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. 2014 Jan 22;35(8):3919–3931. doi: 10.1002/hbm.22448

A functional MRI study of the relationship between naming treatment outcomes and resting state functional connectivity in post‐stroke aphasia

Sophia van Hees 1,2,✉, Katie McMahon 3, Anthony Angwin 2, Greig de Zubicaray 4, Stephen Read 5, David A Copland 1,2,6
PMCID: PMC6869730  PMID: 24453137

Abstract

Background

The majority of studies investigating the neural mechanisms underlying treatment in people with aphasia have examined task‐based brain activity. However, the use of resting‐state fMRI may provide another method of examining the brain mechanisms responsible for treatment‐induced recovery, and allows for investigation into connectivity within complex functional networks

Methods

Eight people with aphasia underwent 12 treatment sessions that aimed to improve object naming. Half the sessions employed a phonologically‐based task, and half the sessions employed a semantic‐based task, with resting‐state fMRI conducted pre‐ and post‐treatment. Brain regions in which the amplitude of low frequency fluctuations (ALFF) correlated with treatment outcomes were used as seeds for functional connectivity (FC) analysis. FC maps were compared from pre‐ to post‐treatment, as well as with a group of 12 healthy older controls

Results

Pre‐treatment ALFF in the right middle temporal gyrus (MTG) correlated with greater outcomes for the phonological treatment, with a shift to the left MTG and supramarginal gyrus, as well as the right inferior frontal gyrus, post‐treatment. When compared to controls, participants with aphasia showed both normalization and up‐regulation of connectivity within language networks post‐treatment, predominantly in the left hemisphere

Conclusions

The results provide preliminary evidence that treatments for naming impairments affect the FC of language networks, and may aid in understanding the neural mechanisms underlying the rehabilitation of language post‐stroke. Hum Brain Mapp 35:3919–3931, 2014. © 2014 Wiley Periodicals, Inc.

Keywords: anomia, rehabilitation, phonology, semantics, language network

INTRODUCTION

Previous studies investigating the neural mechanisms underlying treatment‐induced recovery in people with aphasia post‐stroke have primarily focussed on functional activity or structural integrity within discrete brain regions [e.g., Fridriksson, 2010; Meinzer et al., 2010; Parkinson et al., 2009]. However, focal lesions are likely to disrupt communication within a distributed network, which may also relate to functional outcomes in aphasia recovery [Warren et al., 2009], as well as outcomes of language rehabilitation [Marcotte et al., 2013]. Thus, investigation into the reorganization of functional networks post‐stroke may complement findings from previous studies, and has the potential to provide additional information for predicting treatment outcomes, as well as understanding the mechanisms underlying treatment‐induced recovery [Carter et al., 2012; Meinzer et al., 2013].

Analyses of functional connectivity (FC) are commonly conducted using a resting state condition, which refers to brain activity in the absence of any explicit input or output task. Previous studies have found that the regional amplitude of low frequency fluctuations (ALFF) during resting state fMRI correlate with behavioral measures [e.g., Wei et al., 2012; Zang et al., 2007]. Furthermore, brain regions that have similar functional properties have been found to display coherent fluctuations in activity during a resting state [Biswal et al., 1995]. Resting state functional connectivity (RSFC) patterns have also been found to predict task‐response in specific brain regions across different cortical and subcortical networks [e.g., De Luca et al., 2005], and are sensitive to variations in cognitive abilities across participants [e.g., Koyama et al., 2011]. Thus, by identifying regional activity amplitudes that correlate with behavioral performance during resting state fMRI, these regions can then be used as seeds to examine the functional and anatomical organization of brain systems.

FC analyses have been used to examine differences in connectivity in a variety of disorders, such as Alzheimer's disease [e.g., Supekar et al., 2008], schizophrenia [e.g., Zhou et al., 2007], and autism spectrum disorder [e.g., Weng et al., 2010], as compared to a healthy control group. Longitudinal changes in the FC of different networks have also been examined with respect to recovery following brain damage. For example, Hillary et al. [2011] found increased connectivity in networks to the insula and medial temporal regions during recovery from 3 to 6 months following moderate to severe traumatic brain injury. A study conducted by Park et al. [2011] reported dynamic changes in the lateralization of FC of motor networks in the first six months post‐stroke, where measures of FC at stroke onset were found to be positively correlated with motor outcomes. Additionally, increased frontoparietal integration has been found to correlate with language recovery [Sharp et al., 2010]. Such results suggest that investigation of FC may be useful in examining the mechanisms underlying recovery and identifying predictors of functional outcomes.

