Dimitri Van De Ville

Van De Ville studied computer sciences at Ghent University and received his Master's degree suma cum lauda in 1998. He then pursued a PhD at the same institution and graduated in 2002 with a thesis on "Linear, nonlinear, and fuzzy image interpolation techniques" (Lineaire, niet-lineaire en vaaglogische beeldinterpolatietechnieken) that was supervised by Ignace Lemahieu and Wilfried Philips.{{Cite thesis|title=Lineaire, niet-lineaire en vaaglogische beeldinterpolatietechnieken|url=http://hdl.handle.net/1854/LU-522075|publisher=Ghent University|date=2002|degree=dissertation|first=Dimitri|last=Van De Ville|hdl=1854/LU-522075}}{{Cite journal|last1=Van De Ville|first1=Dimitri|last2=Nachtegael|first2=Mike|last3=Van der Weken|first3=Dietrich|last4=Kerre|first4=Etienne|last5=Philips|first5=Wilfried|last6=Lemahieu|first6=Ignace|date=2003|title=Noise reduction by fuzzy image filtering|url=http://hdl.handle.net/1854/LU-212802|journal=IEEE Transactions on Fuzzy Systems|volume=11|issue=4|pages=429–436|doi=10.1109/TFUZZ.2003.814830|hdl=1854/LU-212802|issn=1063-6706}}{{Citation|last1=Van De Ville|first1=Dimitri|title=Head-controlled mouse system using a low-budget webcam|date=2002|url=http://hdl.handle.net/1854/LU-361876|work=Recent Research Developments in Pattern Recognition|volume=1|pages=247–257|isbn=978-81-86846-61-2|access-date=2021-02-24|last2=Duysens|first2=J.|last3=Rogge|first3=B.|last4=Van de Walle|first4=Rik|last5=Philips|first5=Wilfried|last6=Lemahieu|first6=Ignace|hdl=1854/LU-361876}} He joined the EPFL as a post-doctoral researcher in Michael Unser's Biomedical Imaging Group.{{Cite web|title=EPFL {{!}} Biomedical Imaging Group {{!}} People|url=http://bigwww.epfl.ch/people.html|access-date=2021-02-24|website=bigwww.epfl.ch}} In 2005, he became group leader of the Signal Processing Core Geneva at the CIBM Center for Biomedical Imaging.{{Cite web|title=People – CIBM {{!}} Center for Biomedical Imaging|url=https://cibm.ch/people/|access-date=2021-02-24|website=CIBM {{!}} Center for Biomedical Imaging|language=en-US}}

In 2009, enabled by a SNSF Professorship Grant, he founded the Medical Image Processing Laboratory that is jointly held by EPFL's Institute of Bioengineering and the University of Geneva's Faculty of Medicine, and that is currently situated at the Campus Biotech in Geneva.{{Cite web|title=Lab :: MIP|url=https://miplab.epfl.ch/|access-date=2021-02-24|website=miplab.epfl.ch}}{{Cite web|title=SNF-Förderungsprofessuren|url=http://www.snf.ch/SiteCollectionDocuments/SiteCollectionDocumentsfop_awa_pfs_zusprachen.pdf}} In 2015, he was appointed tenured associate professor at EPFL with an adjunct appointment at the University of Geneva.{{Cite news |last=Evangelista |first=Sandy |date=13 July 2015 |title=Eight appointed professors |url=https://actu.epfl.ch/news/eight-appointed-professors/ |work=EPFL News |language=en}} Since 2015 he has been the head of the CIBM's Signal Processing Section, and since 2020 he has been the ad-interim head of CIBM's Animal Imaging & Technology Section.

Van De Ville's research focuses on functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to measure dynamical and network aspects of human brain activity. He develops analysis methods at the interface of signal processing, data and network science, statistics, and applies them to investigate brain function.

