Cerebral reorganization as a function of linguistic recovery in children: An fMRI study

cortex 47 (2011) 202–216

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Cerebral reorganization as a function of linguistic recovery in children: An fMRI study Odelia Elkana a,b,**, Ram Frost a, Uri Kramer c, Dafna Ben-Bashat b, Talma Hendler b, David Schmidt d and Avraham Schweiger d,e,* a

Psychology Department, Hebrew University, Jerusalem, Israel Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel-Aviv, Israel c Pediatric Neurology, Sourasky Medial Center, Tel-Aviv, Israel d Behavioral Sciences, Academic College of Tel Aviv-Yaffo, Israel e Loewenstein Rehabilitation Center, Raanana, Israel b

article info

abstract

Article history:

Characterizing and mapping the relationship between neuronal reorganization and func-

Received 22 December 2008

tional recovery are essential to the understanding of cerebral plasticity and the dynamic

Reviewed 29 May 2009

processes which occur following brain damage. The neuronal mechanisms underlying

Revised 9 August 2009

linguistic recovery following left hemisphere (LH) lesions are still unknown. Using functional

Accepted 11 November 2009

magnetic resonance imaging (fMRI), we investigated whether the extent of brain lateraliza-

Action editor Myrna Schwartz

tion of linguistic functioning in specific regions of interest (ROIs) is correlated with the level of

Published online 29 December 2009

linguistic performance following recovery from acquired childhood aphasia. The study focused on a rare group of children in whom lesions occurred after normal language acqui-

Keywords:

sition, but prior to complete maturation of the brain. During fMRI scanning, rhyming,

Aphasia

comprehension and verb generation activation tasks were monitored. The imaging data were

Functional MRI

evaluated with reference to linguistic performance measured behaviorally during imaging, as

Language recovery

well as outside the scanner. Compared with normal controls, we found greater right hemi-

Recovery of function

sphere (RH) lateralization in patients. However, correlations with linguistic performance

Reorganization

showed that increased proficiency in linguistic tasks was associated with greater lateralization to the LH. These results were replicated in a longitudinal case study of a patient scanned twice, 3 years apart. Additional improvement in linguistic performance of the patient was accompanied by increasing lateralization to the LH in the anterior language region. This, however, was the result of a decreased involvement of the RH. These findings suggest that recovery is a dynamic, ongoing process, which may last for years after onset. The role of each hemisphere in the recovery process may continuously change within the chronic stage. ª 2009 Elsevier Srl. All rights reserved.

Abbreviations: CVA, cerebrovascular accidents; MCA, middle cerebral artery; LI, laterality index; LIS, language index score; IFG, inferior frontal gyrus; L/R H, left/right hemisphere; IC, insular cortex; IPL, inferior parietal lobule. * Corresponding author. Academic College of Tel Aviv-Yaffo, 14 Rabeinu Yerucham St., P.O. 8401, Yaffo 68114, Israel. ** Corresponding author. Functional Brain Imaging Unit, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, Tel-Aviv 64239, Israel. E-mail addresses: [email protected] (O. Elkana), [email protected] (R. Frost), [email protected] (U. Kramer), [email protected] (D. Ben-Bashat), [email protected] (T. Hendler), [email protected] (D. Schmidt), schweige@mta. ac.il (A. Schweiger). 0010-9452/$ – see front matter ª 2009 Elsevier Srl. All rights reserved. doi:10.1016/j.cortex.2009.12.003

cortex 47 (2011) 202–216

1.

Introduction

Much effort has been directed in recent years towards identifying systematic patterns of neural reorganization which are related to the recovery from brain damage. Characterizing and mapping the relationships between neuronal reorganization and functional recovery are essential to the understanding of cerebral plasticity and the dynamic processes which occur following brain damage. Recent studies based on functional neuroimaging have provided evidence regarding the changes in neuronal activity following left hemisphere (LH) aphasiogenic lesions. Such studies have found, for example, increased language-related brain activation in the perilesional tissue of the LH, and/or an increase of activation in the homologous regions in the right hemisphere (RH) (Abo et al., 2004; Fernandez et al., 2004; Naeser et al., 2004; Saur et al., 2006; Thivard et al., 2005; Voets et al., 2006; Xu et al., 2004). These findings raise an important question: how are these changes in activation in the two hemispheres related to the actual recovery of linguistic functions? This question is particularly intriguing in light of recent research which casts doubt on the capacity of the RH to take over linguistic functions in the event of damage to the LH (Naeser et al., 2004; Rosen et al., 2000). These studies suggest instead that increased RH activation, seen in imaging following an LH lesion, may simply reflect disinhibition of the RH as a result of a breakdown of normal inter-hemispheric coordination, and that this disrupted inter-hemispheric balance could be the reason for the residual aphasic symptoms exhibited by patients. According to this view, during normal language processing, the LH inhibits RH interference. The lesion disrupts this inhibition across the hemispheres, and consequently, this leads to an increase of the RH activation seen in some studies. Thus, despite recent advances in our understanding of cerebral plasticity, the relative contribution of each hemisphere to the recovery from aphasia is still unclear and requires additional research. Evidence from behavioral clinical data presents substantial variability in the degree of linguistic recovery achieved by different aphasic patients in the chronic stage (at least six months following onset) (Lazar et al., 2007). Therefore, two important questions to consider in the present context are: do recovered patients in the chronic stage, who exhibit different degrees of linguistic performance, also exhibit different patterns of cerebral reorganization? And, if so, are there patterns of hemispheric reorganization that characterize good recovery, as opposed to ones that characterize poor recovery? The present study is concerned with the relative contribution of linguistic cortical networks in each of the hemispheres to linguistic performance in the chronic stage, following focal lesions to the LH, and it aims at mapping the relations between functional linguistic performance and cerebral reorganization. We examined cerebral reorganization in terms of the dominant hemisphere for linguistic processing as expressed by laterality index (LI) of presumed regions of interest (ROIs) involved in language functions. This index describes hemispheric dominance in terms of the degree of

203

right or left lateralization (Binder et al., 1995), and was found to be a more reliable representation of true laterality than the technique which relies on signal intensity (Ruff et al., 2008). Moreover, this measure takes into consideration the relative contribution of each of the hemispheres during linguistic processing, as opposed to presenting activations of each hemisphere separately and correlating each with linguistic recovery (Richter et al., 2008). This latter approach ignores the inter-hemispheric coordination during task performance. Since the LI may change according to different lesion sizes, we chose our patient group first on a clinical basis (diagnosed at onset as ‘‘non-fluent aphasics’’), but then on strictly radiological bases (etiology – cerebrovascular accidents – CVA at middle cerebral artery – MCA). This approach to patient selection thus diverges from other similar studies which used more heterogeneous etiologies in their patient groups (Liegeois et al., 2004; Richter et al., 2008; Voets et al., 2006). Based on prior studies, we focused on classical perisylvian areas involved in recovering from non-fluent aphasia – anterior language region (ALR), which includes mainly the inferior frontal gyrus (IFG) and adjacent insular cortex (IC). And posterior language region (PLR), which includes the superior temporal gyrus (STG) and inferior parietal lobule (IPL) (e.g., Rosen et al., 2000; Winhuisen et al., 2005). Most studies investigating linguistic brain reorganization following LH brain damage focused either on individuals with lesions occurring before the onset of speech, namely, cases of pre- or peri-natal CVA (e.g., Curtiss and de Bode, 2003), or on elderly patients with lesions occurring long after speech onset (cf. Price and Crinion, 2005). In the present study, we focused on a population with focal lesions at a time window not studied so far, that is, children in whom lesions occurred after language acquisition, but prior to complete maturation of the brain, during the years when myelination is still in progress (Pujol et al., 2006; Schmithorst et al., 2002; Sowell et al., 2004). In this group, one would expect to find the maximal effect of cerebral reorganization and compensation for deficits in skills already acquired, following focal lesions. This approach enabled us to examine ongoing changes in patterns of cerebral reorganization in relation to the extent of recovery. Linguistic recovery was assessed on the basis of two independent measures: a direct measurement of accuracy and speed performance during two language activation tasks (rhyming and sentence comprehension), and an indirect measurement of linguistic performance, which was obtained outside the scanner (the language index score – LIS, see the Method section for details). Combining linguistic behavioral measurements outside the scanner, with behavioral measurements during scanning, allows for a comprehensive evaluation of patients’ present linguistic capabilities and provides converging evidence regarding their linguistic skills. Indeed, high positive correlations (see Results section below) were found in our study between accuracy performance on language activation tasks during scanning and linguistic tests administered outside the scanner (naming, phonetic and semantic verbal fluency and reading), suggesting that these two measurements reliably reflect similar aspects of linguistic abilities. Our investigation had, therefore, four goals: 1) To examine the relations between hemispheric lateralization for linguistic

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processing and linguistic recovery from focal insults to the language-dominant hemisphere. 2) To study these relations in a population of children, with lesion onset no earlier than age five, to ensure that premorbid establishment of language skills took place, but due to incomplete myelination, their brains are probably still developing, thus allowing for maximal plasticity. 3) To investigate the major aspects of linguistic skills by selecting a wide range of linguistic tasks, and to examine their respective patterns of activation in their respective ROIs. 4) To assess linguistic recovery on the basis of two independent measures rather than one: a direct measurement of accuracy and speed performance during the language activation tasks, in addition to the indirect, traditional comprehensive linguistic evaluations conducted outside the scanner.