Few studies have examined the relationship between changes in FC and outcomes of different behavioral treatments. Leavitt et al. [in press] examined changes in RSFC following a treatment for memory impairments in patients with multiple sclerosis, and found increased connectivity in memory‐related networks post‐treatment, including the hippocampal network and components of the default mode network (DMN). The DMN is a group of regions more active during rest than during performance of a task; including the medial prefrontal cortex, lateral parietal cortex, medial temporal lobes, posterior cingulate, and precuneus, and is thought to be involved in internal mentation or low‐level attentional mechanisms [Buckner et al., 2008]. The DMN was also examined in a group of patients with aphasia who underwent a semantic‐based treatment for anomia, where improved integration of the posterior DMN was found following treatment; however, this change in FC was not correlated with treatment outcome [Marcotte et al., 2013]. Additionally, as this study examined connectivity exclusively within the DMN, there has been no examination of the relationship between RSFC of specific language networks and aphasia therapy outcomes. Furthermore, this study examined FC during a naming task, which can make interpretation difficult given treatment‐induced changes in task performance. One advantage of RSFC is that it is not confounded by changes in behavioral performance from pre‐ to post‐treatment, yet studies of RSFC before and after aphasia treatment are still lacking. The investigation of FC of language networks in aphasia may contribute to the understanding of the neural mechanisms underlying language impairments by identifying the neural mechanisms underlying different treatments, and providing predictors of treatment success.

One of the most common impairments in people with aphasia post‐stroke is difficulty naming (anomia), which is often targeted in the rehabilitation of language [Goodglass and Wingfield, 1997]. The treatment of anomia typically aims to target the underlying impairment using either semantic and/or phonologically‐based therapy tasks, dependant on the individual's primary locus of breakdown in word retrieval [Nickels, 2002]. However, the benefit of a particular treatment approach does not always relate to an individual's locus of impairment [e.g., Howard et al., 1985; Lorenz and Ziegler, 2009] and tasks within these different treatment approaches also differ in their level of difficulty and response demands required [Hickin et al., 2002], making direct comparisons difficult. There is also debate as to whether these different treatment approaches selectively target a particular component of word retrieval, or whether the difference has been overstated [Howard, 2000]. The use of resting‐state fMRI may aid in addressing these issues, by examining differences in the underlying neural networks, which may be responsible for the outcomes of different treatments.

The current study aimed to investigate the resting state activity and FC of specific language regions with respect to the outcomes of two different treatments for anomia that employed similar task structure and response demands, but targeted different cognitive components of word retrieval. Of interest was whether resting state activity and FC pre‐treatment were associated with treatment outcomes, whether changes post‐treatment were associated with treatment success, and whether this differed depending on the type of treatment employed. Competing hypotheses were examined as to whether treatment success would be associated with the normalization of connectivity within left hemisphere networks, or alternatively that compensatory networks in the right hemisphere underlie successful treatment.

METHODS

Participants

Eight participants with aphasia (five female; aged 41–69 years, M 56.38, SD 9.15) completed the study. All had experienced a single left hemisphere stroke (see Fig. 2 for lesion overlap map), had mild or moderate severity of aphasia, and were studied from 17 to 170 months (M 52.25; SD 49.84) post‐stroke (see Table 1 for demographic and lesion information). Twelve healthy older controls (six female; aged 40–81 years, M 63.50, SD 9.74) also participated in the study. All participants had English as a first or primary language, were right handed according to the Edinburgh Handedness Inventory [Oldfield, 1971], had normal or corrected‐to‐normal vision and hearing, and were within normal limits on the Mini Mental State Examination [Folstein et al., 1975] and Glasgow Depression Scale [Feher et al., 1992]. None of the participants had a history of any other neurological disease or disorder, mental illness, head trauma, alcoholism, cerebral tumor or abscess, severe dysarthria or apraxia of speech, or any contraindications for MRI. All participants gave their informed consent prior to participation in the study.

Figure 2.

Figure 2

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Lesion overlay map displaying regions of lesion overlap between participants.

Table 1.

Demographic and lesion information for participants with aphasia

Participant: P01 P02 P03 P04 P05 P06 P07 P08
Age (years) 60 60 41 52 56 48 69 65
Months post‐stroke 38 57 170 55 25 17 36 20
Gender F M F F F F M M
Classification Conduction Anomic Anomic Anomic Conduction Anomic Anomic Anomic
Severity Mild–Moderate Mild Mild Mild Moderate Mild Moderate Mild
Locus of breakdown Postsemantic /Phonological Semantics to phonology Semantics to phonology Semantics to phonology Postsemantic /phonological Semantics to phonology Semantic Semantic
Lesion volume 22.68 cm3 39.51 cm3 135.69 cm3 166.05 cm3 46.26 cm3 22.74 cm3 66.26 cm3 42.53 cm3
Lesion site MTG IFG (oper) IFG (oper) IFG IFG Putamen Inferior IFG

