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Biology subjects

Brickman, A.

Publications and source records attributed to Brickman, A..

2 recordsLinked to original sources

Deep structural brain lesions associated with consciousness impairment early after haemorrhagic stroke

BackgroundThe significance of deep structural lesions on level of consciousness early after intracerebral haemorrhage (ICH) is largely unknown.\n\nMethodsWe studied a consecutive series of patients with spontaneous ICH that underwent MRI within 7 days of the bleed. We assessed consciousness by testing for command following from time of MRI to hospital discharge, and determined 3-months functional outcomes using the Glasgow Outcome Scale-Extended (GOS-E). ICH and oedema volumes, intraventricular haemorrhage (IVH), and midline shift (MLS) were quantified. Presence of blood and oedema in deep brain regions previously implicated in consciousness were assessed. A machine learning approach using logistic regression with elastic net regularization was applied to identify parameters that best predicted consciousness at discharge controlling for confounders.\n\nResultsFrom 158 ICH patients that underwent MRI, 66% (N=105) were conscious and 34% (N=53) unconscious at the time of MRI. Almost half of unconscious patients (49%, N= 26) recovered consciousness by ICU discharge. Focal lesions within subcortical structures predicted persistent impairment of consciousness at discharge together with MLS, IVH, and ICH and oedema volumes (AUC 0.74; 95%-CI 0.73-0.75). Caudate nucleus, midbrain peduncle, and pontine tegmentum were implicated as critical structures. Unconscious patients predicted to recover consciousness had better 3-month functional outcomes than those predicted to remain unconscious (35% vs 0% GOS-E [≥]4; p-value=0.02).\n\nConclusionMRI lesions within key subcortical structures together with measures reflecting the mass effect of the haemorrhage (lesion volumes, IVH, MLS) obtained within one week of ICH can help predict early recovery of consciousness and 3-month functional outcome.

neuroscience

A common statistical misunderstanding in Psychology and Neuroscience: Do we need normally distributed independent or dependent variables for linear regression to work?

Our ability to draw conclusions from experiments often relies on the application of inferential statistics, such as those included within the general-linear-modeling framework. The purpose of this commentary is to direct attention a common fallacy that can be observed in paper reviews and professional exchanges in the field of Psychology and Neuroscience pertaining to multiple regression analysis. The fallacy concerns the confusion of the requirements on the residuals of a linear-regression model with requirements on the dependent or independent variables themselves. A simple thought experiment, algebraic demonstration, and simulation study can demonstrate this misconception. It is our hope that consideration of the fallacy may help clarify distinctions between technical statistical issues and conceptual interpretation of research findings.

scientific communication and education