Van De Ville's research interests through to the end of his post-doctoral studies were dedicated to wavelets and splines, specifically to hex-splines,{{Cite journal|last1=Van De Ville|first1=D.|last2=Blu|first2=T.|last3=Unser|first3=M.|last4=Philips|first4=W.|last5=Lemahieu|first5=I.|last6=Van de Walle|first6=R.|date=June 2004|title=Hex-Splines: A Novel Spline Family for Hexagonal Lattices|url=http://dx.doi.org/10.1109/tip.2004.827231|journal=IEEE Transactions on Image Processing|volume=13|issue=6|pages=758–772|doi=10.1109/tip.2004.827231|pmid=15648867|bibcode=2004ITIP...13..758V|s2cid=9832708|issn=1057-7149}} isotropic polyharmonic B-spline wavelets,{{Cite journal|last1=Van De Ville|first1=D.|last2=Blu|first2=T.|last3=Unser|first3=M.|date=November 2005|title=Isotropic polyharmonic B-splines: scaling functions and wavelets|url=http://dx.doi.org/10.1109/tip.2005.857249|journal=IEEE Transactions on Image Processing|volume=14|issue=11|pages=1798–1813|doi=10.1109/tip.2005.857249|pmid=16279181|bibcode=2005ITIP...14.1798V|s2cid=9321887|issn=1057-7149}} and operator wavelets.{{Cite journal|last1=Tafti|first1=P.D.|last2=Van De Ville|first2=D.|last3=Unser|first3=M.|date=April 2009|title=Invariances, Laplacian-Like Wavelet Bases, and the Whitening of Fractal Processes|url=https://ieeexplore.ieee.org/document/4799376|journal=IEEE Transactions on Image Processing|volume=18|issue=4|pages=689–702|doi=10.1109/TIP.2008.2011451|pmid=19278914|bibcode=2009ITIP...18..689T|s2cid=6013652|issn=1057-7149}} This research also found application in neuro-imaging by inspiring adaptation such as activelets{{Cite journal|last1=Khalidov|first1=Ildar|last2=Fadili|first2=Jalal|last3=Lazeyras|first3=François|last4=Van De Ville|first4=Dimitri|last5=Unser|first5=Michael|date=December 2011|title=Activelets: Wavelets for sparse representation of hemodynamic responses|url=http://dx.doi.org/10.1016/j.sigpro.2011.03.008|journal=Signal Processing|volume=91|issue=12|pages=2810–2821|doi=10.1016/j.sigpro.2011.03.008|issn=0165-1684}} and wavelet-based statistical parametric mapping.{{Cite journal|last1=Van De Ville|first1=Dimitri|last2=Seghier|first2=Mohamed L.|last3=Lazeyras|first3=François|last4=Blu|first4=Thierry|last5=Unser|first5=Michael|date=October 2007|title=WSPM: Wavelet-based statistical parametric mapping|url=http://dx.doi.org/10.1016/j.neuroimage.2007.06.011|journal=NeuroImage|volume=37|issue=4|pages=1205–1217|doi=10.1016/j.neuroimage.2007.06.011|pmid=17689101|s2cid=714379|issn=1053-8119}}

