ArticlePDF Available

Auditory training changes temporal lobe connectivity in ‘Wernicke’s aphasia’: a randomised trial

  • University College London/ and UCL Hospitals NHS Foundation Trust


Introduction Aphasia is one of the most disabling sequelae after stroke, occurring in 25%–40% of stroke survivors. However, there remains a lack of good evidence for the efficacy or mechanisms of speech comprehension rehabilitation. Trial Design This within-subjects trial tested two concurrent interventions in 20 patients with chronic aphasia with speech comprehension impairment following left hemisphere stroke: (1) phonological training using ‘Earobics’ software and (2) a pharmacological intervention using donepezil, an acetylcholinesterase inhibitor. Donepezil was tested in a double-blind, placebo-controlled, cross-over design using block randomisation with bias minimisation. Methods The primary outcome measure was speech comprehension score on the comprehensive aphasia test. Magnetoencephalography (MEG) with an established index of auditory perception, the mismatch negativity response, tested whether the therapies altered effective connectivity at the lower (primary) or higher (secondary) level of the auditory network. Results Phonological training improved speech comprehension abilities and was particularly effective for patients with severe deficits. No major adverse effects of donepezil were observed, but it had an unpredicted negative effect on speech comprehension. The MEG analysis demonstrated that phonological training increased synaptic gain in the left superior temporal gyrus (STG). Patients with more severe speech comprehension impairments also showed strengthening of bidirectional connections between the left and right STG. Conclusions Phonological training resulted in a small but significant improvement in speech comprehension, whereas donepezil had a negative effect. The connectivity results indicated that training reshaped higher order phonological representations in the left STG and (in more severe patients) induced stronger interhemispheric transfer of information between higher levels of auditory cortex. Clinical trial registration This trial was registered with EudraCT (2005-004215-30, https:// Eudract and ISRCTN (68939136,
Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
To cite: Woodhead ZVJ,
Crinion J, Teki S, et al. J
Neurol Neurosurg Psychiatry
Published Online First: [please
include Day Month Year].
1Department of Brain Repair
and Rehabilitation, University
College London, London, UK
2Wellcome Trust Centre for
Neuroimaging, University
College London, London, UK
3Department of Experimental
Psychology, University of Oxford,
Oxford, UK
4Institute of Cognitive
Neuroscience, University College
London, London, UK
5Department of Physiology,
Anatomy and Genetics,
University of Oxford, Oxford, UK
Correspondence to
Dr Zoe VJ Woodhead, Wellcome
Trust Centre for Neuroimaging,
University College London, 12
Queen Square, London WC1N
3BG, UK; z. woodhead@ ucl.
ac. uk
Received 05 August 2016
Revised 24 November 2016
Accepted 01 February 2017
Auditory training changes temporal lobe connectivity
in ‘Wernicke’s aphasia’: a randomised trial
Zoe VJ Woodhead,1,2,3 Jennifer Crinion,4 Sundeep Teki,5 Will Penny,2 Cathy J Price,2
Alexander P Leff 2,4
Introduction Aphasia is one of the most disabling se-
quelae after stroke, occurring in 25%–40% of stroke sur-
vivors. However, there remains a lack of good evidence
for the efficacy or mechanisms of speech comprehension
Trial Design This within-subjects trial tested
two concurrent interventions in 20 patients with
chronic aphasia with speech comprehension
impairment following left hemisphere stroke: (1)
phonological training using ‘Earobics’ software and
(2) a pharmacological intervention using donepezil, an
acetylcholinesterase inhibitor. Donepezil was tested in a
double-blind, placebo-controlled, cross-over design using
block randomisation with bias minimisation.
Methods The primary outcome measure was speech
comprehension score on the comprehensive aphasia test.
Magnetoencephalography (MEG) with an established
index of auditory perception, the mismatch negativity
response, tested whether the therapies altered effective
connectivity at the lower (primary) or higher (secondary)
level of the auditory network.
Results Phonological training improved speech
comprehension abilities and was particularly effective
for patients with severe deficits. No major adverse
effects of donepezil were observed, but it had an
unpredicted negative effect on speech comprehension.
The MEG analysis demonstrated that phonological
training increased synaptic gain in the left superior
temporal gyrus (STG). Patients with more severe speech
comprehension impairments also showed strengthening
of bidirectional connections between the left and right
Conclusions Phonological training resulted in a small
but significant improvement in speech comprehension,
whereas donepezil had a negative effect. The connectivity
results indicated that training reshaped higher order
phonological representations in the left STG and (in
more severe patients) induced stronger interhemispheric
transfer of information between higher levels of auditory
cortex. Clinical trial registration This trial was registered
with EudraCT (2005-004215-30, https:// eudract . ema.
europa. eu ) and ISRCTN (68939136, http://www. isrctn.
com ).
Speech comprehension impairments after stroke
caused by damage to dominant temporoparietal
cortex (ie, 'Wernicke’s aphasia' (WA) and global
aphasia)1–3 are resistant to treatment by conven-
tional methods4; hence, there is a need for new
evidence-based therapies to improve auditory
comprehension in aphasia.
One popular target for WA therapy is the low-level
deficit in auditory phonological analysis.5–7 This
typically involves training auditory discrimina-
tion with phonemes, consonant-vowel-consonant
segments or longer sequences. Previous attempts
have met with mixed results: whereas two case
studies reported positive effects on auditory
discrimination and/or comprehension,8 9 a further
case study10 and case series of eight patients11 failed
to find any significant improvements. We tested the
efficacy of phonological training using Earobics
software12 in a larger sample (n=20).
We also tested whether donepezil, an acetylcho-
linesterase inhibitor, could improve behavioural
outcomes. Studies in rats and bats have shown that
modulating the cholinergic pathway from nucleus
basalis to auditory cortex enhances experience-de-
pendent plasticity,13 whereas suppression of the
cholinergic system reduces it.14 15 As the cholinergic
system is specifically involved in behaviourally
dependent learning,16 cholinergic drugs may be most
effective when paired with behavioural therapy. Two
clinical trials in aphasia by Berthier and colleagues17
18 administered donepezil in conjunction with
conventional speech and language therapy. The
drug improved aphasia quotient scores, particularly
on picture naming. While Berthier and colleagues
studies included patients with aphasia of all types,
we focused on Wernicke’s and global aphasia and
tested whether aphasia severity interacted with the
efficacy of both phonological and donepezil thera-
pies using a within-subject, cross-over design.
As well as behavioural outcome measures, we
employed functional neuroimaging to investi-
gate the neural mechanisms of the therapeutic
effect. Following Teki and colleagues,19 changes
in the auditory cortex’s sensitivity to phonological
contrasts were assayed using the mismatch negativity
response (MMN) recorded with magnetoenceph-
alography (MEG). Participants were habituated to
a repeated spoken syllable (the standard stimulus).
Occasionally, syllables that differed acoustically or
phonologically from the standard stimulus were
presented (the deviant stimuli). The surprise elic-
ited by these unanticipated deviant stimuli results
in a stronger evoked neural response, known as
the MMN.20 By comparing MMN responses with
acoustic and phonemic deviants, we could reveal
when and where neurons were sensitive to the
phonological content of the stimuli.
Cognitive neurology
JNNP Online First, published on March 4, 2017 as 10.1136/jnnp-2016-314621
Copyright Article author (or their employer) 2017. Produced by BMJ Publishing Group Ltd under licence.
2 Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
Dynamic causal modelling (DCM)21–23 was used to investi-
gate how the MMN arises from interactions between multiple
neuronal sources in the temporal lobes. Teki and colleagues
showed that in controls the difference between acoustic and
phonemic deviants was reflected in stronger local adaptation of
representations in the bilateral primary auditory cortex (Heschl’s
gyrus (HG)) and the auditory association cortex (superior
temporal gyrus (STG)). Patients with aphasia showed no such
adaptation in the left HG or STG, but instead showed stronger
feedforward connectivity on the right hemisphere. This study
involved the same participants, MMN paradigm and analysis,
but employed a longitudinal design to test the impact of phono-
logical training and donepezil on connectivity. We hypothesised
that an effective therapy would increase updating of phonolog-
ical representations on the left hemisphere and decrease reliance
on the right hemisphere regions.
