TrueBrainConnect: Advancing the non-invasive assessment of brain communication in neurological disease

Pathological communication between different brain regions has been implicated in various neurological disorders. However, the computational tools for assessing such communication from neuroimaging data are not sufficiently developed. The goal of TrueBrainConnect is to establish brain connectivity analysis using non-invasive electrophysiology as a practical and reliable neuroscience tool. To achieve this, we will develop novel signal processing and machine learning techniques that address shortcomings in state-of-the-art reconstruction and localization of neural activity from sensor data, the estimation of genuine neural interactions, the prediction of external (eg, clinical ) variables from estimated neural interactions, and the interpretation of the resulting models. These techniques will be thoroughly validated and then made publicly available. We will use the TrueBrainConnect methodology to characterize the neural bases underlying dementia and Parkinson's disease (PD), two of the most pressing neurological health challenges of our time. In collaboration with clinical experts, we will address practically relevant issues such as how to determine the onset of 'freezing' episodes in PD patients, and how to detect different variants and precursors of dementia. The outcome of TrueBrainConnect will be a versatile methodology allowing researchers, for the first time, to reliably estimate and anatomically localize important types of interactions between different brain structures in humans within known confidence bounds. two of the most pressing neurological health challenges of our time. In collaboration with clinical experts, we will address practically relevant issues such as how to determine the onset of 'freezing' episodes in PD patients, and how to detect different variants and precursors of dementia. The outcome of TrueBrainConnect will be a versatile methodology allowing researchers, for the first time, to reliably estimate and anatomically localize important types of interactions between different brain structures in humans within known confidence bounds. two of the most pressing neurological health challenges of our time. In collaboration with clinical experts, we will address practically relevant issues such as how to determine the onset of 'freezing' episodes in PD patients, and how to detect different variants and precursors of dementia. The outcome of TrueBrainConnect will be a versatile methodology allowing researchers, for the first time.

Find the project on CORDIS.

• Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework, A Hashemi, C Cai, G Kutyniok, KR Müller, SS Nagarajan, S Haufe, NeuroImage 239, 118309.
• Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm, C Cai, A Hashemi, M Diwakar, S Haufe, K Sekihara, SS Nagarajan, NeuroImage 225, 117411.
• Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age, E Cesnaite, TP Steinfath, MJ Idaji, T Stephani, D Kumral, S Haufe, bioRxiv.
• Relationship between Regional White Matter Hyperintensities and Alpha Oscillations in Older Adults, D Kumral, E Cesnaite, F Beyer, SM Hofmann, T Hensch, C Sander, bioRxiv, 2020.09. 04.283200, 2021.
• Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance, C Vidaurre, S Haufe, T Jorajuría, KR Müller, VV Nikulin, Frontiers in Neuroscience 14, 1278.
• Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI, M Nentwich, L Ai, J Madsen, QK Telesford, S Haufe, MP Milham, LC Parra, NeuroImage 218, 117001.
• Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses, T Stephani, G Waterstraat, S Haufe, G Curio, A Villringer, VV and Nikulin, The Journal of Neuroscience 40 (34), 6572-6583.
• Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation, C Vidaurre, AR Murguialday, S Haufe, M Gómez, KR Müller, VV Nikulin, NeuroImage 199, 375-386.
• Quantifying the effect of demixing approaches on directed connectivity estimated between reconstructed EEG sources, A Anzolin, P Presti, F Van De Steen, L Astolfi, S Haufe, D Marinazzo, Brain topography 32 (4), 655-674.
• A simulation framework for benchmarking EEG-based brain connectivity estimation methodologies, S Haufe, A Ewald, Brain topography 32 (4), 625-642.
• A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG / fMRI, T Tu, J Paisley, S Haufe, P Sajda, Advances in Neural Information Processing Systems 32, 4662-4671.