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TYPE Original Research
PUBLISHED 06 July 2023
DOI 10.3389/fneur.2023.1063408
OPEN ACCESS
EDITED BY
Hari Kishan Reddy Indupuru,
University of Texas Health Science Center at
Houston, United States
REVIEWED BY
Yafeng Li,
The Fifth Hospital of Shanxi Medical
University, China
Arvind Bambhroliya,
University of Texas Health Science Center at
Houston, United States
Nabila Brihmat,
Kessler Foundation, United States
*CORRESPONDENCE
Michael E. Sughrue
sughruevs@gmail.com
RECEIVED 07 October 2022
ACCEPTED 13 June 2023
PUBLISHED 06 July 2023
CITATION
Chen R, Dadario NB, Cook B, Sun L, Wang X,
Li Y, Hu X, Zhang X and Sughrue ME (2023)
Connectomic insight into unique stroke patient
recovery after rTMS treatment.
Front. Neurol. 14:1063408.
doi: 10.3389/fneur.2023.1063408
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©2023 Chen, Dadario, Cook, Sun, Wang, Li,
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comply with these terms.
Connectomic insight into unique
stroke patient recovery after rTMS
treatment
Rong Chen1, Nicholas B. Dadario2, Brennan Cook2, Lichun Sun1,
Xiaolong Wang1, Yujie Li1, Xiaorong Hu3, Xia Zhang3,4 and
Michael E. Sughrue4,5,6*
1The First Aliated Hospital of Hainan Medical University, Haikou, Hainan, China, 2Robert Wood
Johnson Medical School, Rutgers University, New Brunswick, NJ, United States, 3Xijia Medical
Technology Company Limited, Shenzhen, China, 4International Joint Research Center on Precision
Brain Medicine, XD Group Hospital, Xi’an, Shaanxi, China, 5Omniscient Neurotechnology, Sydney,
NSW, Australia, 6Cingulum Health, Sydney, NSW, Australia
An improved understanding of the neuroplastic potential of the brain has allowed
advancements in neuromodulatory treatments for acute stroke patients. However,
there remains a poor understanding of individual dierences in treatment-induced
recovery. Individualized information on connectivity disturbances may help
predict dierences in treatment response and recovery phenotypes. We studied
the medical data of 22 ischemic stroke patients who received MRI scans and
started repetitive transcranial magnetic stimulation (rTMS) treatment on the same
day. The functional and motor outcomes were assessed at admission day, 1 day
after treatment, 30 days after treatment, and 90 days after treatment using four
validated standardized stroke outcome scales. Each patient underwent detailed
baseline connectivity analyses to identify structural and functional connectivity
disturbances. An unsupervised machine learning (ML) agglomerative hierarchical
clustering method was utilized to group patients according to outcomes
at four-time points to identify individual phenotypes in recovery trajectory.
Dierences in connectivity features were examined between individual clusters.
Patients were a median age of 64, 50% female, and had a median hospital
length of stay of 9.5 days. A significant improvement between all time points was
demonstrated post treatment in three of four validated stroke scales utilized. ML-
based analyses identified distinct clusters representing unique patient trajectories
for each scale. Quantitative dierences were found to exist in structural and
functional connectivity analyses of the motor network and subcortical structures
between individual clusters which could explain these unique trajectories on the
Barthel Index (BI) scale but not on other stroke scales. This study demonstrates
for the first time the feasibility of using individualized connectivity analyses in
dierentiating unique phenotypes in rTMS treatment responses and recovery.
This personalized connectomic approach may be utilized in the future to better
understand patient recovery trajectories with neuromodulatory treatment.
KEYWORDS
rTMS, connectomic, DTI, fMRI, networks, stroke, motor
1. Introduction
Stroke has remained a leading cause of death worldwide which has increased
in both incidence and prevalence over recent decades (1,2). Of the patients who
survive, few make a complete recovery and most patients are left with significant
disability (3). Despite this, many patients remain highly open to rigorous recovery
treatments and training services to improve the quality of life and integration back
into society (4,5), and as such, neurological rehabilitation treatments to facilitate
functional recovery after stroke have remained a key priority in stroke research (1).
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Chen et al. 10.3389/fneur.2023.1063408
In particular, an improved understanding of the neuroplastic
potential of the human brain connectome has facilitated increased
use of non-invasive neuromodulatory treatments for stroke
patients (1,6–11).
Non-invasive neuromodulatory treatment, delivered through
transcranial magnetic stimulation (TMS), is a recognized and safe
treatment that works primarily through modulating cortical and
corticospinal excitability across the human cerebrum. While a
number of studies in the literature have suggested clear benefits of
this therapy in regard to post-stroke functional recovery (7,12,13),
these benefits have also been contested in recent large scale studies
suggesting limited improvements (1,14). Notably, differences in
outcomes across controlled trials may be related to differences in
the recovery scale utilized (15,16), the specific neuromodulatory
protocols and targets selected (17,18), and importantly, unique
inter-individual differences in patient physiology (19). Nonetheless,
a poor understanding of the variable responses to TMS treatments
has disbarred the effective application and recommendation of this
safe treatment for stroke patients in larger clinical and research
settings (1), and thus requires further study.
It has become clear that human physiological and
pathophysiological functioning can be best understood in the
context of underlying neural connections across the human brain
connectome (8,20,21). More recently, these connections can
now be rigorously analyzed with the recent advancements in
neuroimaging capabilities and high-throughput approaches (22).
Similar to what has been seen in a number of other neurological
disorders (20,23), connectomic analyses have revealed that stroke
disrupts structural and functional neural connections both near
and spatially distant from the lesion site (24,25), and these
disruptions are highly related to functional outcomes (19,26).
This has caused some to suggest the need for a connectomic-based
approach to stroke treatments and analyses (27).