Study design
Our objectives were to investigate (1) whether phonological
training improved speech comprehension, (2) whether donepezil
facilitated phonological training effects and (3) the impact of the
therapies on directed connectivity within the auditory network.
A randomised, double-blind, placebo-controlled cross-over
design was used (figure 1). Each participant received four 5-week
blocks of treatment:
drug only (5 mg daily dose of donepezil)
drug and Earobics (10 mg daily dose of donepezil, plus
two 40 min daily sessions of Earobics)
placebo only
placebo and Earobics.
Participants were assessed at baseline, then before (1) and
after (2) phonological training, in both drug (D) and placebo
(P) conditions, leading to five distinct assessment time points:
baseline, D1, D2, P1 and P2. This design allowed a factorial
analysis of behavioural and effective connectivity outcome
measures, comparing performance before (D1 and P1) versus
after phonological training (D2 and P2), and when participants
were receiving donepezil (D1 and D2) versus placebo (P1 and
P2). There were no predetermined criteria for termination of the
trial or the definition of outliers.
Participants were assigned to one of two cross-over groups
(drug then placebo or placebo then drug). Block randomisation
with bias minimisation was used: a computer algorithm gener-
ated a random number to allocate each new participant to a
group. An independent researcher ensured that the cross-over
groups did not become unbalanced by more than four patients.
Participants, caregivers and researchers were blind to block allo-
cation. Randomisation codes were held by the study pharmacist.
Tablets were supplied in bottles labelled with the block number
when they should be taken (1–4). Each bottle contained 35 indis-
tinguishable lactose encapsulated tablets of placebo, 5 mg done-
pezil hydrochloride or 10 mg donepezil hydrochloride.
Outcome measures were prospectively selected. The primary
behavioural outcome measure was score on the comprehensive
aphasia test (CAT)24 speech comprehension scale (including
word, sentence and paragraph comprehension; maximum
score=66, aphasia cut-off=56), which was chosen for its high
ecological validity. Other CAT measures (written comprehen-
sion, speech repetition, naming, reading and writing) and a
sustained attention to response test (SART)25 were the secondary
outcomes. The primary neuroimaging outcome was the effect of
phonemic deviants on effective connectivity within the auditory
The participants were patients with chronic post-stroke aphasia.
As the study required approval from the Medicines and Health-
care Products Regulatory Agency, which deemed the study a
clinical trial of an investigational medicinal product, the power
calculation was based on the drug effect. The best evidence
available at the time of designing the study (from Berthier and
colleagues, who used donepezil to treat post-stroke aphasia),17
indicated that 20 patients would be required to detect a 5%
difference given a 7% within-person SD, 5% significance level
and 90% power. Data collection occurred from November 2006
to August 2009 and ended when 20 patients had completed the
protocol. Twenty-seven patients were enrolled, three withdrew
after the baseline time point due to the trial’s time demands and
a further four were excluded from the analysis as extensive left
auditory cortex damage made them unsuitable for the DCM
analysis (figure 2). We report the results from the remaining 20
Figure 1 Study design showing order of the five time points (baseline, D1, D2, P1 and P2) for the two cross-over groups.
Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
patients. Baseline data from all 20 participants were previously
reported by Teki and colleagues.19
All participants had left hemisphere stroke and normal
hearing (three female; mean age (range)=62.4 (43–90) years;
average time since stroke=3.3 (0.6–8.6) years; average lesion
volume=127.3 (24.2–403.6) cm3). The demographics, lesion
details and baseline behavioural scores are shown in table 1. The
behavioural inclusion criteria were speech production impair-
ment in CAT repetition and/or CAT naming, and speech compre-
hension impairment in CAT spoken word comprehension, CAT
spoken sentence comprehension and/or a custom-made vowel
identification task.
During recruitment, it was assumed (wrongly as it turned out)
that the participants’ scores on the main outcome measure would
be normally distributed. It became apparent after the study
closed that they fell into two groups (moderate and severe), as
discussed in a previous report by Schofield and colleagues.26
None of the participants had the sample mean as their baseline
score. A cluster analysis confirmed this bimodal distribution. We
then treated the two groups as a factor in all further analyses,
including when testing the main hypothesis. Group membership,
moderate or severe, is listed in table 1.
The participants were classified into aphasia subtypes
depending on their speech production abilities. Those whose
object naming and repetition total scores placed them in the
bottom quartile for subjects with aphasia in the Predicting
Language Outcome and Recovery After Stroke (PLORAS) data-
base27 were classed as global (G); otherwise we classified them
as Wernicke’s aphasics (W). The majority of severe patients (six
out of eight) had global aphasia (see table 1). A χ2 test showed no
significant differences between the two therapy groups in terms
of aphasia subtype (c2(1, n=20)=0.74, p=0.65).
All participants provided written informed consent according
to the Declaration of Helsinki. Data were collected at the Insti-
tute of Neurology, University College London, and the study
was approved by the Joint Research Ethics Committee of the
National Hospital for Neurology and Neurosurgery and the
Institute of Neurology, University College London.
Phonological training
The phonological training software was Earobics version 1
for adolescents and adults.12 Earobics cycles through six inde-
pendently adaptive tasks: (1) ‘memory matrix’, environmental
sound-to-picture matching to train auditory short-term memory;
(2) ‘sound check’, two-alternative forced-choice task to probe
grapheme-to-phoneme mapping; (3) ‘get rhythm’, non-speech
and speech sounds to probe auditory segmentation; (4) ‘connec-
tivity’, compound word-to-picture matching to train phonological
blending; (5) ‘rhyme time’, rhyme detection task using sequences
of increasing numbers of words and (6) ‘same-different’, auditory
discrimination using phoneme and word pairs. The participants
were asked to complete 10 hours of training per week over each
5-week training block and record training duration in self-report
diaries with some help from their partner or carer.
Figure 2 Consolidated Standards of Reporting Trials (CONSORT) trial flow diagram.
4 Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
Drug administration and monitoring
Donepezil dosage was determined according to the British
National Formulary guidelines, starting at 5 mg for the first
5-week block. If this was tolerated, dose was escalated to 10 mg
for the second block. All patients except one (patient 17) esca-
lated to the higher dose. Adverse effects were monitored at every
time point using a checklist of all possible adverse events (see
online supplementary table).
Behavioural outcome measures
The CAT24 was administered at every time point as shown in
figure 1. Only data from D1, D2, P1 and P2 time points were
entered into the statistical analysis. Domain-general (non-verbal)
effects of donepezil were tested using a modified version of the
SART, a Go/No-Go task loading on sustained attention,25 presented
with E-Prime28 and administered at baseline, D2 and P2 sessions
only. The SART outcome measures included average reaction time
for correct Go trials, accuracy of response inhibition for No-Go
trials and post-error slowing. We also collected carer-reported
measures of activity and participation from the Social Commu-
nication and Daily Planning from the Functional Assessment of
Communication Skills for Adults (ASHA FACS)29 at all time points.
Structural MRI
A T1-weighted structural brain image with 1 mm3 voxel size was
acquired with a Siemens Sonata 1.5T MRI scanner at baseline.
The Automated Lesion Identification toolbox30 for SPM831 iden-
tified lesion location and volume.
MEG and electroencephalogram data acquisition
The evoked response potentials were collected at D1, D2, P1 and
P2 time points. MEG was used in 18 subjects, on a CTF systems
274-channel whole-head MEG scanner with third-order axial
gradiometres and a sampling rate of 480 Hz. Electroencepha-
logram (EEG) was used in two subjects (patients 8 and 19) due
to MEG artefacts. EEG data were acquired with a high-density,
128-channel Bio-Semi headcap system, with a sampling rate of
512 Hz.
MEG/EEG experimental paradigm and stimuli
Spoken auditory stimuli were presented binaurally in a passive
oddball paradigm, with the standard stimulus ‘Bart’, an acoustic
deviant, and two phonemic deviants ‘burt’ and ‘beat’. The deviant
stimuli were created by varying the frequencies of the standard’s
first and second formants, as described by Teki and colleagues.19
At each time point, four stimuli blocks were presented, each
containing 120 presentations of the standard and 30 presenta-
tions of each deviant in a pseudo-randomised order. Stimulus
onset asynchrony was 1080 ms. Stimulus amplitude was initially
set at 60 dB/sound pressure level and adjusted prior to scanning
to a comfortable level.