STG

SMG

Hippocampus

MTG

STG

Insula

Caudate

Putamen

Hippocampus

IFG (tri)

Mid/superior frontal

Rolandic oper insula

Cingulate (mid and anterior)

Caudate

Precentral

SMA

Mid/superior frontal

Rolandic oper

Insula

STG

Heschl's gyrus

SMG

Putamen

Precentral

Postcentral

STG

SMG

Rolandic oper

Precentral

Insula

Amygdala

Insula

Thalamus

Caudate

Temporal

MTG

STG

SMG

Insula

Hippocampus

Putamen

Caudate

Thalamus

STG

Insula

Rolandic Oper

Putamen
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All participants completed an assessment battery that included the Boston Naming Test [Kaplan et al., 1983], the Pyramids and Palm Trees [Howard and Patterson, 1992], Western Aphasia Battery [Kertesz, 2007], as well as the Comprehensive Aphasia Test [Swinburn et al., 2004]. Additionally, 476 object pictures chosen from the International Picture Naming Project Database [Szekely et al., 2004] formed a naming battery, which was administered twice within the same week for participants with aphasia, and once for control participants.

Procedure

For participants with aphasia, the assessment battery was completed over three sessions prior to the first scan. Following the scan, participants received 12 therapy sessions over four weeks (three sessions per week). The treatment task (semantic or phonological) was alternated each session, with the order of delivery counterbalanced among participants, and a second scan conducted within 7–12 days of the final session. Control participants completed the assessment battery over two sessions prior to the scan.

Treatment

Full details of the treatment can be found in van Hees et al. [2013]. The semantic‐based treatment sessions employed Semantic Feature Analysis [SFA; Boyle and Coelho, 1995], and the phonologically‐based treatment sessions employed Phonological Components Analysis [PCA; Leonard et al., 2008]. The administration protocol for both treatment tasks were conducted in accordance with previous studies employing these tasks [e.g., Boyle and Coelho, 1995; Coelho et al., 2000; Leonard et al., 2008]. Three baseline measures of naming were collected (B1–3), which involved naming all items in the treatment and untreated sets for each individual. B1 and B2 were collected during initial assessment sessions and B3 prior to the first treatment session. Two post‐treatment measures of naming accuracy using the same items were also collected (P1–2); P1 immediately following the final therapy session, and P2 during follow‐up assessment 2–3 weeks after the final therapy session. Treatment outcomes were analyzed using a weighted Wilcoxon One‐Sample test. For each condition (PCA, SFA, and Untreated) improvement in naming accuracy over the three baseline measures (B1–B3) was compared for improvement in naming accuracy immediately post‐treatment (P1), and at follow‐up assessment (P2).

Image Acquisition and Processing

Images were acquired using a 4 Tesla Bruker MedSpec MRI system. The resting state scan consisted of 150 contiguous echo planar imaging (EPI) whole‐brain functional volumes (TR = 2100 ms, TE = 30 ms, Slice thickness 3 mm, with 0.6 mm gap, 36 slices, 64 × 64 matrix, FOV = 230 mm). During the resting state scan, participants were instructed to relax and not think of anything in particular. A high resolution 3D T1‐weighted structural image was also acquired in the same session (MPRAGE; TR = 1500 ms; TE = 3.35 ms; TI = 700 ms; 256 × 256 × 192; resolution (0.9 mm3).

Pre‐processing was carried out using Data Processing Assistant for Resting‐State fMRI advanced edition (DPARSFA), version 2.3 [Chao‐Gan and Yu‐Feng, 2010]. The first 5 volumes of the functional images were discarded to allow for signal equilibrium. Slice timing and head motion correction were performed, and a mean functional image was obtained for each participant. Each participant's T1‐weighted structural image was coregistered to their mean functional image and then segmented. Nine nuisance covariates (six head motion parameters, the global signal, white matter signal, and CSF signal) were removed. Functional images were then normalized into the standard Montreal Neurological Institute (MNI) space using the T1 image unified segmentation, resampled to 2 mm, smoothed using an 8 mm full‐width at half maximum Gaussian smoothing kernel, and band pass filtered (0.001 to 0.08 Hz). ALFF and FC analyses were conducted using the DPARSFA toolbox [Chao‐Gan and Yu‐Feng, 2010], and statistical analyses were performed using the Resting‐State fMRI Data Analysis Toolkit (REST), version 1.8 [Song et al., 2011].

