Digital phenotyping for psychiatry: accommodating data and theory with network science methodologies
Year: 2018
Type: review
Abstract: Digital phenotyping is the moment-by-moment quantification of our interactions with digital devices. With appropriate tools, digital phenotyping data afford unprecedented insight into our transactions with the world and hold promise for developing novel signatures of psychopathology that will aid in diagnosis, prognosis, and treatment selection of psychiatric disorders. In this review, we highlight empirical work merging digital phenotyping data, and particularly experience-sampling data collected via smartphone, with network theories of psychopathology and network science methodologies. The intensive, longitudinal, and multivariate data collected through digital phenotyping designs provide the necessary foundation for the application of network science methodologies to parsimoniously test network theories of psychopathology emphasizing causal interactions among psychiatric symptoms, as well as other phenotypes, across time.
Institution: University of Pennsylvania
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,830
Funders: National Science Foundation, John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, Paul G. Allen Family Foundation, Assisi Foundation of Memphis, ASAE Foundation, Institute for Scientific Interchange, National Institutes of Health, National Science Foundation of Sri Lanka, Office of Naval Research, National Institute of Mental Health, National Institute on Deafness and Other Communication Disorders, National Institute of Neurological Disorders and Stroke, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Army Research Office, Army Research Laboratory
Awards: G1024130705, Multimodal brain maturation indices modulating psychopathology and neurocognition, G1483473580, G1997747098, G2138167968, Virtual Resection to Treat Epilepsy, G235261508, G2425642435, G2519463293, G2540474472, G2668337057, G334592757, G3437992771, G3824545241, G4542113549, Network Control and Functional Context: Mechanisms for TMS Response, G572628577, NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control, CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics, Longitudinal multi-modal neuroimaging of irritability in youth, Linguistic and Nonlinguistic Functions of Frontal Cortex, Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence, G7422443152, CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks, WORKSHOP: Quantitative Theories of Learning, Memory, and Prediction, G8020349068, G8895310522, Inter-modal Coupling Image Analytics, Linking the Development of Association Cortex Plasticity to Trans-Diagnostic Psychopathology in Youth, G90128336
OpenAlex ID: W2903691885