Stroke can induce irreversible lesions, disrupting cerebral blood flow (CBF) even beyond the necrotic zone: in the perilesional tissues (PLT). Neurovascular coupling is largely based on blood flow physiological mechanisms, whose estimation is crucial in the context of brain activity detection model used in functional MRI (fMRI). The presence of a chronic lesion therefore questions the detection of brain activity in PLT. In addition, brain plasticity mechanisms are playing a crucial role in post-stroke functional recovery and can occur in PLT. Former studies showed a decreased CBF in PLT, but there is no consensus about the best way to define PLT, any criteria to determine inhomogeneous properties within the PLT or either solution to include them in classical fMRI analysis. To explore these issues, the first research study, presented in this thesis, was conducted on thirteen post stroke participants in chronic phase to investigate the PLT properties through a multimodal MRI protocol. Arterial spin labeling (pcASL) was used to map CBF; fMRI was acquired at resting state, to get the spontaneous hemodynamic time courses and thereby compute brain homotopic functional connectivity. The data were processed in each patient native space. Lesions were manually demarcated, and brain parts were segmented to extract the information from regions of interest. When normalized with homologous control brain regions, results can be related to the distance from the lesion and fitted with linear model. In other words, the further the PLTs from the lesion, the better the CBF and the brain functional connectivity recovers. Moreover, we demonstrated that gradient of perfusion and gradient of homotopic connectivity are highly correlated together in approximately 80% of the remaining tissue of the ipsilateral hemisphere. Those highlighted patterns suggest that, instead of rejecting the PLT tissue, an adjustment of neurovascular model, used in fMRI, could be applied to stroke population

Educational Background

  • Since 2015: PhD in BioSciences: Image processing & Computer Sciences.

  • 2013-2015:Research Engineer, ICM, Paris, France

  • 2013 Master degree, UPEC, honors, Signal & Image for Medicine, University Paris Est Creteil.

  • 2008-2012 Engineer school, ISBS Paris. Majors: Image processing - Bio-informatics - Drug Discovery


  • Berroir, P., Ghazi-Saidi, L., Dash, T., Adrover-Roig, D., Benali, H., and Ansaldo, A. I. (2016). In- terference control at the response level : Functional networks reveal higher efficiency in the bilingual brain. Journal of Neurolinguistics.

  • Philippe, A.-C., Berroir, P., Bardinet, E., Vidailhet, M., and Lehericy, S. (2015). Basal ganglia func- tional parcellation into specific and overlapping territories with resting state f-mri. In Biomedical Imaging(ISBI), 2015 IEEE 12th International Symposium on, pages 1352–1355. IEEE.

  • Durand, E., Berroir, P., & Ansaldo, A. I. (2018). The Neural and Behavioral Correlates of Anomia Recovery following Personalized Observation, Execution, and Mental Imagery Therapy: A Proof of Concept. Neural Plasticity, 2018, e5943759. https://doi.org/10.1155/2018/5943759