We compared manual infarct definition, which is time-consuming and open to bias, with an automated abnormal tissue detection method in measuring corticospinal tract-infarct overlap volumes in chronic stroke patients to help predict motor outcome

Kou, N

Leff, AP

Park, CH

Seghier, ML

Ward, NS

PubMed

UCL Discovery

Nancy Kou, MScChang-hyun Park, PhDMohamed L. Seghier,PhDAlexander P. Leff, PhDNick S. Ward, MDCorrespondence toMs. Kou:n.kou.11@ucl.ac.ukSupplemental data atwww.neurology.orgCan fully automated detection ofcorticospinal tract damage be usedin stroke patients?ABSTRACTObjective:We compared manual infarct definition, which is time-consuming and open to bias, withan automated abnormal tissue detection method in measuring corticospinal tract–infarct overlapvolumes in chronic stroke patients to help predict motor outcome.Methods: Using diffusion tensor imaging and probabilistic tractography, 4 corticospinal tractsfrom the primary motor cortex, dorsal and ventral premotor cortices, and supplementary motorarea to the ipsilateral lower pons were reconstructed in 23 healthy controls. Tract–infarct overlapvolume of each of the 4 corticospinal tracts was determined by overlapping the patients’ lesionsonto the control tract templates, using bothmanually and automatically defined infarcts in 51 patients.Correlations with upper limb motor impairment were assessed and both methods were directly com-pared using intraclass correlations (ICC).Results: Greater impairment was seen in patients with greater corticospinal tract–infarct overlap witheithermethod (rmanual range50.32–0.46; rautomated range50.42–0.57). Consistency betweenmanualand automated methods was good to excellent for all 4 corticospinal tracts (ICC range 5 0.71–0.80).Conclusions: Our results demonstrate that automated infarct identification performs equallyas well as a manual method in quantifying corticospinal tract–infarct overlap following stroke.Neurology 2013;80:2242–2245GLOSSARYARAT 5 Action Research Arm Test; ICC 5 intraclass correlation coefficient;M1 5 primary motor cortex; NHPT 5 Nine-HolePeg Test; PMd 5 dorsal premotor cortex; PMv 5 ventral premotor cortex; SMA 5 supplementary motor area.The extent of corticospinal tract damage after stroke correlates with motor impairment and can becalculated by measuring the overlap between the patient’s infarct and control subjects’ recon-structed corticospinal tracts.1,2 Corticospinal tract volume and integrity can be reliably determinedusing diffusion tensor imaging tractography,3 but the current practice of defining infarcted tissuemanually1,2 is time-consuming and open to bias.4 Automated methods of infarct identificationaddress this problem and increase the translational potential of corticospinal tract injury quanti-fication to clinical practice. The aim of this study therefore was to confirm the relationshipbetween motor impairment, in particular of the upper limbs, and corticospinal tract–infarctoverlap in a cohort of chronic stroke patients, and to compare this approach using manual andautomated methods for defining infarcted tissue.METHODS We recruited chronic stroke patients at least 3 months after first-ever stroke and with weakness of wrist and finger ex-tensors for at least 48 hours after the onset of symptoms. Age-matched healthy volunteers with no history of neurologic disease par-ticipated. Subjects with standard MRI exclusion criteria were not studied.Patients’ upper limb motor performance was assessed with the Action Research Arm Test (ARAT), Nine-Hole Peg Test (NHPT),Motricity Index, and grip strength. The first principal component of these 4 scores was taken as a representative measure of motorimpairment.We used probabilistic tractography5 implemented in FSL (http://www.fmrib.ox.ac.uk/fsl/) to reconstruct descending pathways incontrol participants from the 4 main cortical motor areas that contribute to the corticospinal tract: primary motor cortex (M1), dorsalFrom the Sobell Department of Motor Neuroscience (N.K., C.H.P., N.S.W.) and Wellcome Trust Centre for Neuroimaging (M.L.S.),UCL Institute of Neurology; Institute of Cognitive Neuroscience (N.K., A.P.L.); and The National Hospital for Neurology and Neurosurgery(A.P.L., N.S.W.), London, UK.Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.2242 © 2013 American Academy of Neurologyand ventral premotor cortices (PMd and PMv), and supplementarymotor area (SMA) to the ipsilateral lower pons via the posteriorlimb of the internal capsule and upper pons. The output tracts foreach control subject, mapped into a standard MontrealNeurological Institute space, were superimposed and binarizedso that a value of 1 indicates the presence of a tract in more thanhalf of our healthy subjects and a value of 0 indicates the lack of atract (figure 1).Patients’ infarcts were identified from normalized T1-weightedstructural images using 2 methods. First, infarcts were defined byvisual inspection (N.K. and N.W.) whereby voxels within theinfarct were given a value of 1 and those outside a value of 0.Secondly, the automated lesion identification toolbox6 was usedto estimate weighted lesions by comparing each voxel within thepatient’s gray or white matter to that in the same location in everycontrol subject to yield a degree of abnormality (ranging from 0 to1). Tract–infarct overlap was calculated by multiplying the infarctvalue by the control corticospinal tract template value in each voxel(figure 1).1For each of the 4 corticospinal tracts, Pearson correlationcoefficients were determined between motor impairment andvalues for each infarct identification method separately (SPSS20.0, IBM, New York, NY), and compared using Fisher r–ztransformations (figure 2). Covariates considered were age andtime since stroke. Statistical significance was set at a p value of0.05, Bonferroni corrected for multiple comparisons over 4 cor-ticospinal tracts. Consistency between the 2 methods was evalu-ated with 2-way mixed-model intraclass correlation coefficient(ICC)7 and Pearson correlation coefficients (figure 3). See e-Methodson the Neurology® Web site at www.neurology.org for additionalmethodologic details.Standard protocol approvals, registrations, and patientconsents. Written consent was obtained from all participants.The study was approved by the Joint Ethics Committee of theInstitute of Neurology (UCL) and National Hospital for Neurol-ogy and Neurosurgery (UCLH NHS Trust), London.RESULTS Fifty-one patients (53.9 6 12.7 years)and 23 age-matched healthy volunteers (50.6 6 14.7years) were recruited. Characteristics of the patientgroup are provided in table e-1. Patients had a rangeof upper limb impairments (ARAT: 19–57; NHPT:0–104.5; Motricity Index: 42–100; grip strength:18.2–111.7). The first principal component of these4 scores explained 71.3% of the total behavioral vari-ance. Overlap values correlated with impairment for all4 cortical motor regions with both automated (M1:r 5 0.56, p , 0.001, figure 2; PMd: r 5 0.57,p , 0.001; PMv: r 5 0.45, p 5 0.001; SMA: r 50.42, p5 0.002) and manual methods (M1: r5 0.46,p5 0.001, figure 2; PMd: r5 0.43, p5 0.002; PMv:r 5 0.32, p 5 0.022; SMA: r 5 0.34, p 5 0.010).Fisher r–z transformations showed no significantdifferences between correlation coefficients for anyof the 4 corticospinal tract pairs using each method(table e-2). Further, in comparing automated andmanual methods, Pearson correlation coefficientsand ICCs (range 5 0.71–0.80) from all corticalmotor regions indicate good to excellent consisten-cies between the 2 methods (figure 3, table e-3).ICC values above 0.75 demonstrate an excellentlevel of consistency.7 Controlling for age and timesince stroke had no significant effects in correlationswith impairment.Figure 1 Overview of infarct definition and overlap techniquesFirst, T1-weighted structural images (A) from patients were spatially normalized (B). Then, infarcts were defined manually (C, in red) or automatically (D, inlight gray). Normal template corticospinal tracts were reconstructed through the regions of interest (posterior limb of internal capsule, and upper and lowerpons), and with an exclusion mask (E). Template corticospinal tracts were then overlapped with the manually (F) and automatically (G) defined infarcts.Neurology 80 June 11, 2013 2243DISCUSSION We have demonstrated that manualand automated methods of infarct identification pro-duce similar results when used to assess the degree ofoverlap with the corticospinal tract in chronic strokepatients. Overlap with corticospinal tracts has previ-ously been examined using only manually definedlesion volumes, limiting the generalizability of theapproach.1,2 There is no gold standard for definingthe degree of corticospinal tract damage after stroke,but here we have used correlation with upper limbimpairment as a way of assessing the clinical validityof our measures.In the majority of patients, the automated methodyielded larger tract–infarct overlap volumes. This mayFigure 2 Automated and manual lesion-overlaps with template corticospinal tract from primary motor cortex(n 5 51)Overlap of corticospinal tract from the primary motor cortex (M1) and infarct determined from manual (black squares) andautomated (open circles) methods plotted against motor impairment (automated: r5 0.56, p, 0.001; manual: r5 0.46, p,0.001). Similar correlations with motor impairment between the 2 overlap methods are supported by a Fisher r–z trans-formation (M1: p 5 0.509). Similar results were obtained using weighted tracts defined at the group level without binariza-tion; see e-Results for more details.Figure 3 Correlation between automated and manual lesion-overlaps with corticospinal tract from primarymotor cortex (n 5 51)Pearson correlation coefficient of 0.73 (p , 0.001) and interclass correlations of 0.71 (p , 0.0125) indicate good toexcellent consistency between the 2 overlap methods.2244 Neurology 80 June 11, 2013be attributable to the sensitivity of the fully auto-mated procedure in detecting additional pertinentvoxel abnormalities in T1-weighted images, includingsmall-vessel disease and ventricular dilations.6 A directmeaningful comparison between the 2 methods is notpossible, however, as they are dissimilar in their meas-urements.6 We did not perform tractography in thepatients as a result of insufficient intact fibers withinand around the infarcted area, thus healthy controltemplates were used instead.Further, we have confirmed that the degree of cor-ticospinal tract injury determined with either methodwas consistent and correlated with motor impairmentin agreement with previous research, particularly fortracts originating in M1 and PMd.1,2,8 Independentinjury to the tracts descending from PMv and SMA,however, seems less relevant for predicting impair-ment in comparison to injury to those from PMdand M1.Our results show that corticospinal tract damageto M1 or PMd accounts for only approximately 30%of the interpatient variability in chronic-stage motorimpairment, which is partly attributable to our sampleof chronic stroke patients in whom a degree of reorga-nization is likely to have occurred since the originalinjury (table e-2). The relatively low correspondencebetween upper limb function and corticospinal tract–specific integrity may also be related to damage toimportant non-corticospinal tract cortical regions9 oreven chronic changes in muscle bulk or tone that mayalter upper limb motor performance. Further, correla-tion between corticospinal tract damage and upperlimb impairment is likely to be stronger when assessedearly after stroke,10 suggesting that early evaluation ofcorticospinal tract integrity with long-term follow-upmay be pertinent in inferring motor outcome. Ourresults demonstrate equivalence between automatedand manual lesion identification approaches, openingthe way for large-scale prospective longitudinal stud-ies of the utility of unbiased assessment of cortico-spinal tract integrity early after stroke in predictinglate motor outcome after stroke.AUTHOR CONTRIBUTIONSNancy Kou drafted the manuscript, contributed to data and statisticalanalyses, and made intellectual contribution including design of thestudy. Chang-hyun Park revised the manuscript, contributed to dataand statistical analyses, and made intellectual contribution includingdesign of the