Electrophysiological studies in humans and animals suggest that noninvasive neurostimulation methods such as transcranial direct current stimulation (tDCS) can elicit long-lasting 1, ...polarity-dependent 2 changes in neocortical excitability. Application of tDCS can have significant and selective behavioral consequences that are associated with the cortical location of the stimulation electrodes and the task engaged during stimulation 3–8. However, the mechanism by which tDCS affects human behavior is unclear. Recently, functional magnetic resonance imaging (fMRI) has been used to determine the spatial topography of tDCS effects 9–13, but no behavioral data were collected during stimulation. The present study is unique in this regard, in that both neural and behavioral responses were recorded using a novel combination of left frontal anodal tDCS during an overt picture-naming fMRI study. We found that tDCS had significant behavioral and regionally specific neural facilitation effects. Furthermore, faster naming responses correlated with decreased blood oxygen level-dependent (BOLD) signal in Broca's area. Our data support the importance of Broca's area within the normal naming network and as such indicate that Broca's area may be a suitable candidate site for tDCS in neurorehabilitation of anomic patients, whose brain damage spares this region.
► This is a novel application of concurrent A-tDCS and fMRI during speech production ► Left frontal A-tDCS speeds up spoken naming responses ► Left frontal A-tDCS elicits a regionally specific neural effect in Broca's area ► Decreased BOLD signal in Broca's area correlates with faster naming responses
Comparing families of dynamic causal models Penny, Will D; Stephan, Klaas E; Daunizeau, Jean ...
PLOS computational biology/PLoS computational biology,
03/2010, Letnik:
6, Številka:
3
Journal Article
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Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one ...first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This "best model" approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.
The most difficult clinical questions in stroke rehabilitation are “What is this patient’s potential for recovery?” and “What is the best rehabilitation strategy for this person, given her/his ...clinical profile?” Without answers to these questions, clinicians struggle to make decisions regarding the content and focus of therapy, and researchers design studies that inadvertently mix participants who have a high likelihood of responding with those who do not. Developing and implementing biomarkers that distinguish patient subgroups will help address these issues and unravel the factors important to the recovery process. The goal of the present paper is to provide a consensus statement regarding the current state of the evidence for stroke recovery biomarkers. Biomarkers of motor, somatosensory, cognitive and language domains across the recovery timeline post-stroke are considered; with focus on brain structure and function, and exclusion of blood markers and genetics. We provide evidence for biomarkers that are considered ready to be included in clinical trials, as well as others that are promising but not ready and so represent a developmental priority. We conclude with an example that illustrates the utility of biomarkers in recovery and rehabilitation research, demonstrating how the inclusion of a biomarker may enhance future clinical trials. In this way, we propose a way forward for when and where we can include biomarkers to advance the efficacy of the practice of, and research into, rehabilitation and recovery after stroke.
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.
The most difficult clinical questions in stroke rehabilitation are “What is this patient’s potential for recovery?” and “What is the best rehabilitation strategy for this person, given her/his ...clinical profile?” Without answers to these questions, clinicians struggle to make decisions regarding the content and focus of therapy, and researchers design studies that inadvertently mix participants who have a high likelihood of responding with those who do not. Developing and implementing biomarkers that distinguish patient subgroups will help address these issues and unravel the factors important to the recovery process. The goal of the present paper is to provide a consensus statement regarding the current state of the evidence for stroke recovery biomarkers. Biomarkers of motor, somatosensory, cognitive and language domains across the recovery timeline post-stroke are considered; with focus on brain structure and function, and exclusion of blood markers and genetics. We provide evidence for biomarkers that are considered ready to be included in clinical trials, as well as others that are promising but not ready and so represent a developmental priority. We conclude with an example that illustrates the utility of biomarkers in recovery and rehabilitation research, demonstrating how the inclusion of a biomarker may enhance future clinical trials. In this way, we propose a way forward for when and where we can include biomarkers to advance the efficacy of the practice of, and research into, rehabilitation and recovery after stroke.
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.
•Structural connectivity appears important to language and disorders of language•We asked whether we could use connectivity variables to predict language outcomesafter stroke more accurately•We found no consistent evidence of any predictive advantage for any language outcome
Contemporary models of the neural system that supports reading propose that activity in a ventral occipitotemporal area (vOT) drives activity in higher-order language areas, for example, those in the ...posterior superior temporal sulcus (pSTS) and anterior superior temporal sulcus (aSTS). We used fMRI with dynamic causal modeling (DCM) to investigate evidence for other routes from visual cortex to the left temporal lobe language areas. First we identified activations in posterior inferior occipital (iO) and vOT areas that were more activated for silent reading than listening to words and sentences; and in pSTS and aSTS areas that were commonly activated for reading relative to false-fonts and listening to words relative to reversed words. Second, in three different DCM analyses, we tested whether visual processing of words modulates activity from the following: (1) iO→vOT, iO→pSTS, both, or neither; (2) vOT→pSTS, iO→pSTS, both or neither; and (3) pSTS→aSTS, vOT→aSTS, both, or neither. We found that reading words increased connectivity (1) from iO to both pSTS and vOT; (2) to pSTS from both iO and vOT; and (3) to aSTS from both vOT and pSTS. These results highlight three potential processing streams in the occipitotemporal cortex: iO→pSTS→aSTS; iO→vOT→aSTS; and iO→vOT→pSTS→aSTS. We discuss these results in terms of cognitive models of reading and propose that efficient reading relies on the integrity of all these pathways.
