Biology subjects

Hope, T. M. H.

Publications and source records attributed to Hope, T. M. H..

3 recordsLinked to original sources

bioRxiv2024

Continuous lesion images drive more accurate predictions of outcomes after stroke than binary lesion images

Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by treating the post-stroke prognostic problem as a machine learning problem. Consistent with the observation that these impairments are caused by the brain damage that stroke survivors suffer, information concerning where and how much lesion damage they have suffered conveys useful prognostic information for these models. Much recent research has considered how best to encode this lesion information, to maximise its prognostic value. Here, we consider an encoding that, while not novel, has never before been formally examined in this context: continuous lesion images, which encode continuous evidence for the presence of a lesion, both within and beyond what might otherwise be considered the boundary of a binary lesion image. Current state of the art models employ information derived from binary lesion images. Here, we show that those models are significantly improved (i.e., with smaller Mean Squared Error between predicted and empirical outcome scores) when using continuous lesion images to predict a wide range of cognitive and language outcomes from a very large sample of stroke patients. We use further model comparisons to locate the predictive advantage to the provision of continuous lesion evidence beyond the boundary of binary lesion images. The continuous lesion images thus provide a straightforward way to incorporate details of both lesioned and non-lesioned tissue when predicting outcomes after stroke.

neuroscience↗

bioRxiv2021

Lesion site and therapy time predict responses to a therapy for anomia after stroke: a prognostic model development study

BACKGROUNDStroke 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. METHODS18 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. RESULTSSomewhat 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). DISCUSSIONOur 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.

neuroscience↗

bioRxiv2018

Not so proportional after all?

BackgroundThe proportional recovery rule asserts that most stroke survivors recover a fixed proportion of lost function. Reports that the rule can be used to predict recovery, extraordinarily accurately, are rapidly accumulating. Here, we show that the rule may not be as powerful as it seems.\n\nMethodsWe provide a formal analysis of the relationship between baseline scores (X), outcomes (Y) and recovery (Y-X), to highlight the shortcomings of the proportional recovery rule, and illustrate those problems with simulations in which synthetic recovery data are derived from different types of recovery processes.\n\nFindingsWhen the correlation between baseline scores and recovery is stronger than that between baselines scores and outcomes, the former can create an inflated impression of how predictable outcomes really are given baseline scores. This often happens when outcomes are less variable than baseline scores, as is common in empirical studies of recovery after stroke. Moreover, we cannot use the results of these correlations to distinguish proportional recovery from recovery which is either not consistently proportional, or not proportional at all.\n\nInterpretationAnalyses relating baseline scores to subsequent change are a minefield: our formal analysis applies as consistently outside the area of stroke as it does within it. One implication of our analysis is that the proportional recovery rule is not as predictive of real recovery after stroke as recent empirical studies suggest. Another is that different analytical methods will be required to ascertain whether recovery is even proportional at all.

neuroscience↗