J Rehabil Med 2013; 45: 546–552

ORIGINAL REPORT

Combined information from resting-state functional
connectivity and passive movements with functional
magnetic resonance imaging differentiate fast late-onset
motor recovery from progressive recovery in hemiplegic
stroke patients: a pilot study
Tae-Du Jung, MD1*, Ji -Young Kim, MS2*, Jee-Hye Seo, MS3, Seong-Uk Jin, MS3, Hui Joong
Lee, MD4, So-Hyun Lee, MD1, Yang-Soo Lee, MD1 and Yongmin Chang, PhD4,5
From the 1Department of Physical Medicine and Rehabilitation, Kyungpook National University Hospital/Kyungpook
National University School of Medicine, 2MD-PhD Program, Department of Medical Science, Kyungpook National
University School of Medicine, 3Department of Medical & Biological Engineering, Kyungpook National University,
4
Department of Radiology, Kyungpook National University School of Medicine and 5Department of Molecular Medicine,
Kyungpook National University School of Medicine, Daegu Korea. *Both authors contributed equally to this work

Objective: To investigate the value of combining information from resting-state functional connectivity and passive
movements, measured with functional magnetic resonance
imaging (fMRI), in acute stroke patients with severe motor
impairment.
Subjects: Eight patients with severe left upper limb motor
impairment underwent a passive movement task with fMRI
and resting-state fMRI, 3 weeks following stroke onset. According to the patterns of motor recovery, patients were
divided into groups with, respectively, good or poor motor
recovery. Patients with good recovery were further divided
into two subgroups: progressive and fast late-onset motor
recovery.
Method: Activation and deactivation maps from a passive
movement task with fMRI were obtained. Interhemispheric
connectivity analysis was conducted using resting-state
fMRI.
Results: Interhemispheric connectivity score in patients
with progressive motor recovery was much greater than the
scores in patients with fast late-onset and poor motor recovery. For passive movement, patients with progressive recovery exhibited activation in the ipsilesional sensorimotor area
and no deactivation in the contralesional sensorimotor area.
Patients with fast late-onset motor recovery showed strong
deactivation in both sensorimotor areas. Patients with poor
recovery showed no activation or deactivation in either of
the sensorimotor areas.
Conclusion: Interhemispheric connectivity alone is not
enough to predict delayed motor recovery.
Key words: stroke; motor recovery; functional magnetic resonance imaging.
J Rehabil Med 2013; 45: 546–552
Correspondence address: Yongmin Chang, Department of Molecular Medicine & Radiology, Kyungpook National University
School of Medicine, 200 Dongduk-Ro Jung-Gu, Daegu. Korea.
E-mail: ychang@knu.ac.kr
Accepted Feb 18, 2013; Epub ahead of print May 17, 2013

Introduction
Severe impairment in motor function is the most frequent
complication after stroke (1). For appropriate rehabilitation
strategies in individual stroke patients, it is important to differentiate those with the potential for motor recovery from
those without such potential. To predict motor recovery after
stroke, clinical examinations measuring the degree of motor
impairment are the simplest indicators of prognosis. For example, the Brunnström-Fugl-Meyer motor recovery stage (BMS)
is a valuable clinical outcome measurement to predict motor
recovery (2, 3). In general, greater initial impairment predicts
poorer functional recovery (4). However, inter-individual
variability in the relationship between initial clinical outcome
measurement and subsequent recovery of motor function makes
accurate prognosis for each patient difficult (5). A recent study
suggests that the absence of motor-evoked potential (MEP) of
the affected hand 4 weeks from the onset of stroke might be
an indicator of poor prognosis (6).
Based on functional magnetic resonance imaging (fMRI)
studies of active and passive movements (7–11), patients
with good recovery tend to exhibit a network similar to that
of healthy adults. In addition, Rehme et al. (12) found that
the reinstatement of effective connectivity in the contralateral
(ipsilesional) hemisphere is an important feature for motor
recovery after stroke. In addition, an fMRI study involving
passive movement has shown the potential of the technique as
an alternative paradigm to evaluate possible motor recovery, especially in patients who cannot perform voluntary movements
(13). A previous study has demonstrated that a disrupted interhemispheric functional connectivity in the homologous regions
around the central sulcus within the sensorimotor network is
significantly related to upper extremity impairment (14).
The current study used both resting-state fMRI (rsfMRI)
and a passive movement task with fMRI to investigate whether
the latter technique can provide additional information on
possible motor recovery in patients with severe hemiplegic

© 2013 The Authors. doi: 10.2340/16501977-1165
Journal Compilation © 2013 Foundation of Rehabilitation Information. ISSN 1650-1977