2.

Methods

2.1.

Participants

We reviewed 42 cases of young aphasics with CVA. Only seven individuals (five females and two males) met our inclusion criteria as detailed below. Their age-range at testing was 10–26 years (mean ¼ 17.5; SD ¼ 5.8). The age-range at onset was 5–17 years (mean ¼ 10, SD ¼ 4). Since six of the patients’ age at onset ranged from 5.1 to 12.3, and the seventh patient sustained the lesion at age 16.10, we present the results with and without the older patient (see Table 3). The patients’ characteristics are listed in Table 1. All patients sustained infarcts within the MCA territory (Table 1 and Fig. 1A) and on admission to hospital, all exhibited a similar pattern of functional disability with respect to language functioning (10–30 days of mutism evolving into non-fluent aphasia), as well as with respect to motor functioning (right hemiparesis with the upper right extremity being more affected). Six of the seven patients were premorbidly right-handed.

Inclusion criteria were: 1) a documented lesion in the territory of the left MCA with an intact RH, 2) initial diagnosis of non-fluent aphasia, 3) no history of prior learning disability, attention deficit hyperactive disorder (ADHD) retardation or other psychiatric or serious medical problem, 4) early language development reported to be within normal range and verified via school records and/or previous test results, 5) native speakers of Hebrew, 6) at least one year post-onset at the initiation of the study, 7) good language comprehension and ability to follow simple instructions, 8) intact sensorium (including grossly intact vision and hearing). These criteria were determined via a detailed review of medical history and neurological evaluations (Table 1); in addition, a structured parental interview was conducted in order to obtain early developmental history (e.g., age of speech onset). Handedness was determined by the Edinburgh Handedness Inventory (Oldfield, 1971) for controls. This evaluation was not relevant for the patient group, since all exhibited right hemiparesis, and subsequently switched to using their left hand. A group of 15 healthy right-handed volunteers, ten female and five male participants [range: 10–30 years, mean (SD): 17.9 (6.2) years] (scores on the Edinburgh Handedness test of over 90), were matched to the patients by age at the time of testing. All were free of any clinical evidence of neurological dysfunction or developmental delays. One additional healthy participant was excluded from the study due to an incidental pathological finding on MRI. Informed consent was obtained from all subjects and/or from the parent/guardian of minor participants. The study was approved by the Souraski Medical Center’s Helsinki Committee and by the Israeli Ministry of Health.

2.2.

Study design and fMRI language paradigm

The linguistic activation tasks were constructed in order to engage a wide range of language capabilities, especially

Table 1 – Patient characteristics. Patient No. Sex

Age at onset Site of lesion (years:months) (L MCA territory)

Additional comments from medical records

Premorbid Age at testing handedness (years:months)

NonLanguage verbal index scoreIQ (LIS) (z-score)

MH

1

F

5:1

SC þ Frontal

Right hemiparesis, non-fluent aphasia

Right

26:8

94

HZ

2

F

7:5

Temporoparietal þ SC

Right hemiparesis, non-fluent aphasia

Right

15:11

94

3.04

AH

3

F

7:11

Frontotemporal þ SC

Right hemiparesis, non-fluent aphasia

Right

11:10

103

1.7

DT

4

M

9:2

Frontotemporal

Right hemiparesis, non-fluent aphasia

Left

11:3

123

1.62

TK

5

M

11:6

Frontotemporal þ SC

Right hemiparesis, non-fluent aphasia

Right

16:5

119

.75

DS

6

F

12:3

SC

Right spastic hemiplegia, anomia, non-fluent aphasia

Right

17:6

130

.28

LD

7

F

16:10

Temporoparietal þ SC

Right hemiparesis, non-fluent aphasia

Right

23:6

112

1.22

Note: F ¼ female; M ¼ male; L ¼ left, SC ¼ striatocapsular.

.2

cortex 47 (2011) 202–216

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Fig. 1 – A: Lesion location (circled) in each patient, as shown on T1-weighted MRI. Transversal MRI scans are displayed at the level of maximum infract volume. Right side of the figure corresponds to left side of the brain. B, C: fMRI activation maps (FDR < .05) during rhyming task versus silence blocks, in ALR (1B) and during sentence comprehension task versus silence blocks, in PLR (1C). fMRI images were motion-corrected, pre-processed and superimposed on a T1 anatomical images for clarity. The images were not masked. For each patient, the cross is pointed to his/her dominant hemisphere involved in the task. The LH is depicted on the right.

phonology and sentences comprehension, that are often impaired by aphasiogenic lesions. It was important to keep the tasks short in order to avoid head movements. The tasks and their order of presentation in the scanner remained fixed as follows:

syntactic and semantic comprehension. The sentence comprehension task was chosen because it is a relatively natural linguistic task (Poeppel, 1996) that has been used extensively in language studies (Booth et al., 2000).

1) Rhyming: pictures of common objects were presented visually (e.g., a picture of a table), while at the same time spoken words were presented auditorily (e.g., the spoken word ‘‘cable’’). The subjects were asked to judge whether the object’s name and the spoken word rhymed or not, and to press a button on a response box if they did. The pictures were semantically unrelated to the spoken words, so as to focus on phonological processes only (cf. Pugh et al., 2001). 2) Auditory verb generation (VG): names of common objects were presented auditorily and the subject was asked to covertly generate verbs related to them (i.e., answer covertly the question: ‘‘what do you do with this object?’’). This task was found to be reliable in repeated activation of language areas in fMRI studies (Rutten et al., 2002), and has been widely studied for this purpose (e.g., D’Esposito, 2000; Holland et al., 2001; Wood et al., 2004). 3) Syntactic comprehension: sentences describing simple scenes were presented auditorily while picture of simple scenes was presented visually. Subjects were required to decide whether or not the sentence correctly described the picture. If they matched, subjects were required to press a button on a response box. Sentences were syntactically simple (‘‘the boy is pushing the girl’’), or complex (‘‘the girl is being pushed by the boy’’) in order to have a wide variety of sentence types but without an attempt to represent or balance all syntactic forms. Thus, the task involved both

All tasks were constructed in Presentation Software, version 9.90, in order to achieve precise timing between the auditory and the visual stimuli. Stimuli were projected onto a mirror slanted at 45 , which the subjects viewed as they lied down in the scanner. Auditory stimuli were presented using MRI compatible headphones. Stimuli were arranged in a block design paradigm using silence (fixation cross) as the low-level baseline condition. The total duration of each task was 150 sec (2.5 min). Each activation task began with 24 sec of silence, followed by four blocks of stimuli, each block of 18 sec, interspersed with 9–18 sec of silence (blanks) between blocks. The variable silence was added to create unequal blocks, so as to avoid anticipation of the cycle by participants. A response box was used to measure the subjects’ performance in terms of accuracy and reaction time on the rhyming and comprehension activation tasks. Prior to scanning, all subjects underwent a practice session to familiarize them with the tasks and to reduce anxiety in the scanner environment (this practice was comprised of 2 blocks of different stimuli for each task type).

2.3.

