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Preserved motor learning after stroke is related to the degree of proprioceptive deficit Vidoni, Eric D; Boyd, Lara A Aug 28, 2009

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ralssBioMed CentBehavioral and Brain FunctionsOpen AcceResearchPreserved motor learning after stroke is related to the degree of proprioceptive deficitEric D Vidoni1 and Lara A Boyd*2Address: 1Department of Neurology, University of Kansas Medical Center, Kansas City, USA and 2Department of Physical Therapy, University of British Columbia Vancouver, British Columbia, CanadaEmail: Eric D Vidoni - evidoni@kumc.edu; Lara A Boyd* - lara.boyd@ubc.ca* Corresponding author    AbstractBackground: Most motor learning theories posit that proprioceptive sensation serves animportant role in acquiring and performing movement patterns. However, we recentlydemonstrated that experimental disruption of proprioception peripherally altered motorperformance but not motor learning in humans. Little work has considered humans with centralnervous system damage. The purpose of the present study was to specifically consider therelationship between proprioception and motor learning at the level of the central nervous systemin humans.Methods: Individuals with chronic (> 6mo) stroke and similarly aged healthy participantsperformed a continuous tracking task with an embedded repeating segment over two days andreturned on a third day for retention testing. A limb-position matching task was used to quantifyproprioception.Results: Individuals with chronic stroke demonstrated the ability to learn to track a repeatingsegment; however, the magnitude of behavioral change associated with repeated segment-specificlearning was directly related to the integrity of central proprioceptive processing as indexed by ourlimb-position matching task.Conclusion: These results support the importance of central sensory processing for motorlearning. The confirmation of central sensory processing dependent motor learning in humans isdiscussed in the context of our prior report of preserved motor learning when sensation isdisrupted peripherally.BackgroundIt is commonly held that sensory feedback plays animportant role in motor skill learning [1-3]. Physiologicevidence from animal lesion models suggests that sensorycortex is necessary for learning new skills, includingmates [5] inhibit motor learning. Animals with experi-mentally induced lesions to S1 fail to acquire new motorskills with the contralesional hand. However, once a skillhas been acquired, discrete S1 lesions do not significantlyinterfere with existing skill performance [5]. Though acti-Published: 28 August 2009Behavioral and Brain Functions 2009, 5:36 doi:10.1186/1744-9081-5-36Received: 10 February 2009Accepted: 28 August 2009This article is available from: http://www.behavioralandbrainfunctions.com/content/5/1/36© 2009 Vidoni and Boyd; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 10(page number not for citation purposes)movement sequences. For example, lesions to primarysomatosensory cortex (S1) in cats [4] and non-human pri-vation of the sensory cortex has also been linked to excita-tion of the human motor cortex [6], and disruption ofBehavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36parietal activity interferes with adaptation [7] little otherdirect evidence links somatosensory processing to motorlearning in humans.Further clouding our understanding of the interaction ofsensory and motor systems are reports that somatosensa-tion is not essential to motor adaptation. Several studieshave examined motor performance and adaptation inindividuals lacking proprioception [8-13]. Takentogether, these studies have noted maintenance of adapta-tion and control. Indeed, we recently demonstrated thatperipherally altering proprioception in healthy individu-als did not adversely affect motor sequence learning [14].To disrupt proprioception peripherally, participants' armswere vibrated continuously while they practiced arepeated pattern of continuous movements. We discov-ered that participant performance was compromised byvibration during practice; however, at retention whenvibration was removed, individuals performed therepeated portion of the movement as well as controls. Ourfindings supported previous work and suggested thatwhen peripheral proprioception is disrupted other sen-sory systems can compensate for altered sensation andprovide the feedback that is necessary for motor learning.One apparent difference between our peripheral disrup-tion of