Since 2009 Van De Ville has dedicated his research to computational neuroimaging with the aim to study brain functions related to behavior in health and disorder by employing fMRI and EEG data. He provides an explanation why fast EEG neural correlates (milliseconds timescale) can be correlated with slow fMRI hemodynamic fluctuations (seconds timescale) by demonstrating that sequences of EEG micro-state topographies represent scale-free organization.{{Cite journal|last1=Van De Ville|first1=D.|last2=Britz|first2=J.|last3=Michel|first3=C. M.|date=4 October 2010|title=EEG microstate sequences in healthy humans at rest reveal scale-free dynamics|url= |journal=Proceedings of the National Academy of Sciences|volume=107|issue=42|pages=18179–18184|doi=10.1073/pnas.1007841107|pmid=20921381|pmc=2964192|bibcode=2010PNAS..10718179V|issn=0027-8424|doi-access=free }}{{Cite journal|last1=Britz|first1=Juliane|last2=Van De Ville|first2=Dimitri|last3=Michel|first3=Christoph M.|date=October 2010|title=BOLD correlates of EEG topography reveal rapid resting-state network dynamics|url=https://linkinghub.elsevier.com/retrieve/pii/S105381191000220X|journal=NeuroImage|language=en|volume=52|issue=4|pages=1162–1170|doi=10.1016/j.neuroimage.2010.02.052|pmid=20188188|s2cid=125718}} He also introduced machine learning methods to functional connectivity measures, and thereby initiated the field of connectivity decoding.{{Cite journal|last1=Richiardi|first1=Jonas|last2=Eryilmaz|first2=Hamdi|last3=Schwartz|first3=Sophie|last4=Vuilleumier|first4=Patrik|last5=Van De Ville|first5=Dimitri|date=May 2011|title=Decoding brain states from fMRI connectivity graphs|url=http://dx.doi.org/10.1016/j.neuroimage.2010.05.081|journal=NeuroImage|volume=56|issue=2|pages=616–626|doi=10.1016/j.neuroimage.2010.05.081|pmid=20541019|s2cid=561574|issn=1053-8119}}{{Cite journal|last1=Richiardi|first1=Jonas|last2=Achard|first2=Sophie|author2-link=Sophie Achard|last3=Bunke|first3=Horst|last4=Van De Ville|first4=Dimitri|date=May 2013|title=Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience|url=https://ieeexplore.ieee.org/document/6494687|journal=IEEE Signal Processing Magazine|volume=30|issue=3|pages=58–70|doi=10.1109/MSP.2012.2233865|bibcode=2013ISPM...30...58R|s2cid=6198213|issn=1053-5888}}{{Cite journal|last1=Richiardi|first1=Jonas|last2=Gschwind|first2=Markus|last3=Simioni|first3=Samanta|last4=Annoni|first4=Jean-Marie|last5=Greco|first5=Beatrice|last6=Hagmann|first6=Patric|last7=Schluep|first7=Myriam|last8=Vuilleumier|first8=Patrik|last9=Van De Ville|first9=Dimitri|date=September 2012|title=Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity|url=http://dx.doi.org/10.1016/j.neuroimage.2012.05.078|journal=NeuroImage|volume=62|issue=3|pages=2021–2033|doi=10.1016/j.neuroimage.2012.05.078|pmid=22677149|s2cid=16232878|issn=1053-8119}}

In describing both the method and the application for imaging-based biomarkers, he was among the first to describe the dynamic functional connectome.{{Cite journal|last1=Preti|first1=Maria Giulia|last2=Bolton|first2=Thomas AW|last3=Van De Ville|first3=Dimitri|date=October 2017|title=The dynamic functional connectome: State-of-the-art and perspectives|journal=NeuroImage|volume=160|pages=41–54|doi=10.1016/j.neuroimage.2016.12.061|pmid=28034766|s2cid=6198510|issn=1053-8119|doi-access=free}}{{Cite journal|last1=Karahanoğlu|first1=Fikret Işık|last2=Van De Ville|first2=Dimitri|date=September 2017|title=Dynamics of large-scale fMRI networks: Deconstruct brain activity to build better models of brain function|journal=Current Opinion in Biomedical Engineering|language=en|volume=3|pages=28–36|doi=10.1016/j.cobme.2017.09.008|doi-access=free}}{{Cite journal|last1=Bolton|first1=Thomas A.W.|last2=Morgenroth|first2=Elenor|last3=Preti|first3=Maria Giulia|last4=Van De Ville|first4=Dimitri|date=September 2020|title=Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics|journal=Trends in Neurosciences|volume=43|issue=9|pages=667–680|doi=10.1016/j.tins.2020.06.005|pmid=32682563|s2cid=220527251|issn=0166-2236|doi-access=free}} He also helped to introduce brain states based on sliding-window functional connectivity.{{Cite journal|last1=Leonardi|first1=Nora|last2=Richiardi|first2=Jonas|last3=Gschwind|first3=Markus|last4=Simioni|first4=Samanta|last5=Annoni|first5=Jean-Marie|last6=Schluep|first6=Myriam|last7=Vuilleumier|first7=Patrik|last8=Van De Ville|first8=Dimitri|date=December 2013|title=Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest|url=http://dx.doi.org/10.1016/j.neuroimage.2013.07.019|journal=NeuroImage|volume=83|pages=937–950|doi=10.1016/j.neuroimage.2013.07.019|pmid=23872496|s2cid=14198209|issn=1053-8119}} To allow for reliable identification of spatial patterns of transient brain activity, he introduced a novel method for sparsity-pursuing regularized hemodynamic deconvolution of fMRI time series. This method also enabled an important progress to the classical Wiener deconvolution,{{Cite journal|last1=Karahanoğlu|first1=Fikret Işık|last2=Caballero-Gaudes|first2=César|last3=Lazeyras|first3=François|last4=Van De Ville|first4=Dimitri|date=June 2013|title=Total activation: fMRI deconvolution through spatio-temporal regularization|url=http://dx.doi.org/10.1016/j.neuroimage.2013.01.067|journal=NeuroImage|volume=73|pages=121–134|doi=10.1016/j.neuroimage.2013.01.067|pmid=23384519|s2cid=812802|issn=1053-8119}}{{Cite journal|last1=Karahanoğlu|first1=Fikret Işik|last2=Van De Ville|first2=Dimitri|date=16 July 2015|title=Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks|url= |journal=Nature Communications|volume=6|issue=1|page=7751|doi=10.1038/ncomms8751|pmid=26178017|pmc=4518303|bibcode=2015NatCo...6.7751K|issn=2041-1723}} and brought insights in brain activity dynamics during sleep{{Cite journal|last1=Tarun|first1=Anjali|last2=Wainstein-Andriano|first2=Danyal|last3=Sterpenich|first3=Virginie|last4=Bayer|first4=Laurence|last5=Perogamvros|first5=Lampros|last6=Solms|first6=Mark|last7=Axmacher|first7=Nikolai|last8=Schwartz|first8=Sophie|last9=Van De Ville|first9=Dimitri|date=9 July 2020|title=NREM sleep stages specifically alter dynamical integration of large-scale brain networks|url= |journal=iScience|volume=24|issue=1|page=101923|doi=10.1016/j.isci.2020.101923|biorxiv=10.1101/2020.07.08.193508|pmid=33409474|pmc=7773861}} and alterations owing to neurological conditions.{{Cite journal|last1=Zöller|first1=Daniela|last2=Sandini|first2=Corrado|last3=Karahanoğlu|first3=Fikret Işik|last4=Padula|first4=Maria Carmela|last5=Schaer|first5=Marie|last6=Eliez|first6=Stephan|last7=Van De Ville|first7=Dimitri|date=1 October 2019|title=Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome|url=https://www.sciencedirect.com/science/article/pii/S2451902219301004|journal=Biological Psychiatry: Cognitive Neuroscience and Neuroimaging|language=en|volume=4|issue=10|pages=881–892|doi=10.1016/j.bpsc.2019.04.004|pmid=31171499|s2cid=174816631 |issn=2451-9022}}

