Evidence map›Paper›PMID 38895977›Full record

ArticleHuman brain mapping2024

Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator.

Sukesh Kumar Das, Anil K Sao, Bharat B Biswal

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Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Sukesh Kumar DasSchool of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India.ORCID 0000-0002-6303-0800
Anil K SaoDepartment of Computer Science and Engineering, Indian Institute of Technology Bhilai, Bhilai, Chhattisgarh, India.ORCID 0000-0001-5316-5528
Bharat B BiswalDepartment of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA.ORCID 0000-0002-3710-3500

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

Indexed as

ConnectomeMagnetic Resonance ImagingAdultBrainFemaleHumansImage Processing, Computer-AssistedMaleNerve NetRestYoung Adultcoactivation pattern (CAP)conditional rate mapfunctional connectivityresting‐state fMRIspontaneous BOLD eventzero‐frequency resonator

Identifiers

PMID38895977
PMCPMC11187872

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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.