Dosage and Predictors of Naming Treatment Response in Aphasia
Phase: NA
Conditions: Aphasia, Stroke
Sponsor: VA Office of Research and Development

 
O0832-R 
 
Dosage and Predictors of Naming Treatment Response in Aphasia 
 
NCT02005016 
 
 
Date: August 30, 2019 

A. Background and Significance 
Language and communicative impairments following stroke (aphasia) affect more than 30% of stroke 
survivors, with an incidence of over 180,000 new cases annually and a prevalence greater than that of 
Parkinson’s Disease. The consequences of aphasia are far reaching and can affect psychosocial adjustment, 
family role participation, vocational opportunities and the ability to function independently in society. Recent 
estimates suggest that VHA outpatient clinics see 2000 new cases of aphasia each year[26]. Based on 
estimates of stroke prevalence among veterans[27] and the prevalence of aphasia in stroke[28], it can be 
conservatively estimated that approximately 20,000 enrolled VHA patients and 100,000 US veterans are 
currently living with the condition. Despite the prevalence of aphasia and its broad social and economic 
consequences, access to effective aphasia rehabilitation services for community-dwelling veterans is limited. 
In response to this need, the VA Pittsburgh Healthcare System GRECC established the Program for 
Intensive Residential Aphasia Treatment and Education (PIRATE). PIRATE employs a service delivery model 
that provides 4 weeks of intensive, evidenced-based aphasia treatments to community dwelling veterans while 
they reside in a residential setting on the VAPHS campus. PIRATE currently serves 18 veterans per year in bi-
monthly sessions. Resource limitations associated with PIRATE and aphasia treatment in general require that 
treatments be offered to those veterans most likely to benefit from them, in the most cost-effective doses. 
This proposed research directly addresses these inter-related issues by investigating response to 
intensive semantically-oriented naming treatment for veterans with aphasia. Naming is the most common 
domain of impairment among persons with aphasia.[18] First, the research seeks to identify neural, cognitive, 
and psycholinguistic factors that predict positive treatment response. It builds on promising results indicating 
that individuals with a range of language-impairment profiles may benefit from semantically-oriented naming 
treatment[12, 19], and that cognitive function[10] as well as degree of cortical and subcortical left-hemisphere 
damage may be predictive of naming treatment outcomes[21]. Identifying factors that are prognostic of positive 
treatment response is critical for effective allocation of limited healthcare resources. Second, the research 
examines the dose-response relationship for naming therapy when delivered on an intensive schedule. An 
intensive schedule is widely argued to be important to maximizing treatment gains[29, 30] and it is increasingly 
being adopted as a treatment delivery model[5], with at least 11 organizations in North America currently 
offering intensive aphasia therapy programs. Defining the dose-response relationship for aphasia therapy is a 
critical first step in determining optimum dosage levels.  Providing answers to these two interlocking questions 
– for whom is aphasia therapy effective, and how much of it is needed? – can set transformative new 
standards for how aphasia treatment is delivered within VHA. 
A.1 Rehabilitation of communicative and naming impairments in aphasia 
A.1.1. Stimulation of semantic representations relevant to naming: Semantic Feature Analysis 
As noted above, naming is the most common domain of impairment among persons with aphasia. Over 
85% of PIRATE participants to date have received treatment focused on spoken word production, and word-
finding difficulty is a salient and functionally significant disability for many veterans living with aphasia. 
Psycholinguistically-motivated treatment approaches targeting word-production deficits have shown significant 
evidence of efficacy. These approaches facilitate successful naming by stimulating one or more stages of the 
word-production process, engaging clients in activities designed to strengthen semantic or phonological 
processes through repeated structured practice. These treatment activities are intended to make effortful 
encoding processes more automatic for persons with aphasia, facilitating successful naming performance in 
therapy and broader communicative contexts. Remediating these individuals’ word-production deficits is a 
critical component in the broader effort to improve communicative function among veterans with aphasia. 
The treatment that will be administered to all participants in the proposed research is Semantic Feature 
Analysis (SFA)[14, 31]. SFA is a naming treatment in which participants are first asked to name a target picture. 
Then, regardless of success, they are assisted by the clinician in generating six semantic features of the target.  
Following this, if the participant has not yet successfully produced the target word, the clinician provides it and 
requires the participant to repeat it and review its features. SFA is based on spreading-activation models of 
semantic processing[32] and is consistent with current models of word production, which posit that word 
production begins with activation of conceptual semantic representations, which in turn activate corresponding 
lexical representations[33, 34]. Under this view, early stages of naming an object involve activation of the object’s 
semantic features. This activation spreads through the semantic network, activating concepts sharing those 
features and associated lexical representations. The representation with most activation is ultimately selected 
for phonological encoding. The rationale for SFA is that activation of the semantic network surrounding a target 
item should help increase the activation of the target above the threshold required for successful production[14].  

SFA was originally proposed as a method for remediating the processes underlying word retrieval[35], 
i.e., a treatment that should not require conscious use of a compensatory strategy. However, it has also been 
noted that in some cases SFA may function as a word retrieval strategy[15, 18, 36]. Regardless of whether SFA 
operates by a restorative or a compensatory mechanism in any given case, active generation of semantic 
features by participants appears to be an important component of the approach[37]. SFA will be used as a 
remediatory treatment in the proposed research, in line with the original formulation of SFA and with PIRATE’s 
programmatic focus on impairment-oriented aphasia treatment. 
SFA has several attributes that make it well suited for use in the proposed research. First, there is good 
preliminary evidence that SFA is associated with lasting improvement in the naming ability of persons with 
aphasia. SFA or closely related approaches have been applied to at least 21 participants with aphasia across 
10 single-subject design studies reported in the peer-reviewed literature[14-17, 36, 38-43]. Positive treatment effects 
on production of trained items were observed in 20 of these cases. Interestingly, and potentially relevant to 
Hypothesis 1b of the current proposal, the one individual that failed to respond to SFA had poor performance 
relative to other individuals with aphasia on (unspecified) nonverbal cognitive tests.[38] Clear maintenance of 
improved naming was observed post-treatment in 13 of the 18 cases in which it was assessed. In two of the 
five cases where treatment gains were not clearly maintained, there was maintenance of some but not all 
trained behaviors.[39, 40] Furthermore, as noted by Boyle[37], in four of these five cases the participants were 
administered an atypical variant of SFA: participants were not required to generate semantic features but 
instead reviewed features provided by the clinician. This finding again points to the importance of the active 
generation of semantic features by participants. 
Second, there is evidence that SFA treatment effects generalize to production of untreated words (i.e., 
response generalization) and to connected speech (i.e., stimulus generalization). Among the 20 individuals 
showing positive effects for trained items, response generalization was assessed in 19. In 15 of these cases 
there was positive evidence of response generalization. Three cases in which generalization to untreated items 
was not observed came from a single study[41]. In this study, treatment items were selected from very specific 
closed-set narrative contexts (e.g., recounting how to change a tire), which may have contributed to poor 
response generalization. A distinguishing feature of the remaining case that failed to show response 
generalization was that it was the only one that received treatment on verb rather than noun targets[42]. It is 
noteworthy that in 12 cases, improved naming generalized to items that were unrelated to any of the treated 
items. Also of note, generalization to untreated items was less consistent in 7 cases where bilingual 
participants reviewed but did not generate semantic features during treatment across two languages[39, 40]. 
Stimulus generalization to connected speech was evaluated in 10 cases, with positive results in nine[14-16, 36, 38, 
41, 42]. These findings suggest that SFA shows promise for inducing broad improvement in naming performance. 
A third factor that makes SFA suitable for the current proposal is that it appears to be appropriate for a 
relatively broad range of patients with aphasia, in terms of both language-impairment profile and severity. 
Patients responding positively to SFA have encompassed most of the commonly observed classical aphasia 
categories, including Broca’s, Wernicke’s, anomic, conduction, transcortical motor, and “fluent”. These patients 
have also encompassed a wide range of severity, with Western Aphasia Battery Aphasia Quotients ranging 
from 27 to 90.6. In two cases, participants were also reported to have apraxia of speech[39, 43]. Most 
importantly, the 21 participants discussed above included not only individuals with psycholinguistic profiles 
suggestive of lexical-semantic word production impairments[16, 36, 41-43] but also with combined lexical-semantic 
and phonological impairments[16, 41-43]. There is no specific evidence to date regarding the efficacy of SFA for 
patients with predominantly phonological impairments. However, studies using other semantically-oriented 
treatments suggest that they can result in improved naming in such patients in at least some cases[12, 44, 45].  
These latter findings are relevant to Hypothesis 1a, that semantically-oriented naming treatments will 
benefit individuals with lexical-semantic, phonological, and mixed naming impairments. Data from Wambaugh 
and from Abel[12, 44, 45] suggest that semantic cueing hierarchies may benefit even individuals with primarily 
phonological naming deficits. In fact, close inspection of the data from two of these studies reveals that on 
average, participants with primarily phonological impairments (n =4) responded more favorably to semantically-
oriented treatment than patients with primarily semantic impairments (n=4). The phonologically impaired 
participants showed an average improvement of 37 percentage points (sd = 17), while the semantically 
impaired participants showed an average improvement of 20 percentage points (sd =9). An additional 
participant classified as having mixed semantic and phonological impairments demonstrated an intermediate 
response, with an average improvement of 33 percentage points across two sets of items. Although this 
comparison is based on limited data, it does suggest that psycholinguistic profile is unlikely to be a strong 
predictor of response to SFA treatment. Furthermore, there is reason to expect that improved access to lexical-

semantic representations may stimulate subsequent phonological processes in word production. In all models 
of word production, successful activation of a target lexical-semantic representation feeds forward to activate 
phonological representations needed to produce the associated word form. Successful stimulation of lexical-
semantic processing by SFA may therefore ease later phonological processing, by alleviating bottlenecks 
occurring at earlier levels of processing.  
While it is often assumed that individuals with lexical-semantic deficits will benefit most directly from 
semantically-oriented naming treatments, this assumption has rarely been directly tested in the literature. More 
generally, the question of which individuals are most likely to benefit from a specific course of aphasia 
treatment remains open. Comparing SFA treatment response across impairment-type groups – those with 
primarily lexical-semantic impairments, those with primarily phonological impairments, and those with mixed 
impairments – will provide important evidence regarding these questions in the aphasia treatment literature. 
A.1.2 Dosage of SFA and facilitation of naming function 
Existing studies of SFA, like most studies of impairment-focused aphasia therapy, have provided 
relatively low dosages of the therapy, e.g., 3 hours per week for four weeks[14, 15]. Participants in the research 
proposed below will receive much higher doses of SFA: 20 hours per week for 4 weeks. This dosage of SFA is 
feasible for veterans enrolling in the study: as reported in B.2 below, previous PIRATE participants have 
responded well to similar dosages of impairment-focused treatment and of SFA specifically. Equally 
importantly, this high dosage level is critical for achieving the study aims above, in several respects. First, high 
dosages of behavioral therapy are important for inducing significant and lasting behavioral changes. Evidence 
from the educational literature indicates that extended training – to and even past criterion for a learned 
behavior – improves short-term and long-term retention[46-48], results in greater endurance and resistance to 
distraction[49, 50], and increases generalization to related behaviors[47, 51]. Findings from the motor therapy 
literature also show that high-dosage therapy, particularly when administered on an intensive schedule, leads 
to improved function and retention[52-54]. Consistent with these findings, work examining the effects of 
constraint-induced language therapy[30, 55, 56] suggests that massed practice and multiple repetitions of specific 
training activities is important for maximizing gains. Furthermore, both constraint-induced and traditional 
model-based treatments appear to benefit from such high-dosage administration[57]. These findings all indicate 
that high dosages of aphasia therapy – involving multiple repetitions of therapy activities, training targets to or 
past criterion – are crucial for maximizing improvements, retaining those gains, and generalizing to untrained 
stimuli and other behaviors. 
Second, high treatment dosages like those proposed here are needed to induce neuroplasticity in 
treated individuals. The aim of aphasia therapy is to cause cortical reorganization, reflected in both grey- and 
white-matter changes. The neuroanatomical hypotheses above critically lean on the assumption that treatment 
will induce such neuroplasticity (viz. [11, 29, 57]). There is significant evidence from animal models that cortical 
reorganization of somatosensory areas may occur in response to extensive experience[58, 59], and that 
increasing the amount of sensory input facilitates this reorganization[58, 60, 61]. Furthermore, extended training of 
the sort described above (training to or beyond criterion) appears to be crucial for promoting such lasting 
cortical adaptation. Kleim and colleagues[62] found that while adult rats’ behavioral gains in response to motor 
training plateaued after 3 days, motor-cortex changes appeared only after 7 days or more of training. 
Relatedly, changes in MEG[57] and EEG measures[29] in response to constraint-induced aphasia therapy 
emerge after 30 hours or more of treatment. Extended repetition of therapy activities promotes lasting neural 
changes, of the sort assumed to underlie the structure-function correlations explored under Specific Aim 2. 
Third, there is evidence that higher dosage of training activities specific to SFA yields greater 
improvement for patients receiving SFA. SFA facilitates naming through stimulation of the semantic system, by 
prompting participants to generate semantic features associated with treated items. As discussed above, SFA 
shows evidence of acquisition of treated items as well as generalization to related untreated items, as do other 
naming treatments that stimulate the semantic system[63]. However, the speed of acquisition and the magnitude 
of generalization in SFA appear to depend on the number of repetitions of treatment activities patients 
complete. In two separate studies of SFA, patients reached criterion more quickly and/or showed stronger 
evidence of generalization when they were treated with fewer training exemplars (as few as 7) compared to 
more exemplars (up to 20)[15, 16]. Interestingly, this trend is mirrored in other studies of naming treatment in 
aphasia. In a recent meta-analysis of published naming treatment studies, Snell and colleagues found that the 
magnitude of treatment response was moderately negatively correlated with the number of exemplars used in 
treatment[64]. Both these findings point to the value of repetition in maximizing treatment gains: a larger number 
of treatment exemplars reduces the number of repetitions of each exemplar, as well as the number of 
treatment activities (such as feature generation) patients can complete in response to each exemplar. Parallel 

