Publications | Ana Luísa Pinho

2024

Proceedings Paper

Proceedings of the OHBM Brainhack 2022

Stefano Moia, Hao-Ting Wang, Anibal S. Heinsfeld, Dorota Jarecka, Yu Fang Yang, Stephan Heunis, Michele Svanera, Benjamin De Leener, and 78 more authors

Apert Neuro Feb 2024

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OHBM Brainhack 2022 took place in June 2022. The first hybrid OHBM hackathon, it had an in-person component taking place in Glasgow and three hubs around the globe to improve inclusivity and fit as many timezones as possible. In the buzzing setting of the Queen Margaret Union and of the virtual platform, 23 projects were presented after development. Following are the reports of 14 of those, as well as a recapitulation of the organisation of the event.

@article{Moia2024,
  title = {{Proceedings of the OHBM Brainhack 2022}},
  author = {Moia, Stefano and Wang, Hao-Ting and Heinsfeld, Anibal S. and Jarecka, Dorota and Yang, Yu Fang and Heunis, Stephan and Svanera, Michele and Leener, Benjamin De and Gondov{\'a}, Andrea and Kim, Sin and Basavaraj, Arshitha and Bayer, Johanna M. M. and Bayrak, Roza G. and Bazin, Pierre-Louis and Bilgin, Isil Poyraz and Bollmann, Steffen and Borek, Daniel and Borghesani, Valentina and Cao, Trang and Chen, Gang and Vega, Alejandro de La and Dresbach, Sebastian and Ehses, Philipp and Ernsting, Jan and Esteves, In{\^e}s and Ferrante, Oscar and Garner, Kelly G. and Gau, R{\'e}mi and Germani, Elodie and Ghafari, Tara and Ghosh, Satrajit S. and Goodale, Sarah E. and Praag, Cassandra D. Gould Van and Guay, Samuel and Gulban, Omer Faruk and Halchenko, Yaroslav O. and Hanke, Michael and Herholz, Peer and Heuer, Katja and Hoffstaedter, Felix and Huang, Ruoqi and Huber, Renzo and Jensen, Ole and Keeratimahat, Kan and Kosciessa, Julian Q. and Lukic, Sladjana and Magielse, Neville and Markiewicz, Christopher J. and Martin, Caroline G. and Maumet, Camille and Menacher, Anna and Mentch, Jeff and M{\"o}nch, Christian and More, Shammi and Muller, Leonardo and Muller-Rodriguez, Leonardo and Nastase, Samuel A. and Nicolaisen-Sobesky, Eliana and Nielson, Dylan M. and Nolan, Christopher R. and Paugam, Fran{\c c}ois and Pinheiro-Chagas, Pedro and Pinho, Ana Lu{\'i}sa and Pizzuti, Alessandra and Poldrack, Benjamin and Poser, Benedikt A. and Rocca, Roberta and Sanz-Robinson, Jacob and Sarink, Kelvin and Sitek, Kevin R and Spychala, Nadine and Stirnberg, R{\"u}diger and Szczepanik, Michal and Torabi, Mohammad and Toro, Roberto and Urchs, Sebastian G. W. and Valk, Sofie L. and Wagner, Adina S and Waite, Laura K. and Waite, Alexander Q. and Waller, Lea and Wishard, Tyler J. and Wu, Jianxiao and Zhou, Yuchen and Bijsterbosch, Janine D. and Community, The Physiopy},
  journal = {Apert Neuro},
  doi = {10.52294/001c.92760},
  url = {https://doi.org/10.52294/001c.92760},
  year = {2024},
  month = feb,
  hal_id = {hal-04478342},
  hal_version = {v1},
}

Journal Article
Should one go for individual-or group-level brain parcellations? A deep-phenotyping benchmark

Bertrand Thirion, Himanshu Aggarwal, Ana Fernanda Ponce, Ana Luísa Pinho, and Alexis Thual

Brain Struct Funct Feb 2024

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The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.
@article{Thirion2024, title = {{Should one go for individual-or group-level brain parcellations? A deep-phenotyping benchmark}}, author = {Thirion, Bertrand and Aggarwal, Himanshu and Ponce, Ana Fernanda and Pinho, Ana Lu{\'i}sa and Thual, Alexis}, journal = {Brain Struct Funct}, volume = {229}, number = {1}, pages = {161--181}, year = {2024}, publisher = {Springer}, }

2023

Preprint
A hierarchical atlas of the human cerebellum for functional precision mapping

