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\r\nThe MARVEL Junior Seminar series is continuing in hybrid mode, in order to maintain in-person contacts and allow off-campus attendees to follow the seminars remotely! The MARVEL Junior Seminars aim to intensify interactions between the MARVEL Junior scientists belonging to different research groups. The seminar consists of two 25-minute presentations, followed by time for discussion.
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\r\nZacharie Waysenson
\r\nLaboratoire de Physique de l’École Normale Supérieure, Sorbonne Université
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\r\nMaria Andolfatto
\r\nLaboratory for Materials Simulations, PSI
It is becoming increasingly common to issue forecasts that are probabilistic, in the form of probability distributions over all possible outcomes. To generate good probabilistic forecasts, we must first be able to evaluate how good a forecast is. The evaluation of probabilistic forecasts focuses on two aspects of forecast performance: forecast accuracy and forecast calibration. Forecast accuracy refers to how 'close' the forecast is to the corresponding observation, which can be quantified using proper scoring rules. Forecast calibration considers whether probabilistic forecasts are trustworthy. While most scoring rules and calibration checks treat all possible outcomes equally, some outcomes are often of more interest than others when evaluating forecasts, and one could argue that these outcomes should therefore be emphasised during forecast evaluation. For example, extreme outcomes typically lead to the largest impacts on forecast users, making accurate and calibrated forecasts for these outcomes particularly valuable. In this talk, we discuss methods to focus on particular outcomes when evaluating probabilistic forecasts. We review weighted scoring rules, which allow practitioners to incorporate a weight function into conventional scoring rules when calculating forecast accuracy, and we demonstrate that the theory underlying weighted scoring rules can readily be extended to forecast calibration. Using this, we introduce methods to evaluate the calibration of probabilistic forecasts for extreme events.
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You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/challenges-in-modelling-ion-channels-simulations-meet-experiments-1369.
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\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
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\r\nDescription
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\r\nThe human genome includes more than 300 genes coding for ion channel proteins, representing approximately 2% of the total number of genes. This abundance of ion channels highlights their critical role in numerous biological processes and their involvement in diseases, underscoring their importance as potential drug targets. Ion channels exert their biological roles through three main functional characteristics: the highly efficient selective conduction of ions; the capacity to open and close in response to chemical/physical stimuli (gating); and the decrease in conductance upon sustained stimuli (inactivation). In the last 20 years, the number of experimental atomic structures of ion channels has increased from a few units to hundreds, now including representative structures for most of the ion channel families. Simulations based on these experimental structures have significantly contributed to the current understanding of conduction, selectivity, gating, and inactivation [1-2]. Strengthening the quantitative agreement between simulations and experiments is now essential for advancing in this field. This effort is currently hampered by common issues in biomolecular simulations, such as the limited timescales for observing biologically relevant events and the sub-optimal accuracy of the underlying physical models. Both of these shortcomings are expected to be mitigated by recent methodological developments. For instance, atomic simulations of ion channels with polarizable force fields have been recently reported [3]. Lack of polarization is a well-known limitation of classical force fields, especially when describing ion-protein and ion-water interactions in a crowded environment like the pore cavity. Consequently, the usage of polarizable force fields is considered a promising strategy for improving the agreement with experimental data about ion conduction and selectivity. An alternative strategy for enhancing the model accuracy in critical channel regions is to combine molecular mechanics with quantum approaches. Thanks to the ever-increasing computational resources, now combined with advancements in codes for hybrid QM/MM models, this approach is becoming feasible for ion channel research [4]. Increasing computational resources, coupled with improved algorithms for accelerating rare events and potentially harnessing machine learning, are also opening new possibilities in the study of state transitions. Gating and inactivation events of ion channels are finally becoming accessible to atomic simulations, offering important insights into the mechanistic functioning of this important protein superfamily [5]. The proposed workshop will foster further developments in the field by bringing together leading scientists in the experimental methodologies and computational techniques used in ion channel research in a stimulating and collaborative environment.