It is also important to consider that stroke patient recovery
varies significantly between individuals (19). A connectome-
based TMS approach that considers individual connectivity
disturbances post-craniotomy can facilitate effective improvements
in motor and speech deficits for individual brain tumor patients
(11). Therefore, it is reasonable to hypothesize that similar
patient-specific connectomic analyses may offer additional novel
information to understand and predict individual recovery from
stroke (19). Utilization of this information may help track the
patient recovery course following acute stroke, which could assist in
physician decisions regarding treatment parameters and regimens
by stratifying patients into different TMS treatment recovery
groups (9,11).
In this pilot study, we attempted to examine how patients
could be grouped into specific clusters according to their
clinical treatment phenotypes, and how connectomic information
may provide additional important insight into understanding
these phenotypes.
2. Methods
2.1. Participants
The study was completed with the first affiliated hospital of
Hainan medical university ethics committee approval. Twenty-two
patients with acute strokes provided informed consent to the use of
rTMS treatment from 2020 to 2021.
Inclusion criteria included: ①being between the ages of
18 and 90; ②having the first and unilateral onset within 1
week; ③being able to cooperate with physical examination,
scoring, and treatment; ④met the diagnostic criteria of the
2018 China guidelines for the diagnosis and treatment of acute
ischemic stroke, as confirmed by cranial CT or MRI; and
⑤were diagnosed with infarct lesions in the cerebral hemisphere.
Exclusion criteria included: ①hemorrhage stroke and progressive
stroke; ②intravenous thrombolysis or vascular interventional
therapy; ③metal or foreign matter in the body; and ④other
important organ failure, intracranial hypertension symptoms, or
malignant tumor.
2.2. Functional outcome assessment
Appropriate demographic data and relevant medical history
were collected from each patient. Patient functional status scores
were assessed according to: (1) National Institutes of Health Stroke
Scale (NIHSS), which is an 11-item neurological examination
stroke scale used to evaluate the effect of acute cerebral infarction
on the levels of consciousness, language, neglect, visual-field
loss, extraocular movement, facial palsy, motor strength, ataxia,
dysarthria, and sensory loss. The total scores range from 0 to
42, with higher scores indicating greater severity. (2) Fugl-Meyer
Assessment (FMA) is a 5-domain and 155-item scale to assess
motor functioning, balance, sensation, and joint functioning in
patients with post-stroke hemiplegia at all ages. Each item is scored
by a 3-point ordinal scale, with lower scores indicating greater
severity. (3) Barthel Index (BI), which is a 10-item scale describing
the activities of daily living (ADL) and mobility, and includes 10
personal activities: feeding, personal toileting, bathing, dressing
and undressing, getting on and off a toilet, controlling bladder,
controlling bowel, moving from wheelchair to bed and returning,
walking on a level surface (or propelling a wheelchair if unable to
walk), and ascending and descending stairs. Total scores are 100,
with lower scores indicating greater dependency. (4) Wolf Motor
Function Test (WMFT) includes 15 task performances to measure
the upper extremity function after stroke. The total score is 75 with
a higher score indicating stronger ability to complete the upper
limb tasks (28–31). Each patient’s scores were assessed at four-
time points in order to obtain long-term data: (1) at admission
day, (2) 1 day after treatment, (3) 30 days after treatment, and (4)
90 days after treatment. All the personally identifiable information
has been removed. There were no adverse and unanticipated
events reported.
2.3. Image acquisition
Imaging acquisition was performed within after 48–72 h after
the functional outcome assessment and was performed on a Philips
3T Achieva MRI scanner. Diffusion-weighted imaging (DWI) was
acquired with: 2 ×2×2 mm3voxels, field of view (FOV) =
256 mm, matrix =128 ×128 mm2, slice thickness =2.0 mm, one
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non-zero b-value of 1,000, 40 directions, gap =0.0 mm. Resting-
state functional MRI (rs-fMRI) was acquired as a T2-star EPI
sequence, with 3 ×3×3-mm3voxels, 128 volumes/run, TE =
27 ms, TR =2.8 s, FOV =256 mm, flip angle =90◦. The sequence
time is 230 s. The patient was requested to close their eyes without
thinking or any movement during the scan.
2.4. rTMS treatment
rTMS treatment was performed the day after imaging
acquisition. rTMS was delivered daily, and the patients were
treated twice a day for 5 days, a total of 10 times throughout the
hospital stay.
The rTMS was performed with a TMS stimulator (YINGCHI
Technology, China) using a flat circular coil for accurately targeted
stimulation. The coil were placed tangentially to the scalp with
the handle posterior at 45◦from the mid-line. In order to record
surface electromyography (EMG), electrodes were placed on the
abductor pollicis brevis (AFB) on the unaffected side. Resting
motor threshold (RMT) is defined as the minimum intensity
required eliciting at least five out of 10 MEPs that are >50 µV in a
relaxed target muscle. The coil positioning was guided throughout
a positioning cap with pre-defined brain regions.
Patients were randomly divided into three intervention groups
using an automated random lot drawing technique. Based
on randomization, patients received different TMS treatments
as described in Table 2. The three treatment options were
selected based on previous rTMS evidence-based guidelines that
recommended that low-frequency or high-frequency TMS could
be used as a Class A or B recommendation for the treatment of
post-stroke motor dysfunction in the acute (subacute) stage (32).
While less stated in previous guidelines, intermittent theta burst
stimulation (iTBS) has also been shown to provide benefits in this
context with sustained benefits for at least 3 months and therefore
was also utilized in our study (33,34). Information on the TMS
protocol used in the current study is presented in Table 1.
2.5. MRI image processing
All MRI scans were processed using Infinitome software
(produced by Omniscient Neurotechnology), which has
been described previously (23,35). Diffusion tractography
preprocessing includes standard processing steps (36), which
include motion correction, elimination of excess movement,
gradient distortion correction, eddy correction, and constrained
spherical deconvolution-based deterministic tractography. An
individualized, parcellated brain connectome was then created
according to the Human Connectome Project (37) parcellation
scheme, and structural connectivity is measured between each
parcel pair. Resting-state fMRI image preprocessing steps include
similar steps as outlined above in addition to the removal of high
variance confounds according to the CompCor method and the
regression of motion confounds out of the image and spatial
smoothing (38).