Caroline Nettekoven, Da Zhi, Ladan Shahshahani, Ana Luísa Pinho, Noam Saadon-Grosman, Randy Lee Buckner, and Jörn Diedrichsen

bioRxiv Sep 2023

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The human cerebellum is activated by a wide variety of cognitive and motor tasks. Previous functional atlases have relied on single task-based or resting-state fMRI datasets. Here, we present a functional atlas that integrates information from 7 large-scale datasets, outperforming single dataset parcellations. The new atlas has three further advantages: First, the regions are hierarchically organized across 3 levels, allowing analyses at the appropriate level of granularity. Second, by constraining the boundaries to be the same across hemispheres, we provide a symmetric version of the atlas, which is especially useful in studying functional lateralization. Finally, the atlas allows for precision mapping in individuals: The integration of the probabilistic group atlas with an individual localizer scan results in a marked improvement in prediction of individual boundaries. Overall, the new atlas is an important resource for the study of the interdigitated functional organization of the human cerebellum in health and disease.Competing Interest StatementThe authors have declared no competing interest.
@article{Nettekoven2023, author = {Nettekoven, Caroline and Zhi, Da and Shahshahani, Ladan and Pinho, Ana Lu{\'i}sa and Saadon-Grosman, Noam and Buckner, Randy Lee and Diedrichsen, J{\"o}rn}, title = {A hierarchical atlas of the human cerebellum for functional precision mapping}, elocation-id = {2023.09.14.557689}, month = sep, year = {2023}, doi = {10.1101/2023.09.14.557689}, publisher = {Cold Spring Harbor Laboratory}, journal = {bioRxiv}, }
Preprint
Individual Brain Charting third release, probing brain activity during Movie Watching and Retinotopic Mapping

Ana Luísa Pinho, Hugo Richard, Michael Eickenberg, Alexis Amadon, Elvis Dohmatob, Isabelle Denghien, Juan Jesús Torre, Swetha Shankar, and 18 more authors

Nov 2023

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The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatialresolution, which is intended to facilitate the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals in a fixed environment, covering a broad range of psychological domains that allows, in turn, the investigation of atlasing techniques in functional neuroimaging. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover core structures of brain activation. We rely on the Fast Shared Response Model (FastSRM): an analytical tool that provides a data-driven solution to model naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, thus enabling the extraction of functional networks pertaining to vision, audio and language systems. We also present an in-depth study of the topographic organization of the visual system through a retinotopy task. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.
@article{Pinho2023, title = {{Individual Brain Charting third release, probing brain activity during Movie Watching and Retinotopic Mapping}}, author = {Pinho, Ana Lu{\'i}sa and Richard, Hugo and Eickenberg, Michael and Amadon, Alexis and Dohmatob, Elvis and Denghien, Isabelle and Torre, Juan Jes{\'u}s and Shankar, Swetha and Aggarwal, Himanshu and Ponce, Ana Fernanda and Thual, Alexis and Chapalain, Thomas and Ginisty, Chantal and Becuwe-Desmidt, S{\'e}verine and Roger, S{\'e}verine and Lecomte, Yann and Berland, Val{\'e}rie and Laurier, Laurence and Joly-Testault, V{\'e}ronique and M{\'e}diouni-Cloarec, Ga{\"e}lle and Doubl{\'e}, Christine and Martins, Bernadette and Varoquaux, Ga{\"e}l and Dehaene, Stanislas and Hertz-Pannier, Lucie and Thirion, Bertrand}, url = {https://hal.science/hal-04272993}, note = {working paper or preprint}, year = {2023}, month = nov, hal_id = {hal-04272993}, hal_version = {v1}, }
Journal Article
Controversies and progress on standardization of large-scale brain network nomenclature

Lucina Q. Uddin, Richard F. Betzel, Jessica R. Cohen, Jessica S. Damoiseaux, Felipe De Brigard, Simon B. Eickhoff, Alex Fornito, Caterina Gratton, and 14 more authors

Netw Neurosci May 2023

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The idea that the brain is composed of multiple large-scale networks has steadily gained traction over the past decade. Still, the field has not yet reached consensus on key issues regarding terminology. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide concrete recommendations and reporting guidelines for the scientific community. WHATNET members engaged in regular discussions, conducted a survey to catalog current practices in large-scale brain network nomenclature, identified barriers to progress, and brainstormed tools that could be developed to help standardize reporting in future studies. Here we summarize these activities and provide important considerations and initial recommendations for the network neuroscience community, noting open questions and controversies that require further empirical and theoretical investigation.Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macro-scale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field towards standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including 1) network scale, resolution, and hierarchies; 2) inter-individual variability of networks; 3) dynamics and non-stationarity of networks; 4) consideration of network affiliations of subcortical structures; and 5) consideration of multi-modal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
@article{Uddin2023, author = {Uddin, Lucina Q. and Betzel, Richard F. and Cohen, Jessica R. and Damoiseaux, Jessica S. and De Brigard, Felipe and Eickhoff, Simon B. and Fornito, Alex and Gratton, Caterina and Gordon, Evan M. and Laird, Angela R. and Larson-Prior, Linda and McIntosh, A. Randal and Nickerson, Lisa D. and Pessoa, Luiz and Pinho, Ana Luísa and Poldrack, Russell A. and Razi, Adeel and Sadaghiani, Sepideh and Shine, James M. and Yendiki, Anastasia and Yeo, B. T. Thomas and Spreng, R. Nathan}, title = {{Controversies and progress on standardization of large-scale brain network nomenclature}}, journal = {Netw Neurosci}, pages = {1-111}, year = {2023}, month = may, issn = {2472-1751}, doi = {10.1162/netn_a_00323}, }
preprint
A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets

Da Zhi, Ladan Shahshahani, Caroline Nettekoven, Ana Luísa Pinho, Danilo Bzdok, and Jörn Diedrichsen

bioRxiv May 2023

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One important barrier in the development of complex models of human brain organization is the lack of a large and comprehensive task-based neuro-imaging dataset. Therefore, current atlases of functional brain organization are mainly based on single and homogeneous resting-state datasets. Here, we propose a hierarchical Bayesian framework that can learn a probabilistically defined brain parcellation across numerous task-based and resting-state datasets, exploiting their combined strengths. The framework is partitioned into a spatial arrangement model that defines the probability of a specific individual brain parcellation, and a set of dataset-specific emission models that defines the probability of the observed data given the individual brain organization. We show that the framework optimally combines information from different datasets to achieve a new population-based atlas of the human cerebellum. Furthermore, we demonstrate that, using only 10 min of individual data, the framework is able to generate individual brain parcellations that outperform group atlases.Competing Interest StatementThe authors have declared no competing interest.
@article{Zhi2023, author = {Zhi, Da and Shahshahani, Ladan and Nettekoven, Caroline and Pinho, Ana Lu{\'i}sa and Bzdok, Danilo and Diedrichsen, J{\"o}rn}, title = {A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets}, elocation-id = {2023.05.24.542121}, month = may, year = {2023}, doi = {10.1101/2023.05.24.542121}, publisher = {Cold Spring Harbor Laboratory}, journal = {bioRxiv}, }

2021

Journal Article
Brain topography beyond parcellations: Local gradients of functional maps

Elvis Dohmatob, Hugo Richard, Ana Luísa Pinho, and Bertrand Thirion

Neuroimage Apr 2021

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Functional neuroimaging provides the unique opportunity to characterize brain regions based on their response to tasks or ongoing activity. As such, it holds the premise to capture brain spatial organization. Yet, the conceptual framework to describe this organization has remained elusive: on the one hand, parcellations build implicitly on a piecewise constant organization, i.e. flat regions separated by sharp boundaries; on the other hand, the recently popularized concept of functional gradient hints instead at a smooth structure. Noting that both views converge to a topographic scheme that pieces together local variations of functional features, we perform a quantitative assessment of local gradient-based models. Using as a driving case the prediction of functional Magnetic Resonance Imaging (fMRI) data —concretely, the prediction of task-fMRI from rest-fMRI maps across subjects— we develop a parcel-wise linear regression model based on a dictionary of reference topographies. Our method uses multiple random parcellations —as opposed to a single fixed parcellation— and aggregates estimates across these parcellations to predict functional features in left-out subjects. Our experiments demonstrate the existence of an optimal cardinality of the parcellation to capture local gradients of functional maps.
@article{Dohmatob2021, title = {Brain topography beyond parcellations: Local gradients of functional maps}, journal = {Neuroimage}, volume = {229}, pages = {117706}, month = apr, year = {2021}, issn = {1053-8119}, doi = {https://doi.org/10.1016/j.neuroimage.2020.117706}, url = {https://www.sciencedirect.com/science/article/pii/S1053811920311915}, author = {Dohmatob, Elvis and Richard, Hugo and Pinho, Ana Lu{\'i}sa and Thirion, Bertrand}, keywords = {Parcellation, Functional mapping, Prediction, Model selection, Functional gradients}, }

Journal Article

Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective

Elizabeth Levitis, Cassandra D Gould van Praag, Rémi Gau, Stephan Heunis, Elizabeth DuPre, Gregory Kiar, Katherine L Bottenhorn, Tristan Glatard, and 102 more authors

Gigascience Aug 2021

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As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities.Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design.Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