proprioception and the animal lesion models iscontinuity of the central somatosensory processing sys-tem. Preservation of the central nervous system allows forcomparison and weighting of incoming, sensory informa-tion so that skill learning is maintained, even when feed-back sources are conflicting or aberrant. Conversely, whenthe central structures are damaged, feedback evaluation,sensory reception, interpretation or expectation may bediminished, disrupting motor learning [15,16].The present study was designed as a follow-up investiga-tion, to test whether central proprioceptive processingdysfunction impacts continuous motor learning. It furtherrepresents a corollary study in humans to animal workthat demonstrated the importance of central sensoryprocessing for motor learning [4,5,17]. Continuoussequencing tasks allow for the study of emergent proce-dural learning [18]; because of the need for constantupdating, continuous tasks are highly reliant on the prop-rioceptive system especially in the absence of visual feed-back. This is in contrast to other commonly employedmotor learning paradigms that employ discrete end-pointmovements, which can be largely planned in advance[19]. Continuous tracking also differs from studies requir-ing a reach to discrete targets in a novel environment [20],where a commonly performed behavior is adapted to newdynamics. The task employed here requires extended andcoordinated patterns of movement. Finally, in the presentstudy we considered the pattern of continuous velocitychanges rather than absolute position [18,21,22] as recentwork has emphasized the encoding of velocity-basedinformation by the proprioceptive system [23,24].We hypothesized that if central processing of propriocep-tive feedback were necessary for motor learning, stroke-related damage to somatosensory cortical areas, thalamusand/or the associated white matter tracts would result inimpaired continuous motor learning. Furthermore, weexpected that the magnitude of learning-related change inmotor behavior would be related to the degree of propri-oceptive impairment as measured by limb-positionmatching ability. We tested these hypotheses using a con-tinuous tracking task in which we eliminated useable vis-ual feedback. In addition, we performed a separateretention test to dissociate motor learning from transientperformance-related improvements [25].MethodsParticipantsTwelve individuals presenting with clinical signs of achronic stroke (at least 6 months post) [26,27] in the mid-dle cerebral artery distribution (CVA group), and 9 simi-larly aged individuals neurologically intact individuals(Healthy Control; HC group) were recruited from thegreater Vancouver, British Columbia and Kansas City,Kansas communities and provided informed consent toparticipate in this study. The study was approved by theInstitutional Review Boards of the University of KansasMedical Center and the University of British Columbia.All participants had near vision corrected to at least 20/40,and no history of diabetes or peripheral neurologicaldamage. Prospective participants were screened using theMini-Mental State Exam (MMSE) [28]. Decreased motorlearning capacity and proprioception occurs with advanc-ing age [29-32]. Therefore it was critical that the age of ourHC group share a similar age distribution as our partici-pants with stroke. Group characteristics are summarizedin Table 1. Data from 2 CVA participants were excludedfrom analysis secondary to consistently poor tracking asdefined in the methods section (resulting CVA n = 10).The upper extremity motor portion of the Fugl-Meyerstroke recovery assessment (UEFM) provides a measure ofmotor function and was administered to all participantsin the CVA group [33]. Individuals were compensated fortravel expenses associated with participation in thisresearch.Lesion locationNeuroanatomical scans were performed on 7 of the 10CVA participants included in analysis and MRI reportsPage 2 of 10(page number not for citation purposes)accurate gross motor control of the upper extremity in were obtained for 2 of the remaining 3 individuals. The 3Behavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36individuals for whom scans were not obtained were ineli-gible for MR imaging at the time of the study. Participant-specific lesion information is reported in Table 1.TaskThe continuous tracking task [34] used in the presentstudy was similar to that reported previously [14,35]. Par-ticipants were seated before a computer monitor andasked to grasp a horizontally mounted lever restricted tomovement in the