Van De Ville research is also dedicated to the emerging field of graph signal processing to resolve brain structure-function relationships, where the structural connectome is employed as a graph, on which a function is expressed.{{Cite journal|last1=Huang|first1=Weiyu|last2=Bolton|first2=Thomas A. W.|last3=Medaglia|first3=John D.|last4=Bassett|first4=Danielle S.|last5=Ribeiro|first5=Alejandro|last6=Van De Ville|first6=Dimitri|date=May 2018|title=A Graph Signal Processing Perspective on Functional Brain Imaging|journal=Proceedings of the IEEE|volume=106|issue=5|pages=868–885|doi=10.1109/JPROC.2018.2798928|s2cid=13810090|issn=0018-9219|doi-access=free}} This framework allows to quantify the amount of activity 'coupling' with the underlying structure, and thereby helps to elucidate hierarchical organization of the brain that is relevant for behavior.{{Cite journal|last1=Preti|first1=Maria Giulia|last2=Van De Ville|first2=Dimitri|date=December 2019|title=Decoupling of brain function from structure reveals regional behavioral specialization in humans|url= |journal=Nature Communications|language=en|volume=10|issue=1|pages=4747|doi=10.1038/s41467-019-12765-7|issn=2041-1723|pmc=6800438|pmid=31628329|bibcode=2019NatCo..10.4747P}} Recently his research helped to extend the regular atlas-based graphs including a few hundred nodes to fine-grained voxel-wise graphs representing around a million nodes.{{Cite journal|last1=Tarun|first1=Anjali|last2=Behjat|first2=Hamid|last3=Bolton|first3=Thomas|last4=Abramian|first4=David|last5=Van De Ville|first5=Dimitri|date=June 2020|title=Structural mediation of human brain activity revealed by white-matter interpolation of fMRI|journal=NeuroImage|volume=213|pages=116718|doi=10.1016/j.neuroimage.2020.116718|pmid=32184188|s2cid=202153514|issn=1053-8119|doi-access=free|arxiv=1908.09593}} His underlying theoretical work on graph signal processing encompasses the proposal of multi-slice graph wavelets,{{Cite journal|last1=Leonardi|first1=Nora|last2=Van De Ville|first2=Dimitri|date=July 2013|title=Tight Wavelet Frames on Multislice Graphs|url=http://dx.doi.org/10.1109/tsp.2013.2259825|journal=IEEE Transactions on Signal Processing|volume=61|issue=13|pages=3357–3367|doi=10.1109/tsp.2013.2259825|bibcode=2013ITSP...61.3357L|s2cid=9226088|issn=1053-587X}} graph Slepians,{{Cite journal|last1=Van De Ville|first1=Dimitri|last2=Demesmaeker|first2=Robin|last3=Preti|first3=Maria Giulia|date=July 2017|title=When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum|url=http://dx.doi.org/10.1109/lsp.2017.2704359|journal=IEEE Signal Processing Letters|volume=24|issue=7|pages=1001–1004|doi=10.1109/lsp.2017.2704359|arxiv=1701.08401|bibcode=2017ISPL...24.1001V|s2cid=5628005|issn=1070-9908}}{{Cite journal|last1=Petrovic|first1=Miljan|last2=Bolton|first2=Thomas A. W.|last3=Preti|first3=Maria Giulia|last4=Liégeois|first4=Raphaël|last5=Van De Ville|first5=Dimitri|date=January 2019|title=Guided graph spectral embedding: Application to the C. elegans connectome|url= |journal=Network Neuroscience|volume=3|issue=3|pages=807–826|doi=10.1162/netn_a_00084|pmid=31410381|pmc=6663470|issn=2472-1751}} and community-based graph filtering.{{Cite journal|last1=Petrovic|first1=Miljan|last2=Liegeois|first2=Raphael|last3=Bolton|first3=Thomas A.W.|last4=Van De Ville|first4=Dimitri|date=November 2020|title=Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals|url=http://dx.doi.org/10.1109/msp.2020.3018087|journal=IEEE Signal Processing Magazine|volume=37|issue=6|pages=150–159|doi=10.1109/msp.2020.3018087|arxiv=2008.10375|bibcode=2020ISPM...37f.150P|s2cid=221266473|issn=1053-5888}}

