Tedium vitae in stroke survivors: a comparative cross-sectional study

Objectives: Suicide is best studied by deconstructing the psychological experiences preceding suicidal death. We assessed the characteristics of tedium vitae (feeling tired of life) after first ever stroke in Nigerian survivors.

Methods: Using the Schedule for Clinical Assessment in Neuropsychiatry, tedium vitae was assessed in 130 stroke survivors attending rehabilitation in a large Nigerian university hospital. Global cognitive and executive dysfunctions were evaluated, respectively, using the Mini Mental State Examination and the modi?ed Indiana University Token test. All participants had their index stroke 3 to 24 months before recruitment into the study. We also examined a comparative group of 130 age, gender, and education matched apparently normal persons who were unrelated to the stroke survivors. Associations were explored using univariate and multivariate logistic regression analyses.

Results: Tedium vitae was experienced by 16 (12.3%) stroke survivors compared with 5 (3.9%) in the comparative group (O. R = 3.5, 95% C. I = 1.3–9.9, p = 0.018). Among stroke survivors, those who were retired were more likely to experience tedium vitae (56.2%, p = 0.045). In analyses adjusting for the effect of systemic hypertension, cognitive dysfunction, retirement and marital separation, there was a 3.5-fold increase in the odds of experiencing tedium vitae after surviving a stroke (O. R = 3.5, 95% C. I = 1.1–11.6, p = 0.042).

Conclusions: Tedium vitae is a common suicidal experience after stroke and may be among the earliest perceptible pointer to impending poststroke suicide. It is easy to assess and may be less costly to obtain an adequate sample size in studies aiming to understand the phenomenon of suicide in the stroke population.     

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.