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\r\nReferences
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\r\n[1] E. Flood, C. Boiteux, B. Lev, I. Vorobyov, T. Allen, Chem. Rev., 119, 7737-7832 (2019)
\r\n[2] C. Guardiani, F. Cecconi, L. Chiodo, G. Cottone, P. Malgaretti, L. Maragliano, M. Barabash, G. Camisasca, M. Ceccarelli, B. Corry, R. Roth, A. Giacomello, B. Roux, Advances in Physics: X, 7, (2022)
\r\n[3] V. Ngo, H. Li, A. MacKerell, T. Allen, B. Roux, S. Noskov, J. Chem. Theory Comput., 17, 1726-1741 (2021)
\r\n[4] F. Schackert, J. Biedermann, S. Abdolvand, S. Minniberger, C. Song, A. Plested, P. Carloni, H. Sun, J. Chem. Inf. Model., 63, 1293-1300 (2023)
\r\n[5] S. Pérez-Conesa, L. Delemotte, Free energy landscapes of KcsA inactivation, 2023
Join us for the third edition of the Lemanic Life Sciences Hackathon!
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\r\nOrganized by EPFL School of Life Sciences, EPFL AI Center and the University of Bern, we aim to bring together clinicians, life scientists, and data scientists to test ideas, drive innovation, and tackle real-world challenges in healthcare and research.
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\r\nExplore the untapped potential of AI in life sciences and clinical research\r\n
\r\nFrom digital puzzles to space simulations, play has become a major arena for creation, innovation, and reflection. Through a curated selection of video games, history books, essays, and academic research, the Library invites you to discover the many dimensions of games and gaming culture. Borrow the documents and keep the experience going at home!
","image_description":"CC-BY-NC-SA EPFL Library","creation_date":"2026-03-18T09:56:25","last_modification_date":"2026-03-18T09:56:55","link_label":"Sélection documentaire en ligne","link_url":"https://slsp-epfl.primo.exlibrisgroup.com/discovery/collectionDiscovery?vid=41SLSP_EPF:prod&collectionId=8196759420005516&lang=fr","canceled":"False","cancel_reason":"","place_and_room":"Espace Découverte","url_place_and_room":"","url_online_room":"","spoken_languages":["https://memento.epfl.ch/api/v1/spoken_languages/1/?format=json","https://memento.epfl.ch/api/v1/spoken_languages/2/?format=json"],"speaker":"","organizer":"EPFL Library","contact":"library@epfl.ch","is_internal":"False","theme":"","vulgarization":{"id":1,"fr_label":"Tout public","en_label":"General public"},"registration":{"id":3,"fr_label":"Entrée libre","en_label":"Free"},"keywords":"Jeux, jeux vidéo, théorie des jeux","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120117/","category":{"id":5,"code":"EXPO","fr_label":"Expositions","en_label":"Exhibitions","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/1/?format=json","https://memento.epfl.ch/api/v1/mementos/3/?format=json","https://memento.epfl.ch/api/v1/mementos/5/?format=json","https://memento.epfl.ch/api/v1/mementos/6/?format=json","https://memento.epfl.ch/api/v1/mementos/8/?format=json","https://memento.epfl.ch/api/v1/mementos/145/?format=json","https://memento.epfl.ch/api/v1/mementos/275/?format=json","https://memento.epfl.ch/api/v1/mementos/22/?format=json","https://memento.epfl.ch/api/v1/mementos/27/?format=json"]},{"id":71389,"title":"Call for projects CROSS 2027","slug":"call-for-projects-cross-2027","event_url":"https://memento.epfl.ch/event/call-for-projects-cross-2027","visual_url":"https://memento.epfl.ch/image/32738/200x112.jpg","visual_large_url":"https://memento.epfl.ch/image/32738/720x405.jpg","visual_maxsize_url":"https://memento.epfl.ch/image/32738/max-size.jpg","lang":"en","start_date":"2026-03-18","end_date":"2026-06-15","start_time":"00:00:00","end_time":"23:59:00","description":"The 2027 edition of the CROSS program calls upon researchers at EPFL and the University of Lausanne to submit proposals for joint projects that bring the natural sciences and engineering together with the social sciences and humanities.
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\r\nThrough this annual call for projects, CROSS provides competitive grants to support new seed research endeavors that have the potential to grow into full–scale interdisciplinary research projects.
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\r\nThe 2024 CROSS call is open to all topics.
