NeuroImage 60 (2012) 854–863

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Left hemisphere plasticity and aphasia recovery

Julius Fridriksson a,⁎, Jessica D. Richardson a, Paul Fillmore a, Bo Cai b

a Department of Communication Sciences & Disorders, University of South Carolina, Columbia, South Carolina, USAb Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina, USA

⁎ Corresponding author. Fax: +1 803 777 3081.E-mail address: jfridrik@sc.edu (J. Fridriksson).

1053-8119/$ – see front matter © 2011 Elsevier Inc. Alldoi:10.1016/j.neuroimage.2011.12.057

a b s t r a c t

a r t i c l e i n f o

Article history:Received 18 October 2011Revised 14 December 2011Accepted 15 December 2011Available online 29 December 2011

Keywords:AphasiaAnomiaMRILanguageSpeech

A recent study by our group revealed a strong relationship between functional brain changes in the left hemi-sphere and anomia treatment outcome in chronic stroke patients (N=26) with aphasia (Fridriksson, 2010).The current research represents a continuation of this work in which we have refined our methods and addeddata from four more patients (for a total sample size of 30) to assess where in the left hemisphere treatment-related brain changes occur. Unlike Fridriksson (2010) which only focused on changes in correct naming as amarker of treatment outcome, the current study examined the relationship between changes in left hemi-sphere activity and changes in correct naming, semantic paraphasias, and phonemic paraphasias followingtreatment. We also expanded on the work by Fridriksson by examining whether neurophysiological mea-sures taken at baseline (defined henceforth as the time-point before the start of anomia treatment) predicttreatment outcome. Our analyses revealed that changes in activation in perilesional areas predictedtreatment-related increases in correct naming in individuals with chronic aphasia. This relationship wasmost easily observed in the left frontal lobe. A decrease in the number of semantic and phonemic paraphasiaswas predicted by an activation change in the temporal lobe involving cortical areas that were shown to beactive during picture naming in 14 normal subjects. In contrast, a far less certain relationship was found be-tween baseline neurophysiological measures and anomia treatment outcome. Our findings suggest that im-proved naming associated with behavioral anomia treatment in aphasia is associated with modulation ofthe left frontal lobe whereas a reduction in naming errors is mediated by left posterior regions that classicallyare thought to be involved in language processing.

© 2011 Elsevier Inc. All rights reserved.

Introduction

Recovery from aphasia following stroke varies considerably. Inmost individuals, some spontaneous recovery occurs in the earlyphases of stroke with the greatest return in function seen in thefirst few weeks following stroke onset (Maas et al., in press;Pedersen et al., 1995). The extent of spontaneous recovery is associat-ed with stroke severity and related factors such as lesion size and lo-cation (Plowman et al., in press). It is likely that similar stroke factors,such as sparing and recruitment of specific brain regions, may also re-late to the success of aphasia treatment.

Debates have persisted for over a century concerning the mannerin which the brain compensates in the recovery process (e.g., Calvertet al., 2000; Cao et al., 1999; Heiss and Thiel, 2006; Hillis, 2006; Hillisand Heidler, 2002; Meinzer and Breitenstein, 2008; Pulvermuller etal., 2005; Saur et al., 2006; Thompson, 2000; Weiller et al., 1995). Tra-ditionally, the right hemisphere is thought to support recovery: forexample, left hemisphere stroke patients with accompanying aphasiaexperience further deterioration in language abilities following right

rights reserved.

hemisphere stroke or sodium amytal injection targeting the righthemisphere (Berthier et al., 1991; Kinsbourne, 1971; Levine andMohr, 1979). In a more recent study of a single patient, Turkeltaubet al. (in press) demonstrated how transcranial magnetic stimulationaimed at depressing activation of the right pars triangularis resulted inincreased naming in a patient with non-fluent aphasia. Following asubsequent stroke involving the right hemisphere, the patient experi-enced relatively increased language impairment leading the authorsto suggest that distinct regions of the right hemisphere may play dif-ferent roles in aphasia recovery. Although it is possible that righthemisphere regions may assume language functioning that supportsrecovery from aphasia, several studies have also revealed aphasic lan-guage recovery associated with left hemisphere recruitment (e.g.,Cornelissen et al., 2003; Crinion and Leff, 2007; Fridriksson, 2010;Postman-Caucheteux et al., 2010).

During the past decade, a number of small group and single casestudies have examined functional brain changes associated withaphasia treatment outcome (e.g., Breier et al., 2007; Crosson et al.,2005; Davis et al., 2006; Fernandez et al., 2004; Leger et al., 2002;Martin et al., 2009; Peck et al., 2004; Postman-Caucheteux et al.,2010; Pulvermuller et al., 2005; Rosen et al., 2000; Vitali et al.,2007; Wierenga et al., 2006). Not surprisingly, results have variedwidely with regard to where in the brain favorable changes were

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revealed. Very limited evidence relates the extent of treatment suc-cess to the magnitude of functional brain changes. For example, inone of our earlier studies (Fridriksson et al., 2006a), three individualswith chronic aphasia underwent three functional MRI (fMRI) sessionsbefore and after 40 h of aphasia treatment. As is common in such re-search, they varied considerably with regard to clinical profile and le-sion characteristics. Functional brain changes associated with thetreatment also varied extensively among the patients. Although em-phasizing that change in functional brain activity is somehow relatedto change in language task performance, this study and other similarones contribute only minimally to our overall understanding ofwhere brain changes related to aphasia recovery occur. That is, suchstudies seem more likely to emphasize individual patient differencesrather than similarities.

