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  1. Book: Data Science for Neuroimaging

    Rokem, Ariel / Yarkoni, Tal

    An Introduction

    2023  

    Author's details Ariel Rokem and Tal Yarkoni
    Language English
    Size 392 p.
    Publisher Princeton University Press
    Document type Book
    Note PDA Manuell_25
    Format 175 x 253 x 25
    ISBN 9780691222752 ; 0691222754
    Database PDA

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  2. Article ; Online: Detect-ing brain anomalies with autoencoders.

    Rokem, Ariel

    Nature computational science

    2021  Volume 1, Issue 9, Page(s) 569–570

    Language English
    Publishing date 2021-09-22
    Publishing country United States
    Document type Journal Article
    ISSN 2662-8457
    ISSN (online) 2662-8457
    DOI 10.1038/s43588-021-00128-6
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  3. Article ; Online: Explainable machine learning predictions of perceptual sensitivity for retinal prostheses.

    Pogoncheff, Galen / Hu, Zuying / Rokem, Ariel / Beyeler, Michael

    Journal of neural engineering

    2024  Volume 21, Issue 2

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Humans ; Visual Prosthesis ; Artificial Intelligence ; Electrodes, Implanted ; Retina/physiology ; Machine Learning ; Electric Stimulation/methods
    Language English
    Publishing date 2024-03-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 2170901-4
    ISSN 1741-2552 ; 1741-2560
    ISSN (online) 1741-2552
    ISSN 1741-2560
    DOI 10.1088/1741-2552/ad310f
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  4. Article: Explainable Machine Learning Predictions of Perceptual Sensitivity for Retinal Prostheses.

    Pogoncheff, Galen / Hu, Zuying / Rokem, Ariel / Beyeler, Michael

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: To provide appropriate levels of stimulation, retinal prostheses must be calibrated to an individual's perceptual thresholds ('system fitting'), despite thresholds varying drastically across subjects, across electrodes within a subject, and over time. ... ...

    Abstract To provide appropriate levels of stimulation, retinal prostheses must be calibrated to an individual's perceptual thresholds ('system fitting'), despite thresholds varying drastically across subjects, across electrodes within a subject, and over time. Although previous work has identified electrode-retina distance and impedance as key factors affecting thresholds, an accurate predictive model is still lacking. To address these challenges, we 1) fitted machine learning (ML) models to a large longitudinal dataset with the goal of predicting individual electrode thresholds and deactivation as a function of stimulus, electrode, and clinical parameters ('predictors') and 2) leveraged explainable artificial intelligence (XAI) to reveal which of these predictors were most important. Our models accounted for up to 77% of the perceptual threshold response variance and enabled predictions of whether an electrode was deactivated in a given trial with F1 and AUC scores of up to 0.740 and 0.913, respectively. Deactivation and threshold models identified novel predictors of perceptual sensitivity, including subject age, time since blindness onset, and electrode-fovea distance. Our results demonstrate that routinely collected clinical measures and a single session of system fitting might be sufficient to inform an XAI-based threshold prediction strategy, which may transform clinical practice in predicting visual outcomes.
    Language English
    Publishing date 2023-02-10
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.02.09.23285633
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  5. Article ; Online: Fractional ridge regression: a fast, interpretable reparameterization of ridge regression.

    Rokem, Ariel / Kay, Kendrick

    GigaScience

    2020  Volume 9, Issue 12

    Abstract: Background: Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of ... ...

    Abstract Background: Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable.
    Results: The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets.
    Conclusion: Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.
    MeSH term(s) Algorithms ; Brain ; Linear Models ; Software
    Language English
    Publishing date 2020-11-27
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2708999-X
    ISSN 2047-217X ; 2047-217X
    ISSN (online) 2047-217X
    ISSN 2047-217X
    DOI 10.1093/gigascience/giaa133
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  6. Article: Non-Stationary Dynamic Mode Decomposition.

    Ferré, John / Rokem, Ariel / Buffalo, Elizabeth A / Kutz, J Nathan / Fairhall, Adrienne

    IEEE access : practical innovations, open solutions

    2023  Volume 11, Page(s) 117159–117176

    Abstract: Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal ... ...

