How Does iReadMore Therapy Change the Reading Network of Patients with Central Alexia?

Central alexia (CA) is an acquired reading disorder co-occurring with a generalized language deficit (aphasia). The roles of perilesional and ipsilesional tissue in recovery from poststroke aphasia are unclear. We investigated the impact of reading training (using iReadMore, a therapy app) on the connections within and between the right and left hemisphere of the reading network of patients with CA. In patients with pure alexia, iReadMore increased feedback from left inferior frontal gyrus (IFG) region to the left occipital (OCC) region. We aimed to identify whether iReadMore therapy was effective through a similar mechanism in patients with CA. Participants with chronic poststroke CA (n = 23) completed 35 h of iReadMore training over 4 weeks. Reading accuracy for trained and untrained words was assessed before and after therapy. The neural response to reading trained and untrained words in the left and right OCC, ventral occipitotemporal, and IFG regions was examined using event-related magnetoencephalography. The training-related modulation in effective connectivity between regions was modeled at the group level with dynamic causal modeling. iReadMore training improved participants' reading accuracy by an average of 8.4% (range, -2.77 to 31.66) while accuracy for untrained words was stable. Training increased regional sensitivity in bilateral frontal and occipital regions, and strengthened feedforward connections within the left hemisphere. Our data suggest that iReadMore training in these patients modulates lower-order visual representations, as opposed to higher-order, more abstract representations, to improve word-reading accuracy.SIGNIFICANCE STATEMENT This is the first study to conduct a network-level analysis of therapy effects in participants with poststroke central alexia. When patients trained with iReadMore (a multimodal, behavioral, mass practice, computer-based therapy), reading accuracy improved by an average 8.4% on trained items. A network analysis of the magnetoencephalography data associated with this improvement revealed an increase in regional sensitivity in bilateral frontal and occipital regions and strengthening of feedforward connections within the left hemisphere. This indicates that in patients with CA iReadMore engages lower-order, intact resources within the left hemisphere (posterior to their lesion locations) to improve word reading. This provides a foundation for future research to investigate reading network modulation in different CA subtypes, or for sentence-level therapy.

Recovery after stroke: not so proportional after all?

The proportional recovery rule asserts that most stroke survivors recover a fixed proportion of lost function. To the extent that this is true, recovery from stroke can be predicted accurately from baseline measures of acute post-stroke impairment alone. Reports that baseline scores explain more than 80%, and sometimes more than 90%, of the variance in the patients' recoveries, are rapidly accumulating. Here, we show that these headline effect sizes are likely inflated. The key effects in this literature are typically expressed as, or reducible to, correlation coefficients between baseline scores and recovery (outcome scores minus baseline scores). Using formal analyses and simulations, we show that these correlations will be extreme when outcomes are significantly less variable than baselines, which they often will be in practice regardless of the real relationship between outcomes and baselines. We show that these effect sizes are likely to be over-optimistic in every empirical study that we found that reported enough information for us to make the judgement, and argue that the same is likely to be true in other studies as well. The implication is that recovery after stroke may not be as proportional as recent studies suggest.

Randomized trial of iReadMore word reading training and brain stimulation in central alexia.

