Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Depression is a serious medical condition and is a leading cause of disability worldwide.Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of objective assessments,and assessments that rely on patients'perceptions,memory,and recall.Digital phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes.DP includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects'natural environment.This includes active EMA,data that require active input by the subject,and passive EMA or passive sensing,data passively and automatically collected from subjects'personal digital devices.The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients'clinical status.Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status.These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients.Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines.The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations.A clinically-relevant model for incorporating DP in clinical setting is presented.This model,based on investigations conducted by our group,delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process.Benefits,challenges,and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

Social media and schizophrenia:An update on clinical applications

Social media has redesigned the landscape of human interaction,and data obtained through these platforms are promising for schizophrenia diagnosis and management.Recent research shows mounting evidence that machine learning analysis of social media content is capable of not only differentiating schizophrenia patients from healthy controls,but also predicting conversion to psychosis and symptom exacerbations.Novel platforms such as Horyzons show promise for improving social functioning and providing timely access to therapeutic resources.Social media is also a considerable means to assess and lessen the stigma surrounding schizophrenia.Herein,the relevant literature pertaining to social media and its clinical applications in schizophrenia over the past five years are summarized,followed by a discussion centered on user feedback to highlight future directions.Social media provides valuable contributions to a multifaceted digital phenotype that may improve schizophrenia care in the near future.

INPHOVIS:Interactive visual analytics for smartphone-based digital phenotyping

Digital phenotyping is the characterization of human behavior patterns based on data from digital devices such as smartphones in order to gain insights into the users’state and especially to identify ailments.To support supervised machine learning,digital phenotyping requires gathering data from study participants’smartphones as they live their lives.Periodically,participants are then asked to provide ground truth labels about their health status.Analyzing such complex data is challenging due to limited contextual information and imperfect health/wellness labels.We propose INteractive PHOne-o-typing VISualization(INPHOVIS),an interactive visual framework for exploratory analysis of smartphone health data to study phone-o-types.Prior visualization work has focused on mobile health data with clear semantics such as steps or heart rate data collected using dedicated health devices and wearables such as smartwatches.However,unlike smartphones which are owned by over 85 percent of the US population,wearable devices are less prevalent thus reducing the number of people from whom such data can be collected.In contrast,the‘‘low-level"sensor data(e.g.,accelerometer or GPS data)supported by INPHOVIS can be easily collected using smartphones.Data visualizations are designed to provide the essential contextualization of such data and thus help analysts discover complex relationships between observed sensor values and health-predictive phone-o-types.To guide the design of INPHOVIS,we performed a hierarchical task analysis of phone-o-typing requirements with health domain experts.We then designed and implemented multiple innovative visualizations integral to INPHOVIS including stacked bar charts to show diurnal behavioral patterns,calendar views to visualize day-level data along with bar charts,and correlation views to visualize important wellness predictive data.We demonstrate the usefulness of INPHOVIS with walk-throughs of use cases.We also evaluated INPHOVIS with expert feedback and received encouraging responses.

Digital imaging approaches for phenotyping whole plant nitrogen and phosphorus response in Brachypodium distachyon

This work evaluates the phenotypic response of the model grass （Brachypodium distacbyon （L.） P. Beauv.） to nitrogen and phosphorus nutrition using a combination of imaging techniques and destructive harvest of shoots and roots. Reference line Bd21-3 was grown in pots using 11 phosphorus and 11 nitrogen concentrations to establish a dose-response curve. Shoot biovolume and biomass, root length and biomass, and tissue phosphorus and nitrogen concentrations increased with nutrient concentration. Shoot biovolume, estimated by imaging, was highly correlated with dry weight （R2 〉 0.92） and both biovolume and growth rate responded strongly to nutrient availability. Higher nutrient supply increased nodal root length more than other root types. Photochemical efficiency was strongly reduced by low phosphorus concentrations as early as 1 week after germination, suggesting that this measurement may be suitable for high throughput screening of phosphorus response. In contrast, nitrogen concentration had little effect on photochemical efficiency. Changes in biovolume over time were used to compare growth rates of four accessions in response tonitrogen and phosphorus supply. We demonstrate that a time series image-based approach coupled with mathematical modeling provides higher resolution of genotypic response to nutrient supply than traditional destructive techniques and shows promise for high throughput screening and determina- tion of genomic regions associated with superior nutrient use efficiency.

Evolving technologies for growing,imaging and analyzing 3D root system architecture of crop plants

A plant＇s ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture（RSA）,the three-dimensional distribution of the different root types in the soil. The ability to image,track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three-dimensional RSA of the plant root system,while allowing for aeration,solution replenishment and the imposition of nutrient treatments,as well as high-quality imaging of the root system. The simplicity and flexibility of the system allows for modi fications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity（detection of fine roots and other root details）,higher ef ficiency,and a broad array of growing conditions for plants that more closely mimic those found under field conditions.