A potentially more fruitful approach to this problem is to treat arelatively large number of patients using a single treatment protocoland then relate treatment outcome to functional brain changes in asystematic way. For example, we recently administered the sameanomia treatment protocol (a linguistic cueing hierarchy) to individ-uals (N=26) with chronic stroke and various types and severities ofaphasia (Fridriksson, 2010). Response to treatment varied widelyamong the patients in that at least half showed no benefit or verylimited benefit from treatment while ten patients experienced a rel-atively robust response. Interestingly, treatment outcome was not as-sociated with aphasia severity or type; out of the individuals whobenefited most from the treatment, six had fluent aphasia while theremaining four had non-fluent aphasia. Treatment success was relat-ed to location and extent of functional brain changes. Those whoresponded well showed a corresponding increase in left hemisphereactivity—no similar changes occurred in the poor responders. More-over, damage to certain posterior left hemisphere regions was a neg-ative predictor of treatment success. Individuals whose brain damageinvolved posterior-inferior portions of the left temporal lobe and, to alesser extent, the medial occipital lobe, were less likely to benefitfrom treatment compared to patients in whom those regions werelargely spared. Based on these data, it seems reasonable to suggestthat anomia treatment utilizing a cueing hierarchy approach is prob-ably not warranted in patients with damage to the left posterior tem-poral lobe and adjacent regions. Similarly, it appears that anomiatreatment success, at least when utilizing a hierarchy of linguisticcues, is strongly related to left hemisphere recruitment.

Although considerable evidence suggests left hemisphere plastici-ty supports treated anomia recovery, less is known about the specificcortical patterns of this reorganization. Animal models of acute strokesuggest that brain plasticity is enhanced in perilesional areas (definedhere as cortex immediately adjacent to the frank lesion) where neuralsprouting is enhanced (Nudo, 1999; Stroemer et al., 1995). Thus, itseems plausible that functional brain changes underlying aphasia re-covery rely on the perilesional cortex. Similar findings have beenfound in single case or small group studies (e.g., Fernandez et al.,2004; Fridriksson et al., 2006a; Martin et al., 2009; Rosen et al.,2000; Wierenga et al., 2006). However, treated aphasia recoverycould also possibly rely on the residual left hemisphere language net-work. That is, intact brain regions that premorbidly supported lan-guage processing may now also, in addition, assume some of therole previously played by language regions that were directly affectedby the stroke. Whereas reorganization that primarily relies on the re-sidual language network seems feasible, our previous study(Fridriksson, 2010) suggested that a proportion of the functionalbrain changes that correlated with treated anomia recovery occurredin regions that classically would not be considered primary languagecortex (e.g., left superior parietal cortex and precuneus). However,cortical damage that results in aphasia must (at least indirectly) affectthe cortical language areas, although some of the language networkmay still be structurally intact. Therefore, it is possible that improve-ment in anomia recruits most heavily those residual language regions

that are adjacent to the actual lesion (i.e., areas that are both residualand perilesional).

To date, much of the discussion regarding plasticity associatedwith aphasia recovery has centered on cortical location. Although un-derstanding where favorable cortical changes associated with aphasiarecovery occur in the brain has both theoretical and practical implica-tions (e.g., for targeting brain stimulation treatments), very little ef-fort has focused on the reasons why these changes occur in certainregions rather than somewhere else in the brain. In a recent study,Richardson et al. (2011) found that cerebral perfusion in chronicstroke is significantly reduced in perilesional regions as well as inthe remainder of the ipsilesional hemisphere in comparison to thespared hemisphere. A few studies have demonstrated impairedblood oxygenated level dependent (BOLD) signal in areas of impairedcerebral perfusion (Bonakdarpour et al., 2007; Fridriksson et al.,2006b; Thompson et al., 2010). If decreased cerebral perfusion iscommon in chronic stroke, then changes that favorably mediate re-covery may relate to the extent of cerebral perfusion in areas thatare crucial for treatment outcome. It seems possible that the state ofbrain tissue that appears intact on structural MRI may predict aphasiatreatment outcome and, consequently, the extent of functional brainchanges. Beyond lesion size and location, we know almost nothingabout how other factors such as cerebral perfusion may influenceaphasia treatment success and location of concomitant brain plastici-ty. If, indeed, anomia recovery mediated by language treatment relieson functional brain changes (as measured by fMRI) in the left hemi-sphere, it would seem that such changes would relate to the baselinephysiology (i.e., physiological measures taken before initiation ofanomia treatment) of the left hemisphere. Moreover, it is likely thatthe neurophysiology of the stroke-affected brain may influence theextent and location of functional brain changes associated with apha-sia treatment.

The current research represents a continuation of Fridriksson(2010) where treatment-related improvement in correct namingwas found to be associated with increased left hemisphere activityin patients with chronic aphasia. Specifically, our aim was to bettercharacterize these left hemisphere changes in a larger sample of pa-tients. Accordingly, the purpose of this research was twofold: 1. Tocompare the role of perilesional cortex to that of the residual lan-guage network in the left hemisphere as the locus of favorable brainchanges that support treated anomia recovery in patients with chron-ic stroke-induced aphasia. As these changes may overlap in some pa-tients, we also examined perilesional cortex within the residuallanguage network as a predictor of anomia treatment success. 2. Toincrease our understanding of how cerebral blood flow and brain ac-tivation assessed prior to treatment initiation relate to improved abil-ity to name pictures following treatment.