The left ventral occipito-temporal cortex (LvOT) is thought to be essential for the rapid parallel letter processing that is required for skilled reading. Here we investigate whether rapid written ...word identification in skilled readers can be supported by neural pathways that do not involve LvOT. Hypotheses were derived from a stroke patient who acquired dyslexia following extensive LvOT damage. The patient followed a reading trajectory typical of that associated with pure alexia, re-gaining the ability to read aloud many words with declining performance as the length of words increased. Using functional MRI and dynamic causal modelling (DCM), we found that, when short (three to five letter) familiar words were read successfully, visual inputs to the patient’s occipital cortex were connected to left motor and premotor regions via activity in a central part of the left superior temporal sulcus (STS). The patient analysis therefore implied a left hemisphere “reading-without-LvOT” pathway that involved STS. We then investigated whether the same reading-without-LvOT pathway could be identified in 29 skilled readers and whether there was inter-subject variability in the degree to which skilled reading engaged LvOT. We found that functional connectivity in the reading-without-LvOT pathway was strongest in individuals who had the weakest functional connectivity in the LvOT pathway. This observation validates the findings of our patient’s case study. Our findings highlight the contribution of a left hemisphere reading pathway that is activated during the rapid identification of short familiar written words, particularly when LvOT is not involved. Preservation and use of this pathway may explain how patients are still able to read short words accurately when LvOT has been damaged.
► Word reading can succeed after damage to the left occipito-temporal cortex. ► This is enabled by an alternative pathway via the left superior temporal sulcus. ► Connectivity analysis demonstrated the existence of this pathway in normal readers. ► We hypothesise that this pathway integrates semantics with phonology. ► Our work stresses the importance of mapping alternative degenerate reading pathways.
Sleep is a physiological state necessary for memory processing, learning and brain plasticity. Patients with disorders of consciousness (DOC) show none or minimal sign of awareness of themselves or ...their environment but appear to have sleep-wake cycles. The aim of our study was to assess baseline circadian rhythms and sleep in patients with DOC; to optimize circadian rhythm using an intervention combining blue light, melatonin and caffeine, and to identify the impact of this intervention on brain function using event related potentials. We evaluated baseline circadian rhythms and sleep in 17 patients with DOC with 24-h polysomnography (PSG) and 4-hourly saliva melatonin measurements for 48 h. Ten of the 17 patients (5 female, age 30–71) were then treated for 5 weeks with melatonin each night and blue light and caffeine treatment in the mornings. Behavioral assessment of arousal and awareness Coma recovery scale-revised (CRS-R), 24-h polysomnography and 4-hourly saliva melatonin measurements, oddball mismatch negativity (MMN) and subject's own name (SON) experiments were performed twice at baseline and following intervention. Baseline sleep was abnormal in all patients. Cosinor analysis of saliva melatonin results revealed that averaged baseline % rhythmicity was low (
M
: 31%, Range: 13–66.4%,
SD
: 18.4). However, increase in % Melatonin Rhythm following intervention was statistically significant (
p
= 0.012). 7 patients showed improvement of CRS-R scores with intervention and this was statistically significant (
p
= 0.034). All the patients who had improvement of clinical scores also had statistically significant improvement of neurophysiological responses on MMN and SON experiments at group level (
p
= 0.001). Our study shows that sleep and circadian rhythms are severely deranged in DOC but optimization is possible with melatonin, caffeine and blue light treatment. Clinical and physiological parameters improved with this simple and inexpensive intervention. Optimization of sleep and circadian rhythms should be integrated into rehabilitation programs for people with DOC.
Functional imaging studies of brain damaged patients offer a unique opportunity to understand how sensorimotor and cognitive tasks can be carried out when parts of the neural system that support ...normal performance are no longer available. In addition to knowing which regions a patient activates, we also need to know how these regions interact with one another, and how these inter-regional interactions deviate from normal. Dynamic causal modeling (DCM) offers the opportunity to assess task-dependent interactions within a set of regions. Here we review its use in patients when the question of interest concerns the characterization of abnormal connectivity for a given pathology. We describe the currently available implementations of DCM for fMRI responses, varying from the deterministic bilinear models with one-state equation to the stochastic non-linear models with two-state equations. We also highlight the importance of the new Bayesian model selection and averaging tools that allow different plausible models to be compared at the single subject and group level. These procedures allow inferences to be made at different levels of model selection, from features (model families) to connectivity parameters. Following a critical review of previous DCM studies that investigated abnormal connectivity we propose a systematic procedure that will ensure more flexibility and efficiency when using DCM in patients. Finally, some practical and methodological issues crucial for interpreting or generalizing DCM findings in patients are discussed.
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