Measuring and diagnosing unilateral neglect: a standardized statistical procedure

Unilateral neglect is usually investigated by adminstering stimuli (targets) in different positions, with targets being responded to by the patient (Hit) or omitted. In spite of this homogeneity of data type, neglect indices and diagnostic criteria vary considerably, causing inconsistencies in both clinical and experimental settings. We aimed at deriving a standard analysis which would apply to all tasks sharing this data form.

A-priori theoretical reasoning demonstrated that the mean position of Hits in space (MPH) is an optimal index for correctly diagnosing and quantifying neglect. Crucially MPH eliminates the confounding effects of deficits that are different from neglect (non-lateral) but which decrease Hit rate. We ran a Monte Carlo study to assess MPH's (so far overlooked) statistical behavior as a function of numbers of targets and Hits.

While average MPH was indeed insensitive to non-lateral deficits, MPH's variance (like that of all other neglect indices) increased dramatically with increasing non-lateral deficits. This instability would lead to alarmingly high false-positive rates (FPRs) when applying a classical diagnostic procedure that compares one patient with a control sample. We solved the problem by developing an equation that takes into account MPH instability and provides correct cut-offs and close-to-nominal FPRs, even without control subjects. We developed a computerized program which, given the raw data, yields the MPH, a z-score and a p-value.

We provided a standard method that allows clinical and experimental neuropsychologists to diagnose and measure neglect in a consistent way across the vast majority of tasks.