Materials and methods

Patients

Each of the 30 patients (16 females; age range=33–81 years;M=59.2 years) included in this study incurred a single stroke (ische-mic and/or hemorrhagic) in the left hemisphere at least 6 monthsprior to participation (M=51.1 months, range=6–350 months). Allwere evaluated with the Western Aphasia Battery (WAB; Kertesz,1982). Based on their test scores, each obtained an aphasia quotient(AQ), a measure of aphasia severity ranging from 0 to 100 (>93.8 in-dicates language abilities within normal limits). The mean AQ for thegroup was 57.94 (SD=25.8) with the following aphasia subtypesrepresented: 13 Broca's, 10 anomic, 3 conduction, 2 Wernicke's, 1trans-cortical motor (TCM), and 1 global. Patient data are reportedin Table 1 and a lesion overlay map representing damage in all 30 pa-tients is shown in Fig. 1. Data from 26 of these 30 patients were alsoreported in Fridriksson (2010). The study was approved by the

Table 1Patient biographical and diagnostic information.

No. Gender Age MPO WAB AQ/aphasia type Lesion size (in cc)

1 F 53 14 44.8/Broca's 35.72 F 41 40 79.1/anomic 190.13 M 61 72 72.1/Broca's 130.54 M 77 24 40/Broca's 23.15 F 72 24 87.1/anomic 43.66 F 33 24 31.8/Broca's 42.27 M 54 65 89.7/anomic 92.98 M 73 350 27.5/Broca's 278.19 F 65 107 26.3/Broca's 84.110 M 59 9 79.6/conduction 79.611 F 71 52 91.9/anomic 78.012 F 75 18 93.5/anomic 10.813 M 58 22 92/anomic 63.014 F 63 12 57.4/conduction 50.315 M 63 98 50.7/TCM 420.516 F 45 68 51.9/Broca's 116.717 F 81 13 67.8/anomic 48.018 M 52 23 30.6/Wernicke's 223.119 F 56 291 86.2/anomic 35.020 F 60 41 95.2/anomic 7.721 M 59 28 92.1/anomic 24.222 F 56 17 38.4/Broca's 222.223 F 48 10 31.3/Broca's 129.324 M 53 29 68.7/Broca's 175.525 F 79 6 58.2/conduction 65.226 M 44 11 25.7/Broca's 212.527 M 58 45 47.6/Broca's 118.428 M 57 7 31.2/Wernicke's 31.029 F 60 9 17.2/Global 274.030 M 50 6 32.7/Broca's 172.9

MPO=months post onset; TCM=transcortical motor aphasia; WAB AQ=WesternAphasia Battery Aphasia Quotient.

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University of South Carolina's Institutional Review Board, and all pro-vided informed consent.

Treatment

Patients received 3 h of anomia treatment per weekday for2 weeks, for a total of 30 h. The treatment targeted oral naming ofconcrete, imageable nouns and relied on a cueing hierarchy involvingfive levels of phonological or semantic cues administered in an as-cending order of cueing strength. Half of the patients received treat-ment using the phonological cueing hierarchy during the first weekand, after a week rest period, received treatment for a week usingthe semantic cueing hierarchy; the order of treatment was reversedfor the remaining half of patients. Each cueing hierarchy targeted aseparate corpus of 80 mid- to high-frequency nouns (Kucera and

Fig. 1. A lesion overlay map showing the distribution of brain damage (N=30). The colorshowing regions where at least 10 patients had damage (shown in red).

Francis, 1967). Treatment outcome was defined as the treatment-related change in correct naming attempts, semantic paraphasias,and phonemic paraphasias. Naming improvement was assessed bycomparing overt naming performance during the two pre-treatmentand two post-treatment fMRI sessions where participants attemptedto name pictures targeted in treatment. Specifically, change in nam-ing was quantified by subtracting the number of specific naming at-tempts (correct naming, semantic paraphasias, and phonemicparaphasias) during two pre-treatment naming sessions from thenaming attempts in two post-treatment naming sessions.

Neuroimaging

MRI scanning relied on a 3 T Siemens Trio system equipped with a12-element head-coil. For one patient MRI at 3 T was contraindicated.Thus, twenty-nine patients were scanned with high-resolution(1 mm3 voxels; 160 sagittal slices) T2 and T1 MRI as well as fMRI se-quences described previously (e.g., Fridriksson, 2010). Structural im-ages were prepared for data analyses using software designed andsupported by the Oxford Centre for Functional MRI of the Brain(FMRIB)—FMRIB's Software Library (FSL) version 4.1 (Smith et al.,2004). Lesions and cost-function masks were demarcated on axialslices of native T2-MRI images using MRIcron (http://www.cabiatl.com/mricro/mricron/index.html). Cropped and skull-stripped struc-tural MRI images were normalized to the standard MNI 152 template,employing cost-function mask weighting for improved accuracy. Thetransformation matrix for normalization was applied to the lesion.Normalized images were resliced to 2 mm isotropic.

fMRIDuring fMRI scanning, patients participated in the overt naming

task described previously (Fridriksson, 2010; Fridriksson et al.,2006a,b, 2007, 2009, 2010). Briefly, patients viewed 120 randomlypresented pictures, 80 of which depicted real objects for overt nam-ing and 40 of which were abstract pictures requiring no response.During this naming task, 2 s (TA=2 s) sparse acquisition of wholebrain supratentorial volumes occurred every 10 s (TR=10s). Pictureswere presented during the 8 s silent period between volume acquisi-tions and the timings of inter-stimulus-intervals were jittered to im-prove modeling of the hemodynamic response. Pictured stimuli wereback-projected onto a screen situated at the end of the scanner boreand viewed via a mirror mounted on the head coil. All naming at-tempts were recorded using a non-ferrous microphone and digitallystored for offline scoring by a speech-language pathologist with ex-tensive experience treating aphasia.