Central alexia is an acquired reading disorder co-occurring with a generalized language deficit (aphasia). We tested the impact of a novel training app, 'iReadMore', and anodal transcranial direct current stimulation of the left inferior frontal gyrus, on word reading ability in central alexia. The trial was registered at www.clinicaltrials.gov (NCT02062619). Twenty-one chronic stroke patients with central alexia participated. A baseline-controlled, repeated-measures, crossover design was used. Participants completed two 4-week blocks of iReadMore training, one with anodal stimulation and one with sham stimulation (order counterbalanced between participants). Each block comprised 34 h of iReadMore training and 11 stimulation sessions. Outcome measures were assessed before, between and after the two blocks. The primary outcome measures were reading ability for trained and untrained words. Secondary outcome measures included semantic word matching, sentence reading, text reading and a self-report measure. iReadMore training resulted in an 8.7% improvement in reading accuracy for trained words (95% confidence interval 6.0 to 11.4; Cohen's d = 1.38) but did not generalize to untrained words. Reaction times also improved. Reading accuracy gains were still significant (but reduced) 3 months after training cessation. Anodal transcranial direct current stimulation (compared to sham), delivered concurrently with iReadMore, resulted in a 2.6% (95% confidence interval -0.1 to 5.3; d = 0.41) facilitation for reading accuracy, both for trained and untrained words. iReadMore also improved performance on the semantic word-matching test. There was a non-significant trend towards improved self-reported reading ability. However, no significant changes were seen at the sentence or text reading level. In summary, iReadMore training in post-stroke central alexia improved reading ability for trained words, with good maintenance of the therapy effect. Anodal stimulation resulted in a small facilitation (d = 0.41) of learning and also generalized to untrained items.10.1093/brain/awy138_video1awy138media15796149281001.

How right hemisphere damage after stroke can impair speech comprehension.

Acquired language disorders after stroke are strongly associated with left hemisphere damage. When language difficulties are observed in the context of right hemisphere strokes, patients are usually considered to have atypical functional anatomy. By systematically integrating behavioural and lesion data from brain damaged patients with functional MRI data from neurologically normal participants, we investigated when and why right hemisphere strokes cause language disorders. Experiment 1 studied right-handed patients with unilateral strokes that damaged the right (n = 109) or left (n = 369) hemispheres. The most frequently impaired language task was: auditory sentence-to-picture matching after right hemisphere strokes; and spoken picture description after left hemisphere strokes. For those with auditory sentence-to-picture matching impairments after right hemisphere strokes, the majority (n = 9) had normal performance on tests of perceptual (visual or auditory) and linguistic (semantic, phonological or syntactic) processing. Experiment 2 found that these nine patients had significantly more damage to dorsal parts of the superior longitudinal fasciculus and the right inferior frontal sulcus compared to 75 other patients who also had right hemisphere strokes but were not impaired on the auditory sentence-to-picture matching task. Damage to these right hemisphere regions caused long-term speech comprehension difficulties in 67% of patients. Experiments 3 and 4 used functional MRI in two groups of 25 neurologically normal individuals to show that within the regions identified by Experiment 2, the right inferior frontal sulcus was normally activated by (i) auditory sentence-to-picture matching; and (ii) one-back matching when the demands on linguistic and non-linguistic working memory were high. Together, these experiments demonstrate that the right inferior frontal cortex contributes to linguistic and non-linguistic working memory capacity (executive function) that is needed for normal speech comprehension. Our results link previously unrelated literatures on the role of the right inferior frontal cortex in executive processing and the role of executive processing in sentence comprehension; which in turn helps to explain why right inferior frontal activity has previously been reported to increase during recovery of language function after left hemisphere stroke. The clinical relevance of our findings is that the detrimental effect of right hemisphere strokes on language is (i) much greater than expected; (ii) frequently observed after damage to the right inferior frontal sulcus; (iii) task dependent; (iv) different to the type of impairments observed after left hemisphere strokes; and (v) can result in long-lasting deficits that are (vi) not the consequence of atypical language lateralization.

Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE).

A recently introduced hierarchical generative model unified the inference of effective connectivity in individual subjects and the unsupervised identification of subgroups defined by connectivity patterns. This hierarchical unsupervised generative embedding (HUGE) approach combined a hierarchical formulation of dynamic causal modelling (DCM) for fMRI with Gaussian mixture models and relied on Markov chain Monte Carlo (MCMC) sampling for inference. While well suited for the inversion of complex hierarchical models, MCMC-based sampling suffers from a computational burden that is prohibitive for many applications. To address this problem, this paper derives an efficient variational Bayesian (VB) inversion scheme for HUGE that simultaneously provides approximations to the posterior distribution over model parameters and to the log model evidence. The face validity of the VB scheme was tested using two synthetic fMRI datasets with known ground truth. Additionally, an empirical fMRI dataset of stroke patients and healthy controls was used to evaluate the practical utility of the method in application to real-world problems. Our analyses demonstrate good performance of our VB scheme, with a marked speed-up of model inversion by two orders of magnitude compared to MCMC, while maintaining a similar level of accuracy. Notably, additional acceleration would be possible if parallel computing techniques were applied. Generally, our VB implementation of HUGE is fast enough to support multi-start procedures for whole-group analyses, a useful strategy to ameliorate problems with local extrema. HUGE thus represents a potentially useful practical solution for an important problem in clinical neuromodeling and computational psychiatry, i.e., the unsupervised detection of subgroups in heterogeneous populations that are defined by effective connectivity.

