Spatial and Temporal Quality of Brain Networks for Different Multi-Echo fMRI Combination Methods

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Abstract

The application of multi-echo functional magnetic resonance imaging (fMRI) studies has considerably increased in the last decade due to superior BOLD sensitivity compared to single-echo fMRI. Various methods have been developed that combine fMRI data derived at different echo times to improve data quality. Here, we evaluated five multi-echo combination schemes: ‘optimal combination’ (OC, ${\text {T}_{2}}^{\ast }$ -weighted), ${\text {T}_{2}}^{\ast }$ -FIT ( ${\text {T}_{2}}^{\ast }$ -weighted, calculated per volume), average-weighted (Avg), temporal Signal-to-Noise Ratio (tSNR) weighted, and temporal Contrast-to-Noise Ratio weighted combination. The effect of these combinations, with and without additional postprocessing, on the quality of functional resting-state networks was assessed. Sixteen healthy volunteers were scanned during a 5-minutes resting-state fMRI session. After network extraction, several quality metrics in the temporal and spatial domain were calculated for their respective time-series and spatial maps. Our results showed that OC and ${\text {T}_{2}}^{\ast }$ -FIT outperformed the other methods in both domains. Whereas the OC and ${\text {T}_{2}}^{\ast }$ -FIT time-series were found to be the least associated with artifacts, OC resulted in the highest quality spatial maps. Furthermore, spatial smoothing, bandpass filtering and ICA-AROMA merely improved networks derived from the least performing combinations (Avg and tSNR). Because similar network quality was obtained following OC and ${\text {T}_{2}}^{\ast }$ -FIT without postprocessing, we recommend future studies to implement these combinations without these postprocessing steps. This minimizes the amount of image modifications and processing, potentially leading to enhanced BOLD contrast. The results highlight the benefits of ${\text {T}_{2}}^{\ast }$ -weighted multi-echo combinations on resting-state network quality and raise its potential value in dynamic fMRI analyses or for diagnosis and prognosis purposes of neuropsychiatric disorders.
Original languageEnglish
Article number10283840
Pages (from-to)114536-114549
Number of pages14
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 12 Oct 2023

Funding

This work was supported in part by the Top Consortia for Knowledge and Innovation Public-Private Partnerships (TKI-PPP) under Grant TK11812P07, and in part by the Philips and Eindhoven Engine. This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Medical Ethical Committee of the Academic Center for Epilepsy Kempenhaeghe (Heeze, The Netherlands).

FundersFunder number
Kempenhaeghe, Center for Residential Epilepsy Care
Philips and Eindhoven Engine

    Keywords

    • brain networks
    • fMRI
    • Multi-echo imaging
    • resting-state

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