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T.-D. Jung et al.

stroke who are expected to show poor motor recovery based
on their interhemispheric functional connectivity (FC) scores
from resting-state fMRI measurement. Furthermore, contrary
to previous studies of passive movement task with fMRI, we
evaluated both cortical activation and deactivation patterns
associated with passive hand movements. The fMRI signal
decreases (deactivations) during the task period have recently
become the focus of interest, because these task-related deactivations may be important for successful task performance.
CASE REPORTS
Eight patients with severe hemiplegic stroke were recruited to
the study. Patients with severely impaired cognitive function
(Mini-Mental State Examination (MMSE) score < 24), severe
visual, or perceptual impairment, previous musculoskeletal
abnormality or damage to the upper limb were excluded. Their
demographic and clinical data, including sex, age and MRI
findings, are summarized in Table I. All patients had experienced a right-sided ischaemic stroke in the middle cerebral
arteries (MCA) region (Fig. 1a) and all were right-handed.
They underwent rsfMRI and performed a passive movement
task with the hemiplegic hand using fMRI at about 3 weeks
post-stroke (mean 25.17 days; standard deviation (SD) 6.43
days). At that time, each patient had severe hemiplegia with
no hand and finger motion. We assessed hand motor function
of patients using the Brunnström-Fugl-Meyer motor recovery
stage (BMS) at 3 weeks, 3 months and 6 months after stroke
onset to measure prognosis of hand motor function. At 3 weeks
post-stroke, BMS was one in all patients. MEP, which was
performed once at 3 weeks post-stroke, could not be elicited
in the abductor pollicis brevis of the left hand in the patients.
At 6 months post-stroke, 4 patients had good hand function
with BMS 3 or 4, while 4 patients still had no hand movement with BMS 1. The 4 patients who had good hand functions displayed different recovery patterns. That is, among
these 4 patients, 2 showed progressive improvement in hand
function from the stroke onset to 6 months, while 2 showed
abrupt improvement in hand function at 6 months post-stroke
without improvement at 3 months (Fig. 1b). According to the
Table I. Patients demographic and clinical data
MEP
Age, (abductor
Motor recovery years/ pollicis
patterns
sex
brevis)
BMS
Progressive
recovery
Fast late-onset
recovery
Poor
recovery

67/F
57/F
32/F
70/F
59/M
41/M
68/F
78/M

Absent
Absent
Absent
Absent
Absent
Absent
Absent
Absent

1
1
1
1
1
1
1
1

BMS at
3 months
post-stroke

BMS at
6 months
post-stroke

2
2
1
1
1
1
1
1

3
3
4
4
1
1
1
1

MEP: motor evoked potential; BMS: Brunnström-Fugl-Meyer motor
recovery stage; F: female; M: male.

J Rehabil Med 45

Fig. 1. (a) Representative diffusion-weighted magnetic resonance images
for the stroke patients. (b) Hand function recovery patterns. Progressive
recovery patients with progressive improvement of hand function at
6 months post-stroke, and patients with fast late-onset recovery with
improvement of hand function at 6 months without any improvement up
to 3 months after stroke.

patterns of motor recovery, patients were divided into two
groups, reflecting, respectively, good and poor recovery. The
sensorimotor cortex was spared in all patients except one
patient in poor recovery. Patients with good recovery were
further divided into two subgroups: patients with progressive
improvement of hand function during 6 months, and patients
with abrupt improvement of hand function at 6 months without
any improvement at 3 months.
All subjects signed an informed consent for the study and
agreed to participate in the fMRI study. The study protocol
was approved by the Institutional Review Board.
Methods
Transcranial magnetic stimulation measurement
Transcranial magnetic stimulation (TMS) was performed on the right
primary motor cortex (tangential 3–4 cm lateral and posterior to Cz (the
intersection of the nasion-inion line and both tragus lines) with 90-mm
size round shape coil stimulator (Magstim® Rapid2, Magstim Co. Ltd,

