Integrated multi‐echo denoising strategy improves identification of inherent language laterality

Amemiya, Shonan; Yamashita, Hiroshi; Takao, Hidemasa; Abe, Osamu

doi:10.1002/mrm.27620

Cited by 9 publications

(10 citation statements)

References 27 publications

(84 reference statements)

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“…In an attempt to minimize the contribution of such nuisance factors to our resting data—which is more strongly affected by these undesired sources of fluctuation—we used a ME‐ICA denoising strategy previously shown to improve identification of intrinsic connectivity networks in resting state fMRI (Kundu et al, 2012; Kundu et al, 2013). In fact, one recent study found that while apparent left‐hemisphere language network dominance was seen across several processing approaches, laterality index values were higher when using an integrated ME processing approach (Amemiya, Yamashita, Takao, & Abe, 2019).…”

Section: Discussionmentioning

confidence: 99%

Language lateralization from task‐based and resting state functional MRI in patients with epilepsy

Rolinski

You

Gonzalez‐Castillo

et al. 2020

Human Brain Mapping

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We compared resting state (RS) functional connectivity and task‐based fMRI to lateralize language dominance in 30 epilepsy patients (mean age = 33; SD = 11; 12 female), a measure used for presurgical planning. Language laterality index (LI) was calculated from task fMRI in frontal, temporal, and frontal + temporal regional masks using LI bootstrap method from SPM12. RS language LI was assessed using two novel methods of calculating RS language LI from bilateral Broca's area seed based connectivity maps across regional masks and multiple thresholds (p < .05, p < .01, p < .001, top 10% connections). We compared LI from task and RS fMRI continuous values and dominance classifications. We found significant positive correlations between task LI and RS LI when functional connectivity thresholds were set to the top 10% of connections. Concordance of dominance classifications ranged from 20% to 30% for the intrahemispheric resting state LI method and 50% to 63% for the resting state LI intra‐ minus interhemispheric difference method. Approximately 40% of patients left dominant on task showed RS bilateral dominance. There was no difference in LI concordance between patients with right‐sided and left‐sided resections. Early seizure onset (<6 years old) was not associated with atypical language dominance during task‐based or RS fMRI. While a relationship between task LI and RS LI exists in patients with epilepsy, language dominance is less lateralized on RS than task fMRI. Concordance of language dominance classifications between task and resting state fMRI depends on brain regions surveyed and RS LI calculation method.

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Section: Discussionmentioning

confidence: 99%

Language lateralization from task‐based and resting state functional MRI in patients with epilepsy

Rolinski

You

Gonzalez‐Castillo

et al. 2020

Human Brain Mapping

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“…Collecting more echoes opens up the possibility of applying ICA and classifying independent components into BOLD-related or noise, an approach known as multi-echo independent component analysis (ME-ICA) ( Kundu et al, 2013 , 2012 , 2017 ). Compared to single-echo data denoising, ME-ICA can improve the mapping of task-induced activation ( Amemiya et al, 2019 ; DuPre et al, 2016 ; Evans et al, 2015 ; Gonzalez-Castillo et al, 2016 ; Lombardo et al, 2016 ). It also outperforms single-echo ICA-based denoising of resting-state fMRI data ( Dipasquale et al, 2017 ), which is particularly beneficial to obtain more reliable functional connectivity mapping in individual subjects ( Lynch et al, 2020 ) and in brain regions with reduced signal-to-noise ratio, such as the basal forebrain ( Markello et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

et al. 2021

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Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

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“…independent of TE, related to non-BOLD fluctuations in the net magnetization Δ ρ ), an approach known as multi-echo independent component analysis (ME-ICA) (Kundu et al, 2013(Kundu et al, , 2012(Kundu et al, , 2017. Compared to single-echo data denoising, ME-ICA can improve the mapping of task-induced activation (DuPre, Luh, & Spreng, 2016;Gonzalez-Castillo et al, 2016;Lombardo et al, 2016), for example in challenging paradigms with slow-varying stimuli (Evans, Kundu, Horovitz, & Bandettini, 2015) or language mapping and laterality (Amemiya, Yamashita, Takao, & Abe, 2019). It also outperforms single-echo ICA-based denoising of restingstate fMRI data (Dipasquale et al, 2017), which is particularly beneficial more efficient and reliable functional connectivity mapping in individual subjects (Lynch, Power, Dubin, Gunning, & Liston, 2020) and in brain regions where traditional single-echo acquisitions offer reduced signal-to-noise ratio, such as the basal forebrain (Markello, Spreng, Luh, Anderson, & De Rosa, 2018).…”

Section: Introductionmentioning

confidence: 99%

ICA-based Denoising Strategies in Breath-Hold Induced Cerebrovascular Reactivity Mapping with Multi Echo BOLD fMRI

Moia

Termenon

Uruñuela

et al. 2020

Preprint

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Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models including drifts and motion timecourses as nuisance regressors applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrate the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

show abstract

Integrated multi‐echo denoising strategy improves identification of inherent language laterality

Cited by 9 publications

References 27 publications

Language lateralization from task‐based and resting state functional MRI in patients with epilepsy

Language lateralization from task‐based and resting state functional MRI in patients with epilepsy

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

ICA-based Denoising Strategies in Breath-Hold Induced Cerebrovascular Reactivity Mapping with Multi Echo BOLD fMRI

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