The participants were instructed to attend to an incidental
visual detection task while ignoring the auditory stimuli. Pictures
of outdoor scenes were presented for 60 s, followed by a circle
(92% of trials) or square (8% of trials) for 1.5 s. The partici-
pants responded by button press to the circle and withheld their
response for the square. Average response accuracy was 87%,
Table 1 Demographics, lesion details and baseline behavioural performance of the patients with aphasic stroke
ID Group Sex
edness Severity
Age at
% of ROI
damaged Speech comprehension Speech production
Left A1
CAT word
comp. (/30)
CAT sentence
comp. (/32)
CAT object
1 1 M L M G 69.6 1.0 I 69.5 1.8 0.0 29 28 27 28 6
2 2 M R M W 62.7 1.2 I 42.4 7.2 21.1 30 22 38 34 38
3 1 M R M W 63 8.6 I 429.3 81.0 43.3 27 21 17 50 22
4 1 M L M W 61.5 7.6 I 314.0 52.9 0.0 23 25 16 38 12
5 1 M R M W 67.8 7.4 H 64.3 8.1 30.5 29 21 37 50 30
6 2 M R M W 60.5 5.6 I 161.1 52.7 9.3 27 30 34 54 27
7 1 M R M W 64.9 1.2 I 171.1 39.7 17.9 27 21 38 60 33
8 1 M R S G 61.4 1.9 I 242.9 52.6 52.6 24 6 16 0 0
9 2 F R S G 66.5 5.3 I 195.2 42.3 21.4 21 15 13 23 12
10 1 M R M W 61.5 3.4 M-L* 1.6 0.0 0.0 26 26 40 63 36
11 1 M R S G 63.3 0.6 I 69.0 8.3 33.5 20 13 12 0 0
12 2 F R S G 43.5 1.3 I 69.7 68.7 46.1 14 17 17 18 14
13 2 F R S W 46.3 0.7 I 31.7 60.9 15.3 26 12 26 26 41
14 2 M R S G 71.1 5.1 I 151.1 79.5 29.8 20 14 7 0 0
15 2 M R S G 62.4 3.7 I 168.6 20.2 0.0 22 11 21 30 0
16 1 M R M W 60.9 3.0 H 136.6 11.7 1.5 28 26 40 68 37
17 1M R M G 45.4 3.7 I 61.2 5.8 16.7 27 21 39 31 18
18 2 M R M W 74.7 0.6 I 41.1 7.3 2.9 24 26 24 45 14
19 2 M R M G 50.2 1.8 I 280.5 29.5 0.9 25 26 39 28 2
20 1 M R M W 90.3 3.7 I 62.9 14.2 0.0 27 21 34 68 43
Behavioural measures included CAT spoken word comprehension (score cut-off for impaired performance=25), CAT spoken sentence comprehension (cut-off=27), vowel
identification (36), CAT total repetition score (67) and CAT object naming (43). Values highlighted in bold indicate scores below the threshold for normal performance.
*Patient 10 had four small lacunes in the left hemisphere: (1) superior and lateral occipital lobe (I); (2) deep to the superior frontal sulcus (I); (3) superior longitudinal fasciculus
(L) and (4) inferomedial thalamus (L).
†Patient 17 failed to escalate to 10 mg of donepezil in the second drug block and remained on 5 mg for both blocks.
CAT, comprehensive aphasia test; I, infarct; H, haemorrhagic; M-L, multilacune; ROI, region of interest; STG, superior temporal gyrus.
Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
with no significant differences in the number of errors (false-pos-
itives or false-negatives) as an effect of time (main effect of
chronological order of testing sessions) or due to drug adminis-
tration (main effect of drug vs placebo).
MEG/EEG preprocessing
Preprocessing of MEG data in SPM831 included the following:
high-pass filtering (1 Hz), eye-blink artefact removal using
multiple source eye correction,32 epoching (−100 to 500 ms
peristimulus time), baseline correction (−100 to 0 ms prestim-
ulus time window), low-pass filtering (30 Hz), merging the four
blocks of each dataset, robust averaging and low-pass filtering
again. Preprocessing of EEG data in SPM1231 33 included the
following: high-pass filtering (1 Hz), epoching (−100 to 500 ms
peristimulus time), merging the four runs of each dataset, identi-
fication of blinks from vertical electro-oculography data, correc-
tion of blink artefacts using single value decomposition and
signal-space projection, robust averaging and low-pass filtering
(30 Hz). (Note: Low-pass filtering at 30 Hz would have removed
any time-locked effects in the gamma range from our analyses.)
Source localisation using the variational-Bayesian equivalent
current dipoles34 is described in the online supplementary material.
In brief, this compared different source models, containing five
possible sources identified from previous auditory MMN analyses
(left and right HG, left and right STG and right IFG) in different
combinations. The winning model, containing left and right HG
and STG only (p=0.88), was used as the spatial model for the DCM
analysis. All patients had at least 10% intact cortex within each
anatomical region of interest. All fitted sources fell within intact
cortex, not lesion (see online supplementary material for detail).
Dynamic causal modelling
DCM was conducted on preprocessed evoked responses to stan-
dard and deviant stimuli at each time point separately (D1, D2,
P1 and P2). Evoked responses from 1 to 400 ms peristimulus time
were tapered with a Hanning window. The DCM spatial model
used Equivalent Current Dipole (ECD) sources from bilateral HG
and STG dipole locations (see online supplementary material). The
neural model included exogenous auditory stimulation to the left
and right HG nodes at 60 ms; endogenous connectivity evoked by
standard stimuli in two forward, two backward and four lateral
connections between HG and STG sources (diagonal connections
were not modelled because although heterotopic connections are
present in auditory cortex they are sparse)35 ; and three modula-
tory effects evoked by each deviant stimulus compared with the
standard stimulus. A large model space of 2^8 (256) models was
estimated, each with modulatory effects modelled in a different
combination of the eight independent inter-regional connections.
Modulatory effects on self-connections, modelling a region’s
sensitivity to inputs,36 were present in all models.
Bayesian model averaging
For each connection, Bayesian Model Averaging (BMA) with
random effects37 was used at the subject level to average the
modulation caused by each deviant versus the standard at each
time point (D1, D2, P1 and P2). The difference in modulation
by phonemic versus acoustic deviants (phonemic sensitivity)
was calculated by averaging the modulatory effects for the two
phonemic deviants and subtracting the modulatory effect for the
acoustic deviant.
The phonemic sensitivity values were entered into a repeat-
ed-measures analysis of variance (ANOVA) for each connec-
tion. The ANOVAs had within-subjects variables time (before/
after phonological training) and drug (on drug/placebo), and
between-subjects variables severity (moderate/severe groups)
and cross-over order (drug/placebo first).
Baseline language performance and structural MRI
Exploratory analysis of the baseline CAT speech comprehen-
sion scores using a two-step cluster analysis identified a division
between participants with moderate (n=13) and severe (n=7)
impairments (figure 3). Independent samples t-tests confirmed that
moderate and severe subgroups differed in speech comprehension
ability (t(18)=7.77, p<0.001), as well as in written comprehen-
sion, repetition, naming, reading and writing abilities (all p<0.05).
The subgroups did not differ significantly in age (p=0.35) or time
since stroke (p=0.43). Lesion overlay maps (figure 3) showed the
subgroups shared broadly similar lesion distributions, centring on
the left perisylvian cortex and superior longitudinal fasciculus.
The lesion volume was not significantly different between groups
Phonological training dose
Out of 40 completed training blocks (2 blocks × 20 partici-
pants), 28 self-report diaries were fully completed (70%). Of
those 28 diaries, the average training dose was 36 hours 38 min
per block (SD=16 hours 8 min; range: 6 hours 20 min to 65 hours
5 min; median=36 hours 28 min). Fifteen of the 28 diaries were
from blocks 1 and 13 were from Block 2. The mean training
dose for block 1 and block 2 did not differ significantly (block
1: mean=38 hours 58 min; block 2: mean=36 hours 26 min;
t(26)=0.37, p=0.71).