Resting-passive fMRI in severe stroke
Dyfed, UK). The recording electrode was placed on the left abductor
pollicis brevis (APB). The coil was held tangential to the scalp, with
the handle angled backwards and 45º away from the midline. The
inter-stimulus interval was 10 s. We maintained coil temperature not
to exceed 35ºC. The threshold was defined as the minimum stimulation intensity required to evoke a peak-to-peak amplitude of > 50 μV
in at least 5 of 10 consecutive trials. The latency and the amplitude
were determined by averaging the values from 4 stimuli at 110% of
threshold. The absence of an MEP was defined when we could not
obtain a response after 3 successive discharges.
Magnetic resonance images acquisition
All magnetic resonance images (MRI) were acquired with a 3.0T GE
HD scanner.
T1-weighted and diffusion magnetic resonance images
T1-weighted images were acquired as anatomical images using a
3D-FSPGR sequence with the following parameters: TR = 7.8 ms,
TE = 3 ms, flip angle = 20, matrix = 256 × 256, FOV = 210 mm, 1.3 mm
thickness, and no gap. Diffusion weighted images were obtained as
confirmation of ischaemic lesion using the single-shot spin-echo, echoplanar imaging sequence with the following parameters: TR = 10,000
ms, TE = 92.3 ms, flip angle = 90°, matrix = 128 × 128, FOV = 220 mm,
4-mm thickness, and no gap. Diffusion was measured along 3 noncollinear directions with diffusion weighting (b = 1000 s/mm2) and
without diffusion weighting (b = 0 s/mm2).
Resting-state functional magnetic resonance imaging
rsfMRI was performed using a gradient echo planar-imaging sequence
with the following parameters: TR = 2000 ms, TE = 30 ms, flip angle = 90°, matrix = 64 × 64 and FOV = 210 mm, 4-mm thickness and no
gap. During rsfMRI scans, 240 volumes were acquired and subjects
had no other task but to stay alert and to keep their eyes closed during
the scanning run.
Passive movement task with functional magnetic resonance imaging
The fMRI with a passive hand movement task was acquired using a
gradient echo planar-imaging sequence with the following parameters:
TR = 3000 ms, TE = 40 ms, flip angle = 90°, matrix = 64 × 64, FOV = 210
mm, 4-mm thickness, and no gap. An experimenter, who stood next
to a patient, squeezed the left hand of the patient during the passive
movement task. The passive left hand movement task began with a
30 s resting phase, followed by 30 s during which the left hand was
squeezed by an experimenter. This sequence was repeated 4 times
during 4 min and 80 volumes were acquired.
Analysis of task-related functional magnetic resonance imaging
data
The fMRI data was analysed using the software package of Statistical
Parametric Mapping 5 (SPM5) implemented in MATLAB (The Mathworks Inc., Natick, MA, USA) for imaging processing and statistical
analysis. The images were corrected for slice timing effects and were
realigned to the first image to adjust for residual head movement. The
realigned images were spatially co-registered to the T1 image based
on anatomical brain image of individual subjects and normalized into
a Montreal Neurological Institute template. Subsequently, all images
were smoothed with an isotropic Gaussian kernel, having 8-mm full
width at the half maximum (FWHM). Low-frequency noise was removed using high-pass filter of 128 s.
Statistical analysis of the data from each subject was performed
by using a general linear model approach to create the statistical map
and the theory of Gaussian fields to make statistical inferences about
regional effects with the SPM5. The pre-processed data were entered
into first-level individual analysis by comparing brain activation during
the passive motor task with brain activation during the baseline condition (passive motor task – resting baseline). The passive movement

3

task was modelled as boxcars, of identical length to the total period
of motor task prior to convolution with canonical haemodynamic
response function. The explanatory variables and 6 additional regressors from the realignment procedure were used to generate a design
matrix, and their corresponding regression coefficients were estimated
on a voxel-wise basis by least-squares analysis. Then t-statistics were
used to make statistical inferences about input of each regressor to
the evaluated fMRI signal. The t-statistics created subject-specific
statistical parametric maps (SPM{t}). The contrasts were applied to
the parameter estimates to determine brain regions showing significant task-related signal increases and decreases associated with each
of the passive motor task vs the resting baseline. Brain activations
were identified as task-related signal increases during passive motor
tasks, while brain deactivations were identified as task-related signal
decreases during resting-phase. Significant activations and deactivations were identified at the single-subject level using threshold of
uncorrected p < 0.005 and a minimum cluster extent of 32 contiguous
voxels. Finally, the resulting activation maps were created to identify
the anatomical correlates of the activity, and these were displayed by
projection onto the individual T1-weighted images.
Interhemispheric connectivity scores
For each subject, we determined the bilateral primary motor areas
(spheres of 5-mm radius) based on the motor task activation maps of
individual subjects using MarsBaR (http://marsbar.sourceforge.net/), a
region of interest tool. The interhemispheric connectivity (IHC) score
analysis was conducted using the Functional Connectivity SPM5 toolbox (http://web.mit.edu/swg/software.htm) in the resting-state scan.
To correct for co-fluctuations in blood oxygenation level dependent
(BOLD) signal due to noise, BOLD signal from cerebrospinal fluid,
white matter, and motion parameters were used as nuisance covariates (regressors). In addition, a band-pass filter (0.009–0.08 Hz) was
used. Bivariate correlations were computed between selected regions
of interest. These correlations were transformed into the β values
and the functional connectivity scores using regressors and Fisher
Z transforms, respectively. We represented functional connectivity
maps, which correlated brain regions with ipsilesional sensorimotor
area; and the IHC scores, which demonstrated functional connectivity scores of homologous sensorimotor area. The threshold value
of functional connectivity map is defined in the β unit (voxels with
regression coefficients > 0.65).
Statistical analysis
The IHC scores were analysed by one-way analysis of variance
(ANOVA) among the 3 groups with the Statistical Package for the
Social Sciences (Version 18; SPSS, Chicago, IL, USA). To assess the
IHC differences between one group and another, post-hoc two sample
t-tests were applied.

