The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression.The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression.An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated.Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models , whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression.We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries. Problems related to mental health are widespread, with 1 report estimating that 1 in 5 Canadians experience a problem related to mental health or addiction in any given year . These iThe widespread use of smartphones coupled with their sensing capabilities and persistent network connectivity makes them a promising platform for delivering these technologies. The sensors present on all modern smartphones offer a rich landscape of data that can be used to infer behavior or actions pertinent to an individual’s mental health state. These data could then serve as the basis for screening algorithms for mood and anxiety disorders such as depression, generalized anxiety disorder (GAD), and social anxiety disorder (SAD). The development of automated screening may be a crucial step in mental health care. Automated screeners could offer a low-cost monitoring system to individuals struggling with mental health problems and provide potentially useful information for practitioners of mental health care.and transparent, in that they offer insight into what specific patterns of data, and what points in time, are driving models to make specific decisions or characterizations [A core aspect of this work is the extraction of relevant knowledge from the wide array of smartphone-sensed data. To convey a sense of scale of this class of data, consider that smartphones can sense, measure, and record geolocation, motion, ambient lighting conditions, ambient audio, communication patterns, and app use in real time. In a matter of weeks, tens of thousands of data points across these many data modalities can easily be collected from a single individual. Nonetheless, even with all these data, it remains quite challenging to build predictive models that are both accurate izations . It is oA general approach to this problem of knowledge extraction and interpretability has been to review the diagnostic criteria and the key symptoms of a mental health disorder and to look for data streams that would relate to these. In this approach, data are transformed into relevant knowledge by determining if the presence or absence of a symptom or characteristic of a disorder can be inferred from the available data. For example, key symptoms and criteria for depression include sleep disturbances (insomnia or hypersomnia)  and low social or not, achievable using so-called semantic location data. Measuring the degree to which subjects travel to these inherently social locations (such as pubs and dancing venues) has been shown to be correlated with social anxiety levels [This approach of using characteristics of disorder symptomology to drive the understanding of smartphone-collected data has been used in the modeling of SAD. Individuals with social anxiety are characterized by fear or avoidance of social interactions, ultimately stemming from the fear of negative evaluation from others . Althougy levels . Chow ety levels  demonstrAt the time of this writing, the authors are unaware of any studies that have used passively collected smartphone data to infer behaviors associated with GAD or to otherwise predict or screen for GAD.With respect to depression, Ben-Zeev et al  demonstrThis study aims to expand upon the existing body of work in using mobile sensing to predict certain aspects of mental health. Although the body of knowledge in this field continues to grow, much of it mainly focuses on mood disorders (depression and bipolar disorder), with much less of this work being undertaken in anxiety disorders. Studies have shown key features that act as predictors of depression, but many of these features have been motivated by general trends or associations with mental health that may extend beyond mood disorders to anxiety disorders. Furthermore, because anxiety disorders are commonly comorbid with depression , featureThe subjects were adults recruited from the general population using Prolific, a web-based recruitment platform . RecruitSubjects were entered into a 14-day observational study where a custom Android app was installed on their personal smartphone. The app collected both self-report measures of mental health (collected at the beginning and end of the study) and objective smartphone-sensed data, collected periodically and completely passively throughout the entire 2 weeks of the study.Subjects completed 4 self-report measures, in digital form, within the study app, at the beginning and end of the 14-day study. A review by Belisario et al  found thprevious 2 weeks; therefore, the window of symptom self-assessment at the study exit coincides with the window of electronic data collection. Changes with respect to baseline  were not investigated because the goal was solely to determine whether digital technology could perform automated screenings from short-term data collection.Although all scales were completed at both the study intake and exit, only the values obtained at the exit were used in the analysis presented in this paper. This was done because the self-report measures asked respondents to evaluate symptoms over the Exit values on the LSAS, GAD-7, and PHQ-8 were used to screen for SAD, GAD, and major depressive disorder (MDD), respectively, by applying thresholds to scores. A threshold of 60 was used with the LSAS scores to screen for SAD , as recommended by Mennin et al . This thThe app collected data across 4 streams of data available on an Android smartphone: audio (from the microphone), geolocation (GPS sensor), screen state (screen on or off), and illuminance of the environment (light sensor).Audio data were collected by using devices’ microphones to record the ambient audio of the environment for a 15-second duration, at a nominal rate of one 15-second recording every 5 minutes. To preserve privacy, these audio recordings were processed to yield 3 less invasive streams of data, after which the recordings were automatically destroyed. The first stream of audio-derived data is simply the average volume of each 15-second audio recording. The second stream of audio-derived data is a label for each audio recording, which indicates whether English-language speech was detected in the environmental audio at the time of each recording. Automatic speech recognition (ASR) software  was usedThe GPS location (latitude and longitude) was recorded at a nominal rate of once every 5 minutes. The state of the screen was not sampled periodically, but instead, the app recorded screen transitions  whenever the state of the screen changed. Finally, the illuminance of the subjects’ environments, as measured by the devices’ light sensors, was recorded at a nominal rate of once every 10 minutes. We refer to these data sampling rates as nominal because, in practice, the observed frequencies are often lower than what was specified in the software because of the battery-preserving restriction imposed by the Android operating system in devices running Android version 6 and above .features, were extracted from the raw smartphone-collected sensor data to act as inputs to the predictive models of SAD, GAD, and depression. These features are all hypothesized as capturing behaviors that are relevant to mental state, with the goal of using them as explanatory variables within the prediction models. The following sections describe each of the features that were used.Predictor variables, or daily similarity, which quantifies how periodic the volume of the audio recordings was, with respect to a 24-hour or 