Compliance and tolerability of cholinergic drug therapy
Patients were required to return their medication bottles after
completing each block. Eighty-eight per cent of medication
bottles were returned. In these bottles, only 2.5% of unused
tablets remained.
The participants completed an adverse effects report form at
each time point (see online supplementary table). Three partic-
ipants had incomplete report forms at one time point; hence,
reports were analysed for 17 participants only. We compared
the total number of adverse events when the participants were
on drug (D1 or D2) or placebo (P1 or P2). The participants
reported 32 adverse events when on drug and 28 on placebo. A
non-parametric sign test showed no significant difference in the
frequency of adverse events on drug versus placebo (p=0.79).
The most frequent adverse events were insomnia, head-
aches, dizziness and muscle cramps. For each type of event, the
frequency of occurrence on drug or placebo was compared using
the McNemar tests with binomial distribution. No significant
differences were observed for any type of event (all p>0.2).
Therapy effects on behavioural outcome measures: primary
The effects of phonological training and donepezil on CAT speech
comprehension scores were assessed using a mixed effects ANOVA
with two within-subjects variables: (1) time (before/after phonolog-
ical training) and (2) drug (on drug/on placebo). Two between-sub-
jects variables were also included: (1) aphasia severity (moderate/
severe) and (2) cross-over group (drug first/placebo first). The
results (figure 4) demonstrated that phonological training signifi-
cantly improved speech comprehension, indicated by a significant
main effect of time (F(1, 16)=6.56, p<0.05). Conversely, there
was a significant main effect of drug, with (unexpectedly) lower
6 Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
scores on drug than placebo (F(1, 16)=11.60, p<0.005). There
was no interaction between phonological training and donepezil:
the effects were independent and in opposite directions. Time by
severity and drug by severity interactions were significant (F(1,
16)=6.6, p<0.05 and F(1, 16)=4.9, p<0.05, respectively): both
therapy effects were larger in the severe patient group.
Therapy effects on behavioural outcome measures: secondary
Among the other CAT measures (written comprehension, speech
repetition, naming, reading and writing), the only notable
effect was a significant time by severity interaction for written
comprehension (F(1, 16)=8.56, p<0.05), driven by larger bene-
ficial effects of phonological training in severe patients compared
with moderate patients. There was a non-significant trend towards
better naming on drug than placebo (F(1, 16)=3.64, p=0.075),
consistent with prior observations by Berthier and colleagues.17 18
There were no significant changes in the SART or ASHA FACS
scores. Repeated-measures ANOVAs demonstrated no signifi-
cant effects of time, drug, severity or cross-over group.
Therapy effects on effective connectivity
MMN responses to acoustic and phonemic deviants are shown
in the online supplementary figure 1.
Figure 3 Baseline Comprehensive Aphasia Test data and structural brain imaging. Top: average T-scores for severe (n=7) and moderate (n=13) patients.
Bottom left: speech comprehension T-scores, showing division of severe and moderate subgroups. Bottom right: lesion overlay maps for severe and moderate
Figure 4 Effects of Earobics and donepezil on speech comprehension in severe and moderate patient subgroups.
Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
Modulation of connection strength by phonemic versus acoustic
deviants (phonemic sensitivity) changed as a result of phonological
training (figure 5). The main effects of phonological training were
significant in two connections (figure 5A): (1) the left STG self-con-
nection (F(1, 16)=5.30, p<0.05) and (2) the left HG to left STG
connection (F(1, 16)=11.3, p<0.005). Three connections showed
time by severity interactions driven by stronger training effects in
the severe group (figure 5B): (1 ) the left HG to left STG forward
connection (F(1, 16)=8.30, p<0.05) (2) the left STG to right STG
lateral connection (F(1, 16)=6.65, p<0.05) and (3) the right STG
to left STG lateral connection (F(1, 16)=8.64, p<0.01).
Only one connection, the left HG to STG, showed a main
effect of drug (F(1, 16)=20.70, p<0.001) due to stronger
phonemic sensitivity on drug versus placebo (figure 5C). A
drug by severity interaction (figure 5D) showed that this effect
was larger for severe than moderate patients (F(1, 16)=9.79,
We hypothesised that phonological training would improve
speech comprehension and that donepezil would facilitate this
effect by enhancing the neuroplastic response to training. Only
the first hypothesis was supported: patients showed significantly
better speech comprehension after phonological training, but
worse comprehension on drug than placebo. Both effects were
stronger in more severely impaired patients: the severe subgroup
responded better to training and worse to drug than the moderate
subgroup. The effect of phonological training on comprehen-
sion was significant but clinically small: the average CAT speech
comprehension score during the placebo block increased from
52.0 to 53.2 after 5 weeks of training. The average dose was
36 hours per block. Lower doses of phonological therapy in
previous studies (6–12 hours) may explain why significant effects
have not been consistently observed.10 11 Although the effect of
phonological training was small, it is of clinical importance as
therapeutic interventions in patients with aphasia with severe
impairments of speech perception have been largely written
off, both in textbooks (‘(global aphasia is) sometimes known as
irreversible aphasia syndrome’38) and in systematic reviews (‘No
evidence of benefit’39).
The behavioural effect of phonological training was gener-
alised in two ways: first, training with Earobics stimuli gener-
alised to different stimuli used in CAT speech comprehension
tests and second, written comprehension improved, at least for
severe patients. This latter result suggests that either auditory
training acted on amodal, higher level semantic representations
or that strengthening auditory phonological representations
also facilitates grapheme-to-phoneme translation required for
written comprehension. A third option, that training improved
non-linguistic, domain-general cognitive abilities, is less likely as
there were no improvements on sustained attention.
The surprising result that speech comprehension was worse
on donepezil than placebo contrasts with improved Western
Aphasia Battery Aphasia Quotients40 was reported by Berthier
Figure 5 Significant changes in phonemic sensitivity between time points. (a) Red connections showed significantly stronger phonemic sensitivity after
Earobics training (main effect of Earobics); (b) red connections showed significantly stronger training effects on phonemic sensitivity in the severe patients
than the moderate patients (Earobics by severity interaction); (c) main effect of drug and (d) drug by severity interaction.
8 Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
and colleagues.17 18 Berthier only observed task-specific improve-
ments for picture naming. We saw a trend towards better
naming on drug than placebo, perhaps suggesting that donepezil
is better suited to treating speech production than comprehen-
sion. Husain and Mehta41 observe that cognitive-enhancement
drugs can have opposing effects on different tasks as an inverted
U-shaped relationship applies between neuropharmacological
enhancement and performance. Hence, if acetylcholine stimu-
lation was already high in auditory cortex, increasing it further
with donepezil could have impaired performance.
The effective connectivity (DCM) analysis examined the inter-
actions between auditory areas. We predicted that improved
speech comprehension would result from a tuning of phono-
logical representations in auditory cortex. In DCM such tuning
is expressed as a region’s self-connection, which acts as a gain
mechanism36: that is, a post-therapy increase in a self-connec-
tion’s strength would mean that the region had become more
sensitive to phonological contrasts. The results confirmed our
hypothesis: improved speech comprehension after phonolog-
ical training was associated with stronger modulation of the left
STG’s self-connection by phonological but not acoustic devi-
Phonological training also strengthened phonological sensi-
tivity of the forward connection from left HG to STG, suggesting
that updating of higher order representations of speech sounds
in the left STG was driven by stronger feedforward prediction
errors. The observation that phonological modulation of this
connection was also affected by drug (which did not result in
a behavioural improvement) suggests that strengthening this
forward connection alone is insufficient to improve speech
Our findings were not confined to the left hemisphere. In
severe patients, training led to stronger phonemic sensitivity in
the STG interhemispheric connections. This implies that after
severe left temporal damage, the left STG requires more support
from its right hemisphere homologue to perform phonolog-
ical discrimination. The baseline data in these patients treated
as a single group19 showed that modulation strength of the left
to right STG connection correlated negatively with phonemic
discrimination ability; that is, there was a decoupling of these two
regions in patients with better speech perception. It is not clear
whether this decoupling represented (1) the best the damaged
system could currently manage, with the left and right STGs
maintaining their specialised functions but no longer working
in tandem or (2) a maladaptive response to left-hemisphere
damage that may serve to limit further recovery, as, presumably,
auditory representations in the right hemisphere do not share
the linguistic specialisation of the left hemisphere. The longitu-
dinal data presented here suggest a more complex interpretation
because severity effects indicated (1) moderately affected patients
do not have impaired interhemispheric connectivity and therapy
effects are seen within their dominant temporal lobe and (2) in
more severely affected patients interhemispheric processing of
phonemic stimuli is abnormal, but can still be improved by audi-
tory training.
