Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention

Post-stroke fatigue severity is associated with executive dysfunction in chronic stroke

Following stroke, fatigue is highly prevalent and managing fatigue is consistently rated a key unmet need by stroke survivors and professionals. Domain-specific cognitive impairments have been associated with greater fatigue severity in earlier stages of stroke recovery, but it is unclear whether these associations hold in chronic (>2 years) stroke. The present cross-sectional observational study evaluates the relationship between domain-specific cognitive functioning and the severity of self-reported fatigue among chronic stroke survivors. Participants (N = 105; mean age = 72.92, 41.90% female; mean years post-stroke = 4.57) were assessed in domains of attention (Hearts Cancellation test), language (Boston Naming Test), episodic memory (Logical Memory Test), working memory (Digit Span Backwards task), and executive functioning (set-shifting: Trail Making Test, Part B), as part of the OX-CHRONIC study, a longitudinal stroke cohort. Fatigue was assessed using the Fatigue Severity Scale. In a multiple linear regression analysis inclusive of above cognitive domains, only poorer executive functioning was associated with increased fatigue severity. This provides insight into the cognitive impairment profile of post-stroke fatigue long-term after stroke, with executive functioning deficits as the key hallmark.

The impact of post-stroke fatigue on inpatient rehabilitation outcomes: An observational study

BACKGROUND

Post-stroke fatigue is a typical complication following stroke. However, existing research primarily focused on its underlying mechanisms, and its impact on rehabilitation outcomes has yet to be uncovered.

OBJECTIVE

This study aims to explore the impact of post-stroke fatigue on rehabilitation outcomes during hospitalization.

METHOD

This was a prospective multicenter observational study including 46 stroke patients receiving comprehensive rehabilitation treatment. Patients' basic information was recorded upon admission and patients' functional independence was assessed with Functional Independence Measure (FIM) both upon admission and discharge. One week after rehabilitation treatment, fatigue, positivity in daily activity, attention, and memory were assessed. Serum biochemical indicators and levels of C-reactive protein (CRP) were assessed weekly following admission. The pain scores were assessed during the first week of hospitalization to calculate the average. Correlation analysis, linear regression and propensity score matching (PSM) were used to analyze the impact of fatigue on FIM scores at discharge and length of hospital stay.

RESULT

The proportion of patients with low fatigue was 39.13% and significant improvement was revealed in FIM scores upon admissions and discharge [(50.67±18.61) vs. (75.13±21.04), P<0.05]. Positivity in daily activity, attention, and age are factors that influence post-stroke fatigue. After PSM, low-fatigue group (Fatigue score< 3) showed significant higher motor function independence at discharge [(54.39 ± 15.42) vs. (41.89 ± 14.90), P<0.05] and shorter hospital stay [(28.54±9.13)d vs. (37.32 ± 9.81)d, P<0.05] than high-fatigue group. There was a significant difference (P<0.05) in level of CRP between the first inpatient week and the third week, with declining trend.

CONCLUSION

Post-stroke fatigue can affect the rehabilitation outcomes regarding motor function independence and length of hospital stay.

Influence of Perceptual Load on Attentional Orienting in Post-Stroke Fatigue: A Study of Auditory Evoked Potentials

OBJECTIVE

Increasing perceptual load alters behavioral outcomes in post-stroke fatigue (PSF). While the effect of perceptual load on top-down attentional processing is known, here we investigate if increasing perceptual load modulates bottom-up attentional processing in a fatigue dependent manner.

METHODS

In this cross-sectional observational study, in 29 first-time stroke survivors with no clinical depression, an auditory oddball task consisting of target, standard, and novel tones was performed in conditions of low and high perceptual load. Electroencephalography was used to measure auditory evoked potentials. Perceived effort was rated using the visual analog scale at regular intervals during the experiment. Fatigue was measured using the fatigue severity scale. The effect of fatigue and perceptual load on behavior (response time, accuracy, and effort rating) and auditory evoked potentials (amplitude and latency) was examined using mixed model ananlysis of variances (ANOVA).

RESULTS

Response time was prolonged with greater perceptual load and fatigue. There was no effect of load or fatigue on accuracy. Greater effort was reported with higher perceptual load both in high and low fatigue. p300a amplitude of auditory evoked potentials (AEP) for novel stimuli was attenuated in high fatigue with increasing load when compared to low fatigue. Latency of p300a was longer in low fatigue with increasing load when compared to high fatigue. There were no effects on p300b components, with smaller N100 in high load conditions.

