Curriculum Vitae

General Information

Full Name Ana Luísa Grilo Pinho
Languages Portuguese, English, French

Education

  • 2015
    PhD
    Karolinska Institutet (Stockholm, Sweden) and Faculty of Medicine of the University of Coimbra (Coimbra, Portugal)
    • Thesis
    • My PhD Project was focused on the neurocognitive mechanisms underlying musical creativity within the framework of musical performance. I’ve used musical improvisation as a model behaviour and functional Magnetic Resonance Imaging (fMRI) as a technique in order to access the correspondent brain activity in professional pianists.
    • Study I sought to investigate the specific neurocognitive effects derived from expertise in musical improvisation. Results show a significant negative association between improvisational training and activity in a number of cortical regions in the right hemisphere. Besides, more experienced improvisers showed higher functional connectivity during improvisation between prefrontal, premotor, and motor regions.
    • Study II explored the contribution of the Dorsolateral Prefrontal Cortex (DLPFC) in creative cognition. Main findings highlight that deliberative creative cognition can be implemented in different ways given different circumstances. Higher activity was registered in the right DLPFC as well as in the parietal lobe and right dorsal premotor cortex, when contrasting structural conditions with emotional conditions. Conversely, the DLPFC was functionally connected to the default mode network during emotional conditions.
    • Faculty Supervisors
  • 2008
    MSc + Licentiate Degrees (Integrated Master) in Engineering Physics
    Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

Experience

  • 2021-present
    Tier I BrainsCAN Postdoctoral Fellow
    University of Western Ontario, London ON, Canada
    • My main project in this fellowship pertains to the functional mapping of the cortico-basal ganglia-cerebellar circuitry involved in the cognitive ability of forming temporal predictions during rhythmic and non-rhythmic sequences of events.
    • I am also interested on the development of encoding models of cognitive tasks as means to improve functional specificity in neuroimaging and, to this end, I am studying how to combine task-fMRI data across different datasets that will allow us to improve specificity of the statistical results relative to elementary cognitive components that modulate behaviour. Concretely, I am now working on the development of individualized encoding models, suitable to be used in a fusion framework of fMRI datasets. The main goal is to perform individual parcellations on functional maps of the human brain by systematically account for inter-subject variability, through integration of group-level parcels with individual data and using a hierarchical Bayesian parcellation scheme.
    • Faculty Advisors
  • 2015-2020
    Postdoctoral Researcher
    Parietal Team, Inria Saclay – Île-de-France, France
    • I was involved in the development of the Individual Brain Charting (IBC) dataset, which refers to an open-access neuroimaging initiative concerning the acquisition and analysis of multitask fMRI data for the establishment of a neurocognitive atlas of the human brain. This project is part of the Human Brain Project consortium.
    • Because many fMRI studies are limited to one single task and only report group-average brain maps that hinder effect specificity and a fine demarcation of brain regions, I also developed an individual-functional-atlasing approach to link functional segregation of specialized brain regions to elementary mental functions, by leveraging the IBC dataset.
    • Advisor

Fellowships, Grants & Awards

Academic Interests

  • Cognitive and Systems Neuroscience
    • Development of deep-behavioral-phenotyping strategies to inspect cognitive components of the phenotype and their network consistency/variability across individuals
    • Investigation of high-order neurocognitive mechanisms involved in both musical performance and perception
  • Neuroimaging
    • Focus in Functional Magnetic Resonance Imaging (fMRI)
  • Functional Brain Atlasing
    • Development of encoding models —leveraging machine-learning techniques— to perform functional mapping of cognition in the human brain
    • Development of a common framework of psycho-physiological constructs
  • Data Science and Neuroinformatics
    • Development of big-data and data-sharing frameworks to facilitate mega-analyses and reproducibility in systems neuroscience and neuroimaging
    • Revision of large-scale cognitive ontologies

Open-Source Projects

  • 2022-present
    NeuroCausal
    • Contributer to NeuroCausal: “An open data sharing and metadata synthesis platform for clinical data”
  • 2017-present
    Nilearn
    • Contributer to Nilearn: “Statistics for NeuroImaging in Python”
  • 2015-present
    IBC-analyses pipeline
    • Contributer to the Repository of Public Analysis Code for the IBC Project.

Other Interests