@article{Levitis2021,
  author = {Levitis, Elizabeth and van Praag, Cassandra D Gould and Gau, Rémi and Heunis, Stephan and DuPre, Elizabeth and Kiar, Gregory and Bottenhorn, Katherine L and Glatard, Tristan and Nikolaidis, Aki and Whitaker, Kirstie Jane and Mancini, Matteo and Niso, Guiomar and Afyouni, Soroosh and Alonso-Ortiz, Eva and Appelhoff, Stefan and Arnatkeviciute, Aurina and Atay, Selim Melvin and Auer, Tibor and Baracchini, Giulia and Bayer, Johanna M M and Beauvais, Michael J S and Bijsterbosch, Janine D and Bilgin, Isil P and Bollmann, Saskia and Bollmann, Steffen and Botvinik-Nezer, Rotem and Bright, Molly G and Calhoun, Vince D and Chen, Xiao and Chopra, Sidhant and Chuan-Peng, Hu and Close, Thomas G and Cookson, Savannah L and Craddock, R Cameron and De La Vega, Alejandro and De Leener, Benjamin and Demeter, Damion V and Di Maio, Paola and Dickie, Erin W and Eickhoff, Simon B and Esteban, Oscar and Finc, Karolina and Frigo, Matteo and Ganesan, Saampras and Ganz, Melanie and Garner, Kelly G and Garza-Villarreal, Eduardo A and Gonzalez-Escamilla, Gabriel and Goswami, Rohit and Griffiths, John D and Grootswagers, Tijl and Guay, Samuel and Guest, Olivia and Handwerker, Daniel A and Herholz, Peer and Heuer, Katja and Huijser, Dorien C and Iacovella, Vittorio and Joseph, Michael J E and Karakuzu, Agah and Keator, David B and Kobeleva, Xenia and Kumar, Manoj and Laird, Angela R and Larson-Prior, Linda J and Lautarescu, Alexandra and Lazari, Alberto and Legarreta, Jon Haitz and Li, Xue-Ying and Lv, Jinglei and Mansour L., Sina and Meunier, David and Moraczewski, Dustin and Nandi, Tulika and Nastase, Samuel A and Nau, Matthias and Noble, Stephanie and Norgaard, Martin and Obungoloch, Johnes and Oostenveld, Robert and Orchard, Edwina R and Pinho, Ana Lu{\'i}sa and Poldrack, Russell A and Qiu, Anqi and Raamana, Pradeep Reddy and Rokem, Ariel and Rutherford, Saige and Sharan, Malvika and Shaw, Thomas B and Syeda, Warda T and Testerman, Meghan M and Toro, Roberto and Valk, Sofie L and Van Den Bossche, Sofie and Varoquaux, Gaël and Váša, František and Veldsman, Michele and Vohryzek, Jakub and Wagner, Adina S and Walsh, Reubs J and White, Tonya and Wong, Fu-Te and Xie, Xihe and Yan, Chao-Gan and Yang, Yu-Fang and Yee, Yohan and Zanitti, Gaston E and Van Gulick, Ana E and Duff, Eugene and Maumet, Camille},
  title = {{Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective}},
  journal = {Gigascience},
  volume = {10},
  number = {8},
  year = {2021},
  month = aug,
  issn = {2047-217X},
  doi = {10.1093/gigascience/giab051},
  url = {https://doi.org/10.1093/gigascience/giab051},
  note = {giab051},
}

Journal Article
Subject-specific segregation of functional territories based on deep phenotyping

Ana Luísa Pinho, Alexis Amadon, Murielle Fabre, Elvis Dohmatob, Isabelle Denghien, Juan Jesús Torre, Chantal Ginisty, Séverine Becuwe-Desmidt, and 13 more authors

Hum Brain Mapp Mar 2021

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Functional magnetic resonance imaging (fMRI) has opened the possibility to investigatehow brain activity is modulated by behavior. Most studies so far are bound to one singletask, in which functional responses to a handful of contrasts are analyzed and reported asa group average brain map. Contrariwise, recent data-collection efforts have started to tar-get a systematic spatial representation of multiple mental functions. In this paper, weleverage the Individual Brain Charting (IBC) dataset—a high-resolution task-fMRI datasetacquired in a fixed environment—in order to study the feasibility of individual mapping.First, we verify that the IBC brain maps reproduce those obtained from previous, large-scale datasets using the same tasks. Second, we confirm that the elementary spatial com-ponents, inferred across all tasks, are consistently mapped within and, to a lesser extent,across participants. Third, we demonstrate the relevance of the topographic informationof the individual contrast maps, showing that contrasts from one task can be predicted bycontrasts from other tasks. At last, we showcase the benefit of contrast accumulation forthe fine functional characterization of brain regions within a prespecified network. To thisend, we analyze the cognitive profile of functional territories pertaining to the languagenetwork and prove that these profiles generalize across participants.
@article{Pinho2021, author = {Pinho, Ana Lu{\'i}sa and Amadon, Alexis and Fabre, Murielle and Dohmatob, Elvis and Denghien, Isabelle and Torre, Juan Jes{\'u}s and Ginisty, Chantal and Becuwe-Desmidt, S{\'e}verine and Roger, S{\'e}verine and Laurier, Laurence and Joly-Testault, V{\'e}ronique and M{\'e}diouni-Cloarec, Ga{\"e}lle and Doubl{\'e}, Christine and Martins, Bernadette and Pinel, Philippe and Eger, Evelyn and Varoquaux, Ga{\"e}l and Pallier, Christophe and Dehaene, Stanislas and Hertz-Pannier, Lucie and Thirion, Bertrand}, title = {Subject-specific segregation of functional territories based on deep phenotyping}, journal = {Hum Brain Mapp}, volume = {42}, number = {4}, pages = {841-870}, month = mar, year = {2021}, keywords = {atlases, brain imaging, cognitive function, data set, functional magnetic resonance imaging}, url = {https://doi.org/10.1002/hbm.25189}, annotate = {* This paper introduces several key experiments on a fraction of the IBC dataset, namely the first release. In particular, it introduces the application of dictionary learning to summarize contrast maps to topographies. It studies the stability of the dictionary components across data resamplings. It also shows that some contrast maps can be successfully reconstructed from other contrasts. Finally, it illustrates how the accumulation of functional contrasts can help to distinguish between the functional specialization of several regions taken from the language network.}, }
Dataset
IBC

