Author

Eric Lacosse

Max Planck Institute for Biological Cybernetics

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:imaging· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:cell-cell communication· 1 Research Focus:gene regulatory network inference· 1 Approach:statistical model· 1

Frequent Co-Authors

Gabriele LohmanniD· 3 shared Klaus ScheffleriD· 3 shared Georg MartiusiD· 1 shared Johannes StelzeriD· 1 shared Jürgen JostiD· 1 shared Karsten Müller· 1 shared Thomas EthoferiD· 1 shared Tilo Buschmann· 1 shared

Activity by Year

2017-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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3 shared papersNeuroscience

Gabriele LohmannORCID

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3 papers

Recent·Most cited

Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

Gabriele Lohmann, Eric Lacosse, Thomas Ethofer, Vinod Kumar, Klaus Scheffler, Jürgen Jost

The paper introduces a machine learning algorithm that predicts intelligence from task-based fMRI data with high accuracy and faster scan times than resting state fMRI.

bioRxiv · Mar 18, 2021 · PreprintNeuroscience

Jumping over Baselines with New Methods to Predict Activation Maps from Resting-state fMRI

Eric Lacosse, Klaus Scheffler, Gabriele Lohmann, Georg Martius

The paper evaluates methods for predicting task activation maps from resting-state fMRI and finds that most underperform compared to simple baselines.

Scientific Reports · Feb 10, 2021Peer-reviewedNeuroscience

Inflated False Negative Rates Undermine Reproducibility In Task-Based fMRI

Gabriele Lohmann, Johannes Stelzer, Karsten Müller, Eric Lacosse, Tilo Buschmann, VJ Kumar, Wolfgang Grodd, Klaus Scheffler

The paper assesses reproducibility in task-based fMRI by evaluating false negative rates across different sample sizes and introduces a new statistical method to improve reproducibility.

bioRxiv · Mar 31, 2017 · PreprintNeuroscience