to the educational-literature findings above, extended training using SFA -- involving many repetitions of 
training activities and exemplars – appears to be important for effective acquisition and generalization to 
untreated stimuli. 
A.1.3 Summary 
Word-finding deficits represent a salient impairment for veterans with aphasia, and they are a common 
object of aphasia treatment in PIRATE and aphasia therapy more generally. Research examining response to 
treatment designed to remediate these deficits should employ well-defined naming treatment methods which 
target a single level of language function and are hypothesized to facilitate specific word-production 
processes[37]. SFA is one such naming treatment, which has successfully been used with individuals 
representing a range of aphasia severities, classifications, and language-impairment profiles. Comparing the 
response of individuals with different language-impairment profiles to this single well-defined treatment will 
provide important evidence regarding whether its benefits are specific to one group of individuals or to 
impairments of one word-production process. Furthermore, providing therapy at high dosage levels is 
important for generating lasting and measurable improvements in naming function, in order to test study 
hypotheses regarding the dose-response relationship and predictors of the size of treatment-induced change. 
A.2 Predictors of treatment response 
In addition to determining whether groups of individuals (classified by language-impairment profile) will 
respond to a specific naming treatment, it is also important to identify factors that predict whether particular 
clients will respond to treatment. Identifying such factors can inform decisions about treatment candidacy and 
treatment type. A range of candidate factors have been identified, including demographic variables (age at 
onset of aphasia, education level, pre-morbid intelligence), medical variables (general health, presence of co-
morbid conditions), and impairment-related factors (time post-onset of aphasia, aphasia type, aphasia severity, 
auditory comprehension). There is positive evidence of the effects of at least some of these factors: Robey[1] 
reports that studies treating moderately severe aphasia patients exhibited larger treatment effect sizes than did 
studies treating more mildly impaired patients, and that treatment administered in the acute stage (<3 months 
post-onset) resulted in greater gains than treatment administered in the chronic stage. However, there are also 
counterexamples to these generalizations. For instance, the sentence-treatment studies exhibiting the largest 
effect sizes among those surveyed by Beeson and Robey[65] all involved patients in the chronic phase of 
recovery. The question of how to identify the individuals aphasia treatment will benefit most thus remains open. 
A.2.1 Cognitive factors predicting treatment response 
One set of factors which have been shown to be potentially useful predictors of treatment response are 
non-language cognitive factors. There is a growing body of evidence suggesting that cognitive factors such as 
executive functioning, visuospatial skills, memory, and attention may carry significant predictive value for 
aphasia treatment outcomes[10, 66, 67].  While the exact nature of the relationship between linguistic and 
nonlinguistic cognitive processes remains unclear, it is undisputed that multiple cognitive processes are 
important for learning. Encoding, storage, organization, and access of information are all aspects of cognition 
that are integral to the aphasia treatment process and ultimately impact treatment outcomes[68]. 
A few studies have begun to identify specific cognitive factors and neuropsychological measures 
associated with positive naming-treatment outcomes among people with aphasia. Lambon Ralph and 
colleagues[10] administered several neuropsychological and linguistic assessments to 33 individuals who 
received naming treatment using a phonemic and orthographic cueing hierarchy. Three pre-treatment 
language assessments (Boston Naming Test [BNT], three-picture version of the Pyramids and Palm Trees 
Test [PPT], and PALPA 31 word reading subtest) and three cognitive assessments (Test of Everyday Attention 
Elevator Counting with distraction subtest, Rey figure copy, and Rey Figure delayed recall) were found to 
correlate significantly with confrontation naming improvements post therapy. Additionally, principal component 
analysis identified a cognitive factor and a phonological factor that were independent predictors of immediate 
and long-term therapy gain. The cognitive factor was comprised of measures of executive function (Wisconsin 
Card Sorting Test), sustained and divided attention (elevator counting with and without distraction), and 
visuospatial skills/memory (Rey figure copy and recall).  The PPT also loaded onto this cognitive factor. Further 
analysis revealed that the cognitive factor and the language severity rating from the BNT were the best 
predictors of treatment outcomes, and that phonological factors did not add to the model’s predictive power.      
Fillingham, Sage, and Lambon Ralph[66] and Hinckley and Carr[69] also found executive function abilities 
to have prognostic value for naming treatment outcomes in aphasia. Both studies found that the Wisconsin 
Card Sorting Test significantly correlated with language-performance outcomes. However, Hinckley and Carr 
found this relationship only for participants who received contextually-based treatment instruction (e.g. 
catalogue ordering), not for those receiving therapy using a skill-based treatment approach (like that used in 

impairment-oriented naming therapy like SFA). Hinckley and Carr[69] also found that performance on the 
Ravens Colored Progressive Matrices (RCPM) was related to some outcome measures. Interestingly, they 
also found additional predictive benefit of executive function abilities for the amount of treatment time needed 
for language skill acquisition. Scores on the WCST and the RCPM were significantly negatively correlated with 
the amount of treatment time required to reach performance criteria.   
Recognition memory and attention skills have also been identified as important predictors of treatment 
success when contrasting errorful and errorless therapies for anomia. Fillingham and colleagues[66] found that 
specific subtests on the Camden Memory Test (topographical and word subtests) were significantly correlated 
with language outcomes. The authors highlighted the importance of baseline working memory, delayed recall, 
and attention for the two participants that demonstrated larger treatment effects in the errorful learning 
condition, suggesting that errorful learning techniques may be most beneficial for patients with stronger 
memory and attention skills. 
Interestingly, the predictive value of these cognitive factors may be stronger for treatment-related gains 
in specific language domains rather than for broad measures of communicative function. The relationship 
between nonlinguistic cognitive performance and language recovery as determined by improvements on the 
Boston Diagnostic Aphasia Examination post treatment was examined by Seniow, Litwin, and Lesniak[70].  
When using the RCPM as the primary measure for executive functioning/abstract thinking abilities in people 
with aphasia, the authors did not find significant correlations between post treatment scores on the BDAE and 
the RCPM.  However, they did find a significant correlation between patient’s performance on tests of 
nonverbal visuospatial working memory and naming and comprehension scores of the BDAE.   
While these findings all point to the importance of cognitive factors in mediating treatment response, it 
is unknown how these factors relate to the different naming treatment effects highlighted above. In particular, it 
is unclear how they relate to the likelihood of learning the specific skills or language forms being treated in 
therapy (acquisition) versus generalizing to untreated forms and situations outside the therapy room 
(generalization). Acquisition of treated forms or skills is a necessary prerequisite for generalization[71], but 
generalization is widely considered the ultimate goal of effective therapy. As noted above, there is good 
preliminary evidence that SFA is associated with successful acquisition of trained items, and with both 
generalization to untrained items (response generalization) and to word production in non-therapy context 
(stimulus generalization). Relatively little research to date has been directed at the question of what factors 
underlie successful acquisition versus generalization in aphasia therapy. However, there is emerging evidence 
suggesting that the neural and cognitive mechanisms responsible for the two types of learning may be distinct. 
Dickey and Yoo[9] found that behavioral variables associated with positive treatment response were predictive 
of acquisition of treated forms, but not generalization to untreated but related forms. Furthermore, Meinzer and 
colleagues[20] found that brain areas positively related to acquiring treated items in a naming treatment study 
were not correlated with generalization to untreated words. The relationship between individual differences in 
cognitive capacities and mastery of treated behaviors or generalization to untreated behaviors is unknown.  
A.2.2 Neural correlates of treatment response 
Complementing these findings regarding the importance of various cognitive factors in mediating 
treatment response, recent work using neuroimaging has identified some promising neural correlates of 
positive treatment outcomes[21, 25, 72]. These results point to the importance of regions relevant for basic 
learning and memory (hippocampal regions) and regions relevant to functional reorganization in response to 
treatment (basal ganglia as well as contralesional right-hemisphere and intact left-hemisphere tissue).  
One line of evidence suggests that patterns of damage that lead to greater recruitment of right-
hemisphere regions during recovery are associated with positive response to treatment. Parkinson and 
colleagues[21] studied the relationship between lesion location and extent and response to two different naming 
treatments. They collected structural measurements of cortical and subcortical tissue in 15 individuals with 
aphasia and correlated them with measures of treatment gains. (See Preliminary Studies, below, for a more 
detailed description of this study.) Parkinson and colleagues found that the extent of left anterior cortical lesion 
was positively correlated with improvement in action and object naming. This counterintuitive finding suggests 
that greater loss of left anterior tissue – which may force greater recruitment of homologous right-hemisphere 
tissue during recovery – was related to more positive treatment outcomes. Consistent with this, they also found 
that the extent of left basal ganglia lesion was negatively correlated with treatment effects. Given that inhibitory 
projections from the basal ganglia are helpful in suppressing recruitment of damaged left hemisphere and 
permit greater recruitment of right-hemisphere tissue[22], this finding suggests that patterns of damage 
promoting right-hemisphere recruitment may yield more positive treatment response.  

Consistent with these findings from structural imaging, Menke and colleagues[24] found in a functional 
imaging study that greater naming improvement immediately post-treatment was associated with activations in 
the hippocampus and some right-hemisphere areas (precuneus and cingulate). They also found that naming 
improvements maintained at 8-month follow-up were associated with increased activity in the right Wernicke’s 
area homologue, and less strongly in left peri-lesional areas. Also consistent with these findings, Crosson and 
colleagues[25] found that a group of 4 individuals with aphasia whose naming function improved in response to 
treatment exhibited more right lateralized activation immediately post-treatment, compared to a patient who did 
not improve. Parallel functional imaging work by Fridriksson and colleagues has found that successful naming 
performance by individuals with aphasia is associated with greater activation of either peri-lesional left 
hemisphere tissue or unaffected right-hemisphere tissue[73, 74]. These findings strongly suggest that right 
hemisphere and perilesional left hemisphere tissue can play a critical role in successful treatment response.  
A second line of evidence suggests that the hippocampus may play a role in treatment-induced 
recovery from aphasia. As noted above, Menke and colleagues[24] found that naming improvement was 
associated with increased activation in the hippocampus. Parallel to this finding, Meinzer and colleagues[20] 
found that improvement on treated object naming items among 10 patients with mild to moderate chronic 
aphasia was negatively correlated with damage to the hippocampus. Volume loss to the left hippocampus was 
negatively correlated (-0.65) with improvement on treated items immediately after training, and even more 
strongly negatively correlated (-0.80) at 8-month follow-up. Loss of integrity of left hippocampus-adjacent white 
matter tracts (measured using fractional anisotropy maps) was negatively correlated with naming improvement 
after treatment (-.85) and at follow-up (-0.83). These relationships were specific to treated items: while there 
was some generalization of treatment effects to untreated items, these effects did not correlate with the 
structural measures of the hippocampus. This evidence suggests that the hippocampus, which is critical for 
formation and consolidation of episodic memory, is critical for supporting treatment-related aphasia recovery. 
These two lines of evidence point to the importance of both cortical and subcortical tissue in facilitating 
recovery in response to treatment. However, there are conflicting findings regarding neuroanatomical 
predictors of aphasia recovery, particularly regarding the role of anterior left-hemisphere versus right-
hemisphere tissue. Postman-Caucheteux and colleagues[75] found in an fMRI study of three adults with chronic 
aphasia and naming impairments that trials with incorrect naming performance were associated with right-
hemisphere activation (particularly in anterior regions such as inferior frontal gyrus, the same region recruited 
by patients showing improvement in a previous study by Crosson and colleagues[21]). Successful naming by 
the patients and by a group of unimpaired controls did not elicit right-hemisphere activation, suggesting that 
right-hemisphere regions may play a maladaptive role in aphasic language performance. Converging with this 
finding is functional imaging evidence that right-hemisphere recruitment is associated with poorer outcomes in 
treatment-related recovery. In a study of 16 adults with chronic aphasia, Richter, Miller and Straube[76] found 
that positive response to constraint-induced language therapy was associated with decreased activation in 
right-hemisphere anterior areas (inferior frontal gyrus and insula). Also, in a treatment and neuroimaging study 
of 26 individuals with aphasia, Fridiksson[23] reported that improved naming in response to treatment was 
associated with increased left-hemisphere activation of medial and inferior frontal areas and more posterior 
parietal areas, rather than increased right-hemisphere activation. There is also fMRI evidence that better 
spontaneous recovery is associated with greater recruitment of peri-lesional left-hemisphere areas[77]. 
Furthermore, Fridriksson[23] found that lesions in posterior middle temporal regions were associated with poor 
response to naming treatment, suggesting that damage preventing recruitment of these areas reduces 
treatment response. Thus, the prognostic value of factors permitting functional reorganization to the right 
hemisphere is promising but not definitive. Furthermore, there is conflicting evidence that recruiting left-
hemisphere regions may be more beneficial for aphasia recovery. 
There are also a number of unanswered questions regarding how neuroanatomical factors may be 
associated with treatment response. First, it is unclear whether the previously identified neuroanatomical 
predictors of treatment response are predictive of improvement only for treated language behaviors, or whether 
they are predictive of more general improvements in language function. Studies showing a positive role for 
right-hemisphere recruitment have usually measured treatment-related improvements in specifically treated 
language behaviors[21, 25]. In contrast, studies showing a negative role for right-hemisphere recruitment have 
typically employed broader measures of language function such as standardized assessments[76]. Further data 
are required to settle this issue. Second, the neural correlates of the two types of treatment response 
(acquisition, generalization) are unknown. There is evidence that intact hippocampal tissue and structural 
factors promoting reorganization to the right hemisphere are predictive of improvements on treated 
items/behaviors[20, 21, 24]. However, there is no evidence to date of regions that are associated with 

generalization to untreated items/behaviors. Meinzer and colleagues[20] found that intact hippocampal tissue 
was correlated with improvement on treated items, but not with improvement on untreated items. The neural 
correlates of successful generalization in response to treatment thus remain unknown[9]. 
Interestingly, the neuroanatomical correlates of response to a particular form of naming treatment, or of 
response to a single treatment by different groups of individuals, have not been investigated to date. The fact 
that similar treatment-neuroanatomy relationships have been detected across studies suggest that these 
relationships may reflect global patterns of successful reorganization in response to treatment, rather than 
learning of therapy-specific processes[22]. However, they may also reflect cortical/subcortical resources that are 
needed for improvements in specific processes associated with successful word production (e.g., activation or 
selection of a lexical-semantic representation). Research that delineates the neuroanatomical correlates of 
positive response to a specific and well-defined treatment will lay the foundation for future studies comparing 
the neural bases of response to different types of treatment. 
A.2.3 Summary 
Given that behavioral therapy for chronic aphasia is efficacious but resource-intensive, it is critical to 
identify factors that will be predictive of who is most likely to benefit from such therapy. Studies examining the 
role of cognitive factors in aphasia therapy provide evidence that a broad range of factors relevant to learning 
may be predictive of treatment response. Executive function, visuospatial memory, and attention (both divided 
and focused) have all been found to predict naming treatment outcomes. However, it is unclear whether these 
factors are more or less strongly predictive of outcomes for psycholinguistically-motivated naming treatments 
like SFA than for the cueing hierarchies used in previous studies. It is also unknown whether these cognitive 
factors are predictive of acquisition of trained behaviors, generalization (stimulus or response), or both. 
Studies regarding potential neuroanatomical predictors of treatment response provide similarly 
encouraging but limited evidence for the potential prognostic role of neurological findings for aphasia therapy 
outcomes. Further research is required to determine the prognostic value of the extent of left-hemisphere 
lesion (and corresponding recruitment of right-hemisphere homologues) in forecasting likely treatment 
response. Further research is also required to determine whether neuroanatomical predictors are prognostic 
only of skills/items treated in therapy, or whether they are also prognostic of generalization to untreated items 
and to improvements in broader, standardized measures of naming and language function.  
A.3 Dosage levels and treatment response in aphasia 
There is abundant evidence for the efficacy of behavioral rehabilitation strategies rooted in cognitive 
neuropsychology and psycholinguistics for improving the language function of adults with aphasia[1, 2], including 
naming treatments like those described above. Moreover, the literature also suggests that providing more 
treatment results in greater improvement in language function[1, 6, 7, 78, 79]. However, these findings are 
preliminary, because the total amount of treatment has often been confounded with intensity of treatment. 
Research is therefore needed to address the effects of treatment amount independent of treatment intensity. 
Systematically examining the connection between the amount of treatment and the resulting gains is critical for 
the ultimate goal of identifying optimum dosages for aphasia therapy. 
The general finding is that more treatment results in better treatment response. An early survey of 
aphasia treatment studies[13] identified treatment duration as one factor relevant to positive treatment response, 
finding that studies in which patients received at least 3 hours per week for 5 or more months were likely to 
report improvements. A subsequent large-scale meta-analysis of aphasia treatment studies[1] reported that the 
amount of treatment received (measured in weeks in treatment or number of treatment hours) was positively 
correlated with magnitude of improvement on study measures of language function (r=.76, .64, respectively). 
Similarly, one study of non-intensive, socially-focused group therapy found additional improvements on some 
performance-based outcome measures after 4 months (80 hours) as opposed to 2 months (40 hours) of 
treatment[7]. These findings all suggest that a there is a positive dose-response relationship in aphasia therapy: 
more treatment is likely to lead to better outcomes. However, there is considerable variation across these 
studies in the individuals treated and the outcome measures used, making comparison of treatment gains in 
response to varying amounts of treatment difficult. In addition, there are some findings suggesting that more 
time spent in treatment may not result in greater response. A study surveying treatment research using one 
sentence-level treatment approach[80] found that auditory comprehension scores predicted treatment response, 
but that the number of treatment sessions was not correlated with the magnitude of treatment gains[9].  
Recent studies of intensive aphasia treatment (defined here as >3 hours per day for at least 2 weeks) 
have also drawn attention to the role that intensity along with treatment duration may play in maximizing 
treatment gains[5, 6]. Intensive treatment is a tenet of many current aphasia treatment approaches, such as 
constrained-induced language therapy[30]. Consistent with this emphasis on intensity, Robey[1] reports that 

