Loading and plotting of cortical surface representations in Nilearn

Huntenburg, Julia M.; Alexandre, A.; Loula, João; Liem, Franziskus; Dadi, Kamalaker; Varoquaux, Gaël

doi:10.3897/rio.3.e12342

Cited by 11 publications

(7 citation statements)

References 13 publications

(8 reference statements)

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“…Finally, the signal of each source location was normalized by its variance to counter the depth bias. For visualization, source locations thresholded at 50% of the maximum source activation were plotted on cortical surfaces using the nilearn package (Huntenburg et al, 2017) in Python. Brain regions were identified from the MNI coordinates of source maxima using the Harvard-Oxford cortical structural atlas (Desikan et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

Context-Based Facilitation in Visual Word Recognition: Evidence for Visual and Lexical But Not Pre-Lexical Contributions

Eisenhauer

Fiebach

Gagl

2019

eNeuro

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Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword (PW) familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N = 38 human participants), while experiment 2 assessed behavioral facilitation effects ( N = 24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context-based facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in PWs familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context-based facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism.

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Section: Methodsmentioning

confidence: 99%

Context-Based Facilitation in Visual Word Recognition: Evidence for Visual and Lexical But Not Pre-Lexical Contributions

Eisenhauer

Fiebach

Gagl

2019

eNeuro

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“…Tangent correlation across the time series of the n = 122 regions of the BASC (Multi-level bootstrap analysis of stable clusters) [ 65 ] brain parcellation was computed with nilearn ( http://nilearn.github.io/ ) [ 66 , 67 ].…”

Section: Methodsmentioning

confidence: 99%

Statistical quantification of confounding bias in machine learning models

Spisák¹

2022

GigaScience

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Background The lack of nonparametric statistical tests for confounding bias significantly hampers the development of robust, valid, and generalizable predictive models in many fields of research. Here I propose the partial confounder test, which, for a given confounder variable, probes the null hypotheses of the model being unconfounded. Results The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in machine learning. Applying the proposed test on models trained on large-scale functional brain connectivity data (N= 1,865) (i) reveals previously unreported confounders and (ii) shows that state-of-the-art confound mitigation approaches may fail preventing confounder bias in several cases. Conclusions The proposed test (implemented in the package mlconfound; https://mlconfound.readthedocs.io) can aid the assessment and improvement of the generalizability and validity of predictive models and, thereby, fosters the development of clinically useful machine learning biomarkers.

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“…It does not include support for visualizations. The nilearn toolbox ( Huntenburg et al, 2017 ) does not currently support CIFTI files per se, but it has functions for projecting data onto surface geometry for visualization. Users can read in CIFTI and GIFTI data with NiBabel and then use nilearn to visualize it.…”

Section: Relationships With Other Tools and Packagesmentioning

confidence: 99%