In conclusion, our results demonstrate that phonological
training worked at a behavioural level, but the underlying neuro-
anatomical mechanism varied with aphasia severity. The left STG
mediated therapy effects in both patient groups: in the moderate
group, this occurred independently from the influence of right
temporal regions, whereas in the severe group interhemispheric
interactions were also involved in promoting recovery of speech
Funding This work was supported by the Wellcome Trust and the James S McDon-
nell Foundation (conducted as part of the Brain Network Recovery Group initiative).
APL and ST were supported by personal fellowships from the Wellcome Trust
(ME033459MES and 106084/Z/14/Z, respectively).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Open Access This is an Open Access article distributed in accordance with the
terms of the Creative Commons Attribution (CC BY 4.0) license, which permits
others to distribute, remix, adapt and build upon this work, for commercial use,
provided the original work is properly cited. See: http:// creativecommons. org/
licenses/ by/ 4. 0
© Article author(s) (or their employer(s) unless otherwise stated in the text of the
article) 2017. All rights reserved. No commercial use is permitted unless otherwise
expressly granted.
1 Wernicke C. Der aphasiche symptomenkomplex: eine psychologische studie auf
anatomischer basis. Breslau: Cohen and Weigert, 1874.
2 Eggert GH. Wernicke’s works on aphasia: a sourcebook and review. The Hague:
Mouton Publishers, 1997.
3 Goodglass H, Kaplan E, Barresi B. The assessment of aphasia and related disorders.
3rdedn. Baltimore, MD: Lippincott Williams & Wilkins, 2001.
4 Brady MC, Kelly H, Godwin J, et al. Speech and language therapy for aphasia
following stroke. Cochrane Database Syst Rev 2012;(5):1–145.
5 Luria AR. Basic problems of neurolinguistics. The Hague: Mouton & Co BV, 1976.
6 Robson H, Grube M, Lambon Ralph MA, et al. Fundamental deficits of auditory
perception in Wernicke's aphasia. Cortex 2013;49:1808–22.
7 Whitworth A, Webster J. Howard D. A cognitive neuropsychological approach to
assessment and intervention in aphasia: a clinician’s guide. 2ndedn. Hove andNew
York: Psychology Press, 2013.
8 Morris J, Franklin S, Ellis AW, et al. Remediating a speech perception deficit in an
aphasic patient. Aphasiology 1996;10:137–58.
9 Tessier C, Weill-Chounlamountry A, Michelot N, et al. Rehabilitation of word
deafness due to auditory analysis disorder. Brain Inj 2007;21:1165–74.
10 Maneta A, Marshall J, Lindsay J. Direct and indirect therapy for word sound deafness.
Int J Lang Commun Disord 2001;36:91–106.
11 Woolf C, Panton A, Rosen S, et al. Therapy for auditory processing impairment in
aphasia: an evaluation of two approaches. Aphasiology 2014;28:1481–505.
12 Houghton Mifflin Harcourt Learning Technology. http://www. earobics. com/ demos/
meta. html
13 Kilgard MP, Merzenich MM. Cortical map reorganization enabled by nucleus basalis
activity. Science 1998;279:1714–8.
14 Ji W, Gao E, Suga N. Effects of acetylcholine and atropine on plasticity of central
auditory neurons caused by conditioning in bats. J Neurophysiol 2001;86:211–25.
15 Conner JM, Culberson A, Packowski C, et al. Lesions of the basal forebrain
cholinergic system impair task acquisition and abolish cortical plasticity associated
with motor skill learning. Neuron 2003;38:819–29.
16 Ramanathan D, Tuszynski MH, Conner JM. The basal forebrain cholinergic system
is required specifically for behaviorally mediated cortical map plasticity. J Neurosci
17 Berthier ML, Green C, Higueras C, et al. A randomized, placebo-controlled study of
donepezil in poststroke aphasia. Neurology 2006;67:1687–9.
18 Berthier ML, Hinojosa J, Martín MC, et al. Open-label study of donepezil in chronic
poststroke aphasia. Neurology 2003;60:1218–9.
19 Teki S, Barnes GR, Penny WD, et al. The right hemisphere supports but does not
replace left hemisphere auditory function in patients with persisting aphasia. Brain
20 Näätänen R, Gaillard AWK, Mäntysalo S. Early selective-attention effect on evoked
potential reinterpreted. Acta Psychologica 1978;42:313–29.
21 David O, Kiebel SJ, Harrison LM, et al. Dynamic causal modeling of evoked responses
in EEG and MEG. NeuroImage 2006;30:1255–72.
22 Kiebel SJ, David O, Friston KJ. Dynamic causal modelling of evoked responses in EEG/
MEG with lead field parameterization. NeuroImage 2006;30:1273–84.
23 Kiebel SJ, Garrido MI, Moran R, et al. Dynamic causal modeling for EEG and MEG.
Hum Brain Mapp 2009;30:1866–76.
24 Swinburn K, Porter G, Howard D. The comprehensive aphasia test. Hove: Psychology
Press, 2005.
25 Robertson IH, Manly T, Andrade J, et al. 'Oops!': performance correlates of everyday
attentional failures in traumatic brain injured and normal subjects. Neuropsychologia
26 Schofield TM, Penny WD, Stephan KE, et al. Changes in auditory feedback
connections determine the severity of speech processing deficits after stroke. J
Neurosci 2012;32:4260–70.
27 Price CJ, Seghier ML, Leff AP. Predicting language outcome and recovery after stroke:
the PLORAS system. Nat Rev Neurol 2010;6:202–10.
Woodhead ZVJ, et al. J Neurol Neurosurg Psychiatry 2017;0:1–9. doi:10.1136/jnnp-2016-314621
Cognitive neurology
28 Schneider W, Eschman A. Zuccolotto A. E-Prime reference guide. Pittsburgh:
Psychology Software Tools Inc, 2002.
29 Frattali CM, Thompson CK, Holland AL, et al. Functional assessment of
communication skills for adults (ASHA FACS). Rockville, MD: American Speech-
Language-Hearing Association, 1995.
30 Seghier ML, Ramlackhansingh A, Crinion J, et al. Lesion identification using
unified segmentation-normalisation models and fuzzy clustering. NeuroImage
31 . Wellcome Trust Centre for Neuroimaging; http://www. fil. ion. ucl. ac. uk/ spm/
32 Berg P. Scherg M .A multiple source approach to the correction of eye artefacts.
Electroencephalogr Clin Neurophysiol 1994;90:229–41.
33 Litvak V, Mattout J, Kiebel S, et al. EEG and MEG data analysis in SPM8. Comput
Intell Neurosci 2011;2011:1–32.
34 Kiebel SJ, Daunizeau J, Phillips C, et al. Variational Bayesian inversion of the
equivalent current dipole model in EEG/MEG. NeuroImage 2008;39:728–41.
35 Lee CC, Winer JA. Connections of cat auditory cortex: II. Commissural system. J
Comp Neurol 2008;507:1901–19.
36 Kiebel SJ, Garrido MI, Friston KJ. Dynamic causal
modelling of evoked responses: the role of intrinsic connections. NeuroImage
37 Penny WD, Stephan KE, Daunizeau J, et al. Comparing families of dynamic causal
models. PLoS Comput Biol 2010;6:e1000709.
38 Crystal D. The Cambridge Encyclopedia of Language. 2nd edn. Cambridge:
Cambridge University Press, 2010.
39 Brady MC, Kelly H, Godwin J, et al. Speech and language therapy for aphasia
following stroke. Cochrane Database Syst Rev 2016;(6):CD000425.
