neuroimaging posts

2021 highlight: Decoding brain activity to new cognitive paradigms

Broad decoding models that can specialize to discriminate closely-related mental process with limited data


Decoding models can help isolating which mental processes are implied by the activation of given brain structures. But to support a broad conclusion, they must be trained on many studies, a difficult problem given …

2019: my scientific year in review

My current research spans wide: from brain sciences to core data science. My overall interest is to build methodology drawing insights from data for questions that have often been addressed qualitatively. If I can highlight a few publications from 2019 [1], the common thread would be computational statistics, from dirty …

2018: my scientific year in review

From a scientific perspective, 2018 [1] was once again extremely exciting thank to awesome collaborators (at Inria, with DirtyData, and our local scikit-learn team). Rather than going over everything that we did in 2018, I would like to give a few highlights: We published major work using machine learning to …

Our research in 2017: personal scientific highlights

In my opinion the scientific highlights of 2017 for my team were on multivariate predictive analysis for brain imaging: a brain decoder more efficient and faster than alternatives, improvement clinical predictions by predicting jointly multiple traits of subjects, decoding based on the raw time-series of brain activity, and a personnal …

Our research in 2016: personal scientific highlights

Year 2016 has been productive for science in my team. Here are some personal highlights: bridging artificial intelligence tools to human cognition, markers of neuropsychiatric conditions from brain activity at rest, algorithmic speedups for matrix factorization on huge datasets…

Artificial-intelligence convolutional networks map well the human visual system

Eickenberg et …

Nilearn 0.2: more powerful machine learning for neuroimaging

After 6 months of efforts, We just released version 0.2 of nilearn, dedicated to making machine learning in neuroimaging easier and more powerful.

This release integrates the features of the july sprint, and more.


Better documentation …

Nilearn sprint: hacking neuroimaging machine learning

A couple of weeks ago, we had in Paris the second international nilearn sprint, dedicated to making machine learning in neuroimaging easier and more powerful.

It was such a fantastic experience, as nilearn is really shaping up as a simple yet powerful tool, and there is a lot of enthusiasm …

Job offer: working on open source data processing in Python

We, Parietal team at INRIA, are recruiting software developers to work on open source machine learning and neuroimaging software in Python.

In general, we are looking for people who:

  • have a mathematical mindset,
  • are curious about data (ie like looking at data and understanding it)
  • have an affinity for problem-solving …

PRNI 2016: call for organization

The steering committee of PRNI (Pattern Recognition for NeuroImaging) is opening a call for bid to organize the conference in June 2016, in Europe

Hiring an engineer to mine large functional-connectivity databases

Work with us to leverage leading-edge machine learning for neuroimaging

At Parietal, my research team, we work on improving the way brain images are analyzed, for medical diagnostic purposes, or to understand the brain better. We develop new machine-learning tools and investigate new methodologies for for quantifying brain function from …