TE-dependent analysis of multi-echo fMRI with tedana

DuPré, Elizabeth; Salo, Taylor; Ahmed, Zaki; Bandettini, Peter A.; Bottenhorn, Katherine L.; Caballero-Gaudes, César; Dowdle, Logan T.; Gonzalez‐Castillo, Javier; Heunis, Stephan; Kundu, Prantik; Laird, Angela R.; Markello, Ross; Markiewicz, Christopher J.; Moia, Stefano; Staden, Isla; Teves, Joshua B.; Uruñuela, Eneko; Vaziri-Pashkam, Maryam; Whitaker, Kirstie; Handwerker, Daniel A.

doi:10.21105/joss.03669

Cited by 85 publications

(77 citation statements)

References 23 publications

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“…For the BOLD analysis, the four echoes were combined using the -weighted approach(Posse et al, 1999). The data was then denoised using ME-ICA and the open source python script tedana.py version 0.0.10 (https://tedana.readthedocs.io/en/latest)(DuPre et al, 2019; Kundu et al, 2013; Kundu et al, 2012). This technique, described in detail elsewhere, classifies independent components as BOLD or non-BOLD based on whether or not their amplitudes are linearly dependent on TE, respectively (Kundu et al, 2013; Kundu et al, 2012; Olafsson et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Molecular-enriched functional connectivity in the human brain using multiband multi-echo simultaneous ASL/BOLD fMRI

Dipasquale

Cohen

Martins

et al. 2022

Preprint

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Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) is a novel analytical strategy that enriches functional connectivity (FC) information from functional MRI (fMRI) with molecular information on the neurotransmitter distribution density in the human brain, providing a biological basis to the FC analysis. So far, this integrative approach has been used in blood oxygen level-dependent (BOLD) fMRI studies only, providing new insights into the brain mechanisms underlying specific disorders and its response to pharmacological challenges. In this study, we demonstrate that the application of REACT can be further extended to arterial spin labelling (ASL) fMRI. Some of the advantages of this extension include the combination of neurotransmitter specific information provided by molecular imaging with a quantitative marker of neuronal activity, the suitability of ASL for pharmacological MRI (phMRI) studies assessing drug effects on baseline brain function, and the possibility to acquire images that are not affected by susceptibility artifacts in the regions linked to major neurotransmitter systems.In this work, we tested the feasibility of applying REACT to resting state ASL fMRI and compared the molecular-enriched FC maps derived from ASL data with those derived from BOLD data. We applied REACT to high-resolution, whole-brain simultaneous ASL/BOLD resting-state fMRI data of 29 healthy subjects and estimated the ASL- and BOLD-based FC maps related to six molecular systems, including the transporters of dopamine, noradrenaline, serotonin and vesicular acetylcholine, and the GABA-A and mGlu5 receptors. We then compared the ASL and BOLD FC maps in terms of spatial similarity, using the Dice Similarity Index and the voxel-wise spatial correlation. On a data subsample (N=19) we also evaluated the test-retest reproducibility of each modality using the regional intraclass correlation coefficient, and compared the two modalities.Our results showed robust spatial patterns of molecular-enriched functional connectivity for both modalities, moderate to high similarity between BOLD- and ASL-derived FC maps and mixed results in terms of reproducibility (i.e., none of the modalities outperformed the other). Overall, our findings show that the ASL signal is as informative as BOLD in detecting functional circuits associated with specific molecular pathways, and that the two modalities may provide complementary information related to these circuits.Considering the more direct link of ASL imaging with neuronal acrivity compared to BOLD and its suitability for phMRI studies, this new integrative approach could become a valuable asset in clinical studies investigating functional alterations in patients with brain disorders, or in pharmacological studies investigating the effects of new or existing compounds on the brain.