studies delivering treatment at higher intensities exhibited larger effects. These findings are a significant part of 
the motivation for the intensive delivery schedule used in the PIRATE program at VAPHS. However, those 
studies which provided more intensive treatment have also provided more of it: the improvements seen in 
these studies could be due in part to greater treatment dosage[7]. Furthermore, these studies have not 
systematically examined treatment amount, with most providing approximately 30 hours of treatment over a 2 
week period[29, 30, 55, 57, 81]. It is not known whether additional treatment on the same schedule would produce 
additional gains or would demonstrate diminishing returns.  
Also unknown is how the amount of treatment received relates to acquisition and generalization in 
response to that treatment. As noted above, there is emerging evidence that the neural and cognitive 
mechanisms responsible for the two types of learning may be distinct[9, 20]. In addition, there is evidence from 
the motor-learning literature suggesting that retention of trained behaviors and transfer to untrained behaviors 
may respond differently to different dosage levels[82]. For non-speech motor learning, greater amounts of 
practice typically yield better retention of treated skills[83, 84]. However, large amounts of practice may interfere 
with transfer to untreated skills, particularly under conditions of constant practice. Furthermore, the motor-
learning literature suggests that the slope of performance curves for skill learning may vary widely even for a 
single behavior, depending on the choice of dependent measure[85]. The relationship between treatment 
amount and mastery of treated skills or generalization to untreated skills thus remains unknown.  
As evidence for the efficacy of language rehabilitation for adults with chronic aphasia accumulates, 
especially when delivered on an intensive schedule, it becomes increasingly important to determine in what 
amounts such treatment should be provided. However, current evidence does not permit researchers or 
policymakers to determine optimum dosage amount, or even whether the number of hours of treatment 
determines the size of treatment gains. Research studies that hold the delivery schedule constant while 
systematically measuring the effects found for different dosage levels are required to answer this question. 
Finally, such studies should ideally examine the dose-response relationship for not only acquisition but also 
generalization of treated skills, given the importance of generalization for broadly effective aphasia therapy.  
B. Preliminary Studies 
The proposed study builds on two parallel lines of work carried out by the investigators: a clinical 
program providing intensive impairment-based aphasia therapy to veterans with aphasia, and research 
programs investigating the relationship between neuroanatomical variables and aphasia treatment response.  
B.1 The Program for Intensive Residential Aphasia Treatment and Education 
In January 2009, the VAPHS GRECC initiated the Program for Intensive Residential Aphasia 
Treatment and Education (PIRATE), a novel service delivery program model designed to eliminate barriers 
preventing community-dwelling veterans with aphasia from accessing intensive language rehabilitation 
services. At its inception, the program provided approximately 80 hours of aphasia treatment over a 3-week 
period. Currently, the schedule has been expanded to approximately 110 hours over a 4-week period. After 
being evaluated for candidacy, veterans are enrolled in groups of three and reside in residential villas on the 
VAPHS H.J. Heinz Campus for the duration of the program. 
Treatment provided within the context of PIRATE is grounded in current psycholinguistic and cognitive-
neuropsychological approaches to aphasia therapy. Patients’ language impairments are described in reference 
to current models of language processing, and treatments motivated by these models are applied to remediate 
these well-specified deficits. Treatments used in PIRATE also tend to rely heavily on practice and repeated 
drilling of tasks organized around relatively discrete units of language. While the particular stimuli used may 
have social relevance for a given patient, the focus of treatment is on improving the underlying cognitive-
linguistic processes, rather than on language performance in a particular social context. The proposed 
research thus fits naturally with the treatment approach employed in PIRATE, with its core emphasis on 
repeated practice focused on basic cognitive-linguistic processes.  
In complement to intensive impairment-focused treatment, PIRATE has also included elements of more 
socially-oriented approaches to aphasia treatment. These include group activities focused on conversation 
practice and conversational coaching with patients’ significant others or volunteer partners. The purpose of 
these activities is to promote generalization of performance gains made in the context of psycholinguistically 
motivated treatments to other communicative contexts. Group activities occur once weekly during each 
PIRATE session, for approximately 1.5 hours. However, impairment-focused therapy constitutes the large 
majority of the 25 hours of weekly treatment routinely provided in PIRATE.  Because this study focuses on how 
dosage levels and neuroanatomical and behavioral factors predict response to a well-motivated and specified 
treatment (SFA), and because of the potential confound posed by additional group treatment activities, we will 
deliver individual SFA treatment exclusively in the research proposed below. 

B.1.1 PIRATE participants and outcomes to date 
PIRATE employs both performance-based and patient-reported outcome measures. Performance-
based measures are currently collected at the initial evaluation (time 1), program entry (time 2), program exit 
(time 3), and follow up (time 4), approximately four weeks post-exit. These outcome measures provide pilot 
data that are directly relevant to the current proposal in two ways. First, they suggest that the aphasia 
treatment provided in the context of PIRATE is effective. Second, the natural history of the program permits a 
preliminary evaluation of the dose-response question raised in Specific Aim 2, given that the first six PIRATE 
sessions were three weeks in duration, and the remaining ten sessions were four weeks in length.  
To date, PIRATE has served 53 unique veterans. In one of these cases, the veteran ended program 
participation early due to medical issues. Descriptive demographic and clinical data are presented in Table 1 
for the 52 unique cases in which the veteran completed PIRATE. 
 
Months Post Onset at Program Entry, Median (IQR) 
23 (29) 
Age at Program Entry, Mean (SD) 
58 (13) 
Years of Education, Mean (SD) 
14 
(2.4) 
Gender, N male 
49 
PICA Overall Score, %ile rank (SD) of mean score 
51 (15) 
Etiology of Aphasia, N 
 
Left Hemisphere Stroke 
48 
Bilateral Stroke 
1 
Left Penetrating Head Injury 
1 
Closed Head Injury 
1 
TBI + LH Stroke 
1 
 
Table 1. Demographic and clinical characteristics of veterans who have completed PIRATE (n =43). 
B.1.2 Analysis of Preliminary Data 1: Effectiveness of PIRATE 
The performance-based outcome measure most frequently used in PIRATE has been the Porch Index 
of Communicative Ability (PICA[86]). For the PICA, we have complete data at times 1-3 for 39 cases. We 
excluded from the present analysis four patients who were less than 6 months post onset at the time of initial 
evaluation, in order to limit the influence of physiological recovery across the baseline interval. We converted 
PICA overall raw scores to standardized unit normal scores (z-scores) via the percentile tables published in the 
PICA manual. These z-scores were then analyzed using repeated-measures ANOVA, with planned contrasts 
between successive time points to test the hypotheses that there would be no difference between times 1 and 
2, but significant positive change between times 2 and 3. The analysis demonstrated non-significant change 
between initial evaluation and program entry (F [1,34] = 2.574, MSE = 0.009, p = 0.118, ηp2 = 0.07) and 
significant improvement between program entry and program exit (F[1,34] = 56.364, MSE = 0.01, p < 0.001, 
ηp2 = 0.624). Mean values at each assessment point are displayed in Figure 1. To guard against bias resulting 
from exclusion of cases with missing data, we repeated these analyses with missing observations replaced by 
the mean of each patient’s available observations. This strategy was conservative in that it is likely to 
overestimate positive change across the baseline interval (initial evaluation to program entry) and 
underestimate positive change across the treatment interval (program entry to program exit). The results of this 
re-analysis were similar to the original analysis, with the 
expected larger improvement across the baseline interval: 
There was significant improvement from initial evaluation to 
program entry (F[1,40] = 6.042, MSE = 0.014, p = 0.018, ηp2 = 
0.131) and relatively greater improvement between program 
entry and program exit (F[1,40] = 53.168, MSE = 0.013,p < 
0.001 ηp2 = 0.571). The observed pattern of minimal change 
across the baseline interval coupled with significant 
improvement across the treatment interval supports the 
effectiveness of PIRATE in treating aphasia. In addition, these 
changes on this broad measure of language performance 
likely underestimate the magnitude of treatment-related 
changes in the proposed research. The PICA samples 
communicative functioning at a broad level, encompassing 
performance not only in targeted domains (such as naming) 
Figure 1. Mean PICA z-scores for n=35 patients with 
complete data available at three assessment points. 
Error bars indicate +/- 1 standard error. 

but also in domains not targeted in therapy, which are unlikely to show improvement. The language 
performance assessments to be used in the proposed research measure performance on naming behaviors 
which are the object of treatment, rather than a broad index of both treated and untreated behaviors. 
Consequently, we may expect that the intensive delivery schedule followed in PIRATE and in the proposed 
research will result in even larger gains on study measures than those described here. 
B.1.3 Analysis of Preliminary Data 2: Dose-Response Relationship 
As noted above, the first six PIRATE sessions were three weeks in length, and the remaining sessions 
have been four weeks in length. Comparison of outcomes between groups receiving 3 versus 4 weeks of 
treatment permitted us to estimate the marginal benefit of an additional week of intensive aphasia treatment in 
terms of a broad performance-based outcome. To derive these estimates, we conducted ANCOVAs on the 
PICA overall z-scores, with the post-treatment scores as the dependent variable, treatment group (3-week vs. 
4-week) as the independent variable, and pre-treatment scores collected at program entry as the covariate. 
For the PICA, data at program entry and program exit were available for 15 veterans who received 
three weeks of treatment and 27 veterans who received four weeks. The demographic and clinical 
characteristics of each group are presented in Table 2. 
 
 
3-Week Group 
(n=15) 
4-Week Group 
(n=27) 
Months Post Onset at Program Entry, Median (IQR) 
19 (38) 
21 (31) 
Age at Program Entry, Mean (SD) 
57 (16) 
55 (13) 
Years of Education, Mean (SD) 
14 (3) 
14 (2) 
Gender, % male 
100 
89 
PICA Overall Score, %ile rank (SD) of mean score 
47 (17) 
58 (15) 
Etiology of Aphasia, N 
 
 
Left Hemisphere Stroke 
14 
17 
Bilateral Stroke 
1 
0 
Left Penetrating Head Injury 
0 
1 
Closed Head Injury 
0 
1 
 
Table 2. Demographic and clinical characteristics of the 3-week and 4-week subgroups of PIRATE cases. 
The analysis revealed a non-significant benefit for four weeks of treatment relative to three weeks (F[1,39] = 
0.917, MSE = 0.011, p = 0.344, ηp2 = 0.023, Cohen’s d = 0.37). The difference in overall PICA scores at 
program exit, corrected for PICA score at program entry, was 0.035 standard score points. Interestingly, this 
estimated value of one additional week of treatment in terms of PICA overall z-score is approximately equal to 
33% of the average change score for the three week group and 25% of the average change score for the four-
week group. This estimate suggests that the relationship between improvement on broad measures of 
language performance and total time in treatment may be linear. It is unknown whether a similar trend may 
hold for measures of behaviors specifically targeted in therapy. However, as noted above, we may expect that 
treatment-related gains will be larger for measures that more narrowly sample the targeted behavior (naming 
and word production, in the proposed research). 
B.2 SFA in the context of PIRATE 
 
SFA is already one of the treatments administered on an intensive schedule within PIRATE to 
remediate participants’ naming impairments. PIRATE participants receive 22.5 hours weekly of treatment 
focused on repeated drills targeting specific cognitive-linguistic processes. Veterans who choose to enroll in 
PIRATE (the population from which study participants will be drawn) are thus able to tolerate and remain 
engaged during high dosages of impairment-focused, drill-oriented treatments like SFA, at levels even higher 
than those proposed below.  
Veterans who have been administered SFA as part of their treatment in PIRATE have received up to 
37.25 hours of SFA (mean 16.2 hours), delivered in one or two 90-minute sessions per day, for up to four 
weeks. These veterans have responded positively to high levels of SFA: for those veterans for whom detailed 
probe information is available, mean probe accuracy for treated items increased from 30% at baseline to 72% 
at the time of their final probe. This positive treatment response is larger than that commonly reported in the 
SFA literature, and it suggests than extended SFA training administered intensively may result in significant 
improvement in naming function. 
Furthermore, we present more detailed information for one PIRATE participant who recently received 
SFA as part of his overall treatment plan. His response to an intensive trial of SFA treatment provides pilot data 

regarding the efficacy and the feasibility of the proposed design. The veteran presented with significant naming 
impairments on the naming portion of the CAT, as well as prominent phonological impairments. Demographic 
and language-testing data for this veteran are presented below. 
 
Months Post Onset at Program Entry 
37 
Age at Program Entry 
66 
Years of Education 
16 
PICA Overall Score, %ile rank of mean score 
72 
Detailed Language Testing 
 
CAT Spoken Naming  
25/58 (T-score = 50)* 
CAT Repetition 
43/74 (T-score = 49)* 
CAT Comprehension of Spoken Words 
25/30 (T-score – 51)* 
Pyramids and Palm Trees (3-picture version) 
51/52 (98%) 
PALPA Same-Different Discrimination 
63/72 (83%)* 
PALPA Phonological Segmentation 
33/45 (73%)* 
 
Table 3. Demographic and clinical characteristics of veteran enrolled in SFA protocol 
Consistent with the criteria for study participation (see section C.1 below), this veteran presented with naming 
deficits on the CAT (T-score of 50) with primarily phonological naming errors. His naming impairments would 
be classified as mixed according to participant classification measures described below (section C.2.3). He 
presented with impaired repetition (indicative of impaired phonological processing) and impaired performance 
on PALPA phonological processing subtests. He also exhibited some evidence of impaired lexical-semantic 
processing, as evidenced by his performance on spoken word-to-picture matching tasks. However, his 
conceptual semantic system appeared to be relatively intact, as suggested by his performance on Pyramids 
and Palm Trees.  
This veteran received 13 hours of SFA on 10 training items over a six-day period. His response to 
treatment is summarized in Fig. 2. We probed this veteran’s performance daily on 10 treated items, 10 
untreated related items, and 10 untreated unrelated items. He also received less-frequent assessment with 
short forms derived from Philadelphia Naming Test that are described below in section C.2.2. We note that 
because these data were collected in the context of the PIRATE clinical program, the veteran was receiving 
other treatments (Response Elaboration Training and Kendall’s phoneme-based treatment for anomia) during 
both the SFA baseline and SFA treatment phases presented in Figure 3. Despite the multiple-treatments 
confound, the probe data suggest a clear acquisition effect for the trained items as well as some generalization 
to untrained items. There was also improvement on the PNT short forms. 
 