He is also actively developing real-time neuro-feedback applications of fMRI that allow for the self-regulation of brain activity by the volunteer in the scanner by the principle of biofeedback.{{Cite journal|last1=Koush|first1=Yury|last2=Rosa|first2=Maria Joao|last3=Robineau|first3=Fabien|last4=Heinen|first4=Klaartje|last5=W. Rieger|first5=Sebastian|last6=Weiskopf|first6=Nikolaus|last7=Vuilleumier|first7=Patrik|last8=Van De Ville|first8=Dimitri|last9=Scharnowski|first9=Frank|date=November 2013|title=Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI|url= |journal=NeuroImage|volume=81|pages=422–430|doi=10.1016/j.neuroimage.2013.05.010|pmid=23668967|pmc=3734349|issn=1053-8119}}{{Cite journal|last1=Emmert|first1=Kirsten|last2=Kopel|first2=Rotem|last3=Sulzer|first3=James|last4=Brühl|first4=Annette B.|last5=Berman|first5=Brian D.|last6=Linden|first6=David E.J.|last7=Horovitz|first7=Silvina G.|last8=Breimhorst|first8=Markus|last9=Caria|first9=Andrea|last10=Frank|first10=Sabine|last11=Johnston|first11=Stephen|date=January 2016|title=Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?|url=http://dx.doi.org/10.1016/j.neuroimage.2015.09.042|journal=NeuroImage|volume=124|issue=Pt A|pages=806–812|doi=10.1016/j.neuroimage.2015.09.042|pmid=26419389|hdl=11572/121609|s2cid=18911319|issn=1053-8119|hdl-access=free}}