All fMRI data were analyzed using FSL (FMRIB's Software Library)version 4.1 (Smith et al., 2004). To appreciate how treatment-induced

scale shows the extent of lesion overlap in different regions with an upper threshold

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changes in naming ability relate to modulation of left hemisphere ac-tivity, the fMRI data were subjected to a three-level analysis: 1. T2*fMRI data collected during each of four scanning sessions (two pre-and two post-treatment sessions) were analyzed using the followingpre-processing parameters: high-pass filter cutoff=100 s; motioncorrection using linear image registration (Jenkinson et al., 2002);and spatial smoothing (FWHM)=6 mm. A time-series statisticalanalysis involving all stimulus presentations, regardless of response,relied on general linear modeling with local autocorrelation correc-tion (Woolrich et al., 2001). The time series data were normalizedto each patient's T2-MRI in standard space using linear transforma-tion and 12 degrees of freedom. The second level analysis contrastedthe two fMRI sessions before treatment with the two fMRI sessionsadministered upon treatment completion, creating a single statisticalmap for each patient that represented the change in cortical activa-tion from pre-treatment to post-treatment. The final level includedsummarizing baseline levels of cortical activation (i.e., naming-related cortical activation assessed in the two fMRI sessions adminis-tered before treatment was started) as well as change in activity inthree patient specific volumes of interest (VOIs). Further detail on se-lection of the VOIs is described below under ‘Mask creation forselected cortical areas.’

Pulsed arterial spin labeling (PASL)Two PASL sequences were used to acquire regional cerebral blood

flow (rCBF) measures in this study. Twenty-one patients werescanned with the following parameters: parallel imaging GRAPPAfactor=2, 3.5×3.5×6 mm voxels, 16 axial slices, TR=4000 ms,TE=12 ms. Five were scanned with the following parameters: paral-lel imaging GRAPPA factor=2, 3×3×6 mm voxels, 14 axial slices,TR=2500 ms, TE=11 ms. Images were corrected for head motion.Each patient's perfusion image was coregistered to his or her ownspatially normalized structural T1 image. Note that of the 30 patientsincluded in this study, 26 underwent PASL.

Mask creation for selected cortical areas

Based on aforementioned evidence suggesting the importance ofperilesional areas in aphasia recovery, we sought to compare variousmeasures in the perilesional cortex to areas commonly associatedwith language processing. Thus, we developed perilesional and resid-ual masks (as defined below) for each participant that reflected theirunique pattern of damage, and which could be overlaid on differentimage types allowing us to obtain values for functional activation(fMRI) and cerebral perfusion (CBF). As there was considerable over-lap between the perilesional and residual areas, masks were dividedinto three primary volumes of interest (VOIs): 1. perilesional cortexthat excluded residual naming areas; 2. residual naming areas thatexcluded perilesional cortex; and 3. overlapping perilesional and re-sidual naming areas. These VOIs were each subdivided into thethree lobes (frontal, parietal, temporal) typically implicated in lan-guage processing, to allow comparison across these regions; lobeswere defined using lobe masks derived from the Wake Forest Picka-tlas (Maldjian et al., 2003; Tzourio-Mazoyer et al., 2002). Thus eachsubject had a total of nine masks—three VOIs, each subdivided intothree lobes. Mask creation, as well as data extraction, was performedusing MATLAB 2010b (Mathworks, Inc.).

Perilesional mask creationThere is no current evidence defining the spatial parameters of

what might be coined the ‘perilesional area.’ Therefore, we addressedthis issue here. Because cerebral perfusion deficits correlate both withreduced functional activation and behavioral deficits in acute andchronic stages of stroke recovery (Hillis et al., 2005, 2006; Love etal., 2002; Thompson et al., 2010), we used CBF values to guide our de-cision regarding how far perilesional masks should extend, as

reduced values might represent those cortical areas around the lesionthat need to be ‘recruited’ after stroke for optimal behavioral out-comes. Previous research suggested that patients with chronic stroke,as a group, demonstrate significantly lower perfusion in the perile-sional region extending at least 8 mm beyond the lesion than inboth the remaining intact hemisphere and the contralateral hemi-sphere (Richardson et al., 2011). However, since the ipsilesional in-tact cortex also demonstrated CBF values that were significantlylower in comparison to the contralateral hemisphere, we sought todetermine how far reduced CBF values extend beyond the frank le-sion, and, based thereon, determine the size of the perilesional VOIsused in the current analyses.

To determine the size of the perilesional cortex, CBF was assessedas a function of distance from the actual lesion in a subset of 20 pa-tients (12 females; age range=41 to 81 years; M=61.3) using MRI-cron. Lesions were dilated into seven adjacent 3 mm perilesionalregions expanding 24 mm beyond the lesion's rim (i.e., 3–6 mm,6–9 mm, 9–12 mm, etc.). The region 0 to 3 mm beyond the lesion'srim was excluded to account for partial volume effects (as inRichardson et al, 2011). The seven perilesional regions were maskedusing a probabilistic left hemisphere gray matter mask so that CBFvalues were only obtained from gray matter. The resultant perile-sional gray matter masks were overlaid onto each patient's standard-ized CBF image. Mean CBF for each mask was obtained in units of ml/min/100 g of tissue.

Planned comparisons between successively more distant perile-sional masks were conducted to examine the differences in CBF values,in an attempt to determine the extent of how far reduced CBFmight ex-tend beyond frank cortical damage. When comparing the adjacent re-gions to one another, the analysis produced significant differences(Bonferroni corrected for 6 comparisons) between the following: 1)3–6 mm and 6–9 mm (p=.0001), 2) 6–9 mm and 9–12mm(p=.0003), and 3) 9–12 mm and 12–15 mm (p=.004), suggestingthat significantly reduced CBF extends as far as 12–15 mm beyond thelesion. Significant differences between masks more distant from the le-sion were not observed. Based upon these group results where hypo-perfusion extended as far as 12–15 mm beyond the lesion,perilesional masks extending from 3 to 15 mm beyond the lesion'srim were created on a patient-by-patient basis and are defined as the‘perilesional cortex’ in the frontal, parietal, and temporal lobes (Fig. 2A).