INTERPRETATION

High fatigue specific modulation of p300a component of AEP with increasing load is indicative of distractor driven alteration in orienting response, suggestive of compromise in bottom-up selective attention in PSF.

Lesioned hemisphere-specific phenotypes of post-stroke fatigue emerge from motor and mood characteristics in chronic stroke

BACKGROUND AND PURPOSE

Post-stroke fatigue commonly presents alongside several comorbidities. The interaction between comorbidities and their relationship to fatigue is not known. In this study, we focus on physical and mood comorbidities, alongside lesion characteristics. We predict the emergence of distinct fatigue phenotypes with distinguishable physical and mood characteristics.

METHODS

In this cross-sectional observational study, in 94 first time, non-depressed, moderate to minimally impaired chronic stroke survivors, the relationship between measures of motor function (grip strength, nine-hole peg test time), motor cortical excitability (resting motor threshold), Hospital Anxiety and Depression Scale and Fatigue Severity Scale-7 (FSS-7) scores, age, gender and side of stroke was established using Spearman's rank correlation. Mood and motor variables were then entered into a k-means clustering algorithm to identify the number of unique clusters, if any. Post hoc pairwise comparisons followed by corrections for multiple comparisons were performed to characterize differences among clusters in the variables included in k-means clustering.

RESULTS

Clustering analysis revealed a four-cluster model to be the best model (average silhouette score of 0.311). There was no significant difference in FSS-7 scores among the four high-fatigue clusters. Two clusters consisted of only left-hemisphere strokes, and the remaining two were exclusively right-hemisphere strokes. Factors that differentiated hemisphere-specific clusters were the level of depressive symptoms and anxiety. Motor characteristics distinguished the low-depressive left-hemisphere from the right-hemisphere clusters.

CONCLUSION

The significant differences in side of stroke and the differential relationship between mood and motor function in the four clusters reveal the heterogenous nature of post-stroke fatigue, which is amenable to categorization. Such categorization is critical to an understanding of the interactions between post-stroke fatigue and its presenting comorbid deficits, with significant implications for the development of context-/category-specific interventions.

Mechanisms of Post-Stroke Fatigue: A Follow-Up From the Third Stroke Recovery and Rehabilitation Roundtable

BACKGROUND

Post-stroke fatigue (PSF) is a significant and highly prevalent symptom, whose mechanisms are poorly understood. The third Stroke Recovery and Rehabilitation Roundtable paper on PSF focussed primarily on defining and measuring PSF while mechanisms were briefly discussed. This companion paper to the main paper is aimed at elaborating possible mechanisms of PSF.

METHODS

This paper reviews the available evidence that potentially explains the pathophysiology of PSF and draws parallels from fatigue literature in other conditions. We start by proposing a case for phenotyping PSF based on structural, functional, and behavioral characteristics of PSF. This is followed by discussion of a potentially significant role of early inflammation in the development of fatigue, specifically the impact of low-grade inflammation and its long-term systemic effects resulting in PSF. Of the many neurotransmitter systems in the brain, the dopaminergic systems have the most evidence for a role in PSF, along with a role in sensorimotor processing. Sensorimotor neural network dynamics are compromised as highlighted by evidence from both neurostimulation and neuromodulation studies. The double-edged sword effect of exercise on PSF provides further insight into how PSF might emerge and the importance of carefully titrating interventional paradigms.

CONCLUSION

The paper concludes by synthesizing the presented evidence into a unifying model of fatigue which distinguishes between factors that pre-dispose, precipitate, and perpetuate PSF. This framework will help guide new research into the biological mechanisms of PSF which is a necessary prerequisite for developing treatments to mitigate the debilitating effects of post-stroke fatigue.

Is there evidence for a relationship between cognitive impairment and fatigue after acquired brain injury: a systematic review and meta-analysis

PURPOSE

Fatigue is a major symptom of ABI. Greater fatigue is associated with cognitive impairment. Our aim was to systematically review, describe and analyse the literature on the extent of this relationship.

