Paradoxical lesions, plasticity and active inference

Paradoxical lesions are secondary brain lesions that ameliorate functional deficits caused by the initial insult. This effect has been explained in several ways; particularly by the reduction of functional inhibition, or by increases in the excitatory-to-inhibitory synaptic balance within perilesional tissue. In this article, we simulate how and when a modification of the excitatory-inhibitory balance triggers the reversal of a functional deficit caused by a primary lesion. For this, we introduce in-silico lesions to an active inference model of auditory word repetition. The first in-silico lesion simulated damage to the extrinsic (between regions) connectivity causing a functional deficit that did not fully resolve over 100 trials of a word repetition task. The second lesion was implemented in the intrinsic (within region) connectivity, compromising the model's ability to rebalance excitatory-inhibitory connections during learning. We found that when the second lesion was mild, there was an increase in experience-dependent plasticity that enhanced performance relative to a single lesion. This paradoxical lesion effect disappeared when the second lesion was more severe because plasticity-related changes were disproportionately amplified in the intrinsic connectivity, relative to lesioned extrinsic connections. Finally, this framework was used to predict the physiological correlates of paradoxical lesions. This formal approach provides new insights into the computational and neurophysiological mechanisms that allow some patients to recover after large or multiple lesions.

Effective immunity and second waves: a dynamic causal modelling study

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Effective immunity and second waves: a dynamic causal modelling study

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Dynamic causal modelling of COVID-19

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

Testing and tracking in the UK: A dynamic causal modelling study

By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.

Dynamic causal modelling of COVID-19

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

The impact of early language exposure on the neural system supporting language in deaf and hearing adults

Deaf late signers provide a unique perspective on the impact of impoverished early language exposure on the neurobiology of language: insights that cannot be gained from research with hearing people alone. Here we contrast the effect of age of sign language acquisition in hearing and congenitally deaf adults to examine the potential impact of impoverished early language exposure on the neural systems supporting a language learnt later in life. We collected fMRI data from deaf and hearing proficient users (N ​= ​52) of British Sign Language (BSL), who learnt BSL either early (native) or late (after the age of 15 years) whilst they watched BSL sentences or strings of meaningless nonsense signs.

There was a main effect of age of sign language acquisition (late ​> ​early) across deaf and hearing signers in the occipital segment of the left intraparietal sulcus. This finding suggests that late learners of sign language may rely on visual processing more than early learners, when processing both linguistic and nonsense sign input – regardless of hearing status. Region-of-interest analyses in the posterior superior temporal cortices (STC) showed an effect of age of sign language acquisition that was specific to deaf signers. In the left posterior STC, activation in response to signed sentences was greater in deaf early signers than deaf late signers. Importantly, responses in the left posterior STC in hearing early and late signers did not differ, and were similar to those observed in deaf early signers. These data lend further support to the argument that robust early language experience, whether signed or spoken, is necessary for left posterior STC to show a ‘native-like’ response to a later learnt language.

Generalizing post-stroke prognoses from research data to clinical data

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Lesion-symptom models for aphasia must generalize, if they are to be useful.

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We show that these models can generalize across countries, native languages, neuroimaging technology and from chronic to acute scans.

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The result depends on modelling lesion growth over years post-stroke.

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Lesion growth may be a more significant confound, in previous lesion-symptom analyses, than previously thought.

Around a third of stroke survivors suffer from acquired language disorders (aphasia), but current medicine cannot predict whether or when they might recover. Prognostic research in this area increasingly draws on datasets associating structural brain imaging data with outcome scores for ever-larger samples of stroke patients. The aim is to learn brain-behaviour trends from these data, and generalize those trends to predict outcomes for new patients. The practical significance of this work depends on the expected breadth of that generalization. Here, we show that these models can generalize across countries and native languages (from British patients tested in English to Chilean patients tested in Spanish), across neuroimaging technology (from MRI to CT), and from scans collected months or years after stroke for research purposes, to scans collected days or weeks after stroke for clinical purposes.