40 Kertesz A. Western aphasiabattery. San Antonio, TXCorporation
Psychological, 1982.
41 Husain M, Mehta MA. Cognitive enhancement by drugs in health and disease.
Trends Cogn Sci 2011;15:28–36.
... Donepezil is the most common cholinergic agent used for the treatment of post-stroke aphasia [36][37][38][39]45,109,121]. During the last twenty years several studies have indicated the beneficial effects of donepezil in patients with post-stroke aphasia, either in case [36,113,118] or group studies, mostly in chronic phase of stroke [114][115][116]119], while sparse evidence exists for the acute phase [117]. In most studies patients were assessed at least one-year post-stroke and no more than four years post-stroke see for example: [113][114][115]. ...
... In most studies researchers examined core aspects of language functions to investigate possible gains after administering donepezil. Assessment was mostly accomplished using aphasia batteries and more specifically Western Aphasia Battery (see for example: [36,[117][118][119][120]122]). In most of them, Aphasia Battery Quotient, a measure of aphasia severity was considered as a core metric to quantify any change, while the major language domains assessed were spontaneous speech, comprehension, repetition, and naming functions. ...
... Results revealed that patients presented improved performance in several repetition tasks and aphasia severity index, while donepezil was more effective when combined with more-intensive therapy for a longer period of time. Woodhead and colleagues [119] also reported the effect of specific phonological training via a software in combination with pharmacological intervention using donepezil. Patients presented improved performance in language comprehension. ...
Full-text available
Despite the relative scarcity of studies focusing on pharmacotherapy in aphasia, there is evidence in the literature indicating that remediation of language disorders via pharmaceutical agents could be a promising aphasia treatment option. Among the various agents used to treat chronic aphasic deficits, cholinergic drugs have provided meaningful results. In the current review, we focused on published reports investigating the impact of acetylcholine on language and other cognitive disturbances. It has been suggested that acetylcholine plays an important role in neuroplasticity and is related to several aspects of cognition, such as memory and attention. Moreover, cholinergic input is diffused to a wide network of cortical areas, which have been associated with language sub-processes. This could be a possible explanation for the positive reported outcomes of cholinergic drugs in aphasia recovery, and specifically in distinct language processes, such as naming and comprehension, as well as overall communication competence. However, evidence with regard to functional alterations in specific brain areas after pharmacotherapy is rather limited. Finally, despite the positive results derived from the relevant studies, cholinergic pharmacotherapy treatment in post-stroke aphasia has not been widely implemented. The present review aims to provide an overview of the existing literature in the common neuroanatomical substrate of cholinergic pathways and language related brain areas as a framework for interpreting the efficacy of cholinergic pharmacotherapy interventions in post-stroke aphasia, following an integrated approach by converging evidence from neuroanatomy, neurophysiology, and neuropsychology.
... Dampening enthusiasm with this pharmacotherapy is evidence that increasing synaptic acetylcholine may negatively impact language. In one recent double-blind placebo-controlled cross-over study, donepezil was associated with harm to speech comprehension among patients with chronic poststroke Wernicke's aphasia (Woodhead et al., 2017). Moreover, nearly all reported studies suffered considerable attrition due to poor drug tolerance, echoing the observation that cholinesterase inhibitors frequently cause nausea, vomiting, anorexia, diarrhea, and dizziness as a result of cholinergic overstimulation (Ali et al., 2015). ...
Speech and language therapy is the standard treatment of aphasia. However, many individuals have barriers in seeking this measure of extensive rehabilitation treatment. Investigating ways to augment therapy is key to improving poststroke language outcomes for all patients with aphasia, and pharmacotherapies provide one such potential solution. Although no medications are currently approved for the treatment of aphasia by the United States Food and Drug Administration, numerous candidate mechanisms for pharmaceutical manipulation continue to be identified based on our evolving understanding of the neurometabolic experience of stroke recovery across molecular, cellular, and functional levels of inquiry. This chapter will review evidence for catecholaminergic, glutamatergic, cholinergic, and serotonergic drug therapies and discuss future directions for both candidate drug selection and pharmacotherapy practice in people with aphasia.
... Consequently, a series of drugs targeting improving language deficits have been studied during the last years. Until now, the conclusion is that some agents may be mainly suitable for treating speech output deficits and picture naming with poor influence over comprehension, particularly in severe cases [49]. There are some theories that support the idea that selective serotonin reuptake inhibitors (SSRIs) might be useful for persons with non-fluent aphasia (e.g., Broca's aphasia), which are also associating depression and frustration, but probably they are less suitable for persons with fluent aphasia (coursing with excitement and reduced awareness) [44]. ...
Full-text available
Aphasia denotes an acquired central disorder of language, which alters patient’s ability of understanding and/or producing spoken and written language. The main cause of aphasia is represented by ischemic stroke. The language disturbances are frequently combined into aphasic syndromes, contained in different vascular syndromes, which may suffer evolution/involution in the acute stage of ischemic stroke. The main determining factor of the vascular aphasia’s form is the infarct location. Broca’s aphasia is a non-fluent aphasia, comprising a wide range of symptoms (articulatory disturbances, paraphasias, agrammatism, anomia, and discrete comprehension disorders of spoken and written language) and is considered the third most common form of acute vascular aphasia, after global and Wernicke’s aphasia. It is caused by a lesion situated in the dominant cerebral hemisphere (the left one in right-handed persons), in those cortical regions vascularized by the superior division of the left middle cerebral artery (Broca’s area, the rolandic operculum, the insular cortex, subjacent white matter, centrum semiovale, the caudate nucleus head, the putamen, and the periventricular areas). The role of this chapter is to present the most important acquirements in the field of language and neurologic examination, diagnosis, and therapy of the patient with Broca’s aphasia secondary to ischemic stroke.
... This study is also potentially informative for aphasia therapy. The neural bases of successful speech and language therapy have been rarely explored, and those studies that have done so have yielded varying results (Abel et al. 2015;Nardo et al. 2017;Woodhead et al. 2017). The methods adopted in this study were deliberately designed to mimic those used to treat wordfinding difficulties, where patients aim to re-establish meaningful, native vocabulary through multiple learning sessions and vanishing phonemic cues (Abel et al. 2005), over several weeks (Dignam et al. 2016). ...
Full-text available
The Complementary Learning Systems (CLS) theory provides a powerful framework for considering the acquisition, consolidation, and generalization of new knowledge. We tested this proposed neural division of labor in adults through an investigation of the consolidation and long-term retention of newly learned native vocabulary with post-learning functional neuroimaging. Newly learned items were compared with two conditions: 1) previously known items to highlight the similarities and differences with established vocabulary and 2) unknown/untrained items to provide a control for non-specific perceptual and motor speech output. Consistent with the CLS, retrieval of newly learned items was supported by a combination of regions associated with episodic memory (including left hippocampus) and the language-semantic areas that support established vocabulary (left inferior frontal gyrus and left anterior temporal lobe). Furthermore, there was a shifting division of labor across these two networks in line with the items’ consolidation status; faster naming was associated with more activation of language-semantic areas and lesser activation of episodic memory regions. Hippocampal activity during naming predicted more than half the variation in naming retention 6 months later.
Chronic aphasia, a devastating impairment of language, affects up to a third of stroke survivors. Speech and language therapy has consistently been shown to improve language function in prior clinical trials, but few clinicially applicable predictors of individual therapy response have been identified to date. Consequently, clinicians struggle substantially with prognostication in the clinical management of aphasia. A rising prevalence of aphasia, in particular in younger populations, has emphasized the increasing demand for a personalized approach to aphasia therapy, that is, therapy aimed at maximizing language recovery of each individual with reference to evidence-based clinical recommendations. In this narrative review, we discuss the current state of the literature with respect to commonly studied predictors of therapy response in aphasia. In particular, we focus our discussion on biographical, neuropsychological, and neurobiological predictors, and emphasize limitations of the literature, summarize consistent findings, and consider how the research field can better support the development of personalized aphasia therapy. In conclusion, a review of the literature indicates that future research efforts should aim to recruit larger samples of people with aphasia, including by establishing multisite aphasia research centers.
