11institutetext:  Polytechnique Montreal, Montreal, QC, Canada
22institutetext: Mila - Quebec AI Institute, Montreal, QC, Canada
33institutetext: Palacký University Olomouc, Olomouc, Czechia 44institutetext:
University of Zürich, Zürich, Switzerland 55institutetext: Stanford University
School of Medicine, Stanford, California, USA 66institutetext: University of
Colorado School of Medicine, Aurora, Colorado, USA 77institutetext: Max Planck
Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
88institutetext: CHU Sainte-Justine, Université de Montréal, Montreal, QC,
Canada 99institutetext: Functional Neuroimaging Unit, CRIUGM, Université de
Montréal, QC, Canada 99email<EMAIL_ADDRESS>
# SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in
Spinal Cord Injury

Enamundram Naga Karthik these authors contributed equally to this work1122
0000-0003-2940-5514 Jan Valošek0 112233 0000-0002-7398-4990 Lynn Farner 44
0009-0004-8252-5448 Dario Pfyffer 4455 0000-0002-2406-9251 Simon Schading-
Sassenhausen 44 0000-0003-3969-193X Anna Lebret 44 0009-0004-1908-4328 Gergely
David 44 0000-0002-9379-5193 Andrew C. Smith 66 0000-0001-5020-8094 Kenneth A.
Weber II 55 0000-0002-0916-9174 Maryam Seif 4477 0000-0002-9253-5680 RHSCIR
Network Imaging Group  Patrick Freund joint senior authors4477
0000-0002-4851-2246 Julien Cohen-Adad 0 11228899 0000-0003-3662-9532

###### Abstract

Spinal cord injury (SCI) is a devastating incidence leading to permanent
paralysis and loss of sensory-motor functions potentially resulting in the
formation of lesions within the spinal cord. Imaging biomarkers obtained from
magnetic resonance imaging (MRI) scans can predict the functional recovery of
individuals with SCI and help choose the optimal treatment strategy.
Currently, most studies employ manual quantification of these MRI-derived
biomarkers, which is a subjective and tedious task. In this work, we propose
(i) a universal tool for the automatic segmentation of intramedullary SCI
lesions, dubbed SCIsegV2, and (ii) a method to automatically compute the width
of the tissue bridges from the segmented lesion. Tissue bridges represent the
spared spinal tissue adjacent to the lesion, which is associated with
functional recovery in SCI patients. The tool was trained and validated on a
heterogeneous dataset from 7 sites comprising patients from different SCI
phases (acute, sub-acute, and chronic) and etiologies (traumatic SCI, ischemic
SCI, and degenerative cervical myelopathy). Tissue bridges quantified
automatically did not significantly differ from those computed manually,
suggesting that the proposed automatic tool can be used to derive relevant MRI
biomarkers. SCIsegV2 and the automatic tissue bridges computation are open-
source and available in Spinal Cord Toolbox (v6.4 and above) via the
sct_deepseg -task seg_sc_lesion_t2w_sci and sct_analyze_lesion functions,
respectively.

###### Keywords:

Spinal Cord Injury Segmentation MRI Deep Learning Tissue Bridges

##  1 Introduction

Traumatic and non-traumatic spinal cord injuries (SCI) represent damage to the
spinal cord (SC) with severe consequences, including weakness and paralysis in
patients [4]. Traumatic SCI arises from sudden physical impacts, such as car
accidents or falls, while non-traumatic SCI can be caused by ischemia
(ischemic SCI) or chronic mechanical compression of the SC (degenerative
cervical myelopathy, DCM) [4, 1]. Both traumatic and non-traumatic SCI
commonly involve intramedullary lesions, which are critical areas of tissue
damage within the SC. Magnetic resonance imaging (MRI) is routinely used to
provide information on the extent and the location of these intramedullary
lesions [4, 5, 22]. Importantly, MRI scans can also be used to compute
quantitative biomarkers, such as midsagittal tissue bridges [8]. These help in
quantifying the amount of preserved SC neural tissue (carrying motor and
sensory information to and from the brain) and have been found to predict
functional recovery in patients with traumatic and non-traumatic SCI [8, 16,
17, 20, 23, 18].