2.6. Statistical analyses
Analyses were completed using R 4.1.3 (R Foundation for
statistical computing).
Data were analyzed for mean or median for continuous
variables and as frequency or percentages for categorical data.
Continuous variables were assessed for normality with the Shapiro–
Wilk’s test and homogeneity of variance with the F-test of
variance and then subsequently compared with unpaired t-tests
or Wilcoxon rank-sum tests (with Bonferroni correction for
multiple comparisons) and univariate linear regression analysis
as appropriate. Categorical variables were assessed with chi-
squared tests with Yate’s continuity correction or Fisher’s exact
tests as appropriate. Paired subjects at different time points [(1) at
admission day, (2) 1 day after treatment, (3) 30 days after treatment,
and (4) 90 days after treatment] were assessed using the non-
parametric Friedman’s test for all four scales. The effect size for
possible differences was measured with Kendall’s Wand Dunn’s
pairwise post hoc analyses.
2.7. Structural and functional connectivity
analyses
After completing tractography-based individual patient
connectomes, structural and functional connections between
parcels in the motor network were assessed.
Possible structural connectivity disturbances in the cortical-
spinal tracts (CSTs), cortical–subcortical projection fibers, and
subcortical connections were assessed according to their structural
integrity on a 3-point scale (0 =intact, 1 =visible injured, and
2=absent) as well as the lesion proximity to these structures
(0 =not adjacent, 1 =adjacent (<1 cm), and 2 =inside the
fibers). These structural connectivity analyses were completed by
two independent reviewers (YZ and MES) similar to what has been
completed by others (39).
Functional connectivity disturbances within the motor network
were assessed by identifying individual “anomaly” parcels, referring
to regions functioning outside of the normal range compared
to 200 healthy adults. The source of the data is from healthy
subjects of similar but not age-matched adults from the publicly
available OpenNeuro (https://openneuro.org/) and SchizConnect
(http://schizconnect.org) datasets as previously discussed by our
team (35,40). The personalized atlas created in previous steps
was registered to the T1 image and localized to the gray matter
regions. Although the entire human connectome according to
the atlas published by the Human Connectome Project authors
demonstrates a total of 360 cortical parcellations (37) as well
as an additional 19 subcortical structures (35), we sought to
focus on the motor network and subcortical regions alone.
Therefore, in the current study, the average BOLD time series
from parcellations confined to the motor network and subcortical
structures were extracted, including a total of 45 regions (see the
details of 45 regions in Supplementary Table 1). In order to create
individual functional connectivity anomaly matrices that identify
outliers (“anomalies”), a tangent space connectivity matrix was
performed to determine the range of each functional connectivity
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TABLE 1 TMS protocols.
TMS
protocol
Motor
threshold
Stimulation
frequency
(Hz)
Trains Pulses/
train
Intervals
between
trains (s)
Total
pulses
Duration
(Min)
Side Target N=22
iTBS 80% 5 Hz burst
frequency, 3
pulses/burst at
50 Hz pulses
frequency
20 30 8 600 3 Ipsilesional M1 5 (23%)
High-
frequency
90% 10 Hz 100 10 10 1,000 18 Ipsilesional M1 10 (45%)
Low-frequency 90% 1 Hz 100 10 2 1,000 20 Contralesional M1 7 (32%)
pair in the matrix and create an individual raw functional
connectivity matrix. Then, anomaly matrices were created by
identifying abnormally connected parcels defined as a 3-sigma
outlier for that correlation compared to the normative connectivity
matrix. Connections that were 3-SD above the normative mean
were labeled “hyperconnected,” within 3-SD labeled “normal
connectivity,” and 3-SD below the mean “hypoconnected” (23).
Furthermore, the highest variance 1/3 of pairs were excluded to
further reduce the false discovery rate. This was based on the
hypothesis that since these areas had the highest inter-subject
variance in a normal cohort, these areas may be more prone to
false discovery and therefore should be excluded, as previously
elucidated elsewhere (23,41).
2.8. Hierarchical clustering
An unsupervised machine learning algorithm was utilized to
group patients into similar, unique clusters according to their
recovery profile and treatment response. Namely, an agglomerative
hierarchical clustering method was utilized which groups objects
into clusters based on their similar characteristics in a “bottom
up” approach (42,43). Each node (object) represents a cluster, and
then clusters are subsequently merged based on their dis(similarity)
until the optimal number of clusters K is obtained. Information
about (dis)similarity between clusters is calculated using the
pairwise Euclidean distances between every pair of clusters in a
data matrix. The optimal number of clusters K based on this
distance information is then determined according to the Silhouette
method. In brief, a Silhouette coefficient, which presents a metric to
calculate the goodness of a clustering technique, is obtained and
ranges between −1 and 1, with higher scores representing more
coherent clusters. Mathematically, it models the difference between
cluster separation and cohesion in order to identify the optimal
quality of clustering according to a specific number of clusters
generated (44).
The individual features utilized in the algorithm included
the individual stroke scale scores at four-time points (pre-TMS
at baseline and 1-day, 30-day, and 90-day post-TMS). These
values were chosen for the current clustering analysis in order
to identify individual phenotypes in recovery trajectory (45),
rather than identifying clinical presentation phenotypes first and
then subsequently assessing their relevance to treatment responses
(46). Importantly, we completed this clustering technique for
each individual scale separately. This was done secondary to the
observation that combining elements from each scale into the same
analysis on this relatively small cohort with heterogenous data
resulted in poor statistical fitting consisting of clustering into more
than 14 groups of 1–2 patients per cluster.
3. Results
The 22 patients included in the study were of a median (IQR)
age of 64 (56, 68) years, and split equally of male (n=11) and
female (n=11) patients. All patients suffered from a stroke, and
the median (IQR) hospitalization duration was 9.5 (9, 11) days. The
stroke most occurred in the right hemisphere (n=15, 68%). The
average baseline score on the NIHSS scale was 11.1, on FMA 16.5,
on BI 8.9, and on WFMT 11.8. These data are presented in Table 2.