A generative model of whole-brain effective connectivity.

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data - in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling - for example, for phenotyping individual patients in terms of whole-brain network structure.

Aphasia Recovery: When, How and Who to Treat?

PURPOSE OF REVIEW: We now know that speech and language therapy (SALT) is effective in the rehabilitation of aphasia; however, there remains much individual variability in the response to interventions. So, what works for whom, when and how? RECENT FINDINGS: This review evaluates the current evidence for the efficacy of predominantly impairment-focused aphasia interventions with respect to optimal dose, intensity, timing and distribution or spacing of treatment. We conclude that sufficient dose of treatment is required to enable clinical gains and that e-therapies are a promising and practical way to achieve this goal. In addition, aphasia can be associated with other cognitive deficits and may lead to secondary effects such as low mood and social isolation. In order to personalise individual treatments to optimise recovery, we need to develop a greater understanding of the interactions between these factors.

Right hemisphere structural adaptation and changing language skills years after left hemisphere stroke.

Stroke survivors with acquired language deficits are commonly thought to reach a 'plateau' within a year of stroke onset, after which their residual language skills will remain stable. Nevertheless, there have been reports of patients who appear to recover over years. Here, we analysed longitudinal change in 28 left-hemisphere stroke patients, each more than a year post-stroke when first assessed-testing each patient's spoken object naming skills and acquiring structural brain scans twice. Some of the patients appeared to improve over time while others declined; both directions of change were associated with, and predictable given, structural adaptation in the intact right hemisphere of the brain. Contrary to the prevailing view that these patients' language skills are stable, these results imply that real change continues over years. The strongest brain-behaviour associations (the 'peak clusters') were in the anterior temporal lobe and the precentral gyrus. Using functional magnetic resonance imaging, we confirmed that both regions are actively involved when neurologically normal control subjects name visually presented objects, but neither appeared to be involved when the same participants used a finger press to make semantic association decisions on the same stimuli. This suggests that these regions serve word-retrieval or articulatory functions in the undamaged brain. We teased these interpretations apart by reference to change in other tasks. Consistent with the claim that the real change is occurring here, change in spoken object naming was correlated with change in two other similar tasks, spoken action naming and written object naming, each of which was independently associated with structural adaptation in similar (overlapping) right hemisphere regions. Change in written object naming, which requires word-retrieval but not articulation, was also significantly more correlated with both (i) change in spoken object naming; and (ii) structural adaptation in the two peak clusters, than was change in another task-auditory word repetition-which requires articulation but not word retrieval. This suggests that the changes in spoken object naming reflected variation at the level of word-retrieval processes. Surprisingly, given their qualitatively similar activation profiles, hypertrophy in the anterior temporal region was associated with improving behaviour, while hypertrophy in the precentral gyrus was associated with declining behaviour. We predict that either or both of these regions might be fruitful targets for neural stimulation studies (suppressing the precentral region and/or enhancing the anterior temporal region), aiming to encourage recovery or arrest decline even years after stroke occurs.

Modulation of frontal effective connectivity during speech.