Figure 1: Representative axial and sagittal T2w MRI scans of the lesion in
various SCI etiologies/types.

Identifying MRI biomarkers in SCI automatically is challenging due to the
varying size and location of lesions across patients and injury phases.
Lesions can evolve from hyper- to hypo-intense depending on the underlying
pathological mechanism (Figure 1), and metal implants in the spine often cause
image artifacts [5]. As a result, most studies report biomarkers quantified
from manual lesion annotations [8, 24, 17, 19, 20, 16, 23, 18], which is a
tedious and error-prone task subject to inter-rater variability, making large-
scale studies impractical. While two existing studies proposed automatic
lesion segmentation in SCI, their methods were developed for specific SCI
etiologies: [13] focused on acute preoperative traumatic SCI lesions, whereas,
[14] introduced SCIseg, an open-source model for predominantly chronic, post-
operative traumatic and ischemic SCI lesions. However, maintaining multiple
etiology-specific models is challenging, highlighting the need for a single,
comprehensive model for segmenting any kind of SCI lesions. In this regard,
our contributions in this work are as follows:

  * •

Segmentation tool: We present a comprehensive tool for the automatic
segmentation of intramedullary SCI lesions, dubbed SCIsegV2. Our model was
trained and validated on a heterogeneous, multinational dataset from 7 sites
consisting of (i) traumatic SCI (acute preoperative, sub-acute and chronic
postoperative) and (ii) non-traumatic SCI (ischemic SCI and DCM).

  * •

Analysis tool: We propose a method to automatically compute the midsagittal
tissue bridges, a predictor of functional recovery in SCI patients.

  * •

Packaging in open-source software suite: Both SCIsegV2 and the automatic
tissue bridges computation are open-source and will be integrated into the
Spinal Cord Toolbox (SCT) v6.4 and higher.

##  2 Materials and Methods

###  2.1 Dataset

We used T2-weighted (T2w) MRI images with heterogeneous image resolutions
(isotropic, sagittal, and axial) and magnetic field strengths (1.0T, 1.5T, and
3.0T) from seven sites. The number of patients from each site is as follows:
{site 1: $(n=154)$, site 2: $(n=80)$, site 3: $(n=14)$, site 4: $(n=11)$, site
5: $(n=23)$, site 6: $(n=4)$, site 7: $(n=5)$}. Site 1 contained patients with
both traumatic $(n=97)$ and non-traumatic SCI (mainly, DCM) $(n=57)$. Sites 2
& 3 included both preoperative and postoperative traumatic SCI, while sites 4
to 7 included only acute preoperative traumatic SCI. The timing of the MRI
examination in relation to injury for traumatic SCI patients from sites 1, 2,
and 3 is detailed in Table 1 of [14]. Eight patients from site 1 were followed
up with additional MRI examinations, and 40 patients from site 1 had both
sagittal and axial T2w images. Patients from sites 1 & 2 were split according
to 80-20% train/test ratio. Due to the relatively small size of some datasets,
we decided to use sites 3, 5 & 6 entirely for training and kept the patients
from sites 4 & 7 as held-out (unseen) test sets to evaluate the model’s
generalization performance. The model was trained on a total of 281 T2w images
and tested on 75 images. The ground truth masks of intramedullary lesions
appearing as T2w signal abnormalities were manually annotated by expert raters
at individual sites [14]. The SC masks were automatically segmented using the
sct_deepseg_sc [6] algorithm and manually corrected when necessary.