transverse plane. A push/pull, shoulder/elbow flexion and extension motion was used to controlan on-screen cursor (Figure 1A &1B). Either the dominantarm (HC) as determined by the Edinburgh Inventory [36]or the hemiparetic arm (CVA) was used to manipulate thelever. Participants were instructed to track the target mov-ing vertically at the midline of the screen as accurately aspossible by controlling the cursor with the lever. Targetand lever position were sampled at 40 Hz using customsoftware developed on the LabView platform (v. 7.1;National Instruments, Austin, TX). Lever excursion of 60°,equal to an arc length of 31 cm, was required to accuratelyexperimental software was calibrated such that the maxi-mum excursion necessary to perform the task was ~2 cmless than the participant's achievable excursion. In addi-tion, an elastic wrap was used to maintain the hand posi-tion of two individuals with stroke on the lever due toweak grip strength and/or poor sensation.To maximize dependence on proprioceptive information,we used our previously established protocol to severelyrestrict visual feedback of movements [14]. Draping wasplaced over but not in contact with the participant's upperbody to prevent vision of the arms. Additionally, over thefirst 20 practice trials, visual feedback that had been pro-vided to aid participants' initial understanding of the taskwas faded [14]. To accomplish this on day 1, arm positioninformation (i.e. cursor) was linearly faded from continu-ous presentation on trial 1, to only a 200 ms durationpresentation every 2s by trial 19. Visual feedback wasmaintained at this frequency for the rest of the experi-ment. This frequency of position feedback is well belowthat which Kao [37] reported to be virtually useless forTable 1: Participant characteristicsAge MMSE LPM UEFM LesionCVA1 70 30 1.34 50 L Corona Radiata & PutamenCVA2 67 29 1.16 59 L PLIC & ThalamusCVA3 75 29 2.03 51 R Corona Radiata & PutamenCVA4 45 28 0.91 60 L Occip. lobe, Parahippocampal g & ThalamusCVA5 60 25 1.55 54 R Temporal, Parietal, Insular & Occipital lobesCVA6 65 28 1.18 51 R Corona Radiata, PLIC & ThalamusCVA7 73 29 1.36 32 R Caudate, Corona Radiata, Putamen & PLICCVA8 32 28 2.00 25 L Middle cerebral artery distributionCVA9 62 29 2.17 24 UnavailableCVA10 66 29 0.64 56 R Inferior Parietal & Temporal lobesAvg.(SD)61.5(13.3)28.4(1.3)1.52(0.4)46.2(13.8)HC1 61 29 0.93 -- --HC2 62 30 1.22 -- --HC3 59 30 0.74 -- --HC4 30 30 1.27 -- --HC5 66 30 0.91 -- --HC6 73 29 0.98 -- --HC7 63 30 0.71 -- --HC8 46 30 0.77 -- --HC9 63 30 1.48 -- --Avg.(SD)58.1(12.7)29.8(0.4)1.0(0.3)-- --Individual characteristics and group averages (SD) including Age, Mini-Mental State Exam (MMSE) score, Upper Extremity Fugl-Meyer (UEFM) score and normalized Limb Position Matching (LPM) index. Stroke location is also presented (PLIC = internal capsule, posterior limb). Location of the stroke based on medical records for individuals unable to complete an MRI are presented in italics. CVA9 presented with clinical signs of a right middle cerebral artery infarct, including L hemiparesis and increased muscle tone.Page 3 of 10(page number not for citation purposes)track the target. One participant with stroke could notachieve this range of motion. For this participant, theguiding continuous hand-controlled cursor movements.To encourage and motivate individuals for this difficultBehavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36task, a single summary feedback score regarding overalltracking accuracy was provided after each trial, as a per-centage of time the position cursor spent within a 10°bandwidth of the target.ProcedureIndividuals practiced 50 trials of continuous tracking oneach of two days. Each session lasted approximately 1hour. On a separate third day, participants returned for 10retention test trials. These trials required the same trackingactivity as practiced on the 2 training days. No practice tri-als were allowed prior to testing on any day.ified from Wulf and Schmidt [21]. In continuous trackingparadigms, a segment of the tracked waveform is oftenembedded regularly, such that the participant receivesrepeated practice on the same set of movements. Throughthis repeated regular practice the subject may learn aspectsof the segment motion. Performance on the repeated seg-ment can be compared to performance on random, novelsegments, i.e. those that have never been practiced before,to index segment-specific learning. For this study a unique33s trial was seamlessly constructed from one 3s baselineand two 15s sine-cosine waveforms, or