patient safety in obstetric care.This study aims to compare a digital live seminar with a web-based application intervention and a passive control group and to identify which social cognitive variables determine safe communication behavior and patient safety.In total, 657 pregnant women were recruited, and hereof, 367 expectant mothers from 2 German university hospitals participated in the pre-post study . All interventions targeted intention, planning, self-efficacy, and communication of personal preferences. The 2.5-hour midwife-assisted live seminar included exercises on empathy and clear communication. The fully automated web-based application consisted of 9 consecutive training lessons with the same content as that of the live seminar.P<.001; ηp2=0.043). Perceived patient safety improved more for pregnant women participating in the live seminar than for those participating in the web-based application group (P=.03 ηp2=0.015). A regression analysis revealed that social cognitive variables predicted safe communication behavior.Controlled for sociodemographic characteristics, repeated measures analyses of covariance revealed that pregnant women significantly improved their self-reported communication behavior in all groups. The improvement was more pronounced after the digital live seminar than after the web-based application (Overall, the web-based application intervention appeared to be less effective than the digital live training in terms of communication behavior. Application interventions addressing communication behaviors might require more face-to-face elements. Improving intention, coping planning, and coping self-efficacy appeared to be key drivers in developing safe communication behavior in pregnant women. Future research should include social learning aspects and focus on the practical application of medical internet interventions when aiming to improve pregnant women’s communication and patient safety in obstetrics.ClinicalTrials.gov NCT03855735; https://clinicaltrials.gov/ct2/show/NCT03855735 Medical internet interventions, such as asynchronous applications and synchronous digital live seminars, can be effective behavior change interventions . In healOne of the fields in which digital interventions could be especially useful is antenatal education and care . RespectSafe communication can be described as a multilateral process that involves sharing emotions, cognitions, and actions on a verbal and nonverbal level . AlthougThis is particularly important because poor and ineffective communication in health care settings is a contributing and leading factor for adverse events that are a threat to patient safety, according to the report of the Joint Commission . PatientIt is evident that effective communication is a prerequisite for safe care in obstetrics. Communication behavior can thus be seen as a crucial preventive health behavior . There iThe HAPA model assumes 2 distinct phases: first, in the motivational phase, an intention to act  is developed based on the individual’s outcome expectancies and risk perceptions. In the second phase, the volitional phase, this intention is brought into action through planning. During all the stages of behavior change, situational barriers and facilitators intercorrelate with this process . Self-efTo actually improve safe communication behavior instead of the intention to communicate safely and thus reduce potential preventable adverse events, digital interventions must be tailored to the social cognitive barriers and facilitators for pregnant women. Previous literature has demonstrated that interventions based on motivational and volitional theories, such as the HAPA, are effective in improving health-related behaviors such as safe communication ,30,31. TTaken together, pregnant women’s safe communication behavior in the context of obstetrics requires further examination, especially regarding digital antenatal communication interventions during the COVID-19 pandemic. Therefore, this study will evaluate 2 digital interventions that are hypothesized to improve perceived communication behavior and perceived patient safety within the sample of pregnant women and investigate behavior change determinants. The aim of this study was to compare the effectiveness of a web-based application intervention with a digital live seminar and a passive control group (CG).Thus, the hypotheses are as follows:Hypothesis 1: pregnant women who use the web-based application before giving birth will show greater improvement in the primary outcome of safe communication behavior and the secondary outcome of perceived patient safety than women from a passive CG. Their improvement was comparable with an intervention group that received a web intervention (web live seminar).Hypothesis 2: the HAPA model can explain the safe communication behavior of pregnant women after web-based application interventions; coping planning, coping self-efficacy, and intention are associated with safe communication behavior after digital interventions.Hypothesis 3: perceived patient safety is associated with safe communication behavior after web-based application use.Data collection took place within the TeamBaby Project, which aimed to investigate and improve the psychological mechanisms underlying safe communication behavior in obstetrics, specifically before and during birth. The TeamBaby Project is funded by the German Innovation Fund (project number 01VSF18023) of the Gemeinsamer Bundesausschuss (G-BA) and registered with ClinicalTrials.gov .All participants were pregnant women intending to give birth at 1 of 2 project-affiliated university hospitals providing the highest level of care in affiliated neonatal intensive care units. Within the 2 university hospitals, expecting mothers were approached by a project-affiliated study nurse and a research associate. Recruitment was facilitated by distributing flyers, posters, and registration forms at key locations  as well as through social media posts. In addition, gynecologists in private practice, midwives, counseling centers, and pharmacies were approached with additional material to distribute to their clients. Participants registered via email by filling in a registration form. During the registration process, participants were provided with an informed consent form offering a detailed description, including the outcomes of the study. Furthermore, as part of the informed consent, participants were informed of whether they were randomly allocated to the intervention or passive CG. In addition, participants were informed that no harm or unintended effects were expected as part of their participation. To ensure the privacy and confidentiality of the obtained data, each pregnant woman was asked to generate a unique pseudonymization code and subsequently received a baseline questionnaire (provided that informed consent was given) afterward. Further inclusion criteria were sufficient knowledge of German and age of maturity (>18 years). Expectant mothers created their own participant codes, using the following scheme: (1) the first 2 letters of the father’s surname, (2) the first 2 letters of the mother’s surname, and (3) the birthday of the expectant mother. In accordance with the data security approval obtained, the process of pseudonymizing the data allowed for no conclusions regarding personal data.Between June 2020 and August 2021, participants were randomized to either an intervention group that received a digital live seminar training (live seminar group [IG1]) or a passive CG. Women in the passive CG were fully informed about the study, including the possible intervention, before the randomization. They did not receive any additional intervention or educational material. The randomization for the live seminar was prepared and performed by project-affiliated team members (study nurses and research assistants) at the 2 hospitals. For this purpose, closed envelopes were prepared in a ratio of 3:2 for 77.6% (222/367) of pregnant women.Although a complete randomization was planned, 52 (36.1%) of 144 women had to be allocated to the CG owing to their expected due date. To ensure that the live seminar worked in a group setting, 12 (8.3%) of 144 women who provided postpartum survey data for the intervention group