Jeesung Ahn

All Publications

• Developing Clinically Interpretable Neuroimaging Biotypes in Psychiatry. Biological psychiatry Ahn, J., Foland-Ross, L., Akiki, T. J., Boyar, L., Wydler, I., Bostian, C., Zhang, X., Yang, H. J., Ellsay, A., Ma, E., Rajasekharan, D., Holtzheimer, P., Lim, K., Madore, M., Philip, N., Ajilore, O., Ma, J., Williams, L. M. 2025

  Abstract

  Despite available treatments, major depressive disorder (MDD) remains one of the leading causes of disability across medical conditions. The current symptom-based diagnostic system groups patients with highly heterogeneous presentations, with no biomarkers to guide treatment-akin to diagnosing heart disease solely by chest pain, without imaging to reveal the underlying pathology. Lacking biological guidance, clinicians rely on trial-and-error prescribing. Only 33% of individuals with MDD achieve remission on initial treatments, and most cycle through multiple treatments over an average of seven years. The risk of relapse increases with each treatment failure, rising from 50% to 90%. This critical review synthesizes studies showing how functional MRI (fMRI) can predict treatment outcomes and identify which treatment is most effective for an individual based on their brain circuit profile. We illustrate one such method: a theoretically informed approach that quantifies dysfunction across six large-scale brain circuits, relative to healthy reference norms. The resulting personalized circuit scores serve as predictors of response or failure and as moderators of differential treatment outcomes. Matching treatment to a patient's biotype, defined by their circuit profile, has the potential to double remission rates compared to unmatched treatment. We place this example in the broader context of precision imaging approaches to parsing MDD heterogeneity. We also discuss key challenges, limitations, and future directions for translating fMRI-based tools into clinical practice.

  View details for DOI 10.1016/j.biopsych.2025.08.019

  View details for PubMedID 40930375

• Baseline Physical Activity Moderates Brain-Behavior Relationships in Response to Framed Health Messages. Social cognitive and affective neuroscience Ahn, J., Cooper, N., Kang, Y., O'Donnell, M. B., Green, M. A., Notthoff, N., Carstensen, L. L., Samanez-Larkin, G. R., Falk, E. B. 2025

  Abstract

  Health messaging often employs gain-framing (highlighting behavior benefits) or loss-framing (emphasizing non-engagement risks) to promote behavior change. This study examined how neural responses to gain- and loss-framed messages predict changes in physical activity. We conducted a mega-analysis of raw fMRI and pedometer/accelerometer data from four studies (N = 240) that tracked brain activity during message exposure and real-world physical activity longitudinally. Focusing on brain regions theorized by the Affect-Integration-Motivation (AIM) framework-the anterior insula, ventral striatum, vmPFC, dorsal striatum, and pre-SMA-we found that baseline physical activity levels moderated brain-behavior relationships in response to message framing. More active individuals increased physical activity post-intervention when these brain regions responded more strongly to loss-framed messages, suggesting that neural sensitivity to inactivity risks may reinforce behavior maintenance in this group. Conversely, less active individuals increased physical activity when brain responses were stronger to gain-framed messages, indicating that sensitivity to activity benefits may facilitate action initiation in this group. These findings suggest that message effectiveness depends on the interaction between framing, neural processing, and pre-existing behavioral patterns. By linking neurocognitive mechanisms with real-world outcomes, we highlight the importance of personalized, neuroscience-informed health interventions tailored to individual neural and behavioral characteristics to optimize behavior change strategies.

  View details for DOI 10.1093/scan/nsaf046

  View details for PubMedID 40324902