segments. Duringeach trial, participants attempted to track one novel ran-dom segment and one repeated segment that was identi-cal in every trial (Figure 1C).The use of a random and repeated segment is importantfor two reasons. First, improved tracking can come fromtwo sources: general improvement and understanding oftask dynamics, or improvement on a specific pattern ofmovement. Repeated segment-specific learning involvesacquisition and retention of the precise pattern of a prac-ticed movement, for example, signing your name. In con-trast, general task learning is improvement in the non-specific components of the task. In the signature example,this would include learning the necessary hand force togrip a pen. In continuous tracking experimental para-digms, improvement on the random segment is consid-ered to be an indication of general task learning [21]. Thiscan include familiarity with the apparatus or requirementof the task among other factors. Improvement on therepeated segment is considered to reflect pattern specificmotor learning of the repeated segment beyond generaltask learning [21,26]. The difference between improve-ment on the repeated and random segments indexes theimprovement that is solely related to practice of therepeated motor pattern. Further, comparison of the ran-dom and repeated segment tracking for each individualcontrols for performance differences between subjectssuch as those based on skill level or handedness.Individuals were never informed of the existence of thisrepeated segment. The presentation of this repeated seg-ment, first or last in a trial, was randomized to minimizeorder effects. However, the same overall trial order wasemployed for every participant. That is, each participantpracticed the same trials in the same order throughout thestudy to allow for comparison.Indexing of proprioceptionThe ability to access and discriminate proprioceptiveinformation was indexed via a common clinical assess-ment, limb position matching [38]; In a proprioceptiveassessment for stroke, the hemiparetic limb is moved by aTracking taskFigure 1Tracking task. A) Participants were seated before a com-puter monitor and griped one (tracking task) or both (limb position matching task) horizontally mounted levers. Draping is drawn over the shoulders to prevent visualization of arm movement, represented by a dashed line in B. C) All partici-pants tracked a target following movement patterns similar to these two example trials. Following a 3s stable baseline, a sine-cosine waveforms dictated target movement in degrees up or down at the horizontal center of the screen. Two full trial waveform patterns, each consisting of 1 random and 1 repeated segment, are overlaid in the diagram to demon-strate that all trials had an identical segment common to each. The random segment comes first, followed by the repeated segment during both trials for ease of visualization.Page 4 of 10(page number not for citation purposes)The pattern of target movement was a predefined sine-cosine waveform constructed according to a method mod-clinician while the patient attempts to match those move-ments with the non-hemiparetic limb. We modified thisBehavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36assessment for more precise, numeric quantification ofaltered proprioception and have previously demonstratedits sensitivity [14]. Two near-frictionless, horizontally-mounted levers, the same used in training, were graspedin each hand (Figure 1A). For individuals in the strokegroup, the experimenter supported the more involved,hemiparetic arm at the humeral condyles with minimalcutaneous contact and drove the lever through a 30 sec-ond continuous pattern of random movements. Partici-pants closed their eyes and matched the movement of thedriven arm by moving the opposite, less involved, non-hemiparetic arm. The same procedure was followed forthose in the healthy control group with the dominant armbeing driven by the experimenter. Limb position wassmoothed using a 100 ms moving average [39] to reducenoise and corrected for constant error. As has been previ-ously reported, the area difference between the position ofthe driven and matching arms, root-mean-squared error(RMSE, see Appendix 1), was then calculated [40]. To gen-erate a limb position matching score (LPM) that would becomparable across groups, inter-limb matching error foreach CVA participant was normalized to the average inter-limb matching error from the healthy control group (seeAppendix 2). In this calculation, 1.0 indicates averageinter-limb position matching; greater