1-day period. When the volume of the audio recordings was extracted and treated as time series data, there were clear peaks and troughs in the signal, corresponding to the daytime activity and nighttime inactivity, respectively. It was hypothesized that individuals with more regular patterns of activity and inactivity, as inferred from the peaks and troughs of this time series data, would have better mental health. The daily similarity feature was designed to compute a quantitative measure of the regularity, in time, of the sequence of these peaks and troughs—in other words, to what degree peaks and troughs occur at the same time across days.The ability to establish and maintain regularity in one’s patterns of activities has been associated with positive mental health ,29. AssuMathematically, daily similarity is computed as the autocorrelation function of the volume time series of ambient audio recordings evaluated at a lag time of 24 hours. speech presence feature serves as a measure of social interaction by quantifying the presence of speaking voices in the periodic audio recordings. The speech presence feature is defined as the number of audio recordings labeled as containing speech divided by the total number of audio recordings. This feature was first proposed by Wang et al [any speech in the environment, including the playback of recorded speech, will be detected as speech; therefore, this feature is not a perfect proxy for social interaction. The results of the study by Di Matteo et al [The avoidance of social interaction is a defining characteristic of SAD  and is ang et al  and was ng et al . We noteeo et al  were repweeknight sleep disturbance, which infers a proxy measure of sleep quality from the same audio volume time series data as the daily similarity feature. The underlying assumption of this feature is that when a subject’s environment is noisy during common hours of sleep, then it is likely that that the subject is either awake or not sleeping well. Specifically, this feature is a measure of the noisiness of a subject’s environment during common sleeping hours. The weeknight sleep disturbance feature is computed as the SD of the volume time series of ambient audio recordings for weeknights between midnight and 6 AM. Weekend evenings are excluded because some individuals may shift their sleep schedules on weekends, if they work weekdays and not weekends. The SD of the volume time series is used as a measure of noisiness, and not the volume itself, because noisy environments tend to produce volume recordings that are consistently chaotic , yet the absolute volume of what can be considered noisy is often dependent on the microphone and further confounded by device-specific processing, including automatic gain control. This feature was first presented and analyzed in a previous study by Di Matteo et al [Sleep is also known to be a key factor in mental health disorders, with sleep disturbances being a common feature of anxiety and depression ,33. We ceo et al .affect, which comprises words relating to emotional states (some examples of the words belonging to the affect category are happy and cried). For each word category that is of interest, LIWC determines the percentage of words found in the text being analyzed which fall within the predefined dictionary of words for that category. We use LIWC to count the percentage of all words collected from the person that fall within LIWC’s death category and refer to this as the death-related words feature. In our previous work, we showed that the incidence of death-related words has been shown to have an association with symptoms of SAD, GAD, and depression [In addition to the binary presence or absence of speech, the contents of that speech can be investigated to yield further features that may be related to the symptoms of anxiety and depression. The ASR-detected words produced from subjects’ audio recordings were analyzed using the Linguistic Inquiry and Word Count (LIWC) software tool  to generpression . The prepression  also connumber of locations visited feature is simply the number of clusters produced when clustering a subject’s stationary GPS location data. This feature was adopted from the study by Saeb et al [The number of locations that a person visits may also offer insight into mental state, as the avoidance behavior associated with SAD or the lack of energy associated with depression may result in an individual leaving the home less often than healthy individuals. To estimate the number of locations visited from the GPS location data, location data were first processed to identify the stationary points. A stationary point is defined as one in which a subject travels at a speed less than 1 km/h relative to the last recorded location point in time (assuming direct-line travel). This stationary location data were then processed using a clustering approach  to groupeb et al .number of exits from the home can be computed as the number of cluster transitions from the home cluster to any other nonhome cluster.The number of times a person leaves their home to visit another location is similarly hypothesized to be relevant to symptoms of anxiety and depression. Although subjects do not explicitly provide us with the location of their home, we infer their home location (or cluster) as the cluster in which participants spend the most amount of time between the hours of midnight and 6 AM, a method that has been used in previous studies ,11. Oncescreen use feature is computed as the proportion of time that each subject’s smartphone screen is on. It was computed from the screen on/off time series data. This feature was adopted from the study by Saeb et al [The eb et al .time in darkness feature is computed as the proportion of time that each subject’s light sensor readings measure an illuminance less than 5 lux, which corresponds to a very dark environment.The training data to estimate the mean, SD, and median to perform these operations.Our goal was to assess the capability of this set of 8 features to predict the screening results  of SAD, GAD, and depression. To do so, logistic regression  models wtrain the model). This method is often used in machine learning–based approaches. The cross-validation approach used was repeated k-fold cross-validation [folds with approximately 17 subjects each. Then, four of those five folds were used to train a logistic regression model, and the accuracy was then evaluated by predicting the unseen screening results of the held-out fold. This process is repeated for a total of 20 times, where in each of the 20 iterations, the data set is split into a different random assignment of participant data to the 5 folds. As every repetition of the 5-fold cross-validation scheme produces 5 models to be trained and evaluated (1 for each of the held-out sets), a total of 100 logistic regression models were trained and evaluated for each of the 3 disorders. A repeated k-fold cross-validation scheme was used instead of simple cross-validation because the data set is relatively small and, therefore, the performance of the models can vary by chance depending on how the subjects are randomly assigned to folds.To estimate the predictive performance, a cross-validation  scheme wlidation , with 5 t test was used to assess whether the mean predictive performance of each group of models is significantly better than that of an uninformative model (which would produce random predictions). This corrected repeated k-fold cross-validation t test was developed by Bouckaert and Frank [k-fold cross-validation resampling is not independent, as models built using this strategy share training data.The predictive performance of each of the 3 groups of models  is reported as the mean (across the 100 models) of the area under the receiver operating