1141
Symposium
This symposium surveys the state-of-the-art in the application of brain network modelling techniques to research questions in the field of brain stimulation. Computational brain modelling is an emerging sub-field of computational neuroscience that focuses on simulations of different scale neural activity patterns across distributed brain networks, drawing heavily on the macro- and micro-scale connectomic data that is now readily available in multiple species. Brain stimulation is an evergreen topic in systems neuroscience and neurophysiology, with multiple recent technological and clinical breakthroughs in this space making the proposed symposium a particularly timely one. The four speakers each describe their cutting-edge recent work, spanning a wide range of stimulation modalities, brain activity measurements, spatial scales, species, and clinical applications - all of which are studied within the same unifying computational and theoretical framework. In the first presentation, Dr. Sadeh will discuss his work on modeling neural dynamics to improve brain stimulation, focusing on how targeted perturbations in neuronal microcircuits can illuminate neural processes and exploring models that connect microscale and macroscale neural activity through sophisticated computational frameworks. Afterwards, Dr. Wang will show how stimulation-induced seizures in a virtual brain twin framework diagnosed the epileptic zone network (EZN), and its successful validation with empirical data. Then, Dr. Kurtin will present her work using virtual brain twins to identify personalized stimulation parameters that shift the relationship between neural features that drive task performance. Finally, Dr. Momi will discuss the neurophysiological mechanisms underlying the efficacy of intermittent theta burst stimulation (iTBS) in treatment-resistant depression, highlighting how prefrontal-subgenual interactions and cortical inhibition contribute to clinical improvements observed in responders.
1) Apply computational modeling techniques to analyze and predict the effects of diverse brain stimulation methods (DBS, TMS, TES) on neural dynamics at both mesoscopic and macroscopic scales.
2) Implement personalized brain network models using patient-specific neuroimaging data to simulate resting-state networks and pathological states, with emphasis on parameter optimization and model validation.
3) Evaluate the clinical applications of brain stimulation modeling through case studies demonstrating how theoretical insights can inform therapeutic interventions and treatment optimization.
Our target audience is twofold, encompassing two distinct groups within the neuroscientific community:
1) Cognitive and Clinical Neuroimaging Scientists who are actively involved in or intrigued by brain stimulation. This group seeks to deepen their mechanistic comprehension of the effects of brain stimulation for both general knowledge and to enhance their research endeavors. Whether they are seasoned researchers or novices in the field, our symposium aims to provide valuable insights and the latest developments in neurophysiological modeling related to brain stimulation.
2) Systems and Computational Neuroscientists who are well-versed in whole-brain modeling. This group is particularly interested in delving deeper into the intricate realm of both invasive and noninvasive experimental brain stimulation techniques, including Transcranial Magnetic Stimulation (TMS), Transcranial Direct Current Stimulation (TDCS), Transcranial Alternating Current Stimulation (TACS), and Deep Brain Stimulation (DBS).
Our symposium serves as a platform for these experts to explore and discuss the nuances of these advanced techniques, fostering a cross-disciplinary dialogue that bridges the gap between micro/macro scale brain modeling and experimental applications. By bringing together these two groups, our goal is to facilitate a rich exchange of ideas and knowledge, contributing to the advancement of both cognitive/clinical neuroimaging and systems/computational neuroscience.
Presentations
Estimating the epileptogenic zone network (EZN) is an important part of the diagnosis of drug-resistant focal epilepsy and plays a pivotal role in treatment and intervention. Virtual brain twins, based on personalized whole brain modeling, provide a formal method for personalized diagnosis and treatment. They integrate patient-specific brain topography with structural connectivity from anatomical neuroimaging such as MRI, and dynamic activity from functional recordings such as EEG and stereo-EEG (SEEG). Seizures demonstrate rich spatial and temporal features in functional recordings, which can be exploited to estimate the EZN. Stimulation-induced seizures can provide important and complementary information. We considered invasive SEEG stimulation as the most practical current approach, and temporal interference (TI) stimulation as a potential future approach for non-invasive diagnosis and treatment. I will present a virtual brain twin framework for EZN diagnosis based on stimulation-induced seizures. This framework estimates the EZN and validated the results on synthetic data with ground-truth. It provides an important methodological and conceptual basis for a series of ongoing scientific studies and clinical usage. This framework also provides the necessary step to go from invasive to non-invasive diagnosis and treatment of drug-resistant focal epilepsy.
Presenter
Huifang Wang, Aix Marseille Université Marseille, France
France
The brain comprises complex neuronal networks that underlie its adaptive and versatile functionality. Recent neurotechnological innovations have dramatically expanded our ability to investigate these networks, enabling precise interventions at the microcircuit level. Here, I present modelling research examining how targeted perturbations of excitatory and inhibitory neurons within microcircuits can illuminate neural dynamics. Subsequently, I introduce novel models for decoding neural activity across larger scales. Based on these modelling work, I argue that effectively connecting microscale and macroscale neural phenomena requires more sophisticated computational models for encoding and decoding brain activity. Technological advances are necessary, but they must be complemented by robust theoretical frameworks that can translate insights from localised neural interactions to system-wide neural processes.
Presenter
Sadra Sadeh, Imperial College London London, UK
United Kingdom
Depression remains a significant global health challenge, with Intermittent Theta Burst Stimulation (iTBS) targeting the left dorsolateral prefrontal cortex (DLPFC) as a promising intervention for treatment-resistant depression. However, the neurophysiological mechanisms driving its clinical efficacy are not fully understood. Here, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) with whole-brain computational modeling to investigate iTBS effects in a cohort of 90 participants. Before iTBS, responders exhibited a unique brain state marked by opposite directional interactions between the subgenual cingulate (SGC) and DLPFC, a pattern absent in non-responders. Following iTBS, responders demonstrated significant divergence from baseline neuronal dynamics, especially between 50-120 ms post-stimulation, suggesting enhanced neuroplasticity. Furthermore, responders exhibited a power reduction in the 3-10 Hz range, associated with increased cortical inhibition and a lowered excitation-inhibition ratio. These results suggest that iTBS restores balance within prefrontal-subgenual networks and enhances cortical inhibition, which may be crucial mechanisms underlying clinical improvements in responders.
Presenter
Davide Momi, Stanford University Stanford, CA
United States
Current therapies for addiction largely centre on cognitive strategies and emotional management. Given that more than 66% of people will relapse after treatment, new therapeutic approaches are gravely needed. We are developing virtual brain twins to identify parameters for personalised, non-invasive deep brain stimulation targeting the neural underpinnings of addiction. To construct the virtual brain twin, fMRI data is collected while participants perform the Iowa Gambling Task, which probes reward and decision-making processes disrupted in addiction. After identifying which neural metric best predicts participant task performance, virtual brain twin parameters are optimized so that model outputs that most closely match the target neural metric. A stimulation term is added, and stimulation parameters optimized to shift the target neural metric in the direction that accords with improved task performance. Participants return for a second session of task fMRI, where they receive both personalized stimulation and generic stimulation. While data collection is ongoing, we anticipate that personalised stimulation will more effectively shift brain-behaviour relationships, setting a new course for neuromodulatory interventions for addiction.
Presenter
Danielle Kurtin, PhD, Imperial College London
Department of Brain Sciences
London, UK
United Kingdom