values reflect pro-gressively worse matching.Outcome measuresFor our experimental continuous motor learning task, tar-get and lever position data were differentiated into thevelocity profile and smoothed using a 100 ms movingaverage. We considered the pattern of continuous velocitychanges rather than absolute position [18,21,22] as recentwork has emphasized the encoding of velocity-basedinformation by the proprioceptive system [14,23,24].Correlation between target and arm velocity profiles wasperformed for each trial. Trials were excluded from analy-sis if the correlation coefficient did not reach r = 0.3. Aspresented earlier, two participants were removed fromanalysis, having tracked poorly in more than 50% of trialsbased on this criterion (total CVA n = 10). Anecdotally,these individuals had great difficult with even basic move-ments. For the remaining participants, less than 3% of tri-als were excluded. RMSE between target and arm velocityprofiles was calculated separately for random andrepeated segments and was averaged across sets of 10 con-secutive trials to group data into blocks (each 10 trials = 1block of data). This procedure was repeated for all trialsacross the two practice days and at retention.To quantify repeated segment-specific learning we sepa-rately calculated the improvement in average RMSE forrandom and repeated segments. The difference betweenment-specific improvement score (SSI). Because thischange score is calculated separately for each individual itnormalizes for baseline differences between groups aswell as other inter-subject differences that might berelated to hand dominance, motor experience, or strokeseverity.Statistical analysesFirst, to examine initial tracking performance and seg-ment differences, an omnibus three-way ANOVA ofGroup (CVA, HC), Segment (random, repeated) andBlock (acquisition 1 through 10, and retention) RMSEwith repeated measures correction of Segment and Blockwas conducted. The ANOVA was tested at α = 0.05 andGreenhouse-Geisser correction was employed. To furtherexamine repeated segment-specific learning we then per-formed separately, at Block 1 and Retention, planned two-way ANOVAs (Segment by Group) with repeated meas-ures correction of Segment. The planned, post-hoc ANO-VAs afford the ability to compare initial and finalperformance between those with and without stroke atthe beginning and the end of the study and to confirm theskill was learned.To examine the relationship between repeated segment-specific learning and sensorimotor indices, correlationswere performed between LPM or UEFM and SSI. We addi-tionally performed partial correlations to control for ageeffects on proprioception. The two-way ANOVAs and cor-relations were tested at α = 0.025 to correct for multiplecomparisons.ResultsTracking accuracyIndividuals with stroke were generally less accurate attracking throughout the study. The three-way ANOVA aresult confirmed this observation with significant a maineffect of Group (F(1,17) = 8.76, p = 0.009). Our plannedcomparison of Block 1 revealed no initial Group differ-ence in tracking RMSE (p = 0.129). However, by Retentionthe HC group tracked with significantly less error (F(1,17)= 10.39, p = 0.005). No interaction effect with group wasdetected for any ANOVA.Despite overall difference in tracking accuracy, bothgroups demonstrated repeated segment-specific learningover the course of the experiment. This was confirmed viaa three-way ANOVA which yielded a significant interac-tion of Block and Segment (F(10,170) = 8.14, p = 0.011).Visual inspection indicates this effect is a function ofgreater improvement over practice on the repeated seg-ment (Figure 2) as compared with the random segment.This was confirmed with our two-way ANOVA analysis.Page 5 of 10(page number not for citation purposes)improvement on the random and repeated segment RMSEfrom Block 1 to Retention was defined as the repeated seg-RMSE between segments in Block 1 were not different,indicating similar performance (p = 0.053). However, atBehavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36Retention both groups had lower RMSE on the repeatedsegment than the random segment demonstratingrepeated segment-specific learning (F(1,17) = 29.91, p <0.001).Relationship between learning and proprioceptionAs expected, the CVA group demonstrated worse normal-ized limb position matching ability (LPM, average RMSE= 1.52 ± 0.4) than did the HC group (1.0 ± 0.3) (p =0.017, one tailed t-test). The importance of proprioceptiveprocessing accuracy for motor learning was illustrated bya