ROI analysis

We divided the hemispheres into two known major language areas, each serving as an ROI: one is the ALR, which includes the IFG [Brodmann area (BA) 44, 45, 47] the anterior insula

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(aINS) and the inferior aspect of the middle frontal gyrus (MFG) (BA 9, 46). Previous research has shown rhyming and VGauditory tasks to activate mainly the anterior frontal areas (IFG, MFG) in the LH (see Gernsbacher and Kaschak, 2003). The second ROI is the PLR, which includes the STG (BA 22, 41, 42), the angular gyrus (BA 39) and the supramarginal gyrus (BA 40), both of which are part of the IPL (Holland et al., 2001; Seghier et al., 2004). Previous research has shown that sentence comprehension tasks activate more posterior temporal areas in both hemispheres (Gernsbacher and Kaschak, 2003; Wilson et al., 2008). Because we studied a relatively small group of patients, and since our research group included children and adolescents of different ages, we did not transform their brains into a standard brain (e.g., Talairach space). Thus the above ROIs were defined anatomically for each individual (patients and normal controls) separately, using an atlas of the human brain (Mai et al., 2004) and confirmed by an experienced neuroradiologist. The same ROIs were defined for the healthy controls and for the patients – on both hemispheres prior to co-registration with the fMRI data.

2.4.

Image acquisition

The MRI measurements were performed in a whole-body 1.5 T, General Electric scanner. Anatomical and functional sequences were performed including: spin-echo–T2, fluid attenuation inversion recovery (FLAIR), 3D-SPGR and fMRI. The FLAIR images were used for volumetric measurements of the lesions (no. of slices ¼ 28, slice thickness ¼ 5 mm and spacing between slices ¼ 1 mm). For each subject, anatomical imaging using T1 SPGR sequence with a resolution of 2.2 mm  .97 mm  .97 mm was performed for registrations of activation maps on high-resolution anatomical images. Blood oxygenation level dependent (BOLD) contrast mechanism was used to collect activation data. Functional imaging employed a gradient-echo, echo planar imaging (EPI) sequence, with the following parameters: 23 slices, 4 mm thickness with 0 mm gap, TR/TE ¼ 3000/55 msec, FA ¼ 90, FOV ¼ 19.2 cm, matrix ¼ 64  64 (resolution in-plane: 3 mm  3 mm).

2.5.

Image processing and statistical analysis

Volumetric measurements: the volume of the lesions was calculated semi-manually with ANALYZE image analysis software (version 8.1) according to (Saunders et al., 1995) that was found to be highly accurate and reproducible. In brief, infarct volumes were defined with the volume estimator algorithm, a visual thresholding method that marks over the entire lesion in a three-dimensional array under a specific threshold. The operator then selects/changes those marks according to lesion boundaries. fMRI analysis: data analysis was performed using the BrainVoyager data analysis software package (Brain-Innovation, Germany, 1999–2002 version) for the manual co-registration and BrainVoyager QX 1.38 software package (Goebel et al., 1998) to create the statistical maps. Preprocessing analyses included motion correction (for baseline and drift correction). Data with artifacts or major movement (above 2 mm in each direction) were excluded from the study. The first six functional volumes before signal stabilization were

excluded from the analysis. Functional 2D data were manually aligned and co-registered with 3D anatomical data and transformed into Talairach space for healthy subjects only. Because we did not normalize the patients’ brain into a standard brain, we superimposed the fMRI data onto the T1 SPGR images for each patient’s brain individually. Whole brain analyses were computed using general linear model (GLM) with each task serving as the predictor for brain activations. We then used the false discovery rate (FDR) procedures for the selection of thresholds, which was found to be an effective technique for the analysis of neuroimaging data (Benjamini et al., 2001). Variation across subjects has a critical impact on threshold selection in practice (Genovese et al., 2002). Even with the same scanner and experimental paradigm, subjects vary in the degree of activation they exhibit, in terms of signal-to-noise ratio. Thus, FDR methods offer an objective way to select thresholds, which is automatically adaptive across subjects. Therefore, in the present study, automatic and implicit threshold selection was used (Benjamini and Heller, 2008; Benjamini and Yekutieli, 2005). The actual FDR (q value) chosen in the present study was .05, and the threshold was then automatically calculated by the FDR procedure for each brain. The voxels which were included in the FDR calculation were the voxels within the relevant ROI’s.

2.6.

fMRI laterality index (LI) calculation

After choosing the appropriate threshold using the FDR method, a laterality calculation was performed in each ROI, i.e., for ALR and PLR separately (Constable et al., 1998). The laterality index (LI) was calculated for each subject, separately for each ROI, according to the following familiar formula: laterality index ¼ (Left  Right)/(Left þ Right), where Left and Right equal the total number of voxels activated above threshold in the left and right ROIs, respectively (Liegeois et al., 2004). Activation was considered to be LH dominant if the LI  .20, and RH dominant if the LI  .20. An LI between .20 and .20 was considered as bilateral activation (Gaillard et al., 2001, 2002; Jansen et al., 2006; Springer et al., 1999). Although the LI calculation is unstable when either hemisphere has a small number of activated voxels, no subject in the present group had fewer than 50 activated voxels in either hemisphere. In order to measure the functional cerebral reorganization underlying linguistic operations as a function of the degree of behavioral linguistic performance, correlations between cerebral laterality (i.e., the LI) and language performance indices were calculated.

2.7.

Neuropsychological evaluation outside scanner

This battery was comprised of standardized tests and was devised to provide data on the general cognitive status of the patients, as well as to enable a comparison between underlying intellectual abilities and present verbal skills. The evaluation included: A. Test of Non-Verbal Intelligence (TONI-3, Brown et al., 1997) (M ¼ 100; SD ¼ 15) B. Language testing

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(1) Phonological fluency: letters Bet, Gimel & Shin in Hebrew; and (2) semantic fluency: animals and fruit & vegetable categories (COWAT – Controlled Oral Word Association Test, Hebrew version by Kave, 2006). (3) Naming (Hebrew version by Kave, 2006). (4) Reading accuracy and (5) reading speed (Shalem and Lachman, 1998: Diagnostic Battery for Reading Processes in Hebrew). All tests have norms and take between 2 and 3 h to administer. Altogether, there was a set of five linguistic measures for each patient. These scores were first normalized to age adjusted z-scores for each patient and then averaged into a composite score referred to as the ‘‘Language Index Score’’ (LIS). Thus, the LIS provides an approximate measure of the overall linguistic skills of the patients at this point in time. The battery was administered no later than 4 weeks after the fMRI session. Behavioral measures were analyzed and correlated with the LI using SPSS (Version 14.0, SPSS Inc., Chicago, IL).

3.

Results

3.1. Behavioral results during scanning: patients versus healthy control 3.1.1.

Accuracy performance

All of our patients and controls were able to perform the rhyming and comprehension tasks with above 80% accuracy, albeit with more difficulties observed in the patient group on the rhyming task [t(19) ¼ 1.97, p > .06] [Fig. 2A; Controls: mean rhyming task, accuracy performance ¼ 91% correct, SD ¼ 8.08; mean sentence comprehension task, accuracy performance ¼ 93% correct, SD ¼ 6.7; Patients: mean rhyming task,

Accuracy %

A

accuracy performance ¼ 82% correct, SD ¼ 15.7; mean sentence comprehension task, accuracy performance ¼ 93% correct, SD ¼ 9.1].

3.1.2.

Reaction time performance

Reaction time was longer in the patient group on the rhyming task, but not statistically significant [t(19) ¼ 1.37, p > .1], There were no other task or group effects [Fig. 2B; Controls: mean rhyming task, reaction time performance ¼ 1400 msec, SD ¼ 273 msec; mean sentence comprehension task, reaction time performance ¼ 3058 msec, SD ¼ 225; Patients: mean rhyming task, reaction time performance ¼ 1572 msec, SD ¼ 365 msec; mean sentence comprehension task performance ¼ 3063 msec, SD ¼ 152 msec].

3.2.

