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Background Fatigue is one of the most commonly reported symptoms post-stroke, which has a severe impact on the quality of life. Post-stroke fatigue is associated with reduced motor cortical excitability, specifically of the affected hemisphere. Objective The aim of this exploratory study was to assess whether fatigue symptoms can be reduced by increasing cortical excitability using anodal transcranial direct current stimulation (tDCS). Methods In this sham-controlled, double-blind intervention study, tDCS was applied bilaterally over the primary motor cortex in a single session in thirty stroke survivors with high severity of fatigue. A questionnaire-based measure of trait fatigue (primary outcome) was obtained before, after a week and 5 weeks post stimulation. Secondary outcome measures of state fatigue, motor cortex neurophysiology and perceived effort were also assessed pre, immediately post, a week and 5 weeks post stimulation. Results Anodal tDCS significantly improved fatigue symptoms a week after real stimulation when compared to sham stimulation. There was also a significant change in motor cortex neurophysiology of the affected hemisphere and perceived effort, a week after stimulation. The degree of improvement in fatigue was associated with baseline anxiety levels. Conclusion A single session of anodal tDCS improves fatigue symptoms with the effect lasting up to a week post stimulation. tDCS may therefore be a useful tool for managing fatigue symptoms post-stroke. Trial registration NCT04634864 Date of registration 17/11/2020–“retrospectively registered”.

Funder: Medical Research Council, Funder: The Stroke Association, Stroke is a leading cause of disability, and language impairments (aphasia) after stroke are both common and particularly feared. Most stroke survivors with aphasia exhibit anomia (difficulties with naming common objects), but while many therapeutic interventions for anomia have been proposed, treatment effects are typically much larger in some patients than others. Here, we asked whether that variation might be more systematic, and even predictable, than previously thought. 18 patients, each at least 6 months after left hemisphere stroke, engaged in a computerised treatment for their anomia over a 6-week period. Using only: (a) the patients’ initial accuracy when naming (to-be) trained items; (b) the hours of therapy that they devoted to the therapy; and (c) whole-brain lesion location data, derived from structural MRI; we developed Partial Least Squares regression models to predict the patients’ improvements on treated items, and tested them in cross-validation. Somewhat surprisingly, the best model included only lesion location data and the hours of therapy undertaken. In cross-validation, this model significantly out-performed the null model, in which the prediction for each patient was simply the mean treatment effect of the group. This model also made promisingly accurate predictions in absolute terms: the correlation between empirical and predicted treatment response was 0.62 (95% CI 0.27, 0.95). Our results indicate that individuals’ variation in response to anomia treatment are, at least somewhat, systematic and predictable, from the interaction between where and how much lesion damage they have suffered, and the time they devoted to the therapy.

It has been proposed that accurate motor control relies on Bayesian inference that integrates sensory input with prior contextual knowledge (Bays and Wolpert, 2007; Kording and Wolpert, 2004; Wolpert et al., 1995). Recent evidence has suggested that modulations in beta power (∼12–30 Hz) measured over sensorimotor cortices using electroencephalography (EEG) may represent parameters of Bayesian inference. While the well characterised post-movement beta synchronisation has been shown to correlate with prediction error (H. Tan, Jenkinson, & Brown, 2014; Huiling Tan, Wade, & Brown, 2016), recent evidence suggests that beta power may also represent uncertainty measures (Tan et al., 2016; Tzagarakis et al., 2015). The current study aimed to measure the neurophysiological correlates of uncertainty mediating Bayesian updating during a visuomotor adaptation paradigm in healthy human participants. In particular, sensory uncertainty was directly modulated to measure its effect on sensorimotor beta power. Participant's behaviour was modelled using the Hierarchical Gaussian Filter (HGF) in order to extract the latent variables involved in learning actions required by the task and correlate these with the measured EEG. We found that sensorimotor beta power correlated with inverse uncertainty afforded to sensory prediction errors both prior to and following a movement. This suggests that sensorimotor beta oscillations may more readily represent relative uncertainty within the sensorimotor system rather than error. Neurophysiological models describing the generation of beta oscillations offer a potential mechanism by which this neural signature may encode latent uncertainty parameters. This is essential for understanding how the brain controls behaviour.

