Towards reliable spinal cord fMRI: Assessment of common imaging protocols

Kinany, Nawal; Pirondini, Elvira; Mattera, Loan; Martuzzi, Roberto; Micera, Silvestro; Ville, Dimitri Van De

doi:10.1016/j.neuroimage.2022.118964

Cited by 28 publications

(47 citation statements)

References 74 publications

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“…However, it will be challenging to do this in a robust and systematic manner and appropriately integrate this step into volume realignment (i.e., motion correction) procedures, and this would not fully compensate for inherently poor Adjacent:SC contrast across the scan. These findings support the need for continued improvement in spinal cord fMRI acquisition techniques to improve and stabilize image contrast (and particularly tissue-CSF contrast) along the length of the cord while mitigating flow artifacts, such as improved receive coils [18,[54][55], higher static magnetic field strengths [18,54], sequence and protocol optimization [18,[41][42], and image processing techniques [14][15][16][17][18][19]54].…”

Section: Discussionmentioning

confidence: 56%

“…Specifically, analysis and interpretation of spinal cord fMRI data is hindered by the large changes in magnetic susceptibility of tissues approximating the cord, the small size of the target neural tissues (typically leading to low signal-to-noise ratio), and physiological noise from cardiac and respiratory processes. [14][15][16][17][18] Several analytical tools have been developed to improve characterization of spinal cord fMRI data. These tools include, but are not limited to the Spinal Cord Toolbox, the Neptune Toolbox, and Pantheon (formerly spinalfMRI8).…”

Section: Introductionmentioning

confidence: 99%

“…Standardization of both imaging protocols and processing pipelines has been recommended to improve the robustness of spinal cord fMRI findings. [15, 18] In brain fMRI, differences in functional image processing pipelines have been shown to have large potential impacts on the resultant findings of a study. [32] Bowring et al observed that variability in results from task fMRI in the brain are heterogeneous, depending on the input dataset and potentially each aspect of the image processing pipeline (including registration).…”

Section: Introductionmentioning

confidence: 99%

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Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

Hoggarth

Wang

Hemmerling

et al. 2022

Preprint

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Functional magnetic resonance imaging (fMRI) of the human spinal cord (SC) is an emerging but technically challenging method for understanding neurovascular responses to sensory and motor stimuli. Group-analysis of SC fMRI data involves co-registration of subject-level data to a standard image, which currently requires manual masking of the cord. We hypothesize that this manual input may contribute to bias or poor repeatability in group-level SC fMRI results. To test this, we examined variability in SC masks drawn in fMRI data from 21 healthy participants taken from a completed study mapping responses to sensory stimuli of the C7 dermatome. Masks were drawn on the temporal mean functional image by eight raters with varying levels of neuroimaging experience, and the rater from the original study acted as an expert reference. Spatial agreement between rater and expert masks was measured using the Dice Similarity Coefficient (DSC), and the influence of rater experience and individual dataset was examined using ANOVA. Each rater mask was then used for spatial normalization of functional imaging data to the PAM50 template image. Using tissue regions defined in the template image, the gray matter-white matter signal contrast of the aligned functional data was used to evaluate the relative accuracy of the spatial normalization procedure across raters. Subject- and group-level analysis of activation during the left- and right-sided sensory stimuli were performed for each rater's co-registered data. Agreement with the expert's SC mask was associated with both rater experience level (F(7,140) = 32.12, P < 2x10-16, η2 = 0.29) and dataset (F(20,140) = 20.58, P < 2x10-16, η2 = 0.53). Dataset variations may reflect image quality metrics: the ratio between the signal intensity of spinal cord voxels and surrounding cerebrospinal fluid was correlated with DSC results (p<0.001). As predicted, variability in the manually-drawn masks visibly influenced spatial normalization, and the GM:WM contrast in the registered data showed significant effects of rater and dataset (rater: F(8,160) = 23.57, P < 2x10-16, η2 = 0.24; dataset: F(20,160) = 22.00, P < 2x10-16, η2 = 0.56). These registration differences were propagated into activation mapping results, and subject-level activation maps showed rater-dependent agreement with the expert's maps. Although group-level activation maps differed between raters, no systematic bias was identified. While increasing consistency in manual contouring of spinal cord fMRI data could improve data co-registration and ultimately the inter-rater agreement in activation mapping, our results suggest that other improvements in image acquisition and post-processing may be more critical to address.