Behavioral results outside the scanner

First, high positive correlations were found between behavioral results inside the scanner (accuracy performance on rhyming and sentence comprehension tasks) and behavioral linguistic measures outside the scanner (LIS) (for all correlations computed, Pearson’s r was greater than .6), suggesting that these two measurements reliably reflect similar aspects of linguistic abilities. At the time of testing, all patients exhibited significant recovery from their initial non-fluent aphasia and all returned to regular schooling curriculum. However, residual aphasic symptoms persisted: phonetic fluency and naming were low for all patients (mean z-scores of phonetic fluency ¼ 1.99, SD ¼ .85; mean z-scores of naming ¼ 1.93, SD ¼ 1.44). Based on the LIS, we then divided the patient group into two sub-groups: patients who recovered well versus those who recovered poorly; patients with LIS z-scores above 1 were

Accuracy performance 100 95

Patients Healthy

90 85 80 75

Rhyming

B

Comprehension

Reaction time performance Reaction time (msec)

Reaction time (msec)

3400 2000 1800 1600 1400 1200 1000

Rhyming

3200

3000

2800

2600

Comprehension

Fig. 2 – A: Accuracy performance during scanning of patients (N [ 7) and healthy controls (N [ 14) on the rhyming and comprehension activation tasks [Rhyming: t(19) [ 1.97, p < .63, two tailed; comprehension: not significant – n.s.]. B: Reaction time performance during scanning of patients (N [ 7) and healthy controls (N [ 14) on the rhyming and comprehension activation tasks [Rhyming: t(19) [ L1.37, p > .1, two tailed; comprehension: n.s.].

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defined as ‘‘good performers’’ and those with LIS z-scores below 1 were defined as ‘‘poor performers’’. This division reflects not just the relative recovery status of the patient in relation to the other patients, but also in relation to the average range of performance seen in the normal population of their respective ages. Based on this assessment, patients 1, 5 and 6 were defined as ‘‘good performers’’ and the rest as ‘‘poor performers’’. The difference in the LIS scores between these two groups was statistically significant despite the small number of individuals per group [t(5) ¼ 3.24, p < .02].

3.3. fMRI laterality index (LI): patients versus healthy controls In the control group, the dominant patterns of languagerelated activation were seen in the frontotemporal linguistic networks and were very similar to those found in other studies using similar paradigms (Holland et al., 2001; Kloppel and Buchel, 2005). Thus, activations were noted mostly in the ALR, including the IFG, the MFG, as well as in the PLR, including the STG, bilaterally. Fourteen out of the 15 healthy control children and adolescents showed LH lateralization on all three linguistic tasks. The laterality indices of all participants are presented in Table 2 and Fig. 3 for the three linguistic tasks in the two ROIs. When analyzed as groups, the LI indices demonstrated left lateralization in controls but right lateralization in patients, in both ALR and PLR. As detailed in Fig. 3 and Table 2, the LIs of patients and those of control participants differed significantly on all three tasks and in both ROIs. In Fig. 1B and in Fig. 1C the dominant patterns of hemispheric lateralization in each patient are presented individually for the rhyming and the comprehension activation tasks.

(accuracy of rhyming and comprehension tasks) during scanning for the seven patients. Although the number of patients was relatively small, a statistically significant and highly positive correlation between patients’ accuracy on the rhyming task and their fMRI patterns of cerebral lateralization to the LH in the ALR was found (r ¼ .66, p < .05). Likewise, there were negative correlations between the patients’ reaction times on the rhyming tasks and the involvement of their ALR in the LH (r ¼ .732, p < .031). That is, it appears that the ALR in the LH was more involved for patients who responded more quickly. The correlations between accuracy on the comprehension task and the LI in the PLR were positive as well. However, because accuracy performance on the comprehension task was almost at ceiling (about 93% correct), statistical significance was marginal (r ¼ .5, p < .1). Similarly, the variation in reaction time for the comprehension task was very small and therefore the correlation with cerebral laterality was null (r ¼ .12). Taken together, better linguistic performance was associated with greater involvement of the perisylvian area in the LH relative to the RH, as measured by the LI of the fMRI signal. It should be emphasized that such correlations were not seen in the healthy controls, (controls’ accuracy on the rhyming task and their LI: r ¼ .18, p < .3; controls’ accuracy on the comprehension task and their LI: r ¼ .05, p < .5). However, due to the small variation in the controls’ behavioral performance, this result should be regarded with some caution (Fig. 2A).

Anterior Language Region .80

Laterality Index

.60

3.4. Correlations between laterality indices and behavioral linguistic performance 3.4.1.

Inside the scanner

Fig. 4A shows the Pearson Correlation Coefficients for the laterality index (LI) and behavioral linguistic measures

LI – rhyming LI – comprehension LI – VG-auditory ALR

PLR

ALR

PLR

ALR

PLR

.31 .55

.22 .45

.34 .59

.22 .65

.07 .52

.19 .40

15 Controls AVG .44 STDEV .33

.22 .28

.16 .44

.21 .40

.59 .25

.30 .37

p Values

.00

.02

.04

.01

.01

7 Patient AVG STDEV

.20 .00 -.20 Patients Healthy

-.40 -.60

Rhyming

Comprehension Posterior Language Region

.60

Patients Healthy

.40 .20 .00 -.20 -.40 -.60 Rhyming

.00

VG

.80

Laterality Index

Table 2 – LI for patients and healthy controls. The laterality indices [LI [ (L L R)/(L D R)] for 7 patient and 15 healthy control subjects on three linguistic tasks, in two language ROIs. Signs denote side of higher activation (D [ left, L [ right). The indices demonstrate that in the ALR and in the PLR, there is LH lateralization in controls and RH lateralization in patients.

.40

Comprehension

P<0.05

VG

P<0.01

Fig. 3 – Comparisons of laterality indices (LI) of patient group (N [ 7) versus healthy controls (N [ 15) on 3 linguistic tasks, in the ALR and in the PLR (VG [ verb generation). Two tailed t-test values are shown on each task.

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cortex 47 (2011) 202–216

Inside scanner

A

Comprehension task

Rhyming task 100

Accuracy %

Accuracy %

100

80

60

80

60

R = .66 P< .05

40

R = .51 P< .1

40 -1

-.8

-.6

-.4

-.2

0 .2 Laterality

.4

.6

.8

1

-1

-.8

-.6

-.4

-.2

0

.2

.4

.6

.8

1

Laterality

B

Outside scanner 1.00

1.00

.50

.50 .00

-.50

LIS (z-score)

LIS (z-score)

.00 -1.00 -1.50 -2.00 -2.50

R = .63 P< .06

-3.00 -3.50

-1

-.8

-.6

-.4

-.2

0

.2

.4

.6

.8

-.50 -1.00 -1.50 -2.00 -2.50

R = .751 P< .03

-3.00 -3.50

1

-1

Laterality on rhyming task

-.8

-.6

-.4

-.2

0

.2

.4

.6

.8

1

Laterality on comprehension task

Fig. 4 – A: Pearson Correlation Coefficients for the laterality index (LI) and behavioral linguistic measures during scanning: accuracy performance on rhyming and comprehension tasks. ROIs shown: ALR for rhyming; PLR for comprehension. B: Pearson Correlation Coefficients for the laterality index (LI) and behavioral linguistic measures outside the scanner (LIS). ROIs as in A.

3.4.2.

Outside the scanner

Fig. 4B shows the Pearson Correlation Coefficients for the laterality index (LI) and behavioral linguistic measures outside the scanner (LIS) for the seven patients. Positive correlations were found between brain laterality in the ALR during the rhyming task and performance on behavioral linguistic measures (LIS), taken outside the scanner (r ¼ .63, p < .06), as well as between brain laterality in PLR during the comprehension task and the performance on behavioral LIS taken outside the scanner (r ¼ .75, p < .03). Similar results were obtained when performing the same correlations analyses without the patient LD (age at onset 16:10, – see Table 3).