were not randomized but were assigned when the live intervention started between August 2021 and June 2022, and a third group of expectant mothers were recruited for the web-based application intervention (web-based application group [IG2]). Participants were informed of the study in writing and asked to provide informed consent before participation. As with the digital live trainings, participants were asked to create a unique pseudonymization code to ensure that all privacy and confidentiality regulations were met as part of the data collection and evaluation.Different recruitment periods were planned from the beginning to avoid overlapping recruitment efforts at the 2 hospitals. Nevertheless, the recruitment of pregnant women for the digital live seminar took place during COVID-19 pandemic restrictions, including access restrictions for both support persons and the study personnel. Participants answered the survey questions twice (before and after birth), with the abovementioned interventions conducted before giving birth. The participants did not receive any form of compensation for their participation in the study.A detailed description of the recruitment process and dropout from the interventions is shown in physician and midwife. Details of the training provided to the pregnant women and their partners have been described elsewhere [For the digital live seminar, content for communication training was developed by 2 organizations that consulted on patient safety and communication in collaboration with the research team. The content was delivered through a 2.5-hour web session facilitated by 2 communication trainers, including a lsewhere . The HAPlsewhere . To prepThe training used in the web sessions was adapted for the fully automated web-based application intervention. The web-based application was also based on the HAPA and behavior change techniques (BCTs) , aiming Content and adaptations for the German web-based application were developed with physicians from 2 university hospitals (n=4), psychologists (n=4), and the German Alliance for Patient Safety. The content from the web training was further iterated by project researchers (psychologists), obstetricians at clinics, and web application developers. The development process included a beta version of the web-based application tested by an affiliated health insurance company. The web-based application was accessible through all web browsers. It consisted of 10 consecutive lessons, from basic communication competencies to action plans. The details of the modules in the web-based application are provided in Tables S1 and S2 in The primary outcome of the study was pregnant women’s communication behavior, and the secondary outcome was perceived patient safety. As behavioral determinants, action planning and coping planning were assessed using self-reported questionnaires. Items stemmed from previously validated scales -36, whicCommunication behavior was assessed via 7 items from a self-constructed scale based on the communication competencies by Rider and Keefer , “DuringPerceived patient safety was measured as perceived patient safety risks with 9 items that were adapted to the pregnant women’s perspective from a self-constructed and previously validated scale , “Beforemuch lower compared with other patients) to 5 (much higher compared with other patients).Coping planning was measured with a single item based on previously validated items in other behavioral domains : “I was Coping self-efficacy was assessed via a self-constructed single item on the basis of previously validated items in other behavioral domains : “I was Intention was assessed via 2 self-constructed items on the basis of previously validated items in other behavioral domains , “I inteAge, marital status, highest level of education, and nationality were assessed in categorical data. Age , education , and marital status  were measured in 4 categories. Nationality (1: “German” or 2: “Other”) was measured dichotomously.All analyses were conducted using SPSS software . The authors were not blinded to the analysis. Regarding hypothesis 1, 2 repeated measures analyses of covariance were used to examine and compare changes in safe communication behavior and patient safety across the 3 groups  in a pre-post design. For the repeated measures analyses of covariance, age, nationality, relationship status, and education were recoded into binary variables, so they could be added as covariates. For age, 2 binary variables were used to compare younger patients with patients in the age range of 30 to 39 years and older patients with patients in the age range of 30 to 39 years. Nationality was recoded to compare German participants with pregnant women of a different nationality. Relationship status was recoded to compare pregnant women currently in a relationship with those currently single for different reasons (including separated or divorced). The educational level was recoded as “university degree” versus “other.” Finally, the group factor was added as 2 binary variables to compare IG2 with IG1 and the CG. To test the drivers of safe communication behavior (hypothesis 2) and the association of patient safety with communication behavior (hypothesis 3), regression paths based on the HAPA model were analyzed for all 3 groups . Table S3 in The Declaration of Helsinki was adequately addressed, and this study was approved by the Ethics Committee for Human Research of the University Hospital Ulm (number 114/19) and the Ethics Committee for Medical Research of the University Hospital Frankfurt (number 19-292). Approval for this study was obtained without any exemption.In total, 367  expectant women participated in the 2 survey waves, while providing matchable codes, and were thus included for data analysis. Descriptive statistics on age, relationship, and education level, as well as the nationality of expectant mothers included in the study, are shown in χ²4=4.2; P=.38, education level , marital status , or nationality . More detailed results are presented in Table S4 in χ23=49.7; P≤.001 and nationality with χ21=5.9; P=.02. No significant differences were found in marital status  and education level . The same picture emerged for IG1 for age , nationality , marital status , and education level . Finally, no significant differences were found in the CG for age , nationality , marital status , and education level . The results of all the mentioned groups are depicted in Table S5 in In addition to F1,336=3.322; P=.07; ηp2=0.010). This suggests that across groups, the mean level of communication behavior scores did not exhibit a significant trend over the measurement points . Between the CG and IG2, no significant time × group interaction effect emerged .There was a significant time × group interaction effect, meaning that the change in communication behavior across time differed between IG1 and IG2 . This suggests that, across groups, the mean level of patient safety scores did not exhibit a significant trend across the measurement occasions . There was no significant time × group interaction effect between CG and IG2 .There was a significant time × group interaction effect, meaning that the change in perceived patient safety across time did significantly differ between IG1 and IG2  interventions in addition to simply demonstrating their overall effectiveness . We inveNotably, a different picture emerged in the live seminar and in the CG, where coping self-efficacy was the main determinant of pregnant women’s safe communication behavior. Thus, the web-based application stands out in the sense that multiple HAPA constructs predicted safe communication behavior at T2. Not only does the belief that one can communicate safely in difficult situations (coping self-efficacy) seem to play a role but also does the transfer of knowledge regarding concrete plans for these situations (coping planning). This further illustrates the need to incorporate elements regarding the social aspect of learning as well as further chances to translate theoretical knowledge into practice within a natural setting . UltimatAnother topic of concern was to investigate whether and how participation in the web-based application related to perceived patient safety. We expected that recipients of the