While language impairment is the defining symptom of aphasia, the co-occurrence of non-language cognitive deficits and their importance in predicting rehabilitation and recovery outcomes is well documented. Despite this, people with aphasia (PWA) are rarely tested on assessments of higher order cognitive functions, making it difficult for studies to associate these functions with a consistent lesion correlate. Contrary to classic models of speech and language, cumulative evidence shows that Broca’s area and surrounding regions in the left inferior frontal cortex (LIFC) are involved in, but not specific to, speech production – suggesting that these regions may be involved in higher-level cognitive functions that support language production. A better understanding of language processing in the context of other domain general cognitive functions is essential for improving aphasia treatments.This study aimed to explore the brain-behaviour relationships between tests of individual cognitive skill and language abilities in people with post-stroke aphasia, with a focus on language production deficits and their associated lesion correlates. We predicted our analysis would reveal a latent (non-language specific) cognitive component, that would be driven by damage to LIFC.We analysed the behavioural and neural correlates of an extensive battery of language and non-language cognitive tests in a sample of thirty-six adults with long-term speech production deficits from post-stroke aphasia. All participants were anomic, with relatively intact speech comprehension and no apraxia of speech. The behavioural variables were analysed using Principal Component Analysis and their neural correlates were estimated using Voxel-Based Correlational Morphology. A significant number of anomic adults showed impaired performance on tests of non-language specific cognitive function. The variance underlying behavioural performance was best captured by four orthogonal components, two higher-order cognitive components (executive functions and verbal working memory) and two linguistic processing components (phonology and semantics). Brain-behaviour relationships revealed separable neural correlates for each component in line with previous studies and an executive functions correlate in the left inferior frontal cortex (LIFC).Our findings suggest that in adults with chronic post-stroke language production deficits (anomia), higher-level cognitive functions explain more of the variance in language function than classical models of the condition imply. Additionally, lesions to the LIFC, including Broca’s area, were associated with executive (dys)function, independent of language abilities, suggesting that lesions to this area are associated with non-language specific higher-level cognitive functions that support speech production. These findings support contemporary models of speech production that place language processing within the context of domain-general perception, action and conceptual knowledge.

BACKGROUND: Anomia, or difficulty naming common objects, is the most common, acquired impairment of language. Effective therapeutic interventions for anomia typically involve massed practice at high doses, requiring significant investment from patients and therapists. Aphasia researchers have increasingly looked to neurostimulation to accelerate these treatment effects, but the evidence behind this intervention is sparse and inconsistent.AIM: Here, we hypothesised that group-level neurostimulation effects might belie more systematic structure at the individual level. We sought to test the hypothesis by attempting to predict the immediate (online), individual-level behavioural effect of neurostimulation in patients with anomia.METHODS: 36 stroke patients, each with anomia at least 6 months post-onset, were asked to perform naming and judgement tasks both with concurrent neurostimulation and with sham stimulation. Using clinical, (pre-stimulation) behavioural and MRI data, and Partial Least Squares regression, we attempted to predict the effect of neurostimulation on accuracies and reaction times in both tasks. Model performance was assessed via cross-validation, and performances compared to that of a null model, which predicted the mean neurostimulation effects for all patients.RESULTS: Models derived from pre-stimulation data consistently out-performed the null model when predicting neurostimulation effects on accuracies and reaction times in both judgement and naming. Notably, we could predict declines in performance just as well as improvements.CONCLUSIONS: Inter-patient variation in online responses to neurostimulation is to some extent systematic and predictable. That declines in performance were just as predictable as improvements, implies that the effect of neurostimulation on performance in patients with anomia is unlikely to be a placebo. However, the online effect of the intervention appears to be just as likely to interfere with task performance, as it is to improve performance.