METHODS

Five databases were searched from inception. Studies were included where: participants had a defined clinical diagnosis of ABI which included TBI, stroke or subarachnoid haemorrhage; a fatigue measure was included; at least one objective cognitive measure was used. Three reviewers individually identified studies and determined quality using the Quality Assessment Tool for Observational Cohort and Cross-sectional Studies.

RESULTS

Sixteen of the 412 identified studies, investigating the relationship between cognitive dysfunction and fatigue, comprising a total of 1,745 participants, were included. Quality ranged from fair to good. Meta-analysis found fatigue was significantly associated with an overall pattern of cognitive slowing on tasks of sustained attention. A narrative synthesis found weak associations with fatigue and information processing, attention, memory and executive function.

CONCLUSION

Analysis found sustained attentional performance had stronger associations with fatigue after ABI. Whereas, weak associations were found between fatigue and information processing, attention and to some extent memory and executive function. More focused research on specific cognitive domains is needed to understand the mechanisms of fatigue.

Introspective inference counteracts perceptual distortion

Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.

Interoceptive and metacognitive facets of fatigue in multiple sclerosis

Numerous disorders are characterised by fatigue as a highly disabling symptom. Fatigue plays a particularly important clinical role in multiple sclerosis (MS) where it exerts a profound impact on quality of life. Recent concepts of fatigue grounded in computational theories of brain-body interactions emphasise the role of interoception and metacognition in the pathogenesis of fatigue. So far, however, for MS, empirical data on interoception and metacognition are scarce. This study examined interoception and (exteroceptive) metacognition in a sample of 71 persons with a diagnosis of MS. Interoception was assessed by prespecified subscales of a standard questionnaire (Multidimensional Assessment of Interoceptive Awareness [MAIA]), while metacognition was investigated with computational models of choice and confidence data from a visual discrimination paradigm. Additionally, autonomic function was examined by several physiological measurements. Several hypotheses were tested based on a preregistered analysis plan. In brief, we found the predicted association of interoceptive awareness with fatigue (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep.

Elucidating medial temporal and frontal lobe contributions to approach-avoidance conflict decision-making using functional MRI and the hierarchical drift diffusion model

The prefrontal cortex (PFC) has long been associated with arbitrating between approach and avoidance in the face of conflicting and uncertain motivational information, but recent work has also highlighted medial temporal lobe (MTL) involvement. It remains unclear, however, how the contributions of these regions differ in their resolution of conflict information and uncertainty. We designed an fMRI paradigm in which participants approached or avoided object pairs that differed by motivational conflict and outcome uncertainty (complete certainty vs. complete uncertainty). Behavioral data and decision-making parameters estimated using the hierarchical drift diffusion model revealed that participants' responding was driven by conflict rather than uncertainty. Our neural data suggest that PFC areas contribute to cognitive control during approach-avoidance conflict by potentially adjusting response caution and the strength of evidence generated towards either choice, with differential involvement of anterior cingulate cortex and dorsolateral prefrontal cortex. The MTL, on the other hand, appears to contribute to evidence generation, with the hippocampus linked to evidence accumulation for stimuli. Although findings within perirhinal cortex were comparatively equivocal, some evidence suggests contributions to perceptual representations, particularly under conditions of threat. Our findings provide evidence that MTL and PFC regions may contribute uniquely to arbitrating approach-avoidance conflict.

Role of selective attention in fatigue in neurological disorders

BACKGROUND

Chronic fatigue is a significant symptom in several diseases including traumatic and degenerative neurological disorders. While several studies have investigated the correlates of chronic fatigue, there is as yet no unifying framework to explain chronic fatigue.

METHODS

In this narrative review, I investigate the role of selective attention in the development of chronic fatigue and discuss results within the framework of the sensory attenuation model of fatigue, which posits that fatigue is the phenomenological output of altered attention to sensory input. Following a short introduction of this framework, I present results from investigations that address attentional mechanisms in fatigue in multiple sclerosis, stroke, traumatic brain injury and Parkinson's disease.

RESULTS

Attention was quantified in all four disease models using a variety of outcome measures, including behavioural, neurophysiological, structural and functional brain connectivity. The range of measures precluded direct comparison of results across disease conditions; however, in all four disease models there was evidence of poor selective attention that explained levels of chronic fatigue, supporting the sensory attenuation model of fatigue as a disease-independent mechanism of fatigue. Evidence was lacking to draw any conclusions about the direction of causality.