web-based application would improve more than a passive CG and to a similar degree as recipients of a live seminar intervention. This was not empirically supported. In addition, an association between perceived patient safety and safe communication behavior emerged only in the CG. It is possible that participants in the CG had worse birth experiences and thus perceived higher patient safety risks. As safe communication behavior is central to good birth experiences, their perceptions of birth might have acted as a confounding factor in both perceived risks and communication. IG2 and IG1 focused on safe communication behavior, which is, although important, only one aspect of perceived patient safety and might be overshadowed by more obvious medical aspects and behavioral variables in this specific context, such as the birth experience. In this case, the web-based application in its current form was not able to improve perceived patient safety.This study was the first approach to design and apply a digital training tool in the form of a web-based application to improve expectant mothers’ safe communication. This study has several limitations. First, the lack of available previously published evidence negatively affected the ability to design and tailor such a web-based application to expectant mothers’ needs and the accuracy of the hypothesized effectiveness. Future research in this area will benefit from the insights generated in this study. Consecutive research designs should permit more rigorous testing and a thorough development phase for the design and content according to the needs of participants before using a medical internet intervention. Similarly, optimizing the intervention effect and user experience could be achieved by incorporating face-to-face elements or the possibility of social exchange into the digital intervention design .From a methodological perspective, the group of web-based application users was smaller than the other 2 groups. Absolute sample size and potential distortions  might explain the difference between hypothesized and empirically observed group differences ,64. The In this study, mostly well-educated women participated, which probably had an effect on the results, and thus limited generalizability. The web-based application should also be tested and verified with other sociodemographic groups with lower levels of education and migration backgrounds ,65. ConsIn addition, it should be mentioned that the data collection in the live seminar between June 2020 and August 2021, the COVID-19 pandemic was associated with restrictions at the hospitals. For example, expectant fathers were partially not allowed to be present during the birth, and interpersonal contacts were limited to a minimum to prevent the spread of the pandemic. All these points may have had an impact on the communication within the hospitals, for example, due to higher vigilance of patients in the current situation or a lack of resources.Another limitation concerns the randomized group assignment, in which only a partly randomized allocation could be achieved. In addition, there was a comparably high dropout rate in IG2 that was potentially selective, which is typical for asynchronous web-based interventions ,67,68. IThe evaluated digital interventions had different effects on communication behavior and patient safety. The intervention that was developed and delivered as a web-based training application appeared to be not sufficient in changing communication behavior in pregnant women and perceived patient safety risks when compared with a passive CG. Hence, it seems reasonable to combine the web-based application with other face-to-face interventions to achieve better effectiveness. Changes and adaptations to the existing web-based applications should be examined more closely in the future. In addition, more precise analyses of communication behavior and the interrelation of social cognitive determinants are warranted. Future research should control for more potential confounding variables, such as socioeducational status and prior knowledge of pregnancy and profession. Qualitative methods can be applied to gain more precise insights into the existing web-based applications to adjust. Future web application developers and researchers should also consider the mode of delivery and create a “native app” to make the intervention more accessible.


Oscillations occurring simultaneously in a given area represent a physiological unit of brain states. They allow for temporal segmentation of spikes and support distinct behaviors. To establish how multiple oscillatory components co-vary simultaneously and influence neuronal firing during sleep and wakefulness in mice, we describe a multivariate analytical framework for constructing the state space of hippocampal oscillations. Examining the co-occurrence patterns of oscillations on the state space, across species, uncovered the presence of network constraints and distinct set of cross-frequency interactions during wakefulness compared to sleep. We demonstrated how the state space can be used as a canvas to map the neural firing and found that distinct neurons during navigation were tuned to different sets of simultaneously occurring oscillations during sleep. This multivariate analytical framework provides a window to move beyond classical bivariate pipelines for investigating oscillations and neuronal firing, thereby allowing to factor-in the complexity of oscillation–population interactions. Oscillations, generated by large neuronal ensembles, are a hallmark of the mammalian brain. They are well preserved during evolution  and havestratum radiatum (SR), stratum lacunosum moleculare (SLM), and stratum pyramidale (SP), respectively, are linked to network activity in entorhinal cortex, CA3 and CA1 region, respectively , recorded from the CA1 region of the dorsal hippocampus of freely moving mice, were used to construct the network state space during wakefulness and sleep. This method has allowed to create a compact representation of the state of multiple oscillatory processes, distinct in frequency and anatomical localization. We used this compact representation for studying various oscillations, their intrinsic organization, their temporal progression, and their simultaneous influence on distinct neuronal ensembles and behavior. In addition, the state space provided a window for observing the hippocampal population in the context of the network in which they are embedded. This allowed us to examine how cells fire as a function of the network state and how multiple oscillations simultaneously modulate cell firing. As a proof of concept, this approach has been applied to datasets recorded, in similar experimental conditions, from another animal species (rats), in Dr. Gyorgy Buzsaki’s lab at NYU  during four consecutive trials: (1) Sleep1, (2) Novel Arena-1 Exploration, (3) Novel Arena-2 Exploration, and (4) Sleep2, . Layer-rWe first examined the occupancy of the network state space during sleep and awake exploration by overlaying trial specific states  on the network state space . We obseate data . To asseate data , suggestate data  as well ate data . Similarate data  and obseate data . Hence, To characterize the network restrictions and visualize how different hippocampal rhythms vary simultaneously during sleep and awake trials, we overlaid the power of each oscillation on the network state space . While ctheta/delta power ratio for each state on the network state space . We overlaid the te space . States te space . Similarte space . The remte space  revealednumber of state visits/bin/second. Density on the state space computed during awake trials measures the normalized occurrence of corresponding behaviors during exploration whereas density computed during sleep trials measures the time spent by the network in various sleep states . Mirroring the smaller occupied state space region, the median density of awake trials was significantly higher than that detected during sleep (0.13 ± 0.009 versus 0.03 ± 0.0005). We next investigated the density across sleep trials (Sleep1 and Sleep2) by comparing the median density of REM and non-REM states between the two sleep trials , are coupled with each other during distinct states of sleep