###  2.2 SCIsegV2

We used nnUNet [10] as the backbone architecture for the SCI lesion
segmentation model. The continuing dominance of nnUNet [10, 9] across several
open-source challenges has shown that a well-tuned convolutional neural
network (CNN) architecture is robust and continues to achieve state-of-the-art
results over novel (and more sophisticated) transformer-based architectures in
dense pixel prediction tasks such as image segmentation.

Similar to the recent work building upon the nnUNet framework [21, 11], we
experimented with its easy-to-tweak trainers for developing SCIsegV2.
Specifically, we used: (i) nnUNetTrainer, the default model, and (ii)
nnUNetTrainerDA5, the model applying aggressive data augmentation. The
augmentation methods in the standard nnUNetTrainer include random rotation,
scaling, mirroring, Gaussian noise addition, Gaussian blurring, adjusting
image brightness and contrast, low-resolution simulation, and Gamma
transformation. In addition to the standard augmentations, nnUNetTrainerDA5
applies additional transforms such as random patch replacement with mean
values, sharpening, median filter, gamma correction, and additive intensity
gradients. We note that even though the training data is a collection of
heterogeneous datasets (acute, chronic traumatic and non-traumatic SCI) from 7
sites, we employed stronger data augmentation to improve generalization across
various types of lesions.

The wide spectrum of lesion intensities in different SCI etiologies makes
lesion segmentation an extremely challenging task. For instance, lesions in
acute SCI are mildly T2w hyperintense and chronic lesions are bright T2w
hyperintense (Figure 1), although typically surrounded by metallic implants
causing heavy interference [5]. Therefore, we compared two different training
strategies for lesion segmentation: (i) given just the T2w image as the input,
the model is trained to segment both the SC and lesions in hierarchical
ordering, and (ii) assuming the availability of SC segmentation mask, the
model is trained to segment only the lesions, given a 2-channel input
consisting of the T2w image concatenated with the SC mask. While the first
strategy attempts to implicitly guide the model towards the SC for lesion
segmentation, the latter provides an explicit guidance in the form of the
input channel.

###  2.3 Automatic Quantification of Tissue Bridges

The manual measurement of tissue bridges is performed on a single midsagittal
slice of a volumetric (3D) T2w MRI image [8, 24, 17, 19, 20, 16, 23, 18]
(Figure 2A). The midsagittal slice is defined as the middle slice of all
slices where the SC is visible (Figure 2B). Ventral and dorsal tissue bridges
are quantified as the width of spared tissue at the minimum distance from the
intramedullary lesion edge to the boundary between the SC and cerebrospinal
fluid (Figure 2C).

Figure 2: Illustration of tissue bridges. A) Volumetric T2w image of a spinal
cord injury (SCI) with chronic intramedullary lesion. B) Midsagittal slice
used to compute the tissue bridges. C) Ventral and dorsal tissue bridges are
defined as the width of spared tissue at the minimum distance from the
intramedullary lesion edge to the boundary between the SC and cerebrospinal
fluid.

To automate the measurement of tissue bridges, we propose a method that
computes ventral and dorsal tissue bridges utilizing the lesion and SC
segmentation masks. To compensate for different neck positions and,
consequently, different SC curvatures, we use angle correction, which adjusts
the tissue bridge widths with respect to the SC centerline [7]. The method
computes tissue bridges from all sagittal slices containing the lesion,
allowing quantification of not only midsagittal but parasagittal tissue
bridges as well. For the purpose of this study (and to compare against
existing manual measurements based on midsagittal tissue bridges), we
considered only the midsagittal slice for the automatic measurement of the
tissue bridges.

###  2.4 Experiments

We divided our experiments into 3 categories to investigate the effects of
input types, data augmentation strategies and SCI etiology-specific models.
All images were preprocessed with right-left, posterior-anterior, inferior-
superior (RPI) orientation, resampled to a common resolution ($0.92\times
0.68\times 0.92$ $\textrm{mm}^{3}$, which is the median of all image
resolutions in the training set) and intensity-normalized using Z-score
normalization. The model was trained for 1000 epochs with 5-fold cross-
validation, using a batch size of 2 and the stochastic gradient descent
optimizer with a polynomial learning rate scheduler.