CONCLUSION

The role of selective attention in development of fatigue is indicated. Future studies must focus on establishing causality and exploring attentional circuitry as a potential therapeutic target.

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

Computational Modeling of the n-Back Task in the ABCD Study: Associations of Drift Diffusion Model Parameters to Polygenic Scores of Mental Disorders and Cardiometabolic Diseases

BACKGROUND

Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with polygenic risk scores (PRSs) for mental disorders and comorbid cardiometabolic diseases.

METHODS

We used the drift diffusion model to infer latent computational processes underlying decision making and working memory during the n-back task in 3707 children ages 9 to 10 years from the Adolescent Brain Cognitive Development (ABCD) Study. Single nucleotide polymorphism-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated n-back task performance, and neurocognitive assessments. PRSs were calculated for Alzheimer's disease, bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia, and type 2 diabetes.

RESULTS

Heritability estimates of cognitive phenotypes ranged from 12% to 38%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRSs for CAD and schizophrenia. Longer nondecision time was associated with higher PRSs for Alzheimer's disease and lower PRSs for CAD. Narrower decision threshold was associated with higher PRSs for CAD. Load-dependent effects on nondecision time and decision threshold were associated with PRSs for Alzheimer's disease and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRSs for any of the mental or cardiometabolic phenotypes.

CONCLUSIONS

We identified distinct associations between computational cognitive processes and genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

No add-on effect of tDCS on fatigue and depression in chronic stroke patients: A randomized sham-controlled trial combining tDCS with computerized cognitive training

BACKGROUND

Fatigue and emotional distress rank high among self-reported unmet needs in life after stroke. Transcranial direct current stimulation (tDCS) may have the potential to alleviate these symptoms for some patients, but the acceptability and effects for chronic stroke survivors need to be explored in randomized controlled trials.

METHODS

Using a randomized sham-controlled parallel design, we evaluated whether six sessions of 1 mA tDCS (anodal over F3, cathodal over O2) combined with computerized cognitive training reduced self-reported symptoms of fatigue and depression. Among the 74 chronic stroke patients enrolled at baseline, 54 patients completed the intervention. Measures of fatigue and depression were collected at five time points spanning a 2 months period.

RESULTS

While symptoms of fatigue and depression were reduced during the course of the intervention, Bayesian analyses provided evidence for no added beneficial effect of tDCS. Less severe baseline symptoms were associated with higher performance improvement in select cognitive tasks, and study withdrawal was higher in patients with more fatigue and younger age. Time-resolved symptom analyses by a network approach suggested higher centrality of fatigue items (except item 1 and 2) than depression items.

CONCLUSION

The results reveal no add-on effect of tDCS on fatigue or depression but support the notion of fatigue as a relevant clinical symptom with possible implications for treatment adherence and response.

Post-stroke fatigue is associated with resting state posterior hypoactivity and prefrontal hyperactivity

BACKGROUND

Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown.

AIM

This observational study aimed to identify resting state brain activity markers of post-stroke fatigue.

METHOD

Sixty-three stroke survivors (22 women; age 30-89 years; mean 67.5 ± 13.4 years) from the Cognition And Neocortical Volume After Stroke study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischemic stroke, underwent brain imaging three months post-stroke, including a 7-minute resting state functional magnetic resonance imaging. We calculated the fractional amplitude of low-frequency fluctuations, which is measured at the whole-brain level and can detect altered spontaneous neural activity of specific regions.

RESULTS

Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. Fatigued compared to non-fatigued participants demonstrated significantly lower resting-state activity in the calcarine cortex (p < 0.001, cluster-corrected pFDR = 0.009, k = 63) and lingual gyrus (p < 0.001, cluster-corrected pFDR = 0.025, k = 42) and significantly higher activity in the medial prefrontal cortex (p < 0.001, cluster-corrected pFDR = 0.03, k = 45).

CONCLUSIONS

Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

Structural brain disconnectivity mapping of post-stroke fatigue

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We tested for associations between post stroke fatigue (PSF) and both lesion characteristics and brain structural disconnectome in 84 S patients.

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Results provided no evidence supporting a simple association between PSF severity and lesion characteristics or disconnectivity.

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PSF was strongly correlated with depression.

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Further studies including patients with more severe symptoms are needed to generalize the findings across a wider clinical spectrum.

Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remains unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a structural disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual structural brain disconnectome maps in 84 S survivors in the chronic phase (≥3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue among chronic stroke patients are not simply explained by lesion characteristics or the extent and distribution of structural brain disconnectome, and are discussed in light of methodological considerations.

How peer influence shapes value computation in moral decision-making

Moral behavior is susceptible to peer influence. How does information from peers influence moral preferences? We used drift-diffusion modeling to show that peer influence changes the value of moral behavior by prioritizing the choice attributes that align with peers' goals. Study 1 (N = 100; preregistered) showed that participants accurately inferred the goals of prosocial and antisocial peers when observing their moral decisions. In Study 2 (N = 68), participants made moral decisions before and after observing the decisions of a prosocial or antisocial peer. Peer observation caused participants' own preferences to resemble those of their peers. This peer influence effect on value computation manifested as an increased weight on choice attributes promoting the peers' goals that occurred independently from peer influence on initial choice bias. Participants' self-reported awareness of influence tracked more closely with computational measures of prosocial than antisocial influence. Our findings have implications for bolstering and blocking the effects of prosocial and antisocial influence on moral behavior.

Reliability, sensitivity, and predictive value of fMRI during multiple object tracking as a marker of cognitive training gain in combination with tDCS in stroke survivors

Computerized cognitive training (CCT) combined with transcranial direct current stimulation (tDCS) has showed some promise in alleviating cognitive impairments in patients with brain disorders, but the robustness and possible mechanisms are unclear. In this prospective double-blind randomized clinical trial, we investigated the feasibility and effectiveness of combining CCT and tDCS, and tested the predictive value of and training-related changes in fMRI-based brain activation during attentive performance (multiple object tracking) obtained at inclusion, before initiating training, and after the three-weeks intervention in chronic stroke patients (>6 months since hospital admission). Patients were randomized to one of two groups, receiving CCT and either (a) tDCS targeting left dorsolateral prefrontal cortex (1 mA), or (b) sham tDCS, with 40s active stimulation (1 mA) before fade out of the current. Of note, 77 patients were enrolled in the study, 54 completed the cognitive training, and 48 completed all training and MRI sessions. We found significant improvement in performance across all trained tasks, but no additional gain of tDCS. fMRI-based brain activation showed high reliability, and higher cognitive performance was associated with increased tracking-related activation in the dorsal attention network and default mode network as well as anterior cingulate after compared to before the intervention. We found no significant associations between cognitive gain and brain activation measured before training or in the difference in activation after intervention. Combined, these results show significant training effects on trained cognitive tasks in stroke survivors, with no clear evidence of additional gain of concurrent tDCS.

TVA-based modeling of short-term memory capacity, speed of processing and perceptual threshold in chronic stroke patients undergoing cognitive training: case-control differences, reliability, and associations with cognitive performance

Attentional deficits following stroke are common and pervasive, and are important predictors for functional recovery. Attentional functions comprise a set of specific cognitive processes allowing to attend, filter and select among a continuous stream of stimuli. These mechanisms are fundamental for more complex cognitive functions such as learning, planning and cognitive control, all crucial for daily functioning. The distributed functional neuroanatomy of these processes is a likely explanation for the high prevalence of attentional impairments following stroke, and underscores the importance of a clinical implementation of computational approaches allowing for sensitive and specific modeling of attentional sub-processes. The Theory of Visual Attention (TVA) offers a theoretical, computational, neuronal and practical framework to assess the efficiency of visual selection performance and parallel processing of multiple objects. Here, in order to assess the sensitivity and reliability of TVA parameters reflecting short-term memory capacity (K), processing speed (C) and perceptual threshold (t 0), we used a whole-report paradigm in a cross-sectional case-control comparison and across six repeated assessments over the course of a three-week computerized cognitive training (CCT) intervention in chronic stroke patients (> 6 months since hospital admission, NIHSS ≤ 7 at hospital discharge). Cross-sectional group comparisons documented lower short-term memory capacity, lower processing speed and higher perceptual threshold in patients (n = 70) compared to age-matched healthy controls (n = 140). Further, longitudinal analyses in stroke patients during the course of CCT (n = 54) revealed high reliability of the TVA parameters, and higher processing speed at baseline was associated with larger cognitive improvement after the intervention. The results support the feasibility, reliability and sensitivity of TVA-based assessment of attentional functions in chronic stroke patients.