  
 
Further evidence of this veteran’s improved 
function can be seen in the video Appendix A. The 
Appendix presents two representative treatment 
trials for the same treatment exemplar, drawn from 
the first and final SFA treatment session. As can 
be seen in the two samples, the veteran’s ability to 
name the treatment exemplar as well as to 
generate associated semantic features has 
improved during treatment. He has also remained 
engaged with the treatment and with the repeated 
treatment activities. These pilot data thus provide 
additional evidence suggesting that patients will be 
able to complete the extended course of SFA 
treatment proposed below, and that the multiple 
repetitions of SFA treatment activities can induce 
significant change in function (see section A.2 
above). Furthermore, this veteran’s primarily 
phonological impairments provide preliminary evidence in favor of Hypothesis 1a, that individuals with 
phonologically-based naming deficits will also benefit from SFA. 
Figure 2. Pilot SFA participant’s treatment response, for acquisition of treatment exemplars (top panel), generalization 
to related and unrelated words (middle panel), and more general naming function (bottom panel). 

 
B.3 Correlates of language impairment and treatment response 
B.3.1 Neuroanatomical predictors of treatment response 
The investigators have also carried out work regarding the relationship between lesion site and 
treatment response, in particular investigating the role of recruitment of the non-dominant hemisphere during 
recovery. This work is directly relevant to the current proposal, since it provides preliminary evidence for the 
hypothesized role of selected brain regions in supporting recovery in response to aphasia therapy. In addition, 
we have available brain scan images for 6 veterans served by PIRATE, permitting a preliminary evaluation of 
the relationships between lesion patterns and treatment response relevant to the neuroanatomical hypotheses 
under Specific Aim 1. 
As described briefly above, Parkinson, Raymer, Chang, FitzGerald, and Crosson[21] studied the 
relationship between lesion location and extent and treatment effects in a sample of 15 patients who had 
received one of two semantically-oriented naming treatments. There were two major findings. First, when 
controlling for the extent of basal ganglia lesion, the extent of left anterior cortical lesion was positively 
correlated with improvement in action naming (r = 0.821, p = 0.002) and object naming (r = 0.858, p < 0.0005) 
on treated items (correlations with untreated items and/or standardized outcome measures were not reported). 
Second, when controlling for extent of left anterior cortical lesion, the extent of basal ganglia lesion was 
negatively correlated with treatment effects (r = -0.785 and -0.749 for actions and objects, respectively, both 
p’s ≤ 0.01). Based on these results, Parkinson, Crosson, and colleagues hypothesized that excessively “noisy” 
activity of the left anterior cortex may prevent adaptive re-organization from occurring in the non-dominant 
hemisphere (as well as in non-lesioned areas of the dominant hemisphere) to support recovery. They 
proposed that this interference may be reduced in the presence of more extensive lesions to the left anterior 
cortex and with sparing of the left basal ganglia, which has been argued to suppress peri-lesional activity 
during word production[22]. These findings indicate that increased left-hemisphere involvement and intact left 
basal ganglia tissue should be associated with better treatment response, since both promote adaptive 
recruitment of other cortical resources (especially right-hemisphere tissue[25]). 
Preliminary data regarding the relationship between treatment response and lesioned and spared brain 
regions can also obtained from the sample of PIRATE participants to date. Brain scan images are available for 
6 of the veterans who have participated in PIRATE. Of these six veterans, we have pre/post picture-naming 
probe data on 4, all of whom had aphasia due to left hemisphere stroke of ≥ 19 months post onset. It was not 
possible to conduct voxel-based morphometry or voxel-based lesion symptom mapping analyses on these 
data, but they were descriptively analyzed with respect to the involvement of the regions of interest identified 
as potential predictors of treatment response above. A neuroradiologist who was blind to the behavioral data 
examined the scans and coded each case for involvement in each of four regions: the left anterior cortex 
(defined as the pre-central gyrus plus Brodmann areas 9 and 44-46[21]), the left posterior cortex (defined as 
covering posterior medial temporal, inferior parietal, and anterior occipital areas, including Brodmann’s areas 
37, 39, and 19[23]), the left hippocampus, and the left basal ganglia. The same scale used by Parkinson and 
colleagues[21] was used for this analysis: 0 - no lesion; 1 - equivocal lesion or very small portion has patchy 
lesion; 2 - half of area has patchy or very small portion solid lesion; 2.5 – total area has patchy lesion or less 
than half of area has solid lesion; 3 – half of area has solid lesion; 4 – more than half of area has solid lesion; 5 
– total area has solid lesion. 
 
Pre-Tx 
Probe % 
Correct 
Post-Tx 
Probe % 
Correct 
Probe Change 
Score 
L Ant. 
Cortex 
Lesion 
Rating 
L Post 
Cortex 
Lesion 
Rating 
L Basal 
Ganglia  
Lesion Rating 
Hippocampus 
Lesion Rating 
0.50 
0.64 
0.14 
3 
4 
3 
2 
0.50 
0.80 
0.30 
3 
0 
0 
0 
0.12 
0.47 
0.35 
4 
0 
1 
0 
0.39 
0.81 
0.42 
2 
0 
0 
0 
 
Table 4. Treatment response on naming probes and lesion ratings for regions of interest,  
for a sample of PIRATE participants (n=4). 
These findings are from a small sample but are consistent with some of the neuroanatomical hypotheses 
above. The patient with the greatest degree of left posterior and subcortical involvement (with a 3 and 2 rating 

for basal-ganglia and hippocampus involvement, respectively) showed the poorest response to treatment. 
While this patient had the greatest lesion extent overall, simple lesion size is unlikely to be responsible for his 
poor treatment outcomes: in separate studies, Meinzer and colleagues[20] and Baker and colleagues[87] have 
found that treatment gains were not correlated with lesion size. All three patients showing better treatment 
response (naming-probe change scores between .3 and .42) had some degree of left anterior involvement and 
little or no subcortical involvement, as well as little or no posterior cortical involvement. These findings suggest 
that greater basal-ganglia and hippocampal involvement[20, 21] and/or greater left-posterior involvement[23] may 
be associated with poorer response to treatment in PIRATE. There is less variability in left anterior cortical 
involvement in this sample, making conclusions regarding the role of left anterior cortical damage in treatment 
response difficult. However, the patterns do suggest that positive treatment response may be associated with 
left anterior cortical damage, provided that the subcortical structures needed for basic memory and learning 
and for promoting recruitment of non-damaged cortical areas are intact[21, 22, 25].  
These findings therefore provide preliminary evidence that veterans who have responded positively to 
intensive aphasia treatment in PIRATE have patterns of preserved and damaged brain areas which are in line 
with the hypotheses in Specific Aim 1, specifically Hypothesis 1c. In particular, they suggest that damage to 
left-anterior cortex may be associated with positive treatment outcomes. However, they leave open the 
question of the role of left posterior cortex and subcortical structures in recovery in response to treatment. 
B.3.2 Voxel-based lesion symptom mapping for language impairment 
The proposed research will use voxel-based lesion symptom mapping (VLSM) to test neuroanatomical 
hypotheses relevant to Specific Aim 1. VLSM is a method which has been used productively to examine 
correlations between behavioral variables, such as degree of language impairment or hemispatial neglect[88], 
and brain lesion. In VLSM, continuous measures of performance are compared to the lesion status of cortical 
or subcortical tissue on a voxel-by-voxel basis. This method will be used to examine the relationship between 
treatment-related changes among PIRATE participants and lesions in brain regions which previous research 
has identified as being predictive of treatment response[20, 21, 24]. 
The investigators have used this method to examine the relationship between verbal fluency and lesion 
site and extent in a group of 16 adults with aphasia[89]. Participants’ verbal descriptions of the “Cookie Theft” 
picture from the Boston Diagnostic Aphasia Exam[90] were measured and coded for their relative fluency along 
five different dimensions: speech rate, degree of audible struggle, ratio of filler to meaningful material, type-
token ratio, and ratio of spoken output to periods of silence (a simple global measure of speech productivity). 
These fluency measures were then correlated with these individuals’ lesions, assessed via a high-resolution 
structural MRI which had been coded for lesion size and extent by a neuroradiologist. The relationship 
between speech fluency measures and patterns of brain damage is depicted in Figure 3. 
 
 
 
 
 
 
The correlations between continuous fluency measures and 
the likelihood of lesion in different brain regions revealed 
important connections between fluency and cortical and 
subcortical areas. Factors such as speech rate and audible 
struggle correlated with inferior frontal cortical damage, 
whereas superior temporal lobe damage predicted high rates 
of filler material. In contrast, the compromise of frontal 
subcortical structures was predictive of separate aspects of 
narrative dysfluency. Specifically, damage to the left insula, 
caudate, globus pallidus, and putamen were predictive of 
impairments in type-token ratio and speech productivity. These findings illustrate the potential utility of VLSM 
for investigating the connection between treatment-induced changes in language performance measures (such 
as the PNT) and the degree of cortical and subcortical involvement, as hypothesized under Specific Aim 1. 
C. Methods and Research Plan:  
The proposed study will employ a within-subject repeated measures design to establish the functional 
relationship between amount of treatment and treatment response. We will also randomize half of the sample 
to a delayed treatment condition in order to control for the effects of social contact associated with PIRATE 
participation. Finally, we will supplement this design with between-subjects analyses designed to measure how 
different randomly-selected samples of participants respond to varying amounts of treatment. Study 
Figure 3: Parametric statistical map showing reliable relationships 
between speech fluency measures and lesion location in sample of 
participants with aphasia (Reilly, et al., in prep) 

participants will receive intensive semantically-oriented naming treatment in the context of the PIRATE 
program. Their response to treatment will be measured and compared across the 4-week duration of PIRATE 
using probes of treated and untreated stimuli as well as a standardized measure of naming performance. This 
comparison will address Specific Aim 2, to identify the dose-response relationship for aphasia therapy. 
Measures of naming gains in response to treatment will be compared across groups of participants defined in 
terms of their language-impairment profiles, to determine whether semantically-oriented naming treatment 
benefits all or only a subset of individuals with naming deficits. The study will also employ voxel-based lesion 
symptom mapping analyses to examine the relationship between lesion site and extent and treatment 
response, as well as regression analyses to examine the relationship between cognitive factors and treatment 
response. These analyses will together address Specific Aim 1, to identify psycholinguistic, cognitive, and 
neuroanatomical predictors of positive response to intensive semantically-based naming treatment.  
This design avoids the confounds present in many previous studies of treatment response. First, it 
holds treatment intensity constant while systematically measuring the effects of varying amounts of treatment. 
Second, it uses both between and within-subject design components to control for a variety of potential 
confounding effects, including social contact, inter-subject variability, and the passage of time. Third, it 
examines the relationship between psycholinguistic, cognitive, and neuroanatomical factors and treatment 
response. Previous studies of treatment response have examined the effects of only one of these factors in 
accounting for differential response to treatment. 
C.1 Study participants 
Potential participants will be informed of the study on the PIRATE website as well as during PIRATE 
candidates’ initial evaluation to determine eligibility for PIRATE participation. PIRATE candidates will be 
informed that they may eligible to participate in the study if they meet additional study eligibility requirements. 
As described below, study participants will receive up to $1500 to defray any travel expenses incurred as part 
of study participation, to be pro-rated if they fail to complete all study procedures (including follow-up visits). 
All study participants will be community-dwelling veterans with a positive diagnosis of aphasia and the 
ability to live independently and carry out activities of daily living during their PIRATE program participation. 
Aphasia diagnosis will be provided by the referring provider, based on performance on standardized aphasia-
assessment measures such as the Comprehensive Aphasia Test (CAT), the Western Aphasia Battery, 
Revised (WAB-R), or the Boston Diagnostic Aphasia Exam (BDAE). Aphasia diagnosis will be verified by 
PIRATE staff prior to enrollment based on clinical impression and performance on the CAT, with a language 
modality mean T-score below the 70 being required for aphasia diagnosis. Although this criterion is more 
liberal than the cut-off score of 62.8 described in the CAT manual, our experience suggests that patients may 
score above this cut-off but nevertheless present with clinically significant naming impairments. Additional 
exclusionary criteria for PIRATE participation are listed in Table 5 below. 
 
Inability to carry out activities of daily living necessary for self-care 
Lack of physical independence 
Significant mood or behavioral disorders 
Drug or alcohol dependence 
Inability to tolerate intensive treatment  
Medical conditions which would preclude independent living  
 
Table 5. Exclusionary criteria for PIRATE participants. 
Information regarding these exclusionary factors is obtained by PIRATE staff prior to program entry, based on 
review of veterans’ medical charts, pre-entry applications, and interviews with family members and service 
providers. The absence of medical conditions which would preclude PIRATE participation is verified as part of 
a medical clearance exam provided by a VAPHS physician prior to program entry.  
In addition to the criteria described above, participants in the proposed study must meet five additional 
criteria. First, only veterans with aphasia due to unilateral left-hemisphere stroke of greater than six months 
post-onset will be included in the study. Individuals with right-hemisphere involvement will be excluded 
because of the study’s emphasis on testing hypotheses regarding potential non-dominant hemisphere 
recruitment in treatment response. Individuals less than 6 months post-onset will be excluded to ensure than 
participants’ brain injuries are stable, and to reduce any potential effects of spontaneous recovery on treatment 
response. Second, only participants without progressive neurological disease or prior central nervous system 
injury or disorder will be enrolled in the study, since these comorbid factors could affect treatment response. 
Third, study participants must demonstrate less than or equal to 33% correct performance on at least 120 
items contained in the 361-item baseline probe set (see section C.2.1. below), measured across three 

administrations. This criterion is necessary to permit selection of the necessary treatment and generalization 
probe items as described below, and will minimize ceiling effects on the naming probes. Fourth, in order to 
avoid enrolling into the study participants who have profound naming impairments and are therefore unlikely to 
benefit from the study treatment (e.g., patients with global aphasia and/or verbal output limited to recurrent 
stereotypy), all participants must obtain a CAT Naming modality T-score greater than or equal to 40. Finally, 
we will exclude from participation potential participants with severe motor speech disorder (i.e., apraxia of 
speech and/or dysarthria). Motor speech diagnosis and severity judgments will be made by study investigators 
with experience in this area (Doyle, McNeil, Hula), based on the following samples of behavior: Connected 
speech samples (described below); three recorded attempts at laryngeal diadochokinesis at slow, medium, 
and fast rates; three trials of maximum sustained phonation; and recorded responses to the three subtests of 
the Apraxia Battery for Adults-2, including Speech Diadochokinesis, Repetition of Words of Increasing Length, 
and Word Repetition with Repeated Trials. Two judges will independently diagnose and rate the severity of 
motor speech disorder in each potential participant. A third judge will provide a diagnosis and rating to resolve 
any disagreements regarding presence or severity of motor-speech disorders. Chart review of PIRATE 
participants to date suggests that the above additional five criteria would have excluded approximately 20% of 
the unique veterans served by the program. Thus, out of 72 patients who will be seen in PIRATE over the four-
year duration of the project, approximately 58 should be eligible for enrollment in the study. Of these 58 eligible 
veterans, it is reasonable to expect that at least 50 will enroll in the study and complete the treatment and all 
study visits (including follow-up visits). This sample size should be sufficient for detecting the relationships 
between treatment response, psycholinguistic profile, and cognitive performance relevant to Specific Aim 1, 
and to detect differences relevant to testing the dose-response hypotheses in Specific Aim 2. 
Some participants may also have co-occurring conditions which would prevent them from participation 
in the imaging arm of the study (claustrophobia; presence of metal in the body). These participants will be 
enrolled in the study but will not complete imaging procedures. A chart review of PIRATE participants to date 
indicates that approximately 20% of veterans enrolled in PIRATE may have such exclusionary criteria: roughly 
17% report pacemaker or other implanted metal objects or devices incompatible with MR scanning. Thus, the 
participant sample used to address the neuroanatomical hypotheses proposed under Specific Aim 1 may be 
smaller than the sample contributing data the other proposed analyses. Under the worst case scenario of no 
overlap between these imaging-related exclusionary criteria and those described in the preceding paragraph, 
we expect to obtain a sample size of 40 participants (50 minus 
20%) to conduct the proposed imaging-related analyses. As 
described in section C.3.1.3 below, this sample size should be 
sufficient to detect the relationships relevant to the 
neuroanatomical hypotheses. 
C.2 Procedures 
The schedule of procedures for participants enrolling in the 
study is summarized in the flowchart in Figure 4. Participants 
enrolling in the research study will receive a single pre-
treatment structural MRI and a battery of cognitive assessments 
at program entry (see section C.2.3 below). They will also have 
their performance on standardized measures of naming and 
other language functions assessed repeatedly throughout the 
protocol  (see section C.2.2 below), and they will receive an 
intensive dose of a treatment (SFA) designed to target lexical-
semantic processes (see section C.2.1 below). Participants’ 
performance on treated and untreated lexical items will be 
probed three times weekly, to assess acquisition and 
generalization and to decide when participants will move to a 
novel set of treatment exemplars. 
Half the participants will be randomly assigned to a 
delayed-treatment condition. The purpose of this condition is to 
provide an additional measure of experimental control, to 
ensure that any observed improvements are not due simply to 
the social contact afforded by the residential component of the 
treatment program.  Previous work [7] has found that simple 
exposure or engagement in the absence of specific therapy 
Figure 4: Testing and treatment procedures  
for study participants 