Ana Luísa Pinho, L. Hertz-Pannier, and B. Thirion

OpenNeuro January 2021

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Overview: Functional Magnetic Resonance Imaging (fMRI) has opened the door to brain mapping of perceptual, motor, or cognitive functions. As such, it provides an instrumental basis for the whole field of cognitive neuroimag- ing. However, there exists to date no data collection that systematically maps representations for a wide-variety of mental functions at a fine spa- tial scale. The Individual Brain Charting (IBC) project is collecting a high-resolution multi-task-fMRI dataset, to provide an objective basis for a comprehensive atlas of brain responses. The data refer to a cohort of twelve participants performing many different tasks. Acquiring a large amount of tasks on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. Additionally, the dataset comes with high-resolution anatomical and diffusion images, to achieve a fine anatomical characterization of these brains.
@article{Pinho2021DCa, author = {Pinho, Ana Lu{\'i}sa and Hertz-Pannier, L. and Thirion, B.}, title = {{IBC}}, journal = {{OpenNeuro}}, note = {{ds002685}}, month = {{January}}, year = {2021}, url = {https://doi.org/10.18112/openneuro.ds002685.v1.3.1}, }
Dataset
Individual Brain Charting (IBC) v3.0

A. L. Pinho, S. Shankar, H. Richard, A. Amadon, S. Nishimoto, A. G. Huth, M. Eickenberg, I. Denghien, and 18 more authors

EBRAINS September 2021

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Functional Magnetic Resonance Imaging (fMRI) has opened the door to brain mapping of perceptual, motor, or cognitive functions. As such, it provides an instrumental basis for the whole field of cognitive neuroimaging. However, there exists to date no data collection that systematically maps representations for a wide-variety of mental functions at a fine spatial scale. The Individual Brain Charting (IBC) project is collecting a high-resolution multi-task-fMRI dataset to provide an objective basis for a comprehensive atlas of brain responses. The data refer to a cohort of participants performing many different tasks. Acquiring a large amount of tasks on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. Additionally, the dataset comes with high-resolution anatomical and diffusion images, to achieve a fine anatomical characterization of these brains.
@article{Pinho2021DCb, author = {Pinho, A. L. and Shankar, S. and Richard, H. and Amadon, A. and Nishimoto, S. and Huth, A. G. and Eickenberg, M. and Denghien, I. and Torre, J. J. and Aggarwal, H. and Ginisty, C. and Becuwe-Desmidt, S. and Roger, S. and Lecomte, Y. and Berland, V. and Laurier, L. and Joly-Testault, V. and M{\'e}diouni-Cloarec, G. and Doubl{\'e}, C. and Martins, B. and Haxby, J. V. and Gallant, J. and Varoquaux, G. and Dehaene, S. and Hertz-Pannier, L. and Thirion, B.}, title = {{Individual Brain Charting (IBC) v3.0}}, journal = {{EBRAINS}}, month = {{September}}, year = {2021}, url = {https://doi.org/10.25493/SM37-TS4}, }
Journal Article
From deep brain phenotyping to functional atlasing

Bertrand Thirion, Alexis Thual, and Ana Luísa Pinho

Curr Opin Behav Sci Aug 2021

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How can neuroimaging inform us about the function of brain structures? This simple question immediately brings out two pertinent issues: Firstly, an inference problem, namely the fact that the function of a region can only be asserted after observing a large array of experimental conditions or contrasts; and second, the fact that the identity of a region can only be defined with accuracy at the individual level, because of intrinsic differences between subjects. To overcome this double challenge, we consider an approach based on the deep phenotyping of behavioral responses from task data acquired using functional magnetic resonance imaging. The concept of functional fingerprint—which subsumes the accumulation of functional information at a given brain location—is herein discussed in detail through concrete examples taken from the Individual Brain Charting dataset.
@article{Thirion2021, title = {From deep brain phenotyping to functional atlasing}, journal = {Curr Opin Behav Sci}, volume = {40}, pages = {201-212}, month = aug, year = {2021}, note = {Deep Imaging - Personalized Neuroscience}, issn = {2352-1546}, doi = {https://doi.org/10.1016/j.cobeha.2021.05.004}, url = {https://www.sciencedirect.com/science/article/pii/S2352154621001121}, author = {Thirion, Bertrand and Thual, Alexis and Pinho, Ana Lu{\'i}sa}, }

2020

Journal Article
Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping

Ana Luísa Pinho, Alexis Amadon, Baptiste Gauthier, Nicolas Clairis, André Knops, Sarah Genon, Elvis Dohmatob, Juan Jesús Torre, and 20 more authors