and wakefulness. Furthermore, projecting the correlation matrices on 2D plane provides a compact tool that allows to visualize the cross-frequency interactions among various hippocampal oscillations. Altogether, this approach reveals the complex nature of coupling dynamics occurring in hippocampus during distinct behavioral states.To investigate state-dependent coupling among various oscillatory processes, we computed pairwise correlation matrix using binned power in all 18 frequency bands (see ‘Methods’). Each correlation matrix represents how various bands are coupled during sleep and awake trials . We obseAfter characterizing the static properties of state space such as occupancy, power distribution, localization of sleep states, network state density, and coupling among oscillations, in the following analyses we addressed the dynamic properties of state space such as network flow, state transitions, and speed on state space.To investigate how learning during exploration affects the consequent sleep, we characterized state transition patterns during Sleep1 and Sleep2. By plotting outgoing trajectories on the network state space , we visuWe further examined which transitions on the state space are significantly altered across sleep trials. We computed AACP specifically for transition from REM/non-REM/intermediate sleep state to REM/non-REM/intermediate state. We found that transitions occurring from REM-to-REM sleep and non-REM-to-non-REM sleep (intra-state transitions) are more vulnerable to plasticity after exploration as compared to inter-state transitions  . These cCoverage speed determines how fast the network sweeps the area of the state space. To calculate the coverage speed, we binned the state space as shown in We next investigated the characteristics of stabilization during transition to REM. We wondered how the power in frequency bands is reorganized during the transition. We investigated this by plotting a spectrogram of median power for all 18 frequency bands during the entire sleep trial . This alIn the following section, we explored two applications of this analytical framework. We first used the network state space as a canvas (or context) to map the firing (activation signatures) of cells from CA1 pyramidal layer.signature space  mouse, an animal model of autism spectrum disorders (ASD). NLGs are postsynaptic adhesion molecules that bind to their presynaptic partners neurexins to functionally couple the postsynaptic densities with the transmitter release machinery, thus contributing to synapses organization and stabilization . Mice cadeficits . Here, Ldeficits .We first constructed the composite network state space of hippocampal oscillations by pooling network states from all trials and all animals . AssessiIn this study, we introduced an analytical framework for investigating the organization of hippocampal oscillations. This framework is established by constructing a state space of multiple network variables: in our case, the power of 18 hippocampal frequency bands from three layers of the CA1 region. While we used predefined frequency bands to filter oscillations from LFPs, alternatively, unsupervised methods see  can be eTraditional approaches usually consist of analyzing spectral features of individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). This multivariate analytical approach provides a unique window to assess the combinatorial effect of more than two oscillations on the state of the network, the neuronal population and the behavior. This provides a formal framework to uncover the potential combinations of neural signals underlying various cognitive functions during sleep and wakefulness. By constructing the state space of power in various frequency bands from distinct layers of a brain region, our analysis allows to characterize the network landscape and how various oscillatory processes co-vary as a function of the overall state of the network. Additionally, we demonstrated that the state space can be used as a canvas to map the activity of individual cells, thus allowing to study how various oscillations can simultaneously influence the firing of neuronal ensembles. Although the scope of this study was limited to demonstrate the state space approach for power of network oscillations, the general idea can be extended to create a state space with additional attributes of oscillations such as instantaneous frequency, phase, etc. The resulting state space may provide additional insights about the organization of network oscillations. For instance, characterization of the state space constructed with instantaneous phase of the oscillations may provide novel information on how cell spiking is phase-locked to multiple oscillations simultaneously and how those phase-locking properties vary with time and behavioral state of an animal.We first used the network state space to visualize the restricted nature of access to the state space during wakefulness compared to sleep. We then characterized that restriction by overlaying the power of individual oscillations on the state space. This helped us to visualize how oscillations are intrinsically organized and co-vary simultaneously on the state space during wakefulness and sleep. Compared to sleep, in our experimental settings, during awake trials, the delta power is low, and the network is more dominated by theta and gamma supporting the ongoing navigational needs . InteresWe next identified REM and non-REM states on the network state space. Alternatively, they can also be identified using oscillations from multiple brain areas . We obseFurther, we characterized the coupling of various frequency bands during awake and sleep trials and across distinct network states. We reported state-dependent coupling of distinct hippocampal oscillations, suggesting that their sources change their functional link during distinct states on state space. For instance, during REM sleep, we observed beta-frequency range oscillations coupled with slow gamma power and medium gamma coupled with fast gamma. We also observed delta decoupled from medium and fast gamma. These observations are in agreement with a general understanding of REM sleep as corresponding to a state of low coherence across the cortex and between the cortex and the hippocampus  while arWe next investigated the state transitions on the network state space. We quantified trajectories on state space and observed that state transition probabilities vary between two sleep trials and two awake trials. During exploration trials, the flow of the hippocampal network state is strongly affected by ongoing spatial navigation. The incoming and outgoing state space trajectories are in fact bounded by the details of the exploration activity statistics (such as speed). Changes in transition probabilities thus reflect alterations in behavioral sequences exhibited by the animal during exploration. During sleep trials, the state of the hippocampal network flows without any interference from environmental inputs. The incoming and outgoing trajectories bounds are instead characteristics of specific sleep states. Thus, changes in transition probabilities across sleep trials represent modifications in the probable paths on the network state space. These changes might be due to learning and/or experience acquired during awake exploration trials. The experience of running on a track or in an arena may change the state of the circuit in many ways: (1) by changing the neuromodulatory state due to arousal, physical exertion; (2) by changing brain temperature, which is known to modify brain potentials ; and (3)We then characterized the speed of coverage on the network state space and reported the stabilization of network during transitions to REM sleep. We also described how frequency bands are reorganized during the transition. We observed specific increase in power of medium and fast gamma, but not slow gamma, during the transition to REM. State-dependent cross-frequency coupling between theta and gamma was also reported during REM sleep in EEG recordings from mouse parietal cortex . These rFurther, we demonstrated two applications of this analytical method. Firstly, rather than using individual oscillations to study the firing properties of hippocampal neurons, we