Evidence map›Paper›PMID 37981126›Full record

ArticleJournal of neuroscience methods2024

Optimal approaches to analyzing functional MRI data in glioma patients.

Ki Yun Park, Joshua S Shimony, Satrajit Chakrabarty, Aaron B Tanenbaum, Carl D Hacker, Kara M Donovan, Patrick H Luckett, Mikhail Milchenko, Aristeidis Sotiras, Daniel S Marcus and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of neuroscience methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.2field-weighted citation impact, top 21% of its field
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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
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

12 authors at 3 institutions in 1 country.

Ki Yun ParkDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA; Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO, USA; Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO 63110, USA. Electronic address: k.park@wustl.edu.
Joshua S ShimonyMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Satrajit ChakrabartyDepartment of Electrical and Systems Engineering, Washington University, St. Louis, MO 63130, USA.
Aaron B TanenbaumDepartment of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Carl D HackerDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA.
Kara M DonovanDepartment of Biomedical Engineering, Washington University, St. Louis, MO 63130, USA; Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Patrick H LuckettDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA; Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Mikhail MilchenkoMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Aristeidis SotirasMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA.
Daniel S MarcusMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Eric C LeuthardtDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, USA; Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, MO 63130, USA; Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO 63110, USA; Brain Laser Center, Washington University School of Medicine, St. Louis, MO 63110, USA; Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Abraham Z SnyderMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Washington University in St. Louis · USMallinckrodt (United States) · USNeurological Surgery · US

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI AIMEE S JAMES · 2001 to 2026
$128.0M
WUIDDRC Supplement-Supporting the health and well-being of children with intellectual and developmental disability during COVID-19 pandemicP50HD103525 · NICHD · WASHINGTON UNIVERSITY · PI JOSEPH D DOUGHERTY, Natasha Marrus · 2020 to 2026
$15.5M
Training and DisseminationP41EB018783 · NIBIB · WADSWORTH CENTER · PI Theresa M. Vaughan · 2014 to 2026
$15.4M
Augmented Neurosurgical Navigation Software Using Resting State MRIR01CA203861 · NCI · WASHINGTON UNIVERSITY · PI Eric CLAUDE Leuthardt, JOSHUA S SHIMONY · 2017 to 2026
$5.8M
BCI2000: Software Resource for Adaptive Neurotechnology ResearchU24NS109103 · NINDS · WASHINGTON UNIVERSITY · PI BRUNNER, PETER · 2019 to 2024
$2.8M
BCI2000+: A Software Platform for Adaptive NeurotechnologiesR01EB026439 · NIBIB · WASHINGTON UNIVERSITY · PI BRUNNER, PETER · 2018 to 2022
$1.5M
Dynamics and Causal Functions of Large-Scale Cortical and Subcortical NetworksU01NS108916 · NINDS · WADSWORTH CENTER · PI WOLPAW, JONATHAN RICKEL · 2018 to 2019
$1.4M
NCI NIH HHS P30 CA091842NCI NIH HHS R01 CA203861NIBIB NIH HHS P41 EB018783NIBIB NIH HHS R01 EB026439NICHD NIH HHS P50 HD103525NINDS NIH HHS U01 NS108916NINDS NIH HHS U24 NS109103
6 · The paper itself

Abstract

backgroundResting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results. NEW

methodWe first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163).

resultsOur results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density. COMPARISON WITH EXISTING METHODS AND

conclusionsOur current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.

Indexed as

ConnectomeGliomaBrainHumansMagnetic Resonance ImagingAtlas registrationGliomaGraph-theoretic measuresParcellation granularityParcellation schemesResting-state fMRI

Identifiers

PMID37981126
PMCPMC10926951
OpenAlexW4388799146

What OpenQuestion holds

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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.