Inverse transformed encoding models - a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding.

Joram Soch, Carsten Allefeld, John-Dylan Haynes

April 2020 Neuroimage

Synopsis of Social media discussions

Several posts highlight the novelty and practical value of the inverse transformed encoding models, describing it as 'a solution to the problem of correlated trial-by-trial estimates.' The choice of words like 'solution,' 'final journal PDF,' and 'open access' emphasize both their interest and belief in its potential impact. The tone is generally professional and enthusiastic, with some posts actively encouraging further exploration or dissemination of the research, reflecting deep engagement and recognition of its importance.

A
Agreement
 Moderate agreement

Most posts acknowledge the significance of the publication, with some expressing support or recognizing it as a notable advancement in fMRI decoding research.

I
Interest
 High level of interest

The discussions show a high level of curiosity, especially with statements like 'our solution' and mentions of open access, reflecting genuine enthusiasm and relevance to ongoing research.

E
Engagement
 High engagement

Numerous posts reference the methodology (inverse transformed encoding models) and its implications, indicating active engagement through sharing links, summaries, and personal comments.

I
Impact
 Moderate level of impact

While the posts suggest potential for meaningful impact, they predominantly focus on the technical advancement and its availability, implying a perceived but not yet fully realized high impact.

Social Mentions

YouTube

1 Videos

Twitter

35 Posts

Metrics

Video Views

105

Total Likes

63

Extended Reach

236,390

Social Features

36

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Inverse Transformed Encoding Models Improve fMRI Trial-Wise Decoding Precision

Inverse Transformed Encoding Models Improve fMRI Trial-Wise Decoding Precision

Inverse Transformed Encoding Models address trial-by-trial parameter estimate correlations in fast event-related fMRI, improving decoding accuracy and reconstructing visual information effectively.

Joram Soch

October 25, 2023

105 views

  • Joram Soch
    @JoramSoch (Twitter)

    In this #PaperVideo, I'm summarizing the 2020 NeuroImage paper about our solution for the problem of correlated trial-by-trial estimates in fMRI decoding. Don't suppress the correlations - estimate their extent and explain them away! #ITEM #fMRI https://t.co/A19e3Pd5s2
    view full post

    November 6, 2023

  • City Research Online
    @City_Research (Twitter)

    #openaccess New research in CRO: July 12, 2021 at 02:08PM Inverse Transformed Encoding Models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding https://t.co/h2KGFjroa3
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    July 12, 2021

  • Lorenz Deserno
    @ldeserno (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 25, 2020

    18

  • Joram Soch
    @JoramSoch (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 25, 2020

    18

  • Magdalena Kachlicka @mkachlicka.bsky.social
    @mkachlicka (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 21, 2020

    18

  • Daniel Lindh
    @lajnd (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 21, 2020

    18

  • TheDecisionGuy
    @DecisionGuy (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 21, 2020

    18

  • Thomas Christophel
    @tbchristophel (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 21, 2020

    18

  • Polina
    @I_amPolina (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 21, 2020

    18

  • Taku Ito
    @taku_ito1 (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • Christine Heim
    @HeimMedPsych (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • katja seeliger
    @seelikat (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • Martin Hebart
    @martin_hebart (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • Furkan Özçelik
    @OzceFurkan (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • Armin W. Thomas
    @athmsx (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • Bernstein Network Computational Neuroscience
    @BernsteinNeuro (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • yoshiyuki nishio
    @nishiokov (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • M. Werkle-Bergner
    @WB_Markus (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • Diego Lozano
    @dieloz10 (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • Max Planck School of Cognition
    @MPS_cog (Twitter)

    RT @johndylanhaynes: Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter esti…
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    January 20, 2020

    18

  • John-Dylan Haynes
    @johndylanhaynes (Twitter)

    Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding" is online (with @JoramSoch @C_Allefeld). Open access: https://t.co/ORAg0jiKe9
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    January 20, 2020

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  • Joram Soch
    @JoramSoch (Twitter)

    @NeuroImage_EiC @biorxiv_neursci @github @OpenNeuroOrg @C_Allefeld @johndylanhaynes The final journal PDF can now be found here: https://t.co/JFz1EF4MrJ. (It's OA since NI was waiving the APC.) #NeuroImage #OpenAccess @C_Allefeld @johndylanhaynes
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    January 20, 2020

    2

  • City Research Online
    @City_Research (Twitter)

    #openaccess New research in CRO: January 07, 2020 at 03:27PM Inverse transformed encoding models - A solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding https://t.co/NOWmXxZsOK
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    January 7, 2020

  • Avi
    @avimimoun (Twitter)

    RT @JoramSoch: @NeuroImage_EiC @biorxiv_neursci @github @OpenNeuroOrg @C_Allefeld @johndylanhaynes Our paper "Inverse Transformed Encoding…
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    January 7, 2020

    1

  • Joram Soch
    @JoramSoch (Twitter)

    @NeuroImage_EiC @biorxiv_neursci @github @OpenNeuroOrg @C_Allefeld @johndylanhaynes Our paper "Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding" is online! Find pre-proof here: https://t.co/Hwzy64zSBJ. Will update when final journal PDF is available here: https://t.co/be8yi2m7EB.
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    January 7, 2020

    6

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  • Stefan Frässle
    @stefan_fraessle (Twitter)

    "Inverse transformed encoding models – A solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding" by @JoramSoch Carsten Allefeld and @johndylanhaynes https://t.co/jyIQUgY37q
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    January 3, 2020

    4

  • zuxfoucault
    @zuxfoucault (Twitter)

    RT @mripapers: NI: Inverse transformed encoding models – A solution to the problem of correlated trial-by-trial parameter estimates in fMRI…
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    December 22, 2019

    1

  • MRI Papers
    @mripapers (Twitter)

    NI: Inverse transformed encoding models – A solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding https://t.co/0xg39hrPPW
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    December 22, 2019

    1

    1

  • Allie Sinclair
    @sinclair_allie (Twitter)

    RT @biorxiv_neursci: Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fM…
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    April 17, 2019

    4

  • Shuntaro Aoki
    @shuntaro_aoki (Twitter)

    Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding | bioRxiv https://t.co/fHo4JfHPeI
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    April 17, 2019

  • Harrison Ritz
    @harrison_ritz (Twitter)

    RT @biorxiv_neursci: Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fM…
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    April 16, 2019

    4

  • Thomas A Carlson
    @CompCogNeuro (Twitter)

    RT @biorxiv_neursci: Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fM…
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    April 16, 2019

    4

  • Raymundo Neto
    @Raymundo_Pardal (Twitter)

    RT @biorxiv_neursci: Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fM…
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    April 16, 2019

    4

  • bioRxiv Neuroscience
    @biorxiv_neursci (Twitter)

    Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding https://t.co/kvoKw5EOYp #biorxiv_neursci
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    April 16, 2019

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  • bioRxiv
    @biorxivpreprint (Twitter)

    Inverse Transformed Encoding Models - a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding https://t.co/yQeuQmJpWU #bioRxiv
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    April 16, 2019

Abstract Synopsis

  • Inverse Transformed Encoding Models (ITEM) address the problem of highly variable and correlated trial-by-trial parameter estimates in fast event-related fMRI experiments, especially when trials are close together in time.
  • ITEM works by properly accounting for serial dependencies introduced by the hemodynamic response function, leading to more accurate decoding of experimental conditions or variables from brain activity without increasing computational load.
  • Through simulations and real data analysis, the authors show that ITEM outperforms existing methods in decoding accuracy and can even reconstruct visual information from fMRI signals effectively.]