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\r\nPROPOSALS:
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\r\nProposals must come from joint UNIL-EPFL teams, which bring together specialists in the human and social sciences on the one hand, with specialists from life sciences, natural sciences or engineering on the other. Up to six projects will be selected, with a maximum of CHF 60,000 awarded per project.\r\n
In this seminar, I will discuss our efforts to design conformationally constrained helical macrocyclic peptides and outline the principles that enable effective targeting of protein–protein interactions beyond classical stapled peptides. In the second part, I will show how a subtle isosteric backbone modification—thioamidation—can be used to impart oral bioavailability to bioactive macrocyclic peptides. I will further illustrate how conformational restriction enhances membrane permeability and highlight the critical role of metabolic stability in achieving oral bioavailability.
","image_description":"Prof. Jayanta Chatterjee","creation_date":"2026-03-30T19:14:23","last_modification_date":"2026-03-30T19:24:00","link_label":"Publications Prof. Jayanta Chatterjee","link_url":"https://scholar.google.com/citations?user=r0aQPNIAAAAJ&hl=en","canceled":"False","cancel_reason":"","place_and_room":"BCH 3118","url_place_and_room":"https://plan.epfl.ch/?room==BCH%203118","url_online_room":"","spoken_languages":["https://memento.epfl.ch/api/v1/spoken_languages/2/?format=json"],"speaker":"Prof. Jayanta Chatterjee, Indian Institute of Science, Bangalore, India","organizer":"C. Heinis","contact":"C. Heinis","is_internal":"False","theme":"","vulgarization":{"id":2,"fr_label":"Public averti","en_label":"Informed public"},"registration":{"id":3,"fr_label":"Entrée libre","en_label":"Free"},"keywords":"cbseminar","file":null,"icalendar_url":"https://memento.epfl.ch/event/export/120241/","category":{"id":1,"code":"CONF","fr_label":"Conférences - Séminaires","en_label":"Conferences - Seminars","activated":true},"academic_calendar_category":null,"domains":[],"mementos":["https://memento.epfl.ch/api/v1/mementos/9/?format=json","https://memento.epfl.ch/api/v1/mementos/5/?format=json","https://memento.epfl.ch/api/v1/mementos/14/?format=json"]},{"id":70900,"title":"FAIR Data Management of Theoretical Spectroscopy and Green’s Function Methods","slug":"fair-data-management-of-theoretical-spectroscopy-a","event_url":"https://memento.epfl.ch/event/fair-data-management-of-theoretical-spectroscopy-a","visual_url":"https://memento.epfl.ch/image/32289/200x112.jpg","visual_large_url":"https://memento.epfl.ch/image/32289/720x405.jpg","visual_maxsize_url":"https://memento.epfl.ch/image/32289/max-size.jpg","lang":"en","start_date":"2026-04-20","end_date":"2026-04-24","start_time":null,"end_time":null,"description":"You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/fair-data-management-of-theoretical-spectroscopy-and-greens-function-methods-1377.
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\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
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\r\nDescription
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\r\nBig-data-driven methodologies have emerged as a fundamental paradigm of science, but require an enormous amount of resources to achieve their promised impact. The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [1] ensure that scientific data can be shared and reutilized, providing an efficient route for accumulating data and taking advantage of these powerful techniques. FAIR data management allows essential knowledge to be systematically extracted from data, accelerating discoveries and innovations across various domains [2]. Furthermore, open science is essential for the verifiability and reproducibility of results and has been a topic of major discussion over the last decade. In materials science, data-driven methodologies, coupled with the appropriate FAIR data management practices, are invaluable for the discovery of new materials due to the vast combinatorial space of chemical systems that emerge from the periodic table [3, 4]. Such methodologies have been successfully applied, e.g., to design and predict new materials with desired properties using ab-initio ground state simulations, i.e., data generated from Density Functional Theory (DFT) calculations [5]. However, there remains a critical gap in replicating this success in the context of other simulation frameworks.
\r\nTheoretical spectroscopy and Green's function method simulations [6, 7], including data simulated using the GW approximation, Time-Dependent Density Functional Theory (TDDFT), the Bethe-Salpeter equation (BSE), Dynamical Mean-Field Theory (DMFT), and Korringa-Kohn-Rostoker (KKR), pose especially difficult challenges in the context of FAIR data management. These simulations not only involve extensive computational resources and produce large datasets with associated complex workflows but are also executed using a large variety of public and in-house simulation software. At the same time, these methodologies are essential for understanding excited state properties of complex materials; they are more accurate than DFT calculations and provide better comparisons with experimental results since they incorporate excited states and electronic correlation effects in a more consistent manner [8].