overlaid the firing of hippocampal neurons on state space and used those sleep signatures to distinguish cells in CA1. We reported a gradient in the sleep state space signatures of cells, differentiating cells with sparse activation in the arena, from more active ones. This suggests that the sparse firing cells in the hippocampus are modulated differently by network oscillations occurring simultaneously during sleep.Secondly, this method was applied to study the organization of hippocampal network state space in mice lacking NLG3, animal models of autism, which exhibit social deficits reminiscent of those found in autistic children . In lineA-mediated neurotransmission in both CA1 and CA2 hippocampal regions. This would cause an enhancement of network excitability with consequent excitatory/inhibitory unbalance, critical for controlling spike rate and information processing.Oscillatory activities, and in particular the gamma ones, depend on the synchronous firing of large neuronal ensembles of excitatory cells within and across brain regions, which are paced by GABAergic interneurons . TherefoLastly, we highlight some other potential applications of the network state space framework:(i) Assessment of inter-areal communication. The state space approach can be applied to study oscillations from multiple brain regions simultaneously and how one set of oscillations from one region influence the others (ii) Analysis of EEG datasets. Various oscillations can be extracted from EEG recorded from multiple brain areas and used to assess how they are organized during various behavioral tasks during wakefulness  or during sleep. Additionally, this analytical framework can be applied to study alterations in the organization of brain-wide network oscillations as signatures or biomarkers in various neurodevelopmental and neurodegenerative disorders. (iii) Network assessment of novel therapeutics. Newly developed drugs are required to be extensively tested for their harmful side effects. Using network state space method will allow assessing how multiple brain regions, and their oscillations are simultaneously affected under pharmacological treatment. Additionally, this information can be used to predict potential side effects and develop better therapeutics in drug development.e others . (ii) AnFour male C57BL/6J mice (Charles River) were used in this study, all implanted with a Hybrid Drive. All animals received the implant between 12 and 16 wk of age. After surgical implantation, mice were individually housed on a 12 hr light-dark cycle and tested during the light period. Water and food were available ad libitum.Experiments were performed on offspring male derived from heterozygous mating after 10 backcrossing with C57BL/6J. Results were analyzed blindly before genotyping. Control experiments were performed on wild‐type littermates and C57BL/6J (WT). Genotyping was carried out on tail biopsy DNA by PCR using a standard protocol. After surgical implantation, mice were individually housed on a 12 hr light-dark cycle and tested during the light period. Water and food were available ad libitum.Datasets of extracellular recordings from right dorsal hippocampus of three Long-Evans rats using silicon probes were used for validating the findings. Detailed information is published in https://github.com/wonkoderverstaendige/dataman; The fabrication of the Hybrid Drives and the implantation surgeries were done as described earlier . These e2, and 1 MgCl2, was lowered to target CA1 pyramidal layer using micromanipulator . Location of electrode was assessed by visually inspecting LFP trace, depth information, and was confirmed post hoc using brain sectioning and immunohistochemistry. LFPs were acquired with Multiclamp 700B amplifier  and digitized with an A/D converter . Data were acquired at sampling rate of 10 kHz and were further downsampled to 1 kHz using MATLAB for all the further analysis of state space. Multiple 3 min trials were recorded from a mouse, and artifact-free trials were considered for all further analyses.Separate set of mice were used for awake head-fixed recordings at EBRI, Rome, Italy. Seven male mice (four WT and three NLG3 KO) aged 3–5 mo were implanted with stainless steel head bar . Mice were individually housed after implantation and were allowed to recover for 1 wk. Mice were progressively habituated to head-fixation on a horizontal platform until they no longer attempt to escape (3–4 wk). For craniotomy, they were anesthetized with i.p. injection of a mixture of tiletamine/zolazepam  and xylazine , a day before the recording. A glass electrode  with the resistance of 1–2 MΩ, filled with standard Ringer’s solution containing (in mM) 35 NaCl, 5.4 KCl, 5 HEPES, 1.8 CaClIn the open-field experiments, mice were free to explore two arenas for about 20 min each. The arenas were either square (45 cm × 45 cm) or circle (45 cm diameter) or rectangle (50 cm × 25 cm). This was preceded and followed by a rest session in the animal’s home cage (Sleep1 and Sleep2) that typically lasted 60 min each.Experiments on rats were performed in Buzsaki lab at NYU. Each session consisted of a long (~4 hr) PRE rest/sleep (Sleep1) epoch home cage recordings performed in a familiar room, followed by a Novel MAZE running epoch (~45 min) in which the animals were transferred to a novel room and water-rewarded to run on a novel maze. These mazes were either (1) a wooden 1.6 m linear form, (2) a wooden 1 m diameter circular platform, or (3) a 2 m metal linear platform. Animals were rewarded either at both ends of the linear platform or at a predetermined location on the circular platform. The animal was gently encouraged to run unidirectionally on the circular platform. After the MAZE epochs, the animals were transferred back to their home cage in the familiar room where a long (~4 hr) POST rest/sleep (Sleep2) was recorded. Detailed paradigm and recording conditions are described in After the final recording day, tetrodes were not moved. Animals were administered an overdose of pentobarbital (300 mg/ml) before being transcardially perfused with 0.9% saline, followed by 4% paraformaldehyde solution. Brains were extracted and stored in 4% paraformaldehyde for 24 hr. Then, brains were transferred into 30% sucrose solution until sinking. Finally, brains were quickly frozen, cut into coronal sections with a cryostat (30 microns), mounted on glass slides, and stained with cresyl violet. The location of the tetrode tips was confirmed from stained sections .eegfilt from EEGLAB, an open-source toolbox in MATLAB . Threshold were adjusted by visual inspection of raw traces. Data points with amplitude greater than threshold were eliminated from analysis pipeline. Raw LFPs recorded at 30 kHz were first low-pass filtered (<1000 Hz) and then downsampled to 1 kHz for all further analyses of state space. Oscillations were extracted in six frequency bands: delta (1–5 Hz), theta (6–10 Hz), beta (10–20 Hz), slow gamma (20–45 Hz), medium gamma (60–90 Hz), and fast gamma (100-200) Hz from each of the three layers of CA1 using n MATLAB . CSD wernearest neighbors: 25, minimum distance: 0.1, distance metric: Euclidean.Filtered LFP or CSD bands were used to compute power. Power time series were binned in 200 ms time bins to compute median power. They were further smoothed using Gaussian kernel with standard deviation of three bins. The resulting smoothened, binned, 18 power time series were given as input to UMAP to obtain 2D projection with parameters: LFPs from all WT and NLG3 KO mice were normalized by scaling from 0 to 1. The scaled LFPs were then filtered to obtain the following frequency bands: delta (1–5 Hz), theta (6–10 Hz), beta (10–20 Hz), slow gamma (20–45 Hz), medium gamma (60–90 Hz), and fast gamma (100–200 Hz). The median band power was computed in bins of 200 ms for all six frequency bands. The network states from all the animals were merged and projected into 2D using UMAP with parameters as described before.3-Axis accelerometer data were used to identify periods of mobility and prolonged immobility during recordings in home cage . Raw accThe projected state space was divided into 100 × 100 bins. The occupancy was computed by counting the number of bins visited during each trial. The visited bin count was