Evaluation and Optimization of Motion Correction in Spinal Cord fMRI Preprocessing

Dehghani, Hamed; Weber, Kenneth A.; Batouli, Seyed Amir Hossein; Oghabian, Mohammad Ali; Khatibi, Ali

doi:10.1101/2020.05.20.103986

Cited by 3 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Slice-wise motion correction that allows non-rigid transformations (as implemented in SCT and NEPTUNE toolbox) ( Cohen-Adad, 2017 ; Deshpande and Barry, 2022 ) may be better suited for spinal cord fMRI than conventional rigid-body motion correction commonly employed for brain imaging. Different motion-correction techniques (slice-wise or rigid-body) have been employed by different spinal and corticospinal fMRI studies ( Weber et al, 2016a ; Tinnermann et al, 2017 ; Kinany et al, 2019 ; Oliva et al, 2022 ), and further investigation is necessary to compare the efficacy of different motion-correction techniques ( Dehghani et al, 2020 ).…”

Section: Technical Considerations For Corticospinal Imagingmentioning

confidence: 99%

Recent developments and future avenues for human corticospinal neuroimaging

Kaptan,

Pfyffer,

Konstantopoulos

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Non-invasive neuroimaging serves as a valuable tool for investigating the mechanisms within the central nervous system (CNS) related to somatosensory and motor processing, emotions, memory, cognition, and other functions. Despite the extensive use of brain imaging, spinal cord imaging has received relatively less attention, regardless of its potential to study peripheral communications with the brain and the descending corticospinal systems. To comprehensively understand the neural mechanisms underlying human sensory and motor functions, particularly in pathological conditions, simultaneous examination of neuronal activity in both the brain and spinal cord becomes imperative. Although technically demanding in terms of data acquisition and analysis, a growing but limited number of studies have successfully utilized specialized acquisition protocols for corticospinal imaging. These studies have effectively assessed sensorimotor, autonomic, and interneuronal signaling within the spinal cord, revealing interactions with cortical processes in the brain. In this mini-review, we aim to examine the expanding body of literature that employs cutting-edge corticospinal imaging to investigate the flow of sensorimotor information between the brain and spinal cord. Additionally, we will provide a concise overview of recent advancements in functional magnetic resonance imaging (fMRI) techniques. Furthermore, we will discuss potential future perspectives aimed at enhancing our comprehension of large-scale neuronal networks in the CNS and their disruptions in clinical disorders. This collective knowledge will aid in refining combined corticospinal fMRI methodologies, leading to the development of clinically relevant biomarkers for conditions affecting sensorimotor processing in the CNS.

show abstract

Section: Technical Considerations For Corticospinal Imagingmentioning

confidence: 99%

Recent developments and future avenues for human corticospinal neuroimaging

Kaptan,

Pfyffer,

Konstantopoulos

et al. 2024

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…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%

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI

Mobarak-Abadi,

Mahmoudi-Aznaveh,

Dehghani

et al. 2024

Front. Psychiatry

View full text Add to dashboard Cite

Background and purposeThere are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor.MethodsIn this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing.ResultsTo evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco.ConclusionOur findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

show abstract

Evaluation and Optimization of Motion Correction in Spinal Cord fMRI Preprocessing

Cited by 3 publications

References 41 publications

Recent developments and future avenues for human corticospinal neuroimaging

Recent developments and future avenues for human corticospinal neuroimaging

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

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI

Contact Info

Product

Resources

About