Evidence map›Paper›PMID 40800834›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2025

WhiFuN: A toolbox to map the white matter functional networks of the human brain.

Pratik Jain, Andrew M Michael, Pan Wang, Xin Di, Bharat Biswal

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

5 authors.

Pratik JainNew Jersey Institute of Technology, Newark, NJ, United States.
Andrew M MichaelDuke Institute for Brain Sciences, Duke University, Durham, NC, United States.
Pan WangThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Xin DiNew Jersey Institute of Technology, Newark, NJ, United States.
Bharat BiswalNew Jersey Institute of Technology, Newark, NJ, United States.ORCID https://orcid.org/0000-0002-3710-3500

Funding

Functional Connectivity and Baseline Networks of the White Matter Brain: Development and Dissemination of Algorithms and ToolsR01MH131335 · NIMH · NEW JERSEY INSTITUTE OF TECHNOLOGY · PI BISWAL, BHARAT BHUSAN · 2022 to 2025
$2.1M
Functional Networks of White Matter in Alzheimer's Disease and their Associations with Cognitive DeclineR01AG085665 · NIA · NEW JERSEY INSTITUTE OF TECHNOLOGY · PI Bharat Bhusan Biswal · 2024 to 2026
$1.8M
Investigating the neurophysiological basis of circuit-specific laminar rs-fMRIR01NS124778 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI BISWAL, BHARAT BHUSAN, BROWN, EMERY N · 2025 to 2025
$697k
Functional brain developments during movie watching and resting-state in autism spectrum disorderR15MH125332 · NIMH · NEW JERSEY INSTITUTE OF TECHNOLOGY · PI DI, XIN · 2021 to 2021
$305k
NIA NIH HHS R01 AG085665NIMH NIH HHS R01 MH131335NIMH NIH HHS R15 MH125332NINDS NIH HHS R01 NS124778
6 · The paper itself

Abstract

Functional connectivity (FC) computed using functional magnetic resonance imaging (fMRI) of the gray matter (GM) regions of the human brain has been successfully used to find reliable markers of healthy and clinical populations. Approximately 50% of the human brain consists of white matter (WM), and previous studies have shown the presence of blood oxygen level-dependent (BOLD) signals in the WM. However, current FC analysis by researchers is limited to GM regions of the brain, and fMRI data from WM are typically not analyzed. Here, we present the White Matter Functional Networks (WhiFuN) Toolbox specifically designed for WM-FC analysis, incorporating preprocessing steps that minimize signal contamination due to GM, optimized methods for extracting meaningful WM signals, and dedicated statistical and visualization tools for WM-FC. WhiFuN is based on SPM12 preprocessing and contains statistical tools for group-level analyses. WhiFuN provides an intuitive graphical user interface allowing users to execute all steps from preprocessing to final group-level analyses and does not require prior knowledge of computer programming. To demonstrate the features and capabilities of WhiFuN, 98 healthy controls from the publicly available HCP 100 unrelated dataset were used to identify sex differences in WM-FC. We found significant WM-FC sex differences between the left body of the corpus callosum (CC) and the WM-FN that included the left and right posterior corona radiata and the left and right posterior thalamic region. WhiFuN will provide a platform for the neuroimaging community, offering new dimensions to elucidate human brain function as an integrated system of both GM and WM.

Indexed as

functional connectivitysoftware toolboxwhite matterwhite matter fMRI

Identifiers

PMID40800834
PMCPMC12319995

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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.