then divided by the total number of bins in state space to get the fraction of state space occupied during each trial.sleep-awake occupancy was used to compare surrogate data with the observed data.The 18 power time series were time randomly time shifted to create 100 surrogate state spaces. UMAPs were obtained for each surrogate data and occupancy was computed. The measure The projected state space was divided into 20 × 20 bins. For each trial and for each oscillation, median power in each bin was computed. The range of each oscillation was determined between minimum value and 95th percentile to prevent skewness of sequential color scale by extreme artifacts. Sequential colors were assigned to power values in the determined range to generate overlay plots . UnvisitWe adapted the methods from previous study for identification of REM and non-REM sleep . The proThe projected state space was divided into 20 × 20 bins. For each trial, we computed the number of network state visits in each bin and then divided the count by trial duration to get normalized states count, also known as density. Color assignment to density is as described above in power overlay methods.corrcoef function in MATLAB. Input to corrcoef were trial-specific power of 18 oscillations binned in 200 ms bins . corrcoef computes pairwise Pearson correlation for all 18 oscillations for a given trial. For state-dependent coupling of frequency bands, the projected state space was divided into 3 × 3 bins. For each bin, a correlation matrix was generated using the corrcoef. The inputs to corrcoef were bin-specific power of all 18 oscillations . To generate correlation matrix space, we took the lower triangular values of correlation matrices of all bins in all trials and used this as an input to UMAP. The embedding was generated using cosine distance as it brings similar points  together, thus, allowing us to separate similar bins. Each bin was then assigned its status based on the following: (1) awake bins – bins collected from awake trials. (2) REM bins – bins from sleep trials having >10% of its network states identified as REM as described above. (3) Non-REM bins – bins from sleep trials having >10% of its network states identified as non-REM as described above. (4) Intermediate – the remaining bins from sleep trials.Correlation matrices for each trial were created using The projected state space was divided into 3 × 3 bins for sleep trials and 10 × 10 for awake trials. For all the network states in each bin, we show the lines (trajectories) connecting five states (1 s of time) preceding and proceeding the state. We label set of preceding states as incoming trajectories and proceeding states as outgoing trajectories.Transition probability from, say, bin A to bin B was computed as follows: for every state in bin A, we collected outgoing trajectory states . Transition probability from A to B was then computed by fraction of total trajectory states that ended in B. Transition probabilities were then represented in the form of matrix.Difference matrices were computed by subtracting corresponding bin transition probabilities from two sleep or two awake trials. The mean of absolute values from difference matrices was used to generate summary plot for all animals.State transition occurring in both sleep (Sleep1 and Sleep2) and awake (Awake1 and Awake2) trials was merged to create a pool of state transitions. The sleep or awake pool was then split randomly into two surrogate sleep or awake trials, respectively. A total of 1000 randomly generated surrogate sleep or awake trials were generated to compute the change in transition probabilities between two surrogate sleep or surrogate awake trials and compute its average absolute change.The projected state space was divided into 50 × 50 bins. We counted the number of bins visited by the network trajectory in a time frame of 10 s. Thus, the speed is measured in units: bins/s. The median values of coverage speed from all time frame of sleep and awake trails were used to plot summary.Entire sleep trial was divided into nonoverlapping time frames of 10 s. We then computed median theta/delta ratio for all the states in each time frame. REM peaks were identified by detecting peaks in median theta/delta ratio. Median power on state space from each oscillation in each time frame was also computed to generate spectrogram. Summary plots were generated by averaging power in oscillations from pyramidal layer, speed of coverage, theta/delta ratio, and delta × beta product for all REM peaks.Spike trains of each cell were binned into nonoverlapping bins 200 ms (same bin size as applied during the construction of network state space). Thus, each network state gets a corresponding bin of spike train. For generating overlay maps, the projected state space was divided into 15 × 15 bins. For each bin on state space, we identified a set of network states in that bin and computed their mean firing rate from the corresponding bins in spike train. Plots demonstrating firing rate map in the arena were generated using the methods described in We computed the cell’s firing rate on sleep state space (sleep signature) as above. We used these sleep signatures of all cells of a given animal as an input to UMAP with cosine distance and 10 neighbors. The resulting projected space is known as sleep signature space. We computed mean firing rate and mean sparsity of each cell during awake trials and overlaid it on sleep signature space. Traditional approaches for the study of brain oscillations are typically based on analyzing spectral features of individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). This manuscript presents a different, multivariate, approach to simultaneously analyze interactions between multiple oscillations and applied it to rodent hippocampal LFPs. This innovative and important method provides a comprehensive and convincing approach to characterize oscillatory network states, opening new avenues for studying the complex interactions that characterize neural circuit dynamics. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Antonio Fernandez-Ruiz as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Laura Colgin as the Senior Editor.Thank you for submitting your article "State Dependent Coupling of Hippocampal Oscillations" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. All reviewers found the method as an interesting novel approach with the potential to provide new insights into multivariate oscillatory coupling dynamics. However, they agree that the present manuscript fails to clearly illustrate what kinds of information this method can provide that cannot be derived from more traditional analysis and what is the physiological significance of the observations made from the state space model. This may require both performing a new analysis of the existing data and a more profound characterization and interpretation of the present results. The results presented need to be interpreted in physiological terms and, in several cases, additional quantifications are needed in addition to the qualitative descriptions provided.2. The method relies on filtering LFPs in discrete frequency bands. The terminology needs to be clarified not to confound power in a certain frequency band with specific oscillatory events (e.g. spindles). The limitations of using power time series on predefined frequency bands rather than physiologically defined events (like. spindles or ripples) need to be discussed.3. Analysis of single-cell spiking dynamics is not well documented which makes it difficult to properly interpret them. Were pyramidal cells and interneurons classified? Can this approach be useful to uncover more 'subtle' physiological differences that those existing between pyramidal cells and fast-spiking interneurons; or between REM and non-REM sleep stages in previous analysis? These questions can be addressed with additional descriptions and further analysis.4. Several aspects of the analysis are not well justified or explained including the shuffling procedure for state occupancy, the choice of the number of states in Figure 6, how changes in oscillatory coupling between states were quantified, what is the relation of the state space with behavioral variables such as animal