2026 ECNP Congress

PS01-1149

Towards neuroimaging-based biotypes in the neuromodulation of depression

Daniel Faber — L. van de Mortel, A. Bastami, A. Hammermayer, H. Bartsch, A. Gaspert, A. Neyazi, G. van Wingen, K. Narr, B. Draganski, M. Argyelan, C. Abbott, A. Anand, L. Emsell, P. Nordanskog, P. van Eijndhoven, A. Jørgensen, A. Dols, J. van Waarde, R. Redlich, C. Soriano-Mas, A. Takamiya, J. Camprodon, A. Sartorius, L. van Diermen, A. Yrondi, J. Prudic, N. Tuovinen, T. Bracht, U. Mehta, S. Lisanby, O.T. Ousdal, L. Oltedal, M. Kiebs, H. Maier · Hannover Medical School, Department of Psychiatry- Social Psychiatry- and Psychotherapy, Hannover, Germany

2026 ECNP Congress

Background

A persistent challenge in psychiatric neuroimaging is the limited generalizability of findings, with many reported neural correlates of depression failing to replicate across cohorts, scanners, or analytic pipelines [1]. Group-averaged analyses obscure the substantial heterogeneity of depression and have yielded only modest individual-level predictive value [2,3], suggesting that progress toward clinically useful biomarkers requires individualized approaches and a shift from broad network-level connectivity toward more anatomically and functionally constrained circuit-level connectivity. Building on a standardized framework [4], Tozzi et al. (2024) defined personalized brain circuit biotypes from three a priori resting-state networks - Default Mode (DMN), Salience (SN), and Attention (AN) - by aggregating connectivity between predefined ROIs into composite scores expressed in SD units relative to healthy controls [5]. Two reproducible resting-state biotypes emerged: a tri-network hyperconnectivity biotype (DC+SC+AC+) and an attention hypoconnectivity biotype (AC+), each with distinct clinical profiles and differential response to psychotherapy. Whether these resting-state biotypes validate, replicate and g to neuromodulatory treatment remains untested.

Aims

In depressed patients undergoing electroconvulsive therapy (ECT), we aim to (i) validate the Tozzi et al. (2024) biotype framework by applying it directly to our cohort and testing whether the AC+ and DC+SC+AC+ biotypes can be reliably identified; (ii) replicate the clustering methodology end-to-end to assess whether the same biotypes emerge from our data and characterize their clinical and demographic correlates; and (iii) evaluate within- and between-network normalization of circuit dysfunction from pre- to post-ECT, and test whether such normalization tracks symptom change.

Methods

We will use pre- and post-ECT resting-state fMRI data from the Global ECT MRI Research Collaboration (GEMRIC), which since 2015 has combined datasets from 22 sites worldwide and currently includes more than 700 subjects, providing statistical power that far exceeds any single-site cohort. Several hundred subjects with complete pre/post rs-fMRI are expected to be eligible for the present analyses. Multimodal data will include clinical measures (HDRS, MADRS), structural MRI and rs-fMRI. Preprocessing will mirror Tozzi et al. (2024) using fMRIPrep (slice timing/motion correction, susceptibility distortion correction, ICA-AROMA, CompCor, band-pass filtering 0.009–0.08 Hz, MNI normalization; >25% high-motion frames excluded). Time series will be extracted from the predefined DMN, SN, and AN ROIs, Fisher z-transformed, and averaged into composite circuit scores standardized in SD units against a healthy reference dataset. For the validation aim, patients will be stratified using the original thresholds (AC+: AN ≤ −0.5 SD; DC+SC+AC+: DMN, SN, AN all ≥ +0.5 SD). For the replication aim, biotypes will be derived data-driven by applying hierarchical clustering to independently defined resting-state biotypes circuit scores.

Analyses. Linear mixed-effects models will test Time × Biotype interactions on symptom trajectories, adjusting for age, sex, site, baseline severity, and mean framewise displacement. Logistic regression will model response (≥50% reduction) and remission as a function of baseline biotype. Secondary analyses will assess within- and between-network normalization post-ECT and correlate ΔDMN/ΔSN/ΔAN with ΔHDRS/ΔMADRS to quantify brain–symptom coupling.

References

  1. Botvinik-Nezer, R., Holzmeister, F., Camerer, C.F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J.A., Adcock, R.A., et al., 2020. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582 (7810), 84-88.
  2. Gray, J.P., Müller, V.I., Eickhoff, S.B., Fox, P.T., 2020. Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am. J. Psychiatry 177 (5), 422-434.
  3. Marek, S., Tervo-Clemmens, B., Calabro, F.J., Montez, D.F., Kay, B.P., Hatoum, A.S., Donohue, M.R., Foran, W., Miller, R.L., Hendrickson, T.J., et al., 2022. Reproducible brain-wide association studies require thousands of individuals. Nature 603 (7902), 654-660.
  4. Drysdale, A.T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R.N., Zebley, B., Oathes, D.J., Etkin, A., et al., 2017. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23 (1), 28-38.
  5. Tozzi, L., Zhang, X., Pines, A., Olmsted, A.M., Zhai, E.S., Anene, E.T., Chesnut, M., Holt-Gosselin, B., Chang, S., Stetz, P.C., et al., 2024. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat. Med. 30 (7), 2076-2087.