activities for participants is insufficient to explain their gains in communicative function. However, inclusion of a 
delayed-treatment condition will help establish this more conclusively for the current study. The specifics of the 
delayed-treatment condition are given in section C.2.1.  
PIRATE involves a single 2-day visit to VAPHS 4-6 weeks prior to entry for a medical clearance exam 
and an initial comprehensive speech-language evaluation and to initiate treatment planning. During this visit, 
the CAT and PALPA subtests (for confirmation of aphasia diagnosis and for participant classification and 
treatment planning) are administered. Accepted candidates return to VAPHS at program entry and reside in 
residential villas at the H.J. Heinz campus of VAPHS for 4 weeks (or 5 weeks in the delayed-treatment 
condition). At program entry, a second CAT is administered to participants, and PIRATE candidates enrolling in 
the research study will be given structural MRI scans and the battery of cognitive assessments described 
above. Therapy is provided in existing, dedicated PIRATE treatment rooms, also located on the Heinz campus. 
The treatment schedule is five days a week, approximately 5 hours a day, for a total of 25 hours of therapy 
each week. As described below, PIRATE participants enrolled in the proposed research will receive treatment 
five days a week, for 4 hours per day. All therapy will be provided by PIRATE-dedicated licensed clinical 
providers with multiple years of experience delivering aphasia therapy. Participants return to VAPHS for a 
single follow-up visit 4-6 weeks after program exit. 
C.2.1 Treatment: 
Over 85% of PIRATE participants to date have received treatment focused on spoken word production. 
This is also the unit of language function and the output modality targeted in the majority of treatment-
neuroanatomy/neuroimaging studies reviewed in section A.2 above, as well as the majority of treatment-
cognitive prediction studies reviewed in section A.2. SFA treatment will be implemented as described by Boyle 
& Coelho8 and Boyle.9  At the beginning of a treatment trial, the target picture will be placed in the center of a 
chart containing the following entries: Group, use, action, properties, location, and association. A treatment trial 
will proceed through the following steps: 
 
1. 
The participant is asked to name the picture. 
 
2. 
Regardless of accuracy in Step 1, the participant is asked to verbally produce at least six 
distinguishing semantic features of the target. For each feature, the clinician asks a question (e.g., 
What is it used for?). If the patient responds with an appropriate feature (or features), the clinician 
repeats the feature, provides optional verbal reinforcement, and writes the response(s) in the chart. The 
clinician may also prompt the participants to provide additional and/or novel responses before 
proceeding to the next feature. If the participant is unable to produce an appropriate response, the 
clinician provides a cloze sentence cue (e.g., This is a kind of _____). If the participant is still unable to 
provide an appropriate response, the clinician provides the feature verbally and in writing, and asks the 
participant to repeat the feature name. 
 
3. 
After all six features are written on the chart, the clinician again asks the participant to 
name the target picture, regardless of whether the participant has named the picture successfully at 
any earlier point during the same trial. 
 
4. 
If the participant is unable to name the picture, the clinician says the name aloud, 
requires repetition, and requires the participant to verbally produce the six features. 
 
Treatment will be initiated with the first set of 10 treatment items for each patient. The pictures will be 
presented in a different pseudorandom order on each cycle through the set. If the patient names 90% of the 
treated items correctly on three of four consecutive probes, treatment on that set will be discontinued and a 
new set will be introduced. Since naming probes will be given 3 times per week, this criterion ensures that 
participants who show rapid acquisition will move on to a new treatment set as often as once per week. This in 
turn means that participants will receive treatment on up to 40 treatment exemplars during their time in the 
study. This number is greater than the number of treatment exemplars used in the majority of published 
naming treatment studies recently surveyed by Snell and colleagues[64]. 
 
 
Treatment will be conducted five days a week (Monday through Friday) across two daily 120-minute 
intervals, one morning session and one afternoon session, for a total of 4 hours of treatment per day and 20 
hours per week.  A 20-minute break will be provided within each 120-minute treatment interval and a 120 
minute break will be provided between each 120-minute treatment interval. Time in treatment will be recorded 
in daily logs, as will  the number of exposures to each training exemplar, the number of treatment trials, and the 
number of features (both novel and recurring) generated for each item on a given treatment trial. In addition, 
participants will be engaged in planned social and recreational activities on the weekends, such as visits to 

local sites and events (like a baseball game). These activities will not target or specifically stimulate language 
function or the skills trained in therapy. 
As indicated above, half the participants will be enrolled in treatment immediately upon their arrival at 
VAPHS, and half will be entered into the delayed-treatment condition. Participants assigned to the delayed-
treatment condition will arrive one week prior to treatment initiation. During this week, they will be in residence 
at the HJ Heinz campus and engage in non-language recreational activities in 2 two-hour sessions per day (the 
same schedule as will be followed during treatment). These activities will be led by a study staff member 
experienced in working with stroke survivors. The morning session will consist of recreational activities 
regularly conducted at the H.J. Heinz campus, listed in Table 6 below: 
 
Billiards  
Recreational Hall, H.J. Heinz campus 
Physical exercise 
Gym, H.J. Heinz campus 
Ceramics, other visual arts 
Art Studio, H.J. Heinz campus 
Puzzles and board games 
Recreational Hall, H.J. Heinz campus 
 
Table 6. Non-language-focused recreational activities for participants enrolled in delayed-treatment condition 
The afternoon session will consist of either complementary activities drawn from Table 6 or off-site visits to 
local destinations. While these sessions will afford multiple social contacts and interactions, typical of 
structured recreational programs, they will not target specific speech and language behaviors. Participants 
randomized to the delayed treatment group will receive probes (naming and PNT and other assessments 
[CAT, discourse measures]) on the same schedule as the no-delay group. 
 
The following procedures will be implemented to monitor and ensure treatment fidelity. All treatment 
sessions will be video recorded. For each participant and each day of treatment, study staff not providing 
treatment will randomly select 15 minutes (~ 7% of total treatment time) of video and review it for adherence to 
the semantic feature analysis (SFA) protocol. All deviations will be discussed with the treating clinicians. 
Monitoring will occur and feedback will be provided to clinicians by the end of the business day following the 
monitored sessions. When monitoring of a given clinician-participant pair detects no more than one deviation 
on three consecutive days, the pair will be monitored weekly for the remainder of the 4-week session. If 
subsequent weekly monitoring reveals more than one deviation from the SFA protocol, daily monitoring will be 
re-instituted until the criterion for weekly monitoring is again reached. 
Treatment and probe stimuli for study participants will consist of picturable nouns, consistent with 
methods used in existing SFA treatment studies. All pictures used as treatment and probe stimuli will be drawn 
from a subset of 361 of the 520 object pictures published by the UCSD Center for Research in Language 
International Picture Naming Project[91]. These 361 pictures were selected by removing from the full set 159 
picture stimuli that are also included in the Philadelphia Naming Test[92], which will serve as a secondary 
outcome measure for Specific Aims 1 and 2.  Participants will be asked to name this set of 361 pictures on 
three occasions across the two days of their initial evaluation, with all administrations separated by at least 5 
hours. All treatment and probe pictures will be selected from the pictures 
named correctly on fewer than two of those occasions. Four sets of 10 
items each will be selected as potential treatment items, along with four 
sets of 10 related generalization probe items and four sets of 10 
unrelated generalization probe items. For each treatment item, a 
category coordinate will be selected as a generalization probe item. The 
unrelated generalization probes will be selected from remaining items 
belonging to categories not occurring within the treatment set. These 
items will also be selected to minimize associative and other semantic 
relationships with items in the treatment sets. Within the constraints of 
semantic relatedness and baseline performance level, we will balance 
the treatment and generalization sets for lexical frequency and length. 
 
 
 
 
C.2.2  Probe stimuli, procedures and schedule 
Following baseline assessment of all 361 UCSD CRL items, the 
40 items selected for training, 40 related items, and 40 unrelated items 
will be probed at treatment initiation, three times per week during 
Figure 5. Panel A: Expected score for each of the seven 25-item PNT alternate forms, 
compared to empirical data of 254 aphasic patients. Panel B: Mean Rasch-scaled score for 
each of the seven alternate PNT forms, for a subsample of 57 aphasic patients.  

treatment, at program exit, and at one-month follow-up.  As an additional measure of within-subject control, we 
will also obtain baseline measures and weekly probes on the non-word repetition subtest of the PALPA. 
Generalization of SFA treatment to improved performance on nonword repetition is theoretically possible, but 
unexpected. The Philadelphia Naming Test (PNT)[92], a standardized measure of naming performance, will be 
administered weekly during baseline and treatment phases of the study. In order to eliminate the possibility 
that observed performance gains on the PNT could be attributed to repeated exposure to the same picture 
stimuli, we used methods based in item response theory[93] (IRT) to organize the 175 PNT items into seven 
equivalent 25-item alternate forms. We accomplished this in the following steps, using data taken from the 
Moss Aphasia Psycholingustics Project Database (MAPPD)[94], which contains item-level PNT response data 
collected from 254 persons with aphasia. First, we confirmed that a single underlying factor can satisfactorily 
account for aphasic performance on the PNT. We fit a unidimensional model to the data using NOHARM[95], 
and found that fit was excellent (root mean square of the model residuals = 0.010 vs. criterion of < 0.25; 
Tanaka Goodness of Fit Index = 0.985 vs. criterion of >0.95 for good fit). Next, we fit the data to a 1-parameter 
IRT model (Rasch) for dichotomous items  using WINSTEPS[96, 97]. We found that all items obtained 
information-weighted mean-square statistics <1.4, suggesting adequate fit to the model[98]. We then used the 
model-estimated item difficulty values to construct the seven alternate forms such that they were of equivalent 
overall difficulty and gave equivalent expected proportion correct scores as a function of latent naming ability. 
Panel A of Figure 5 shows the model predicted and empirical raw score for each alternate form as a function of 
Rasch-scaled latent naming ability, estimated based on the responses to the full PNT. The model curves for 
the seven forms are essentially identical and well-approximated by the empirical data. We then cross-validated 
these forms using new data from a subsample of 57 patients who were administered the PNT a second time. 
For these 57 patients, we computed the latent naming ability score for each alternate form and found that they 
adequately fit a strictly parallel test model (i.e., had equivalent means, true score variances, and error 
variances), χ2 = 37.757 with 32 df, p = 0.223. The average correlation between forms was 0.87 (min-max = 
0.82-0.93). The means and standard deviations for each of the seven forms are displayed in Panel B of Figure 
5. 
 
The naming probes and the PNT will be presented via computer by study staff not involved in 
treatment. The PNT alternate forms will be administered according to the procedures described by Roach et al. 
The naming probes will be administered in a different random order at each measurement point according to 
the same procedures as the PNT, with one modification: In order to make initial testing with a large number of 
potential treatment items feasible, participants will be given 15 rather than 30 seconds to respond to each item. 
Responses to the naming probes and the PNT will be audio recorded for subsequent transcription and 
scoring according to the conventional scoring procedures described by Mirman et al.[94] and available on the 
MAPPD website (www.mappd.org). Specifically, dichotomous correct/incorrect (1/0) scoring will be applied to 
the first complete attempt at each item. Minor sound distortions, additions, and omissions attributable to will be 
ignored for the purposes of dichotomous scoring. For the naming probes, the primary dependent variable will 
be the sum of these dichotomous item scores. For the PNT alternate forms, the primary dependent variable will 
be the logit-scaled latent ability score derived from the sum of the dichotomous item scores. The lexical and 
phonological error codes contained in the conventional and model coding system for the PNT responses will 
also be collected for all naming probe and PNT responses for use in secondary analyses. The nonword 
repetition control probe stimuli will be audio recorded by study staff and administered in pseudo-random order 
at each measurement point. Responses will be audio recorded for subsequent transcription and scoring. 
Scoring will be dichotomous correct/incorrect. 
For all naming probes, nonword repetition control probes, and the PNT, initial dichotomous scoring will 
be completed online by the study staff administering the stimuli. This online scoring will be used for the 
purposes of establishing inter-rater reliability. Primary scoring of each of these variables will be completed by a 
different member of the study staff from the audio-recorded responses. Prior to primary scoring, the filenames 
of audio recordings will be scrambled with respect to administration date so that the scorer will be blind to the 
measurement point. Inter-rater reliability between primary scoring and online scoring will be assessed for each 
participant by percentage point-to-point agreement and across participants by intraclass correlation coefficient. 
 