Residual mask creationTo determine the size and location of residual naming-related cor-

tical areas among the current sample of patients with aphasia, four-teen healthy, right-handed subjects (age range=26 to 77 years)with normal speech–language abilities participated in the samefMRI naming task used with the aphasic patients. The fMRI data anal-ysis relied on default setup in FSL including the following parametersfor preprocessing: motion correction, spatial smoothing using Gauss-ian kernel of FWHM 8.0 mm, grand-mean intensity normalization ofthe entire 4D dataset by a single multiplicative factor, and high-passtemporal filtering (sigma=60.0 s). All functional images were nor-malized before being entered into a group analysis. Z statistics imagesfor the contrast ‘naming>watching abstract pictures’ were thre-sholded using clusters determined by Z>2.3 and a (corrected) clustersignificance threshold of p=.05. Then, a group mean activation mapof the network associated with naming was defined, as shown inFig. 2B. To maintain consistency with perilesional mask creation, thefrank lesion and the surrounding 3 mm were deleted from the maskfor each subject. The resulting map was divided according to its distri-bution within the frontal, parietal, and temporal lobes; these imagesformed each subject's residual network masks.

Volumes of interestTo create each subject's final VOIs, the residual masks were over-

laid onto the perilesional masks, so as to subdivide the perilesional

Fig. 2. The following images detail the creation of our volumes of interest for one representative patient. Panel A delineates the areas marked as lesioned tissue (frank lesion plus3 mm surround, to account for partial volume effects), as well as the perilesional masks (3–15 mm beyond the lesion) divided by lobe. Panel B illustrates the group-level functionalactivation pattern seen in our healthy control group (n=14) for the same naming task performed by the patients with aphasia. Panel C shows the final masks (lobes are not shownhere for purposes of clarity) used as our volumes of interest: the intact residual language network, the perilesional areas, and the areas of overlap between the two.

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areas which were within the residual naming network (perile-sional–residual overlap) from those that were not (perilesionalonly); this also yielded masks which were specific to the residualnetwork (residual only). The residual activation maps were thenthresholded separately within each lobe, so that the average num-ber of voxels in the residual only masks matched the average num-ber of voxels in the perilesional only masks allowing for directcomparison between these areas. Z-score thresholds and averagenumber of voxels (SD) for each volume of interest were asfollows—frontal: z=2.366, residual only voxels=5402.1(3273.0),overlap=2604.7(1397.8), perilesional only=5405.6(3272.9); parietal:z=1.874, residual only voxels=2087.8(1609.6), overlap=1542.5(755.5), perilesional only=2085.1(965.5); temporal: z=1.427,residual only voxels=2435.3(2517.0), overlap=2636.4(1154.6), peri-lesional only=2436.0(1348.0). Fig. 2C illustrates the three differentVOIs (overlap, perilesional only, and residual only) in a single patient.

Data analyses

Independent factorsSummary measures were determined in each of the VOIs within

each lobe, for each of the three data sets (baseline fMRI activation,change in fMRI activation, and CBF). Because our masks often includ-ed areas of large spatial extent, we used the average value of the topten percent of voxels from each mask as the summary measure forfMRI data. This constrained the analyses to the most robust datapoints. Visual inspection of the frequency histograms for CBF datayielded slight positive skew; accordingly, the median was used asthe summary measure.

Dependent factorsTo assess overall change in naming, three dependent factors de-

fined ‘change’: 1. correct naming (ACC); 2. semantic paraphasias(SEM); and 3. phonemic paraphasias (PHON). As stated earlier, thechange in correct naming and paraphasias was determined by sub-tracting the number of correct naming attempts, semantic parapha-sias, and phonemic paraphasias before the start of the anomiatreatment phase from the same kinds of naming attempts once treat-ment was completed.

Statistical analysesStatistical analyses were completed using SPSS 19.0.0 (SPSS, Inc.).

For aim one, to evaluate the brain regions in which activation changeis most predictive of how one responds in treatment, multiple linearregression analyses were performed. First, for the three VOIs (overlap,perilesional only, residual only), data from each of three left hemi-sphere lobes (frontal, parietal, and temporal lobes) were enteredinto the analysis, with missing cases excluded in a pairwise fashion.The explanatory power of the resulting regression model was deter-mined by the R2 (proportional reduction in error). Then, to determineif certain cortical areas were relatively stronger predictors of out-comes, factors of interest were entered into a regression analysisusing a stepwise approach. For the second aim, to evaluate baselineneurophysiological factors as predictors of treatment response, thesame regression analyses described for aim one were employed—one for each data type (baseline fMRI and CBF) separately, again be-ginning with the full model, followed by stepwise regression.