characteristic (AUROC) , which ind Frank  to accoufull models will then be presented and discussed. These coefficients are x-standardized as all features were scaled to have mean of 0 (SD 1) before model building. This standardization was necessary because we wish to compare the effects of features that do not share a common unit of measurement or scale.Finally, to inspect and interpret the models and the relative influence of each feature within the model, a single model was trained using the entire data set for each of the 3 disorders under investigation. The coefficients of these All recruited subjects retained anonymity and were provided with randomized account log-ins for use in the study app. All data were transmitted to the study servers over encrypted channels and stored in an encrypted form. Audio recordings were processed entirely by automated software with no human intervention and were deleted immediately upon processing. The words contained in the transcripts of audio recordings were stored in a randomized order to prevent the recreation of speech, after which each transcript was destroyed. The study was approved by the University of Toronto Health Sciences Research Ethics Board (protocol #36687).Of the subjects who completed the study, 75% (84/112) yielded sufficient data for analysis.Missing data were encountered because of the smartphone app; in some cases, sampling data were obtained at rates below the intended rate. For each participant and for every data stream , the number of data samples collected in that stream was compared with the number of samples that would be collected given perfect periodic sampling at the desired sampling rate. If the number of samples was less than half of what would be expected, then features would not be computed from that data stream, and those associated features would be considered missing. Participants having four or more missing features were then considered to have insufficient data and were excluded from the analysis.The gender balance of the study sample was 42% (35/84) female and 58% (49/84) male, with an average age of 28.8 (SD 8.6) years. The 84 subjects included in the analysis and the 28 subjects excluded from the analysis did not differ significantly in mean age, gender distribution, or mean score of any of the 4 self-report measures.Subjects were screened for SAD, GAD, and depression using the LSAS, GAD-7, and PHQ-8 instruments, respectively. The screening criteria and the number and percentage of positive screenings are summarized in Methods section. The remainder of the features are quite simple and can be easily interpreted in the context of everyday behaviors. The speech presence feature has a mean of 0.15, which indicates that, on average, subjects spent 15% of their time (including sleeping time) in the presence of smartphone-detected speech. The mean value of the death-related words feature was 0.16%, indicating that these words were detected infrequently. The mean of the number of locations visited feature was 15, and the mean of the number of exits from the home feature was 15. The subjects’ devices had their screens in an on state 23% of the time, on average, and devices were in darkly lit environments 63% of the time.Summary statistics for all eight features extracted from the subjects’ smartphone-collected data are presented in Methods section, a repeated k-fold cross-validation scheme was used to acquire an unbiased estimate of the models’ predictive performance. The mean AUROC for each of the 3 disorder models is shown in Three independent logistic regression models were built and evaluated using all eight features to predict SAD, GAD, and depression. As noted in the t test to compare whether the mean AUROC of each model was significantly better than that of an uninformative model (which performs with AUROC=0.5). Both the SAD model and the depression model performed significantly better than the uninformative model at a 5% significance level. The GAD model effectively performed on par with an uninformative model (mean AUROC 0.55), showing no effective capability to predict GAD.Methods section, these coefficients come from a single model that was built on the entire data set of 84 subjects for each disorder, in contrast with the results in We sought to determine the relative importance of the features and to compare how feature importance may differ between the models for the 3 different disorders. The standardized logistic regression coefficients of the models of SAD and depression are shown in decreased odds of a positive screening (a characteristic of the logistic regression modeling used). The following features were associated with decreased odds of a positive screen for both social anxiety and depression: daily similarity, number of locations visited, number of exits from the home, and time in darkness. This indicates that these features generally capture healthy behaviors with respect to social anxiety and depression. In contrast, the death-related words and screen use features were both associated with increased odds of positive screening for SAD and depression.First, by looking at the signs of the feature coefficients, we can determine which features were found to decrease the odds of a positive screening, as negative coefficients correspond to a feature that is associated with decreased odds of a depression screening; however, speech presence was associated with a slight increase in the odds of a SAD screening. In addition, weeknight sleep disturbance was associated with increased odds of a depression screening but with decreased odds of a SAD screening.Two features, namely, speech presence and weeknight sleep disturbance, were found to have contrasting directionality with respect to screening for SAD and depression. Speech presence was associated with greatly Second, in terms of the relative magnitudes of their associated coefficients, several features stand out. For SAD, the daily similarity and number of locations visited features seem to be the strongest indicators of health, whereas the death-related words feature is the greatest risk factor for SAD. For depression, the speech presence and number of exits from the home features seem to be the strongest indicators of health, whereas weeknight sleep disturbance and death-related words features are the greatest risk factors for depression.The moderate success of the predictive models of SAD and depression is interesting when compared with the failure of the GAD model. This particular feature set seems unable to predict GAD, which may be related to how generalized anxiety manifests itself in subjects’ feelings and behaviors in a manner different from social anxiety or depression. Individuals with SAD commonly use behavioral avoidance to avoid situations that trigger their anxiety symptoms; they may choose specific jobs to avoid public speaking or avoid going to social gatherings . The relbehavioral avoidance can be contrasted with the cognitive avoidance that is common in individuals with GAD [This with GAD , which iwith GAD . These aA similar line of reasoning was presented in an earlier study of the audio-based features and how they were less strongly correlated with GAD symptom severity than the symptom severity of SAD and MDD . It is wThe predictive models of SAD and depression have similar performance, achieving mean AUROC values of 0.64 (SD 0.13) and 0.72 (SD 0.12), respectively. Numerous features appear to be of near-equal importance for both models, whereas some features are much more important for one of the two disorders (SAD and MDD). Comparing the 2 models, the proportion of death-related words detected in the environmental audio recordings was the greatest risk factor for both SAD and depression. The link between the use of death-related words and depression has