Noninvasive neurostimulation methods such as transcranial direct current stimulation (tDCS) can elicit long-lasting, polarity-dependent changes in neocortical excitability. In a previous concurrent tDCS-fMRI study of overt picture naming, we reported significant behavioural and regionally specific neural facilitation effects in left inferior frontal cortex (IFC) with anodal tDCS applied to left frontal cortex (Holland et al., 2011). Although distributed connectivity effects of anodal tDCS have been modelled at rest, the mechanism by which 'on-line' tDCS may modulate neuronal connectivity during a task-state remains unclear. Here, we used Dynamic Causal Modelling (DCM) to determine: (i) how neural connectivity within the frontal speech network is modulated during anodal tDCS; and, (ii) how individual variability in behavioural response to anodal tDCS relates to changes in effective connectivity strength. Results showed that compared to sham, anodal tDCS elicited stronger feedback from inferior frontal sulcus (IFS) to ventral premotor (VPM) accompanied by weaker self-connections within VPM, consistent with processes of neuronal adaptation. During anodal tDCS individual variability in the feedforward connection strength from IFS to VPM positively correlated with the degree of facilitation in naming behaviour. These results provide an essential step towards understanding the mechanism of 'online' tDCS paired with a cognitive task. They also identify left IFS as a 'top-down' hub and driver for speech change.

Distinguishing the effect of lesion load from tract disconnection in the arcuate and uncinate fasciculi.

Brain imaging studies of functional outcomes after white matter damage have quantified the severity of white matter damage in different ways. Here we compared how the outcome of such studies depends on two different types of measurements: the proportion of the target tract that has been destroyed ('lesion load') and tract disconnection. We demonstrate that conclusions from analyses based on two examples of these measures diverge and that conclusions based solely on lesion load may be misleading. First, we reproduce a recent lesion-load-only analysis which suggests that damage to the arcuate fasciculus, and not to the uncinate fasciculus, is significantly associated with deficits in fluency and naming skills. Next, we repeat the analysis after replacing the measures of lesion load with measures of tract disconnection for both tracts, and observe significant associations between both tracts and both language skills: i.e. the change increases the apparent relevance of the uncinate fasciculus to fluency and naming skills. Finally we show that, in this dataset, disconnection data explains significant variance in both language skills that is not accounted for by lesion load or volume, but lesion load data explains no unique variance in those skills, once disconnection and lesion volume are taken into account.

The PLORAS Database: A data repository for Predicting Language Outcome and Recovery After Stroke.

The PLORAS Database is a relational repository of anatomical and functional imaging data that has primarily been acquired from stroke survivors, along with standardized scores on a wide range of sensory, motor and cognitive abilities, demographic details and medical history. As of January 2015, we have data from 750 patients with an expected accrual rate of 200 patients per year. Expansion will accelerate as we extend our collaborations. The main aim of the database is to Predict Language Outcome and Recovery After Stroke (PLORAS) on the basis of a single structural (anatomical) brain scan that indexes the stereotactic location and extent of brain damage. Predictions are made for individual patients by indicating how other patients with the most similar brain damage, cognitive abilities and demographic details recovered their language skills over time. Predictions are validated by longitudinal follow-ups of patients who initially presented with speech and language difficulties. The PLORAS Database can also be used to predict recovery of other cognitive abilities on the basis of anatomical brain scans. The functional imaging data can be used to understand the neural mechanisms that support recovery from brain damage; and all the data can be used to understand the main sources of inter-subject variability in structure-function mappings in the human brain. Data will be made available for sharing, subject to: funding, ethical approval and patient consent.

Comparing language outcomes in monolingual and bilingual stroke patients.