In addition to the probes described above, we will also collect data on two standardized assessments of 
language performance to examine the generalized effects of intensive lexical-semantic naming treatment.  
These include the Comprehensive Aphasia Test[99] and the connected speech task described by Nicholas and 
Brookshire[100]. The language battery of the CAT is comprised of several subtests designed to assess both 
word and sentence level performance in the areas of auditory and reading comprehension, repetition, naming, 
spoken and written picture description, oral reading and writing. T-scores based upon a well-described 

normative sample of adults with aphasia (n = 146) may be derived for each of the eight modalities to provide a 
profile of the measured impairment, and a mean modality T-score is computed to provide an overall estimate of 
the severity of the impairment. We will use modality T-scores to examine changes in reading, writing and 
comprehension performance in response to treatment.  
The Nicholas and Brookshire task employs 10 stimuli: 4 single pictures, 2 picture sequences, 2 
requests for personal information and 2 requests for procedural information. The stimuli are presented in 
random order to elicit connected speech samples from which the following measures may be derived; number 
of words (#words) in the sample, number of words that are correct information units (#CIUs), CIUs per minute 
(CIUs/min), and percentage of words a that are CIU (%CIUs).  We will employ the measures of CIUs/min and 
%CIUs to assess the effects of intensive lexical-semantic naming treatment on the informativeness and 
efficiency of study participants' connected speech.  These measures may be calculated with high inter- (words 
= 98%; CIUs = 90%) and intra-rater (words = 99%; CIUs = 95%) reliability, and have been reported to be 
stable across repeated assessments separated by 10 minutes, and by 7-10 days (mean absolute difference 
words = 6.82; mean absolute difference %CIUs = 3.50) in speech samples elicited by 5 stimuli, presented in 
random order, to adults with aphasia[101].   
Both the CAT and the connected speech task will be administered on 4 occasions: at baseline, study 
entry, study exit and follow-up. The connected speech task will employ a different, randomly selected set of 5 
stimuli across repeated assessments, be audio recorded, orthrographically transcribed and coded according to 
the conventions described by Nicholas and Brookshire[100]. Both of these tasks will be administered by study 
staff in line with the procedures used for probe and PNT measures above (i.e., samples will be blinded to time 
point and coded and scored by study staff rather than PIRATE clinicians). 
C.2.3 Participant classification and predictor measures: Psycholinguistic profile, cognitive assessment, and 
neuroimaging methods 
Participants’ naming-impairment profiles will be classified based on their performance on tests of 
lexical-semantic and phonological processing. These classification criteria will be the same as those used in 
Wambaugh and colleagues’ previous study examining the effects of language-impairment profile on naming 
treatment response[12]. Wambaugh and colleagues’ criteria are summarized in Figure 6.  
The semantic categorization task 
will involve sorting of 70 pictures into 
seven different superordinate categories 
(e.g., transportation, animals, tools, etc.). 
Rhyming, repetition, word-picture 
matching, oral reading, and repetition will 
be assessed using appropriate PALPA 
subtests. Picture naming error type data 
will be collected from the initial 
presentation of the 361-picture set 
described below, and from the subset of 
50 Philadelphia Naming Test items that 
each participant will receive by program 
entry, also as described below. 
Phonemic cueing responsiveness will be 
measured by presenting the pictures of 
the Boston Naming Test along with a 
single-segment phonemic cue. Those 
cues will either overlap with the first 
segment of the target (first sound cueing: 
e.g., [b] for target ‘broom’) or will overlap with the first segment of a semantic competitor (miscueing: e.g., [m] 
for target ‘broom,’ overlapping with semantic coordinate ‘mop’)[102]. 
All participants will also be administered a set of cognitive assessments immediately prior to program 
entry. These cognitive tasks will include measures of executive function abilities, visuospatial skills, recognition 
memory, and tasks of sustained and divided attention. The WCST and the picture and word versions of the 
Pyramids and Palm Trees Test (PPT) will be administered to assess executive function skills. The PPT was 
found to correlate with the cognitive factors identified in Lambon Ralph et al.’s study and performance has 
been shown to depend partially on reasoning and problem-solving abilities[103].  Additionally, two of the studies 
found significant correlations between PPT performance and treatment response following naming therapy[10, 
Figure 6. Criteria for classification of patients’ naming deficits, 
taken from Wambaugh et al.[12] 

66]. The immediate and delayed recall of the Complex Rey Figure will be administered to evaluate visuospatial 
abilities and memory. This test has been found to be significantly correlated with naming treatment 
outcomes[10].  Two of the short recognition subtests of the Camden Memory Test, the topographical and word 
subtests, will be administered to assess recognition memory[66].  Additionally, study participants will be given 
the sustained and divided attention subtests of the TEA. As noted above, Lambon Ralph and colleagues[10] 
found that the elevator counting with distraction subtest on the TEA was one of the significant cognitive factors 
that predicted naming treatment outcome. The TEA provides an aphasia-friendly, ecologically valid 
assessment of multiple forms of attention that has been standardized on a group of stroke patients[104].   
Finally, participants will receive structural magnetic resonance imaging scans immediately prior to 
program entry. Not all participants will be eligible to take part in imaging procedures (due to presence of 
exclusionary conditions such as claustrophobia or metal in the body). As noted above, current estimates are 
that approximately 20% of PIRATE participants to date have such exclusionary conditions.  
At program entry, all participants eligible for imaging will have a single structural magnetic-resonance 
imaging scan. Scans will be collected in the scanning facility located at the University Drive campus of VAPHS. 
The facility includes a new 3T Siemens Magnetom Verio Scanner (70 cm open bore) with a 12 channel 
MATRIX head-coil. The gradient coil set achieves 45mT/m maximum amplitude and a slew rate of 200mT/m/s 
with up to FoV 50 cm. The scanner is operated by registered MR technologists and is equipped with MRI-
compatible cardiac ECG equipment and blood oxygen monitors. All conventional and echo planar MR imaging 
functions are supported. The system is interfaced to a high-speed local area network for data transfer to 
workstations for analysis. All images will be collected under supervision of Dr. Forman. 
For each patient, we will obtain whole brain, high resolution T1 and T2-weighted structural MR images 
on the VAPHS 3T scanner., using an MPRAGE sequence with an axial slice orientation [Repetition Time (TR)= 
9.90ms, Echo Time (TE)=4.60ms, Voxel Size =.94mm x.94mm x 1.0 mm, Matrix Size=256mm x 256 mm, Flip 
Angle=8º]. Images will be reviewed by Co-Investigator Steven Forman, MD, Ph.D. and a staff neuro-
radiologist. Any unanticipated findings from the MR scan will be shared with VAPHS medical staff and may be 
referred to participants’ medical caregivers, as appropriate. These images will be housed on the VA network 
and will be viewed using ViSTA Image. We will manually trace all lesions in native space using the ITK-Snap 
image processing program[105]. We will then normalize each scan and its associated lesion mask to a standard 
neurological template (i.e., Montreal Neurological Institute) using SPM-08’s spatial normalization tool. Once all 
of the lesions have been oriented to a common template, we will conduct the VLSM analysis using the 
MRIcron nonparametric analysis toolbox[106]. 
C.3 Data analysis  
Three separate sets of analyses will be carried out on study measures. The first set of analyses will 
examine differences between participants randomized to the delayed-treatment and immediate-treatment 
conditions. The second set of analyses will test hypotheses relevant to Specific Aim 1, identifying correlates of 
response to intensive semantically-oriented naming treatment. The third set will test the dose-response 
hypotheses relevant to Specific Aim 2. 
The first analysis will evaluate whether the social contact associated with PIRATE participation results 
in improved performance on study measures, independent of any actual aphasia treatment. We will use a 
multivariate mixed model to test whether there are any differences between the immediate-treatment and 
delayed-treatment groups at the initiation of treatment, and whether there is any group-by-time interaction 
suggesting a positive effect of the delayed treatment week. The dependent variables will include performance 
on treated and untreated naming probes and PNT short forms.  
If this analysis finds no significant differences between the groups, it will suggest that any observed 
treatment gains can be attributed to the SFA treatment rather than general effects of the social contact 
associated with PIRATE. In this case, the two groups will be collapsed for further analysis. In the unexpected 
event that this analysis finds a significant difference favoring the delayed treatment condition, it will suggest 
that observed treatment gains should be attributed to the combination of SFA and the socialization inherent in 
a residential treatment program, rather than to SFA alone. In this case, we will use the data to estimate the 
size of the socialization effect and statistically control for it in subsequent analyses, where possible. 
C.3.1 Psycholinguistic, cognitive, and neuroanatomical predictors of treatment response 
The primary dependent variable for analyses relevant to Specific Aim 1 will be the proportion of 
correctly-named treated stimuli at study exit (post-treatment), minus the proportion of correctly-named treated 
stimuli at study entry (pre-treatment). This difference score provides the simplest measure of naming treatment 
response. Supplementary analyses may be carried out using a related dependent variable, the percentage of 
possible naming improvement[10]. This is a derived measure which divides the simple pre-post difference score 

by the difference between pre-treatment naming performance and ceiling performance (100% accuracy minus 
observed pre-treatment accuracy). This measure helps correct for observed differences in baseline accuracy. 
Secondary dependent variables for these analyses will include accuracy on untreated related stimuli 
(accuracy at study exit minus accuracy at study entry) and Rasch-scaled scores for PNT stimuli (scores at exit 
minus scores at entry). Previous studies have not found any predictive relationships between neuroanatomical 
or other variables and generalization to untrained stimuli[9, 20]. However, those studies did not administer 
treatment in the amounts or intensities provided in PIRATE or in the proposed research. It is possible that more 
intensive and/or larger doses of aphasia treatment may uncover stronger generalization effects (as well as 
stronger predictive relationships) than have previously been reported. 
C.3.1.1 Group effects on treatment response  
 
Participants will be classified as having primarily phonological, primarily lexical-semantic, or mixed 
naming impairments, using the methods described in section C.2.3 above. A between-participants analysis of 
variance will be carried out on the primary and secondary dependent measures of treatment response, with 
language-impairment group as an independent variable. As predicted by Hypothesis 1a and consistent with 
previous findings in the naming-treatment literature[12, 44], we anticipate that there will not be a main effect of 
language-impairment group on treatment response. The different groups of naming-impaired individuals will 
show comparable benefit from intensive, semantically-oriented naming treatment. If group differences are 
found in the ANOVA, we will explore their source using Tukey’s multiple comparisons to control type-I error. 
To determine whether the proposed sample size will be sufficient to detect these between-group 
differences, a power analysis  was carried out based on data from Wambaugh et al[12] and Abel et al.[44]. 
Together, these two studies provide pre-treatment and post-treatment naming probe data for four aphasic 
participants classified as having primarily semantic impairment, four participants classified as having primarily 
phonological impairment, and one patient with mixed impairments, all of whom received semantically-oriented 
naming treatment. As noted above in section A.1, the effects of psycholinguistic profile on response to 
semantically oriented treatment were potentially counter-intuitive: there was better response in phonologically 
impaired vs. semantically impaired participants. Nevertheless, we used the pre-treatment/post-treatment probe 
data from these 9 patients to estimate the variance in change scores. We assume that a 20 percentage-point 
difference in treatment effect (i.e., an average of 2 items, given the 10-item probe sets) is needed for the effect 
to be considered clinically meaningful. Assuming that 12 of the anticipated 50 participants will have primarily 
semantic impairments and 12 will have primarily phonological impairments, the proposed between-subjects 
ANOVA will have power of 0.82 to detect a 20-percentage point difference between these two groups. 
If no group differences are found in treatment response, as predicted under Hypothesis 1a, the 
treatment-response data will be collapsed across language-impairment groups for subsequent analysis. If 
group differences in the magnitude of treatment response are found, separate analyses for the relevant groups 
will be carried out for the cognitive and neuroanatomical hypotheses, in the analyses described below. 
C.3.1.2 Prediction of treatment effects by cognitive factors 
 
Participants’ performance on the set of cognitive measures identified above (section C.2.3) will be 
transformed into z-scores, to ensure that the scores may be compared across tasks. These transformed 
scores will be submitted to principal component analysis in order to identify one or more factors which account 
for performance across the measures. Based on previous findings[10, 69], we anticipate that measures of 
executive function (WCST) and performance on the PPT will load on one factor, while measures of visuo-
spatial memory (Rey Complex Figure) and other memory function (Camden Memory Test, topographical and 
word subtests) will load on a separate factor. Performance on tests of focused and divided attention (TEA) may 
also load on a separate factor. 
 
These factors will be entered as predictors into separate linear regressions targeting primary and 
secondary dependent measures, to determine which cognitive factors predict these measures of treatment 
response. Based on previous results, we anticipate that the executive-function factor[10, 71], the memory 
factor[10, 66], and the attention measures[10] will all be significant predictors of naming treatment response. If no 
reliable predictive relationships are found between these cognitive factors and treatment response, we will 
perform additional correlation analyses to examine the relationship between performance on individual 
cognitive tasks and treatment response. We may also subtract variance associated with aphasia severity (as 
assessed by the CAT mean language modality T-score) using stepwise linear regression, since different 
aphasia severities may be associated with different responses to treatment[1]. Given that the studies referenced 
above have demonstrated the hypothesized relationships with sample sizes ranging from 7 to 33 participants, 
we expect that the present analyses will be sufficiently powered, given the proposed sample size of 50. 
 

C.3.1.3 Voxel-based lesion symptom mapping (VLSM) 
For each participant, lesion size and extent will be quantified and related to treatment response using 
voxel-based lesion symptom mapping (VLSM), to test the neuroanatomical hypotheses relevant to Specific 
Aim 1. These analyses will compare the structural MRI data collected for study participants with change scores 
based on their performance on outcome measures. 
VLSM analysis will proceed in several steps. First, we will employ a multi-step lesion tracing procedure, 
first tracing lesions volume-by-volume in a descending axial plane while simultaneously referencing the coronal 
and saggital views in the ITK-Snap image processing program[105]. These initial tracings will be conducted via 
consensus among study staff. We will then subject these initial tracings to a second round of consensus with 
different raters and conduct minor adjustments as needed. Dr. Forman will have final say in any conflicts 
regarding lesion tracing. He will also be blind to each participant’s corresponding behavioral data.  
Upon reaching consensus on the lesion distributions, we will warp the original brains and their 
respective lesion masks into a standard neurological space (i.e., Montreal Neurological Institute template). We 
will first re-align each brain along its AC-PC axis and then segment it into distinct tissue types (i.e., gray matter, 
white matter, CSF, skull). After these initial pre-processing steps, we will normalize each brain using SPM-08. 
During normalization, we will apply the lesion tracings as masks with the goal of preventing tissue distortion in 
and around the lesion. We will then apply the original normalization parameters to the masks themselves in 
order to derive a set of normalized lesion patterns that may then be overlaid on a canonical brain and 
contrasted across subjects. 
We will use the MRIcron program’s nonparametric lesion mapping (NPM) feature to complete the lesion 
symptom correlations[106]. These data will be structured such that there is a binary classification at the voxel 
level (i.e., ±lesion) and continuous behavioral data (change score associated with the different outcome 
measures: PNT scores and scores on acquisition/generalization probes). Since these data are likely not to 
satisfy parametric statistical assumptions, we will use the NPM program’s Brunner-Munzel nonparametric test 
to relate behavioral performance on each of the treatment-outcome predictors (change scores) to lesion 
distributions[107-109]. We will apply a cluster threshold of 10 contiguous voxels and threshold for a false 
discovery rate of .05 in order to control for type I error while maintaining sufficient power to detect effects.  We 
will also include voxels that have at least a 25% lesion overlap across patients. VLSM analyses will focus on 
four principal regions of interest (ROIs): left anterior cortex, left posterior cortex, left basal ganglia, and left 
hippocampus. Previous work (reviewed in section B.2 above) has shown that positive response to aphasia 
treatment is associated with intact left hippocampus and left basal ganglia. In addition, some previous work 
suggests that negative treatment response is associated with more intact left anterior cortex[21], but other work 
suggests that greater recruitment of left anterior cortex is associated with positive treatment response[23]. 
Additional work has also found that more intact left posterior cortex is associated with more positive treatment 
response[23]. 
Change scores from program entry to program exit and/or follow-up will be computed for primary and 
secondary study measures: weekly PNT scores and acquisition/generalization probes. These scores will be 
correlated with lesion extent using VLSM, with analysis focusing on the four ROIs identified above. These 
correlations will serve to test the hypotheses relevant to Specific Aim 1 above. As predicted by Hypothesis 1c 
and consistent with the preliminary findings from the PIRATE participants (see B.2.1 above), we anticipate that 
there will be a significant negative correlation between change scores and lesion extent in left basal ganglia 
and hippocampus. Greater damage to structures required for learning and memory function (hippocampus) 
and to structures which can control maladaptive activation of damaged left-hemisphere tissue (basal ganglia) 
will result in poorer treatment outcomes. Also as predicted by Hypothesis 1c and consistent with preliminary 
data by Parkinson and colleagues (see B.2.1), we anticipate that extent of left-anterior hemisphere lesion will 
be positively correlated with change scores. Greater damage to left anterior cortex will prompt greater 
recruitment of right hemisphere cortex or other non-lesioned left hemisphere cortex, resulting in more positive 
treatment outcomes[20, 25]. Furthermore, we anticipate that extent of left posterior cortex will be associated with 
poorer treatment outcomes, given Fridriksson’s findings[23].  
Additional exploratory analyses will be carried out to examine how these neuroanatomical factors 
predict performance for the different outcome measures. We anticipate that brain regions which are positively 
correlated with change scores for acquisition probes will not be positively correlated with change scores for 
generalization probes, given the behavioral[9] and neuroimaging evidence[20] that treatment response for treated 
and untreated items/behaviors leans on different cognitive and neural mechanisms. We do not have any a 
priori predictions regarding what brain regions may be correlated with change scores for generalization probes. 

