Synopsis
This study investigated changes
in functional and effective connectivity with M1 and anterior cerebellum using psychophysiological
interaction (PPI) and resting-state fMRI (rsfMRI), applied to a motor task fMRI dataset in healthy subjects and
multiple sclerosis (MS) patients.
Results show that M1 in MS patients has reduced long-range connectivity
to the contra-lateral hemisphere and the cerebellum and vice versa. Furthermore, MS patients lose visuo-motor integration
with parietal areas. This is in contrast to rsfMRI functional connectivity,
where connectivity of M1 to areas identified by the PPI network is increased. Results
indicate a task-specific disconnection reflecting increased disability,
associated also with low frequency maladaptive increased rsfMRI connectivity. Purpose
To
investigate the relationship of task-dependent with resting-state fMRI (rsfMRI)
functional connectivity of the motor network in multiple sclerosis (MS)
Background
Patients with MS have abnormal functional
connectivity patterns compared to healthy subjects (HS) (1); these differences
are based on rsfMRI signal correlations, which generally show increased
functional connectivity in MS. However, it is unclear what this means in terms
of distributed processing during a task and this is the focus of our investigation. The ultralow frequency correlations of
rsfMRI data may or may not reflect task-related changes in effective
connectivity mediated by physiological events at neuronal and synaptic levels.
We therefore used Psychophysiological Interaction (PPI) analysis (2) to
characterise task-dependent connectivity changes, where a task is seen as the modulator of connectivity. Given that in
MS the motor system is heavily
affected, we focused on motor areas, aiming to:
1. Define
the changes in effective connectivity with the primary motor cortex (M1) and anterior
cerebellum (aCBL), using PPI in the context of a visuo-motor task in HS and MS;
2. Compare
these changes with the functional (rsfMRI) connectivity of M1 and aCBL within
the PPI-defined ROIs, both in HS and MS.
Methods
18 Right-handed subjects (9 HS: 5 females; mean
(sd) age 30 (3.75) years and 9 relapsing-remitting MS:
7 females; mean age 34 (2.23) years; median (range) expanded disability status scale
(EDSS) = 3 (1.5, 6.5)) were recruited.
MRI: (3.0T Philips Achieva scanner): 1) T2*-weighted
EPI: TE/TR=35/2500ms, voxel size=3×3×3mm3, SENSE=2, Slices=46, FOV=192mm2,
volumes=200 (for task fMRI); 2) Same as 1) but with volumes=120 (for rsfMRI (3));
3) PD/T2 clinical scan; 4) 1mm isotropic
3D-T1-weighted scan.
Task
paradigm: visually-guided
event-related task with 150 jittered trials, randomized with rest, squeezing a
ball using their right hand.
fMRI
pre-processing: slice timing; realignment;
co-registration; normalization and smoothing.
PPI statistical-analysis
(figure-1) (using SPM12):
o The
main effect of movement (for each subject) was entered into random effects
analysis testing for group effects and between-group differences.
o A
conjunction of motor activations (over both groups) was masked with anatomical
regions of interest (ROIs) in left (contralateral) M1 and right (ipsilateral) aCBL.
o Centres
of these two ROIs (seeds) were identified as the highest t-value (testing
subject-specific movement effects) in each ROI. A subject-specific sphere of
8mm was selected for M1 and aCBL.
o Two
PPI models were evaluated per subject including: M1 or aCBL responses as
physiological factors, the main effect of motor task as the psychological
factor, and the (PPI) interaction term, being the effect of interest.
o A
second level analysis of PPI-M1 or PPI-aCBL effects was performed using one- or
two-sample t-tests for within- and between-group comparisons.
o Maps
were thresholded at the cluster level (one-sample=P<0.05, FWE;
two-sample=P<0.0001).
rsfMRI statistical-analysis
(figure-1) (using CONN (4)):
o Bivariate
correlations measured ROI-to-ROIs rsfMRI connectivity with random effects
analysis at group level.
o Source
ROIs were M1 and aCBL, as in PPI, with targets being PPI network ROIs.
o ROI-to-ROIs
significance used P<0.05, FWE.
Also, EDSS was used as a second level
covariate.
Results
Main findings (see figures-2-5) are:
o In
HS, the task-related (PPI) M1 network was organized around hubs for motor
planning/execution, visual response and integration. Furthermore, clusters were
symmetric in the brain and asymmetric in the cerebellum.
o In
MS, there was loss of task-related (PPI) connectivity with M1, preserving a
motor hub but losing visuo-motor integration and cerebellar connectivity. Half
of the connections of M1 and aCBL were lost within the M1 network, becoming
largely lateralized. However, regional functional responses of disconnected
ROIs were greater (mostly) in MS than HS.
o Two-sample
t-tests showed that even when connectivity is preserved in M1 local hubs, this is reduced in MS and the reduction involves all domains. Furthermore,
thalamic connectivity is reduced.
o rsfMRI
connectivity of M1 was increased in MS. This was less evident for aCBL.
o Lower
task-related (PPI) connectivity local to M1 and between aCBL and M1 was associated
with increased disability. In contrast, rsfMRI showed an opposite correlation,
where increased functional connectivity was associated with increased
disability.
Conclusion
In this study, we identified effective (task-related) and functional
(task-free) M1 and aCBL connectivity. Our results suggest that MS is
characterised by a disconnection of long-range connectivity during task
performance, while there are local compensatory attempts through increased
activation (5). At ultralow frequencies, during rest, the same regions show
increased connectivity, compared to HS. Given the task-related PPI disconnectivity
and opposite correlation of connectivity with EDSS, we speculate whether
increased rsfMRI functional connectivity in MS reflects a compensatory mechanism
or may actually reflect a maladaptive frequency specific (task-related)
functional disconnection, possibly also representing a reduction in white
matter fibres’ ability to support task connectivity in MS (6).
Acknowledgements
The UK MS Society and the UCL-UCLH Biomedical Research Centre for ongoing support; The Wellcome Trust.References
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