been demonstrated in some previous studies ,50, but Methods section. That is, higher regularity in the subjects’ patterns of daily activities may be associated with better mental health.Continuing to compare the models of SAD and MDD, the daily similarity feature appears as a factor of good health with respect to both SAD and depression, in line with the hypotheses outlined in the description of this feature in the A difference between the 2 models is that 2 features appear to act as predictors in opposite directions. First, the speech presence feature appears to be a critical indicator of health with respect to depression: subjects who spend more time in the presence of speech in their environment had greatly reduced odds of depression. The same is not true for SAD, in which the direction of this effect was reversed. Nonetheless, the value of the coefficient for speech presence in the SAD model is so low that it is considered an insignificant risk factor. The result that links more environmental speech to weaker symptoms of depression replicates results from previous studies ,15. It iThe weeknight sleep disturbance feature is the second of 2 features that act in different directions within the models of SAD and depression. Sleep disturbance is a risk factor in the model of depression, a result that aligns with our original hypothesis. However, sleep disturbance was shown to decrease the odds of a positive screening of SAD. This indicates that sleep disturbance may be a positive factor with respect to social anxiety, if enhanced nighttime activity is indeed the result of social interaction.Finally, there are 2 features that serve as positive factors for both SAD and depression, but with marked differences in magnitude. The number of locations visited feature is much more impactful in the model of SAD than in the model of depression, whereas the number of exits from the home feature is much more impactful for depression than for SAD. This may suggest that for depression, simply leaving the home and engaging in some form of activity is enough to preclude a positive screening. For SAD, simply leaving the home may not provide enough opportunity for social engagement to indicate a lack of social anxiety; however, visits to numerous locations  may suggest that an individual does not have SAD.A number of studies have used smartphone-collected data in a similar fashion to build and evaluate predictive models of depression. Saeb et al  achievedBoth the studies by Wang et al  and PlacStudies of anxiety disorders using smartphone-collected data are much less represented in the literature. A study by Boukechba et al  demonstrOne limitation of this study is that the subjects’ state of SAD, GAD, and depression was determined using self-report measures as screeners. Although all the instruments used demonstrated acceptable accuracy for doing so, it is not clear how these results would compare had the diagnoses been performed using structured clinical interviews by trained clinicians.Another limitation is related to the speech presence feature, which captures any intelligible speech within range of participants’ devices, including television or other media. More information about whom the subject is speaking with  and where the individual is situated at the time of the recording may improve the ability to identify the key contexts in which social interactions are relevant to a subject’s symptoms of SAD.Another set of key limitations involves the use of a particular smartphone system to collect data. First, all subjects were users of Android smartphones. Although our study excluded iOS users, 1 study of depression prediction from smartphone data that separately considered both iOS and Android users found similar results for both subgroups . Second,P=.02) and depression . Although the prediction of GAD was unsuccessful, we believe that we are the first to evaluate that disorder using this methodology and to propose key considerations that may yield success in predicting that disorder from smartphone-collected data.This work contributes to the development of automated technology capable of screening individuals for SAD, GAD, and depression. To our knowledge, this is the first study to evaluate and compare how the same set of predictor variables derived from objective smartphone-collected data perform in predicting depression and 2 classes of anxiety disorders. This set of features was used to build models that had a significant capability in predicting SAD (mean AUROC 0.64, SD 0.13;


Snudda is a software for the creation and simulation of large scale networks of detailed microcircuits consisting of multicompartmental neuron models. We have developed an extension to Snudda to incorporate neuromodulation in large scale simulations. The extended Snudda framework implements neuromodulation at the level of single cells incorporated into large-scale microcircuits. We also developed Neuromodcell, a software for optimizing neuromodulation in detailed multicompartmental neuron models. The software adds parameters within the models modulating the conductances of ion channels and ionotropic receptors. Bath application of neuromodulators is simulated and models which reproduce the experimentally measured effects are selected. In Snudda, we developed an extension to accommodate large scale simulations of neuromodulation. The simulator has two modes of simulation – denoted replay and adaptive. In the replay mode, transient levels of neuromodulators can be defined as a time-varying function which modulates the receptors and ion channels within the network in a cell-type specific manner. In the adaptive mode, spiking neuromodulatory neurons are connected via integrative modulating mechanisms to ion channels and receptors. Both modes of simulating neuromodulation allow for simultaneous modulation by several neuromodulators that can interact dynamically with each other. Here, we used the Neuromodcell software to simulate dopaminergic and muscarinic modulation of neurons from the striatum. We also demonstrate how to simulate different neuromodulatory states with dopamine and acetylcholine using Snudda. All software is freely available on Github, including tutorials on Neuromodcell and Snudda-neuromodulation.Neuromodulation is present throughout the nervous system and serves a critical role for circuit function and dynamics. The computational investigations of neuromodulation in large scale networks require supportive software platforms.  The nervous system depends on fast interaction via ionotropic receptors, which are activated by a variety of neurotransmitters including glutamate, GABA, and glycine. Besides these ionotropic receptors, neuromodulation via metabotropic receptors have a profound effect on network dynamics via slower processes. On a single cell level, these neuromodulators act via a variety of receptor subtypes, which modulate the excitability of neurons and influence their synaptic properties . The intThere are models of both invertebrate and different types of vertebrate which attempt to bridge the gap from cellular and synaptic properties to circuit function, and where the effects of neuromodulation is considered . For neuin silico striatal microcircuit . The simulations of large networks use Snudda .e Snudda , 2021, aNeuromodcell and Snudda require multicompartmental models specified for NEURON simulator described by three files: a morphology file (SWC), a parameter file (JSON) and a mechanism file (JSON) including a directory with the ion channel model files . The models used in this publication were optimized using BluePyOpt (luePyOpt  in previluePyOpt  with modDA to indicate dopamine), based on previous formalism developed in ∗’, ‘level∗’ and ‘mod∗’ and the ∗ should be replaced with a specific name for the neuromodulator. The