The rTMS treatment targeted the primary motor cortex (M1)
in all patients. The targets were at equal proportions of the right
(n=11) and left hemispheres (n=11), although varied based
on the frequency of rTMS targeting ipsilateral or contralateral to
the lesion varied further by rTMS protocol (Table 3). Decisions
on which hemisphere rTMS was delivered to relative to the lesion
site were made by two independent stroke neurologists based
on radiographic findings at patient presentation. The treatment
intensity was most commonly of high frequency (n=10, 45%). The
type of TMS protocol was not associated with scores at any time
point on the NIHSS, BI, or WFMT scales (p>0.05 each). However,
the use of iTBS was associated with lower scores on the FMA scale
at 1-day (p=0.03) and 30-day (p=0.02) post-stroke.
3.1. Functional assessment outcomes
Functional outcomes were examined between four
standardized stroke scales between four-time points (baseline
pre-TMS and 1-day, 30-day, 90-day post-TMS). A significant
improvement between all time-points was demonstrated according
to the NIHSS (Kendall’s W=0.51, large), FMA (Kendall’s W=
0.59, large), and WFMT (Kendall’s W=0.02, small) scales (each
p<0.0001). The change in the BI scale was non-significant (p=
0.67). Mean values at each time point are presented in Figure 1.
Post hoc testing demonstrated significant differences between the
time points of baseline before TMS and 1-day (p=0.001) as well
as 30-day post-TMS (p<0.0001) on the NIHSS scale; significant
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TABLE 2 Demographics by stroke scale and cluster.
Characteristic All
data
NIHSS cluster FMA clusters BI cluster WMFT clusters
N=22a
1, N=4a
2, N=3a
3, N=8a
4, N=4a
5, N=2a
6, N=1a
p-valueb
1, N=18a
2, N=4a
p-valuec
1, N=2a
2, N=5a
3, N=6a
4, N=3a
5, N=6a
p-valueb
1, N=16a
2, N=6a
p-valuec
Lesion side
Left 7 (32%) 1 (25%) 1 (33%) 3 (38%) 2 (50%) 0 (0%) 0 (0%) >0.9 6 (33%) 1 (25%) >0.9 0 (0%) 3 (60%) 1 (17%) 2 (67%) 1 (17%) 0.3 5 (31%) 2 (33%) >0.9
Right 15 (68%) 3 (75%) 2 (67%) 5 (62%) 2 (50%) 2 (100%) 1 (100%) 12 (67%) 3 (75%) 2 (100%) 2 (40%) 5 (83%) 1 (33%) 5 (83%) 11 (69%) 4 (67%)
Gender
Female 11 (50%) 3 (75%) 1 (33%) 4 (50%) 2 (50%) 0 (0%) 1 (100%) 0.6 8 (44%) 3 (75%) 0.6 2 (100%) 2 (40%) 2 (33%) 2 (67%) 3 (50%) 0.7 7 (44%) 4 (67%) 0.6
Male 11 (50%) 1 (25%) 2 (67%) 4 (50%) 2 (50%) 2 (100%) 0 (0%) 10 (56%) 1 (25%) 0 (0%) 3 (60%) 4 (67%) 1 (33%) 3 (50%) 9 (56%) 2 (33%)
Patient age 64 (56,
68)
58 (55,
62)
49 (48,
59)
64 (62,
66)
64 (55,
72)
78 (77,
80)
67 (67,
67)
0.2 64 (56,
67)
69 (64,
72)
0.2 57 (55,
59)
66 (65,
68)
69 (52,
74)
64 (61,
65)
60 (56,
66)
0.8 64 (58,
67)
62 (54,
70)
>0.9
Hospitalization (days) 9.50
(9.00,
10.75)
10.00
(9.00,
11.25)
9.00
(8.50,
9.00)
10.00
(8.75,
10.00)
11.50
(10.75,
12.00)
8.50
(8.25,
8.75)
8.00
(8.00,
8.00)
0.073 9.00
(9.00,
10.00)
10.50
(9.50,
11.25)
0.5 11.50
(11.25,
11.75)
9.00
(9.00,
9.00)
9.00
(8.00,
10.75)
12.00
(10.50,
12.00)
9.50
(9.00,
10.00)
0.2 9.00
(8.75,
10.00)
11.50
(10.25,
12.00)
0.045
History of cerebrovascular disease
No 22 (100%) 4 (100%) 3 (100%) 8 (100%) 4 (100%) 2 (100%) 1 (100%) 18 (100%) 4 (100%) 2 (100%) 5 (100%) 6 (100%) 3 (100%) 6 (100%) 16 (100%) 6 (100%)
Hypertension 12 (55%) 2 (50%) 1 (33%) 5 (62%) 3 (75%) 1 (50%) 0 (0%) 0.9 10 (56%) 2 (50%) >0.9 0 (0%) 4 (80%) 3 (50%) 1 (33%) 4 (67%) 0.4 9 (56%) 3 (50%) >0.9
Diabetes 9 (41%) 1 (25%) 2 (67%) 4 (50%) 1 (25%) 1 (50%) 0 (0%) 0.9 8 (44%) 1 (25%) 0.6 0 (0%) 4 (80%) 2 (33%) 0 (0%) 3 (50%) 0.2 8 (50%) 1 (17%) 0.3
Coronary Heart
Disease
2 (9.1%) 1 (25%) 0 (0%) 1 (12%) 0 (0%) 0 (0%) 0 (0%) >0.9 2 (11%) 0 (0%) >0.9 0 (0%) 1 (20%) 0 (0%) 1 (33%) 0 (0%) 0.4 2 (12%) 0 (0%) >0.9
Hyperlipidemia 9 (41%) 0 (0%) 2 (67%) 5 (62%) 1 (25%) 1 (50%) 0 (0%) 0.3 8 (44%) 1 (25%) 0.6 0 (0%) 4 (80%) 2 (33%) 0 (0%) 3 (50%) 0.2 8 (50%) 1 (17%) 0.3
TMS protocol
High freq 10 (45%) 2 (50%) 0 (0%) 4 (50%) 2 (50%) 1 (50%) 1 (100%) 0.7 7 (39%) 3 (75%) 0.3 0 (0%) 2 (40%) 3 (50%) 1 (33%) 4 (67%) 0.5 7 (44%) 3 (50%) 0.7
iTBS 5 (23%) 2 (50%) 1 (33%) 1 (12%) 1 (25%) 0 (0%) 0 (0%) 4 (22%) 1 (25%) 2 (100%) 0 (0%) 1 (17%) 1 (33%) 1 (17%) 3 (19%) 2 (33%)
Low freq 7 (32%) 0 (0%) 2 (67%) 3 (38%) 1 (25%) 1 (50%) 0 (0%) 7 (39%) 0 (0%) 0 (0%) 3 (60%) 2 (33%) 1 (33%) 1 (17%) 6 (38%) 1 (17%)
TMS side
Contralateral 10 (45%) 2 (50%) 2 (67%) 4 (50%) 1 (25%) 1 (50%) 0 (0%) >0.9 10 (56%) 0 (0%) 0.1 1 (50%) 3 (60%) 2 (33%) 2 (67%) 2 (33%) 0.9 9 (56%) 1 (17%) 0.2
Ipsilateral 12 (55%) 2 (50%) 1 (33%) 4 (50%) 3 (75%) 1 (50%) 1 (100%) 8 (44%) 4 (100%) 1 (50%) 2 (40%) 4 (67%) 1 (33%) 4 (67%) 7 (44%) 5 (83%)
an (%); median (IQR).