Sci Data Oct 2020

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We present an extension of the Individual Brain Charting dataset –a high spatial-resolution, multi-task, functional Magnetic Resonance Imaging dataset, intended to support the investigation on the functional principles governing cognition in the human brain. The concomitant data acquisition from the same 12 participants, in the same environment, allows to obtain in the long run finer cognitive topographies, free from inter-subject and inter-site variability. This second release provides more data from psychological domains present in the first release, and also yields data featuring new ones. It includes tasks on e.g. mental time travel, reward, theory-of-mind, pain, numerosity, self-reference effect and speech recognition. In total, 13 tasks with 86 contrasts were added to the dataset and 63 new components were included in the cognitive description of the ensuing contrasts. As the dataset becomes larger, the collection of the corresponding topographies becomes more comprehensive, leading to better brain-atlasing frameworks. This dataset is an open-access facility; raw data and derivatives are publicly available in neuroimaging repositories.
@article{Pinho2020, author = {Pinho, Ana Lu{\'i}sa and Amadon, Alexis and Gauthier, Baptiste and Clairis, Nicolas and Knops, Andr{\'e} and Genon, Sarah and Dohmatob, Elvis and Jes{\'u}s Torre, Juan and Ginisty, Chantal and Becuwe-Desmidt, S{\'e}verine and Roger, S{\'e}verine and Lecomte, Yann and Berland, Val{\'e}rie and Laurier, Laurence and Joly-Testault, V{\'e}ronique and M{\'e}diouni-Cloarec, Ga{\"e}lle and Doubl{\'e}, Christine and Martins, Bernadette and Salmon, Eric and Piazza, Manuela and Melcher, David and Pessiglione, Mathias and Van Wassenhove, Virginie and Eger, Evelyn and Varoquaux, Ga{\"e}l and Dehaene, Stanislas and Hertz-Pannier, Lucie and Thirion, Bertrand}, title = {{Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping}}, journal = {{Sci Data}}, volume = {7}, number = {1}, month = oct, year = {2020}, url = {https://doi.org/10.1038/s41597-020-00670-4}, }
Dataset
Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping

Ana Luísa Pinho, A. Amadon, T. Ruest, M. Fabre, B. Gauthier, N. Clairis, A. Knops, S. Genon, and 25 more authors

NeuroVault Oct 2020

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The individual Brain Charting (IBC) Project is using high resolution fMRI to map 13 subjects that undergo a large number of tasks: the HCP tasks, the so-called ARCHI tasks, a specific language task, video watching, low-level visual stimulation etc. The native resolution of the data is 1.5mm isotropic. Their main value lies in the large number of contrasts probed, the level of detail and the high SNR per subject. This dataset is meant to provide the basis of a functional brain atlas. We upload here smoothed individual SPMs. The uploaded maps comprise session-specific and fixed effects across maps acquired with AP and PA phase encoding directions. Note that Neurovault collection #4438 is a subset of that one. In the present collections, some details have been fixed, including mroe accurate and unique file naming.
@article{Pinho2020DCa, author = {Pinho, Ana Lu{\'i}sa and Amadon, A. and Ruest, T. and Fabre, M. and Gauthier, B. and Clairis, N. and Knops, A. and Genon, S. and Dohmatob, E. and Denghien, I. and Torre, J.J. and Ginisty, C. and Becuwe-Desmidt, S. and Roger, S. and Lecomte, Y. and Berland, V. and Laurier, L. and Joly-Testault, V. and M{\'e}diouni-Cloarec, G. and Doubl{\'e}, C. and Martins, B. and Salmon, E. and Piazza, M. and Melcher, D. and Pessiglione, M. and van Wassenhove, V. and Pinel, P. and Eger, E. and Varoquaux, G. and Pallier, C. and Dehaene, S. and Hertz-Pannier, L. and Thirion, B.}, title = {{Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping}}, journal = {{NeuroVault}}, note = {{id collection=6618}}, year = {2020}, url = {https://identifiers.org/neurovault.collection:6618}, }

2019

Preprint
Fast shared response model for fMRI data

Hugo Richard, Lucas Martin, Ana Luísa Pinho, Jonathan W. Pillow, and Bertrand Thirion

CoRR Dec 2019

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The shared response model provides a simple but effective framework to analyse fMRI data of subjects exposed to naturalistic stimuli. However when the number of subjects or runs is large, fitting the model requires a large amount of memory and computational power, which limits its use in practice. In this work, we introduce the FastSRM algorithm that relies on an intermediate atlas-based representation. It provides considerable speed-up in time and memory usage, hence it allows easy and fast large-scale analysis of naturalistic-stimulus fMRI data. Using four different datasets, we show that our method matches the performance of the original SRM algorithm while being about 5x faster and 20x to 40x more memory efficient. Based on this contribution, we use FastSRM to predict age from movie watching data on the CamCAN sample. Besides delivering accurate predictions (mean absolute error of 7.5 years), FastSRM extracts topographic patterns that are predictive of age, demonstrating that brain activity during free perception reflects age.
@article{Richard2019, author = {Richard, Hugo and Martin, Lucas and Pinho, Ana Lu{\'i}sa and Pillow, Jonathan W. and Thirion, Bertrand}, title = {Fast shared response model for fMRI data}, journal = {CoRR}, volume = {abs/1909.12537}, month = dec, year = {2019}, eprinttype = {arXiv}, eprint = {1909.12537}, timestamp = {Mon, 29 Aug 2022 09:33:25 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1909-12537.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}, }