Results
Analysis of resting-state functional magnetic resonance
imaging
Functional connectivity maps of the cortical sensorimotor networks in stroke patients are shown in Fig. 2a. All patients displayed
ipsilesional dominant functional connectivity. The groups with
progressive and late-onset recovery both have SMA activation
during rsfMRI as well as ipsilesional dorsal premotor activation.
The mean IHC score between sensorimotor regions in patients
with progressive motor recovery was much greater than the IHC
scores in patients with fast late-onset recovery and in patients with
poor recovery (Fig. 2b). Comparison with one-way ANOVA test
showed a significant difference between the 3 groups (p < 0.05).
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T.-D. Jung et al.

Fig. 2. (a) Representative resting-state functional connectivity maps of the cortical sensorimotor networks in stroke patients. (b) Mean interhemispheric
connectivity score between primary sensorimotor regions in 3 recovery groups. Comparison with one-way analysis of variance test showed a significant
difference between the 3 groups (p < 0.05).

Post-hoc comparisons with two-sample t tests indicated a significant difference between IHC scores in the progressive recovery
group and the other two groups (p < 0.05). However, there was no
significant difference between IHC scores in the group with fast
late-onset recovery and the group with poor recovery (p = 0.538).

tive BOLD activation in the ipsilesional sensorimotor area and
exhibited deactivation in the contralesional sensorimotor areas
(Fig. 3B). Patients with fast late-onset recovery also showed
deactivations in SMA. In patients with poor recovery, both
activation and deactivation were not observed for passive left/
hemiplegic hand movements (Fig. 3C).

Passive movement task with functional magnetic resonance
imaging

Discussion

For the passive left/hemiplegic hand movement task, patients
with progressive recovery exhibited positive BOLD activation
in the ipsilesional sensorimotor area and no negative BOLD
activity (deactivation) in the contralesional sensorimotor area
(Fig. 3A). Patients with fast late-onset recovery showed posi-

The present study is, to the best of our knowledge, the first
to combine rsfMRI and a passive movement task with fMRI
measurements in relation to motor recovery after stroke in
patients who initially had no voluntary hand movements and
no MEP of the hemiplegic hand. This study demonstrates that

J Rehabil Med 45

Resting-passive fMRI in severe stroke

5

Fig. 3. Activation and deactivation patterns for the passive left/hemiplegic hand movement task. (a) Patients with progressive recovery, patients with
fast late-onset recovery and patients with poor recovery. Red circles indicates the location of primary sensorimotor area and supplementary motor
cortex. (b) Plotting mean deactivation intensities for contralesional sensorimotor area in 3 recovery groups.

the combined use of rsfMRI and a passive movement task with
fMRI can improve estimates of the potential for recovery of
motor function in stroke patients. In particular, the deactivation
pattern of the passive movement task with fMRI is important
to estimate possible long-term motor recovery.
The rsfMRI data showed that the mean IHC scores between
ipsilesional and contralesional motor regions were significantly
higher in patients with progressive recovery than in patients
with poor recovery. This finding was consistent with a previous
observation that disruption of IHC scores between sensorimotor
regions significantly correlated with upper extremity impairment (14). However, the current findings from IHC analysis also
revealed that IHC alone is insufficient to predict delayed motor

recovery shown in patients with fast late-onset recovery. That is,
based on IHC scores at the time of rsfMRI examination (3 weeks
after stroke onset), patients with fast late-onset recovery would be
regarded as those who had less potential for motor recovery and
were expected to show poor recovery. The present findings from
the IHC analyses therefore suggest that, although the spontaneous activities in the motor network at rest would have a good
prognostic value, it is necessary to measure the movement-related
cortical activities in the motor network for better estimates of the
potential capacities for motor recovery in stroke patients.
While passive movement is a form of sensory stimulation,
provided by proprioceptive input, a previous study used passive
movement to demonstrate that it can be useful in assessment
J Rehabil Med 45

