Back to overview

Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI

Applicant Esteban Sanz-Dranguet Oscar
Number 185872
Funding scheme Ambizione
Research institution Service Radiologie CHUV
Institution of higher education University of Lausanne - LA
Main discipline Information Technology
Start/End 01.06.2020 - 31.05.2024
Approved amount 903'000.00
Show all

All Disciplines (5)

Information Technology
Electrical Engineering
Neurophysiology and Brain Research
Neurology, Psychiatry
Biomedical Engineering

Keywords (6)

functional MRI; connectome; diffusion MRI; thalamus; brain; magnetic resonance imaging

Lay Summary (French)

Découvrir l'interaction de la structure, de la fonction et de la dynamique de la connectivité cérébrale à l'aide de l'IRM
Lay summary

L'imagerie par résonance magnétique (IRM) est potentiellement le moyen le plus direct mais non invasif de sonder l'architecture et l'activité du cerveau in vivo, avec une résolution spatiale et temporelle suffisante pour dévoiler comment la structure du cerveau définit sa fonction distribuée. Cependant, les limites de l'IRM entravent le lien entre la structure, la fonction et la dynamique. En particulier, les mesures obtenues avec l'IRM sont très indirectes, spatio-temporelles incertaines, et sont confondues par d'autres sources de signal RM. Cette complexité constitue un immense défi informatique qui traverse de multiples modalités d'imagerie, y compris des approches de connectivité structurelle, fonctionnelle et dynamique pour comprendre le cerveau humain. Dans ce projet, nous aborderons la fiabilité du flux de travail de neuroimagerie scientifique en testant l'hypothèse selon laquelle la minimisation des effets de confusion sur le prétraitement de l'IRM fonctionnelle et structurelle permettra une approche de modélisation intégrée qui est fondamentale pour la compréhension du cerveau humain.

Dans l'ensemble, ce projet dotera les chercheurs d'un cadre pour l'extraction de réseaux structurels, fonctionnels et dynamiques fiables et précis qui permettent leur modélisation et leur analyse conjointes avec des méthodes interprétables et reproductibles. Nous publierons publiquement deux ensembles de données très utiles pour l'évaluation des méthodes d'extraction du réseau. Enfin, sur la base de l'amélioration du flux de travail de neuroimagerie, nous déchiffrons le rôle de contrôle des noyaux thalamiques sur la connectivité fonctionnelle dynamique, qui est considérée comme un biomarqueur clé de la progression de la maladie.

Direct link to Lay Summary Last update: 28.04.2020

Responsible applicant and co-applicants


Associated projects

Number Title Start Funding scheme
157040 Advanced high-field MR imaging and quantitative image analysis for segmentation of the thalamic nuclei 01.04.2015 Project funding (Div. I-III)
156874 Quantitative characterization of the connectome in the progression of psychosis 01.05.2015 Project funding (Div. I-III)


Unveiling how the brain's structure defines its distributed function and modulates the dynamics of processing holds the promise of triggering a revolution in neuroscience and applications to mental health and neurodegenerative diseases. Magnetic resonance imaging (MRI) has proven a valuable, non-invasive way of probing both the architecture and activity of the brain in-vivo, with sufficient spatial and temporal resolution to understand many aspects of its function. However, limitations in MRI impede the link between structure, function, and dynamics. In particular, the measurements obtained with MRI are highly indirect, spatio-temporally uncertain, and are confounded by other sources of MR signal. This complexity provides an immense informatics challenge that crosses multiple imaging modalities, including structural, functional and dynamic connectivity approaches to understanding the human brain.In this project, we will address the reliability of the scientific neuroimaging workflow by testing the hypothesis that minimizing the confounding effects on preprocessing of functional and structural MRI will enable an integrated modeling approach that is fundamental to the understanding of the human brain. Using a dense sampling approach on a single healthy participant (an approach that has been dubbed "precision neuroimaging"), optimal acquisition and processing for each modality will be determined across four scanner models. The wealth of repeated data in this dataset will allow us to define a set of "gold" standards necessary in the validation of workflows to extract functional and structural networks. We will comprehensively evaluate the multiplicity of processing alternatives to select those that achieve the highest sensitivity and specificity. We hypothesize that such improvements in sensitivity and specificity of functional and structural networks generalize across scanners and subjects, allowing the univocal identification (or "fingerprinting") of networks. We will test whether gains in reliability of connectomes are reproduced when applied on data from ten new subjects (5M / 5F), whose data will be acquired using the optimized protocols from the previous step and the precision neuroimaging approach. Our approach will focus specifically on the thalamus, which mediates the dynamical activity propagation through the cortex, and thus is thought to regulate the trajectories of connectivity when the brain switches between task-oriented and internal functional networks. Additional evidence suggests that the Thalamus may be a relevant descriptor of disease and have associated the disruption in thalamo-cortical connectivity with Schizophrenia and Bipolar Disorder. To attain the necessary topological resolution of regions anatomically connected to the Thalamus, we will use very-high resolution dMRI by limiting the field of view to our region of interest. Preprocessed data will be then integrated into a common framework for the analysis and modelling of brain functional dynamics. In particular, we will test the hypothesis that integrative thalamic nuclei control the dynamics of functional networks that aperiodically oscillate between integrated and segregated topological states. Overall, this project will equip researchers with a framework for the extraction of reliable and precise structural, functional and dynamic networks that permit their joint modeling and analysis with interpretable and reproducible methods. The project will publicly release two highly valuable datasets necessary in the improvement of the workflow for structural and functional network extraction. Finally, with the support of the improved neuroimaging workflow, we will decipher the control role of the integrative thalamic nuclei over dynamic functional connectivity, which is thought to be a key biomarker of disease progression in Schizophrenia and Bipolar Disorder.