Ana Luísa Pinho
BrainsCAN Postdoctoral Fellow at Diedrichsenlab and The Music and Neuroscience Lab.

WIN-WIRB, office #4130
Western Univ., Dock #76
1151 Richmond St N
London, Ontario
N6A 3K7, Canada
I am a Neuroscientist with a background in Engineering Physics. My research focuses on the application of functional Magnetic Resonance Imaging (fMRI) and statistical techniques to map the neurocognitive mechanisms in the human brain involved in higher-order cognition.
News
Mar 31, 2023 | It was great to give a talk at the MNI Feindel Brain and Mind Lecture Series ! ![]() |
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Nov 7, 2022 | I am now on Mastodon! Join and follow me: https://fediscience.org/@ALuisaPinho. |
Oct 10, 2022 | My new website is out! ![]() ![]() |
Selected Publications
- Journal ArticleConnecting to create: expertise in musical improvisation is associated with increased functional connectivity between premotor and prefrontal areasAna Luísa Pinho, Örjan Manzano, Peter Fransson, Helene Eriksson, and Fredrik UllénJ Neurosci 2014
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}, year = {2014}, publisher = {Society for Neuroscience}, url = {https://doi.org/10.1523/JNEUROSCI.4769-13.2014}, }
- Journal ArticleAddressing a paradox: dual strategies for creative performance in introspective and extrospective networksAna Luísa Pinho, Fredrik Ullén, Miguel Castelo-Branco, Peter Fransson, and Örjan ManzanoCereb Cortex 2015
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}, year = {2015}, publisher = {Oxford University Press}, url = {https://doi.org/10.1093/cercor/bhv130}, }
- Journal ArticleIndividual Brain Charting, a high-resolution fMRI dataset for cognitive mappingAna Luísa Pinho, Alexis Amadon, Torsten Ruest, Murielle Fabre, Elvis Dohmatob, Isabelle Denghien, and 15 more authorsSci Data Jun 2018
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}, }
- Journal ArticleIndividual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mappingAna Luísa Pinho, Alexis Amadon, Baptiste Gauthier, Nicolas Clairis, André Knops, Sarah Genon, and 22 more authorsSci Data Jun 2020
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}, year = {2020}, url = {https://doi.org/10.1038/s41597-020-00670-4}, }
- Journal ArticleSubject-specific segregation of functional territories based on deep phenotypingAna Luísa Pinho, Alexis Amadon, Murielle Fabre, Elvis Dohmatob, Isabelle Denghien, Juan Jesús Torre, and 15 more authorsHum Brain Mapp Jun 2021
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}, 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.}, }
- Journal ArticleFrom deep brain phenotyping to functional atlasingBertrand Thirion, Alexis Thual, and Ana Luísa PinhoCurr Opin Behav Sci Jun 2021
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}, 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}, }