Scalable Biomarkers and Generative Digital Twins for Personalized Neurostimulation in Depression

用于抑郁症个性化神经刺激的可扩展生物标志物和生成数字双胞胎

• 批准号: 10556838
• 负责人: LOGAN GROSENICK
• 金额: $ 63.43万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10556838
• 关键词: Acute; Affect; Aftercare; American; Biological; Biological Markers; Brain; COVID-19; COVID-19 pandemic; Chronic stress; Clinical; Clinical Trials; Custom; Data; Depressed mood; Development; Disease remission; Effectiveness; Electroencephalography; Functional Magnetic Resonance Imaging; Gold; Hour; Human; Individual; Individual Differences; Intervention; Learning; Life; Literature; Magnetism; Major Depressive Disorder; Measurable; Measurement; Measures; Mental Depression; Modality; Modeling; Neurobiology; Patients; Pattern; Persons; Pharmacology; Prevalence; Protocols documentation; Research; Rest; Risk; Social isolation; Symptoms; Techniques; Time; Training; Trauma; Treatment Protocols; Twin Multiple Birth; United States; Woman; Work; base; density; depressed patient; digital; disability; generative adversarial network; improved; in silico; individual response; individualized medicine; innovation; insight; machine learning framework; neural network; neurophysiology; novel strategies; outcome prediction; personalized medicine; physical model; psychologic; repetitive transcranial magnetic stimulation; simulation; socioeconomics; treatment response; treatment-resistant depression; trend

Project Summary/Abstract More than 100 million people in the United States currently show signs of clinical depression, approximately three times more than before the onset of the COVID-19 crisis. Currently, the main treatment options for such depressed individuals include pharmacological and psychological interventions, the acute and long-term effectiveness of which are significantly limited: up to one-third of patients develop treatment-resistant depression. Noninvasive neurostimulation therapies such as repetitive Transcranial Magnetic Stimulation (rTMS)–where a magnetic coil placed over the cortex is used to focally stimulate the brain–have recently emerged as promising low-risk interventions for treatment-resistant depression. However, the mechanisms and appropriate parameters for this treatment remain poorly understood. In the best cases, rTMS can have dramatic effects, changing the course of a patient's life in hours. In many cases, however, it has little to no measurable effect. This raises the obvious question: why do current rTMS protocols work well for some individuals but not for others? Could we adapt protocols to work well for everyone, potentially providing reliable personalized treatment or even a lasting cure? A growing literature suggests this is possible if we learn to tailor treatment to individual differences in human neurophysiology. Here we propose an innovative and unique approach towards precision psychiatric neurostimulation: personalized modeling of treatment using “Generative Digital Twins”. To affordably build and scale Generative Digital Twins, we propose combining high density electroencephalography (HD-EEG)–which is non-invasive, inexpensive, and easily deployable–to measure longitudinal changes in brain connectivity during an accelerated rTMS treatment protocol for depression. Then, using controllable generative neural networks that allow detailed predictive simulations of individual response trajectories given rTMS treatment, we can begin to predict outcomes prior to treatment, understand individual responses, and personalize treatment parameters.

项目总结/文摘