To determine whether the proposed sample size will be sufficient to detect the lesion-behavioral 
correlations described above, a power analysis was carried out on the correlational data from Parkinson and 
colleagues[21]. The correlation coefficients reported by Parkinson range from 0.65 to 0.85.  Power analysis 
indicates that a sample size of 40 will provide power >0.99 for detecting effects of this magnitude and power of 
0.80 for detecting correlations with r values of 0.38 or greater. Given the likely greater variability among the 
patients who participate in PIRATE, both in terms of their demographic characteristics and their language 
impairment profiles, we anticipate that any correlations between neuroanatomical markers and language 
performance and recovery may be somewhat weaker than those observed in the Parkinson and colleagues 
study[21], which had a much more uniform sample. However, the projected sample size of 40 should be 
sufficient to detect weaker correlations than those observed by Parkinson and colleagues[21]. 
If no significant correlations are found between the above ROIs and change scores for the outcome 
measures, we will perform additional VLSM analyses to examine the relationship between lesion site and 
extent and treatment response in subgroups of study participants. Since participants in this study will likely be 
more heterogeneous than those in previous studies, the data from the current study may be noisier due to 
greater inter-subject variability. To explore the effects of this variability on neural correlates of treatment 
response, participants will be divided by aphasia severity, their CAT T-scores, since different aphasia 
severities may be associated with different responses to treatment[1]. Additional analyses may explore the 
lesion-treatment relationships found in different subgroups of veterans. 
C.3.2 Dose-response analyses 
Study measures will be subjected to two sets of analyses which test the dose-response hypotheses 
relevant to Specific Aim 2. These analyses will compare the PNT scores and acquisition and generalization 
probe scores across the 4 weeks of PIRATE treatment, to determine the dose-response relationship and to 
identify the points of largest change in response to treatment. The last of the three weekly naming probes will 
serve as a measure of weekly acquisition and generalization performance. This will ensure that performance 
on PNT and naming-probe measures may be directly compared, as they will be on the same schedule. 
First, the data for the PNT scores and the acquisition/generalization probes will be analyzed with a 
multivariate mixed model with polynomial trend contrasts. To simplify interpretation, only time points 2 through 
6 (program entry through program exit) will be included in the trend analyses. These trend analyses will first 
test whether there is a linear relationship between time in treatment and treatment response for study 
measures, as predicted by Hypothesis 2a. Second, they will test whether acquisition and generalization probes 
show additional curvilinear relationships to time in treatment, as predicted by Hypothesis 2b. We anticipate that 
there will be a positive linear trend between program entry and program exit for PNT scores and for acquisition 
and generalization probes. This finding would indicate that the four weeks of treatment results in significant 
score gains. We further predict that the trend for acquisition probes will also have a significant curvilinear 
(quadratic or higher-order) component, consistent with decelerating improvement across the four weeks of 
treatment. This finding would indicate that the largest gains for specifically treated skills/items occur early in 
treatment, with declining gains in later weeks. Finally, we predict that the generalization probes will also 
demonstrate a curvilinear relationship to time, due to accelerating improvement across the four week program. 
This finding would indicate that the largest gains for related but untreated items occur late in treatment, 
emerging later than the gains for specifically treated forms.  
Second, six pairwise planned comparisons of scores across all seven consecutive measurement 
intervals will be carried out, using Bonferroni-corrected t-tests to maintain the familywise type-I error rate at 0.1. 
As predicted by Hypothesis 2a and consistent with the preliminary outcome data from PIRATE (see B.1.3 
above), we anticipate that there will be a significant increase in both PNT score and acquisition/generalization 
probe performance for each additional one-week increment of treatment. We also predict non-significant 
changes between the initial evaluation and program entry and between program exit and follow-up.  
In case a linear relationship is not found between time in treatment and outcome measures, additional 
analyses will be carried out examining the performance of different subsets of veterans. For example, veterans 
will be divided into groups based on their aphasia severity, since previous treatment findings have suggested 
that milder aphasia severity may be associated with less change on language-performance measures[1]. 
Separate trend analyses will be carried out for the different severity groups, to examine whether different dose-
response relationships may be observed for different aphasia groups.  
In addition to these within-participant analyses examining dose-response relationships, a 
complementary set of between-participants analyses will be carried out, examining the effects of two versus 
four weeks of treatment for different randomly-selected groups of participants. It is standard practice in the 
dose-response literature to compare the response of groups exposed to different amounts of a therapeutic 

agent, to see the incidence of positive or negative responses to the agent in the groups (e.g., frequency of 
remission or toxicity[110]). These complementary between-participant analyses will simulate this comparison for 
the current study, providing an additional randomized-control measure of the effect of high dosage SFA on 
individuals with naming impairments. For these analyses, participants will be randomized to one of two groups. 
For half the participants, outcome measures (naming and PNT scores) will be sampled after two weeks’ 
exposure, while for the other half, outcome measures will be sampled after the full four weeks of treatment. We 
anticipate that changes in study measures will be larger for the four-week group than for the two-week group. 
C.3.3 Additional analyses of general language performance and connected speech  
 
In order to evaluate whether the study treatment is associated with broad gains in communicative 
function beyond confrontation naming, we will analyze data obtained from the Comprehensive Aphasia Test 
(CAT) and the connected speech samples described above in section C.2.2. Specifically, we will conduct a 
multivariate linear mixed model analysis with measurement point (initial evaluation, program entry, program 
exit, follow-up) as a fixed factor, subjects as a random factor, and the following dependent variables: CAT 
reading, writing, auditory comprehension and mean modality T-scores, and, from the connected speech 
samples, the percentage of correct information units (%CIUs) and the number of correct information units per 
minute (CIUs/min). We anticipate that there will be significant increases in the CAT auditory comprehension 
and mean modality T-scores and in the two connected speech measures (%CIUs, CIUs/min) from program 
entry to program exit, showing generalized improvement in communicative function. We have no strong 
predictions regarding the generalized effects of SFA on reading and writing abilities as measured by the CAT.   
C.4 Timetable  
This proposal is for a four-year research project. Based on an October 1, 2012 start date, recruitment of 
participants should begin with the November 2012 PIRATE session of 2012. Enrollment will take place 
continuously through September 2016, with dissemination of results continuing through December 2016. 
C.5 Investigators 
Michael Walsh Dickey, Ph.D. and Patrick J. Doyle, Ph.D., CCC-SLP will serve as principal 
investigators. Dr. Dickey has expertise in the study and application of sentence processing treatments for 
aphasia. Dr. Doyle has expertise in aphasia assessment, treatment and clinical outcomes evaluation. The 
following individuals will serve as co-investigators: Steven D. Forman, M.D., Ph.D. has expertise in collection 
and analysis of brain imaging data and will oversee collection of structural imaging data and VLSM analyses; 
Bruce Crosson, Ph.D., has expertise in the application of brain imaging to aphasia treatment research and will 
advise regarding analysis and interpretation of imaging data and correlation analyses; Malcolm R. McNeil, 
Ph.D., CCC-SLP, has expertise in aphasia diagnosis, assessment, and treatment; William D. Hula, Ph.D., 
CCC-SLP, has expertise in analysis methods and measurement in aphasia; and Jamie J. Reilly, Ph.D., CCC-
SLP, has expertise in language rehabilitation for aphasia and dementia, and in structural and functional 
neuroimaging and will advise regarding analysis and interpretation of imaging data. 
C.6 Project resources 
The VA Pittsburgh Healthcare System possesses the physical facilities and other resources necessary 
for the successful and timely completion of the proposed research project. The VAPHS Geriatric Research, 
Education and Clinical Center (GRECC) is a VHA supported Center of Excellence focused on stroke 
prevention, treatment, rehabilitation, and outcomes. Centralized GRECC funding supports a core staff of 12 
FTEE including Drs. Dickey (2/8ths) and Doyle (8/8ths). Support for 2 FTEE PIRATE SLPs is provided by VISN 
4 and GRECC Clinical Innovation Award Funds. The physical infrastructure of the GRECC includes office 
space and research labs equipped with VHA-networked secure computers and dedicated research servers. In 
addition, PIRATE has a dedicated residential villa located on the adjacent VAPHS Heinz campus. Daily 
PIRATE treatment sessions are also conducted on the VAPHS H.J. Heinz Campus in dedicated clinic space 
containing 3 individual treatment rooms, VHA-networked computers, and 6 dedicated non-VA-networked 
laptops used to design and manage treatment stimuli and deliver customized treatment programs. Dedicated 
PIRATE staff handle patient referrals, inquiries, scheduling, and logistical arrangements.  
The VAPHS scanning facility includes a 3T Siemens Magnetom Verio Scanner (70 cm open bore) with 
a 12 channel MATRIX head-coil. The scanner is operated by registered MR technologists and is equipped with 
MRI-compatible cardiac ECG equipment and blood oxygen monitors. All conventional and echo planar MR 
imaging functions needed for the research are supported. The system is interfaced to the secure VHA network. 
PIRATE receives enrollment applications from VA Medical Centers and DOD Hospitals nationwide and 
from key local referral sources. During its first 3 years of operation PIRATE received 136 applications for 
enrollment, with 19 veterans on the waiting list and several evaluations pending. This application rate is well in 
excess of the rates required to achieve study enrollment targets within the 4 years of the proposed project. 

REFERENCES 
 
1. 
Robey, R.R., A meta-analysis of clinical outcomes in the treatment of aphasia. Journal of Speech, 
Language, and Hearing Research, 1998. 41(1): p. 172-187. 
2. 
Holland, A.L., et al., Treatment efficacy: aphasia. Journal of Speech and Hearing Research, 1996. 
39(5): p. S27-S36. 
3. 
Wertz, R.T., et al., Comparison of clinic, home, and deferred language treatment for aphasia. A 
Veterans Administration Cooperative Study. Archives of Neurology, 1986. 43(7): p. 653-658. 
4. 
Poeck, K., W. Huber, and K. Willmes, Outcome of intensive language treatment in aphasia. Journal of 
Speech and Hearing Disorders, 1989. 54(3): p. 471-479. 
5. 
Cherney, L.R., et al., Evidence-based systematic review: effects of intensity of treatment and 
constraint-induced language therapy for individuals with stroke-induced aphasia. J Speech Lang 
Hear.Res., 2008. 51(5): p. 1282-1299. 
6. 
Bhogal, S.K., R. Teasell, and M. Speechley, Intensity of aphasia therapy, impact on recovery. Stroke, 
2003. 34(4): p. 987-993. 
7. 
Elman, R.J. and E. Bernstein-Ellis, The efficacy of group communication treatment in adults with 
chronic aphasia. Journal of Speech, Language, and Hearing Research, 1999. 42   p. 411-419. 
8. 
de Jong-Hagelstein, M., et al., Efficacy of early cognitive-linguistic treatment and communicative 
treatment in aphasia after stroke: a randomised controlled trial (RATS-2). Journal of Neurology, 
Neurosurgery, and Psychiatry, 2010. 
9. 
Dickey, M.W., Yoo, H., Predicting outcomes for linguistically-specific sentence treatment protocols. 
Aphasiology, 2010. 24: p. 787-801. 
10. 
Lambon Ralph, M.A., et al., Predicting the outcome of anomia therapy for people with aphasia post 
CVA: Both language and cognitive status are key predictors. Neuropsychological Rehabilitation, 2010. 
20: p. 289-305. 
11. 
Parkinson, B.R., et al., Lesion characteristics related to treatment improvement in object and action 
naming for patients with chronic aphasia. Brain and Language, 2009. 110(2): p. 61-70. 
12. 
Wambaugh, J.L., et al., Effects of two cueing treatments of lexical retrieval in aphasic speakers with 
different levels of deficit. Aphasiology, 2001. 15(10/11): p. 933-950. 
13. 
Wertz, R.T., Language treatment for aphasia is efficacious, but for whom? Topics in Language 
Disorders, 1987. 8(1): p. 1-10. 
14. 
Boyle, M. and C.A. Coelho, Application of semantic feature analysis as a treatment for aphasic 
dysnomia. American Journal of Speech-Language Pathology, 1995. 4(4): p. 94-98. 
15. 
Coelho, C.A., R.E. McHugh, and M. Boyle, Semantic feature analysis as a treatment for aphasic 
dysnomia: A replication. Aphasiology, 2000. 14(2): p. 133-142. 
16. 
Boyle, M., Semantic feature analysis treatment for anomia in two fluent aphasia syndromes. American 
Journal of Speech-Language Pathology, 2004. 13(3): p. 236-249. 
17. 
Boyle, M. and C.A. Coelho, Application of semantic feature analysis as a treatment for aphasic 
dysnomia. American Journal of Speech-Language Pathology, 2008. 4: p. 94-98. 
18. 
Nickels, L., Therapt for naming disorders: revisiting, revising, and reviewing. Aphasiology, 2002. 16: 
935-979. 
19. 
Wambaugh, J.L., A comparison of the relative effects of phonologic and semantic cueing treatments. 
Aphasiology, 2003. 17(5): p. 433-441. 
20. 
Meinzer, M., et al., Integrity of the hippocampus and surrounding white matter is correlated with 
language training success in aphasia. NeuroImage, 2010. 53: p. 283-290. 
21. 
Bruce Parkinson, R., et al., Lesion characteristics related to treatment improvement in object and action 
naming for patients with chronic aphasia. Brain and Language, 2009. 110(2): p. 61-70. 
22. 
Crosson, B., et al., Role of the right and left hemispheres in recovery of function during treatment of 
intention in aphasia. J Cogn Neurosci., 2005. 17(3): p. 392-406. 
23. 
Fridriksson, J., Preservation and modulation of specific left hemisphere regions is vital for treated 
recovery from anomia in stroke. J Neurosci., 2010. 30(35): p. 11558-11564. 
24. 
Menke, R., et al., Imaging short- and long-term training success in chronic aphasia. BMC.Neurosci., 
2009. 10: p. 118. 
25. 
Crosson, B., et al., Regional changes in word-production laterality after a naming treatment designed to 
produce a rightward shift in frontal activity. Brain and Language, 2009. 111(2): p. 73-85. 

