Statistical analysis All statistical analysis was completed wi

.. Statistical analysis All statistical analysis was completed with SPSS v 16.0 (SPSS, Chicago, IL). Group comparisons were completed on each of the demographic variables and saccade variables using one-way analysis of variance (ANOVA). When the ANOVA assumption of equal variances was not met (e.g., prosaccade errors, antisaccade: error rates, corrections, fixations, Inhibitors,research,lifescience,medical and omissions), the Welch’s robust tests of equality was utilized (Welch 1947). To assess antisaccade error rates in milder levels of AD, additional analysis was conducted on the subgroup with MMSE scores >22 and another subgroup with MMSE scores >24. The first subgroup, with MMSE scores >22 was selected to provide a

direct comparison with the study of Boxer and colleagues (Boxer et al. 2006), while the second Inhibitors,research,lifescience,medical group (MMSE >24) was selected because an MMSE score of 24 is usually considered a cutoff point for dementia. Sensitivity, specificity, positive predictive value, and negative predictive

value were calculated to assess the diagnostic capacity of antisaccade errors, uncorrected errors, and JAK inhibitor fixation errors. Sensitivity and specificity calculation required binary classification of performance; therefore, antisaccade performance was categorized as impaired (two standard deviations above the normal controls [NC] mean) or unimpaired (under two standard deviations of the NC mean). The effect Inhibitors,research,lifescience,medical size of antisaccade error rates was calculated with Cohen’s d, (the mean difference in antisaccade errors between the two groups, divided by the pooled standard deviation) (Cohen 1998). Values derived from the Cohen’s Inhibitors,research,lifescience,medical d test are categorized into effect sizes that are small (0.2–0.5), medium (0.5–0.8), and large (≥0.8) (Cohen 1992). Results Demographic data for the 61 participants showed no significant baseline differences as summarized in Table 1. Performance metrics

are summarized in Table 2. Patients with AD not only made significantly more errors on both the prosaccade (F(1,47.6)= 4.76, P < 0.05) and antisaccade tasks (F(1,47.6)= 24.72, P < 0.001), but also left significantly more antisaccade errors uncorrected (F(1,29.5)= Inhibitors,research,lifescience,medical 22.3, P < 0.001). During the antisaccade task, patients made significantly more fixation errors (F(1,31.7)= Mephenoxalone 23.6, P < 0.01) and omission errors (F(1,31.4)= 8.1, P < 0.01) compared with controls. Both subgroups with MMSE scores >22 (F(1,31.6)= 18.24, P < 0.001) and MMSE scores >24 (F(1,22.3)= 14.5, P < 0.01) made significantly more antisaccade errors than NC. Table 2 Antisaccade performance. Sensitivity and specificity, and cutoff scores are outlined in Table 3. While all of the metrics provided specificities greater than 0.9, sensitivity was low, with uncorrected errors showing the highest sensitivity (sensitivity = 0.63). Prosaccade errors were not included in sensitivity and specificity as the amount of performance overlap between the two groups was large (Cohen’s d = 0.56). Table 3 Diagnostic capacity of antisaccade metrics.

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