####  2.4.1 Inputs

We trained 2 different models: (i) a model that segments both the SC and
lesions given just the T2w image as input (referred to as single), and (ii) a
model that segments only the lesion given a 2-channel input consisting of T2w
image and the SC segmentation (referred to as multi). As briefly discussed in
Section 2.2, this experiment is to understand whether providing additional
(localization) context in the form of SC segmentation as input would improve
the lesion segmentation performance.

####  2.4.2 Data augmentation

Given the increasing literature towards unrealistic transformations leading to
better test-time performance [2], we compared 2 models with and without
aggressive data augmentation to understand which model leads to better
generalization on external test sets with acute preoperative SCI images. These
are referred to as defaultDA and aggressiveDA, respectively.

####  2.4.3 SCI Etiology-specific models

This crucial experiment will provide insight into the initial hypothesis,
asking whether a comprehensive SCI model is achievable. Toward this end, we
compared our SCIsegV2 model trained on all 7 sites against etiology-specific
models individually trained on non-traumatic SCI and acute preoperative SCI
data, respectively.

####  2.4.4 SCIseg

We also evaluate our model against SCIseg [14] which was trained on data from
three sites comprising traumatic and ischemic SCI lesions. The model used a
three-phase training strategy involving active learning and is available in
SCT. Details about the training strategy can be found in [14].

####  2.4.5 Tissue Bridges

To validate the automatic measurements of the tissue bridges, we compared the
method against manual and semi-automatic techniques in 15 individuals with
traumatic SCI from site 1. Specifically, we compared the following: (1) manual
— manual measurement of tissue bridges on manually segmented intramedullary
lesions, (2) semi-automatic — automatic measurement of the tissue bridges
using the proposed method on manually segmented intramedullary lesions, and
(3) fully-automatic — automatic measurement of tissue bridges using SCIsegV2
predictions. Statistical analysis was performed using the SciPy v1.10.0. The
distribution of the data was assessed with the D’Agostino and Pearson
normality test. Subsequently, the Kruskal-Wallis H-test was performed to
compare the methods independently for ventral and dorsal bridges.

####  2.4.6 Evaluation Metrics

We used MetricsReloaded [12] and contributed to its development by adding
lesion-wise metrics, specifically, lesion-wise sensitivity, positive
predictive value, and $F_{1}$-score in addition to the existing metrics.

##  3 Results

Figure 3 shows the test Dice scores averaged across 5 folds for all models
described in Section 2.4. Starting with the etiology-specific models, we
observed that the model trained only on acute preoperative (AcuteSCI) data
does not perform well on sites 1 and 2 containing non-traumatic and traumatic
SCI data. While it performs relatively better on test sets from unseen sites
(sites 4 and 7), it does not outperform the SCIsegV2 models. Likewise, the
model trained only on non-traumatic SCI data (DCM) performs well on a similar
test set but fails in generalizing to acute preoperative SCI of sites 4 and 7.

Figure 3: Comparison of Dice scores for different SCI models. Each bar plot
shows the test Dice scores averaged across 5 folds (the error bar represents
the standard deviation).

Since SCIsegV1 [14] was trained on a dataset of 3 sites predominantly
consisting of traumatic SCI, it outperforms SCIsegV2 models in sites 1 and 2.
However, we observed that even SCIsegV1 performs poorly on sites 4 and 7
suggesting that the segmentation of acute preoperative SCI lesions is
extremely challenging. Within SCIsegV2 models, we noted that training with
aggressive data augmentation only results in marginal improvements in lesion
segmentation performance. However, concatenating the SC segmentation as a
second channel along with the input image (resulting in a 2-channel input)
showed considerable improvements, especially in acute preoperative images,
compared to a single-channel input with just the T2w image.