strong [41] and significant relationship, between LPMand sequence-specific learning (SSI), (r = -0.74, p = 0.015;Figure 3A). Controlling for age using partial correlationmade only nominal change (r = -0.76, p = 0.019). Therelationship between general motor function (UEFM) andrepeated segment-specific learning (SSI) was not signifi-cant prior to (r = 0.46, p = 0.117; Figure 3B) or after con-trolling for age (r = 0.41, p = 0.279). Because plotting ofpartial correlations involves difficult-to-interpret residualerror, Figures 3A and 3B plot the simple relationshipbetween primary measures, not accounting for age.Discussionpart of a continuous tracking task where minimal visualfeedback is present. However, we also discovered thatafter the central nervous system (CNS) is damaged, prop-rioceptive integrity is closely related to the amount ofbehavioral change associated with repeated segment-spe-cific learning. In contrast, general motor function demon-strated during random segment tracking is not stronglyrelated to motor learning. Our findings support andextend previous research implicating proprioception inmotor learning to include repeated segment-specific con-tinuous tracking [42-44]. Importantly, the present study isamong the first to examine the impact of damage to cen-tral proprioceptive processing ability on continuousmotor learning.Motor learning taskAt the beginning of practice both groups performedequally well on the random and repeated segments withsufficient visual feedback, suggesting that significant dif-ferences at retention were not the result of any baselineeffects. Over the course of practice, a differential ability toimprove on the random versus repeated segments of ourtracking task became apparent. Prior tracking studies con-sistently report progressive spatial tracking improvementacross practice, specifically on the repeated segment of theskill [21,22,34]. The findings presented here are consist-ent with this past work despite our use of velocity profilesrather than spatial movements as our dependent measure.Both groups in the present study were able to improvetheir general tracking ability and showed better perform-ance for the specific, repeated portion of movement.Importantly, both groups improved on the repeated seg-ment of the tracking task to a greater extent than the ran-dom portion with practice and thus show repeatedsegment-specific motor learning.Because a random tracking segment was embeddedwithin each trial, including the retention tests, we wereable to dissociate effects of altered limb position sense ongeneral task performance from the development of alearned plan for movement. Indeed, both groups demon-strated significant improvement on the repeated segmentas compared to the random segment at retention, sup-porting previous findings of preserved motor learningcapacity following stroke [26,45-47]. Often, work investi-gating the role of proprioception for motor performancehas only considered single time points [42,43,48-50]. Ifwe had employed such a design we may have not noted arelationship between motor learning related change andproprioceptive processing ability. However, because weexamined multiple days of practice, and employed adelayed retention test, we were able to discover that themagnitude of learning related change was directly relatedTask performance and learningFigure 2Task performance and learning. RMSE (+- 1 SD) of velocity over the course of training and retention testing for CVA (triangles) and HC (circles) participants. Throughout the study, the HC group was more accurate. At the begin-ning of practice, Block 1, there is no difference between ran-dom (black) and repeated (white) segments. Over the course of training both groups improved on the repeated segment to a greater extent than the random segment. This improve-ment persisted at retention testing with both groups exhibit-ing repeated segment-specific learning.Page 6 of 10(page number not for citation purposes)We demonstrated that following stroke, some individualscan learn a specific, repeating pattern of movement as ato the preservation of proprioception as indexed via ourlimb matching task. To our knowledge, no prior studiesBehavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36regarding the role of proporioception in continuousmotor learning and/or proprioception following strokehave employed a separate retention test design. Therefore,it has not been clear whether altered central processing ofproprioceptive information would deleteriously impactmotor learning. Taken together, these data indicate