Van De Ville is the recipient of the 2016 Leenaards Prize,{{Cite web|title=Attribution des Prix & Bourses scientifiques Leenaards 2016|url=https://www.leenaards.ch/wp-content/uploads/2016/09/1_Communique_presse_PrixScientifique2016.pdf|website=Fondation Leenaards}} the 2014 NARSAD Independent Investigator Award,{{Cite web|title=Dimitri Van De Ville|url=https://ieeexplore.ieee.org/author/37085367307|access-date=2021-02-25|website=IEEE}} the 2013 NeuroImage Editors’ Choice Award,{{Cite web|title=Editor's Choice Award 2013|url=https://www.journals.elsevier.com/neuroimage/editors-choice-award/editors-choice-award-2013|url-status=live|website=NeuroImage|archive-url=https://web.archive.org/web/20150114025510/http://www.journals.elsevier.com:80/neuroimage/editors-choice-award/editors-choice-award-2013/ |archive-date=2015-01-14 }} and the 2012 Pfizer Prize.{{Cite web|title=Lauréates et lauréats 2012|url=https://www.pfizerforschungspreis.ch/sites/pfizerforschungspreis.ch/files/g10028091/f/201601/Preistraeger2012-Arbeiten_fr.pdf|website=Fondation du Prix Pfizer de la Recherche}}

He is a distinguished lecturer of the 2021–2022 IEEE Signal Processing Society (SPS),{{Cite web|date=1 December 2020|title=SPS Announces 2021 Class of Distinguished Lecturers and Distinguished Industry Speakers|url=https://signalprocessingsociety.org/newsletter/2020/12/sps-announces-2021-class-distinguished-lecturers-and-distinguished-industry|access-date=2021-02-25|website=IEEE Signal Processing Society|language=en}} and a Fellow of the IEEE (2020).{{Cite web|date=2 January 2020|title=37 SPS Members Elevated to Fellow|url=https://signalprocessingsociety.org/newsletter/2020/01/37-sps-members-elevated-fellow|access-date=2021-02-25|website=IEEE Signal Processing Society|language=en}}

He is the president of the board of the Swiss Society for Biomedical Engineering (SSBE; since 2020).{{Cite web|title=SSBE – Discover our organization|url=https://www.ssbe.ch/Cms/Content/about-ssbe|access-date=2021-03-08|website=www.ssbe.ch}} He was founding chair of the Special Attention Team for Biomedical Image & Signal Analytics of the European Association for Signal Processing (EURASIP; 2016–2019),{{Cite web|title=TAC Biomedical Image & Signal Analytics|url=https://www.eurasip.org/index.php?option=com_content&view=article&id=151&Itemid=1151|access-date=2021-03-08|website=www.eurasip.org}} and vice-chair (2011), chair (2012–2013), and past chair (2014) of the Biomedical Imaging & Signal Processing (BISP) Technical Committee of the IEEE Signal Processing Society (SPS).{{Cite web|date=6 March 2018|title=BISP TC Home|url=https://signalprocessingsociety.org/community-involvement/bio-imaging-and-signal-processing/bisp-tc-home|access-date=2021-03-08|website=IEEE Signal Processing Society|language=en}}

He has been a senior associate editor of the IEEE Transactions on Signal Processing (since 2019),{{Cite web|date=29 February 2016|title=Editorial Board|url=https://signalprocessingsociety.org/publications-resources/ieee-transactions-signal-processing/editorial-board|access-date=2021-03-08|website=IEEE Signal Processing Society|language=en}} an associate editor of the SIAM Journal of Imaging Sciences (since 2018),{{Cite web|title=SIIMS {{!}} Editorial Board {{!}} SIAM|url=https://www.siam.org/publications/journals/siam-journal-on-imaging-sciences-siims/editorial-board|access-date=2021-03-08|website=www.siam.org}} a founding associate editor of Elsevier NeuroImage Reports (since 2020),{{Cite book|url=https://www.journals.elsevier.com/neuroimage-reports/editorial-board|title=NeuroImage: Reports Editorial Board}} an associate editor of the IEEE Transactions on Image Processing (2006–2009), and an associate editor of the IEEE Signal Processing Letters (2004–2006).