maxMod∗ defines the modulation degree . The level∗ defines the transient level of modulation throughout the simulation. The mod∗ defines the activation of modulation . Within the examples, dopaminergic and muscarinic modulation was included (with muscarinic modulation using key ACh). The list of modulated ion channels for each cell type is presented in To modulate the models during the simulation, we introduce additional parameters for the specific neuromodulator within the .mod files. The modulation is implemented in a phenomenological manner, where a modulation parameter is multiplied with the target (for example the conductance of an ion channel). Hence, the conductance (in this case) can be regulated through the simulation. For example, for dopamine, the three parameters are maxModDA, modDA and levelDA level in the circuit (for example concDA to levelDA for dopamine). Hence, neuromodulation within the simulation can either follow a fixed recipe (‘replay’ mode), or dynamically change based on the activity in the network (‘adaptive’ mode). The mode used within a particular simulation will therefore depend on the network structure. Using either approach, the level of neuromodulation would be updated every time step via the ∗-levelparameter. The replay mode loads an array which has set values for the level∗ for each time step of the simulation. This would suffice if the neuromodulation is synchronous and the neuromodulatory neurons are not affected by activity of the circuit. On the other hand, if the activity of the microcircuit could for example inhibit the neuromodulatory neurons, like striosomal dSPNs in the striatum, replay mode does not suffice and adaptive mode would be used instead, as the presynaptic neuromodulatory neurons can interact with the microcircuit throughout the simulation.Neuromodulation in replay, the maxMod∗, mod∗ and level∗ are introduced as RANGE variables. In the adaptive mode, which is based on pointers, the parameters are introduced with the keyword POINTERS4 and ‘ptr’ is added to the filename .For Neuromodulation class  or NeuromodulationSynapse . For examples of how to use these classes (see text footnote 4).Lastly, both types of simulations require a configuration file (JSON) which is created using either Snudda.neuromodulation, we simulated several networks using both replay and adaptive mode. Firstly, using the Snudda framework, a network of 10 000 neurons was created and simulated with dopamine and acetylcholine transients; and with a cortical activation at 1 s, with cortical and thalamic background activity. The network is based on previous publications . The activity of the neurons within the simulations were measured by the percentage of spiking neurons and the mean firing frequency during the cortical activations using custom Python code and Electrophysiology Analysis Toolkit5.To demonstrate the features of ications  and the Neuromodcell and an example of dopaminergic modulation of dSPN are available (see text footnote 1).A tutorial on Snudda.neuromodulation module are available (see text footnote 4).Examples of simulations using Neuromodcell performs parameter variation of ion channels and receptor models within multicompartmental models to simulate neuromodulation. The components of the software are presented in DefineModulation . The parameter variations of the models which pass validation are saved in a separate file. The code for defining the selection criteria is freely available (module selection_criteria and selection_function) and can be modified to measure a specific electrophysiological feature of the simulation, for example firing frequency or the number of action potentials.Following the simulation, a selection process is performed, where the models are compared to the experimental data provided in the setup .Below, we describe the process of setting up, optimizing and selecting the models that pass validation. Following the completion of the optimization, the results are transferred to Neuromodcell. We instantiated the DefineModulation class as shown in define_modulation_parameter method within the class. The modulation can also be defined to be somatic, dendritic  or axonal.We performed optimizations of dopaminergic and muscarinic modulation for dSPN, iSPN, and FS. In the following examples, the dopaminergic optimization of dSPNs is used to demonstrate the features of modset, with a current clamp protocol and bath application of dopamine . Both the define_modulation_function and define_selection_criteria can select several pre-defined functions but they can also be customized to accommodate specific transients or criteria, respectively.The experimental data used in the example optimization was taken from Figure 6C in dopamine . FurtherOptimiseModulation, from optimise module in Neuromodcell. The optimization for dSPN used a custom Python script called optimise_dspn.py, which loaded the path_to_model and the specific seed for the optimization  are simulated in parallel with the specific model (defined in DefineModulation), as illustrated in The optimization uses the mization . The seedSPNanalysis   . The claSnudda mechanism directory.Following the optimization, the parameter sets which reproduce the neuromodulation are saved as modulation.json and moved to the model directory . The modified .mod files should also be transferred to a common replay mode in Snudda.neuromodulation, we simulated a network of 10 000  to modulate the network. In We created a small network of dSPNs to illustrate the effect of changing the firing frequency of the presynaptic neurons. We placed spontaneously active neurons to modulate the dSPNs via D1 receptors. In control, 50% of the dSPNs within the network were spiking and the spontaneous activity of the presynaptic neurons did not change the percentage . AlthougNeuromodcell and Snudda. Our priority was to create an extension to Snudda, a software for large scale simulation of networks of multicompartmental models, which also simulates transients of neuromodulator(s). Firstly, we developed Neuromodcell which enables the user to optimize and select parameters for a specific type of neuromodulator. The neuromodulation is defined by introducing parameters within the ion channel and receptors models, hence enabling dynamic control of for example conductance. Secondly, we developed an extension to Snudda, which incorporates the neuromodulation parameters found using Neuromodcell. Thirdly, we simulated a range of different neuromodulatory states with the striatal microcircuit to show the versatility of the tool.In this article, we present a framework for simulating neuromodulation in detailed multicompartmental neuronal models being part of large-scale microcircuits, using Neuromodcell to optimize for parameters sets which reproduced the available experimental data on dopaminergic modulation of these cell types. The simulations of dopaminergic modulation showed that DA can modulate the excitability of the network. Hence, the simulations of dopamine are in line with known effects of DA in the striatum , the degeneration of dopaminergic neurons results in a change in the balance between dopamine and acetylcholine  and GABA receptors with short-term plasticity. In several brain areas, neuromodulators can regulate the long-term potentiation (LTP) and long-term depression (LTD)  . Hence, Snudda is a general tool for simulating large scale networks of neurons in any part of the nervous system with NEURON simulator. Our aim was to develop an extension to Snudda, which simulates neuromodulation on a large scale and can include any neuromodulator. We then developed Neuromodcell, which provides a tool for investigating neuromodulation at single cell level, in addition to the integration into the Snudda framework. These tools can provide a link between the single cell experiments and circuit level experiments of neuromodulation, which is currently not possible with the available simulation tools.github.com/jofrony/Neuromodcell.git and Snudda.neuro modulation is available at github.com/Hjorthmedh/Snudda.git.