Post-stroke prognoses are usually inductive, generalizing trends learned from one group of patients, whose outcomes are known, to make predictions for new patients. Research into the recovery of language function is almost exclusively focused on monolingual stroke patients, but bilingualism is the norm in many parts of the world. If bilingual language recruits qualitatively different networks in the brain, prognostic models developed for monolinguals might not generalize well to bilingual stroke patients. Here, we sought to establish how applicable post-stroke prognostic models, trained with monolingual patient data, are to bilingual stroke patients who had been ordinarily resident in the UK for many years. We used an algorithm to extract binary lesion images for each stroke patient, and assessed their language with a standard tool. We used feature selection and cross-validation to find 'good' prognostic models for each of 22 different language skills, using monolingual data only (174 patients; 112 males and 62 females; age at stroke: mean = 53.0 years, standard deviation = 12.2 years, range = 17.2-80.1 years; time post-stroke: mean = 55.6 months, standard deviation = 62.6 months, range = 3.1-431.9 months), then made predictions for both monolinguals and bilinguals (33 patients; 18 males and 15 females; age at stroke: mean = 49.0 years, standard deviation = 13.2 years, range = 23.1-77.0 years; time post-stroke: mean = 49.2 months, standard deviation = 55.8 months, range = 3.9-219.9 months) separately, after training with monolingual data only. We measured group differences by comparing prediction error distributions, and used a Bayesian test to search for group differences in terms of lesion-deficit associations in the brain. Our models distinguish better outcomes from worse outcomes equally well within each group, but tended to be over-optimistic when predicting bilingual language outcomes: our bilingual patients tended to have poorer language skills than expected, based on trends learned from monolingual data alone, and this was significant (P < 0.05, corrected for multiple comparisons) in 13/22 language tasks. Both patient groups appeared to be sensitive to damage in the same sets of regions, though the bilinguals were more sensitive than the monolinguals. media-1vid1 10.1093/brain/awv020_video_abstract awv020_video_abstract.

Cognitive control and the salience network: an investigation of error processing and effective connectivity.

The Salience Network (SN) consists of the dorsal anterior cingulate cortex (dACC) and bilateral insulae. The network responds to behaviorally salient events, and an important question is how its nodes interact. One theory is that the dACC provides the earliest cortical signal of behaviorally salient events, such as errors. Alternatively, the anterior right insula (aRI) has been proposed to provide an early cognitive control signal. As these regions frequently coactivate, it has been difficult to disentangle their roles using conventional methods. Here we use dynamic causal modeling and a Bayesian model evidence technique to investigate the causal relationships between nodes in the SN after errors. Thirty-five human subjects performed the Simon task. The task has two conditions (congruent and incongruent) producing two distinct error types. Neural activity associated with errors was investigated using fMRI. Subjects made a total of 1319 congruent and 1617 incongruent errors. Errors resulted in robust activation of the SN. Dynamic causal modeling analyses demonstrated that input into the SN was most likely via the aRI for both error types and that the aRI was the only region intrinsically connected to both other nodes. Only incongruent errors produced behavioral adaptation, and the strength of the connection between the dACC and the left insulae correlated with the extent of this behavioral change. We conclude that the aRI, not the dACC, drives the SN after errors on an attentionally demanding task, and that a change in the effective connectivity of the dACC is associated with behavioral adaptation after errors.

Auditory-motor interactions for the production of native and non-native speech.

During speech production, auditory processing of self-generated speech is used to adjust subsequent articulations. The current study investigated how the proposed auditory-motor interactions are manifest at the neural level in native and non-native speakers of English who were overtly naming pictures of objects and reading their written names. Data were acquired with functional magnetic resonance imaging and analyzed with dynamic causal modeling. We found that (1) higher activity in articulatory regions caused activity in auditory regions to decrease (i.e., auditory suppression), and (2) higher activity in auditory regions caused activity in articulatory regions to increase (i.e., auditory feedback). In addition, we were able to demonstrate that (3) speaking in a non-native language involves more auditory feedback and less auditory suppression than speaking in a native language. The difference between native and non-native speakers was further supported by finding that, within non-native speakers, there was less auditory feedback for those with better verbal fluency. Consequently, the networks of more fluent non-native speakers looked more like those of native speakers. Together, these findings provide a foundation on which to explore auditory-motor interactions during speech production in other human populations, particularly those with speech difficulties.

Between thought and expression, a magnetoencephalography study of the "tip-of-the-tongue" phenomenon.