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Section: Discussionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

Hoggarth

Wang

Hemmerling

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Standardization of both imaging protocols and processing pipelines has been recommended to improve the robustness of spinal cord fMRI findings ( 15 , 18 ). In brain fMRI, differences in functional image processing pipelines have been shown to have large potential impacts on the resultant findings of a study ( 32 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

et al. 2022

View full text Add to dashboard Cite

Functional magnetic resonance imaging (fMRI) of the human spinal cord (SC) is a unique non-invasive method for characterizing neurovascular responses to stimuli. Group-analysis of SC fMRI data involves co-registration of subject-level data to standard space, which requires manual masking of the cord and may result in bias of group-level SC fMRI results. To test this, we examined variability in SC masks drawn in fMRI data from 21 healthy participants from a completed study mapping responses to sensory stimuli of the C7 dermatome. Masks were drawn on temporal mean functional image by eight raters with varying levels of neuroimaging experience, and the rater from the original study acted as a reference. Spatial agreement between rater and reference masks was measured using the Dice Similarity Coefficient, and the influence of rater and dataset was examined using ANOVA. Each rater's masks were used to register functional data to the PAM50 template. Gray matter-white matter signal contrast of registered functional data was used to evaluate the spatial normalization accuracy across raters. Subject- and group-level analyses of activation during left- and right-sided sensory stimuli were performed for each rater's co-registered data. Agreement with the reference SC mask was associated with both rater (F(7, 140) = 32.12, P < 2 × 10−16, η2 = 0.29) and dataset (F(20, 140) = 20.58, P < 2 × 10−16, η2 = 0.53). Dataset variations may reflect image quality metrics: the ratio between the signal intensity of spinal cord voxels and surrounding cerebrospinal fluid was correlated with DSC results (p < 0.001). As predicted, variability in the manually-drawn masks influenced spatial normalization, and GM:WM contrast in the registered data showed significant effects of rater and dataset (rater: F(8, 160) = 23.57, P < 2 × 10−16, η2 = 0.24; dataset: F(20, 160) = 22.00, P < 2 × 10−16, η2 = 0.56). Registration differences propagated into subject-level activation maps which showed rater-dependent agreement with the reference. Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.

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“…Spinal cord functional magnetic resonance imaging (fMRI) has become increasingly popular for exploring intrinsic neural networks and their role in pain modulation, motor learning and sexual arousal (Alexander et al, 2016; Kinany et al, 2019). There are unique challenges in data acquisition and preprocessing, such as relatively small cross-sectional dimension, the variable articulated structure of the spine between individuals, low signal intensity in standard gradient-echo echo-planar T2 ∗ -weighted fMRI and voluntary (bulk motion) or involuntary (fluctuation of cerebrospinal fluid due to respiration and heartbeat) movements during image acquisition (Dehghani, Oghabian, Batouli, Arab Kheradmand, & Khatibi, 2020; Kinany et al, 2022; Powers, Ioachim, & Stroman, 2018). Spinal cord motions can cause signal alterations across volumes, which decrease the temporal stability of the signal and ultimately increase false positive and negative discovery rates (Cohen-Adad, Piche, Rainville, Benali, & Rossignol, 2007; Dehghani, Weber, Batouli, Oghabian, & Khatibi, 2020; Stroman, Figley, & Cahill, 2008).…”

Section: Introductionmentioning

confidence: 99%

DeepRetroMoCo: Deep neural network-based Retrospective Motion Correction Algorithm for Spinal Cord functional MRI

Mobarak-Abadi¹,

Mahmoudi-Aznave²,

Dehghani

et al. 2022

Preprint

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There are unique challenges in the preprocessing of spinal cord fMRI data, particularly voluntary or involuntary movement artifacts during image acquisition. Despite advances in data processing techniques for movement detection and correction, there are challenges in extrapolating motion correction algorithm developments in the brain cortex to the brainstem and spinal cord. We trained a Deep Learning-based convolutional neural network (CNN) via an unsupervised learning algorithm, called DeepRetroMoCo, to detect and correct motions in axial T2*-weighted spinal cord data. Spinal cord fMRI data from 27 participants were used for training of the network (135 runs for training and 81 runs for testing). We used average temporal signal-to-noise-ratio (tSNR) and Delta Variation Signal (DVARS) of raw and motion-corrected images to compare the outcome of DeepRetroMoco with sct_fmri_moco implemented in the spinal cord toolbox. The average tSNR in the cervical cord was significantly higher when DeepRetroMoco was used for motion correction compared to sct_fmri_moco method. Average DVARS was lower in images corrected by DeepRetroMoco than those corrected by sct_fmri_moco. The average processing time for DeepRetroMoco was also significantly shorter than sct_fmri_moco. Our results suggest that DeepRetroMoCo improves motion correction procedures in fMRI data acquired from the cervical spinal cord.

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Towards reliable spinal cord fMRI: Assessment of common imaging protocols

Cited by 28 publications

References 74 publications

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

DeepRetroMoCo: Deep neural network-based Retrospective Motion Correction Algorithm for Spinal Cord functional MRI

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