Persistent fatigue is a major debilitating symptom in many psychiatric and neurological conditions, including stroke. Post-stroke fatigue has been linked to low corticomotor excitability. Yet, it remains elusive as to what the neuronal mechanisms are that underlie motor cortex excitability and chronic persistence of fatigue. In this cross-sectional observational study, in two experiments we examined a total of 59 non-depressed stroke survivors with minimal motoric and cognitive impairments using ‘resting-state’ MRI and single- and paired-pulse transcranial magnetic stimulation. In the first session of Experiment 1, we assessed resting motor thresholds—a typical measure of cortical excitability—by applying transcranial magnetic stimulation to the primary motor cortex (M1) and measuring motor-evoked potentials in the hand affected by stroke. In the second session, we measured their brain activity with resting-state MRI to assess effective connectivity interactions at rest. In Experiment 2 we examined effective inter-hemispheric connectivity in an independent sample of patients using paired-pulse transcranial magnetic stimulation. We also assessed the levels of non-exercise induced, persistent fatigue using Fatigue Severity Scale (FSS-7), a self-report questionnaire that has been widely applied and validated across different conditions. We used spectral dynamic causal modelling in Experiment 1 and paired-pulse transcranial magnetic stimulation in Experiment 2 to characterize how neuronal effective connectivity relates to self-reported post-stroke fatigue. In a multiple regression analysis, we used the balance in inhibitory connectivity between homologue regions in M1 as the main predictor, and have included lesioned hemisphere, resting motor threshold and levels of depression as additional predictors. Our novel index of inter-hemispheric inhibition balance was a significant predictor of post-stroke fatigue in Experiment 1 (β = 1.524, P = 7.56 × 10−5, confidence interval: 0.921 to 2.127) and in Experiment 2 (β = 0.541, P = 0.049, confidence interval: 0.002 to 1.080). In Experiment 2, depression scores and corticospinal excitability, a measure associated with subjective fatigue, also significantly accounted for variability in fatigue. We suggest that the balance in inter-hemispheric inhibitory effects between primary motor regions can explain subjective post-stroke fatigue. Findings provide novel insights into neural mechanisms that underlie persistent fatigue.

The central and autonomic nervous systems can be defined by their anatomical, functional and neurochemical characteristics, but neither functions in isolation. For example, fundamental components of autonomically mediated homeostatic processes are afferent interoceptive signals reporting the internal state of the body and efferent signals acting on interoceptive feedback assimilated by the brain. Recent predictive coding (interoceptive inference) models formulate interoception in terms of embodied predictive processes that support emotion and selfhood. We propose interoception may serve as a way to investigate holistic nervous system function and dysfunction in disorders of brain, body and behaviour. We appeal to predictive coding and (active) interoceptive inference, to describe the homeostatic functions of the central and autonomic nervous systems. We do so by (i) reviewing the active inference formulation of interoceptive and autonomic function, (ii) survey clinical applications of this formulation and (iii) describe how it offers an integrative approach to human physiology; particularly, interactions between the central and peripheral nervous systems in health and disease.

Contains fulltext : 175110.pdf (Publisher’s version ) (Open Access) Research suggests that perception and imagination engage neuronal representations in the same visual areas. However, the underlying mechanisms that differentiate sensory perception from imagination remain unclear. Here, we examine the directed coupling (effective connectivity) between fronto-parietal and visual areas during perception and imagery. We found an increase in bottom-up coupling during perception relative to baseline and an increase in top-down coupling during both perception and imagery, with a much stronger increase during imagery. Modulation of the coupling from frontal to early visual areas was common to both perception and imagery. Furthermore, we show that the experienced vividness during imagery was selectively associated with increases in top-down connectivity to early visual cortex. These results highlight the importance of top-down processing in internally as well as externally driven visual experience. 9 p.