Dissociating the functions of superior and inferior parts of the left ventral occipito-temporal cortex during visual word and object processing

During word and object recognition, extensive activation has consistently been observed in the left ventral occipito-temporal cortex (vOT), focused around the occipito-temporal sulcus (OTs). Previous studies have shown that there is a hierarchy of responses from posterior to anterior vOT regions (along the y-axis) that corresponds with increasing levels of recognition - from perceptual to semantic processing, respectively. In contrast, the functional differences between superior and inferior vOT responses (i.e. along the z-axis) have not yet been elucidated. To investigate, we conducted an extensive review of the literature and found that peak activation for reading varies by more than 1 cm in the z-axis. In addition, we investigated functional differences between superior and inferior parts of left vOT by analysing functional MRI data from 58 neurologically normal skilled readers performing 8 different visual processing tasks. We found that group activation in superior vOT was significantly more sensitive than inferior vOT to the type of task, with more superior vOT activation when participants were matching visual stimuli for their semantic or perceptual content than producing speech to the same stimuli. This functional difference along the z-axis was compared to existing boundaries between cytoarchitectonic areas around the OTs. In addition, using dynamic causal modelling, we show that connectivity from superior vOT to anterior vOT increased with semantic content during matching tasks but not during speaking tasks whereas connectivity from inferior vOT to anterior vOT was sensitive to semantic content for matching and speaking tasks. The finding of a functional dissociation between superior and inferior parts of vOT has implications for predicting deficits and response to rehabilitation for patients with partial damage to vOT following stroke or neurosurgery.

A special role for the right posterior superior temporal sulcus during speech production

This fMRI study of 24 healthy human participants investigated whether any part of the auditory cortex was more responsive to self-generated speech sounds compared to hearing another person speak. The results demonstrate a double dissociation in two different parts of the auditory cortex. In the right posterior superior temporal sulcus (RpSTS), activation was higher during speech production than listening to auditory stimuli, whereas in bilateral superior temporal gyri (STG), activation was higher for listening to auditory stimuli than during speech production. In the second part of the study, we investigated the function of the identified regions, by examining how activation changed across a range of listening and speech production tasks that systematically varied the demands on acoustic, semantic, phonological and orthographic processing. In RpSTS, activation during auditory conditions was higher in the absence of semantic cues, plausibly indicating increased attention to the spectral-temporal features of auditory inputs. In addition, RpSTS responded in the absence of any auditory inputs when participants were making one-back matching decisions on visually presented pseudowords. After analysing the influence of visual, phonological, semantic and orthographic processing, we propose that RpSTS (i) contributes to short term memory of speech sounds as well as (ii) spectral-temporal processing of auditory input and (iii) may play a role in integrating auditory expectations with auditory input. In contrast, activation in bilateral STG was sensitive to acoustic input and did not respond in the absence of auditory input. The special role of RpSTS during speech production therefore merits further investigation if we are to fully understand the neural mechanisms supporting speech production during speech acquisition, adult life, hearing loss and after brain injury.

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In right auditory cortex, a region is more sensitive to own than another’s speech.

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This region (RpSTS) responds to phonological input in the absence of auditory input.

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RpSTS may match auditory feedback with internal representations of speech sounds.

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.

Acquisition of sensorimotor fMRI under general anaesthesia: Assessment of feasibility, the BOLD response and clinical utility

We evaluated whether task-related fMRI (functional magnetic resonance imaging) BOLD (blood oxygenation level dependent) activation could be acquired under conventional anaesthesia at a depth enabling neurosurgery in five patients with supratentorial gliomas. Within a 1.5 T MRI operating room immediately prior to neurosurgery, a passive finger flexion sensorimotor paradigm was performed on each hand with the patients awake, and then immediately after the induction and maintenance of combined sevoflurane and propofol general anaesthesia. The depth of surgical anaesthesia was measured and confirmed with an EEG-derived technique, the Bispectral Index (BIS). The magnitude of the task-related BOLD response and BOLD sensitivity under anaesthesia were determined. The fMRI data were assessed by three fMRI expert observers who rated each activation map for somatotopy and usefulness for radiological neurosurgical guidance. The mean magnitudes of the task-related BOLD response under a BIS measured depth of surgical general anaesthesia were 25% (tumour affected hemisphere) and 22% (tumour free hemisphere) of the respective awake values. BOLD sensitivity under anaesthesia ranged from 7% to 83% compared to the awake state. Despite these reductions, somatotopic BOLD activation was observed in the sensorimotor cortex in all ten data acquisitions surpassing statistical thresholds of at least p < 0.001_uncorr. All ten fMRI activation datasets were scored to be useful for radiological neurosurgical guidance. Passive task-related sensorimotor fMRI acquired in neurosurgical patients under multi-pharmacological general anaesthesia is reproducible and yields clinically useful activation maps. These results demonstrate the feasibility of the technique and its potential value if applied intra-operatively. Additionally these methods may enable fMRI investigations in patients unable to perform or lie still for awake paradigms, such as young children, claustrophobic patients and those with movement disorders.