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The code for single neuron optimizations available at Neuromodcell and generalization of neuromodulation in Snudda and wrote the manuscript with some input from the co-authors. JHj, SG, and JH contributed throughout the study by discussing the results. All authors contributed to the article and approved the submitted version.JF designed The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The disruption of traditional, in-person learning due to the COVID-19 pandemic necessitated the rapid development and use of revised and novel learning opportunities using a variety of remote instructional methodologies. This viewpoint describes the process used by an undergraduate Public Health program to transition a traditional, in-person, semester-long, 480-hour internship to a virtual-only learning experience guided by the existing student learning outcomes. Working closely with public health professionals at existing internship agencies, alumni from the program, student interns, and program faculty developed a modified virtual internship composed of 6 components. The development of this modified virtual internship model was guided by previous research on the components of successful internships and the elements of high-impact learning practices. The COVID-19 pandemic has brought unprecedented changes for college students. As SARS-CoV-2, the virus that causes COVID-19, is primarily spread through airborne transmission, institutions of higher education were noted as communities of concern, as dormitories have close living quarters, and classrooms are often set up in a way to allow collaboration rather than social distancing ,4. In adIn response to the issues that arise due to what has been lost during the pandemic, many efforts have been made to retain high-quality and engaging learning experiences in a virtual format. In addition to coursework, other examples include experiences such internships, which can better equip students for the workforce upon graduation. As many students have yet to gain this experience, the value of maintaining internship opportunities cannot be understated. More than half of graduating college students in the United States participated in at least 1 internship during their academic training . InternsThe purpose of this viewpoint is to describe the process and experience of transitioning a traditional (in-person) internship to a virtual experience in the Public Health undergraduate program at a public university in New Jersey. The traditional internship is a 12-credit course where interns complete (1) an in-person, classroom-based, didactic experience; and (2) an in-person field placement.Prior to the semester in which the field placement occurs, students complete a 1-credit Introduction to Internship course. This in-person seminar-style course is designed to assist students in the development of the professional skills necessary for the successful completion of an internship in public health education. Topics of study include professional conduct, professional communication, resume development, interviewing skills, and job search strategies. Concurrent to the course, students work closely with the internship site coordinator to review the various types of public health education field placement settings and select their placement for the following semester. The internship site coordinator is a professional staff member who works in the academic department to coordinate and supervise the field placements.During the 12-credit field placement semester, interns participate in classroom-based instruction necessary for the completion of a Capstone Project and faculty-led review for the certified health education specialist examination, administered by the National Commission for Health Education Credentialing, which interns take at the end of the semester. The Capstone Project provides interns the opportunity to demonstrate their understanding of public health education program planning and is the final assessment of their ability to develop a comprehensive health education program from conception through assessment, planning, implementation, and evaluation. While preparation for the certification exam does not demonstrate an intern’s potential professional competence, it does provide both the faculty member and the intern an important opportunity to gauge the intern’s application and interpretation of knowledge.The field placement is a supervised experience designed to allow interns to apply their newly acquired knowledge and skills in a professional work setting. The field placement occurs at an approved public health agency in the community where the intern works under the supervision of a public health professional, the internship site supervisor. Student learning outcomes center on the five following major themes: (1) exposure to the roles and responsibilities of an entry level public health educator in a public health agency; (2) examination of the ways in which theoretical concepts are applied to the realities of the field of public health education; (3) exploration of strategies for communicating and working with public health professionals; (4) self-reflection regarding career goals and lifelong learning; and (5) improvement of public speaking and audience management skills.Interns spend approximately 28-30 hours per week at their field placement over the course of 15 weeks. During this time, interns work with their internship site supervisor, other partners at the agency, and stakeholders in the community to complete activities and projects that fall within the professional scope of practice for entry-level health education specialists. During this experience, 2 site visits are conducted by the internship site coordinator to review the intern’s progress toward meeting the student learning outcomes of the field placement portion of the internship experience.As the pandemic changed the nature of in-person instruction for institutions of higher education, an alternate structure for the internship needed to be developed. It was necessary that the new structure include robust activities and rich experiences that would substantively address the 5 student learning outcomes. The following is a description of the 6 components that were included in the virtual internship experience in the spring semester of 2021 to accommodate all students and provide what proved to be an experiential learning opportunity. A comparison of the components of traditional and virtual internships is displayed in The previously described faculty-led coursework was moved to a fully web-based format and maintained focus on the completion of the Capstone Project and preparation for the certification exam. Instruction for completing the Capstone Project was modified to suit the web-based learning environment. It included weekly content review using the University’s web-based learning management platform, several required individual feedback sessions with the faculty member, and the frequent review of each intern’s progress toward completion of the project. Web-based exam preparation consisted of faculty-supervised web-based study groups that met several times each week, weekly virtual practice exams, and faculty-recorded review sessions for each weekly exam.The Introduction to Internship course completed by students the semester prior to their field placement was moved to a web-based format and retained all the course content previously described. The main challenge during this semester was the identification