ALFF values were extracted from each voxel within a mask of left hemisphere language‐related regions and their right hemisphere homologs (including the inferior frontal gyrus (IFG), superior/middle/inferior temporal gyri, supramarginal, and angular gyri) and divided by the global mean ALFF value for each participant. A correlation analysis was then performed to identify the relationship between pre‐ and post‐treatment ALFF maps and percent improvement scores following treatment. Regions that positively correlated with treatment outcome were then used as seeds for the FC analyses [see Wei et al., 2012 for similar approach]. Seed regions were placed at the peak MNI coordinates from the ALFF‐ behavior correlation analysis using a 4 mm sphere. Fisher z‐score transformations were then performed to generate z‐FC maps for each participant.

One‐sample t‐tests were conducted on the control group z‐FC maps to identify the normal network. Two sample t‐tests were then conducted to compare z‐FC maps between participants with aphasia and the control group both pre‐ and post‐treatment. Paired t‐tests were also performed to identify changes on pre‐versus post‐treatment z‐FC maps. Corrected cluster thresholds were determined using Monte Carlo simulations with the program AlphaSim in AFNI, implemented in the REST toolbox [Song et al., 2011]. Using a cluster connectivity criterion of 5 mm (edge connected), spatial smoothness of 8 mm, and a height threshold of p < 0.001, family wise error rates of p < 0.05 were achieved with a minimum cluster threshold of 22 contiguous voxels for both the ALFF and FC analyses. The locations of the peak maxima of significant clusters were determined using xjview (http://www.alivelearn.net/xjview).

RESULTS

Treatment Results

A weighted Wilcoxon One‐Sample test was used to identify significant differences between improvement in naming accuracy over the three baseline measures (B1–B3) and improvement immediately post‐treatment (P1), as well as at follow‐up assessment (P2), for each condition (PCA, SFA, and Untreated items). Seven of the eight participants with aphasia (P01, P03, P04, P05, P06, P07, and P08) showed significant improvements in naming accuracy immediately post‐treatment for items treated with PCA (p < 0.05). Six of these seven participants maintained significant improvements at follow‐up assessment (P01, P03, P04, P06, P07, and P08) (p < 0.05). Four of the eight participants (P01, P03, P04, and P05) showed significant improvements in naming accuracy immediately post‐treatment for items treated with SFA, (p < 0.05). Three of these four participants maintained significant improvements at follow‐up assessment (P01, P04, and P05) (p < 0.05). No significant improvements in naming accuracy were found for untreated items (p > 0.05). See Figure 1 for individual results.

Figure 1.

Figure 1

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Naming accuracy data for each participant: B1–3 = average of baseline 1–3 (pre‐treatment), P1 = immediately post‐treatment, P2 = follow‐up 2–3 weeks post‐treatment, PCA = items treated using Phonological Components Analysis, SFA = items treated using Semantic Feature Analysis, (*p < 0.05, **p < 0.01, ***p < 0.001).

In addition to accuracy, changes in the type of errors made from pre‐ to post‐treatment were also examined. For this analysis, errors were coded as semantic paraphasias, phonological paraphasias, or no‐responses. A McNemar's test was used to identify any significant differences in the total number of each error type from the final baseline as compared to both post‐treatment and follow‐up scores for each treatment set. Two participants, P02 and P07, showed no significant changes in any error type at either time point (p > 0.05, two tailed). For items treated with PCA, P01, P03, P04, and P06 all showed a significant decrease in no‐response errors at both time points (p < 0.05, two tailed). P08 showed a significant decrease in semantically‐related errors at both time points (p < 0.05, two tailed). P05 showed a significant decrease in phonologically‐related errors immediately post‐treatment (p < 0.05, two tailed); however this was not maintained at follow‐up. For items treated with SFA, P01, P03, and P04 showed a significant decrease in no‐response errors at both time points (p < 0.05, two tailed). P06 also showed a significant decrease in no‐response errors at follow‐up assessment only (p < 0.05, two tailed). Similarly, P05 showed a significant decrease in phonologically‐related errors at follow‐up assessment only (p < 0.05, two tailed). However, significant changes may only have been possible for individuals who had a large number of errors pre‐treatment, or errors limited to one type.

Resting‐State fMRI Results

Because of technical problems, scan acquisitions were ended prematurely for three participants with aphasia; however, the acquisitions were not affected. Thus, analyses were conducted using 119 volumes for P01 and 99 volumes for both P03 and P04. For all other participants, analyses were conducted using 150 volumes.