thatthe individuals with stroke who had poor limb positionsense were at a disadvantage during learning of a continu-ous motor pattern as compared to those with intact posi-tion sense.Proprioception and motor learningThe present study supplements previous reports of alterednovel skill learning following induced lesions to the sen-sory cortex in animal models [4,5]. We found that propri-oception was strongly related to the magnitude ofbehavioral change associated with learning to accuratelytrack a repeated pattern of movement, even after account-ing for age. Because our measure of repeated segment-spe-cific learning (SSI) reflects improvement beyond general,non-specific task learning, this finding argues against thesuggestion that proprioception is merely important forchanges in general motor control. Rather, it appears thatspecific to the practiced, repeating pattern of movement.This is consistent with previous suggestions that proprio-ception may be important for forming and helping toupdate a template of appropriate velocity-based motorcommands for successful execution of a motor skill[2,23].The relationship between proprioception and motor skilllearning stands in contrast to our findings concerning armmotor function. Following stroke, arm motor function asindexed by the Fugl-Meyer scale did not correlate with theability to improve tracking of repeated movement pat-terns. Because in the past, many studies of motorsequence learning following stroke required the use of theipsilesional, less involved upper extremity [18,45,46], thisrelationship had previously been poorly characterized.It may be argued that vision could have been used to sup-plement or compensate for proprioceptive deficit. Indeed,vision and proprioception have received considerableattention in the literature regarding their role duringmotor learning [49]. And it has been previously noted thatvision is critical when proprioceptive sensation is dimin-Proprioception, motor control and repeated segment-specific learningFigu e 3Proprioception, motor control and repeated segment-specific learning. Proprioceptive processing ability indexed as the normalized limb position matching RMSE (LPM) is plotted against the segmental difference in repeated segment-specific improvement over the course of practice (A). LPM values greater than 1.0 represent worse matching error than the average HC participant. Increasing values of repeated segment-specific improvement (SSI) represent greater improvement on the repeated segment as compared to the random segment. A strong relationship was detected between LPM and repeated seg-ment learning (r = -0.74, *p = 0.015). B) Motor function of the hemiparetic arm as indexed by the Upper Extremity Fugl-Meyer assessment (UEFM) is plotted against the segmental difference in repeated segment-specific improvement over the course of practice. A moderate but non-significant (r = 0.46, p = 0.117) relationship between motor function and repeated segment-spe-cific improvement (SSI) was detected. Increasing UEFM values represent better motor function. In both panels the horizontal dotted line represents general task improvement over practice; values above the line indicate repeated segment-specific learn-ing. CVA group numbers corresponding to participants as identified in Table 1 are placed alongside each datapoint.Page 7 of 10(page number not for citation purposes)proprioception was crucial for improved tracking accuracy ished or absent [8,9]. To explore the contribution of pro-Behavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36prioception without the confound of visual feedback, wereduced visual information available to the participant viaseveral controls. First, we occluded vision of the arm viadraping. Next, we quickly faded feedback regarding cursorposition over the first 20 trials to an intermittency exceed-ing that which Kao [37] cited as being disruptive to con-tinuous tracking. Finally, no vision was used in ourproprioceptive measure, the limb position matching task(LPM). However, we chose to preserve some visual feed-back to reduce cumulative error which might haveobscured improved motor control associated with learn-ing [51] by displaying the arm position cursor for 200 msat 1800 ms intervals. It is possible that even this minimalvisual information may have allowed participants to eval-uate their performance and adjust accordingly in theabsence of trustworthy proprioceptive feedback. How-ever, based on the past work of Kao [27] and our own pre-vious study [14] we find this explanation of ourconclusions