\r\nThere has recently been a number of individual efforts to improve the accessibility of data produced by theoretical spectroscopy and Green’s function methods through the usage of publicly accessible databases. For example, the Computational Materials Repository (CMR) [9] contains several individual databases, amongst which the Computational 2D Materials Database (C2DB) [10] contains GW and BSE data for a specific set of parameters and properties. The MaterialsCloud [11] database has some individual datasets published for these methodologies, however there is not a clear data structure for them. The NIST-JARVIS [12] database has a specific app for BeyondDFT simulations with DMFT data, but only for a specific simulation code. By making datasets findable, these efforts aim to avoid redundant computations and thus build upon existing work more efficiently. While these efforts represent an important step in the right direction, they fall short of fully achieving their goal due to a continued lack of consistency (i.e., interoperability) between individual databases. Moreover, these self-managed databases typically lack the ability to store the complete provenance of the simulated workflow, which is essential to ensure reproducibility.
\r\nRecently, FAIRmat [13], a consortium of the German research data infrastructure (NFDI) association, was formed to construct a scalable data infrastructure for Materials Science that can be easily customized for individual communities. This infrastructure consists of a primary software and repository called NOMAD [14]—a free web-service that enables the organization, analysis, sharing, and publishing of materials science data. One of the tasks within FAIRmat’s scope is to build support for theoretical spectroscopy and Green’s function simulations within NOMAD. Support for several of these methodologies have now been successfully built, and there already exists over 10 000 entries in the NOMAD repository containing GW [15], BSE [16], and DMFT [17] data, along with the full provenance of the corresponding complex workflows. The next step to developing a FAIR data infrastructure for these methods is to tackle the interoperability problem.
\r\nInteroperability within this domain is extremely challenging due to the heterogeneous character of theoretical spectroscopy and Green’s function simulations. Consequently, the adoption of common structures (e.g., describing the Green’s function, the self-energy, or the dielectric function) is the key for improving interoperability. Thus, various members of the community, including method developers, materials and data scientists, and data management experts, must come together to reach a consensus on specific common data structures.
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\r\nReferences
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\r\n[1] Computational Materials Repository (CMR) website
\r\n[2] S. Di Cataldo, P. Worm, J. Tomczak, L. Si, K. Held, Nat. Commun., 15, 3952 (2024)
\r\n[3] F. Meng, B. Maurer, F. Peschel, S. Selcuk, M. Hybertsen, X. Qu, C. Vorwerk, C. Draxl, J. Vinson, D. Lu, Phys. Rev. Materials, 8, 013801 (2024)
\r\n[4] M. van Setten, F. Caruso, S. Sharifzadeh, X. Ren, M. Scheffler, F. Liu, J. Lischner, L. Lin, J. Deslippe, S. Louie, C. Yang, F. Weigend, J. Neaton, F. Evers, P. Rinke, J. Chem. Theory Comput., 11, 5665-5687 (2015)
\r\n[5] M. Scheidgen, L. Himanen, A. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. Márquez, S. Brockhauser, S. Brückner, L. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. Rudzinski, N. Daelman, J. Pizarro, M. Kuban, C. Salazar, P. Ondračka, H. Bungartz, C. Draxl, JOSS., 8, 5388 (2023)
\r\n[6] FAIRmat website
\r\n[7] K. Choudhary, K. Garrity, A. Reid, B. DeCost, A. Biacchi, A. Hight Walker, Z. Trautt, J. Hattrick-Simpers, A. Kusne, A. Centrone, A. Davydov, J. Jiang, R. Pachter, G. Cheon, E. Reed, A. Agrawal, X. Qian, V. Sharma, H. Zhuang, S. Kalinin, B. Sumpter, G. Pilania, P. Acar, S. Mandal, K. Haule, D. Vanderbilt, K. Rabe, F. Tavazza, npj. Comput. Mater., 6, 173 (2020)