study. Mohamed L. Seghier revised the manuscript andcontributed to data interpretation. Alexander P. Leff revised the manu-script and contributed to data interpretation. Nick S. Ward revised themanuscript, contributed to data interpretation, and made intellectualcontribution including design of the study.STUDY FUNDINGSupported by The Wellcome Trust (N.W., C.H.P.) and European Com-mission under seventh Framework Program—HEALTH—CollaborativeProject Plasticise (contract no. 223524)—www.plasticise.eu (N.W.). Thestudy is not industry-sponsored.DISCLOSUREThe authors report no disclosures relevant to the manuscript. Go toNeurology.org for full disclosures.Received November 12, 2012. Accepted in final form March 1, 2013.REFERENCES1. Zhu LL, Lindenberg R, Alexander MP, Schlaug G. Lesionload of the corticospinal tract predicts motor impairmentin chronic stroke. Stroke 2010;41:910–915.2. Riley JD, Le V, Der-Yeghiaian L, et al. Anatomy of strokeinjury predicts gains from therapy. Stroke 2011;42:421–426.3. Newton JM, Ward NS, Parker GJM, et al. Non-invasivemapping of corticofugal fibres from multiple motor areas:relevance to stroke recovery. Brain 2006;129:1844–1858.4. Ashton EA, Takahashi C, Berg MJ, Goodman A,Totterman S, Ekholm S. Accuracy and reproducibility ofmanual and semiautomated quantification of MS lesionsby MRI. J Magn Reson Imaging 2003;17:300–308.5. Behrens TE, Woolrich MW, Jenkinson M, et al. Character-ization and propagation of uncertainty in diffusion-weightedMR imaging. Magn Reson Med 2003;50:1077–1088.6. Seghier ML, Ramlackhansingh A, Crinion J, Leff AP,Price CJ. Lesion identification using unified segmentation-normalisation models and fuzzy clustering. Neuroimage 2008;41:1253–1266.7. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessingrater reliability. Psychol Bull 1979;86:420–428.8. Schulz R, Park CH, Boudrias MH, Gerloff C, Hummel FC,Ward NS. Assessing the integrity of corticospinal pathwaysfrom primary and secondary cortical motor areas afterstroke. Stroke 2012;43:2248–2251.9. Gauthier LV, Taub E, Mark VW, Barghi A, Uswatte G.Atrophy of spared gray matter tissue predicts poorer motorrecovery and rehabilitation response in chronic stroke.Stroke 2012;43:453–457.10. Swayne OB, Rothwell JC, Ward NS, Greenwood RJ.Stages of motor output reorganization after hemispheric strokesuggested by longitudinal studies of cortical physiology. CerebCortex 2008;18:1909–1922.Neurology 80 June 11, 2013 2245DOI 10.1212/WNL.0b013e318296e9772013;80;2242-2245 Published Online before print May 8, 2013Neurology Nancy Kou, Chang-hyun Park, Mohamed L. Seghier, et al. Can fully automated detection of corticospinal tract damage be used in stroke patients?This information is current as of May 8, 2013ServicesUpdated Information & http://www.neurology.org/content/80/24/2242.full.htmlincluding high resolution figures, can be found at:Supplementary Material 96e977.DC1.htmlhttp://www.neurology.org/content/suppl/2013/05/08/WNL.0b013e3182Supplementary material can be found at: References http://www.neurology.org/content/80/24/2242.full.html##ref-list-1This article cites 10 articles, 6 of which you can access for free at: Subspecialty Collections http://www.neurology.org//cgi/collection/mriMRI http://www.neurology.org//cgi/collection/infarctionInfarction http://www.neurology.org//cgi/collection/all_rehabilitationAll Rehabilitationfollowing collection(s): This article, along with others on similar topics, appears in the  Permissions & Licensing http://www.neurology.org/misc/about.xhtml#permissionsits entirety can be found online at:Information about reproducing this article in parts (figures,tables) or in  Reprints http://www.neurology.org/misc/addir.xhtml#reprintsusInformation about ordering reprints can be found online:rights reserved. 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Can fully automated detection of corticospinal tract damage be used in stroke patients?

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Can fully automated detection of corticospinal tract damage be used in stroke patients?

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