26. 
Basvaraju, A. and S.R. Pratt, Aphasia data extraction report. Center for Health Equity Research & 
Promotion and Geriatric Research Education & Clinical Center, VA Pittsburgh Healthcare, 2008. 
27. 
Yu, W., et al., Prevalence and costs of chronic conditions in the VA health care system. Med.Care 
Res.Rev., 2003. 60(3 Suppl): p. 146S-167S. 
28. 
Kauhanen, M.L., et al., Aphasia, depression, and non-verbal cognitive impairment in ischaemic stroke. 
Cerebrovascular Diseases, 2000. 10(6): p. 455-461. 
29. 
Pulvermuller, F., et al., Therapy-related reorganization of language in both hemispheres of patients with 
chronic aphasia. NeuroImage, 2005. 28(2): p. 481-489. 
30. 
Pulvermuller, F., et al., Constraint-induced therapy of chronic aphasia after stroke. Stroke, 2001. 32(7): 
p. 1621-1626. 
31. 
Massaro, M. and C.A. Tompkins, Feature Analysis for Treatment of Communication Disorders in 
Traumatically Brain-Injured Patients: An Efficacy Study. Clinical Aphasiology, 1994. 22: p. 245-256. 
32. 
Collins, A.M. and E.F. Loftus, A Spreading-Activation Theory of Semantic Processing. Psychological 
Review, 1975. 82(6): p. 407-428. 
33. 
Levelt, W.J., A. Roelofs, and A.S. Meyer, A theory of lexical access in speech production. Behavioral 
and Brain Sciences, 1999. 22(1): p. 1-38. 
34. 
Dell, G.S., et al., Lexical access in aphasic and nonaphasic speakers. Psychological Review, 1997. 
104(4): p. 801-838. 
35. 
Haarbauer-Krupa, J., et al., Cognitive rehabilitation therapy: Late stages of recovery, in Head injury 
rehabilitation: Children and adolescents, M. Ylvisaker, Editor 1985, College Hill Press: San Diego, CA. 
p. 311-340. 
36. 
Peach, R.K. and K.A. Reuter, A discourse-based approach to semantic feature analysis for the 
treatment of aphasic word retrieval failures. Aphasiology, 2010. 24(9): p. 971-990. 
37. 
Boyle, M., Semantic feature analysis treatment for aphasic word retrieval impairments: what's in a 
name? Topics in Stroke Rehabilitation, 2010. 17: p. 411-422. 
38. 
Lowell, S., P.M. Beeson, and A.L. Holland, The efficacy of a semantic cueing procedure on naming 
performance of adults with aphasia. American Journal of Speech-Language Pathology,1995.4:109-114. 
39. 
Edmonds, L.A. and S. Kiran, Effect of semantic naming treatment on crosslinguistic generalization in 
bilingual aphasia. Journal of Speech, Language, and Hearing Research, 2006. 49(4): p. 729-748. 
40. 
Kiran, S. and P.M. Roberts, Semantic feature analysis treatment in Spanish-English and French-
English bilingual aphasia. Aphasiology, 2010. 24: p. 231-261. 
41. 
Rider, J.D., et al., Using semantic feature analysis to improve contextual discourse in adults with 
aphasia. American Journal of Speech Language Pathology, 2008. 17: p. 161-172. 
42. 
Wambaugh, J. and M. Ferguson, Application of semantic feature analysis to retrieval of action names in 
aphasia. Journal of Rehabilitation and Research Development, 2007. 44: p. 381-394. 
43. 
Hashimoto, N. and A. Frome, The use of a modified semantic features analysis approach in aphasia. 
Journal of Communication Disorders, 2011. 44: p. 459-469. 
44. 
Abel, S., K. Willmes, and W. Huber, Model-oriented naming therapy: Testing predictions of a 
connectionist model. Aphasiology, 2007. 21: p. 411-447. 
45. 
Wambaugh, J.L., et al., Effects of two lexical retrieval cueing treatments on action naming in aphasia. 
Journal of Rehabilitation and Research Development, 2002. 39: p. 455-466. 
46. 
Ivarie, J.J., Effects of proficiency rates on later performance of a recall and writing behavior. Remedial 
and Special Education, 1986. 7(5): p. 25-30. 
47. 
Bloom, B.S., The hands and feet of genius. Educational Leadership, 1986. 43: p. 70-77. 
48. 
Olander, C.P., et al., Retention among college students: A comparison of traditional versus precision 
teaching. Journal of Precision Teaching, 1986. 6(4): p. 80-82. 
49. 
Binder, C., Behavioral fluency: A new paradigm. Educational Technology, 1993. 33(10): p. 8-14. 
50. 
LaBerge, D. and S.J. Samuels, Toward a theory of automatic information-processing in reading. 
Cognitive Psychology, 1974. 6: p. 293-323. 
51. 
White, O.R., Aim*star wars (setting aims that compete). Journal of Precision Teaching, 1984. 5: 55-63. 
52. 
Kunkel, A., et al., Constraint-induced movement therapy for motor recovery in chronic stroke patients. 
Archives of Physical Medicine and Rehabilitation, 1999. 80(624-628). 
53. 
Taub, E., G. Uswatte, and R. Pidikiti, Constraint induced movement therapy: A new family of 
techniques with broad application to physical rehabilitation—A clinical review. Journal of Rehabilitation 
and Research Development, 1999. 36: p. 237–251. 

54. 
Taub, E., G. Uswatte, and T. Elbert, New treatments in rehabilitation founded on basic research. Nature 
Reviews: Neuroscience, 2002. 3: p. 228-236. 
55. 
Meinzer, M., et al., Long-term stability of improved language functions in chronic aphasia after 
constraint-induced aphasia therapy. Stroke, 2005. 36(7): p. 1462-1466. 
56. 
Meinzer, M., et al., Extending the Constraint-Induced Movement Therapy (CIMT) approach to cognitive 
functions: Constraint-Induced Aphasia Therapy (CIAT) of chronic aphasia. NeuroRehabilitation., 2007. 
22(4): p. 311-318. 
57. 
Meinzer, M., et al., Intensive language training enhances brain plasticity in chronic aphasia. BMC 
Biology, 2004. 2. 
58. 
Kilgard, M.P. and M.M. Merzenich, Cortical map reorganization enabled by nucleus basalis activity. 
Science, 1998. 279(5357): p. 1714-1718. 
59. 
Merzenich, M.M., et al., Topographic reorganiation of somatosensory cortical areas 3b and 1 in adult 
monkeys following restricted deafferentation. Neuroscience, 1983. 8: p. 33-55. 
60. 
Bach-y-Rita, P. and S.W. Kercel, Sensory substitution and the human–machine interface. Trends Cogn 
Sci., 2003. 7(12): p. 541-546. 
61. 
Jenkins, W.M., Functional reorganization of primary somatosensory cortex in adult owl monkeys after 
behaviorally controlled tactile stimulation. Journal of Neurophysiology, 1990. 63: p. 82-104. 
62. 
Kleim, J.A., T.A. Jones, and T. Schallert, Motor enrichment and the induction of plasticity before or after 
brain injury. Neurochemistry Research, 2003. 28(1757-1769). 
63. 
Kiran, S. and C.K. Thompson, The Role of Semantic Complexity in Treatment of Naming Deficits: 
Training Semantic Categories in Fluent Aphasia by Controlling Exemplar Typicality. Journal of Speech, 
Language, and Hearing Research, 2003. 46(4): p. 773-787. 
64. 
Snell, C., K. Sage, and M.A.L. Ralph, How many words should we provide in anomia therapy? A meta-
analysis and a case series study. Aphasiology, 2010. 24(9): p. 1064-1094. 
65. 
Beeson, P., Robey, R., Meta-analysis of aphasia treatment outcomes: Examining the evidence, in 
Clinical Aphasiology Conference2008: Jackson Hole, Wy. 
66. 
Fillingham, J.K., K. Sage, and M.A. Lambon Ralph, The treatment of anomia using errorless learning. 
Neuropsychol.Rehabil., 2006. 16(2): p. 129-154. 
67. 
Van de Sandt-Koenderman, W.M., et al., MAAS (Multi-Axial Aphasia System): Realistic goal setting in 
aphasia rehabilitation. International Journal of Rehabilitation Research, 2008. 31: p. 314-320. 
68. 
Helm-Estabrooks, N., Cognition and aphasia: A discussion and a study. Journal of Communication 
Disorders, 2002. 35: p. 171-186. 
69. 
Hinckley, J.J. and J. Carr, Differential contributions of cognitive abilities to success in skill-based versus 
context-based aphasia treatment. Brain and Language, 2001. 79: p. 3-9. 
70. 
Seniow, J., M. Litwin, and M. Lesniak, The relationship between non-linguistic cognitive deficits and 
language recovery in patients with aphasia. Journal of the Neurological Sciences, 2009. 283: p. 91-94. 
71. 
Hinckley, J.J., J.P. Patterson, and T.H. Carr, Differential effects of context- and skill-based treatment 
approaches: Preliminary findings. Aphasiology, 2001. 15(5): p. 463-476. 
72. 
Meinzer, M., et al., Recent developments in functional and structural imaging of aphasia recovery after 
stroke. Aphasiology, 2011. 25(3): p. 271-290. 
73. 
Fridriksson, J., Baker, J. M., & Moser, D., Cortical mapping of naming errors in aphasia. Human Brain 
Mapping, 2009. 30: p. 2487–2498. 
74. 
Fridriksson, J., Bonilha, L., Baker, J. M., Moser, D., & Rorden, C., Activity in preserved left hemisphere 
regions predicts anomia severity in aphasia. Cerebral Cortex, 2010. 20: p. 1013-1019. 
75. 
Postman-Caucheteux, W.A., et al., Single-trial fMRI shows contralesional activity linked to overt naming 
errors in chronic aphasic patients. J Cogn Neurosci., 2010. 22(6): p. 1299-1318. 
76. 
Richter, M., W.H.R. Miltner, and T. Straube, Association between therapy outcome and right-
hemispheric activation in chronic aphasia. Brain, 2008. 131: p. 1391-1401. 
77. 
Saur, D., et al., Dynamics of language reorganization after stroke. Brain, 2006. 129: p. 1371-1384. 
78. 
Basso, A. and A. Caporali, Aphasia therapy or the importance of being earnest. Aphasiology, 2001. 
15(4): p. 307-332. 
79. 
Basso, A., How intensive/prolonged should an intensive prolonged treatment be? Aphasiology, 2005. 
19(10/11): p. 975-984. 
80. 
Thompson, C.K. and L. Shapiro, Treating agrammatic aphasia within a linguistic framework: Treatment 
of underlying forms. Aphasiology, 2005. 19(10/11): p. 1021-1036. 

81. 
Barthel, G., et al., Intensive language therapy in chronic aphasia: Which aspects contribute most? 
Aphasiology, 2007. 22(4): p. 408-421. 
82. 
Maas, E., Robin, D., Austermann Hula, S., Freedman, S., Wulf, B., Ballard, K., Schmidt, R., Principles 
of Motor Learning in Treatment of Motor Speech Disorders. American Journal of Speech-Language 
Pathology, 2008. 17: p. 277–298. 
83. 
Park, J.H., & Shea, C. H., Effect of practice on effector independence.J. of Motor Behav,2003.35:33-40. 
84. 
Park, J.H., & Shea, C. H., Sequence learning: Response structure and effector transfer. 
Q.J.Exp.Psychol.A, 2005. 58: p. 387-419. 
85. 
Schmidt, R.A. and T.D. Lee, Motor Control and Learning: A Behavioral Emphasis2005, Champaign, IL: 
Human Kinetics. 
86. 
Porch, B., Porch index of communicative ability2001, Albuquerque, NM  PICA Programs. 
87. 
Baker, J., Rorden, C., Fridriksson, J., Using transcranial direct current stimulation to treat stroke 
patients with aphasia. Stroke, 2010. 41: p. 1229-1236. 
88. 
Verdon, V., Schwartz, S., Lovblad, K-O., Hauert, C-A., Vuilleumier, P., Nueroanatomy of hemispatial 
neglect and its functional components: a study using voxel-based lesion-symptom mapping. Brain, 
2010. 133: p. 880-894. 
89. 
Reilly, J., Park. H., Bennet, J., Towler, S. Benjamin, M., Crosson, B., Broca and beyond: Decomposing 
the Lesion Correlates of Narrative Speech Fluency in Aphasia. in preparation. 
90. 
Goodglass, H., E. Kaplan, and B. Barresi, The assessment of aphasia and related disorders. Vol. 3rd  
2001, Baltimore: Lippincott, Williams, & Wilkins. 
91. 
Szekely, A., et al., A new on-line resource for psycholinguistic studies. [References]2004: Journal of 
Memory and Language. Vol.51(2), Aug 2004, pp. 247-250. 
92. 
Roach, A., M.F. Schwartz, and N. Martin, The Philadelphia Naming Test: Scoring and rationale, in 
Clinical Aphasiology, M.L. Lemme, Editor 1996, Pro-Ed: Austin, TX. p. 121-133. 
93. 
De Ayala, R.J., The theory and practice of item response theory, 2009, New York: The Guilford Press. 
94. 
Mirman, D., et al., A large, searchable, web-based database of aphasic performance on picture naming 
and other tests of cognitive function. Cognitive Neuropsychology, 2010. 27(495-504). 
95. 
Fraser, C. and R.P. McDonald, NOHARM: A windows program for fitting both unidimensional and 
multidimensional normal ogive models of latent trait theory, 2003, Niagra College: Welland, ON. 
96. 
Linacre, J.M. and B.D. Wright, WINSTEPS: Multiple-choice, rating scale, and partial credit Rasch 
analysis, 2003, MESA Press  Chicago. 
97. 
Linacre, J.M., WINSTEPS, 2009, Winsteps.com: Chicago. 
98. 
Wright, B.D. and J.M. Linacre. Reasonable mean-square fit values. 1994 2009/11/30/ [cited 8; 3:[370]. 
Available from: http://rasch.org/rmt//rmt83.htm. 
99. 
Swinburn, K., G. Porter, and D. Howard, Comprehensive aphasia test. 2004, NY: Psychology Press. 
100. 
Nicholas, L.E. and R.H. Brookshire, A system for quantifying the informativeness and efficiency of the 
connected speech of adults with aphasia. Journal of Speech and Hearing Research, 1993.36: 338-350. 
101. 
Brookshire, R.H. and L.E. Nicholas, Speech sample size and test-retest stability of connected speech 
measures for adults with aphasia. Journal of Speech and Hearing Research, 1994. 37(2): p. 399-407. 
102. 
Howard, D. and C. Gatehouse, Distinguishing semantic and lexical word retrieval deficits in people with 
aphasia. Aphasiology, 2006. 20  (9/10/11): p. 921-950. 
103. 
Jefferies, E. and M.A. Lambon Ralph, Semantic impairment in stroke aphasia versus semantic 
dementia: A case-series comparison. Brain, 2006. 129  p. 2132-2147. 
104. 
Murray, L.L., Attention deficits in aphasia: presence, nature, assessment, and treatment. Seminars in 
Speech and Language, 2002. 23(2): p. 107-116. 
105. 
Yushkevich, P., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G., User-guided 3D 
active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. 
NeuroImage, 2006. 31: p. 1116-1128. 
106. 
Rorden, C., MRIcro: Imaging analysis toolbox., 2007. 
107. 
Brunner, E., Munzel, U., Puri, M.L., Rank-score tests in factorial designs with repeated measures. 
Journal of Multivariate Analysis, 1999. 70: p. 286-317. 
108. 
Rorden, C., Bonilha, L., Nichols, T., Rank-order versus mean based statistics for neuroimaging. 
NeuroImage, 2007. 35: p. 1531-1537. 
109. 
Rorden, C., Karnath, H-O., Bonilha, L., Improving lesion-symptom mapping. Journal of Cognitive 
Neuroscience, 2007. 19: p. 1081-1088. 
110. 
Ting, N., Dose Finding in Drug Development. 2006, New York: Springer.