nipy/nibabel: 3.2.1

Brett, Matthew; Markiewicz, Christopher J.; Hanke, Michael; Côté, Marc-Alexandre; Cipollini, Ben; McCarthy, Paul; Jarecka, Dorota; Cheng, Christopher P.; Halchenko, Yaroslav; Cottaar, Michiel; Larson, Eric; Ghosh, Samik; Wassermann, Demián; Gerhard, Stephan; Lee, Gregory R.; Wang, Hao-Ting; Kastman, Erik K.; Kaczmarzyk, Jakub; Guidotti, Roberto; Duek, Or; Daniel, Jonathan; Rokem, Ariel; Madison, Cindee; Moloney, Brendan; Morency, Félix C.; Goncalves, Mathias; Markello, Ross; Riddell, Cameron; Burns, Christopher; Millman, Jarrod; Gramfort, Alexandre; Leppäkangas, Jaakko; Sólon, Anibal; Bosch, Jasper J.F. van den; Vincent, Robert D.; Braun, Henry; Subramaniam, Krish; Gorgolewski, Krzysztof J.; Raamana, Pradeep Reddy; Klug, Julian; Nichols, B. Nolan; Baker, Eric M.; Hayashi, Soichi; Pinsard, Basile; Haselgrove, Christian; Hymers, Mark; Estéban, Oscar; Koudoro, Serge; Pérez‐García, Fernando; Oosterhof, Nikolaas N.; Amirbekian, Bago; Nimmo‐Smith, Ian; Nguyen, Ly; Reddigari, Samir; St-Jean, Samuel; Panfilov, Egor; Garyfallidis, Eleftherios; Varoquaux, Gaël; Legarreta, Jon Haitz; Hahn, Kevin S.; Hinds, Oliver P.; Fauber, Bennet; Poline, Jean‐Baptiste; Stutters, Jonathan; Jordan, Kesshi; Cieslak, Matthew; Moreno, Miguel Estevan; Haenel, Valentin; Schwartz, Yannick; Baratz, Zvi; Darwin, Benjamin C; Thirion, Bertrand; Gauthier, Carl; Orfanos, Dimitri Papadopoulos; Solovey, Igor; González, Ivan; Palasubramaniam, Jath; Lecher, Justin; Leinweber, Katrin; Raktivan, Konstantinos; Calábková, Markéta; Fischer, P.; Gervais, Philippe; Gadde, Syam; Ballinger, Thomas; Roos, Thomas; Reddam, Venkateswara Reddy; freec,

doi:10.5281/zenodo.4295521

Cited by 37 publications

(24 citation statements)

References 0 publications

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“…Data analysis, modeling, and figure creation were done using a variety of custom scripts written in Python 3.6.3 (Van Rossum and Drake, 2009), all found in the GitHub repository associated with this paper. The following packages were used: snakemake (Mölder et al, 2021), Jupyter Lab (Kluyver et al, 2016), numpy ("Array programming with NumPy", 2020), matplotlib (Hunter, 2007), scipy (Virtanen et al, 2020), seaborn (Waskom, 2021), pandas (McKinney, 2010;pandas development team, 2020), nipype (Gorgolewski et al, 2018;Gorgolewski et al, 2011), nibabel (Brett et al, 2020), scikit-learn (Pedregosa et al, 2011), neuropythy , pytorch (Paszke et al, 2019), psychopy (Peirce et al, 2019b), FSL (Smith et al, 2004), freesurfer (Dale et al, 1999), vistasoft, and GLMdenoise (Kay et al, 2013a). We start by analyzing the data as a function of spatial frequency alone (i.e., averaging over orientation), which requires fewer assumptions and is easier to visualize.…”

Section: Softwarementioning

confidence: 99%

Mapping Spatial Frequency Preferences Across Human Primary Visual Cortex

Broderick

Simoncelli

Winawer

2021

Preprint

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Neurons in primate visual cortex (area V1) are tuned for spatial frequency, in a manner that depends on their position in the visual field. Several studies have examined this dependency using fMRI, reporting preferred spatial frequencies (tuning curve peaks) of V1 voxels as a function of eccentricity, but their results differ by as much as two octaves, presumably due to differences in stimuli, measurements, and analysis methodology. Here, we characterize spatial frequency tuning at a millimeter resolution within human primary visual cortex, across stimulus orientation and visual field locations. We measured fMRI responses to a novel set of stimuli, constructed as sinusoidal gratings in log-polar coordinates, which include circular, radial, and spiral geometries. For each individual stimulus, the local spatial frequency varies inversely with eccentricity, and for any given location in the visual field, the full set of stimuli span a broad range of spatial frequencies and orientations. Over the measured range of eccentricities, the preferred spatial frequency is well-fit by a function that varies as the inverse of the eccentricity plus a small constant. We also find small but systematic effects of local stimulus orientation, defined in both absolute coordinates and relative to visual field location. Specifically, peak spatial frequency is higher for tangential than radial orientations and for horizontal than vertical orientations.

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Section: Softwarementioning

confidence: 99%

Mapping Spatial Frequency Preferences Across Human Primary Visual Cortex

Broderick

Simoncelli

Winawer

2021

Preprint

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“…The “dcm2niix2bids” plugin is a wrapper around the well-known pydicom ( Mason et al, 2020 ), nibabel ( Brett et al, 2020 ), and dcm2niix tools ( Li et al, 2016 ) for interacting with and converting the DICOM and Philips PAR(/REC)/XML source data. Pydicom is used to read DICOM attributes, nibabel is used to read PAR/XML attribute values, and dcm2niix is used to convert the DICOM and PAR/XML source data to NIfTI 23 and create BIDS sidecar files.…”

Section: Methodsmentioning

confidence: 99%

BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

Zwiers

Moia

Oostenveld

2022

Front. Neuroinform.

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Analyses of brain function and anatomy using shared neuroimaging data is an important development, and have acquired the potential to be scaled up with the specification of a new Brain Imaging Data Structure (BIDS) standard. To date, a variety of software tools help researchers in converting their source data to BIDS but often require programming skills or are tailored to specific institutes, data sets, or data formats. In this paper, we introduce BIDScoin, a cross-platform, flexible, and user-friendly converter that provides a graphical user interface (GUI) to help users finding their way in BIDS standard. BIDScoin does not require programming skills to be set up and used and supports plugins to extend their functionality. In this paper, we show its design and demonstrate how it can be applied to a downloadable tutorial data set. BIDScoin is distributed as free and open-source software to foster the community-driven effort to promote and facilitate the use of BIDS standard.

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“…The whole pipeline is automated with

3.7 [ 58 ]. We utilized

as the data manipulation package [ 59 ],

and

for numerical analysis and matrices operations [ 60 ],

for performing statistical tests [ 61 ],

for reading and writing Freesurfer files [ 62 ]…”

Section: Methodsmentioning

confidence: 99%

The Role of Structure MRI in Diagnosing Autism

et al. 2022

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This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

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nipy/nibabel: 3.2.1

Cited by 37 publications

References 0 publications

Mapping Spatial Frequency Preferences Across Human Primary Visual Cortex

Mapping Spatial Frequency Preferences Across Human Primary Visual Cortex

BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

The Role of Structure MRI in Diagnosing Autism

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