Polymeris Alexandros A., Curtze Sami, Erdur Hebun, Hametner Christian, Heldner Mirjam R., Groot Adrien E., Zini Andrea, Béjot Yannick, Dietrich Annina, Martinez‐Majander Nicolas, von Rennenberg Regina, Gumbinger Christoph, Schaedelin Sabine, De Marchis Gian Marco, Thilemann Sebastian, Traenka Christopher, Lyrer Philippe A., Bonati Leo H., Wegener Susanne, Ringleb Peter A., Tatlisumak Turgut, Nolte Christian H., Scheitz Jan F., Arnold Marcel, et al. (2019), Intravenous thrombolysis for suspected ischemic stroke with seizure at onset, in Annals of Neurology
Baumgartner Philipp, El Amki Mohamad, Bracko Oliver, Luft Andreas R., Wegener Susanne (2018), Sensorimotor stroke alters hippocampo-thalamic network activity, in Scientific Reports
, 8(1), 15770-15770.
(2018), Cohort profile: Thrombolysis in Ischemic Stroke Patients (TRISP): a multicentre research collaboration., in BMJ Open
Liberale Luca, Carbone Federico, Montecucco Fabrizio, Gebhard Cathérine, Lüscher Thomas F., Wegener Susanne, Camici Giovanni G. (2018), Ischemic stroke across sexes: What is the status quo?, in Frontiers in Neuroendocrinology
, 50, 3-17.
Kurmann Rebekka, Engelter Stefan T., Michel Patrik, Luft Andreas R., Wegener Susanne, Branscheidt Meret, Eskioglou Elissavet, Sirimarco Gaia, Lyrer Philippe A., Gensicke Henrik, Horvath Thomas, Fischer Urs, Arnold Marcel, Sarikaya Hakan (2018), Impact of Smoking on Clinical Outcome and Recanalization After Intravenous Thrombolysis for StrokeMulticenter Cohort Study, in Stroke
, 49(5), 1170-1175.
Wegener Susanne, Katan Mira (2017), Getting the First Grant, in Stroke
, 49(1), e7-e9.
El AmkiMohamad, WegenerSusanne (2017), Improving Cerebral Blood Flow after Arterial Recanalization: A Novel Therapeutic Strategy in Stroke, in International Journal of Molecular Sciences
, 18(12), 2669-2669.
El AmkiMohamad, BinderNAdine, Steffen Riccardo, SchneiderHannah, LuftAndreas, WellerMichael, ImthurnBruno, Merki-FeldGabriele, WegenerSusanne, Contraceptive drugs mitigate experimental stroke-induced brain injury, in Cardiovascular Research
Despite improvements in primary prophylaxis and acute recanalization treatments, stroke remains one of the leading causes of death and disability worldwide. In order to achieve the best outcome possible for the individual patient, therapies have to be administered rapidly. However, the longer the time from symptom onset, the lower the efficacy and the higher the risk of treatment side effects. Although brain imaging is the mainstay of acute stroke diagnostics, current imaging strategies that aim to predict therapeutic success or failure in acute stroke patients remain insufficient. Here, we propose a novel prediction approach that is based on immediate and long-term vascular adaptations affecting the contralateral side of stroke. From data obtained through animal models of stroke and imaging in patients with proximal vessel occlusions, contralateral cerebral blood flow (CBF) appears to be particularly suited to predict clinical benefit from recanalization therapies. Contralateral CBF and related perfusion parameters may indicate the ability of the individual to withstand longer durations of ischemia. In the experimental part of the project, we will use a thrombin-injection stroke model that does not artificially impact collateral supply. Reperfusion will be achieved through intravenous injection of recombinant tissue plasminogen activator (rtPA). To assess CBF and ischemic tissue damage, repeated magnetic resonance imaging (MRI) combined with positron emission tomography (PET) will be performed during ischemia, after reperfusion and in the chronic phase of stroke along with behavioral assessments. In the same stroke model, processes influencing CBF on the microvascular level will be directly observed using advanced optical imaging methods. Structural adaptations of the microvascular bed as well as gene and protein expression profiles contralateral to stroke will be analyzed in brain samples. In the clinical part of the project, we will analyze stroke patient imaging data in relation to clinical outcome. In addition to a traditional region-of interest (ROI) based analysis we will apply a novel, “unbiased” machine learning algorithm to extract relevant outcome predictors from patient imaging data.Using a translational approach we aim for a mechanistic characterization of the concept of contralateral flow changes in stroke, and will generate and apply imaging predictors to clinical patient data. With the multidisciplinary study proposed here, I envision i) to deepen the understanding of the basic mechanisms regulating brain perfusion after ischemic stroke, and ii) to improve therapeutic decision-making in acute stroke patients.