3.5. Partial correlations between laterality indices and behavioral linguistic performance Note that lesion size is a potential confounding variable that can modulate both linguistic performance and cerebral laterality scores. Consequently, we examined the partial correlation between linguistic performance and laterality when the effects of lesion size are excluded. Table 4 shows the bivariate and the partial correlations obtained for the laterality index (LI) and behavioral linguistic measures inside and outside the scanner (LIS) for the 7 patients. As can be seen, the exclusion of lesion size as a mediating variable had little effect on the bivariate correlations in spite of the small number of subjects. For example, the correlation between laterality of rhyming task and

Table 3 – Correlations of LI and linguistic measures, with and without patient LD. Pearson Correlation Coefficients between LI (laterality index) and linguistic performances in the patient group, during scanning (accuracy) and outside the scanner (LIS). With LD (7 patients)

Linguistic performance during scanning (Task Accuracy) Linguistic performance outside scanner (LIS)

Without LD (6 patients)

LI – rhyming

LI – comprehension

LI – rhyming

LI – comprehension

.66 (N ¼ 7, p < .05)

.51 (N ¼ 7, p < .1)

.69 (N ¼ 6, p < .06)

.53 (N ¼ 6, p < .1)

.63 (N ¼ 7, p < .06)

.75 (N ¼ 7, p < .03)

.77 (N ¼ 6, p < .04)

.75 (N ¼ 6, p < .04)

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Table 4 – Bivariate and partial correlations of LI and linguistic measures. Partial correlationa

Bivariate correlation

Linguistic performance during scanning (Task Accuracy) Linguistic performance outside scanner (LIS)

LI – rhyming

LI – sentences

LI – rhyming

LI – sentences

.66 (N ¼ 7, p < .05)

.51 (N ¼ 7, p < .1)

.66 (d.f ¼ 4, p < .07)

.91 (d.f ¼ 4, p < .006)

.63 (N ¼ 7, p < .06)

.75 (N ¼ 7, p < .03)

.6 (d.f ¼ 4, p < .1)

.73 (d.f ¼ 4, p < .05)

a Partial correlation: correlation between LI (laterality index) and linguistic performances in the patient group, during scanning (accuracy) and outside the scanner (LIS) when lesions size is controlled (whilst taking away the effects of lesion size).

3.6.

Does LI differ in good and poor performers?

We conducted an additional ANOVA to compare the three groups (‘‘poor performers,’’ ‘‘good performers’’ and healthy control) in terms of their LI in three linguistic tasks (Fig. 5). The results demonstrate a significant difference between the three groups [F(2,21) ¼ 4.3; p < .03]. In order to determine the sources of this effect, post-hoc (Tukey) analysis of the ANOVA results was performed. The post-hoc analysis indicated a significant difference between ‘‘poor performers’’ and normal controls in the rhyming and the VG tasks, but no difference between the ‘‘good performers’’ and the normal controls (p < .05).

was comprised of 5 linguistic tests) was 1.22, whereas on the second LIS her z-score was .92. Her performance on the linguistic activation tasks during scanning supports these findings (accuracy performance on rhyming task at first scanning ¼ 88%; reaction time ¼ 1337 msec, at the second scanning ¼ 92%; reaction time ¼ 1073 msec; with regard to the sentence comprehension task, she achieved a perfect accuracy score in both scanning sessions, and very similar reaction times: during first scanning ¼ 2987 msec, during the second scanning ¼ 2971 msec). In other words, during the follow-up session three years after the first testing, LD showed concomitant improvement in her language skills, as well as increased lateralization to the LH during the performance of linguistic tasks. Moreover, as can be seen in Fig. 6B the increase in LD’s left lateralization in the ALR over time was the result of a decrease in the activation of the RH, and not due to a prominent increase in

Anterior Language Region .80 .60 Laterality index

accuracy performance was identical using both procedures (bivariate correlation ¼ .66, partial correlation ¼ .66). Surprisingly, the correlation between laterality of comprehension task and accuracy performance became even stronger when the effect of lesion size was partialed-out (bivariate correlation ¼ .51, p < .1; partial correlation ¼ .91; p < .006). Correlations between laterality of rhyming task and behavioral linguistic measures outside the scanner (LIS) remained much the same using both procedures (bivariate correlation ¼ .63; partial correlation ¼ .60). Similarly, correlations between laterality of comprehension task and behavioral linguistic measures outside the scanner (LIS) did not change (bivariate correlation ¼ .75; partial correlation ¼ .73). These findings clearly suggest that lesion size did not affect our results.

.40 .20 .00 -.20 -.40

Poor Good Healthy

-.60 -.80

3.7. data

-1.00

A case study: converging evidence from longitudinal

Rhyming

Comprehension

VG

Posterior Language Region .80 .60 Laterality index

To further examine reorganization of language in relation to linguistic recovery, we repeated the fMRI study on one of our patients, LD, three years after the first session, in order to follow her course of recovery over an extended period of time. This enabled us to examine activation changes over time in relation to any change in functional language performance within a single patient. All of the scanning parameters, tasks and behavioral tests were repeated in this second session in a manner identical to that of the first session described above. In comparison with the first scanning (age at onset 16:10; age at testing 24), in the second scanning (age at testing 27) the patient demonstrated increased lateralization to the LH in the ALR, while performing all three linguistic tasks (Fig. 6A). Interestingly, when tested again on all of the linguistic behavioral measures during the second testing session, LD showed evidence of further improvement, as expressed by her LIS. During the first scanning session, her LIS z-score (which

.40 .20 .00 -.20 -.40

Poor Good Healthy

-.60 -.80 -1.00

Rhyming

Comprehension

VG

Fig. 5 – Comparing means of laterality indices in three linguistic tasks of the three groups: ‘‘poor performers’’, ‘‘good performers’’ and healthy controls in the ALR and in the PLR. The ANOVA post-hoc (Tukey) analyses (shown as stars) indicated a significant difference (p > .05) between ‘‘poor performers’’ and healthy controls, but no difference between the ‘‘good performers’’ and the normal controls.

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Longitudinal Data

A 1.00

EX 2004 Ex 2007

Laterality Index

.80 .60 .40 .20 .00 Rhyming

B

Comprehension

N. of voxels- Right hemisphere

N. of voxels- Left hemisphere 6000 5000

6000

Ex 2004 Ex 2007

5000

4000

Ex 2004 Ex 2007

4000 N.voxels

N.voxels

VG

3000

3000

2000

2000

1000

1000 0

0 Rhyming

Comprehension

VG

Rhyming

Comprehension

VG

Fig. 6 – A: Comparing patient LD’s laterality indices on three linguistic tasks in two different sessions. The data demonstrate a significant increase in left lateralization in the ALR on the second scan, on all three linguistic tasks (even on a single subject, which lowers the power of the test, a paired samples one tail t-test resulted in probability p < .038). B: Patient LD’s longitudinal data, using an activated voxel count in the two sessions, on three linguistic tasks. The increase in left lateralization of patient LD over time (A) can be seen as the result of a decrease in the activated voxel count in the ALR in the RH (paired samples t-test two tail p < .065), and not due to increased activation in the LH. This phenomenon was found consistently across all 3 linguistic tasks.

activation of the LH. This phenomenon was consistent across all 3 linguistic tasks. In Fig. 7, the decrease in activation in the RH can be seen in the coronal slice of the threedimensional images. The question can be raised here as to whether or not this change seen in LD represents ‘‘recovery’’ or just a natural variation which may be seen in the general population. To answer this question, we conducted the same procedure with four control subjects. The longitudinal data of four healthy controls that were scanned twice, 3 years apart demonstrate that there were no changes in activation between sessions in the relevant ROIs, contrary to the case of patient LD.

4.