ALFF correlations

No significant correlations were found between ALFF maps and percent improvement scores for items treated with SFA either pre‐ or post‐treatment. For items treated with PCA, the ALFF of four regions significantly correlated with improvement scores. These regions included the right middle temporal gyrus (MTG) pretreatment, and the left MTG, pars triangularis of the right IFG, and left supramarginal gyrus (SMG) post‐treatment (see Fig. 3). These four regions were then used as ROIs for the seed‐based FC analyses. The seeds were also checked against patient lesion volumes to determine if any overlapped. The lesion maps were subtracted from the seed spheres and the final volume checked using FSL software v 4.1.6 [Jenkinson et al., 2012]. The only overlap was the SMG sphere with the lesion of P07, where 22% of the sphere overlapped with lesion volume. Additionally, to examine whether other relevant factors influenced the prediction of outcome, lesion volumes, and percent accuracy on the pretreatment naming battery were included as separate covariates in the correlation analyses. Including naming accuracy scores did not alter the results of the ALFF correlations, although when lesion volume was included as a covariate the left SMG result was no longer significant.

Figure 3.

Figure 3

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Correlations between pre‐ and post‐treatment ALFF maps and percent naming improvement for items treated with PCA. Images are displayed in radiological orientation.

Functional connectivity results

Control group

Activity in the right MTG seed was correlated with activity in the right lingual and precentral gyri, left inferior temporal, and postcentral gyri, as well as the fusiform and middle occipital gyri bilaterally. The left MTG seed correlated with activity in the left fusiform, lingual, and superior occipital gyri, as well as the right inferior/middle temporal and supramarginal gyri. The left SMG correlated with activity in the insula bilaterally, as well as the left cerebellum (VIII), right SMG, and superior temporal pole. Finally, the right IFG (pars triangularis) correlated with activity in the right thalamus and SMG, as well as the left IFG (pars triangularis), inferior parietal lobe, middle frontal gyrus, insula, putamen, and supplementary motor area (see Fig. 4 and Table 2).

Figure 4.

Figure 4

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Control group functional connectivity results for the four ROIs. MTG: middle temporal gyrus; SMG: supramarginal gyrus; IFG: inferior frontal gyrus. Images are displayed in radiological orientation.

Table 2.

Control group functional connectivity results (p < 0.001, k > 22)

Region Volume x y z Peak intensity
Right Middle Temporal Gyrus (50 −72 10)
Inferior Temporal 75 −48 −42 −20 6.5186
Fusiform 156 32 −42 −18 6.9505
Fusiform 30 −24 −52 −12 5.0301
Middle occipital 4954 50 −72 10 52.0854
Middle occipital 1924 −42 −64 2 8.6002
Lingual 22 10 −70 0 4.7944
Postcentral 59 −46 −26 66 5.9615
Precentral 43 44 −18 66 6.0035
Precentral 42 34 −14 72 6.5932
Left Middle Temporal Gyrus (−52 −60 8)
Inferior temporal 158 48 −40 −22 6.5838
Fusiform 43 −32 −62 −16 7.3174
Lingual 40 −18 −88 −18 5.6859
Fusiform 49 −32 −44 −12 7.0695
Middle temporal 3324 −52 −60 10 46.7789
Middle temporal 1246 40 −78 6 14.4783
Superior occipital 27 −16 −86 26 5.2765
Supramarginal 27 62 −24 28 5.3417
Right Inferior Frontal Gyrus (48 30 14)
Middle frontal 62 −32 60 −4 6.0152
Insula 42 −34 16 0 5.8899
Thalamus 48 4 2 2 6.1588
IFG (triangularis) 2971 48 30 14 62.9846
Putamen 46 −22 −2 2 7.3007
IFG (triangularis 1398 −48 20 24 9.5131
Inferior parietal 42 −50 −44 40 5.6504
Supramarginal 100 50 −40 40 6.5273
SMA 311 6 16 50 6.6637
Left Supramarginal Gyrus (−46 −42 26)
Cerebellum (VIII) 213 −20 −70 −46 6.4273
Insula 27 −36 4 −6 5.1965
Superior temporal pole 80 56 10 −4 6.4594
Insula 37 40 0 2 5.8327
Supramarginal 2545 −46 −42 26 42.721
Supramarginal 251 48 −30 32 6.0972
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Table 3.

