Motor Sequence Learning and Consolidation in Unilateral <i>De Novo</i> Patients with Parkinson’s Disease

<div><p>Previous research investigating motor sequence learning (MSL) and consolidation in patients with Parkinson’s disease (PD) has predominantly included heterogeneous participant samples with early and advanced disease stages; thus, little is known about the onset of potential behavioral impairments. We employed a multisession MSL paradigm to investigate whether behavioral deficits in learning and consolidation appear immediately after or prior to the detection of clinical symptoms in the tested (left) hand. Specifically, our patient sample was limited to recently diagnosed patients with pure unilateral PD. The left hand symptomatic (LH-S) patients provided an assessment of performance following the onset of clinical symptoms in the tested hand. Conversely, right hand affected (left hand asymptomatic, LH-A) patients served to investigate whether MSL impairments appear before symptoms in the tested hand. LH-S patients demonstrated impaired learning during the initial training session and both LH-S and LH-A patients demonstrated decreased performance compared to controls during the next-day retest. Critically, the impairments in later learning stages in the LH-A patients were evident even before the appearance of traditional clinical symptoms in the tested hand. Results may be explained by the progression of disease-related alterations in relevant corticostriatal networks.</p></div

Image_1_Integrated fMRI Preprocessing Framework Using Extended Kalman Filter for Estimation of Slice-Wise Motion.pdf

<p>Functional MRI acquisition is sensitive to subjects' motion that cannot be fully constrained. Therefore, signal corrections have to be applied a posteriori in order to mitigate the complex interactions between changing tissue localization and magnetic fields, gradients and readouts. To circumvent current preprocessing strategies limitations, we developed an integrated method that correct motion and spatial low-frequency intensity fluctuations at the level of each slice in order to better fit the acquisition processes. The registration of single or multiple simultaneously acquired slices is achieved online by an Iterated Extended Kalman Filter, favoring the robust estimation of continuous motion, while an intensity bias field is non-parametrically fitted. The proposed extraction of gray-matter BOLD activity from the acquisition space to an anatomical group template space, taking into account distortions, better preserves fine-scale patterns of activity. Importantly, the proposed unified framework generalizes to high-resolution multi-slice techniques. When tested on simulated and real data the latter shows a reduction of motion explained variance and signal variability when compared to the conventional preprocessing approach. These improvements provide more stable patterns of activity, facilitating investigation of cerebral information representation in healthy and/or clinical populations where motion is known to impact fine-scale data.</p

Block Duration in Session 1 (A) and Session 2 (B).

<p>Data points represent group means for each block and error bars depict standard errors. Black squares = healthy controls; blue circles = left hand asymptomatic (LH-A) patients with PD; red crosses = left hand symptomatic patients (LH-S) with PD. Thick solid lines represent group-averaged trajectories based on a single exponential fit.</p

Performance Index (PI) in Session 1 (A) and Session 2 (B).

<p>Data points represent group means for each block and error bars depict standard errors. Black squares = healthy controls; blue circles = left hand asymptomatic (LH-A) patients with PD; red crosses = left hand symptomatic patients (LH-S) with PD. Thick solid lines represent group-averaged trajectories based on a single exponential fit.</p

Participant characteristics.

<p>Values are means ± SD. MMSE = Mini Mental State Examination; GDS = Geriatric Depression Scale; UPDRS = Unified Parkinson’s Disease Rating Scale. L-UPDRS III = left side scores of UPDRS part III; R-UPDRS III = right side scores of UPDRS part III. PIGD = postural instability—gait difficulty. PD-dominance was based on method employed in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134291#pone.0134291.ref044" target="_blank">44</a>].</p><p>Participant characteristics.</p

Experimental Design.

<p>See text for details of each phase. W/U = warm-up.</p

Behavioral and imaging protocols.

<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

Neural correlates of motor practice in the spinal cord.

<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

Spinal cord–brain functional interactions during motor sequence learning.

<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.

<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