Journal Article

Gender bias in (neuro) science: facts, consequences, and solutions

Jessica Schrouff, Doris Pischedda, Sarah Genon, Gregory Fryns, Ana Luísa Pinho, Eliana Vassena, Antonietta G Liuzzi, and F Santos Ferreira

Eur J Neurosci Feb 2019

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@article{Schrouff2019,
  title = {Gender bias in (neuro) science: facts, consequences, and solutions},
  author = {Schrouff, Jessica and Pischedda, Doris and Genon, Sarah and Fryns, Gregory and Lu{\'i}sa Pinho, Ana and Vassena, Eliana and Liuzzi, Antonietta G and Santos Ferreira, F},
  journal = {Eur J Neurosci},
  volume = {50},
  number = {7},
  pages = {3094--3100},
  month = feb,
  year = {2019},
  publisher = {Blackwell Publishing},
  url = {https://doi.org/10.1111/ejn.14397},
}

2018

Book Chapter
The Neuropsychological Aspects of Musical Creativity

Ana Luísa Pinho

Feb 2018

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Creativity emerges from the individual or collective intellect, in order to unfold the conundrum of life and give rise to meaningful deliberations for the attainment of a flourishing life. More specifically, creativity is commonly defined, within the framework of psychology, as an act or product that shall fulfill three main criteria: originality, unexpectedness, and usefulness. The cognitive science approach to creativity investigates the intellectual processes and representations concerned with the creative thinking. The methodologies of cognitive science, derived from the technological advancements of the past sixty years, have begun to adopt a more definitive and systemic perspective. Neuroscience has emerged, under this context, as the scientific study dedicated to explore the biological substrates of the nervous system, by utilizing a multitude of techniques such as neuroimaging. Cognitive neuroscience, in particular, studies the neural correlates of mental processes, and it constitutes the central approach herein adopted to examine musical creativity as a product of the human mind. In the present section, a definition plus historical evolvement of creativity are firstly provided together with an overview of its developments in psychometry. Secondly, a comprehensive description regarding the scientific advances about the topic, and within the field of cognitive neuroscience, is described according to: (1) the model on the four types of creativity and (2) the main categories of experimental designs implemented so far. Lastly, the latest advancements on the study of musical creativity, in particular musical improvisation, will be addressed under the neuroimaging framework.
@inbook{Pinho2018, author = {Pinho, Ana Lu{\'i}sa}, editor = {Kapoula, Zo{\"i} and Volle, Emmanuelle and Renoult, Julien and Andreatta, Moreno}, title = {The Neuropsychological Aspects of Musical Creativity}, booktitle = {Exploring Transdisciplinarity in Art and Sciences}, year = {2018}, publisher = {Springer International Publishing}, address = {Cham}, pages = {77--103}, isbn = {978-3-319-76054-4}, doi = {10.1007/978-3-319-76054-4_4}, url = {https://doi.org/10.1007/978-3-319-76054-4_4}, }
Journal Article
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping

Ana Luísa Pinho, Alexis Amadon, Torsten Ruest, Murielle Fabre, Elvis Dohmatob, Isabelle Denghien, Chantal Ginisty, Séverine-Becuwe, and 13 more authors

Sci Data Jun 2018

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Functional Magnetic Resonance Imaging (fMRI) has furthered brain mapping on perceptual, motor, as well as higher-level cognitive functions. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a cohort of 12 participants performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The present article gives a detailed description of the first release of the IBC dataset. It comprises a dozen of tasks, addressing both low- and high- level cognitive functions. This openly available dataset is thus intended to become a reference for cognitive brain mapping.
@article{Pinho2019, author = {Pinho, Ana Lu{\'i}sa and Amadon, Alexis and Ruest, Torsten and Fabre, Murielle and Dohmatob, Elvis and Denghien, Isabelle and Ginisty, Chantal and S{\'e}verine-Becuwe and Roger, S{\'e}verine and Laurier, Laurence and Joly-Testault, V{\'e}ronique and M{\'e}diouni-Cloarec, Ga{\"e}lle and Doubl{\'e}, Christine and Martins, Bernadette and Pinel, Philippe and Eger, Evelyn and Varoquaux, Ga{\"e}l and Pallier, Christophe and Dehaene, Stanislas and Hertz-Pannier, Lucie and Thirion, Bertrand}, title = {Individual {B}rain {C}harting, a high-resolution f{MRI} dataset for cognitive mapping}, journal = {Sci Data}, year = {2018}, month = jun, volume = {5}, pages = {180105}, url = {https://doi.org/10.1038/sdata.2018.105}, }
Conference Paper
Optimizing deep video representation to match brain activity

