# Project 3: Modulating repetitive negative thinking related brain networks in young adults with depression > **NIH NIH P20** · LAUREATE INSTITUTE FOR BRAIN RESEARCH · 2024 · $291,250 ## Abstract PROJECT SUMMARY: Project 3 – Tsuchiyagaito Major Depressive disorder (MDD) is among the most common mental health conditions in young adulthood (ages 18-25) occurring in 11%, and has serious effects on long-term outcomes such as comorbid mental disorders, unemployment and suicide, if not effectively treated. Repetitive negative thinking (RNT) is a recurrent thought process which is negative in valence and difficult to control. RNT is consistently linked to a higher frequency, duration, and severity of depression, and also predicts suicidality in early adulthood. Although effective treatments for MDD have been established, nearly two-thirds of patients will not respond, and treatment of MDD with currently available modalities leave as many as 90% with residual symptoms, including RNT. Higher RNT is also linked to a slower response and poorer outcome to both antidepressant medication and psychotherapy. Thus, RNT and the underlying neural circuit would be ideal to be directly targeted and it would boost the relevant regulatory brain functions. Real-time functional Magnetic Resonance Imaging neurofeedback (rtfMRI-nf) training is an ideal, non-invasive method to translate RNT-related brain networks into a targetable disease-modifying process. This proposal builds on our previous work, in which we identified the brain functional connectivity associated with RNT, i.e., we determined that the connectivity between the right anterior insular (rAI) and the right superior temporal sulcus (rSTS) was positively correlated with higher RNT in individuals with MDD. In this proposal, we use rtfMRI-nf to causally relate the dysfunction of rAI-rSTS connectivity with the intensity of RNT. We will conduct a randomized double-blind, sham-controlled trial of rtfMRI-nf in n=110 (n=100 completers, assuming a 10% attrition) young adult MDD individuals (ages 18- 25) with RNT symptoms. We will evaluate how rtfMRI-nf can be used to attenuate RNT, and thereby reduce depression. We aim to investigate the degree to which reducing rAI-rSTS connectivity alleviates RNT and depressive symptoms in young adults with MDD receiving active rtfMRI-nf (n=55, 50 completers) compared to sham (neurofeedback with artificially generated feedback signals; n=55, 50 completers) groups. Specific aims are to determine (1) an acute effect of real vs. sham rtfMRI-nf on rAI-rSTS functional connectivity; (2) and on RNT and depression. An exploratory aim will examine the degree to which acute modulation of rAI-rSTS connectivity leads to changes in RNT as well as depression and sub-acute rAI-rSTS connectivity change one week after rtfMRI-nf. The overarching goal is to establish that (1) rAI-rSTS connectivity is involved in RNT and can be modulated as a targetable disease-modifying process of MDD, and (2) by modulating rAI-rSTS connectivity to reduce RNT we can reduce depression severity. The systematic approach of this proposal embodies the goals of the NIH RDoC Initiative by i... ## Key facts - **NIH application ID:** 10894860 - **Project number:** 5P20GM121312-07 - **Recipient organization:** LAUREATE INSTITUTE FOR BRAIN RESEARCH - **Principal Investigator:** Aki Tsuchiyagaito - **Activity code:** P20 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $291,250 - **Award type:** 5 - **Project period:** 2017-09-15 → 2025-04-09 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10894860 ## Citation > US National Institutes of Health, RePORTER application 10894860, Project 3: Modulating repetitive negative thinking related brain networks in young adults with depression (5P20GM121312-07). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10894860. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*