speed. In general, some parts of the result remain rather qualitative and descriptive. For those, additional quantifications and interpretations of their physiological significance need to be provided.Reviewer #1 (Recommendations for the authors):1. One important limitation of this method, that is not mentioned in the text, is its reliance on filtering LFPs in pre-defined frequency bands. While this is a common approach in the field, there is also numerous evidence that some oscillations, such as 'γ', do not occur in a restricted narrow band. Another approach would be to first extract oscillatory components in an unbiased manner and then construct the state space. These caveats and alternative possibilities should be discussed.2. On several occasions throughout the text causal language is used without proper justification (for example line 344). Such sentences need to be reworded carefully to distinguish what are mere correlations from actual causality.3. Although several analyses were conducted to illustrate potential applications of the method, in most cases is not clear what type of new information can be obtained that is also not possible to obtain with more traditional approaches. An example of this can be found in the last paragraph of page 8. Quantifying the power (and other features) of different oscillations across brain states is something that has been done multiple times employing traditional approaches. The authors need to do a better job in trying to illustrate which specific type of analysis can be done or what type of information can be extracted with his method beyond what traditional ones offer. The analysis in Figures 5 to 7 are good steps in that direction, that could be further elaborated at the expense of reducing the first, more descriptive, part of the results.4. Related to the previous point, a comparison that could be more interesting than REM vs non-REM is the same sleep state before and after the novel experience. While spectral changes between REM and non-REM are so obvious that can be shown with simpler analytical approaches, one could expect more subtle changes from pre to post-sleep for the same state. This could be an opportunity to showcase the power of this approach.5. Results regarding Figure 5 are interesting but its description is merely qualitative. For example, in lines 223-226 some statements regarding the variable coupling of oscillations are made without any quantification/ statistics to back them up. Those should be provided either in the main text or figure legend.6. The language describing Figure 6 is too vague and makes it difficult to understand what the author means exactly with expressions such as "that transitions of the network from one region of the state space to another are plastic in nature".7. In a similar way, some statements about results throughout the paper are made without statistical justification. As an example of this, an important demonstration of the robustness of the method would be to compare how similar is the state space across subjects. Such data is provided in Figure S4 and mentioned in the first section of the results but without any qualifications or statistics. These need to be provided. Are high and low firing rate cells mentioned as two subclasses of pyramidal cells or like the former includes fast-spiking interneurons? Both cell classes should not be merged.8. I could not find anywhere a proper description of the single unit analysis. Even basic information such as if pyramidal cells and interneurons were separated is missing.9. In the last section of the results Figure S12 is referred to as Figure 12 in most places. Reviewer #1 (Recommendations for the authors):1. One important limitation of this method, that is not mentioned in the text, is its reliance on filtering LFPs in pre-defined frequency bands. While this is a common approach in the field, there is also numerous evidence that some oscillations, such as 'γ', do not occur in a restricted narrow band. Another approach would be to first extract oscillatory components in an unbiased manner and then construct the state space. These caveats and alternative possibilities should be discussed.We thank the reviewer for allowing us to highlight the scope of our method. To this end, limitations, description of caveats, alternative possibilities and key advantages over traditional approaches are now added in the first and second paragraph of discussion of the manuscript.New Additions:While we used pre-defined frequency bands to filter oscillations from local field potentials, alternatively, unsupervised methods  can be employed to extract oscillations in an unbiased manner and construct the state space of network oscillations. We applied this framework to characterize various static and dynamic properties of the network state space during wakefulness and sleep and demonstrated multiple applications of the framework.Traditional approaches usually consist in analyzing spectral features of individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). This multi-variate analytical approach provides a unique window to assess the combinatorial effect of more than two oscillations on the state of the network, the neuronal population and the behavior. This provides a formal framework to uncover the potential combinations of neural signals underlying various cognitive functions during sleep and wakefulness.2. On several occasions throughout the text causal language is used without proper justification (for example line 344). Such sentences need to be reworded carefully to distinguish what are mere correlations from actual causality.We thank the reviewer for allowing us to modify the text. These sentences have been modified.3. Although several analyses were conducted to illustrate potential applications of the method, in most cases is not clear what type of new information can be obtained that is also not possible to obtain with more traditional approaches. An example of this can be found in the last paragraph of page 8. Quantifying the power (and other features) of different oscillations across brain states is something that has been done multiple times employing traditional approaches. The authors need to do a better job in trying to illustrate which specific type of analysis can be done or what type of information can be extracted with his method beyond what traditional ones offer. The analysis in Figures 5 to 7 are good steps in that direction, that could be further elaborated at the expense of reducing the first, more descriptive, part of the results.We are grateful to the reviewer for his constructive insights. We have used this direction to modify the results of the figures 4,5 and 6 have been modified to clearly describe the advantages and new information that this method provides over the traditional ones.Additions in results of Figure 4: In particular, non-REM states in sleep2 tended to concentrate in a region of increased power in the δ and β bands, which could be the results of increased interactions with cortical activity modulated in the same range. It is also likely that such effect was induced by the exposure to relevant behavioral experience. In fact, changes in density of individual oscillations after learning have been reported using traditional analytical methods and are thought to support memory consolidation . Nevertheless, while traditional methods provide information about individual components, the novel approach used here provides additional information about the combinatorial shift in the dynamics of network oscillations after learning or exploration. Thus, it provides the basis for identifying how coordinated activity among different oscillations supports memory consolidation processes, as those occurring during non-REM sleep after exploration, which cannot be elucidated using traditional analytical methods.Additions in results of Figure 5: Γ segregation and δ decoupling offer a picture of hippocampal REM sleep as being more akin to awake locomotion (with the major difference of a stronger medium γ presence) while also suggesting a substantial independence from cortical slow oscillations. On the other hand, the across-scale coherence of non-REM sleep is consistent with this sleep stage being dominated by brain-wide collective