In Table 1, we present a quantitative comparison of different models using
lesion-wise metrics. We noticed that the models’ performance depends heavily
on the specific SCI phases and etiologies. Except for site 1 (traumatic SCI),
SCIsegV2 outperforms SCIsegV1 in all other sites. Comparing within the
SCIsegV2 models, one of downsides of the multi\- models, despite achieving
higher Dice scores (Figure 3), is its dependency on the SC masks as input.

Table 1: Comparison of lesion-wise metrics for SCIsegV1 and various SCIsegV2
models (with aggressiveDA) across the 5 testing sites. Metrics correspond to
lesion-wise positive predictive value (PPVL), sensitivity (SensL) and
$F_{1}$-score; higher the value the better ($\uparrow$). For a given site,
bold values in each column represent the best model.

Model | Metric | Test Sites
---|---|---
site-01 (DCM) |  site-01 (tSCI) |  site-02 (tSCI) |  site-04 (acuteSCI) |  site-07 (acuteSCI)
SCIsegV1 |  $(\uparrow)$ PPVL | 0.63 $\pm$ 0.43 | 0.81 $\pm$ 0.31 | 0.95 $\pm$ 0.14 | 0.37 $\pm$ 0.50 | 0.62 $\pm$ 0.50
$(\uparrow)$ SensL | 0.78 $\pm$ 0.44 | 0.95 $\pm$ 0.16 | 0.97 $\pm$ 0.12 | 0.55 $\pm$ 0.49 | 0.72 $\pm$ 0.49
$(\uparrow)$ $F_{1}$ScoreL | 0.67 $\pm$ 0.42 | 0.84 $\pm$ 0.26 | 0.95 $\pm$ 0.12 | 0.35 $\pm$ 0.47 | 0.64 $\pm$ 0.49
SCIsegV2 single |  $(\uparrow)$ PPVL | 0.55 $\pm$ 0.45 | 0.73 $\pm$ 0.33 | 0.96 $\pm$ 0.13 | 0.55 $\pm$ 0.50 | 0.78 $\pm$ 0.36
$(\uparrow)$ SensL | 0.68 $\pm$ 0.49 | 0.91 $\pm$ 0.24 | 0.97 $\pm$ 0.12 | 0.63 $\pm$ 0.48 | 0.84 $\pm$ 0.36
$(\uparrow)$ $F_{1}$ScoreL | 0.59 $\pm$ 0.45 | 0.77 $\pm$ 0.28 | 0.95 $\pm$ 0.12 | 0.53 $\pm$ 0.49 | 0.80 $\pm$ 0.35
SCIsegV2 multi |  $(\uparrow)$ PPVL | 0.61 $\pm$ 0.41 | 0.70 $\pm$ 0.36 | 0.90 $\pm$ 0.20 | 0.75 $\pm$ 0.39 | 0.54 $\pm$ 0.41
$(\uparrow)$ SensL | 0.80 $\pm$ 0.42 | 0.90 $\pm$ 0.26 | 0.97 $\pm$ 0.12 | 0.90 $\pm$ 0.25 | 0.80 $\pm$ 0.42
$(\uparrow)$ $F_{1}$ScoreL | 0.67 $\pm$ 0.40 | 0.74 $\pm$ 0.32 | 0.91 $\pm$ 0.15 | 0.75 $\pm$ 0.37 | 0.60 $\pm$ 0.40

Table 2 shows the comparison of the midsagittal tissue bridges obtained using
different methods (manual vs semi-automatic vs fully-automatic; see Section
2.4.5 for details) for 15 patients with traumatic SCI from site 1. For the
fully-automatic technique, we used the SCIsegV2_single_aggressiveDA model to
obtain the lesion segmentations. There was no statistically significant
($p>.05$) difference between the bridges computed using different methods.

Table 2: Comparison of ventral and dorsal midsagittal tissue bridges between
manual, semi-automatic, and automatic measurements. Values are reported in
millimetres.