Functional connectivity results of two‐sample t‐tests between controls and participants with aphasia pre‐ and post‐treatment (p < 0.001, k > 22)

Region Volume x y z Peak intensity Region Volume x y z Peak intensity
Right middle temporal gyrus seed (50 −72 10)
Controls > Participants with aphasia Pretreatment Controls < Participants with aphasia pretreatment
– – – – – – Superior frontal gyrus 49 −14 26 38 5.2774
Controls > Participants with aphasia Post‐treatment Controls < Participants with aphasia Post‐treatment
Lingual gyrus 22 20 −88 −4 4.3594 Middle frontal gyrus 47 −34 38 20 5.3714
Mid cingulum 100 2 −12 30 5.3664
Left middle temporal gyrus seed (−54 −60 8)
Controls > Participants with aphasia Pretreatment Controls < Participants with aphasia pretreatment
Superior temporal gyrus 40 −56 −44 18 5.5055 Thalamus 92 −12 −16 20 6.1777
Caudate 27 16 −2 22 5.6430
Controls > Participants with aphasia Post‐treatment Controls < Participants with aphasia post‐treatment
Mid cingulum 34 2 −50 32 4.2904 Inferior frontal gyrus (orbitalis) 30 −46 46 −14 5.6955
Postcentral 39 −32 −26 44 5.1788
Left supramarginal gyrus seed (−46 −42 26)
Controls > Participants with aphasia Pretreatment Controls < Participants with aphasia pretreatment
Cerebellum (Crus 2) 27 −2 −78 −34 4.4851 – – – – – –
Controls > Participants with aphasia Post‐treatment Controls < Participants with aphasia post‐treatment
Cerebellum (VIII) 181 −24 −64 −56 Superior medial frontal gyrus 34 −8 70 2 6.4228
Insula 31 −36 6 −6 Superior parietal lobe 39 −16 −62 48 4.5778
Middle frontal gyrus 51 −26 30 50 4.9315
Right inferior frontal gyrus seed (48 30 14)
Controls > Participants with aphasia Pretreatment Controls < Participants with aphasia pretreatment
Middle frontal gyrus 36 −38 54 −14 4.9612 Superior temporal pole 33 36 16 −30 5.5967
Insula 38 −32 14 0 4.6336 Angular gyrus 24 48 −70 30 4.5687
Caudate 91 −18 20 2 5.3480
Superior medial frontal 30 12 48 0 5.2144
Inferior frontal gyrus (tri) 22 −50 24 6 4.4641
Inferior frontal gyrus (oper) 97 −50 10 10 5.0699
Inferior frontal gyrus (oper) 48 −40 14 22 5.1358
Supplementary motor area 38 0 22 54 4.8598
Controls > Participants with aphasia Post‐treatment Controls < Participants with aphasia post‐treatment
Cerebellum (VI) 37 32 −22 −36 6.0633 – – – – – –
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Participants with aphasia

For the right MTG pre‐treatment, participants with aphasia only showed greater FC with the left superior frontal gyrus as compared to controls. Post‐treatment, participants with aphasia showed greater FC with the left middle frontal gyrus as well as the right mid cingulum, whereas the controls showed greater FC with the right lingual gyrus. For the left MTG pre‐treatment, participants with aphasia showed greater connectivity with the left thalamus and right caudate as compared to controls, whereas controls showed greater connectivity with the left superior temporal gyrus (STG). Post‐treatment, participants with aphasia no longer showed less connectivity with the left STG, although controls did show greater connectivity with the right mid cingulum as well as the left postcentral gyrus. Participants with aphasia showed greater connectivity between the left MTG and the left IFG (pars orbitalis) as compared to controls post‐treatment. However, no significant changes in FC were found from pre‐ to post‐treatment for either the left or right MTG.

For the right IFG (pars triangularis), the control group showed greater FC with the left IFG, middle frontal gyrus, SMA, insula, caudate, and right superior frontal gyrus as compared to participants with aphasia pre‐treatment, whereas participants with aphasia showed greater FC with the right superior temporal pole and angular gyrus. Post‐treatment, there was no longer a significant difference in the connectivity between the right IFG and left hemisphere regions, where only the right cerebellum showed significantly greater FC for controls as compared to participants with aphasia. Additionally, connectivity between the right IFG and right superior temporal pole and angular gyrus was no longer significantly greater than controls post‐treatment.

For the left SMG, the controls only showed greater FC with the left cerebellum as compared to participants with aphasia pre‐treatment. Post‐treatment, controls still showed greater FC with the left cerebellum, as well as the left insula, whereas participants with aphasia showed greater FC with the left superior and middle frontal gyri, and the superior parietal lobe. For both the right IFG and left SMG, increased connectivity was found with the right inferior temporal gyrus (ITG), as well as the left insula for the right IFG from pre‐ to post‐treatment. The right IFG also showed reduced connectivity with the right caudate and mid cingulum, and the left SMG showed reduced connectivity with the right cerebellum post‐treatment. See Table III for a summary of the FC results.

