Evidence map›Paper›PMID 37396616›Full record

ArticleArXiv2024

Reliability of energy landscape analysis of resting-state functional MRI data.

Pitambar Khanra, Johan Nakuci, Sarah Muldoon, Takamitsu Watanabe, Naoki Masuda

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. 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

5 · Who and what money

Authors and funding

5 authors.

Pitambar KhanraDepartment of Mathematics, State University of New York at Buffalo, Buffalo, USA.
Johan NakuciSchool of Psychology, Georgia Institute of Technology, Atlanta, USA.
Sarah MuldoonDepartment of Mathematics, State University of New York at Buffalo, Buffalo, USA.
Takamitsu WatanabeInternational Research Centre for Neurointelligence, The University of Tokyo, Japan.
Naoki MasudaDepartment of Mathematics, State University of New York at Buffalo, Buffalo, USA.

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
Washington University Intellectual and Developmental Disabilities Research Center-Administrative Down Syndrome SupplementU54HD087011 · NICHD · WASHINGTON UNIVERSITY · PI CONSTANTINO, JOHN N. · 2015 to 2019
$7.1M
PEDIATRIC BRAIN INJURY RECOVERY VIA USE-DRIVEN FUNCTIONAL NETWORK REORGANIZATIONK23NS088590 · NINDS · WASHINGTON UNIVERSITY · PI DOSENBACH, NICO · 2015 to 2019
$886k
PREDICTING OUTCOME IN CHILDREN WITH TIC DISORDERS USING NEUROIMAGING DATAK01MH104592 · NIMH · WASHINGTON UNIVERSITY · PI GREENE, DEANNA JACQUELYN · 2014 to 2018
$723k
NCATS NIH HHS UL1 TR000448NCATS NIH HHS UL1 TR002345NICHD NIH HHS U54 HD087011NIMH NIH HHS K01 MH104592NIMH NIH HHS U54 MH091657NINDS NIH HHS K23 NS088590
6 · The paper itself

Abstract

Energy landscape analysis is a data-driven method to analyze multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e., within-participant reliability) than across different sets of sessions from different participants (i.e., between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Indexed as

Bayesian approximationFingerprintingfunctional magnetic resonance imagingIsing modelMaximum entropy modelPermutation test

Identifiers

PMID37396616
PMCPMC10312792

What OpenQuestion holds

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Registered trials

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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.