Discussion

In the present study, we examined whether the extent of recovery of linguistic functions in the chronic stage is associated with differing patterns of cerebral reorganization for language processing following childhood insults to the language-dominant hemisphere. The novel feature of our investigation lies in our brain damaged child population, characterized by lesions occurring after normal language acquisition. Since by the time of lesion onset the brains of our patients had been already organized for language processing (Wood et al., 2004), the recovery of linguistic functions following a stroke required some form of cerebral

reorganization. The main advantage in investigating children and adolescents is that this population represents the upper limit of plasticity for language reorganization following brain damage. In other words, we expected to see maximal functional recovery and, therefore, a larger extent of cerebral reorganization (Brown et al., 2005; Chugani et al., 1987; Sowell et al., 2004) when compared with the well-studied adult stroke population (see a recent review article by Crinion and Leff, 2007). In the first stage of our analysis of the fMRI and behavioral data, we compared laterality of linguistic functions in the patients, as a group, to healthy normal controls. This was done across three linguistic tasks: cross-modal rhyming, sentence comprehension and auditory-based verb generation. We found that our patients, as a group, exhibited right lateralization in the ROIs while performing the three linguistic tasks, in contrast to the controls, who exhibited left lateralization. This finding is consistent with previous results which examined RH versus LH dominance in patients, following acquired brain damage (e.g., Cappa et al., 1997; Liegeois et al., 2004; Richter et al., 2008; Voets et al., 2006). As behavioral clinical data show substantial variability in the degree of language recovery achieved by different patients (Lazar et al., 2007), the main goal of the present study was to investigate whether an increased level of recovery is reflected in some systematic pattern of cerebral reorganization. Specifically, we examined whether the extent

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Fig. 7 – Patient LD’s longitudinal activations’ maps of fMRI data during rhyming versus silent blocks. The cross is localized on the IFG, perilesional area in the LH. As can be seen, the activation of the perilesional area in the LH is quite similar over the two sessions (2004; 2007). In contrast, the homologues involvement in the RH decreased over time, as seen clearly in the coronal slice (circled), as well as in the reduction of mean signal change seen in the time course below.

of brain lateralization of linguistic functioning in the relevant ROI is correlated with the level of behavioral/linguistic performance. Our results show positive and significant correlations between LH lateralization in the patients and their linguistic performance inside and outside the scanner. Thus, increased LH laterality correlated with better recovery as reflected by better linguistic performance, whereas decreased LH lateralization was associated with poorer recovery. The one exception to these findings was the nonsignificant (albeit positive) correlation between laterality and accuracy on the comprehension task (Fig. 4, r ¼ .5, p < .1). This result could be attributed to the small variance which

may have stemmed from the ceiling effect obtained in this task (Fig. 2). One possible alternative account to consider in this context is that perhaps laterality is correlated with age (Holland et al., 2007) rather than with recovery. According to this view, the greater laterality indices obtained with our better-recovered patients is confounded with their older age. However, if this hypothesis were correct, one would expect to find greater laterality indices for older children relative to younger ones in our control group (N ¼ 15) as well. This, however, was not the pattern of results we obtained. The correlation of laterality indices and age in our control group was very small and

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insignificant on all three tasks, in both ROIs (age and laterality of rhyming task, r ¼ .06, r ¼ .07, in the ALR and the PLR, respectively; age and laterality of comprehension task, r ¼ .11, r ¼ .3; age and laterality of auditory verb generation task, r ¼ .12, r ¼ .25). The second possible alternative account is that our obtained findings simply reflect a correlation between linguistic recovery and lesion size. By this view, damage to the LH leads to both language impairment and reduced activity in the LH, and consequently, reduced linguistic recovery merely reflects greater damage to the language areas. Our volumetric analyses do not support this interpretation. These analyses clearly show that when lesion volume is held constant the main findings remain unchanged. Hence, the correlation between linguistic performance and laterality, at least in the present study, seems independent of the size of the lesion. Moreover the fact that the result is unchanged by partialling out lesion size makes it unlikely that differences in initial severity of the functional impairment have a major role in explaining our data. The results of the present analysis converge with various findings obtained for a wide range of linguistic disorder syndromes such as dyslexia (Heim et al., 2003; Penolazzi et al., 2006) and specific language impairment (SLI) (Ors et al., 2005; Pecini et al., 2005). These studies demonstrated less lateralization for language processing and more symmetric language cerebral organization in patients relative to the normal population, suggesting that specialization in the form of network lateralization is probably required for effective linguistic performance. The behavioral data of the linguistic tests (LIS) presented in Table 1 show that some patients performed within the normal range for their age, whereas others performed below that level. Therefore, in the final stage of our investigation, we divided the patients into two sub-groups, those with relatively poor performance and those showing good performance, based on their overall linguistic performance, as indicated by their LISs. This distinction allowed us to investigate whether or not the quality of linguistic recovery is related to the lateralization of linguistic functioning. We found significant differences in laterality indices between the ‘‘poor performers’’ and the healthy controls, whereas no difference was found between the ‘‘good performers’’ and the healthy controls in any of the three linguistic tasks, in both ROIs. Overall, these findings suggest that the ALR in the LH plays a central role in the recovery of linguistic function. Our findings speak to the ongoing controversy regarding the involvement of areas within the non-dominant hemisphere, usually the RH, in language functions and their contribution to recovery from aphasia. Whereas the majority of investigators stress the role of the dominant LH in language recovery (e.g., Breier et al., 2004; Fernandez et al., 2004; Heiss et al., 1997; Karbe et al., 1998; Leger et al., 2002; Warburton et al., 1999), some argue for a complementary or compensatory function of the RH (Blasi et al., 2002; Cappa et al., 1997; Musso et al., 1999; Thivard et al., 2005). Recent results from studies focusing on recovery of motor function are in line with the findings of the present study (Liepert et al., 2000; Ward et al., 2003). For example, evidence from a study by (Murase et al., 2004), supports the hypothesis

213

that recovery of motor function in chronic stroke patients who recovered well, relies predominantly on reorganized activity in the damaged hemisphere. The latter finding is also consistent with data from primate studies (Nudo, 2003). Finally, evidence from studies reporting on therapeutic approaches supports this view as well. For instance, application of slow, presumably inhibitory, repetitive transcranial magnetic stimulation (rTMS: 1 Hz) to the pars triangularis of the right IFG resulted in an improvement of picture naming in four patients with non-fluent chronic aphasia (Naeser et al., 2005), suggesting that suppression of the right IFG activity may have actually prevented its interference with LH functioning. Several studies investigated the reproducibility of fMRI laterality measures over time and established the reliability of this approach (Fernandez et al., 2004; Rutten et al., 2002). Therefore, our longitudinal data from one patient, LD, who was scanned twice, provide converging evidence which validates our cross sectional conclusions above. When compared with the first scanning, LD demonstrated increased lateralization to the LH in the ALR during her second scanning. This increased lateralization to the LH in the relevant ROI was associated with further improvement of language processing, resulting in a near-complete recovery. Note that 4 healthy controls that underwent test– retest showed no change in activation over time in the relevant ROI. Detailed examination of the patient longitudinal data indicates that the increase in left lateralization for language processing with time was the result of a decrease in the relative involvement of the ALR in the RH, whereas activations of the LH remained much the same. These findings are in line with a recent study by Richter et al. (2008), who investigated the role of the RH in 16 chronic adult patients who suffered from non-fluent aphasia and underwent speech therapy. The authors’ documented therapeutic success which was correlated with a relative decrease of activation in right-hemispheric areas, presumably reflecting increased efficiency of language processing. Therefore, as a result of sufficient improvement, less effort might be required in the non-dominant RH, or perhaps, there is more inhibition of the RH as a result of increased efficiency of the LH. The study most relevant to our findings is that of Saur et al. (2006), who demonstrated different patterns of hemispheric progression of language activation across the first year of recovery. According to Saur et al. (2006), RH involvement is characterized by a biphasic course, with an early strong increase and a later decrease of activation, while the LH language areas show a monophasic course with a continuous increase of activation during the first year of recovery. Nevertheless, these authors limited their model to the first year of recovery (and see also Cardebat et al., 2003) under the assumption that examining the reorganization of language in patients in the ‘‘chronic stage’’ reflects the already reorganized language network, rather than the ongoing process of reorganization. As noted above, we first scanned LD when she was in the ‘‘chronic stage’’, at age 24, seven years post-lesion onset. The second scanning was performed three years later, when she was 27 years old. In this sense, the current study takes Saur et al.’s model one step further and extends its time

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scale, raising the possibility that recovery is a dynamic, ongoing process, lasting for years after onset. And therefore the role of each of the hemispheres in the recovery process may be continually changing across time. Thus, our longitudinal findings, if replicated with other patients, may challenge the view of recovery as ending within the first year, or at most two years, following onset.

4.1.

