Mapping Human Brain Networks with Invasive EEG: From Neural Dynamics to Individual Connectomes
Dr Enrico Amico
Prof Fabian Spill
Applications accepted all year round
Funded PhD Project (UK Students Only)
About the Project
How does information propagate through the human brain, and what can direct recordings of neural activity tell us about its network organisation?
This PhD project will develop and apply network neuroscience methods to invasive electrophysiological recordings, including intracranial EEG (iEEG) and stereo-EEG (SEEG). Unlike non-invasive techniques such as fMRI, intracranial recordings provide millisecond-scale measurements directly from the human brain, offering a unique opportunity to investigate how distributed neural systems communicate and reorganise over time.
The project will combine signal processing, network science, computational neuroscience and data-driven modelling to construct and analyse time-resolved brain networks from intracranial recordings. A central objective will be to move beyond studying individual electrodes or isolated brain regions and instead characterise neural activity as a dynamically interacting system.
Depending on the candidate's interests and available datasets, possible research questions include:
- How do functional brain networks reorganise across different temporal and spectral scales?
- Which network features are stable characteristics of an individual brain — a neural fingerprint — and which reflect changes in cognitive or pathological state?
- How does information propagate between distant brain regions?
- Can network topology identify critical regions or pathways involved in seizure generation and propagation?
- Can invasive electrophysiology reveal aspects of brain communication that cannot be observed with fMRI or MEG?
Methodologically, the project may investigate functional and effective connectivity, graph theory, network communication models, dynamic networks, spectral analysis, dimensionality reduction and machine learning. There will also be opportunities to explore multivariate and higher-order approaches in which interactions between groups of neural signals are considered rather than conventional pairwise connectivity alone.
iEEG connectivity is increasingly being investigated for characterising distributed epileptic networks and may offer complementary information to standard clinical localisation approaches. The project therefore provides an opportunity to connect fundamental questions about human brain organisation with potential clinical applications in neurological disorders.
The exact direction will be shaped jointly by the student and supervisory team. Candidates will be encouraged to develop new mathematical and computational approaches rather than simply applying existing pipelines.
This project would particularly suit applicants with backgrounds in mathematics, physics, computer science, engineering, neuroscience or related quantitative disciplines. Previous neuroscience experience is desirable but not essential. Strong motivation to work with complex datasets and develop computational methods is more important.
Funding Notes
This project does not currently have a guaranteed studentship attached. Applications are welcomed from candidates who are self-funded or who wish to apply for the competitive School-funded scholarship. Prospective applicants are strongly encouraged to contact the supervisor before applying to discuss potential funding opportunities and eligibility.
References
Sinha, N. et al. (2026). Intracranial electroencephalographic connectivity analysis to localize epileptogenic networks: systematic review and meta-analysis. Epilepsia, 67, 2707–2724.
Almeida, J. & Cunha, J.P.S. (2026). Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy. Journal of Neural Engineering, 23.
Novitskaya, Y., Dümpelmann, M. & Schulze-Bonhage, A. (2023). Physiological and pathological neuronal connectivity in the living human brain based on intracranial EEG signals.
Amico, E. & Goñi, J. (2018). The quest for identifiability in human functional connectomes. Scientific Reports, 8, 8254.
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