\r\n[8] L. Talirz, S. Kumbhar, E. Passaro, A. Yakutovich, V. Granata, F. Gargiulo, M. Borelli, M. Uhrin, S. Huber, S. Zoupanos, C. Adorf, C. Andersen, O. Schütt, C. Pignedoli, D. Passerone, J. VandeVondele, T. Schulthess, B. Smit, G. Pizzi, N. Marzari, Sci. Data., 7, 299 (2020)
\r\n[9] S. Haastrup, M. Strange, M. Pandey, T. Deilmann, P. Schmidt, N. Hinsche, M. Gjerding, D. Torelli, P. Larsen, A. Riis-Jensen, J. Gath, K. Jacobsen, J. Jørgen Mortensen, T. Olsen, K. Thygesen, 2D Mater., 5, 042002 (2018)
\r\n[10] M. Wilkinson, M. Dumontier, I. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J. Boiten, L. da Silva Santos, P. Bourne, J. Bouwman, A. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. Evelo, R. Finkers, A. Gonzalez-Beltran, A. Gray, P. Groth, C. Goble, J. Grethe, J. Heringa, P. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. Lusher, M. Martone, A. Mons, A. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, B. Mons, Sci. Data., 3, 160018 (2016)
\r\n[11] L. Reining et al., Comptes Rendus Physique 10, 6 (2009)
\r\n[12] V. Blum, R. Asahi, J. Autschbach, C. Bannwarth, G. Bihlmayer, S. Blügel, L. Burns, T. Crawford, W. Dawson, W. de Jong, C. Draxl, C. Filippi, L. Genovese, P. Giannozzi, N. Govind, S. Hammes-Schiffer, J. Hammond, B. Hourahine, A. Jain, Y. Kanai, P. Kent, A. Larsen, S. Lehtola, X. Li, R. Lindh, S. Maeda, N. Makri, J. Moussa, T. Nakajima, J. Nash, M. Oliveira, P. Patel, G. Pizzi, G. Pourtois, B. Pritchard, E. Rabani, M. Reiher, L. Reining, X. Ren, M. Rossi, H. Schlegel, N. Seriani, L. Slipchenko, A. Thom, E. Valeev, B. Van Troeye, L. Visscher, V. Vlcek, H. Werner, D. Williams-Young, T. Windus, Electron. Struct., (2024)
\r\n[13] L. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. Koch, M. Kühbach, A. Ladines, P. Lambrix, M. Himmer, S. Levchenko, M. Oliveira, A. Michalchuk, R. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, M. Scheffler, Sci. Data., 10, 626 (2023)
\r\n[14] J. Schmidt, M. Marques, S. Botti, M. Marques, npj. Comput. Mater., 5, 83 (2019)
\r\n[15] L. Himanen, A. Geurts, A. Foster, P. Rinke, Advanced Science, 6, (2019)
\r\n[16] M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H. Bungartz, C. Felser, M. Greiner, A. Groß, C. Koch, K. Kremer, W. Nagel, M. Scheidgen, C. Wöll, C. Draxl, Nature, 604, 635-642 (2022)
\r\n[17] C. Draxl, M. Scheffler, MRS Bull., 43, 676-682 (2018)
Hosted by Frédéric Grouiller, CIBM MRI HUG-UNIGE Section head, we are pleased to invite you to attend the CIBM 7T MRI Seminar on April 21st at 10:00 CEST by Caroline Le Ster from the CEA Neurospin, Paris who will be sharing on “Leveraging the potential of UHF scanners for fMRI studies”.
\r\nAbstract
\r\nThere is a growing availability of 7T scanners, with approximately 100 systems currently installed worldwide. These ultra-high-filed (UHF) scanners are particularly relevant for neuroimaging, where anatomical and functional images can be acquired with higher spatial and temporal resolution than on conventional scanners. However, UHF scanners remain difficult to operate, and understanding their specific limitations is essential. These systems indeed face several challenges arising from MR physics and biological effects that must be addressed within engineering constraints. Leverage the potential of UHF scanners, for instance through field monitoring, is therefore important to make the most of these scanners and benefit from the intrinsic SNR gain.
\r\nfMRI particularly benefits from the increase in magnetic field strength, enabling studies to be performed at higher resolution than at lower fields. In order to maintain reasonable acquisition times despite larger imaging matrices, several acceleration strategies have been developed, e.g. multi-slice EPI (SMS-EPI), three-dimensional EPI (3D-EPI) and non-Cartesian sampling schemes. In addition, functional contrasts also benefit from the field increase, including improved spatial localization of the BOLD contrast and the possibility to exploit VASO contrast.