Methodological considerations

As noted above, focal brain lesions to the LH following the acquisition of language in young children are fortunately very rare; therefore, only a small number of patients participated in this study. To obtain a more robust conclusion, one needs more patients. Although an attempt was made to create as much homogeneity inside the patient group as possible by choosing the same etiology and the same clinical diagnosis at onset, there was heterogeneity regarding the lesion volume and the precise lesion location, which are slightly different among patients. Furthermore, since we did not wish to exclude too many potential subjects, we chose children with lesions acquired over a relatively long period of development (anywhere between the age of 5 and 17 years). Note that six of the patients’ age of onset ranged from 5.1 to 12.3, whereas the seventh patient’s age was 16.10. However, as can be seen in Table 3, when we excluded this patient from the analyses the results remain much the same. One potential caveat regarding the LI calculations in fMRI studies is that the specificity and sensitivity of this index to true neural activity may change with the application of different statistical thresholds. As a result, it is expected that language dominance as measured by the LI will vary as the statistical confidence threshold is changed (Ruff et al., 2008). To address this issue, we used the FDR procedure which determines the threshold for each individual patient, by controlling the expected proportion of the hypotheses that are falsely rejected, instead of setting the threshold at one arbitrary level for all subjects (Benjamini et al., 2001; Genovese et al., 2002). Finally, we should note that since a stroke is a vascular event, it most likely leads to changes in blood flow. Therefore, it is conceivable that changes seen during task activation in fMRI studies actually reflect also changes in blood flow, and the respective hemodynamic response, rather than in neural activity. It also seems reasonable to assume that such blood flow changes are related to lesion size. This possibility, common to most fMRI analyses, may be more prominent with the use of short blocks. But note that if compromised blood flow is related to lesion size, then taking into account lesion size in the calculations (as done in this study by partial correlation) should mitigate this effect.

5.

Conclusions

We investigated a rare group of aphasic children and adolescents in the chronic stage of recovery from aphasia using fMRI, while performing three linguistic tasks. In addition to their linguistic performance (accuracy and speed) during imaging, detailed language examinations were carried out. As

a result, we were able to examine the association of cerebral patterns for language processing with language performance. Correlational analysis of brain lateralization in the relevant ROIs and linguistic performance revealed a systematic association between the relative involvement of each hemisphere and the recovery of linguistic functions. These findings corroborate the hypothesis of the increasingly prominent role of intact anterior language areas in the LH in recovering from aphasia. In addition, we obtained fMRI data from one patient during two different scanning and testing sessions, three years apart. These longitudinal data corroborated our group’s findings as well. Despite the fact that these two sessions were both in the chronic stage, we noted an improvement of linguistic functioning in the second session, relative to the first. This improvement was accompanied by increased lateralization in the ALR of the LH, which was due to a substantial reduction in RH activity, rather than to increased activation in the LH. Therefore, the current study extended Saur et al.’s (2006) model, suggesting that recovery is a dynamic ongoing process, lasting for years following onset.

6.

Funding

This work was supported by a grant from the Israel Science Foundation (ISF no. 1173/05) and Martin and Vivian Levin Center for the Normal and Psychopathological Development of the Child and Adolescent.

Acknowledgments We wish to thank Amichai Brezner, Moshe Vardi and Ofer Keren for their assistance in recruiting our rare group of patients. We would also like to thank Anat Maril and Shlomo Bentin for helpful comments on previous drafts of this paper.

references

Abo M, Senoo A, Watanabe S, Miyano S, Doseki K, Sasaki N, et al. Language-related brain function during word repetition in post-stroke aphasics. NeuroReport, 15: 1891–1894, 2004. Benjamini Y, Drai D, Elmer G, Kafkafi N, and Golani I. Controlling the false discovery rate in behavior genetics research. Behavioural Brain Research, 125: 279–284, 2001. Benjamini Y and Heller R. Screening for partial conjunction hypotheses. Biometrics, 2008. Benjamini Y and Yekutieli D. Quantitative trait Loci analysis using the false discovery rate. Genetics, 171: 783–790, 2005. Binder JR, Rao SM, Hammeke TA, Frost JA, Bandettini PA, Jesmanowicz A, et al. Lateralized human brain language systems demonstrated by task subtraction functional magnetic resonance imaging. Archives of Neurology, 52: 593–601, 1995. Blasi V, Young AC, Tansy AP, Petersen SE, Snyder AZ, and Corbetta M. Word retrieval learning modulates right frontal cortex in patients with left frontal damage. Neuron, 36: 159–170, 2002. Booth JR, MacWhinney B, Thulborn KR, Sacco K, Voyvodic JT, and Feldman HM. Developmental and lesion effects in brain

cortex 47 (2011) 202–216

activation during sentence comprehension and mental rotation. Developmental Neuropsychology, 18: 139–169, 2000. Breier JI, Castillo EM, Boake C, Billingsley R, Maher L, Francisco G, et al. Spatiotemporal patterns of language-specific brain activity in patients with chronic aphasia after stroke using magnetoencephalography. NeuroImage, 23: 1308–1316, 2004. Brown TT, Lugar HM, Coalson RS, Miezin FM, Petersen SE, and Schlaggar BL. Developmental changes in human cerebral functional organization for word generation. Cerebral Cortex, 15: 275–290, 2005. Brown L, Sherbenou RJ, and Johnson SK. Test of Nonverbal Intelligence, Third edition (TONI-3). Pro-ed, 1997. Cappa SF, Perani D, Grassi F, Bressi S, Alberoni M, Franceschi M, et al. A PET follow-up study of recovery after stroke in acute aphasics. Brain and Language, 56: 55–67, 1997. Cardebat D, Demonet JF, De Boissezon X, Marie N, Marie RM, Lambert J, et al. Behavioral and neurofunctional changes over time in healthy and aphasic subjects: A PET language activation study. Stroke, 34: 2900–2906, 2003. Chugani HT, Phelps ME, and Mazziotta JC. Positron emission tomography study of human brain functional development. Annals of Neurology, 22: 487–497, 1987. Constable RT, Skudlarski P, Mencl E, Pugh KR, Fulbright RK, Lacadie C, et al. Quantifying and comparing region-of-interest activation patterns in functional brain MR imaging: Methodology considerations. Magnetic Resonance Imaging, 16: 289–300, 1998. Crinion JT and Leff AP. Recovery and treatment of aphasia after stroke: Functional imaging studies. Current Opinion in Neurology, 20: 667–673, 2007. Curtiss S and de Bode S. How normal is grammatical development in the right hemisphere following hemispherectomy? The root infinitive stage and beyond. Brain and Language, 86: 193–206, 2003. D’Esposito M. Functional neuroimaging of cognition. Seminars in Neurology, 20: 487–498, 2000. Fernandez B, Cardebat D, Demonet JF, Joseph PA, Mazaux JM, Barat M, et al. Functional MRI follow-up study of language processes in healthy subjects and during recovery in a case of aphasia. Stroke, 35: 2171–2176, 2004. Gaillard WD, Balsamo L, Xu B, Grandin CB, Braniecki SH, Papero PH, et al. Language dominance in partial epilepsy patients identified with an fMRI reading task. Neurology, 59: 256–265, 2002. Gaillard WD, Grandin CB, and Xu B. Developmental aspects of pediatric fMRI: Considerations for image acquisition, analysis, and interpretation. NeuroImage, 13: 239–249, 2001. Genovese CR, Lazar NA, and Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15: 870–878, 2002. Gernsbacher MA and Kaschak MP. Neuroimaging studies of language production and comprehension. Annual Review of Psychology, 54: 91–114, 2003. Goebel R, Khorram-Sefat D, Muckli L, Hacker H, and Singer W. The constructive nature of vision: Direct evidence from functional magnetic resonance imaging studies of apparent motion and motion imagery. The European Journal of Neuroscience, 10: 1563–1573, 1998. Heim S, Eulitz C, and Elbert T. Altered hemispheric asymmetry of auditory P100m in dyslexia. The European Journal of Neuroscience, 17: 1715–1722, 2003. Heiss WD, Karbe H, Weber-Luxenburger G, Herholz K, Kessler J, Pietrzyk U, et al. Speech-induced cerebral metabolic activation reflects recovery from aphasia. Journal of the Neurological Sciences, 145: 213–217, 1997. Holland SK, Plante E, Weber Byars A, Strawsburg RH, Schmithorst VJ, and Ball Jr WS. Normal fMRI brain activation patterns in children performing a verb generation task. NeuroImage, 14: 837–843, 2001.