"Tip-of-the-tongue" (TOT) is the phenomenon associated with the inaccessibility of a known word from memory. It is universally experienced, increases in frequency with age, and is most common for proper nouns. It is a good model for the symptom of anomia experienced much more frequently by some aphasic patients following brain injury. Here, we induced the TOT state in older participants while they underwent brain scanning with magnetoencephalography to investigate the changes in oscillatory brain activity associated with failed retrieval of known words. Using confrontation naming of pictures of celebrities, we successfully induced the TOT state in 29% of trials and contrasted it with two other states: "Know" where the participants both correctly recognized the celebrity's face and retrieved their name and "Don't Know" when the participants did not recognize the celebrity. We wished to test Levelt's influential model of speech output by carrying out two analyses, one epoching the data to the point in time when the picture was displayed and the other looking back in time from when the participants first articulated their responses. Our main findings supported the components of Levelt's model, but not their serial activation over time as both semantic and motor areas were identified in both analyses. We also found enduring decreases in the alpha frequency band in the left ventral temporal region during the TOT state, suggesting ongoing semantic search. Finally, we identified reduced beta power in classical peri-sylvian language areas for the TOT condition, suggesting that brain regions that encode linguistic memories are also involved in their attempted retrieval.

Gradual lesion expansion and brain shrinkage years after stroke.

BACKGROUND AND PURPOSE: Lesioned brains of patients with stroke may change through the course of recovery; however, little is known about their evolution in the chronic phase. Here, we aimed to quantify the extent of lesion volume change and brain atrophy in the chronic poststroke brain using magnetic resonance imaging. METHODS: Optimized T1-weighted scans were collected more than once (time between visits=2 months to 6 years) in 56 patients (age=36-90 years; time poststroke=3 months to 20 years). Volumetric changes attributable to lesion growth and atrophy were quantified with automated procedures. We looked at how volumetric changes related to time between visits, using nonparametric statistics, after controlling for age, time poststroke, and brain and lesion size at the earlier time. RESULTS: Lesions expanded more in patients who had longer time-intervals between their imaging sessions (partial rank correlation ρ=0.56; P<0.001). The median rate of lesion growth was 1.59 cm(3) per year. Across patients, the whole-brain atrophy rate was 0.95% per year, with accelerated atrophy in the ipsilesional hemisphere. CONCLUSIONS: We show gradual lesion expansion many years after stroke, beyond that expected by normal aging and after controlling for other variables. Future studies need to understand how structural reorganization enables long-term recovery even when the brain is shrinking.

Distinct frontal networks are involved in adapting to internally and externally signaled errors.

Errors trigger changes in behavior that help individuals adapt to new situations. The dorsal anterior cingulate cortex (dACC) is thought to be central to this response, but more lateral frontal regions are also activated by errors and may make distinct contributions. We investigated error processing by studying 2 distinct error types: commission and timing. Thirty-five subjects performed a version of the Simon Task designed to produce large number of errors. Commission errors were internally recognized and were not accompanied by explicit feedback. In contrast, timing errors were difficult to monitor internally and were explicitly signaled. Both types of error triggered changes in behavior consistent with increased cognitive control. As expected, robust activation within the dACC and bilateral anterior insulae (the Salience Network) was seen for commission errors. In contrast, timing errors were not associated with activation of this network but did activate a bilateral network that included the right ventral attentional system. Common activation for both error types occurred within the pars operculari and angular gyri. These results show that the dACC does not respond to all behaviorally salient errors. Instead, the error-processing system is multifaceted, and control can be triggered independently of the dACC when feedback is unexpected.

Prevalence, patterns, and predictors of patient-reported non-motor outcomes at 30 days after acute stroke: Prospective observational hospital cohort study.