bFisher’sexact test; Kruskal–Wallis rank-sum test.
cFisher’sexact test; Wilcoxon rank-sum test.
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TABLE 3 Patient demographics by TMS protocol.
Characteristic High frequency, N=10 iTBS, N=5 Low frequency, N=7p-value
Lesion side
Left 4 (40%) 1 (20%) 2 (29%) 0.9
Right 6 (60%) 4 (80%) 5 (71%)
Gender
Female 5 (50%) 4 (80%) 2 (29%) 0.3
Male 5 (50%) 1 (20%) 5 (71%)
Age 66 (64, 70) 53 (52, 58) 65 (56, 68) 0.035
Hospitalization duration (days) 9.00 (9.00, 10.00) 11.00 (10.00, 12.00) 9.00 (8.50, 11.00) 0.3
History of cerebrovascular disease
No 10 (100%) 5 (100%) 7 (100%)
History of hypertension 8 (80%) 0 (0%) 4 (57%) 0.020
History of diabetes 6 (60%) 1 (20%) 2 (29%) 0.3
History of coronary heart disease 2 (20%) 0 (0%) 0 (0%) 0.5
History of hyperlipidemia 6 (60%) 1 (20%) 2 (29%) 0.3
TMS side
Contralateral 3 (30%) 1 (20%) 6 (86%) 0.041
Ipsilateral 7 (70%) 4 (80%) 1 (14%)
differences between the time points of baseline before TMS and
30-day (p=0.001) as well as 90-day post-TMS (p<0.001) and also
between 1-day post-TMS, 30-day post-TMS (p=0.02), and 90-day
(p<0.001) post-TMS on the FMA scale; significant differences
between the time points of baseline before TMS and 1-day (p=
0.006), 30-day post-TMS (p<0.0001), and 90-day post-TMS (p<
0.0001) as well as between 1-day post-TMS and 90-day post-TMS
(p=0.002).
3.2. Connectivity outcomes
Structural and functional connectivities were measured based
on individualized connectomic analyses. A case example is
presented in Figure 2. These outcomes were addressed below in the
next section based on clustering analyses.
3.3. Cluster analysis based on standardized
stroke scales
Cluster analyses based on total scores at four-time points
revealed unique clusters, suggesting the presence of different types
of patient recovery trajectories in this cohort. These ML-based
clustering analyses were completed for each standardized stroke
scale (Figure 3). According to the optimal number of unique
clusters by the silhouette coefficient, six unique patient trajectories
existed for the NIHSS scale, two for the FMA scale, five for the
BI scale, and two for the WFMT scale. The silhouette coefficients
for each of these scales were 0.59 (NIHSS), 0.52 (FMA), 0.57 (BI),
and 0.57 (WFMT). A table comparing patient demographics in the
total study sample and by individual clusters is presented in Table 2.
There were no significant differences between individual clusters
according to individual patient demographics alone except a higher
length of hospital duration for cluster 2 compared to cluster 1 on
the WFMT scale.
Further inspection of the recovery trajectory profile of each
of these scales reveals some important trends. Most importantly,
despite some similarities between clusters for each scale (e.g., high-
or low-functional status prior to TMS and at the final 90-day time
point following TMS), individual clusters varied significantly in
terms of whether or not they experienced transient 1- and 30-day
declines. These trends in trajectories can be seen in Figure 3. As
an example, visually clusters 1 and 4 had similar baseline stroke
impairment and 1-day post-TMS scores on the NIHSS scale, but
cluster 1 then went on to improve 30 days and 90 days later,
while cluster 4 remained the same. Interestingly, while there were
no significant differences on the BI scale overall for the cohort,
ML-based analyses were able to highlight those patients who did
respond (e.g., cluster 3), and how other groups who had similar
initial scores to these patients then go on to decline (e.g., clusters
1 and 5).
3.4. Connectivity dierences between
individual clusters
After ML-based analyses were able to identify individual stroke
recovery trajectories according to each scale, we next sought to
examine differences in structural and functional connectivities
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FIGURE 1
Changes in functional outcomes after rTMS treatment. Patient functional status scores for each scale (NIHSS, FMA, BI, and WMFT) were assessed at
four-time points: baseline at presentation, 1-day after rTMS, 30 days after rTMS, and 90 days after rTMS. Top lines connect each time point. *p<0.05,
**p<0.001, and *** p<0.0001.
between these trajectories. Although some observable trends
were noted between clusters on the NIHSS, FMA, and WFMT
scales in structural and functional connectivity elements, these
visual trends did not reach statistical significance (p>0.05).