of public health agencies that were willing and able to work with interns virtually. The internship site coordinator queried all existing internship site supervisors to determine if their agency had the ability to host an intern during the pandemic. As remote field placement sites were identified, it became apparent that many field placement sites would not have the capacity for a semester-long field placement, as was the case in prepandemic years. Therefore, the faculty decided to proceed with a shortened field placement experience that would be completed in 1 month, rather than the full semester. Furthermore, to increase the likelihood of meeting student learning outcomes and better manage the remote placements, fewer field placement sites were selected. Those that were selected agreed to take a different intern each month . As a result, instead of having to recruit 60 unique field placements, only 20 were needed. This structure provided multiple benefits as the internship site supervisors were able to develop a 4-week pattern of learning activities for the intern and then replicate that same pattern during the subsequent 2 months. Furthermore, this allowed the faculty to hand select those public health agencies who were best positioned to provide robust learning experiences in a truncated format. Finally, the new structure of the field placement assisted the internship site coordinator to quickly identify and rectify any emergent issues during an intern’s field placement. This last point is particularly important because issues needed to be addressed quickly as the duration of the field placement was only 4 weeks.Once field placement sites were identified, interns were assigned to a public health agency that aligned with their interests. Similar to what occurs during a traditional internship, during this virtual site rotation, interns met several times each week with agency staff and their internship site supervisor, completed assigned tasks, developed public health projects, learned about how the agency functions, attended work-related meetings, and engaged in other related learning activities. During the field placement, interns also worked on an Internship Site Project, a new addition to the virtual internship created to ensure that interns would have the opportunity to demonstrate the community-based health program planning principles that are central to the program’s learning outcomes. The topic of this project was identified during the first 2 days of the field placement in consultation with the intern’s site supervisor. The project was a collaboration between the intern and the agency and could be on any of the following topics: “Press release,” “Strengths/ Weaknesses/Opportunities/Threats analysis,” “PowerPoint presentation,” “Brochure,” “Infographic,” “Data analysis summary report,” “Logic model,” “Flyer,” “Website development,” and “Social-media posting.”Due to the shortened and web-based nature of the field placement, the faculty were concerned about the loss of opportunities for professional development, as the potential to attend professional meetings and conferences was limited during the pandemic. To highlight the need for public health educators to remain updated on current public health information and developments and to emphasize the importance of lifelong learning, all interns engaged in professional development activities in the form of online trainings, webinars, and live virtual conferences. Interns earned “Professional Development Points” by participating in professional development opportunities selected from a faculty-created course catalog of free online opportunities offered by regional public health training centers, state and local health departments, and federal health agencies, such as the Centers for Disease Control and Prevention’s Train network.When selecting professional development opportunities to include in the catalog, the faculty focused on choosing opportunities that reinforced issues around diversity, health equity, and social justice, as these topics are central to the program’s learning outcomes. Furthermore, opportunities for learning about topics that are not fully covered in the undergraduate curriculum  were also included in the training catalog. The interns were required to complete a minimum of 40 hours of training, with 1 hour of training equivalent to 1 professional development point. As evidence of the completion of a professional development opportunity, the interns were required to complete a post-training evaluation to earn a Certification of Completion, which was then uploaded to their e-portfolio.While the interns would be in contact with their internship site supervisor several times each week during their 4-week field placement, the faculty were concerned about the lack of periodic mentorship during the 2 months when the intern was not assigned to a field placement. To provide a consistent point of contact during the whole of the semester, each intern was assigned to a successful alumnus who served as their mentor. Alumni mentors were recruited by the program faculty the semester prior to field placement. All alumni mentors participated in an online training prior to the start of the field placements.Over a 12-week period during the spring semester, the intern and the mentor met at least 8 times to review a predetermined set of discussion topics designed to help the intern gain a deeper understanding of what it is like to work in the public health field. Meetings with mentors were designed to help the intern better understand the transition from college to the professional public health work environment and identity the major issues that arise when working in the public health field. To facilitate these meetings, 8 discussion guides, which acted as a set of guiding questions that the intern should ask their mentor, were created. There was 1 discussion guide for each of the 8 required meetings. At the end of the semester, both the intern and the mentor provided written evaluations of this experience.To provide a directed opportunity for self-reflection, career exploration, and preparation for the job search, all interns were required to compile an e-portfolio using the free web-based software system, WIX (Wix.com Inc). Training on how to develop the e-portfolio was provided by a representative from WIX, and supervision of this component of the internship was provided by the internship site coordinator.An e-portfolio serves several purposes. First, it is a digital compilation of student work upon which they can reflect on the tangible work they have produced. Second, it serves as a valuable means of assessment for faculty to comment on the strengths of the student’s work and areas for improvement. e-Portfolios can be accessed globally and are highly visible, a limitation of traditional portfolios. As they are easily accessible, e-portfolios can be shared with mentors, colleagues, and family members for review and comment . FinallyAs the components of the virtual internship were being developed, careful attention was given to identifying what learning opportunities might be compromised in a virtual-only format and how program student learning outcomes would be met with revised and new activities. The shift of the fieldwork experience to the web-based environment called into question the quantity and quality of the interpersonal aspects that a traditional, in-person internship offers to student interns. The areas of primary concern were potentially lost opportunities to network, to be mentored by a professional within a physical work environment, and to demonstrate the skills and competencies necessary for entry-level health education practice.The model that was developed for the remote internship experience considered these factors. The faculty wanted