Accuracy of Physiotherapist Predictions for Independent Walking After Stroke.

BACKGROUND: The use of prediction tools in stroke rehabilitation research and clinical practice is increasing, but it is not clear whether these prediction tools out-perform clinician predictions. OBJECTIVE: This study aimed to compare physiotherapist predictions for independent walking with the Time to Walking Independently after STroke (TWIST) prediction tool. METHODS: Adults with new lower limb weakness and unable to walk independently (Functional Ambulation Category [FAC] < 4) were recruited. At 1 week post-stroke, the treating physiotherapist was asked to predict whether their patient would achieve independent walking by 4, 6, 9, 12, 16, or 26 weeks, or remain dependent. Predictions were also made using the TWIST prediction tool, but not shared. Binary logistic regressions were conducted with the time independent walking was achieved as the dependent variable and independent variables were the physiotherapist and TWIST predictions. RESULTS: Ninety-one participants were included (median age 71 years, 36 [40%] female). Most participants (67 [74%]) were non-ambulatory (FAC = 0) at 1-week post-stroke. Thirty-seven physiotherapists were recruited. Physiotherapists made accurate predictions for time taken to achieve independent walking for 39 participants (43%). Prediction accuracy was not related to physiotherapist confidence or years of stroke-specific experience. TWIST out-performed physiotherapist predictions (Physiotherapists 76%-77%, TWIST 86%-88% accurate) for participants who achieved independent walking by 4, 6, and 9 weeks post-stroke. Accuracy of physiotherapist and TWIST predictions was similar for 16 and 26 weeks post-stroke. CONCLUSIONS: The TWIST prediction tool is more accurate than physiotherapists at predicting whether a patient will achieve independent walking by 4, 6, or 9 weeks post-stroke, but not for 16 or 26 weeks post-stroke. TWIST may be useful to inform early rehabilitation and discharge planning. Clinical Trial Registration-URL: www.anzctr.org.au Unique Identifier: ACTRN12617001434381.

Randomness impacts the building of specific priors, visual exploration, and perception in object recognition.

Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack a full understanding of the role visual information sampling plays in this process, and its relation to the individual's priors. To bridge this gap, the eye-movements of 18 adult participants were recorded during a free-viewing object-recognition task using Dots stimuli1. Participants viewed the stimuli in one of three orders: from most visible to least (Descending), least visible to most (Ascending), or in a randomised order (Random). This dictated the strength of their priors along the experiment. Visibility order influenced the participants' recognition performance and visual exploration. In addition, we found that while orders allowing for stronger priors generally led participants to visually sample more informative locations, this was not the case of Random participants. Indeed, they appeared to behave naïvely, and their use of specific object-related priors was fully impaired, while they maintained the ability to use general, task-related priors to guide their exploration. These findings have important implications for our understanding of perception, which appears to be influenced by complex cognitive processes, even at the basic level of visual sampling during object recognition.

Effects of expectation on face perception and its association with expertise.

Perceptual decisions are derived from the combination of priors and sensorial input. While priors are broadly understood to reflect experience/expertise developed over one's lifetime, the role of perceptual expertise at the individual level has seldom been directly explored. Here, we manipulate probabilistic information associated with a high and low expertise category (faces and cars respectively), while assessing individual level of expertise with each category. 67 participants learned the probabilistic association between a color cue and each target category (face/car) in a behavioural categorization task. Neural activity (EEG) was then recorded in a similar paradigm in the same participants featuring the previously learned contingencies without the explicit task. Behaviourally, perception of the higher expertise category (faces) was modulated by expectation. Specifically, we observed facilitatory and interference effects when targets were correctly or incorrectly expected, which were also associated with independently measured individual levels of face expertise. Multivariate pattern analysis of the EEG signal revealed clear effects of expectation from 100 ms post stimulus, with significant decoding of the neural response to expected vs. not stimuli, when viewing identical images. Latency of peak decoding when participants saw faces was directly associated with individual level facilitation effects in the behavioural task. The current results not only provide time sensitive evidence of expectation effects on early perception but highlight the role of higher-level expertise on forming priors.

A tale of two tumors: differential, but detrimental, effects of glioblastoma extracellular vesicles (EVs) on normal human brain cells.

Glioblastomas (GBMs) are dreadful brain tumors with abysmal survival outcomes. GBM EVs dramatically affect normal brain cells (largely astrocytes) constituting the tumor microenvironment (TME). EVs from different patient-derived GBM spheroids induced differential transcriptomic, secretomic, and proteomic effects on cultured astrocytes/brain tissue slices as GBM EV recipients. The net outcome of brain cell differential changes nonetheless converges on increased tumorigenicity. GBM spheroids and brain slices were derived from neurosurgical patient tissues following informed consent. Astrocytes were commercially obtained. EVs were isolated from conditioned culture media by ultrafiltration, ultraconcentration, and ultracentrifugation. EVs were characterized by nanoparticle tracking analysis, electron microscopy, biochemical markers, and proteomics. Astrocytes/brain tissues were treated with GBM EVs before downstream analyses. EVs from different GBMs induced brain cells to alter secretomes with pro-inflammatory or TME-modifying (proteolytic) effects. Astrocyte responses ranged from anti-viral gene/protein expression and cytokine release to altered extracellular signal-regulated protein kinase (ERK1/2) signaling pathways, and conditioned media from EV-treated cells increased GBM cell proliferation. Thus, astrocytes/brain slices treated with different GBM EVs underwent non-identical changes in various 'omics readouts and other assays, indicating "personalized" tumor-specific GBM EV effects on the TME. This raises concern regarding reliance on "model" systems as a sole basis for translational direction. Nonetheless, net downstream impacts from differential cellular and TME effects still led to increased tumorigenic capacities for the different GBMs.

Biomarkers of Motor Outcomes After Stroke.

Predicting motor outcomes after stroke based on clinical judgment alone is often inaccurate and can lead to inefficient and inequitable allocation of rehabilitation resources. Prediction tools are being developed so that clinicians can make evidence-based, accurate, and reproducible prognoses for individual patients. Biomarkers of corticospinal tract structure and function can improve prediction tool performance, particularly for patients with initially moderate to severe motor impairment. Being able to make accurate predictions for individual patients supports rehabilitation planning and communication with patients and families.