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Section: Methodsmentioning

confidence: 99%

Molecular-enriched functional connectivity in the human brain using multiband multi-echo simultaneous ASL/BOLD fMRI

Dipasquale

Cohen

Martins

et al. 2022

Preprint

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“…Whole brain structural and functional MRI data were acquired using a 3T Siemens This study used a multiband multi-echo (MBME) scanning sequence. We used TEDANA to combine the images (DuPre et al, 2021, Kundu et al, 2013, Kundu et al, 2012. Before images were combined, some pre-processing was performed.…”

Section: Fmri Acquisitionmentioning

confidence: 99%

Flexing the principal gradient of the cerebral cortex to suit changing semantic task demands

Gao

Zheng

Krieger-Redwood

et al. 2022

Preprint

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Understanding how thought emerges from the topographical structure of the cerebral cortex is a primary goal of cognitive neuroscience. Recent work has revealed a principal gradient of intrinsic connectivity capturing the separation of sensory-motor cortex from transmodal regions of the default mode network (DMN); this is thought to facilitate memory-guided cognition. However, studies have not explored how this dimension of connectivity changes when conceptual retrieval is controlled to suit the context. We used gradient decomposition of informational connectivity in a semantic association task to establish how the similarity in connectivity across brain regions changes during familiar and more original patterns of retrieval. Multivoxel activation patterns at opposite ends of the principal gradient were more divergent when participants retrieved stronger associations; therefore when long-term semantic information is sufficient for ongoing cognition, regions supporting heteromodal memory are functionally separated from sensory-motor experience. In contrast, when less related concepts were linked, this dimension of connectivity was reduced in strength as semantic control regions separated from the DMN to generate more flexible and original responses. We also observed fewer dimensions within the neural response towards the apex of the principal gradient when strong associations were retrieved, reflecting less complex or varied neural coding across trials and participants. In this way, the principal gradient explains how semantic cognition is organised in the human cerebral cortex: the separation of DMN from sensory-motor systems is a hallmark of the retrieval of strong conceptual links that are culturally shared.Significance statementA central task in neuroscience is to understand how cognition emerges from the topographical structure of cerebral cortex. We used fMRI during a semantic task to assess informational connectivity as participants retrieved stereotypical or more unusual associations. The principal gradient of variation in informational connectivity captured the separation of heteromodal memory regions from unimodal cortex. This separation was reduced when weaker associations were retrieved; these trials also produced higher-dimensional neural responses in heteromodal regions. We conclude that the separation of DMN from sensory-motor systems is a hallmark of the retrieval of strong conceptual links that are culturally shared, while more complex and diverse cognition can be generated as this separation in connectivity is reduced.

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“…The ME-ICA data were denoised using TE-dependent multi-echo ICA denoising (tedana.py, version 0.0.1 (DuPre et al, 2021(DuPre et al, , 2019Evans et al, 2015;Kundu et al, 2012)). In brief, PCA was applied, and thermal noise was removed using the Kundu decision tree method.…”

Section: Multi-echo Icamentioning

confidence: 99%

“…One promising data acquisition and preprocessing procedure is multi-echo fMRI (Poser et al, 2006; Posse, 2012) combined with echo-time (TE) dependent ICA denoising (hereafter referred to collectively as ME-ICA (Kundu et al, 2017, 2012)). The ME-ICA procedure is described in detail elsewhere (DuPre et al, 2019; Evans et al, 2015; Kundu et al, 2017, 2012), but in brief, ME-ICA involves two steps. First, during acquisition, researchers acquire multiple TEs at each repetition time (TR) (Kundu et al, 2017; Poser et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI

Steel

Garcia

Silson

et al. 2022

Preprint

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fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance on data acquired during a visual population receptive field (pRF) mapping paradigm (N=13 participants) after using one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyses and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including task fMRI studies, voxel-wise modeling, and naturalistic paradigms.

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TE-dependent analysis of multi-echo fMRI with tedana

Cited by 85 publications

References 23 publications

Molecular-enriched functional connectivity in the human brain using multiband multi-echo simultaneous ASL/BOLD fMRI

Molecular-enriched functional connectivity in the human brain using multiband multi-echo simultaneous ASL/BOLD fMRI

Flexing the principal gradient of the cerebral cortex to suit changing semantic task demands

Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI

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