Results

Thirty patients completed all behavioral testing and 30 h ofanomia treatment. A comparison of pre- and post-treatment namingperformance across the whole group revealed a statistically signifi-cant increase in correct naming, t(29)=4.76, pb .001, and a decreasein semantic paraphasias, t(29)=3.79, pb .001, but not in phonemicparaphasias, t(29)=.23, p=.82. Although not a direct goal of this re-search, the influence of age, time post-stroke, aphasia severity, andaphasia type upon treatment outcome was explored. Neither age ortime post-stroke (tp-s) were associated with change in correct nam-ing (age: r=−25, p=.18; tp-s: r=.04, p=.85), semantic parapha-sias (age: r=−.28, p=.14; tp-s: r=.01, p=.99), or phonemicparaphasias (age: r=.02, p=.94; tp-s: r=−.14, p=.46). Similarly,no relationship was found between overall aphasia severity (mea-sured as AQ) and change in correct naming (r=−.27, p=.18), se-mantic paraphasias (r=.22, p=.26), or phonemic paraphasias (r=−.13, p=.49). To better understand if aphasia type was related totreatment outcome, a regression analysis including a categorical pre-dictor (aphasia type) was performed with change in specific namingattempts as the dependent factor. This analysis revealed that aphasiatype (summarized in three categories: 1. anomic aphasia; 2. Broca's,

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TCM, and global aphasia; and 3. Wernicke's and conduction aphasias)was not related to change in correct naming, F(2,27)=1.11, p=.34,or phonemic paraphasias, F(2,27)=1.11, p=.34. However, changein semantic paraphasias was related to aphasia type, F(2,27)=9.9,pb .001. Patients with Broca's, TCM, or global aphasia experienced asmaller reduction in semantic errors than those with anomic aphasia(p=.001). Although a similar analysis did not reveal less reduction insemantic paraphasias in patients with Broca's, TCM, or global aphasiacompared to those withWernicke's or conduction aphasia, a trend to-wards a statistically significant difference was found (p=.054).

Brain changes associated with treatment outcome

To examine the relationship between treatment-related increasein correct naming and change in left hemisphere activity, we calculat-ed three linear regression analyses separated according to VOI—overlap, perilesional only, and residual only. Then, the strength ofthe relationship between increase in correct naming and activationchange (in frontal, parietal, and temporal lobes) was comparedamong the three VOIs based on proportional reduction in error (R2).The strongest predictor of increase in correct naming was found inperilesional cortex, R2=.30, p=.035 (Table 2). More specifically,step-wise regression revealed that the perilesional frontal lobe wasthe most robust predictor of correct naming improvement, moststrongly in the residual naming areas, F(1,25)=5.27, p=.03(Fig. 3A), but also in perilesional areas not previously recruited fornaming, F(1,26)=5.08, p=.033. To examine whether the above re-sults reflected overall change in naming attempts, rather than actual

Table 2Relationships between pre-post brain measures and behavioral measures of change.

Full model

Volume of interest R2 F p

ChgfMRIACC Overlap .20 1.89 .160

Perilesional .30 3.38 .035Residual .10 0.88 .464

SEM Overlap .15 1.31 .294Perilesional .07 0.59 .625Residual .30 3.32 .038

PHON Overlap .34 3.93 .021Perilesional .11 1.00 .412Residual .50 7.72 .001

PrefMRIACC Overlap .22 2.11 .127

Perilesional .10 0.85 .480Residual .02 0.16 .923

SEM Overlap .38 4.79 .010Perilesional .25 2.61 .074Residual .11 0.99 .417

PHON Overlap .05 0.37 .772Perilesional .00 0.00 1.00Residual .03 0.26 .853

rCBFACC Overlap .14 1.07 .383

Perilesional .05 0.35 .789Residual .36 3.94 .022

SEM Overlap .26 2.39 .099Perilesional .19 1.66 .206Residual .18 1.58 .224

PHON Overlap .19 1.52 .239Perilesional .11 0.88 .467Residual .14 1.09 .374

Statistically significant predictive models are indicated in bold.Regression analyses showing the relationships between treatment-related change in namPHON=change in phonemic paraphasias) and treatment-induced change in functional actiblood flow (rCBF) in three volumes of interests (Overlap=overlapping perilesional and rResidual=residual naming areas excluding perilesional cortex).

improvement in correct naming, the regression analyses were runutilizing the same independent factors but with total change in nam-ing responses, regardless of accuracy, as the dependent factor; no sig-nificant results were revealed.

The regression analyses described above were also calculated in-cluding treatment-related change in phonemic and semantic para-phasias as dependent factors. Change in semantic paraphasias,R2=.30, p=.038, and phonemic paraphasias, R2=.50, p=.001,was predicted by activation change occurring in the residual languagenetwork (Table 2). More specifically, a step-wise regression analysisrevealed that change in semantic paraphasias was most robustly pre-dicted by activation change in residual naming areas in the tempo-ral lobe, F(1,25)=9.57, p=.005 (Fig. 3B). A similar analysisrevealed that change in phonemic paraphasias was predicted by ac-tivation change involving residual naming areas in the temporaland parietal lobes, F(1,25)=11.89, p=.0001 (Table 2; Fig. 3C). Ascan be seen in Figs. 3B and C, one patient with Broca's aphasia expe-rienced a considerably greater increase in temporal lobe activationcompared to the rest of the group. To determine the influence ofthis single patient on the overall results for naming errors, hisdata were removed and the analyses were repeated. Without thisdata point included, change in semantic errors was not predictedby overall change in activation in the residual language areas,R2=.21, p=.15. However, a step-wise regression revealed that ac-tivation change in temporal lobe regions involved in picture nam-ing in normal participants was related to change in semanticerrors, F(1,24)=5.28, p=.031. The analyses were also repeatedfor change in phonemic paraphasias. Overall, the same results

Stepwise

Cortical area R2 F p

Frontal .18 5.27 .030Frontal .16 5.09 .033None – – –

None – – –

None – – –

Temporal .28 9.57 .005Temporal .23 7.63 .011None – – –

Temporal, parietal .50 11.89 .0001

None – – –

None – – –

None – – –

None – – –

Frontal .22 7.37 .012None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

None – – –

ing (ACC=change in correct naming; SEM=change in semantic paraphasias; andvation (ChgfMRI), pre-treatment functional activation (prefMRI), and regional cerebralesidual areas; Perilesional=perilesional cortex excluding residual naming areas; and

Fig. 3. The relationships between treatment related changes in cortical activity andnaming responses: A. activation change in the perilesional frontal lobe and change incorrect naming; B. activation change in residual naming areas in the temporal lobeand change in semantic paraphasias; C. activation change in residual naming areas inthe temporal lobe and change in phonemic paraphasias.

