Evidence map›Paper›PMID 30721750›Full record

ArticleNeuroImage2019

Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.

Fatemeh Mokhtari, Milad I Akhlaghi, Sean L Simpson, Guorong Wu, Paul J Laurienti

Abstract read
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

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  10. Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.Neural networks : the official journal of the International Neural Network Society · 2025
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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.

Fatemeh MokhtariLaboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA. Electronic address: mfatemeh@vt.edu.
Milad I AkhlaghiIndependent Researcher, Cupertino, CA, USA.
Sean L SimpsonLaboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, USA.
Guorong WuBiomedical Research Imaging Center, Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Paul J LaurientiLaboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA; Translational Science Center, Wake Forest University, Winston Salem, NC, USA.

Funding

Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Wake Forest Alzheimer's Disease Core CenterP30AG049638 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI WILLIAMSON, JEFF DOUGLAS · 2016 to 2020
$12.2M
Analytical Tools for Complex Brain Networks: Fusing Novel Statistical Methods and Network Science to Understand Brain FunctionR01EB024559 · NIBIB · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SIMPSON, SEAN L · 2018 to 2021
$1.5M
Statistical Methods for Whole-Brain Connectivety NetworksK25EB012236 · NIBIB · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SIMPSON, SEAN L · 2012 to 2016
$796k
Longitudinal Analysis for Early Diagnosis of Alzheimer's DiseaseK01AG049089 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WU, GUORONG · 2016 to 2019
$478k
NCATS NIH HHS UL1 TR001420NIA NIH HHS K01 AG049089NIA NIH HHS P30 AG049638NIBIB NIH HHS K25 EB012236NIBIB NIH HHS R01 EB024559
6 · The paper itself

Abstract

The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.

Indexed as

Models, TheoreticalBrainComputer SimulationConnectomeData Interpretation, StatisticalHumansMagnetic Resonance ImagingNerve NetConnectivity network statesDynamic brain connectivityModulated rectangular windowNetwork states transitionResting stateSliding window correlation analysis

Identifiers

PMID30721750
PMCPMC6513676

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.