Machine learning for neuroimaging with scikit-learn

Alexandre, A.; Pedregosa, Fabián; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

doi:10.3389/fninf.2014.00014

Cited by 1,855 publications

(1,341 citation statements)

References 38 publications

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“…It contains age, white matter, 12 motion components -6 motion parameters and their first order derivatives -and 6 noise components from CompCor (Behzadi et al, 2007). Confounds are removed by subtracting the time-series projected into an orthonormal basis 260 of the confounds, using Nilearn v0.2.6 (Abraham et al, 2014). All rs-fMRI scans are quality-checked.…”

Section: Data Preprocessingmentioning

confidence: 99%

Joint prediction of multiple scores captures better individual traits from brain images

et al. 2017

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To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements is important: they jointly characterize the disease status of patient groups. In particular, mapping imaging features to behavioral scores with predictive models opens the way toward more precise diagnosis. Here we propose to jointly predict all the dimensions (behavioral scores) that make up the individual profiles, using so-called multi-output models. This approach often boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two independent resting-state fMRI datasets targeting different brain disorders (Alzheimer's Disease and schizophrenia). Furthermore, the model with joint prediction generalizes much better to a new cohort: a model learned on one study is more accurately transferred to an independent one. Finally, we show how multi-output models can easily be extended to multi-modal settings, combining heterogeneous data sources for a better overall accuracy.

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Section: Data Preprocessingmentioning

confidence: 99%

Joint prediction of multiple scores captures better individual traits from brain images

et al. 2017

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“…In order to limit external dependencies to standard*1 Python libraries, we implemented loading of surface data using Nibabel (Brett et al 2016) and rendering of the triangular surface meshes using Matplotlib (Hunter 2007). Beyond these two packages, only Numpy (van der Walt et al 2011) is required.…”

Section: Approachmentioning

confidence: 99%

“…Here we present a project that departs from this landscape in two ways: it strives 1) to provide plotting for cortical surface data in Python under minimal dependencies, and 2) to integrate surface data with multivariate processing in the Nilearn toolbox (Abraham et al 2014). …”

Section: Introductionmentioning

confidence: 99%

Loading and plotting of cortical surface representations in Nilearn

Huntenburg¹,

Alexandre²,

Loula³

et al. 2017

RIO

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Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed.

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“…Software used: We use SPM8 for preprocessing, Nilearn [20] for feature extraction, Scikit-learn [15] for classification, and Statsmodels [21] for post-hoc comparisons.…”

Section: B Experiments Designmentioning

confidence: 99%

Comparing functional connectivity based predictive models across datasets

Dadi

Alexandre

Rahim

et al. 2016

2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)

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To cite this version:Kamalaker Dadi, Alexandre Abraham, Mehdi Rahim, Bertrand Thirion, Gaël Varoquaux. Comparing functional connectivity based predictive models across datasets. Abstract-Resting-state functional Magnetic Resonance Imaging (rs-fMRI) holds the promise of easy-to-acquire and widespectrum biomarkers. However, there are few predictivemodeling studies on resting state, and processing pipelines all vary. Here, we systematically study resting state functionalconnectivity (FC)-based prediction across three different cohorts. Analysis pipelines consist of four steps: Delineation of brain regions of interest (ROIs), ROI-level rs-fMRI time series signal extraction, FC estimation and linear model classification analysis of FC features. For each step, we explore various methodological choices: ROI set selection, FC metrics, and linear classifiers to compare and evaluate the dominant strategies for the sake of prediction accuracy. We achieve good prediction results on the three different targets. With regard to pipeline selection, we obtain consistent results in two pipeline steps -FC metrics and linear classifiers-that are vital in the diagnosis of rs-fMRI based disease biomarkers. Regarding brain ROIs selection, we observe that the effects of different diseases are best characterized by different strategies: Schizophrenia discrimination is best performed in dataset-specific ROIs, which is not clearly the case for other pathologies. Overall, we outline some dominant strategies, in spite of the specificity of each brain disease in term of FC pattern disruption.

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Machine learning for neuroimaging with scikit-learn

Cited by 1,855 publications

References 38 publications

Joint prediction of multiple scores captures better individual traits from brain images

Joint prediction of multiple scores captures better individual traits from brain images

Loading and plotting of cortical surface representations in Nilearn

Comparing functional connectivity based predictive models across datasets

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