However, a number of significant differences in structural and
functional connectivity changes were identified between clusters
on the BI scale. Importantly, these differences prominently
differed for the patients who did improve on this scale compared
to other clusters. Given our ML-based analyses identified
individual trajectories according to each scale regardless of
how the overall cohort responded on that specific scale, we
focus on connectivity differences for the BI scale below in
further detail.
We provide a heatmap of these connectivity differences for each
scale and related clusters in Figure 4 as well as expanded results in
the Supplementary material.
3.4.1. Functional connectivity dierences
between BI clusters
The number of functional connectivity 3-sigma outliers
(“anomalies”) between clusters was investigated for both
cortical and subcortical connections and the total number of
hypoconnected and hyperconnected anomalies.
When investigating specific individual cortical parcels, a
number of significant motor regions differed between clusters.
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FIGURE 2
Case example. (A) Patient with right-sided stroke presented significant left upper and lower extremity motor deficits. (B) Structural tractography
revealed the lesion was directly inside the CST and cortical–subcortical projection fibers and an appreciable visual decrease in the integrity of the
right CST fibers was identified (represented by yellow arrows). Subcortical fibers were relatively intact from the lesion. (C) Functional connectivity
revealed a number of hyperconnected (red) and hypoconnected (blue) cortical and subcortical regions compared to the normative functional
connectivity of healthy adults. As detailed in the methods, the highest variance 1/3 of pairs were excluded to further reduce the false-discovery rate
given these areas may be prone to false discovery due to inter-individual variability in normal subjects. These areas are represented as black in the
connectivity matrix. White boxes represent areas within the normative distribution compared to healthy subjects.
FIGURE 3
Unique stroke recovery trajectories. Dierent groups are presented according to cluster analyses using outcomes on the four standardized stroke
scales at four-time points. Patient functional status scores were assessed according to: (1) National Institutes of Health Stroke Scale (NIHSS) (top
left), (2) Barthel Index (BI) (top right), (3) Fugl-Meyer Assessment (FMA) (bottom left), and (4) Wolf Motor Function Test (WMFT) (bottom right). Each
patient score was assessed at four-time points in order to obtain long-term data: (1) at presentation, (2) 1-day after treatment, (3) 30-days after
treatment, and (4) 90-days after treatment. While our sample included n=22, individual clusters contained occasional overlapping lines in patients
with the same scores. On the NIHSS panel, two patients in cluster 3 had the same score. On the BI panel, two patients in cluster 5 had the same
score. On the WMFT, two patients in cluster 1 had the same score.
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FIGURE 4
Dysfunctional connectivity between patient clusters. Connectivity anomalies are demonstrated on a heat map between patients according to
clustering analyses for the (1) National Institutes of Health Stroke Scale (NIHSS) (top left), (2) Barthel Index (BI) (top right), (3) Fugl-Meyer Assessment
(FMA) (bottom left), and (4) Wolf Motor Function Test (WMFT) (bottom right). Hyperconnected parcels are demonstrated in red, with a higher mean
number of hyperconnections in dark red and a lower mean number of hyperconnections in light red. Hypoconnected parcels are demonstrated in
blue, with a higher mean number of hypoconnections in dark blue and a lower mean number of hypoconnections in light blue. Each brain region,
ipsilateral or contralateral to the stroke site, is labeled on the y-axis. Individual patient clusters are on the x-axis. These outcomes are further
demonstrated in the Supplementary material.
Individual groups differed in the mean number of ipsilateral
hyperconnected supplementary and cingulate eye field (SCEF)
areas of the pre-supplementary motor area (cluster 3 =0.7
anomalies, cluster 1 =1 anomaly, no anomalies for other clusters; p
=0.04). Although, these differences were not statistically significant
between individual clusters on post hoc analyses but rather just for
all groups together. Similar overall differences were found for SCEF
on the ipsilateral side for hypoconnections, where only cluster 1
demonstrated an anomaly (p=0.04). Post hoc testing revealed
that these ipsilateral hypoconnections were significantly different
between group 1 with all other clusters, including clusters 2 (p=
0.02), 3 (p=0.02), 4 (p=0.04), and 5 (p=0.04). Differences
were also present for the number of hypoconnections with area
24dd contralateral to the lesion side (p=0.02), although post hoc
analyses revealed differences between individual groups did not
reach statistical significance (p>0.05).
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When examining subcortical structures, differences mostly
existed between groups for subcortical connections which were
hypoconnected rather than hyperconnected, specifically with the
pallidum, caudate, and thalamus. Significant differences were found
for the number of hypoconnections with the contralateral pallidum
(cluster 1 =1.0 anomaly, 2 =1.0, 3 =0, 4 =3.0, 5 =0.7; p=0.02).
Post hoc analyses revealed clusters 3 and 4 significantly differed the
most (p=0.007). Significant differences were found for the number
of hypoconnections with the contralateral thalamus (cluster 1 =
3.5 anomalies, 2 =1.0, 3 =0, 4 =0.3, 5 =1.0; p=0.02). Post
hoc analyses revealed that clusters 1 and 3 significantly differed the
most (p=0.05). Significant differences were found for the number
of hypoconnections with the ipsilateral caudate (cluster 1 =0.5
anomalies, 2 =0.4, 3 =0, 4 =2.7, 5 =0.5; p=0.02). Post hoc
analyses revealed that clusters 3 and 4 significantly differed the most
(p=0.02).
The mean number of contralateral cortical parcels which were
hypoconnected differed between clusters (cluster 1 =12 anomalies,
2=4.4, 3 =3.8, 4 =10, 5 =4.2; p=0.05). The mean number of
hypoconnected ipsilateral cortical parcels between clusters followed
a similar trend but did not reach statistical significance (cluster 1 =
19 anomalies, 2 =6.6, 3 =4.0, 4 =9.0, 5 =5.3; p=0.09).
Differences between other individual parcellations are
demonstrated in Figure 4 and in the Supplementary material which
did not reach statistical significance.