This scientific commentary refers to ‘Cholinergic and hippocampal systems facilitate cross-domain cognitive recovery after stroke’ by O’Sullivan et al. (
Electrophysiologic methods have been used to investigate neural changes in individuals with poststroke aphasia. The major types of electrophysiologic measures include the event-related potential (ERP) and spectral power, and aspects of both (including amplitude, topography, and power) have been shown to differ in people with aphasia. Not only that, these measures are sensitive to spontaneous and treatment-induced language change. The purpose of this chapter is to review evidence of poststroke reorganization in the language network that has been identified in the acute and chronic phases of poststroke aphasia. The chapter will begin with a brief introduction to electrophysiologic methods and then focus on evidence from the most commonly studied ERPs and spectral bands in aphasia.
Language is one of the most complex and specialized higher cognitive processes. Brain damage to the distributed, primarily left-lateralized language network can result in aphasia, a neurologic disorder characterized by receptive and/or expressive deficits in spoken and/or written language. Most often, aphasia is the consequence of stroke-termed poststroke aphasia (PSA)-yet, aphasia can also manifest due to neurodegenerative disease, specifically, a disorder called primary progressive aphasia (PPA). In recent years, functional connectivity neuroimaging studies have provided emerging evidence supporting theories regarding the relationships between language impairments, structural brain damage, and functional network properties in these two disorders. This chapter reviews the current evidence for the "network phenotype of stroke injury" hypothesis (Siegel et al., 2016) as it pertains to PSA and the "network degeneration hypothesis" (Seeley et al., 2009) as it pertains to PPA. Methodologic considerations for functional connectivity studies, limitations of the current functional connectivity literature in aphasia, and future directions are also discussed.
Stroke is a leading cause of disability, with deficits encompassing multiple functional domains. The heterogeneity underlying stroke poses significant challenges in the prediction of post-stroke recovery, prompting the development of neuroimaging-based biomarkers. Structural neuroimaging measurements, particularly those reflecting corticospinal tract injury, are well-documented in the literature as potential biomarker candidates of post-stroke motor recovery. Consistent with the view of stroke as a ‘circuitopathy’, functional neuroimaging measures probing functional connectivity may also prove informative in post-stroke recovery. An important step in the development of biomarkers based on functional neural network connectivity is the establishment of causality between connectivity and post-stroke recovery. Current evidence predominantly involves statistical correlations between connectivity measures and post-stroke behavioral status, either cross-sectionally or serially over time. However, the advancement of functional connectivity application in stroke depends on devising experiments that infer causality. In 1965, Sir Austin Bradford Hill introduced nine viewpoints to consider when determining the causality of an association: [1] Strength, [2] Consistency [3] Specificity, [4] Temporality, [5] Biological gradient, [6] Plausibility, [7] Coherence, [8] Experiment, and [9] Analogy. Collectively referred to as the Bradford Hill Criteria, these points have been widely adopted in epidemiology. In this review, we assert the value of implementing Bradford Hill’s framework to stroke rehabilitation and neuroimaging. We focus on the role of neural network connectivity measurements acquired from task-oriented and resting-state functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and functional near-infrared spectroscopy in describing and predicting post-stroke behavioral status and recovery. We also identify research opportunities within each Bradford Hill tenet to shift the experimental paradigm from correlation to causation.
Full-text available
Background and Purpose Optimizing speech and language therapy (SLT) regimens for maximal aphasia recovery is a clinical research priority. We examined associations between SLT intensity (hours/week), dosage (total hours), frequency (days/week), duration (weeks), delivery (face to face, computer supported, individual tailoring, and home practice), content, and language outcomes for people with aphasia. Methods Databases including MEDLINE and Embase were searched (inception to September 2015). Published, unpublished, and emerging trials including SLT and ≥10 individual participant data on aphasia, language outcomes, and time post-onset were selected. Patient-level data on stroke, language, SLT, and trial risk of bias were independently extracted. Outcome measurement scores were standardized. A statistical inferencing, one-stage, random effects, network meta-analysis approach filtered individual participant data into an optimal model examining SLT regimen for overall language, auditory comprehension, naming, and functional communication pre-post intervention gains, adjusting for a priori–defined covariates (age, sex, time poststroke, and baseline aphasia severity), reporting estimates of mean change scores (95% CI). Results Data from 959 individual participant data (25 trials) were included. Greatest gains in overall language and comprehension were associated with >20 to 50 hours SLT dosage (18.37 [10.58–26.16] Western Aphasia Battery–Aphasia Quotient; 5.23 [1.51–8.95] Aachen Aphasia Test–Token Test). Greatest clinical overall language, functional communication, and comprehension gains were associated with 2 to 4 and 9+ SLT hours/week. Greatest clinical gains were associated with frequent SLT for overall language, functional communication (3–5+ days/week), and comprehension (4–5 days/week). Evidence of comprehension gains was absent for SLT ≤20 hours, <3 hours/week, and ≤3 days/week. Mixed receptive-expressive therapy, functionally tailored, with prescribed home practice was associated with the greatest overall gains. Relative variance was <30%. Risk of trial bias was low to moderate; low for meta-biases. Conclusions Greatest language recovery was associated with frequent, functionally tailored, receptive-expressive SLT, with prescribed home practice at a greater intensity and duration than reports of usual clinical services internationally. These exploratory findings suggest critical therapeutic ranges, informing hypothesis-testing trials and tailoring of clinical services. Registration URL: ; Unique identifier: CRD42018110947.
Full-text available
In this study, we used magnetoencephalography and a mismatch paradigm to investigate speech processing in stroke patients with auditory comprehension deficits and age-matched control subjects. We probed connectivity within and between the two temporal lobes in response to phonemic (different word) and acoustic (same word) oddballs using dynamic causal modelling. We found stronger modulation of self-connections as a function of phonemic differences for control subjects versus aphasics in left primary auditory cortex and bilateral superior temporal gyrus. The patients showed stronger modulation of connections from right primary auditory cortex to right superior temporal gyrus (feed-forward) and from left primary auditory cortex to right primary auditory cortex (interhemispheric). This differential connectivity can be explained on the basis of a predictive coding theory which suggests increased prediction error and decreased sensitivity to phonemic boundaries in the aphasics' speech network in both hemispheres. Within the aphasics, we also found behavioural correlates with connection strengths: a negative correlation between phonemic perception and an inter-hemispheric connection (left superior temporal gyrus to right superior temporal gyrus), and positive correlation between semantic performance and a feedback connection (right superior temporal gyrus to right primary auditory cortex). Our results suggest that aphasics with impaired speech comprehension have less veridical speech representations in both temporal lobes, and rely more on the right hemisphere auditory regions, particularly right superior temporal gyrus, for processing speech. Despite this presumed compensatory shift in network connectivity, the patients remain significantly impaired.
Full-text available
This paper describes the assessment and treatment of an aphasic patient, J.S., who had multiple language deficits. In particular, pre-speech and speech-level perceptual processes were both found to be impaired. The availability of lip-reading information improved his performance on certain tasks of speech discrimination. Remediation based on the assessment findings was then undertaken. Therapy focused on auditory discrimination at a phonemic level, utilizing lip-reading, and was based on minimal pairs contrasts. J.S. showed improvement on tests of phoneme discrimination, and a trend of improvement was seen for the other auditorily presented tasks. Performance on the pre-speech tests also showed improvement following therapy. Performance on tests of naming and written word comprehension did not change, indicating that the effects of therapy were specific to auditory input and were not the result of spontaneous recovery.
Full-text available
We compared brain structure and function in two subgroups of 21 stroke patients with either moderate or severe chronic speech comprehension impairment. Both groups had damage to the supratemporal plane; however, the severe group suffered greater damage to two unimodal auditory areas: primary auditory cortex and the planum temporale. The effects of this damage were investigated using fMRI while patients listened to speech and speech-like sounds. Pronounced changes in connectivity were found in both groups in undamaged parts of the auditory hierarchy. Compared to controls, moderate patients had significantly stronger feedback connections from planum temporale to primary auditory cortex bilaterally, while in severe patients this connection was significantly weaker in the undamaged right hemisphere. This suggests that predictive feedback mechanisms compensate in moderately affected patients but not in severely affected patients. The key pathomechanism in humans with persistent speech comprehension impairments may be impaired feedback connectivity to unimodal auditory areas.