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T.-D. Jung et al.

of physiological changes associated with motor recovery
after stroke, when patients show little mobility (15). More
specifically, Matteis et al. (15) demonstrated that the increase
in mean flow velocity in the MCA of the affected hemisphere
is higher than in the MCA of the unaffected hemisphere during
passive elbow movements in patients with good recovery, while
there is only a slight bilateral increase in mean flow velocity
with prevalence for the MCA in the unaffected hemisphere
in patients with poor recovery. Although active and passive
tasks are quite different, studies that used passive movement
and active motor tasks also found largely overlapping patterns
of activation (9, 13, 16). Thus, passive movement paradigms
seem to be a suitable way to investigate the functionality of
the sensorimotor network recruited during active movements,
particularly in patients with severe impairment. However, our
passive movement task with fMRI data revealed that patients
with progressive recovery were not differentiated from patients
with fast late-onset recovery, because both groups showed
ipsilesional sensorimotor activation. Furthermore, a previous
observation demonstrated that the ipsilesional sensorimotor
activation, occurring/generated during passive movements of
the affected hand in the early stage of stroke, had a low predictive value for the motor recovery of the hemiplegic hand (9).
Interestingly, however, patients with fast late-onset recovery showed significant deactivation in the contralesional
sensorimotor area and SMA, while both progressive and poor
recovery patients showed no deactivation in both ipsilesional
and contralesional sensorimotor area for passive movement of
the affected hand. The underlying physiological mechanism
of the widespread deactivation in the patients with fast lateonset recovery remains unclear. However, a previous study
demonstrated that the increased neuronal activation of the
motor cortex in one hemisphere might induce reduced neuronal activity in the opposite hemisphere (17). Based on this
previous observation, a possible interpretation for widespread
deactivation in the contralesional sensorimotor area and SMA
is that these brain areas reduce their neuronal activity in order
to increase neuronal activation of sensorimotor area in the
ipsilesional hemisphere. That is, strong deactivation patterns
shown in patients with fast late-onset recovery might suggest
that, although activation in the sensorimotor area was not
involved during early recovery processes, the deactivation
pattern might be a predictive factor for possible functional
recovery of the affected hand at the late stage. Another possible
explanation is “diaschisis” or “distributed injury hypothesis”,
in which focal structural lesions lead to dysfunction in brain
regions that are structurally intact, but connected to the area of
structural damage (18, 19). Our IHC analysis revealed remote
network dysfunction after a focal lesion in the motor network,
suggesting that widespread alterations in motor network functional connectivity take place, while stroke induces a sudden
local structural lesion. In terms of “diaschisis” or “distributed
injury hypothesis”, our findings from IHC analysis seem to
reflect the degree of dysfunction in remote brain regions that
are structurally intact and thus, in part, reflect the potential
capacity for motor recovery.
J Rehabil Med 45

This study has some limitations. First, a small number of
patients in the hospital were recruited. Secondly, we did not
evaluate the longitudinal changes in activation and deactivation
fMRI patterns during motor recovery after stroke. In particular,
since there were no fMRI data 6 months after onset of stroke,
we could not compare brain activation patterns between patients with fast late-onset recovery and those with progressive
recovery at 6 months after stroke, in which both groups showed
significant motor recovery. Thirdly, the results were reported
at uncorrected p < 0.005 level, and thus we could not exclude
a chance of type 1 error (a false-positive error). Finally, in the
current study, we used a hand squeezing procedure performed
by another person. This was not an automatized procedure for
moving the upper limb passively, and thus there might be a
difference in activation patterns due to inconsistency in the
squeezing procedure.
In summary, this study demonstrates that the combined use
of rsfMRI and a passive movement task with fMRI may be
important to evaluate the potential recovery of motor function in stroke patients. The rsfMRI data show that IHC scores
between homologous motor regions are a good indicator for
progressive upper limb motor recovery after stroke, but that
IHC alone is not enough to predict delayed motor recovery
shown in patients with fast late-onset recovery. In patients
with fast late-onset recovery, the strong deactivation of passive movement task with fMRI in the sensorimotor system,
including contralesional sensorimotor area, is an indicator of
possible delayed motor recovery.
AcknowledgementS
This work was supported by a grant from the Ministry of Health &
Welfare (A092106) of Korea, and by Kyungpook National University
Research Fund, 2013.

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