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Buch: Data Science for Neuroimaging

Rokem, Ariel / Yarkoni, Tal

An Introduction

2023

Verfasserangabe	Ariel Rokem and Tal Yarkoni
Sprache	Englisch
Umfang	392 p.
Verlag	Princeton University Press
Dokumenttyp	Buch
Anmerkung	PDA Manuell_25
Format	175 x 253 x 25
ISBN	9780691222752 ; 0691222754
Datenquelle	PDA

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Artikel ; Online: Detect-ing brain anomalies with autoencoders.

Rokem, Ariel

Nature computational science

2021 Band 1, Heft 9, Seite(n) 569–570

Sprache	Englisch
Erscheinungsdatum	2021-09-22
Erscheinungsland	United States
Dokumenttyp	Journal Article
ISSN	2662-8457
ISSN (online)	2662-8457
DOI	10.1038/s43588-021-00128-6
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

Rokem, Ariel / Benson, Noah C

Neuroinformatics

2024

Abstract: NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing ... ...

Abstract	NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.
Sprache	Englisch
Erscheinungsdatum	2024-05-20
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2111941-7
ISSN	1559-0089 ; 1539-2791
ISSN (online)	1559-0089
ISSN	1539-2791
DOI	10.1007/s12021-024-09666-6
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Explainable machine learning predictions of perceptual sensitivity for retinal prostheses.

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

Journal of neural engineering

2024 Band 21, Heft 2

Abstract: Objective. ...

Abstract	Objective.
Mesh-Begriff(e)	Humans ; Visual Prosthesis ; Artificial Intelligence ; Electrodes, Implanted ; Retina/physiology ; Machine Learning ; Electric Stimulation/methods
Sprache	Englisch
Erscheinungsdatum	2024-03-19
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2170901-4
ISSN	1741-2552 ; 1741-2560
ISSN (online)	1741-2552
ISSN	1741-2560
DOI	10.1088/1741-2552/ad310f
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: 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.
Sprache	Englisch
Erscheinungsdatum	2023-02-10
Erscheinungsland	United States
Dokumenttyp	Preprint
DOI	10.1101/2023.02.09.23285633
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Fractional ridge regression: a fast, interpretable reparameterization of ridge regression.

Rokem, Ariel / Kay, Kendrick

GigaScience

2020 Band 9, Heft 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-Begriff(e)	Algorithms ; Brain ; Linear Models ; Software
Sprache	Englisch
Erscheinungsdatum	2020-11-27
Erscheinungsland	United States
Dokumenttyp	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
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; 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 Band 44, Heft 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-Begriff(e)	Humans ; Child ; Adolescent ; Child, Preschool ; Young Adult ; Adult ; White Matter/diagnostic imaging ; Alpha Rhythm ; Diffusion Magnetic Resonance Imaging ; Visual Perception ; Visual Pathways ; Brain/physiology
Sprache	Englisch
Erscheinungsdatum	2024-02-07
Erscheinungsland	United States
Dokumenttyp	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
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Non-Stationary Dynamic Mode Decomposition.

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

IEEE access : practical innovations, open solutions

2023 Band 11, Seite(n) 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.
Sprache	Englisch
Erscheinungsdatum	2023-10-20
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2687964-5
ISSN	2169-3536
ISSN	2169-3536
DOI	10.1109/access.2023.3326412
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: 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 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.
Sprache	Englisch
Erscheinungsdatum	2023-08-13
Erscheinungsland	United States
Dokumenttyp	Preprint
DOI	10.1101/2023.08.08.552333
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; 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.
Schlagwörter	Statistics - Methodology ; Computer Science - Machine Learning ; Statistics - Machine Learning
Thema/Rubrik (Code)	006
Erscheinungsdatum	2020-05-06
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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