860 J. Fridriksson et al. / NeuroImage 60 (2012) 854–863

prevailed as when the patient's data were included: change in pho-nemic paraphasias was predicted by overall activation change inresidual naming areas, R2=.48, p=.002. More specifically, astep-wise regression revealed that activation change in residualnaming areas involving the temporal and parietal lobes predictedchange in phonemic paraphasias, F(1,24)=10.14, p=.001.

Ipsilesional neurophysiology associated with treatment outcome

To examine whether baseline physiology (CBF and pre-treatmentactivation associated with picture naming) related to anomia treat-ment outcome, linear regression analyses were implemented usingthe VOIs described in the previous section. That is, levels of CBF andbaseline cortical activation in the three VOIs were used to predicttreatment-related change in correct naming, semantic paraphasias,and phonemic paraphasias; see Table 2 for results. Increase in correctnaming was predicted by CBF values in the residual language net-work, R2=.36, p=.022. Change in semantic paraphasias was pre-dicted by baseline activation in overlapping areas, R2=.38, p=.01.More specifically, step-wise regression analyses revealed that changein semantic paraphasias was predicted by baseline activation in peri-lesional areas in the frontal lobe, F(1,26)=7.37,p=.012.

Discussion

The current work represents an extension of our previous studythat showed that left hemisphere increase in brain activation is asso-ciated with treatment-related increase in correct naming among pa-tients with chronic aphasia (Fridriksson, 2010). Whereas that studyused a univariate analysis to appreciate the relationship betweentreatment-related change in correct naming and brain activation,the current study applied a multivariate approach. Based on workshowing enhanced neural sprouting in the perilesional cortex follow-ing brain damage (Nudo, 1999; Stroemer et al., 1995), our aim was totarget the regions immediately surrounding frank damage in the lefthemisphere. However, visual inspection of the data suggested thatdamage to the residual naming network (qualified as naming-related brain activation in 14 normal participants) varied substantial-ly among the patients studied. Therefore, we refined our search with-in the left hemisphere to include what happens in brain regionstypically activated during picture naming outside the perilesionalcortex (residual naming network only) as well as naming-relatedareas within the perilesional network (overlap between perilesionalareas and naming-related areas). Finally, the aforementioned perile-sional cortex that did not include residual naming areas (perilesionalregions, only) was examined.

Overall activation increase in left hemisphere perilesional areaswas found to be a significant predictor of treatment-related improve-ment in correct naming; this relationship was strongest in the frontallobe. As is evident in Fig. 3A, slightly fewer than half of the patientsdemonstrated little or no improvement in naming whereas theremaining patients were able to name at least 10 more pictures (outof 80 total) than they were able to name at baseline following treat-ment completion. This improvement was not related to aphasiatype or severity. Therefore, we conclude that treatment-related in-crease in brain activation among regions surrounding frank corticaldamage supports improved naming accuracy, regardless of aphasiatype. One account for increased left frontal activity associated withan increase in correct naming might suggest that participants weresimply naming more items, regardless of accuracy, after treatmentwas complete. However, no relationship was found between changesin cortical activity and overall increase in naming attempts. This find-ing would suggest that a pure motor-speech explanation could notaccount for the data. In a recent meta-analysis involving the anatomyof language in 100 studies (Price, 2010), activation in the left middle-frontal gyrus was found to be associated with lexical retrieval. Al-though several competing hypotheses could be proposed to explainour findings, we suggest that greater reliance on the left frontal lobecould reflect treatment-related improvements in lexical retrieval.

Change in the number of naming errors, both semantic and pho-nological, was primarily driven by temporal lobe regions that, priorto the stroke, were involved in picture naming. Unlike what wasseen for treatment-related change in correct naming, where greater

861J. Fridriksson et al. / NeuroImage 60 (2012) 854–863

accuracy was associated with increased activity, reduction in seman-tic and phonological errors was negatively associated with increasesin temporal lobe activation, such that a relatively smaller increase inactivation was related to a decrease in naming errors. In the case ofphonological errors, the same was true for residual naming areas inthe parietal lobe. Based thereon, anomia treatment that promotes in-crease in correctly named items would be associated with increasedfrontal activity whereas reduction in naming errors would beexpected to be reflected in relatively less activation change in poste-rior regions. This relationship was particularly salient in regard tophonemic paraphasias, where 50% of the variance for error reductionwas explained by activation changes in residual naming areas in thetemporal and parietal lobes. Notably, the areas implicated here areroughly the same as those classically associated with normal lan-guage processing (e.g., Binder et al., 1997; Hickok and Poeppel,2007). Therefore, it is perhaps not surprising that changes in semanticerrors are reflected by activation changes in cortical areas that werecrucial for semantic processing before the initial stroke (i.e., the lefttemporal lobe). In regard to changes in phonemic paraphasias, a sim-ilar account could be proposed. That is, phonological processing innormal participants is commonly found to tax both temporal and pa-rietal regions (e.g., Binder et al., 1997, 2000; Buchsbaum et al., 2001;Démonet et al., 1996; Hickok and Poeppel, 2007); accordingly, itseems plausible that reductions in phonemic paraphasias would bereflected in functional reorganization among areas that are recruitedfor phonological processing in normal subjects.

