# Neural Markers of Treatment Mechanisms and Prediction of Treatment Outcomes in Social Anxiety

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2024 · $91,113

## Abstract

PROJECT SUMMARY/ABSTRACT
Social anxiety disorder (SAD) is one of the most common mental disorders. For unknown reasons, many
patients do not respond to existing treatments. Treatment guidelines and systematic reviews often recommend
CBT as the first line treatment, and then to start an SSRI adjunctively for patients who show no or only partial
response to initial CBT. A major advance and step toward personalized medicine would be to identify reliable
treatment predictors and to clarify the neuromechanism of treatment change. One promising approach toward
improving patient outcomes is to examine the key neurocircuitry of SAD that may also serve as neuromarkers
to predict treatment response. We have gathered convincing pilot data pointing to such neuromarkers to
predict response to CBT for SAD. The next translational step and our primary aim is to apply state of the art
computational psychiatry approaches to further establish the evidence of these neuromarkers, in line with
moving psychiatry toward precision medicine. This aim will be efficiently achieved by collecting multimodal data
to better elucidate key neurocircuitry in SAD compared to controls with state-of-the art neuroimaging in a well
powered sample, as well as differential treatment related changes in neural circuitry (target engagement). The
ultimate goal is to effectively treat all patients, not only a few and without knowing why, and to illuminate the
brain circuitry associated with effective treatments in order to inform psychopathology, nosology, and therapy
of common mental disorders. For these reasons, we propose to recruit a large number of patients with SAD (n
= 190) and healthy controls (n = 50) to examine differences in relevant neurocircuitries that will also be used as
neuromarkers of treatment response. Patients with SAD will first receive CBT group therapy. Those who show
no or only partial response will then receive individual and tailored CBT plus SSRI. In addition to MRI
measures, we will examine EEG and behavioral measures to determine whether there may be less expensive
correlates of neuropredictors that can be easily implemented in clinical practice. We have assembled a team of
skilled researchers with complementary expertise at the Massachusetts Institute of Technology (MIT; John D.
E. Gabrieli, Ph.D.), Boston University (BU; Stefan G. Hofmann, Ph.D.), and McLean Hospital (Daniel Dillon,
Ph.D.), as well as outstanding consultants in neuroimaging analysis (Northeastern University: Susan Whitfield-
Gabrieli, Ph.D.) and machine learning applications in psychiatry (McLean Hospital: Christian Webb, Ph.D.).

## Key facts

- **NIH application ID:** 11093048
- **Project number:** 3R01MH128377-03S1
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** DANIEL G DILLON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $91,113
- **Award type:** 3
- **Project period:** 2022-09-01 → 2025-04-23

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11093048

## Citation

> US National Institutes of Health, RePORTER application 11093048, Neural Markers of Treatment Mechanisms and Prediction of Treatment Outcomes in Social Anxiety (3R01MH128377-03S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11093048. Licensed CC0.

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