3.4.2. Structural connectivity dierences between
BI clusters
Differences in the visual appearance and lesion proximity of
different clusters were examined given the importance of white
matter integrity in post-stroke outcomes and treatment responses
(47–49). When examining the proximity of the lesion to white
matter fibers on DTI, there was a significant difference between
groups for cortical–subcortical projection fibers (p=0.03), but not
for subcortical fibers (p=0.71) or the CST (p=0.68). For cortical–
subcortical projection fibers, proximity was significantly different
between clusters (p=0.033). Proximity was not a predictor of
90-day BI score alone (p>0.05). Similarly, when examining the
disruption of white matter fibers on DTI, there was a significant
difference between groups regarding the visual integrity of cortical–
subcortical projection fibers (p=0.04), but not for subcortical
fibers (p=0.52) or the CST (p=0.38). For cortical–subcortical
projection fibers, visual integrity was significantly different between
clusters (p=0.047). Visual integrity was not a predictor of 90-day
BI score alone (p>0.05).
4. Discussion
Despite a clear understanding that stroke patients vary
significantly in regard to their recovery trajectory, there remains a
poor understanding of how to gain further insight into this process
during motor recovery treatment. Many scales which assess patient
functional outcomes (motor, sensory, and cognitive) have been
developed to predict individual stroke recovery in order to guide
treatment decisions; however, these scales remain heterogenous
and there is little consensus on their clinical value across the field
(50). In this study, a novel approach was taken to identify different
recovery phenotypes following rTMS treatment for acute stroke
patients and specifically with unique insight from personalized
connectomic information. Namely, a reverse approach was taken
which clustered patients with machine learning analyses according
to baseline and post-rTMS functional scores on validated stroke
scales, rather than just grouping patients according to clinical
presentation characteristics alone (45). While we found significant
improvements in functional recovery for patients from baseline up
to 90-day post-rTMS treatment across our entire sample, evidence
was found for clusters of specific patients with distinct recovery
trajectories. Furthermore, these treatment response phenotypes
could partially be differentiated according to their unique structural
and functional connectivity disruptions in the motor network
despite all suffering from “similar” acute strokes.
In many controlled trials, stroke patients are largely treated
as if they have the same underlying problem, despite it being
known that there are unique neurobiological differences between
patients (19). Thus, it is unsurprising to find that there have
been many conflicting results in functional outcomes for similar
stroke treatments, such as TMS, across different trials (1,14).
What is interesting in the current study is that despite not being
a largely powered study, a number of quantitative differences
were found existing in structural and functional connectivity
between individuals and this information could differentiate
unique phenotypes in rTMS treatment responses and recovery
on a standardized stroke scale. Thus, functional and structural
connectivity analyses may allow for additional assistance in
determining the prognosis of the patient as well as for trial designs
in more appreciable ways at the single subject level than many
other predicting tools which do not account for neurobiological
differences between individuals (51).
Spontaneous stroke recovery in functional ability, such as
motor functions, has been reiteratively demonstrated to be
dependent on underlying brain network damage and the network’s
capacity for functional re-organization (19,24–26). Based on
our study, different phenotypes according to the Barthel scale
varied in their total number of abnormal functional connections
to cortical parcellations. The connectivity of these parcellations
in the sensorimotor network has been well-described previously
(52,53) and are well-known regions involved in motor functioning
(54). In particular, the mean total of hypoconnected parcels
contralateral to the lesion side differed between specific trajectories.
Similar results have been found in previous study with less
anatomic specificity (55,56), although early identification of the
specific contralateral hypoconnected sensorimotor connections
which can be normalized with neuromodulatory treatments is
important for facilitating clinical improvements in the functional
activity and motor impairments (48). Furthermore, significant
abnormalities included dysfunctional connectivity of ipsilateral
pre-supplementary motor (pre-SMA) areas, ipsilateral caudate
connections, and contralateral pallidum connections. As an
example, patients in Barthel clusters 1 and 2 were similar in
their lower long-term 90-day scores but differed in their trajectory
such that cluster 1 had a transient improvement at 30 days
before declining in function. Simultaneously, cluster 1 had a
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greater number of hypoconnected ipsilateral connections to the
supplementary and cingulate eye field (SCEF) of the pre-SMA.
SCEF is a motor planning and initiation area believed to be a likely a
major point of informational outflow from higher-order networks
into the motor system due to shared network affiliation (57), and
damage to its connections may be a major cause of problems with
the initiation of goal-directed behaviors, such as in SMA syndrome
(58–60). Another example can be seen with clusters 3 and 4 which
had similar low Barthel starting points but varied in their long-
term scores (high vs. low). Cluster 4 had high functional scores at
90 days, and also had a greater number of abnormally decreased
connections with the ipsilateral caudate and contralateral pallidum
compared to cluster 4. Damage to each of these structures has
been extensively correlated with a variety of functional deficits (48,
61), and therefore, identifying these functional connections may
provide important connectomic features to model stroke severity
and recovery moving forward.
In addition to the insight provided by functional connectivity,
structural connectivity analyses have also been suggested to provide
additional information to better understand stroke recovery (19,
62,63). In the current study, individual clusters on the Barthel
scale were significantly different in regard to their projection
fiber integrity. Projection fibers are white matter connections that
link cortical and subcortical structures and facilitate a variety
of motor and non-motor functions. Although stroke studies
incorporating structural connectivity analyses focus on the CST
and its connections in the motor network (64), projection fibers
are also extensively damaged in stroke patients and are important
in understanding post-stroke deficits despite not being extensively
studied to date (47). In our sample, the integrity of these fibers
alone was not predictor of post-TMS scores; although this is
not entirely surprising given, these connectomic elements are
just one important structure that likely contributes to overall
function and recovery ability. Tools may be created which can
model the severity of white matter integrity of projection fibers
in addition to the CST and other white matter connections
(e.g., commissural fibers) to better understand motor impairment
(47), but additional studies should also examine their non-motor
correlates post-stroke. By mapping this lesion topography to white
matter connections, structural anatomic correlates can be identified
for overall stroke severity and post-stroke outcomes which may aid
in decisions for early rehabilitation strategies tailored to specific
patients but also perhaps for individual symptoms in future
studies (11,48,65).