improbable.Peripheral vs. central disruption of proprioceptionOur present finding that proprioceptive deficit is associ-ated with capacity for change during motor learning ini-tially appears at odds with our previous report thatdisruption of proprioception did not interfere with motorlearning. However, we suggest that this difference is rec-onciled when the location of damage and integrity of thecentral nervous system is considered.In our prior report [14] we found that disrupting proprio-ception with vibration interfered with performance, per-haps by masking proprioceptive signals used by thecentral nervous system to coordinate movements. How-ever, this ultimately did not prevent participants fromlearning a specific set of movements. Several studies haveexamined motor performance and adaptation in individ-uals with large fiber neuropathy resulting in absent prop-rioception [8,9,13,49]. Taken together these studies havenoted that skill learning is possible following deafferenta-tion, though movements are clearly disrupted. Mainte-nance of the ability to learn new movements has also beenreported after peripheral lesions such as in dorsal rhizoto-mies in non-human primates [52,53].In contrast, the present finding that central processing ofproprioceptive information is related to learning supportsprior work in animals [5,6,17,54]. We suggest that thisdichotomy is a function of the integrity of the sensoryprocessing system, specifically somatosensory corticalareas, thalamus and the associated white matter tracts.Parietal cortex is known to maintain representations ofthe body [55]. When these sensory-associated regions aredisrupted, by insult or by transient disturbance such asTMS [7] learning is compromised. If these structuresremain intact, they are available to create representations,of behavior through intra-cortical interaction even whenone or more sources of feedback are not dependable.Therefore it is not clear that the study of individuals withperipheral neuropathy or temporarily disrupted proprio-ception adequately addresses the role of central proprio-ceptive processing in motor skill learning.ConclusionWe recruited individuals with chronic stroke in the mid-dle cerebral artery distribution, and similarly aged healthycontrols to perform a continuous motor learning task. Weseverely restricted visual feedback and in this manner wereable to examine the role of proprioception in motor learn-ing. Despite the presence of a stroke, some individualswere able to demonstrate behavioral change and thusshow learning of the practiced pattern of continuousmovements. However, the degree of proprioceptive deficitwas strongly related to the amount of change made. Itshould be noted that the size of the cohort and heteroge-neity of the lesions warrant care when interpreting thesefindings. However, the findings support prior animalwork implicating central proprioceptive capability asimportant for learning patterns of movement.AppendicesAppendix 1RMSE = SQRT(∑(xi - Xi)2/n) where xi = driven arm posi-tion (or target velocity during sequence training) and Xi =matching arm position (or tracking arm velocity duringsequence training).Appendix 2LPM = (Σxi/n)/Σ (Σyi/n)/r where xi = limb matching trialRMSE for CVA group, yi = limb matching trial RMSE forHC group, n = # of limb matching trials, r = # of HC par-ticipants.Competing interestsNeither author has any personal or financial relationshipto declare regarding this manuscript.Authors' contributionsEDV participated in all aspects of the study. LAB partici-pated in study design, interpretation of findings and draft-ing the manuscript. Both authors have read and approvedthe final version of this manuscript.AcknowledgementsThis work was supported in part by the Kansas Partners in Progress, Inc., and the VGH and UBC Hospital Foundation. We appreciate the assistance Page 8 of 10(page number not for citation purposes)Behavioral and Brain Functions 2009, 5:36 http://www.behavioralandbrainfunctions.com/content/5/1/36of Tara Klassen, Rebecca Maletsky, Barbara Quaney and Brenda Wessel on various aspects of this study.References1. Schmidt RA, Lee TD: Ch. 10  Motor Learning Concepts andResearch Methods.  In Motor control and learning: a behavior empha-sis 3rd edition. Champaign, IL: Human Kinetics; 1999. 2. Thoroughman KA, Shadmehr R: Electromyographic correlatesof learning an internal model of reaching movements.  J Neu-rosci 1999, 19:8573-8588.3. Hwang EJ, Shadmehr R: Internal models of limb dynamics andthe encoding of limb state.  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