    Abstract Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator. However, these methods apply best to time-translationally invariant or stationary data, while in many typical cases, dynamics vary across time and conditions. To capture this temporal evolution, we developed a method, Non-Stationary Dynamic Mode Decomposition, that generalizes Dynamic Mode Decomposition by fitting global modulations of drifting spatiotemporal modes. This method accurately predicts the temporal evolution of modes in simulations and recovers previously known results from simpler methods. To demonstrate its properties, the method is applied to multi-channel recordings from an awake behaving non-human primate performing a cognitive task.
    Language English
    Publishing date 2023-10-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2687964-5
    ISSN 2169-3536
    ISSN 2169-3536
    DOI 10.1109/access.2023.3326412
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  7. Article: Non-Stationary Dynamic Mode Decomposition.

    Ferré, John / Rokem, Ariel / Buffalo, Elizabeth A / Kutz, J Nathan / Fairhall, Adrienne

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal ... ...

    Abstract Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator. However, DMD methods apply best to time-translationally invariant or stationary data, while in many typical cases, dynamics vary across time and conditions. To capture this temporal evolution, we developed a method, Non-Stationary Dynamic Mode Decomposition (NS-DMD), that generalizes DMD by fitting global modulations of drifting spatiotemporal modes. This method accurately predicts the temporal evolution of modes in simulations and recovers previously known results from simpler methods. To demonstrate its properties, the method is applied to multi-channel recordings from an awake behaving non-human primate performing a cognitive task.
    Language English
    Publishing date 2023-08-13
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.08.08.552333
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  8. Article ; Online: Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance.

    Caffarra, Sendy / Kanopka, Klint / Kruper, John / Richie-Halford, Adam / Roy, Ethan / Rokem, Ariel / Yeatman, Jason D

    The Journal of neuroscience : the official journal of the Society for Neuroscience

    2024  Volume 44, Issue 6

    Abstract: Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and ... ...

    Abstract Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI
    MeSH term(s) Humans ; Child ; Adolescent ; Child, Preschool ; Young Adult ; Adult ; White Matter/diagnostic imaging ; Alpha Rhythm ; Diffusion Magnetic Resonance Imaging ; Visual Perception ; Visual Pathways ; Brain/physiology
    Language English
    Publishing date 2024-02-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 604637-x
    ISSN 1529-2401 ; 0270-6474
    ISSN (online) 1529-2401
    ISSN 0270-6474
    DOI 10.1523/JNEUROSCI.0684-23.2023
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1668: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: Multidimensional analysis and detection of informative features in human brain white matter.

    Richie-Halford, Adam / Yeatman, Jason D / Simon, Noah / Rokem, Ariel

    PLoS computational biology

    2021  Volume 17, Issue 6, Page(s) e1009136

    Abstract: The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance ... ...

    Abstract The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.
    MeSH term(s) Aging/pathology ; Algorithms ; Amyotrophic Lateral Sclerosis/diagnostic imaging ; Case-Control Studies ; Computational Biology ; Connectome/statistics & numerical data ; Diffusion Tensor Imaging/statistics & numerical data ; Humans ; Models, Neurological ; Multivariate Analysis ; Nerve Net/diagnostic imaging ; Neuroimaging/statistics & numerical data ; Principal Component Analysis ; Regression Analysis ; Software ; White Matter/diagnostic imaging
    Language English
    Publishing date 2021-06-28
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1009136
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  10. Book ; Online: Fractional ridge regression

    Rokem, Ariel / Kay, Kendrick

    a fast, interpretable reparameterization of ridge regression

    2020  

    Abstract: Ridge regression (RR) is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using RR is the need to set a hyperparameter ($\alpha$) that controls the amount of regularization. Cross- ... ...

    Abstract Ridge regression (RR) is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using RR is the need to set a hyperparameter ($\alpha$) that controls the amount of regularization. Cross-validation is typically used to select the best $\alpha$ from a set of candidates. However, efficient and appropriate selection of $\alpha$ can be challenging, particularly where large amounts of data are analyzed. Because the selected $\alpha$ depends on the scale of the data and predictors, it is not straightforwardly interpretable. Here, we propose to reparameterize RR in terms of the ratio $\gamma$ between the L2-norms of the regularized and unregularized coefficients. This approach, called fractional RR (FRR), has several benefits: the solutions obtained for different $\gamma$ are guaranteed to vary, guarding against wasted calculations, and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. We provide an algorithm to solve FRR, as well as open-source software implementations in Python and MATLAB (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems, and delivers results that are straightforward to interpret and compare across models and datasets.
    Keywords Statistics - Methodology ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-05-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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