DISCUSSION

A detailed interpretation of treatment outcome can be found in van Hees et al. [2013]. Briefly, seven out of the eight participants significantly improved in naming items treated with the phonological task, whereas only four participants significantly improved in naming items treated with the semantic task. The two participants with predominantly semantic impairments did not significantly improve in naming items treated with the semantic task, whereas the two participants with predominantly phonological impairments significantly improved for both treatments, with greater maintenance of items treated with the semantic task. The four participants with impaired mapping between semantics and phonology had mild anomia, where ceiling effects limited the ability to compare the outcome of the different treatments. Additionally, analysis of changes in error types following treatment was also examined, although as the majority of participant's showed decreases in “no response” errors, no strong conclusions can be made regarding the mechanisms of treatment effects for these participants. However, overall the treatment results provide some evidence that targeting the relatively spared process may be beneficial, and that treatment outcome may differ dependent on the primary process targeted. The aim of the current study was to examine resting state activity and FC of the language network, with respect to the outcomes of these two treatments.

Correlations Between Pre‐ and Post‐treatment ALFF and Improved Naming

For the phonological treatment, the ALFF in the right MTG pre‐treatment correlated with greater outcomes, with a shift to the left MTG and SMG, as well as right IFG, post‐treatment. These results suggest that the ALFF within language regions at rest is affected by the amount of improvement following naming treatment focussed on word form. In contrast, no significant correlations were found between the ALFF and outcomes for the semantic treatment either pre‐ or post‐treatment. The absence of results for the semantic treatment differ from a previous study conducted by Marcotte et al., [2013], which found improved integration of the posterior DMN following SFA. Such discrepancies between the results of the current study and those of Marcotte et al. may be due to a number of factors. First, Marcotte et al. investigated connectivity exclusively within the DMN, whereas the current study examined connectivity of regions within the language network. Second, Marcotte et al. investigated connectivity using a naming task, which may have been confounded by task performance. Finally, differences may relate to the smaller treatment gains found for the semantic approach in the current study, as fewer participants showed significant improvements in naming items treated with SFA.

However, the shift from right to left MTG activity following successful phonologically‐based treatment supports previous studies that suggest the left MTG plays a crucial role in naming ability. For example, Hillis et al. [2006] examined 87 participants and found reperfusion of this region during the acute stage 3–5 days poststroke to be associated with improved naming. Baldo et al. [2012] used voxel‐based lesion symptom mapping with 96 participants and found the mid‐posterior MTG and the underlying white matter to be critical for retrieval of object names. Damage to this region has also been found to have a negative effect on treatment outcome [Fridriksson, 2010]. Additionally, the structural and functional connectivity of the left MTG has been found to involve a large network, including left hemisphere language regions, right hemisphere homologs, as well as regions outside the traditional language network, suggesting a central role in language processing [Turken and Dronkers, 2011].

Previous studies have suggested that right IFG activity may be caused by disinhibition of the right hemisphere in the presence of left hemisphere lesions, which may interfere with language recovery [e.g., Rosen et al., 2000]. However, other studies have suggested that activity in the right IFG may be compensatory and support language recovery [e.g., Blasi et al., 2002]. Activity in the right IFG may represent the up‐regulation of non‐linguistic cognitive processing, which may support greater improvements in treatment [van Oers et al., 2010]. It has also been suggested that a relationship exists between lesion size and the success of hemispheric transfer, where larger lesions may result in more complete transfer of functions to the contralateral hemisphere. Conversely, in the presence of a smaller lesion, intact areas of the damaged hemisphere may inhibit complete transfer [Grafman, 2000]. Such an account of hemispheric transfer may explain why the two participants with large lesions (P03 and P04) both showed significant improvements following treatment. However, the successful transfer of functions to the right hemisphere may also depend on the site of lesion. In a meta‐analysis of neuroimaging studies in chronic aphasia, patients with lesions involving the left IFG have been found to more reliably activate the right IFG during language tasks than those with lesions sparing the left IFG [Turkeltaub et al., 2011]. In the current study, more than half of the participants with aphasia had lesions involving the left IFG, which may explain why activity in the right IFG was found to correlate with improvements following treatment.

Additionally, in order to examine whether other relevant factors influenced the prediction of treatment outcome, lesion volumes and percent accuracy on the naming battery were included as separate covariates in the ALFF correlation analyses. Including naming accuracy scores pre‐treatment did not alter the results of the ALFF correlations, and when lesion volume was included as a covariate only the left SMG result was no longer significant. Additionally, one participant's lesion was found to overlap with the left SMG seed. Overall these results suggest that ALFF provides strong predictive power for treatment outcome, and that resting‐state activity may provide an alternative method for investigating the neural mechanisms underlying treatment‐induced recovery. However, lesion size and damage to the left SMG may also contribute to treatment outcome, particularly for phonologically‐based treatment.

