Sensory burden, a momentary experience of being bothered by sensory stimuli, is a frequent challenge following acquired brain injury (ABI). This study quantitatively tested a theoretical model conceptualizing sensory burden as a dynamic interaction between situational triggers and an individual’s biopsychosocial resources using an experience sampling method.

41 individuals with ABI (median age = 59 years, median time since injury = 6.3 years) provided real-time data at seven semi-random intervals per day over seven consecutive days. Multilevel regression modeling assessed the influence of situational triggers (setting, company, effort, activity dissatisfaction, and negative affect) and individual resources (processing speed, fatigue, and sleep quality) on sensory burden.

Momentary fluctuations in sensory burden varied in severity and variability across individuals. Sensory burden was associated with higher levels of negative affect (β = .58, p < .01), activity dissatisfaction (β = .07, p < .01), effort (β = .09, p < .01), and being in company (β = .39, p < .01). Moreover, sensory burden was related to slower processing speed (β = −0.04, p = .02) and higher fatigue (β = .19, p < .01). However, no interaction effects were found. Effort was the only positive, significant between-person predictor (β = .56, p < .01).

These findings underscore the dynamic and individualized nature of sensory burden after ABI, emphasizing the need for personalized interventions targeting sensory hypersensitivity. Future research should explore additional triggers, resources, and causal pathways to further elucidate the proposed mechanisms and inform treatment development.

Post-stroke neurocognitive disorders are highly prevalent, yet screening tools that are fit for culturally diverse populations are scarce. This study evaluates the impact of cultural differences on the Oxford Cognitive Screen (OCS), a stroke-specific screening tool.

To evaluate cultural differences, we compared two populations with varying degrees of cultural diversity and Western, Educated, Industrialized, Rich and Democratic (WEIRD) characteristics. We adapted the Dutch OCS for Suriname through a multi-stage process. Using Bayesian hierarchical regression analysis, we compared 264 Surinamese participants, assessed with the adapted Dutch OCS, with 247 Belgian participants, assessed with the Dutch OCS, while controlling for age and education. We further investigated whether the associations of age and education with performance were comparable between the two populations.

Our findings revealed minimal differences in OCS performance between the Belgian and Surinamese populations. Both populations showed similar age-related decline and education-related improvement across all subtests, except for Picture naming, where the age-related decline was more pronounced in the Belgian population.

These findings suggest that with minimal adaptation, the OCS is a viable tool for screening post-stroke neurocognitive disorders in culturally diverse populations.

Clinical neuropsychology has been slow in adopting novelties in psychometrics, statistics, and technology. Researchers have indicated that the stationary nature of clinical neuropsychology endangers its evidence-based character. In addition to a technological crisis, there may be a statistical crisis affecting clinical neuropsychology. That is, the frequentist null hypothesis significance testing framework remains the dominant approach in clinical practice, despite a recent surge in critique on this framework. While the Bayesian framework has been put forward as a viable alternative in psychology in general, the possibilities it offers to clinical neuropsychology have not received much attention.

In the current position paper, we discuss and reflect on the value of Bayesian methods for the advancement of evidence-based clinical neuropsychology.

We aim to familiarize clinical neuropsychologists and neuropsychological researchers to Bayesian methods of inference and provide a clear rationale for why these methods are valuable for clinical neuropsychology.

We argue that Bayesian methods allow for a more intuitive answer to our diagnostic questions and form a more solid foundation for sequential and adaptive diagnostic testing, representing uncertainty about patients’ observed test scores and cognitive modeling of test results.

To diagnose egocentric neglect after stroke, the spatial bias of performance on cancellation tasks is typically compared to a single cutoff. This standard procedure relies on the assumption that the measurement error of cancellation performance does not depend on non-spatial impairments affecting the total number of cancelled targets. Here we assessed the impact of this assumption on false-positive diagnoses.

We estimated false positives by simulating cancellation data using a binomial model. Performance was summarised by the difference in left and right cancelled targets (R-L) and the Centre of Cancellation (CoC). Diagnosis was based on a fixed cutoff versus cutoffs adjusted for the total number of cancelled targets and on single test performance versus unanimous or proportional agreement across multiple tests. Finally, we compared the simulation findings to empirical cancellation data acquired from 651 stroke patients.

Using a fixed cutoff, the rate of false positives depended on the total number of cancelled targets and ranged from 10% to 30% for R-L scores and from 10% to 90% for CoC scores. The rate of false positives increased even further when diagnosis was based on proportional agreement across multiple tests. Adjusted cutoffs and unanimous agreement across multiple tests were effective at controlling false positives. For empirical data, fixed versus adjusted cutoffs differ in estimation of neglect prevalence by 13%, and this difference was largest for patients with non-spatial impairments.

Our findings demonstrate the importance of considering non-spatial impairments when diagnosing neglect based on cancellation performance.