Although increases in correct naming are typically reported as anindicator of treatment-related anomia recovery, it is clear that re-duction in naming errors would also have to be considered a markerof improved naming accuracy for most patients with aphasia. Takentogether, it appears that increased activity in specific left hemi-sphere regions (primarily in the residual naming areas) may be im-portant for anomia treatment success. However, the extent ofrecovery, qualified as increases in correct naming or decreases in er-rors, may rely on dynamic activation changes that vary by corticalregion. For example, too much increase in temporal lobe activationmay signify greater phonological or semantic impairment, at leastas assessed with overt naming, while more moderate increase in ac-tivation may be more favorable for anomia treatment outcome. Incontrast, greater activation in perilesional frontal lobe regions sug-gested improvements in correct naming whereas those patientswith relatively less increase in activation showed less favorabletreatment outcome.

Although we believe that the multivariate approach used in thecurrent study constitutes a significant methodological improvementover Fridriksson (2010), it is likely that a network analysis involvingsuccessful multi-modal integration of MRI data (e.g. fMRI and ASL)would better capture brain changes that drive aphasia recovery.Only a few studies have utilized network analyses of functional orstructural connectivity (Carter et al., 2010; Crofts et al., 2011;Solodkin et al., 2004), or a combination of the two (Specht et al.,2009), in patients with stroke. No group studies have yet comprehen-sively integrated such data in a prospective study of aphasia recovery.Whereas the current study is believed to be the first to comprehen-sively reveal a relationship between specific left hemisphere regionsand treated anomia improvement in chronic stroke, further method-ological improvements in future studies that emphasize cortical con-nectivity (both structural and functional), as well as integration ofdata from multiple modalities, are key for better understanding ofneurophysiological changes underlying aphasia recovery.

Our findings suggest that the left hemisphere supports treatedanomia recovery in aphasia (Fridriksson, 2010). More specifically,this work suggests that such recovery is mediated by specific lefthemisphere regions either involving the perilesional cortex or corticalregions activated by picture naming in normal participants. However,the relationship between CBF and the BOLD signal measured at

baseline and the success of anomia treatment was far less clear.Even though a couple of regression analyses yielded statistically sig-nificant results, a general pattern indicating that the level of baselinecortical activation (during picture naming) or CBF, and anomia treat-ment outcome did not emerge. Although baseline fMRI has beenshown to predict recovery in aphasia (e.g., Richter et al., 2008), ourresults do not provide strong corroborating evidence, as baselinebrain activation predicted only the change in semantic errors. Indeed,from these results, it seems that measuring language-related activa-tion in specific left hemisphere regions before treatment initiationmight be of limited clinical importance. Regarding perfusion, we re-cently reported that CBF is decreased in the perilesional cortex inchronic stroke (Richardson et al., 2011). The current study did notshow the level of CBF in perilesional cortex to be associated withanomia treatment success in chronic stroke. However, CBF level in re-sidual naming areas was a significant predictor of change in correctnaming, perhaps providing some evidence that cerebral perfusion inchronic stroke might be related to treatment potential. Whereas ASLprovides an absolute measure of CBF (Lee et al., 2006) and has beencross-validated with H2O15 positron emission tomography (PET;Chen et al., 2011; Ye et al., 2000), it is primarily used for research ap-plications (Alexopoulos et al., in press; Lim et al., 2010; Pimentel etal., in press) and has not undergone rigorous testing for clinical appli-cations; the results presented here should be interpreted accordingly.It is also worth noting that a handful of studies have examined the re-lationship between abnormal hemodynamics and the BOLD signal instroke. For example, a case study by Fridriksson et al. (2006b)revealed slower than normal cerebral perfusion in specific cortical re-gions in a patient with chronic stroke and that this slowing was relat-ed to an abnormal BOLD signal. Utilizing data from six patients withchronic stroke, Thompson et al. (2010) looked specifically at the rela-tionship between time-to-peak (TTP) of the BOLD signal and CBFmeasured with arterial spin labeling and found that slower TTP hada modest correlation with lower CBF. Based thereon, further studiesthat relate measures of cerebral hemodynamics to brain activationin larger samples of stroke seem necessary to determine the specificnature of this relationship.

In an attempt to define the boundaries of what we have referredto here as ‘perilesional’ cortex, we examined the level of CBF as afunction of distance (in 3 mm spatial increments) from the actual le-sion in a subset of patients enrolled in this study. A gradual increasein CBF was found for areas extending to 15 mm from the lesionboundary and we defined this boundary as the edge of the perile-sional cortex. Other physiological measures may emerge in futurestudies to better qualify what constitutes perilesional cortex. In theabsence of available evidence defining the boundaries of perilesionalcortex, we felt that our method was the most principled way to de-fine this region. We are unaware of other studies that used this ap-proach to scrutinize potential CBF changes surrounding damage inchronic stroke.

Conclusions

In summary, our findings suggest that functional brain changes inphysiologically defined perilesional cortex, especially in the frontallobe, predict improvement in correct naming following anomia treat-ment in patients with aphasia post-stroke. In contrast, treatment-related changes in semantic and phonological naming errors were as-sociated with modulation of posterior cortex, primarily in temporallobe regions that in normal subjects are activated during picture nam-ing. Parietal lobe modulation involving naming-related regions alsopredicted changes in phonemic paraphasias. The relationship betweenanomia treatment outcome and neurophysiological measures—baseline naming-related activation and CBF—was far less certain. Al-though the current study strongly implicates the left hemisphere intreated anomia recovery, further studies in this area are imperative to

862 J. Fridriksson et al. / NeuroImage 60 (2012) 854–863

better appreciate the relationship between brain activation, neuro-physiology, and frank cortical damage.

Acknowledgments

This work was supported by the following grants from the NIH/NIDCD: DC008355 and DC009571. The authors wish to thank AstridFridriksson, M.A. CCC-SLP, who collected all the behavioral data forthis work.

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