ID |  Manual Lesions & Manual Measurements |  Manual Lesions & Automatic Measurements |  SCIsegV2 Predictions & Automatic Measurements
---|---|---|---
Ventral |  Dorsal |  Ventral |  Dorsal |  Ventral |  Dorsal
sub-zh101 | 0 | 2.65 | 0 | 2.39 | 0.34 | 2.39
sub-zh102 | 2.10 | 0.83 | 2.25 | 0 | 2.27 | 0.67
sub-zh104 | 0 | 0 | 0.54 | 0 | 0.55 | 0
sub-zh105 | 2.70 | 0 | 2.38 | 0.60 | 2.99 | 0
sub-zh106 | 0 | 0 | 0 | 0 | 0 | 0
sub-zh107 | 0 | 0.76 | 0 | 0.67 | 0 | 0.65
sub-zh108 | 1.32 | 0.52 | 1.96 | 0.66 | 2.03 | 0.68
sub-zh109 | 1.13 | 1.03 | 0.71 | 0 | 1.08 | 0.73
sub-zh110 | 0 | 0.99 | 0 | 0 | 0 | 0.39
sub-zh112 | 3.01 | 0.36 | 1.70 | 0.44 | 2.64 | 0.44
sub-zh114 | 0 | 0 | 0 | 0.38 | 0 | 0
sub-zh115 | 0 | 0 | 0 | 2.12 | 0 | 0.42
sub-zh116 | 3.12 | 0.50 | 2.38 | 0 | 2.49 | 0.80
sub-zh118 | 0.40 | 0 | 0 | 0 | 0 | 0
sub-zh119 | 2.93 | 2.98 | 1.04 | 0.50 | 1.48 | 0.95

##  4 Discussion

In this work, we proposed SCIsegV2, a DL-based universal tool for the
segmentation of intramedullary lesions across different SCI etiologies and
phases. We also automated the calculation of midsagittal tissue bridges, a
metric representing spared spinal tissue adjacent to the lesion. This metric
is relevant as it is associated with functional recovery in individuals with
SCI. Both SCIsegV2 and the automatic tissue bridges computation are open-
source and available in Spinal Cord Toolbox (v6.4 and above) via the
sct_deepseg -task seg_sc_lesion_t2w_sci and sct_analyze_lesion functions,
respectively.

The heterogeneity in the appearance of intramedullary lesions across different
SCI phases (acute, sub-acute, chronic) and etiologies (traumatic SCI, ischemic
SCI, DCM) makes lesion segmentation extremely challenging, even for trained
radiologists. Relatively low prevalence of traumatic SCI and the need for
early surgical intervention [1] result in a low number of preoperative MRI
scans, which adds to the difficulty in training a robust automatic
segmentation model, that performs well on "real world" clinical data across
multiple sites. Moreover, MRI scans of individuals with chronic SCI frequently
exhibit image distortions caused by metallic implants, further complicating
the segmentation process.

As a way of simplifying the lesion segmentation problem, using the SC mask to
explicitly guide the model towards the cord showed improved results on certain
etiologies. However, it introduced a dependency on the SC mask to be
concatenated to the input image, preventing a smooth transition from the
model’s automatic predictions to the computation of tissue bridges, which
requires both the cord and lesion masks. In contrast, the SCIsegV2_single
model capable of segmenting both SC and lesions has a higher utility as it is
also applicable in scenarios where SC masks are unavailable. Lastly, as the
lesion appearance varies substantially across different SCI types, universal
models like SCIsegV2 learn the differences in lesion distributions across
etiologies and even outperform etiology-specific models while showing good
generalization across sites.