A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.

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This guide walks through a group effective connectivity study using DCM and PEB.

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Part 1, presented here, covers first level analysis using DCM for fMRI.

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It clarifies the specific neural and haemodynamic models in DCM and their priors.

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An accompanying dataset is provided with step-by-step analysis instructions.

A guide to group effective connectivity analysis, part 2: Second level analysis with PEB

This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses.

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This guide walks through a group effective connectivity study using DCM and PEB.

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It explains recently developed tools for hierarchical Bayesian modelling.

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The appendices clarify the technical detail of the PEB framework and its priors.

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An accompanying dataset is provided with step-by-step analysis instructions.

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.

The evolution of cognitive models: From neuropsychology to neuroimaging and back

This paper provides a historical and future perspective on how neuropsychology and neuroimaging can be used to develop cognitive models of human brain functions. Section 1 focuses on the emergence of cognitive modelling from neuropsychology, why lesion location was considered to be unimportant and the challenges faced when mapping symptoms to impaired cognitive processes. Section 2 describes how established cognitive models based on behavioural data alone cannot explain the complex patterns of distributed brain activity that are observed in functional neuroimaging studies. This has led to proposals for new cognitive processes, new cognitive strategies and new functional ontologies for cognition. Section 3 considers how the integration of data from lesion, behavioural and functional neuroimaging studies of large cohorts of brain damaged patients can be used to determine whether inter-patient variability in behaviour is due to differences in the premorbid function of each brain region, lesion site or cognitive strategy. This combination of neuroimaging and neuropsychology is providing a deeper understanding of how cognitive functions can be lost and re-learnt after brain damage - an understanding that will transform our ability to generate and validate cognitive models that are both physiologically plausible and clinically useful.

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed.

Predicting language outcomes after stroke: Is structural disconnection a useful predictor?

For many years, researchers have sought to understand whether and when stroke survivors with acquired language impairment (aphasia) will recover. There is broad agreement that lesion location information should play some role in these predictions, but still no consensus on the best or right way to encode that information. Here, we address the emerging emphasis on the structural connectome in this work - specifically the claim that disrupted white matter connectivity conveys important, unique prognostic information for stroke survivors with aphasia. Our sample included 818 stroke patients extracted from the PLORAS database, which associates structural MRI from stroke patients with language assessment scores from the Comprehensive Aphasia Test (CAT) and basic demographic. Patients were excluded when their lesions were too diffuse or small (<1 cm3) to be detected by the Automatic Lesion Identification toolbox, which we used to encode patients' lesions as binary lesion images in standard space. Lesions were encoded using the 116 regions defined by the Automatic Anatomical Labelling atlas. We examined prognostic models driven by both "lesion load" in these regions (i.e. the proportion of each region destroyed by each patient's lesion), and by the disconnection of the white matter connections between them which was calculated via the Network Modification toolbox. Using these data, we build a series of prognostic models to predict first one ("naming"), and then all of the language scores defined by the CAT. We found no consistent evidence that connectivity disruption data in these models improved our ability to predict any language score. This may be because the connectivity disruption variables are strongly correlated with the lesion load variables: correlations which we measure both between pairs of variables in their original form, and between principal components of both datasets. Our conclusion is that, while both types of structural brain data do convey useful, prognostic information in this domain, they also appear to convey largely the same variance. We conclude that connectivity disruption variables do not help us to predict patients' language skills more accurately than lesion location (load) data alone.

Interpreting and Utilising Intersubject Variability in Brain Function

We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.

"Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach"

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations.

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Evidence for at least partially language- and modality-independent semantic neural representations.

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With a decoding approach, we tested whether brain activity during the semantic access of individual nouns in one language and modality (e.g. production) allowed predicting the semantic access of the same concepts in the other language and modalities (e.g. word listening, word reading).

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Across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading.

