2139
Symposium
The contribution of neuroimaging to informing basic, behavioural and clinical applications of brain stimulation is rapidly growing. The use of brain circuits to derive neurobiologically informed targets has become one of the hottest and most rapidly evolving topics in the field at the moment. The importance of this research to identifying new therapeutic targets is now widely recognised and has spurred several large clinical trials internationally, underscoring its importance and timeliness. This research has relevance to neuroscientists, neuroimaging experts and clinicians and is fundamental to those interested in moving from correlation to causation, or translating their findings to better clinical outcomes. The symposium will offer attendees the opportunity to learn the most recent developments in the field and how to leverage the opportunities presented by connectomics-based neuromodulation.
The talks will detail robust new findings (2024 published/2025 in progress) on brain circuitry and biotypes mediating depression, derived from large neuroimaging datasets. These are likely to provide a critically important new avenue in helping to stratify patients to different therapies and inform brain stimulation targeting. The talks will also include novel findings (in prep) from TMS performed in an MRI scanner, now demonstrating the causal importance of target site in determining activation across brain networks in patients with depression. In addition, the symposium will cover new clinical outcomes from a trial utilizing connectivity guided personalised brain stimulation in depression by the authors (2024, submitted).
-Learn how brain-behaviour biotypes have recently been derived and how these differentially respond to brain stimulation and other therapies.
-Learn how the circuits responding to brain stimulation can be tested causally in the scanner, and how research can be clinically translated to better therapeutic or behavioural outcomes.
-Learn the advantages, evidence and outstanding questions relating to circuit-based targeting, with relevance across a range of conditions from neurological to psychiatric disorders and basic science or behavioural applications.
The target audience comprises researchers and clinicians involved in basic, behavioural and clinical neuroscience, and in invasive or non-invasive neuromodulation. Neuroimaging researchers who are interested in moving from correlation to causation will find this session of great interest. Others interested in how they can leverage brain stimulation to clinically translate their findings across different indications will benefit from this symposium.
Presentations
Emerging research indicates that many psychiatric disorders are characterized by dysfunctional brain circuits. In this talk I will briefly describe how this circuit-based framework has recently reshaped and improved the capacity to identify optimal brain stimulation targets for psychiatric disorders. I will discuss how these circuits can be targeted on a personalized basis and briefly overview the evidence for this approach from basic and clinical research, including our recent clinical outcomes. I will describe new research where we have employed network communication models based on structural connectivity to understand and predict propagation of a TMS pulse through the human connectome. I will present our recent findings from TMS performed in the MRI scanner illustrating differential circuit activation at distinct DLPFC TMS targets (all personalized). Lastly, I will briefly highlight our novel hardware solution that provides a cheaper, simpler and faster alternative to neuronavigation for circuit-based or personalised targeting where desired.
Major depressive disorder (MDD) is a complex and heterogeneous condition. While various methods for subtyping MDD have been proposed, there is no consensus on whether these subtypes can guide treatment selection. In my talk, I will present a robust, data-driven model developed from a large, single-site dataset (n=328) that combines clinical assessment data with resting-state fMRI to identify MDD subtypes. Our analysis revealed four distinct MDD subtypes, each characterized by unique symptom profiles, atypical functional connectivity, and responses to transcranial magnetic stimulation (TMS). We further validated these subtypes by applying them to an independent dataset of individuals treated with escitalopram (n=130) and found that subtype membership predicted differential responses to either TMS or pharmacotherapy. These findings suggest that subtyping approaches to classifying neurobiological and symptom variability in MDD are stable, generalizable, and capable of predicting treatment outcomes across different interventions.
In this presentation, I will discuss the use of interleaved intermittent theta-burst stimulation (iTBS) and functional MRI to investigate real-time neural circuit engagement in patients with depression. This approach addresses the challenge of individual variability in response to TMS by allowing direct observation of neural activity at the time of stimulation. By capturing these temporal dynamics, interleaved TMS-fMRI provides unique insights into the neurobiological mechanisms underlying TMS effects. It reveals how stimulation influences the DLPFC-sgACC target network, a critical circuit implicated in depression. Through data from our clinical study, I will demonstrate how this method not only identifies acute brain activity following stimulation but also distinguishes how these patterns differ between responders and non-responders, highlighting individual differences in circuit engagement. By identifying these differences, we’re taking crucial steps toward tailoring treatments to each individual.
Presenter
Maria Vasileiadi, Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, University of Toronto Toronto, Ontario
Canada
Reliable neural biomarkers for psychiatric disorders including Major Depressive Disorder (MDD) have remained difficult to define, partially due to the variability in brain alterations. In this talk, I will discuss how network mapping analyses can help bridge this gap. This approach considers whether apparently isolated brain changes are actually linked in terms of distributed brain networks. While studies using normative connectomes have shown depression-related alterations converge on specific neural circuits, transdiagnostic validation in clinical populations has yet to be explored. We examined depression networks using diffusion-weighted and resting-state fMRI data from a large cohort (N=2592), including individuals with MDD, bipolar disorder (BD), schizophrenia (SZ), and healthy controls (HC). By analyzing brain connectivity patterns, we aimed to determine their relationship with psychiatric symptoms, cognitive deficits, genetic risks, and early-life stress. Using regression models and machine learning, we explored whether connectivity differences can distinguish diagnostic groups and reveal shared neurobiological features. This work advances our understanding of depression’s neural signatures by integrating multimodal data and transdiagnostic analyses, paving the way for more personalized and effective treatments.
Presenter
Marius Gruber, Goethe University
Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy
Frankfurt, Hesse
Germany