Deep Generative Analysis for Task-Based Functional MRI Experiments.

Daniela de Albuquerque, Jack Goffinet, Rachael Wright, John Pearson

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniela de AlbuquerqueDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.

Jack GoffinetDepartment of Computer Science, Duke University, Durham, NC 27708, USA.

Rachael WrightCenter for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA.

John PearsonDepartment of Biostatistics & Bioinformatics, Department of Electrical and Computer Engineering, Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA.

Funding

Medical Scientist Training Program Training GrantT32GM145449 · NIGMS · DUKE UNIVERSITY · PI Christopher D Kontos · 2022 to 2026

$6.6M

Neurocomputational Approaches to Emotion RepresentationR01MH124112 · NIMH · DUKE UNIVERSITY · PI LABAR, KEVIN S · 2020 to 2023

$3.1M

NIGMS NIH HHS T32 GM145449NIMH NIH HHS R01 MH124112

6 · The paper itself

Abstract

While functional magnetic resonance imaging (fMRI) remains one of the most widespread and important methods in basic and clinical neuroscience, the data it produces-time series of brain volumes-continue to pose daunting analysis challenges. The current standard ("mass univariate") approach involves constructing a matrix of task regressors, fitting a separate general linear model at each volume pixel ("voxel"), computing test statistics for each model, and correcting for false positives

Identifiers

PMID35224507

PMCPMC8871581

What OpenQuestion holds

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LicenceTDM

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.