How many deficits in the same dyslexic brains? A behavioural and fMRI assessment of comorbidity in adult dyslexics

Dyslexia can have different manifestations: this has motivated different theories on its nature, on its underlying brain bases and enduring controversies on how to best treat it. The relative weight of the different manifestations has never been evaluated using both behavioural and fMRI measures, a challenge taken here to assess the major systems called into play in dyslexia by different theories. We found that adult well-compensated dyslexics were systematically impaired only in reading and in visuo-phonological tasks, while deficits for other systems (e.g., motor/cerebellar, visual magnocellular/motion perception) were only very occasional. In line with these findings, fMRI showed a reliable hypoactivation only for the task of reading, in the left occipito-temporal cortex (l-OTC). The l-OTC, normally a crossroad between the reading system and other systems, did not show the same level of intersection in dyslexics; yet, it was not totally silent because it responded, in segregated parts, during auditory phonological and visual motion perception tasks. This minimal behavioural and functional anatomical comorbidity demonstrates that a specific deficit of reading is the best description for developmental dyslexia, at least for adult well-compensated cases, with clear implications for rehabilitation strategies. The reduced intersection of multiple systems in the l-OTC suggests that dyslexics suffer from a coarser connectivity, leading to disconnection between the multiple domains that normally interact during reading.

Using multi-level Bayesian lesion-symptom mapping to probe the body-part-specificity of gesture imitation skills

Past attempts to identify the neural substrates of hand and finger imitation skills in the left hemisphere of the brain have yielded inconsistent results. Here, we analyse those associations in a large sample of 257 left hemisphere stroke patients. By introducing novel Bayesian methods, we characterise lesion symptom associations at three levels: the voxel-level, the single-region level (using anatomically defined regions), and the region-pair level. The results are inconsistent across those three levels and we argue that each level of analysis makes assumptions which constrain the results it can produce. Regardless of the inconsistencies across levels, and contrary to past studies which implicated differential neural substrates for hand and finger imitation, we find no consistent voxels or regions, where damage affects one imitation skill and not the other, at any of the three analysis levels. Our novel Bayesian approach indicates that any apparent differences appear to be driven by an increased sensitivity of hand imitation skills to lesions that also impair finger imitation. In our analyses, the results of the highest level of analysis (region-pairs) emphasise a role of the primary somatosensory and motor cortices, and the occipital lobe in imitation. We argue that this emphasis supports an account of both imitation tasks based on direct sensor-motor connections, which throws doubt on past accounts which imply the need for an intermediate (e.g. body-part-coding) system of representation.

Less is more: neural mechanisms underlying anomia treatment in chronic aphasic patients

See Thompson and Woollams (doi:10.1093/brain/awx264) for a scientific commentary on this article. Previous research with aphasic patients has shown that picture naming can be facilitated by concurrent phonemic cueing [e.g. initial phoneme(s) of the word that the patient is trying to retrieve], both as an immediate word retrieval technique, and when practiced repeatedly over time as a long-term anomia treatment. Here, to investigate the neural mechanisms supporting word retrieval, we adopted—for the first time—a functional magnetic resonance imaging task using the same naming procedure as it occurs during the anomia treatment process. Before and directly after a 6-week anomia treatment programme, 18 chronic aphasic stroke patients completed our functional magnetic resonance imaging protocol—a picture naming task aided by three different types of phonemic cues (whole words, initial phonemes, final phonemes) and a noise-control condition. Patients completed a naming task based on the training materials, and a more general comprehensive battery of language tests both before and after the anomia treatment, to determine the effectiveness and specificity of the therapy. Our results demonstrate that the anomia treatment was effective and specific to speech production, significantly improving both patients’ naming accuracy and reaction time immediately post-treatment (unstandardized effect size: 29% and 17%, respectively; Cohen’s d: 3.45 and 1.83). Longer term gains in naming were maintained 3 months later. Functional imaging results showed that both immediate and long-term facilitation of naming involved a largely overlapping bilateral frontal network including the right anterior insula, inferior frontal and dorsal anterior cingulate cortices, and the left premotor cortex. These areas were associated with a neural priming effect (i.e. reduced blood oxygen level-dependent signal) during both immediate (phonemically-cued versus control-cue conditions), and long-term facilitation of naming (i.e. treated versus untreated items). Of note is that different brain regions were sensitive to different phonemic cue types. Processing of whole word cues was associated with increased activity in the right angular gyrus; whereas partial word cues (initial and final phonemes) recruited the left supplementary motor area, and right anterior insula, inferior frontal cortex, and basal ganglia. The recruitment of multiple and bilateral areas may help explain why phonemic cueing is such a successful behavioural facilitation tool for anomia treatment. Our results have important implications for optimizing current anomia treatment approaches, developing new treatments, and improving speech outcome for aphasic patients.