An increase in the number of studies has attempted
to incorporate structural–functional analyses to predict motor
recovery following stroke. These studies have mainly focused
on the CST in relation to predicting motor impairment with
variable outcomes (66–69), and have also suggested the volume
of the acute lesion (70) may be less important to motor recovery
compared to the actual lesion location (71) and integrity of specific
underlying white matter bundles (19,72). These observations
highlight one of the main benefits of our analyses, namely
the utilization of an anatomically fine surface-based, multi-
modal parcellation scheme published by the Human Connectome
Project. Parcel-guided analyses may improve our ability to
better analyze underlying pathophysiological mechanisms and
communicate more anatomically fine results between studies for
hypothesis generation (18). Furthermore, parcel-guided treatments
can provide us a step forward to more accurate therapeutic
targeting (9–11,73). The efficacy of rTMS treatment is highly
dependent on the target location, which can be incorrectly
estimated with standard craniometric measurements that often
underestimate the localization of underlying structures that often
only have millimeter differences across the human scalp (74). While
parcel-guided TMS was not utilized in the current study, and rather
only to analyze and report our data, this study provides an example
of the feasibility and importance of such specific analyses which
should be examined further in future study for the clinical relevance
of such analyses.
The current study sought to use machine learning to identify
unique patient trajectories following acute stroke and then to
examine how connectivity information may provide additional
insight into these differences. While accomplishing this goal in
this current study, it is important to note that the current study
did not attempt to examine the intricacies and mechanisms of
TMS treatment or associated patient responses. It is well-known
that differences in TMS parameters may affect patient responses
(75,76), but this was not examined in the current study and
instead, our results may at most in this context point to the need
to identify precise anatomic neuromodulatory targets, but not the
efficacy in targeting these regions. Furthermore, an obvious point
brought out by our analyses is how stroke patients may have
unique recovery trajectories but also that these trajectories may
vary between different scales such that a select group of patients
“responding” on one scale may or may not be a responder on
a different scale. Although not the focus of study in the current
work a large body of research has also attempted to look at
these differences which presents an important area of research
moving forwards which connectomics may also provide valuable
information (77). Nonetheless, our results instead highlight the
ability of ML-based analyses to identify and highlight trajectories
irrespective of a responder or non-responder status, and then how
connectomic features can differentiate some of these patients, as
seen with the Barthel Index.
Our study included a small sample size of patients from a
single institution. Thus, while individualized connectivity analyses
produced a large amount of data for each single patient, these
biases could have influenced our statistical analyses and therefore
although connectivity differences may have existed between
clusters on other scales, these differences may not have been
identified in the current dataset. Our methods utilized a unique way
to investigate functional connectivity analyses using connectivity
“anomalies.” Given small changes in functional connectivity can be
difficult and too vague to interpret, our use of 3-sigma anomalies
provides a novel way to highlight likely meaningful changes in
a patients connectome in response to pathology or intervention;
however, our structural connectivity-based analyses relied on the
visual inspection of DTI as other have completed (39) and therefore
may have been subject to additional bias. Structural connectivity
provides a meaningful way to examine major differences in
a patient’s white matter bundles and identify gross patterns
between individuals, but when examined alone without additional
information these data should not be over-interpreted. In light
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of these limitations, future studies with larger datasets and
additional statistical power should look to examine individual scale
subcomponents with greater statistical certainty as it relates to
precise connectivity features (65). This is an important area of
future research as we transition toward a period where technology
now exists for highly specialized targeting according to individual
deficits (9,11,73).
Despite having limited power, a number of quantitative
differences in structural and functional connectivity were identified
which could differentiate unique patient recovery trajectories
on a standardized stroke scale and provide insight into their
treatment response. A larger sample size may have allowed us to
more confidently identify more specific individual parcellations
for each cluster and among varying scales. Instead, the current
results demonstrate the value of including additional connectomic
information on individual patients that may have unique
pathophysiological profiles despite similar injuries in order to
appropriately guide clinical decision-making and understand
treatment capabilities moving forward.
5. Conclusion
This study demonstrates the ability to identify unique patient
rTMS recovery trajectories between patients and how functional
and structural connectivity features can provide additional
information in this context. Additional personalized connectivity
analyses may allow for an improved understanding of the
patient’s disease burden or estimate their trajectory and capability
for neuromodulatory treatments and therefore represents an
important area for future study in larger prospective studies.
Data availability statement
The original contributions presented in the study are included
in the article/Supplementary material, further inquiries can be
directed to the corresponding author.
Ethics statement
The studies involving human participants were reviewed
and approved by First Affiliated Hospital of Hainan
Medical University Ethics Committee. Written informed
consent for participation was not required for this study
in accordance with the national legislation and the
institutional requirements.
Author contributions
RC: conceptualization, methodology, original draft, and
writing—review and editing. ND and BC: writing—formal
analysis, visualization, original draft, and writing—review and
editing. XZ: project administration and supervision. XH: project
administration, supervision, and writing—review and editing.
MS: conceptualization, methodology, and writing—review and
editing. LS: rehabilitation function assessment. XW: imaging data
acquisition. YL: clinical data collection and analysis. All authors
contributed to the article and approved the submitted version.
Funding
This study was supported by the 2019 Natural Science
Foundation High-level Talent Project of Hainan Province (item no:
2019RC379) and Hainan Province Clinical Medical Center.
Conflict of interest
XH and XZ were employed by Xijia Medical Technology
Company Limited. MS is the co-founder and chief medical officer
of Omniscient Neurotechnology. XZ and XH are employees of
Omniscient Neurotechnology. No products directly related to this
company were discussed other than for the purpose of explaining
data analyses.
The remaining authors declare that the research was conducted
in the absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fneur.2023.
1063408/full#supplementary-material
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