Hugo Richard, Ana Luísa Pinho, Bertrand Thirion, and Guillaume Charpiat

In CCN 2018 - Conference on Cognitive Computational Neuroscience Sep 2018

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The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions. Here we study fMRI activity in ten subjects watching color natural movies and compute deep representations of these movies with an architecture that relies on optical flow and image content. The association of activity in visual areas with the different layers of the deep architecture displays complexity-related contrasts across visual areas and reveals a striking foveal/peripheral dichotomy.
@inproceedings{Richard2018, title = {{Optimizing deep video representation to match brain activity}}, author = {Richard, Hugo and Pinho, Ana Lu{\'i}sa and Thirion, Bertrand and Charpiat, Guillaume}, url = {https://hal.archives-ouvertes.fr/hal-01868735}, booktitle = {{CCN 2018 - Conference on Cognitive Computational Neuroscience}}, address = {Philadelphia, United States}, year = {2018}, month = sep, keywords = {video encoding ; brain mapping ; deep learning}, hal_id = {hal-01868735}, hal_version = {v1}, }

2015

Journal Article
Addressing a paradox: dual strategies for creative performance in introspective and extrospective networks

Ana Luísa Pinho, Fredrik Ullén, Miguel Castelo-Branco, Peter Fransson, and Örjan Manzano

Cereb Cortex Jun 2015

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Neuroimaging studies of internally generated behaviors have shown seemingly paradoxical results regarding the dorsolateral prefrontal cortex (DLPFC), which has been found to activate, not activate or even deactivate relative to control conditions. On the one hand, the DLPFC has been argued to exert top–down control over generative thought by inhibiting habitual responses; on the other hand, a deactivation and concomitant decrease in monitoring and focused attention has been suggested to facilitate spontaneous associations and novel insights. Here, we demonstrate that prefrontal engagement in creative cognition depends dramatically on experimental conditions, that is, the goal of the task. We instructed professional pianists to perform improvisations on a piano keyboard during fMRI and play, either with a certain emotional content (happy/fearful), or using certain keys (tonal/atonal pitch-sets). We found lower activity in primarily the right DLPFC, dorsal premotor cortex and inferior parietal cortex during emotional conditions compared with pitch-set conditions. Furthermore, the DLPFC was functionally connected to the default mode network during emotional conditions and to the premotor network during pitch-set conditions. The results thus support the notion of two broad cognitive strategies for creative problem solving, relying on extrospective and introspective neural circuits, respectively.
@article{Pinho2015, title = {Addressing a paradox: dual strategies for creative performance in introspective and extrospective networks}, author = {Pinho, Ana Lu{\'i}sa and Ull{\'e}n, Fredrik and Castelo-Branco, Miguel and Fransson, Peter and de Manzano, {\"O}rjan}, journal = {Cereb Cortex}, volume = {26}, number = {7}, pages = {3052--3063}, month = jun, year = {2015}, publisher = {Oxford University Press}, url = {https://doi.org/10.1093/cercor/bhv130}, }

2014

Journal Article
Connecting to create: expertise in musical improvisation is associated with increased functional connectivity between premotor and prefrontal areas

Ana Luísa Pinho, Örjan Manzano, Peter Fransson, Helene Eriksson, and Fredrik Ullén

J Neurosci Apr 2014

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Musicians have been used extensively to study neural correlates of long-term practice, but no studies have investigated the specific effects of training musical creativity. Here, we used human functional MRI to measure brain activity during improvisation in a sample of 39 professional pianists with varying backgrounds in classical and jazz piano playing. We found total hours of improvisation experience to be negatively associated with activity in frontoparietal executive cortical areas. In contrast, improvisation training was positively associated with functional connectivity of the bilateral dorsolateral prefrontal cortices, dorsal premotor cortices, and presupplementary areas. The effects were significant when controlling for hours of classical piano practice and age. These results indicate that even neural mechanisms involved in creative behaviors, which require a flexible online generation of novel and meaningful output, can be automated by training. Second, improvisational musical training can influence functional brain properties at a network level. We show that the greater functional connectivity seen in experienced improvisers may reflect a more efficient exchange of information within associative networks of importance for musical creativity.
@article{Pinho2014, title = {Connecting to create: expertise in musical improvisation is associated with increased functional connectivity between premotor and prefrontal areas}, author = {Pinho, Ana Lu{\'i}sa and de Manzano, {\"O}rjan and Fransson, Peter and Eriksson, Helene and Ull{\'e}n, Fredrik}, journal = {J Neurosci}, volume = {34}, number = {18}, pages = {6156--6163}, month = apr, year = {2014}, publisher = {Society for Neuroscience}, url = {https://doi.org/10.1523/JNEUROSCI.4769-13.2014}, }