to offer experiences that, as best as possible, would mimic traditional work settings while at the same time offering alternate experiences for networking and mentoring; it also aimed to enable the intern to reflect on their skills and competencies and prepare for job search. As the importance of professional development and lifelong learning may be lost in the virtual context, it was critical that a professional development component be created for the virtual internship experience.While developing the structure of the virtual internship, 2 frameworks were used to guide the selection of activities and experiences to ensure that the virtual internship would offer a high-quality experience that allowed interns to achieve the intended learning outcomes.King and Sweitzer  describeThe professional dimension of an internship allows for the exploration of career pathways, the application of skills in professional settings, and the opportunity to observe the patterns of work and behavior that occur in a potential work setting . In the The academic dimension of the internship focuses on the development of patterns of thinking that are consistent with the academic discipline, resulting in the intern’s ability to think, in this case, like a health education specialist . The acaThe third dimension of an internship, the personal dimension, centers on the exploration of life skills beyond those necessary for professional practice. This includes the development of intellectual and emotional qualities such as flexibility, openness to differences, self-awareness, self-efficacy, and sound judgement . The perThe civic dimension addresses the ways in which professionals work with and serve society through their professional role . This diWhile moving toward the completion of the virtual internship model based on these 4 dimensions of learning and development, the faculty felt it was important to ensure that the new internship model remained a high-impact practice. Internships are routinely identified as being a high-impact practice. A high-impact educational practice is any teaching or learning experience that education research has shown to have a positive impact on students . This anA high-impact practice features 6 common elements. First, it requires work, where students are expected to engage in activities that require considerable time and effort. Next, it requires that students engage with others is an intentional way, including faculty, peers, supervisors, and coworkers. A high-impact practice also requires that students interact with and develop ways of working with individuals who are different from themselves. The provision of quality and frequent feedback on performance in both formal and informal manners is another element of a high-impact practice. Students must also be provided with opportunities for the application of knowledge in new situations. Finally, a high-impact practice must provide students with the opportunity for self-reflection on their personal and professional development .While each component of the virtual internship addresses most of the elements of a high-impact practice to some degree, it was important for the faculty to identify where they are addressed in a substantive and intentional manner. While conversion of a traditional internship to a virtual format described above maintained the key elements of a high-impact educational practice, there are some limitations to an exclusively online internship that deserve attention. First, revision of the traditional internship program into a virtual format was challenging as most elements of a virtual internship naturally rely on the use of remote technologies. Varied technologies were used in this virtual internship model, including campus-based learning management platforms, webinars, various presentation software, the opportunity to attend meetings and conferences online, working on team projects virtually, and meeting with mentors via social media platforms. Selection of specific technologies must consider issues such as accessibility, internet speed, as well as hardware and memory capacity. Alternate arrangements for students who do not have the necessary level of accessibility must be made available.Furthermore, in a virtual internship experience, interns spend most of their time in front of a computer screen engaging in the various components of the program. As a result, the types of interpersonal interactions that naturally occur in an in-person environment are different in a virtual setting. This presents a particular challenge to educators when attempting to identify learning activities that closely replicate the work environment. Further research in this area is needed.Moreover, as the intern is not able to engage with the community served by the field placement site in traditional ways, it is uncertain if the intern is fully able to understand the needs of the community. Therefore, a virtual internship must intentionally include opportunities for interaction with faculty, peers, mentors, and colleagues, as well as opportunities for deep engagement with community members so that interns develop a greater understanding and empathy for the individuals in the community served by the field placement site.Finally, as remote field placement sites were identified, it became apparent that a semester-long experience would not be possible, as most internship site supervisors indicated that they would not have the capacity for such a time-intensive field placement. This loss of contact hours is a limitation for which alternate activities needed to be identified. Mentorship opportunities and the emphasis on engagement in professional development opportunities were 2 ways in which the loss of contact hours was addressed in the virtual internship described in this viewpoint. The development of additional activities to address this loss of contact hours is warranted.There is no doubt that technology in educational settings will continue to play a major role in student learning. In recent decades, there has been accumulating evidence supporting the value of interventions delivered through digital technology, including but not limited to texting, telecommunications, and real time monitoring of symptoms and emotions -16. ConsThe benefits of a virtual internship include the ability for students to learn and practice skills related to communication with technology, the availability for interaction with supervisors with convenient timing and modalities, more frequent contacts at lower costs, and adapting to different  implementation contexts ,22. ThesIn addition, the virtual internship experience enabled students to learn and practice new skills and for community members to benefit from education and outreach while helping to ensure the safety of students and the community, as well as reducing travel costs and increasing convenience. Finally, while the need for virtual learning opportunities were prompted by the COVID-19 pandemic, it seems almost certain that this need will likely persist, which increases availability and access to public health education for some populations who would otherwise suffer consequences from unanswered questions, and for students to become adept at skills that will be needed going forward .The existing traditional undergraduate public health internship described in this viewpoint has undergone many modifications in its nearly 40-year history. However, no past revision was as extensive, necessary, or rapid as the transition to a virtual internship due to the COVID-19 pandemic. Guided by conceptual frameworks and the sincere desire on the part of the faculty to ensure that students received a robust learning experience, a virtual internship experience was developed that provided a well-organized and high-impact learning opportunity.