Treatment‐Induced Changes in Functional Connectivity

Compared to controls, participants with aphasia showed reduced connectivity between the left MTG and STG pretreatment. However, this difference was no longer significant post‐treatment, which may represent a normalization of this network following successful treatment. Furthermore, the STG has been associated with phonological processing [Vigneau et al., 2006; Wilson et al., 2009], suggesting that treatment focussed on word form information may improve connectivity between regions associated with accessing word forms. In contrast, no difference was found in the connectivity of the left MTG and IFG between controls and participants with aphasia pre‐treatment. However, participants with aphasia showed greater connectivity between these regions post‐treatment as compared to controls. This change may reflect up‐regulation of FC between left hemisphere frontal and temporal language regions post‐treatment. Saur et al. [2008] identified two routes connecting the frontal and temporal language regions; a dorsal route associated with phonological processing, and a ventral route associated with semantic processing. As the ALFF in the left MTG post‐treatment was found to correlate with treatment outcome for the phonological treatment, increased fronto‐temporal connectivity may reflect treatment‐induced changes in the dorsal route. However, as some participants also improved on naming items treated with the semantic task, this increased connectivity may also reflect changes in the ventral route.

Similarly, up‐regulation of connectivity between the left SMG and superior/mid frontal gyri and the superior parietal gyrus was found for participants with aphasia post‐treatment. The left SMG and dorsolateral prefrontal cortex (DLPFC) have been implicated in verbal working memory [Buchsbaum and D'Esposito, 2008], where the left SMG has been associated with the phonological store of the phonological loop [Awh et al., 1996; Paulescu et al., 1993], or alternatively in the focussing of attention [Chein et al., 2003], whereas the DLPFC has been associated with top‐down executive control [Curtis and D'Esposito, 2003]. Thus, up‐regulation of connectivity between these regions may underlie improved word retrieval following treatment focussing on word form.

Furthermore, the control group also showed greater connectivity between the right and left IFG as compared to participants with aphasia pre‐treatment, which was no longer significant post‐treatment, suggesting a normalization of interhemispheric connectivity following treatment. Similarly, participants with aphasia showed significantly greater connectivity between the right IFG and the right superior temporal pole and angular gyrus as compared to controls pre‐treatment, regions that have been identified as part of the semantic network in the left hemisphere [Sharp et al., 2000]. However, greater right hemisphere connectivity in these regions was no longer significantly different to controls post‐treatment, suggesting a shift to the left hemisphere and normalization in the connectivity of the right IFG post‐treatment. Furthermore, this result suggests some semantic involvement in the modulation of connectivity, which may be attributed to functional changes in those participants who improved following SFA, or that the PCA treatment also engaged semantic processing.

When FC was directly compared pre‐ and post‐treatment; however, both the left SMG and right IFG showed an increase in connectivity with the right ITG, whereas no significant change was found in the connectivity of the left MTG. These results suggest that right hemisphere mechanisms may also play a role in supporting treatment‐induced recovery in the presence of left hemisphere damage. Although differences were identified when compared to the control group, the absence of significant changes from pre‐ to post‐treatment for the connectivity of the left MTG highlights the need to interpret these results with some caution. As the current study is based on a small sample size with heterogeneous lesion sites and language symptoms, larger numbers of participants will be required to identify consistent changes associated with successful treatment across participants. Larger numbers of participants would also allow for sub‐grouping based on lesion location, language symptoms, or treatment response. For example, the participants who did not benefit from the semantic therapy, or did not maintain improvements at follow‐up, all had lesions involving the left caudate/striatum, which has been associated with impaired selection amongst competing semantic representations [Copland, 2003]. Such differences in lesion involvement could be more systematically examined with greater numbers of participants.

Another limitation of the current study was the use of an alternating treatment design, which limits the ability to exclusively attribute changes in neural activity to one treatment approach, as each treatment may have had some effect on the other. Although it should be noted that no generalization was found to untreated items and some participants showed a selective benefit from one approach over the other, and so it can be argued that each treatment was having distinct effects. However, with limited improvements for the semantic‐based treatment in this group, no significant correlations between treatment outcome and resting state activity were found for items treated with SFA, precluding any conclusions being drawn regarding this relationship. Despite these limitations, the results provide preliminary evidence that the neural mechanisms underlying successful treatment for anomia may involve distributed networks, where the use of resting state fMRI allows for the investigation of complex neural changes associated with treatment‐induced recovery. Thus, future studies in this area may aid in determining predictors of treatment outcome for different therapy approaches, in order to provide more effective targeted treatment for people with aphasia.

ACKNOWLEDGMENTS

The authors would like to acknowledge the University of Queensland's Aphasia Registry for the recruitment of participants with aphasia, and the University of Queensland's Aging Mind Initiative for recruitment of control participants. They also thank Aiman Al Najjar and Charlene Pearson for their contribution to data collection.

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