####  4.0.1 Limitations and Future Work

One limitation of the model is its higher sensitivity to traumatic SCI
lesions, as approximately ($\sim 70\%$) of our dataset consists of this
population. This skew is due to the relatively low availability of acute
preoperative SCI scans. Acquiring additional data in SCI is generally
challenging, but is possible with the help of ongoing clinical trials and
consortiums for creating large databases for SCI research [15, 3]. While this
work used a relatively small and unbalanced cohort, we presented a preliminary
proof-of-concept toward a universal tool for SCI lesion segmentation. The
current literature only quantifies tissue bridges manually, from a single
midsagittal slice. However, as lesions are 3D blob-like objects, the
midsagittal slice might not necessarily contain the largest portions of the
lesion and does not consider parasagittally running fiber tracts. Therefore,
combining both parasagittal and midsagittal slices could provide a
comprehensive evaluation of the width of the spared tissue bridges.

####  4.0.2 Prospect of application

Automatic segmentation of the lesions and spinal cord could mitigate the
bottleneck and inter-rater variability associated with manual annotations.
Automating the measurements of tissue bridges could provide an objective,
unbiased way in guiding rehabilitation decision making and stratifying
patients into homogeneous subgroups of recovery in clinical trials.

####  4.0.3 Acknowledgements

We thank Mathieu Guay-Paquet and Joshua Newton for their assistance with the
management of the datasets and the implementation of the algorithm to SCT. We
thank Maxime Bouthillier for the help with manual annotations. We thank Dr.
Serge Rossignol and the Multidisciplinary Team on Locomotor Rehabilitation
(Regenerative Medicine and Nanomedicine, CIHR), and all the patients. The
authors would also like to thank the RHSCIR participants and network,
including all the participating local RHSCIR sites: Vancouver General
Hospital, Foothills Hospital, Royal University Hospital, Toronto Western
Hospital, St. Michael’s Hospital, Sunnybrook Health Sciences Centre, Hamilton
General Hospital, The Ottawa Hospital Civic Campus, Hôpital de l’Enfant Jésus,
Hôpital du Sacre Coeur de Montréal, QEII Health Sciences Centre, Saint John
Regional Hospital.

Funded by the Canada Research Chair in Quantitative Magnetic Resonance Imaging
[CRC-2020-00179], the Canadian Institute of Health Research [PJT-190258], the
Canada Foundation for Innovation [32454, 34824], the Fonds de Recherche du
Québec - Santé [322736, 324636], the Natural Sciences and Engineering Research
Council of Canada [RGPIN-2019-07244], the Canada First Research Excellence
Fund (IVADO and TransMedTech), the Courtois NeuroMod project, the Quebec
BioImaging Network [5886, 35450], INSPIRED (Spinal Research, UK; Wings for
Life, Austria; Craig H. Neilsen Foundation, USA), Mila - Tech Transfer Funding
Program, the Association Française contre les Myopathies (AFM), the Institut
pour la Recherche sur la Moelle épinière et l’Encéphale (IRME), the National
Institutes of Health Eunice Kennedy Shriver National Institute of Child Health
and Development (R03HD094577). ACS is supported by the National Institutes of
Health – K01HD106928 and R01NS128478 and the Boettcher Foundation’s Webb-
Waring Biomedical Research Program. KAW is supported by the National
Institutes of Health – K23NS104211, L30NS108301, R01NS128478. The Rick Hansen
Spinal Cord Injury Registry and this work are supported by funding from the
Praxis Spinal Cord Institute through the Government of Canada and the Province
of British Columbia. For more information about RHSCIR(9), please visit
www.praxisinstitute.org. JV received funding from the European Union’s Horizon
Europe research and innovation programme under the Marie Skłodowska-Curie
grant agreement No 101107932 and is supported by the Ministry of Health of the
Czech Republic, grant nr. NU22-04-00024. ENK is supported by the Fonds de
Recherche du Québec Nature and Technologie (FRQNT) Doctoral Training
Scholarship. The authors thank Digital Research Alliance of Canada for the
compute resources used in this work.

####  4.0.4

The authors have no competing interests to declare that are relevant to the
content of this article.

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