fluctuations engaging oscillations at every range. Distinct cross frequency coupling among various individual pairs of oscillations such as theta-γ, δ-γ etc., have been already reported . However, computing cross frequency coupling on the state space provides the additional information on how multiple oscillations, obtained from distinct CA1 hippocampal layers , are coupled with each other during distinct states of sleep and wakefulness. Furthermore, projecting the correlation matrices on 2D plane, provides a compact tool that allows to visualize the cross-frequency interactions among various hippocampal oscillations. Altogether, this approach reveals the complex nature of coupling dynamics occurring in hippocampus during distinct behavioral states.Modifications in results of Figure 6: To investigate how learning during exploration affects the consequent sleep, we characterized state transition patterns during Sleep1 and Sleep2. By plotting outgoing trajectories on the network state space , we visualized state transitions during sleep. We quantified the probability of state transitions among various sleep states  in a transition matrix . We calculated changes in transition probabilities across sleep trials by measuring average absolute change in probability . This allowed to quantify the amount of sleep state transitions altered after exploration/learning. To assess whether these changes in transition probabilities of sleep states are random or not, we generated 1000 pairs of sleep and awake trials by randomly shuffling state transitions from sleep data and computed transition matrices, difference matrix and their corresponding AACP. The AACP value of real data was then compared to the distribution of randomly shuffled trials. We observed that the AACP value of real data was beyond the 95th percentile of the distribution of shuffled data , Altogether, this data highlights the specific alterations in general sleep architecture and hippocampal oscillatory landscape following learning/novel exploration.4. Related to the previous point, a comparison that could be more interesting than REM vs non-REM is the same sleep state before and after the novel experience. While spectral changes between REM and non-REM are so obvious that can be shown with simpler analytical approaches, one could expect more subtle changes from pre to post-sleep for the same state. This could be an opportunity to showcase the power of this approach.If we understood it correctly, the reviewer is suggesting comparing the spectral changes of ‘same’ oscillatory states across sleep trials. The ‘same’ states have identical spectral power and are thus overlapping on the state space. Alternatively, to assess the state specific alteration across sleep trials, we computed transition probabilities emerging from a given state across sleep trial. This provides an avenue to assess subtle changes in oscillatory state transition patterns after learning.5. Results regarding Figure 5 are interesting but its description is merely qualitative. For example, in lines 223-226 some statements regarding the variable coupling of oscillations are made without any quantification/ statistics to back them up. Those should be provided either in the main text or figure legend.Statistical quantification has been added in the Figure 5 : p < 0.001 using multivariate ANOVA (manova1 in MATLAB).6. The language describing Figure 6 is too vague and makes it difficult to understand what the author means exactly with expressions such as "that transitions of the network from one region of the state space to another are plastic in nature".We thank the reviewer for allowing us to clarify these findings. To this regard, the language has been modified to indicate altered sleep state transitions after exploration/ learning.Modifications in results describing Figure 6:Alterations in Sleep State Transitions after Exploration / LearningTo investigate how learning during exploration affects the consequent sleep, we characterized state transition patterns during Sleep1 and Sleep2. By plotting outgoing trajectories on the network state space , we visualized state transitions during sleep. We quantified the probability of state transitions among various sleep states  in a transition matrix . We calculated changes in transition probabilities across sleep trials by measuring average absolute change in probability . This allowed to quantify the amount of sleep state transitions altered after exploration/learning. To assess whether these changes in transition probabilities of sleep states are random or not, we generated 1000 pairs of sleep and awake trials by randomly shuffling state transitions from sleep data and computed transition matrices, difference matrix and their corresponding AACP. The AACP value of real data was then compared to the distribution of randomly shuffled trials. We observed that the AACP value of real data was beyond the 95th percentile of the distribution of shuffled data , Altogether, this data highlights the specific alterations in general sleep architecture and hippocampal oscillatory landscape following learning/novel exploration.Intra-state sleep transitions are more plastic than Inter-state TransitionsWe further examined which transitions on the state space are significantly altered across sleep trials. We computed AACP specifically for transition from REM/non-REM/intermediate sleep state to REM/non-REM/intermediate state. We found that transitions occurring from REM-to-REM sleep and non-REM-to-non-REM sleep (intra-state transitions) are more vulnerable to plasticity after exploration as compared to inter-state transitions  . These changes in intra-state transitions were observed to be beyond randomness  indicating a specificity in plastic changes in state transitions after exploration. In particular, while the average REM period duration is unaltered after exploration , REM temporal structure is reorganized. In fact, increased probability of REM to REM transitions indicates a significant prolongation of REM bout duration. Similarly, the increase in non-REM to non-REM transition probability reflects an increased duration of non-REM bouts. Therefore, environment exploration was accompanied by an increased separation between REM and non-REM periods, possibly as a response to increased computational demands. More in general, the network state space allows to characterize the state transitions in hippocampus and how they are affected by novel experience or learning. By observing the state transition patterns, this analytical framework allows to detect and identify state-specific changes in the hippocampal oscillatory dynamics, beyond the possibilities offered by more traditional univariate and bivariate methods. We next investigated how fast the network flows on the state space and assessed whether the speed is uniform, or it exhibits specific region-dependent characteristics.7. In a similar way, some statements about results throughout the paper are made without statistical justification. As an example of this, an important demonstration of the robustness of the method would be to compare how similar is the state space across subjects. Such data is provided in Figure S4 and mentioned in the first section of the results but without any qualifications or statistics. These need to be provided. Are high and low firing rate cells mentioned as two subclasses of pyramidal cells or like the former includes fast-spiking interneurons? Both cell classes should not be merged.Comparison of data across animals is added in figure 2 —figure supplement 1. The animals exhibit high overlap on the state space.8. I could not find anywhere a proper description of the single unit analysis. Even basic information such as if pyramidal cells and interneurons were separated is missing.A proper description of single unit analysis has been added in the methods section of the manuscript. For this analysis / dataset, pyramidal and interneurons were not separated. All cells were included in the analysis. However, we have used hc11 datasets from Buzsaki lab with pre-classified information of pyramidal and interneurons to study sleep signatures .9. In the last section of the results Figure S12 is referred to as Figure 12 in most places.This has been corrected.