Full-text available
SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.
Full-text available
Attempts to improve cognitive function in patients with brain disorders have become the focus of intensive research efforts. A recent emerging trend is the use of so-called cognitive enhancers by healthy individuals. Here, we consider some of the effects - positive and negative - that current drugs have in neurological conditions and healthy people. We conclude that, to date, experimental and clinical studies have demonstrated relatively modest overall effects, most probably because of substantial variability in response both across and within individuals. We discuss biological factors that might account for such variability and highlight the need to improve testing methods and to extend our understanding of how drugs modulate specific cognitive processes at the systems or network level.
Background: Aphasia is an acquired language impairment following brain damage that affects some or all language modalities: expression and understanding of speech, reading, and writing. Approximately one third of people who have a stroke experience aphasia. Objectives: To assess the effects of speech and language therapy (SLT) for aphasia following stroke. Search methods: We searched the Cochrane Stroke Group Trials Register (last searched 9 September 2015), CENTRAL (2015, Issue 5) and other Cochrane Library Databases (CDSR, DARE, HTA, to 22 September 2015), MEDLINE (1946 to September 2015), EMBASE (1980 to September 2015), CINAHL (1982 to September 2015), AMED (1985 to September 2015), LLBA (1973 to September 2015), and SpeechBITE (2008 to September 2015). We also searched major trials registers for ongoing trials including (to 21 September 2015), the Stroke Trials Registry (to 21 September 2015), Current Controlled Trials (to 22 September 2015), and WHO ICTRP (to 22 September 2015). In an effort to identify further published, unpublished, and ongoing trials we also handsearched the International Journal of Language and Communication Disorders (1969 to 2005) and reference lists of relevant articles, and we contacted academic institutions and other researchers. There were no language restrictions. Selection criteria: Randomised controlled trials (RCTs) comparing SLT (a formal intervention that aims to improve language and communication abilities, activity and participation) versus no SLT; social support or stimulation (an intervention that provides social support and communication stimulation but does not include targeted therapeutic interventions); or another SLT intervention (differing in duration, intensity, frequency, intervention methodology or theoretical approach). Data collection and analysis: We independently extracted the data and assessed the quality of included trials. We sought missing data from investigators. Main results: We included 57 RCTs (74 randomised comparisons) involving 3002 participants in this review (some appearing in more than one comparison). Twenty-seven randomised comparisons (1620 participants) assessed SLT versus no SLT; SLT resulted in clinically and statistically significant benefits to patients' functional communication (standardised mean difference (SMD) 0.28, 95% confidence interval (CI) 0.06 to 0.49, P = 0.01), reading, writing, and expressive language, but (based on smaller numbers) benefits were not evident at follow-up. Nine randomised comparisons (447 participants) assessed SLT with social support and stimulation; meta-analyses found no evidence of a difference in functional communication, but more participants withdrew from social support interventions than SLT. Thirty-eight randomised comparisons (1242 participants) assessed two approaches to SLT. Functional communication was significantly better in people with aphasia that received therapy at a high intensity, high dose, or over a long duration compared to those that received therapy at a lower intensity, lower dose, or over a shorter period of time. The benefits of a high intensity or a high dose of SLT were confounded by a significantly higher dropout rate in these intervention groups. Generally, trials randomised small numbers of participants across a range of characteristics (age, time since stroke, and severity profiles), interventions, and outcomes. Authors' conclusions: Our review provides evidence of the effectiveness of SLT for people with aphasia following stroke in terms of improved functional communication, reading, writing, and expressive language compared with no therapy. There is some indication that therapy at high intensity, high dose or over a longer period may be beneficial. HIgh-intensity and high dose interventions may not be acceptable to all.
Aims: This study evaluated two forms of discrimination therapy for auditory processing impairment in aphasia. It aimed to determine whether therapy can improve speech perception and/or help participants use semantic information to compensate for their impairment. Changes in listening were also explored by recording the level of facilitation needed during therapy tasks. Finally the study examined the effect of therapy on an everyday listening activity: a telephone message task.Method: The study employed a repeated measures design. Eight participants received 12 sessions each of phonological and semantic–phonological therapy. Both programmes used minimal pair judgement tasks, but the latter embedded such tasks within a meaningful context, so encouraged the strategic use of semantic information (semantic bootstrapping). Experimental measures of auditory discrimination and comprehension were administered twice before therapy, once after each programme, and again six weeks later. The telephone message task was also administered at each time point. Test data were subjected to both group and individual analyses. Records of progress during therapy (i.e., changes in support needed to carry out therapy tasks) were completed during treatment and analysed across the group.Results: Group analyses showed no significant changes in tests of word and nonword discrimination as a result of therapy. One comprehension task improved following therapy, but two did not. There was also no indication that therapy improved the discrimination of treated words, as assessed by a priming task. The facilitation scores indicated that participants needed less support during tasks as therapy progressed, possibly as a result of improved listening. There was a significant effect of time on the telephone message task. Across all tasks there were few individual gains.Conclusions: The results offer little evidence that therapy improved participants’ discrimination or semantic bootstrapping skills. Some changes in listening may have occurred, as indicated by the facilitation scores. Reasons for the null findings are discussed.
Objective: This work investigates the nature of the comprehension impairment in Wernicke's aphasia (WA), by examining the relationship between deficits in auditory processing of fundamental, non-verbal acoustic stimuli and auditory comprehension. WA, a condition resulting in severely disrupted auditory comprehension, primarily occurs following a cerebrovascular accident (CVA) to the left temporo-parietal cortex. Whilst damage to posterior superior temporal areas is associated with auditory linguistic comprehension impairments, functional-imaging indicates that these areas may not be specific to speech processing but part of a network for generic auditory analysis. Methods: We examined analysis of basic acoustic stimuli in WA participants (n = 10) using auditory stimuli reflective of theories of cortical auditory processing and of speech cues. Auditory spectral, temporal and spectro-temporal analysis was assessed using pure-tone frequency discrimination, frequency modulation (FM) detection and the detection of dynamic modulation (DM) in "moving ripple" stimuli. All tasks used criterion-free, adaptive measures of threshold to ensure reliable results at the individual level. Results: Participants with WA showed normal frequency discrimination but significant impairments in FM and DM detection, relative to age- and hearing-matched controls at the group level (n = 10). At the individual level, there was considerable variation in performance, and thresholds for both FM and DM detection correlated significantly with auditory comprehension abilities in the WA participants. Conclusion: These results demonstrate the co-occurrence of a deficit in fundamental auditory processing of temporal and spectro-temporal non-verbal stimuli in WA, which may have a causal contribution to the auditory language comprehension impairment. Results are discussed in the context of traditional neuropsychology and current models of cortical auditory processing.
Discusses neuropsychological and psychophysiological approaches to the exploration of the relationship between language and the brain, and stresses concepts from neuropsychology which have great significance for the further development of linguistics. (43 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Insufficient attention to tasks can result in slips of action as automatic, unintended action sequences are triggered inappropriately. Such slips arise in part from deficits in sustained attention, which are particularly likely to happen following frontal lobe and white matter damage in traumatic brain injury (TBI). We present a reliable laboratory paradigm that elicits such slips of action and demonstrates high correlations between the severity of brain damage and relative-reported everyday attention failures in a group of 34 TBI patients. We also demonstrate significant correlations between self-and informant-reported everyday attentional failures and performance on this paradigm in a group of 75 normal controls. The paradigm (the Sustained Attention to Response Task—SART) involves the withholding of key presses to rare (one in nine) targets. Performance on the SART correlates significantly with performance on tests of sustained attention, but not other types of attention, supporting the view that this is indeed a measure of sustained attention. We also show that errors (false presses) on the SART can be predicted by a significant shortening of reaction times in the immediately preceding responses, supporting the view that these errors are a result of `drift' of controlled processing into automatic responding consequent on impaired sustained attention to task. We also report a highly significant correlation of −0.58 between SART performance and Glasgow Coma Scale Scores in the TBI group.