BACKGROUND: Adverse non-motor outcomes are common after acute stroke and likely to substantially affect quality of life, yet few studies have comprehensively assessed their prevalence, patterns, and predictors across multiple health domains. AIMS: We aimed to identify the prevalence, patterns, and the factors associated with non-motor outcomes 30 days after stroke. METHODS: This prospective observational hospital cohort study-Stroke Investigation in North and Central London (SIGNAL)-identified patients with acute ischemic stroke or intracerebral hemorrhage (ICH) admitted to the Hyperacute Stroke Unit (HASU) at University College Hospital (UCH), London, between August 1, 2018 and August 31, 2019. We assessed non-motor outcomes (anxiety, depression, fatigue, sleep, participation in social roles and activities, pain, bowel function, and bladder function) at 30-day follow-up using the Patient-Reported Outcome Measurement Information System-Version 29 (PROMIS-29) scale and Barthel Index scale. RESULTS: We obtained follow-up data for 605/719 (84.1%) eligible patients (mean age 72.0 years; 48.3% female; 521 with ischemic stroke, 84 with ICH). Anxiety (57.0%), fatigue (52.7%), bladder dysfunction (50.2%), reduced social participation (49.2%), and pain (47.9%) were the commonest adverse non-motor outcomes. The rates of adverse non-motor outcomes in ⩾ 1, ⩾ 2 and ⩾ 3 domains were 89%, 66.3%, and 45.8%, respectively; in adjusted analyses, stroke due to ICH (compared to ischemic stroke) and admission stroke severity were the strongest and most consistent predictors. There were significant correlations between bowel dysfunction and bladder dysfunction (κ = 0.908); reduced social participation and bladder dysfunction (κ = 0.844); and anxiety and fatigue (κ = 0.613). We did not identify correlations for other pairs of non-motor domains. CONCLUSION: Adverse non-motor outcomes were very common at 30 days after stroke, affecting nearly 90% of evaluated patients in at least one health domain, about two-thirds in two or more domains, and almost 50% in three or more domains. Stroke due to ICH and admission stroke severity were the strongest and most consistent predictors. Adverse outcomes occurred in pairs of domains, such as with anxiety and fatigue. Our findings emphasize the importance of a multi-domain approach to effectively identify adverse non-motor outcomes after stroke to inform the development of more holistic patient care pathways after stroke.

Efficacy of a gamified digital therapy for speech production in people with chronic aphasia (iTalkBetter): behavioural and imaging outcomes of a phase II item-randomised clinical trial.

Background: Aphasia is among the most debilitating of symptoms affecting stroke survivors. Speech and language therapy (SLT) is effective, but many hours of practice are required to make clinically meaningful gains. One solution to this 'dosage' problem is to automate therapeutic approaches via self-supporting apps so people with aphasia (PWA) can amass practice as it suits them. However, response to therapy is variable and no clinical trial has yet identified the key brain regions required to engage with word-retrieval therapy. Methods: Between Sep 7, 2020 and Mar 1, 2022 at University College London in the UK, we carried out a phase II, item-randomised clinical trial in 27 PWA using a novel, self-led app, 'iTalkBetter', which utilises confrontation naming therapy. Unlike previously reported apps, it has a real-time utterance verification system that drives its adaptive therapy algorithm. Therapy items were individually randomised to provide balanced lists of 'trained' and 'untrained' items matched on key psycholinguistic variables and baseline performance. PWA practised with iTalkBetter over a 6-week therapy block. Structural and functional MRI data were collected to identify therapy-related changes in brain states. A repeated-measures design was employed. The trial was registered at ClinicalTrials.gov (NCT04566081). Findings: iTalkBetter significantly improved naming ability by 13% for trained items compared with no change for untrained items, an average increase of 29 words (SD = 26) per person; beneficial effects persisted at three months. PWA's propositional speech also significantly improved. iTalkBetter use was associated with brain volume increases in right auditory and left anterior prefrontal cortices. Task-based fMRI identified dose-related activity in the right temporoparietal junction. Interpretation: Our findings suggested that iTalkBetter significantly improves PWAs' naming ability on trained items. The effect size is similar to a previous RCT of computerised therapy, but this is the first study to show transfer to a naturalistic speaking task. iTalkBetter usage and dose caused observable changes in brain structure and function to key parts of the surviving language perception, production and control networks. iTalkBetter is being rolled-out as an app for all PWA and anomia: https://www.ucl.ac.uk/icn/research/research-groups/neurotherapeutics/projects/digital-interventions-neuro-rehabilitation-0 so that they can increase their dosage of practice-based SLT. Funding: National Institute for Health and Care Research, Wellcome Centre for Human Neuroimaging.