46 research outputs found

    Data_Sheet_1_Histology-informed automatic parcellation of white matter tracts in the rat spinal cord.PDF

    No full text
    The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.</p

    Neural correlates of motor practice in the spinal cord.

    No full text
    <p>(A) Activation maps representing the main effect of practice during the CS (red) and SS (blue) conditions are overlaid on the structural image of a reference subject. The yellow box indicates the sagittal section (<i>x</i> = -2.6 mm left), and the oblique yellow lines indicate the location of different transversal sections that are then displayed on the left and right sides of the figure. Note that the peaks of BOLD responses in both conditions are centered on the C7 spinal segment, mostly ipsilateral to the side of finger movements. The upper plots illustrate the percent change of the BOLD signal, averaged across blocks and subjects, during the CS (red) and the SS (blue) conditions. For averaging purposes, the BOLD signal of each block was resampled to obtain an equal number of points per block. The bright gray box represents the average duration of each block. The shaded area represents SEM; the color bars indicate <i>Z-</i>score values; all activation maps are corrected for multiple comparisons using GRF, <i>p</i> < 0.01. (B) There is a significant difference in mean amplitude of the BOLD signal change between the CS and SS conditions. (C) Similarly, the spatial extent of activation within the C6–C8 spinal segments is significantly larger in the CS as compared to the SS condition. Error bars represent SEM; * indicates <i>p</i><0.05.</p

    Behavioral and imaging protocols.

    No full text
    <p>(A) The complex (CS; 4-1-3-2-4) and simple (SS; 4-3-2-1) motor sequence learning tasks were executed with the left (nondominant) hand. Subjects were required to execute 12 CS and 12 SS blocks of practice, with 60 movements each. (B) The CS and SS conditions were split evenly across blocks and alternated in a pseudorandom fashion. A 15-s rest period preceded and followed each block. (C) Functional axial slices (displayed here over the anatomical image of a representative subject) were acquired and covered both brain and cervical spinal cord up to the first thoracic (T1) segment, and they were placed at an angle that was perpendicular to the C4 vertebral segment. (D) Performance speeds (i.e., block duration) averaged across all subjects show that the learning curves differed between the CS (red) and SS (blue) conditions. Participants reached asymptotic performance after the fourth block in the SS and after the eighth block in the CS condition. (E) Learning index (mean duration of the last two blocks subtracted from the first two blocks’ mean) revealed a significant difference in performance between the CS and SS conditions. Error bars represent standard error of the mean (SEM); * indicates <i>p</i><0.05.</p

    Spinal cord–brain functional interactions during motor sequence learning.

    No full text
    <p>(A) Activation maps show brain regions that changed their functional connectivity during the CS condition with a spinal cord ROI centered on the C7 spinal segment (yellow circle, middle of the figure). This change was proportional with subjects’ improvement in performance speed. Red and blue activation clusters indicate positive and negative relationship between functional interaction magnitude and performance speed, respectively (<i>p</i> < 0.01, corrected for multiple comparisons using GRF). (B) Bar plots show Pearson’s correlation values between the spinal cord and brain clusters’ time series in the early (the first two blocks) versus late (the last two blocks) phases of learning, averaged across subjects. Results revealed a significant increase in negative correlation with the cerebellum (CB—red bars), but a significant decrease in positive correlation with the primary sensorimotor cortex (SMC—blue bars) as learning progresses in the CS condition. There is no significant change in correlation during the SS condition (shown in gray). Error bars represent SEM; *, <i>p</i><0.05; **, <i>p</i><0.01, all corrected for multiple comparisons.</p

    Distinct spinal cord contribution to motor sequence learning.

    No full text
    <p>(A and B) Two cervical clusters located at C7–C8 spinal segments showed significant changes in BOLD signal, which were modulated by performance speed. Importantly, activity in those spinal segments was independent of concomitant signals originating from both (A) brain structures that typically project to the spinal cord and (B) brain areas that show learning-related activity changes. Axial slices (colored lines) show the location of brain seed regions, highlighted by yellow circles, whose activities were regressed out in the spinal cord modulation analysis. The color bars indicate <i>Z-</i>score values; all activation maps are corrected for multiple comparisons using GRF, <i>p</i> < 0.01. (C) Activity in both the spinal cord and the brain accounted for nonoverlapping portions of behavioral variability. The Venn diagram illustrates, proportionally, the amount of performance speed variability, which is explained independently by each of the cortical, subcortical, and spinal cord ROIs, as well as their shared variance. Numbers in parentheses indicate the percentage of total variance explained by each ROI (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002186#pbio.1002186.s014" target="_blank">S3 Table</a>).</p

    Neural correlates of motor sequence learning.

    No full text
    <p>Distinct cortical, subcortical, and spinal clusters showed learning-related modulation in activity only during the CS condition. All clusters of activation are positively correlated with the performance speed. At the cortical level, the activation cluster was located in the contralateral sensorimotor cortex. At the subcortical level, one cluster was found in the contralateral putamen, while the other was observed in the ipsilateral lobule V-VI of the cerebellum. In the spinal cord, activation clusters were centered on the C7–C8 spinal segments, similar to those observed in the main effect of practice. The color bars indicate <i>Z-</i>score values; all activation maps are corrected for multiple comparisons using GRF, <i>p</i> < 0.01.</p

    Comparison of Current Segmentation Method to Prior Studies that Calculate Comparable Metrics.

    No full text
    <p>*–Represents the best outcome based on comparison between two manual raters</p><p><sup>^</sup>–Represents the best outcome from two different methods</p><p><sup>#</sup>–Denotes methods where user initialization is required</p><p>Note: In the above table, the Dice coefficient depends on the spatial resolution, which in our case is favourable, however the Hausdorff distance does not, and therefore results suggest high performance of the proposed method.</p><p>Comparison of Current Segmentation Method to Prior Studies that Calculate Comparable Metrics.</p

    An axial slice of the MR image (left) and the gradient image (right) of the cervical spinal cord.

    No full text
    <p>Ď€The light green shading illustrates the manual segmentation. The red radial line rotating about the spinal cord centerline axis was used for extracting the MRI signal.</p

    Example of the difference between two raters segmentation of a smoothed T2-weighted MR image.

    No full text
    <p>The green region represents the area segmented by Rater 1 and the red region is Rater 2 less Rater 1. Although the segmentations are very similar, and both could subjectively be considered accurate, there is an 8 mm<sup>2</sup>, or 9%, difference in CSA.</p

    One manually segmented axial slice of a MR image.

    No full text
    <p>This image was shown to the raters to instruct them on how to manually segment the spinal cord.</p
    corecore