ObjectiveTo test the hypothesis that poststroke fatigue, a chronic, pathologic fatigue condition, is driven by altered effort perception.MethodsFifty-eight nondepressed, mildly impaired stroke survivors with varying severity of fatigue completed the study. Self-reported fatigue (trait and state), perceived effort (PE; explicit and implicit), and motor performance were measured in a handgrip task. Trait fatigue was measured with the Fatigue Severity Scale-7 and Neurologic Fatigue Index. State fatigue was measured with a visual analog scale (VAS). Length of hold at target force, overshoot above target force, and force variability in handgrip task were measures of motor performance. PE was measured with a VAS (explicit PE) and line length estimation, a novel implicit measure of PE.ResultsRegression analysis showed that 11.6% of variance in trait fatigue was explained by implicit PE (R = 0.34; p = 0.012). Greater fatigue was related to longer length of hold at target force (R = 0.421, p < 0.001). A backward regression showed that length of hold explained explicit PE in the 20% force condition (R = 0.306, p = 0.021) and length of hold and overshoot above target force explained explicit PE in the 40% (R = 0.399, p = 0.014 and 0.004) force condition. In the 60% force condition, greater explicit PE was explained by higher force variability (R = 0.315, p = 0.017). None of the correlations were significant for state fatigue.ConclusionTrait fatigue, but not state fatigue, correlating with measures of PE and motor performance, may suggest that altered perception may lead to high fatigue mediated by changes in motor performance. This finding furthers our mechanistic understanding of poststroke fatigue.

ObjectivePersistent post-stroke fatigue is a major debilitating condition that has been linked to low corticomotor excitability and aberrant attention, both phenomena that are associated with the inter-hemispheric inhibition balance in the brain. In this study, we examined the relationship between inter-hemispheric inhibitory effective connectivity, motor cortex excitability and chronic persistence of post-stroke fatigue.MethodsWe tested eighteen non-depressed stroke survivors with minimal motoric and cognitive impairments using spectral dynamic causal modelling (spDCM) of ‘resting state’ magnetic resonance imaging (rs-fMRI) and transcranial magnetic stimulation (TMS) measures of cortical excitability. We also assessed the levels of non-exercise induced, persistent fatigue using Fatigue Severity Scale (FSS) - a self-report questionnaire which has been widely applied and validated across different conditions. To understand neural effective connectivity mechanisms involved in fatigue and corticomotor excitability we examined the balance in inhibitory connectivity between homologue regions in M1, anterior insula, caudate and thalamus of the resting brain.ResultsInter-hemispheric inhibition balance between left and right M1 accounted for 67% of variability in the reported fatigue (R=.82, pConclusionOur findings suggest that the balance in inter-hemispheric effective connectivity between primary motor regions is involved in regulation of corticomotor excitability and could explain subjective post-stroke fatigue.

Contains fulltext : 157745.pdf (Publisher’s version ) (Open Access) In daily life, complex events are perceived in a causal manner, suggesting that the brain relies on predictive processes to model them. Within predictive coding theory, oscillatory beta-band activity has been linked to top-down predictive signals and gamma-band activity to bottom-up prediction errors. However, neurocognitive evidence for predictive coding outside lower-level sensory areas is scarce. We used magnetoencephalography to investigate neural activity during probability-dependent action perception in three areas pivotal for causal inference, superior temporal sulcus, temporoparietal junction and medial prefrontal cortex, using bowling action animations. Within this network, Granger-causal connectivity in the beta-band was found to be strongest for backward top-down connections and gamma for feed-forward bottom-up connections. Moreover, beta-band power in TPJ increased parametrically with the predictability of the action kinematics-outcome sequences. Conversely, gamma-band power in TPJ and MPFC increased with prediction error. These findings suggest that the brain utilizes predictive-coding-like computations for higher-order cognition such as perception of causal events. 8 p.