215

Holland SK, Vannest J, Mecoli M, Jacola LM, Tillema JM, Karunanayaka PR, et al. Functional MRI of language lateralization during development in children. International Journal of Audiology, 46: 533–551, 2007. Jansen A, Menke R, Sommer J, Forster AF, Bruchmann S, Hempleman J, et al. The assessment of hemispheric lateralization in functional MRI – Robustness and reproducibility. NeuroImage, 33: 204–217, 2006. Karbe H, Thiel A, Weber-Luxenburger G, Herholz K, Kessler J, and Heiss WD. Brain plasticity in poststroke aphasia: What is the contribution of the right hemisphere? Brain and Language, 64: 215–230, 1998. Kave G. The development of naming and word fluency: Evidence from Hebrew-speaking children between ages 8 and 17. Developmental Neuropsychology, 29: 493–508, 2006. Kloppel S and Buchel C. Alternatives to the Wada test: A critical view of functional magnetic resonance imaging in preoperative use. Current Opinion in Neurology, 18: 418–423, 2005. Lazar RM, Speizer AE, Festa JR, Krakauer JW, and Marshall RS. Variability in language recovery after first-time stroke. Journal of Neurology, Neurosurgery, and Psychiatry, 2007. Leger A, Demonet JF, Ruff S, Aithamon B, Touyeras B, Puel M, et al. Neural substrates of spoken language rehabilitation in an aphasic patient: An fMRI study. NeuroImage, 17: 174–183, 2002. Liegeois F, Connelly A, Cross JH, Boyd SG, Gadian DG, VarghaKhadem F, et al. Language reorganization in children with early-onset lesions of the left hemisphere: An fMRI study. Brain, 127: 1229–1236, 2004. Liepert J, Hamzei F, and Weiller C. Motor cortex disinhibition of the unaffected hemisphere after acute stroke. Muscle & Nerve, 23: 1761–1763, 2000. Mai Juergen K, Paxinos George, and Assheuer Joseph K. Atlas of the Human Brain. 2nd ed. Academic Press, 2004. Murase N, Duque J, Mazzocchio R, and Cohen LG. Influence of interhemispheric interactions on motor function in chronic stroke. Annals of Neurology, 55: 400–409, 2004. Musso M, Weiller C, Kiebel S, Muller SP, Bulau P, and Rijntjes M. Training-induced brain plasticity in aphasia. Brain, 122: 1781–1790, 1999. Naeser MA, Martin PI, Baker EH, Hodge SM, Sczerzenie SE, Nicholas M, et al. Overt propositional speech in chronic nonfluent aphasia studied with the dynamic susceptibility contrast fMRI method. NeuroImage, 22: 29–41, 2004. Naeser MA, Martin PI, Nicholas M, Baker EH, Seekins H, Kobayashi M, et al. Improved picture naming in chronic aphasia after TMS to part of right Broca’s area: An openprotocol study. Brain and Language, 93: 95–105, 2005. Nudo RJ. Adaptive plasticity in motor cortex: Implications for rehabilitation after brain injury. Journal of Rehabilitation Medicine, 7–10, 2003. Oldfield RC. The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9: 97–113, 1971. Ors M, Ryding E, Lindgren M, Gustafsson P, Blennow G, and Rosen I. SPECT findings in children with specific language impairment. Cortex, 41: 316–326, 2005. Pecini C, Casalini C, Brizzolara D, Cipriani P, Pfanner L, and Chilosi A. Hemispheric specialization for language in children with different types of specific language impairment. Cortex, 41: 157–167, 2005. Penolazzi B, Spironelli C, Vio C, and Angrilli A. Altered hemispheric asymmetry during word processing in dyslexic children: An event-related potential study. NeuroReport, 17: 429–433, 2006. Poeppel D. A critical review of PET studies of phonological processing. Brain and Language, 55: 317–351. discussion 352–385, 1996.

216

cortex 47 (2011) 202–216

Price CJ and Crinion J. The latest on functional imaging studies of aphasic stroke. Current Opinion in Neurology, 18: 429–434, 2005. Pugh KR, Mencl WE, Jenner AR, Katz L, Frost SJ, Lee JR, et al. Neurobiological studies of reading and reading disability. Journal of Communication Disorders, 34: 479–492, 2001. Pujol J, Soriano-Mas C, Ortiz H, Sebastian-Galles N, Losilla JM, and Deus J. Myelination of language-related areas in the developing brain. Neurology, 66: 339–343, 2006. Richter M, Miltner WH, and Straube T. Association between therapy outcome and right-hemispheric activation in chronic aphasia. Brain, 131: 1391–1401, 2008. Rosen HJ, Petersen SE, Linenweber MR, Snyder AZ, White DA, Chapman L, et al. Neural correlates of recovery from aphasia after damage to left inferior frontal cortex. Neurology, 55: 1883–1894, 2000. Ruff IM, Petrovich Brennan NM, Peck KK, Hou BL, Tabar V, Brennan CW, et al. Assessment of the language laterality index in patients with brain tumor using functional MR imaging: Effects of thresholding, task selection, and prior surgery. American Journal of Neuroradiology, 29: 528–535, 2008. Rutten GJ, Ramsey NF, van Rijen PC, and van Veelen CW. Reproducibility of fMRI-determined language lateralization in individual subjects. Brain and Language, 80: 421–437, 2002. Saunders DE, Clifton AG, and Brown MM. Measurement of infarct size using MRI predicts prognosis in middle cerebral artery infarction. Stroke, 26: 2272–2276, 1995. Saur D, Lange R, Baumgaertner A, Schraknepper V, Willmes K, Rijntjes M, et al. Dynamics of language reorganization after stroke. Brain, 129: 1371–1384, 2006. Schmithorst VJ, Wilke M, Dardzinski BJ, and Holland SK. Correlation of white matter diffusivity and anisotropy with age during childhood and adolescence: A cross-sectional diffusion-tensor MR imaging study. Radiology, 222: 212–218, 2002. Seghier ML, Lazeyras F, Pegna AJ, Annoni JM, Zimine I, Mayer E, et al. Variability of fMRI activation during a phonological and semantic language task in healthy subjects. Human Brain Mapping, 23: 140–155, 2004.

Shalem Z and Lachman D. Diagnostic Battery for Hebrew Reading. Tel Aviv: Nitzan, 1998. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, and Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. The Journal of Neuroscience, 24: 8223–8231, 2004. Springer JA, Binder JR, Hammeke TA, Swanson SJ, Frost JA, Bellgowan PS, et al. Language dominance in neurologically normal and epilepsy subjects: A functional MRI study. Brain, 122: 2033–2046, 1999. Thivard L, Hombrouck J, du Montcel ST, Delmaire C, Cohen L, Samson S, et al. Productive and perceptive language reorganization in temporal lobe epilepsy. NeuroImage, 24: 841–851, 2005. Voets NL, Adcock JE, Flitney DE, Behrens TE, Hart Y, Stacey R, et al. Distinct right frontal lobe activation in language processing following left hemisphere injury. Brain, 129: 754–766, 2006. Warburton E, Price CJ, Swinburn K, and Wise RJ. Mechanisms of recovery from aphasia: Evidence from positron emission tomography studies. Journal of Neurology, Neurosurgery, and Psychiatry, 66: 155–161, 1999. Ward NS, Brown MM, Thompson AJ, and Frackowiak RS. Neural correlates of outcome after stroke: A cross-sectional fMRI study. Brain, 126: 1430–1448, 2003. Wilson SM, Molnar-Szakacs I, and Iacoboni M. Beyond superior temporal cortex: Intersubject correlations in narrative speech comprehension. Cerebral Cortex, 18: 230–242, 2008. Winhuisen L, Thiel A, Schumacher B, Kessler J, Rudolf J, Haupt WF, et al. Role of the contralateral inferior frontal gyrus in recovery of language function in poststroke aphasia: A combined repetitive transcranial magnetic stimulation and positron emission tomography study. Stroke, 36: 1759–1763, 2005. Wood AG, Harvey AS, Wellard RM, Abbott DF, Anderson V, Kean M, et al. Language cortex activation in normal children. Neurology, 63: 1035–1044, 2004. Xu XJ, Zhang MM, Shang DS, Wang QD, Luo BY, and Weng XC. Cortical language activation in aphasia: A functional MRI study. Chinese Medical Journal, 117: 1011–1016, 2004.