• {{Cite journal|last1=Preti|first1=Maria Giulia|last2=Bolton|first2=Thomas AW|last3=Van De Ville|first3=Dimitri|date=October 2017|title=The dynamic functional connectome: State-of-the-art and perspectives|journal=NeuroImage|volume=160|pages=41–54|doi=10.1016/j.neuroimage.2016.12.061|pmid=28034766|s2cid=6198510|issn=1053-8119|doi-access=free}}
• {{cite journal |doi=10.1016/j.neuroimage.2010.02.052|title=BOLD correlates of EEG topography reveal rapid resting-state network dynamics|year=2010|last1=Britz|first1=Juliane|last2=Van De Ville|first2=Dimitri|last3=Michel|first3=Christoph M.|journal=NeuroImage|volume=52|issue=4|pages=1162–1170|pmid=20188188|s2cid=125718}}
• {{cite journal |doi=10.1016/j.neuroimage.2014.09.007|title=On spurious and real fluctuations of dynamic functional connectivity during rest|year=2015|last1=Leonardi|first1=Nora|last2=Van De Ville|first2=Dimitri|journal=NeuroImage|volume=104|pages=430–436|pmid=25234118|s2cid=472092|url=http://infoscience.epfl.ch/record/203912}}
• {{cite journal |doi=10.1073/pnas.1007841107|title=EEG microstate sequences in healthy humans at rest reveal scale-free dynamics|year=2010|last1=Van De Ville|first1=D.|last2=Britz|first2=J.|last3=Michel|first3=C. M.|journal=Proceedings of the National Academy of Sciences|volume=107|issue=42|pages=18179–18184|pmid=20921381|pmc=2964192|bibcode=2010PNAS..10718179V|doi-access=free }}
• {{cite journal |doi=10.1109/TFUZZ.2003.814830|title=Noise reduction by fuzzy image filtering|year=2003|last1=Van De Ville|first1=D.|last2=Nachtegael|first2=M.|last3=Van Der Weken|first3=D.|last4=Kerre|first4=E.E.|last5=Philips|first5=W.|last6=Lemahieu|first6=I.|journal=IEEE Transactions on Fuzzy Systems|volume=11|issue=4|pages=429–436|url=http://infoscience.epfl.ch/record/63105}}
• {{cite journal |doi=10.1016/j.neuroimage.2013.07.019|title=Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest|year=2013|last1=Leonardi|first1=Nora|last2=Richiardi|first2=Jonas|last3=Gschwind|first3=Markus|last4=Simioni|first4=Samanta|last5=Annoni|first5=Jean-Marie|last6=Schluep|first6=Myriam|last7=Vuilleumier|first7=Patrik|last8=Van De Ville|first8=Dimitri|journal=NeuroImage|volume=83|pages=937–950|pmid=23872496|s2cid=14198209|url=http://infoscience.epfl.ch/record/195506}}
• {{cite journal |doi=10.1038/ncomms8751|title=Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks|year=2015|last1=Karahanoğlu|first1=Fikret Işik|last2=Van De Ville|first2=Dimitri|journal=Nature Communications|volume=6|page=7751|pmid=26178017|pmc=4518303|bibcode=2015NatCo...6.7751K|s2cid=605370}}
• {{cite journal |doi=10.1109/JPROC.2018.2798928|title=A Graph Signal Processing Perspective on Functional Brain Imaging|year=2018|last1=Huang|first1=Weiyu|last2=Bolton|first2=Thomas A. W.|last3=Medaglia|first3=John D.|last4=Bassett|first4=Danielle S.|last5=Ribeiro|first5=Alejandro|last6=Van De Ville|first6=Dimitri|journal=Proceedings of the IEEE|volume=106|issue=5|pages=868–885|s2cid=13810090|doi-access=free}}
• {{cite journal |doi=10.1109/TSP.2013.2259825|title=Tight Wavelet Frames on Multislice Graphs|year=2013|last1=Leonardi|first1=Nora|last2=Van De Ville|first2=Dimitri|journal=IEEE Transactions on Signal Processing|volume=61|issue=13|pages=3357–3367|bibcode=2013ITSP...61.3357L|s2cid=9226088|url=https://archive-ouverte.unige.ch/unige:39809}}

{{reflist}}

• {{Google Scholar id|id=kFz4LNMAAAAJ}}
• [https://miplab.epfl.ch/ Medical Image Processing Laboratory]

{{Authority control}}

{{DEFAULTSORT:Van De Ville, Dimitri}}

Category:1975 births

Category:Living people

Category:Ghent University alumni

Category:Academic staff of the École Polytechnique Fédérale de Lausanne

Category:Swiss computer scientists

Category:Swiss neuroscientists

Category:Belgian computer scientists