Contains fulltext : 143441.pdf (Publisher’s version ) (Closed access) Recent accounts of understanding goal-directed action underline the importance of a hierarchical predictive architecture. However, the neural implementation of such an architecture remains elusive. In the present study, we used functional neuroimaging to quantify brain activity associated with predicting physical movements, as they were modulated by conceptual-expectations regarding the purpose of the object involved in the action. Participants observed object-related actions preceded by a cue that generated both conceptual goal expectations and movement goal predictions. In 2 tasks, observers judged whether conceptual or movement goals matched or mismatched the cue. At the conceptual level, expected goals specifically recruited the posterior cingulate cortex, irrespectively of the task and the perceived movement goal. At the movement level, neural activation of the parieto-frontal circuit, including inferior frontal gyrus and the inferior parietal lobe, reflected unpredicted movement goals. Crucially, this movement prediction error was only present when the purpose of the involved object was expected. These findings provide neural evidence that prior conceptual expectations influence processing of physical movement goals and thereby support the hierarchical predictive account of action processing. 8 p.

This article offers a formal account of curiosity and insight in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how people attain insight and understanding using just a handful of observations, which are solicited through curious behavior. We use simulations of abstract rule learning and approximate Bayesian inference to show that minimizing (expected) variational free energy leads to active sampling of novel contingencies. This epistemic behavior closes explanatory gaps in generative models of the world, thereby reducing uncertainty and satisfying curiosity. We then move from epistemic learning to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries (i.e., invariances or rules) in their generative models. The ensuing Bayesian model reduction evinces mechanisms associated with sleep and has all the hallmarks of “aha” moments. This formulation moves toward a computational account of consciousness in the pre-Cartesian sense of sharable knowledge (i.e., con: “together”; scire: “to know”).

The main question that Firestone & Scholl (F&S) pose is whether “what and how we see is functionally independent from what and how we think, know, desire, act, and so forth” (sect. 2, para. 1). We synthesize a collection of concerns from an interdisciplinary set of coauthors regarding F&S's assumptions and appeals to intuition, resulting in their treatment of visual perception as context-free.

The pathophysiology of primary progressive aphasias remains poorly understood. Here, we addressed this issue using activation fMRI in a cohort of 27 patients with primary progressive aphasia (nonfluent, semantic, and logopenic variants) versus 15 healthy controls. Participants listened passively to sequences of spoken syllables in which we manipulated 3-key auditory speech signal characteristics: temporal regularity, phonemic spectral structure, and pitch sequence entropy. Relative to healthy controls, nonfluent variant patients showed reduced activation of medial Heschl's gyrus in response to any auditory stimulation and reduced activation of anterior cingulate to temporal irregularity. Semantic variant patients had relatively reduced activation of caudate and anterior cingulate in response to increased entropy. Logopenic variant patients showed reduced activation of posterior superior temporal cortex to phonemic spectral structure. Taken together, our findings suggest that impaired processing of core speech signal attributes may drive particular progressive aphasia syndromes and could index a generic physiological mechanism of reduced computational efficiency relevant to all these syndromes, with implications for development of new biomarkers and therapeutic interventions.

Highlights • Own mental states depend on inferred interoceptive states. • Understanding another’s mental states (ToM) also entails interoceptive predictions. • Multimodal expectations at deep hierarchical levels contribute to ToM., Inferring the intentions and beliefs of another is an ability that is fundamental for social and affiliative interactions. A substantial amount of empirical evidence suggests that making sense of another’s intentional and belief states (i.e. theory of mind) relies on exteroceptive (e.g. visual and auditory) and proprioceptive (i.e. motor) signals. Yet, despite its pivotal role in the guidance of behaviour, the role of the observer’s interoceptive (visceral) processing in understanding another’s internal states remains unexplored. Predicting and keeping track of interoceptive bodily states – which inform intentions and beliefs that guide behaviour – is one of the fundamental purposes of the human brain. In this paper, we will focus on the role of interoceptive predictions, prescribed by the free energy principle, in making sense of internal states that cause another’s behaviour. We will discuss how multimodal expectations induced at deep (high) hierarchical levels – that necessarily entail interoceptive predictions – contribute to inference about others that is at the heart of theory of mind.