https://www.researchgate.net/profile/Lee_Reid3/publication/303920970_Motor_Learning_Induced_Neuroplasticity_Revealed_By_fMRI-Guided_Diffusion_Imaging/links/575dd58a08aed88462166ece.pdf
Conference Paper (PDF Available)
Abstract
Synopsis:
Detecting neuroplasticity requires highly sensitive measurements that
may be outside the bounds of standard parcellation-seeded tractography.
Earlier attempts to measure neuroplasticity induced by motor learning
have utilised voxelwise analyses. Such analyses are reliant on precise
registration, can have low statistical power, and provide little
certainty as to the functional relevance of areas of detected change. We
have measured motor-learning-induced neuroplasticity along corticomotor
and thalamocortical tracts using fMRI-seeded diffusion-MRI, finding
that changes uniquely occur in the corticomotor tract. Unlike previous
analyses, we reveal that these changes occur throughout the corticomotor
tract, not just near the grey-/white-matter interface.
Purpose: Very
few longitudinal studies have investigated white matter changes
associated with motor-skill learning. Of those reported, most have
focussed on visuo-motor regions and utilised voxel-based morphometry or
tract-based spatial statistics. 1 Change has been reported near the
grey-/white-matter interface, but these analyses are reliant on accurate
registration, provide little certainty as to the functional relevance
of an area, and can have low statistical power. Parcellation-seeded
tractography can provide another way to seed specific tracts, but will
include fibres innervating functionally-irrelevant muscles, and so may
be too insensitive to detect change. We sought to measure neuroplastic
change, in subjects who learned a novel non-visual motor task, by using
fMRI-guided diffusion MRI.
Methods: T1 (MPRAGE), HARDI (64 directions;
b=3000s/mm 2), and task-based fMRI images were collected from 23 healthy
adults immediately before and after four weeks of practicing a
(10min/day) finger-thumb opposition task with their non-dominant hand.
Subjects were processed individually. For each brain, a mesh of the
grey-matter/white-matter interface was created from T1
tissue-segmentations. Functional MRI analyses were performed entirely on
the mesh, without reslicing, to avoid implicit or explicit cross-sulcal
smoothing that could affect tractography. An 8mm FWHM surface smoothing
was used. Learned and unlearned finger-thumb opposition-sequence blocks
were pooled and contrasted with rest blocks. T-value meshes were then
moved into dMRI space and triangles of three connected
statistically-significantly activated (p<0.05 FWE) nodes were used to
define seeding regions for tractography (Figure 1 & 2). Activation
outside of the primary sensorimotor cortex (S1M1) was discarded.
Tractography was restricted to the thalamocortical and corticospinal
tracts with manually drawn inclusion masks of the thalamus, posterior
limb of the internal capsule, and brain stem. The mesh surface was used
to proactively constrain tractography to white matter. As corticomotor
and thalamocortical tracts often can pass through the same voxels near
the midbrain, k-means clustering was used to classify each track as
either corticomotor or thalamocortical, based on node locations near
these ROIs. Fractional anisotropy (FA) and mean diffusivity (MD) values
were calculated for each voxel. To assess diffusion metrics, for each
tract, voxels passed through were classified into seven bins, based on
their mean proportional distance along the tracts (Figure 3). Voxels
with an FA below 0.2 were excluded. A GLM was then used to determine
whether change of FA or MD occurred in each bin between sessions,
controlling for subject. Each voxel's contribution to this linear model
was weighted by the proportion of tracks that passed through it.
Results: Sequence performance rates were similar before training
(p=0.34; Paired TTest). After training, the trained sequence was
performed 40% faster than the untrained-sequence (p<0.0001), and
performance for both sequences improved (both p<0.01). fMRI
activation was consistently in the hand area of S1M1 (Figure 1). Bins
were consistently sized and located between sessions and subjects. FA of
the corticomotor tract increased significantly for every bin by 1.8 –
4.7% (all p<0.05, Holm-Bonferroni adjusted for multiple comparisons;
Figure 4). MD of the corticomotor tract decreased in six of seven bins
by-0.15% to-1.75%, increasing only near the internal capsule (0.58%),
but these changes did not consistently reach statistically significance.
No consistent trend of change in FA or MD was seen for the
thalamocortical tract.
Discussion: This study has demonstrated
white-matter changes induced by motor learning, by utilising a novel
data processing pipeline that allows diffusion metrics of sensorimotor
tracts to be compared between both subjects and time points. This method
does not rely on voxel-perfect registration. This is important, as both
type-I and type-II errors might otherwise be introduced through
reslicing and/or registration error, due to the minute changes that are
being measured. We found that FA change in motor tracts can take place
with motor learning, and found no consistent change in thalamocortical
tracks. Unlike previous studies, our fMRI-guided method provides
confidence that the areas of change are functionally relevant. We also
demonstrated that this change occurs across the full length of the motor
tract and so is unlikely to be due to partial volume contamination by
grey matter (registration error). This suggests that learning is not
purely due to highly-local rewiring of the cortex; connections to the
alpha motor neurons themselves may strengthen. It is known that
electrical impulses can induce myelination in vivo within a two week
period. 2 Given the pattern of change observed after four weeks of motor
training, we cautiously speculate that activity-induced myelination of
the white matter tracts may have taken place. Future studies utilising
myelin-water imaging may ascertain whether this mechanism plays a role
in motor learning. References 1 Chang, Y., 2014. Reorganization and
plastic changes of the human brain associated with skill learning and
expertise. Front. Hum. Neurosci. 8, 35.
Finally a study that can track the dynamic changes in HUMAN brains after motor training.
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