The Effects of Aging on Conflict Detection

Several cognitive changes characterize normal aging; one change regards inhibitory processing and includes both conflict monitoring and response suppression. We attempted to segregate these two aspects within a Go/No-go task, investigating three age categories. Accuracy, response times and event-related potentials (ERPs) were recorded. The ERP data were analyzed, and the Go and No-go trials were separated; in addition, the trials were organized in repeat trials (in which the subjects repeated the action delivered in the previous trial) and switch trials (in which the subjects produced a response opposite to the previous response). We assumed that the switch trials conveyed more conflict than the repeat trials. In general, the behavioral data and slower P3 latencies confirmed the well-known age-related speed/accuracy trade-off. The novel analyses of the repeat vs. switch trials indicated that the age-related P3 slowing was significant only for the high conflict condition; the switch-P3 amplitude increased only in the two older groups. The ‘aging switch effect’ on the P3 component suggests a failure in the conflict conditions and likely contributes to a generalized dysfunction. The absence of either a switch effect in the young group and the P3 slowing in middle-aged group indicate that switching was not particularly demanding for these participants. The N2 component was less sensitive to the repeat/switch manipulation; however, the subtractive waves also enhanced the age effects in this earlier time window. The topographic maps showed other notable age effects: the frontal No-go N2 was nearly undetectable in the elderly; in the identical time window, a large activity in the posterior and prefrontal scalp regions was observed. Moreover, the prefrontal activity showed a negative correlation with false alarms. These results suggest that the frontal involvement during action suppression becomes progressively dysfunctional with aging, and additional activity was required to reach a good level of accuracy.     

Premature birth and circadian preference in young adulthood: evidence from two birth cohorts

A preference for eveningness (being a “night owl”) and preterm birth (<37 weeks of gestation) are associated with similar adversities, such as elevated blood pressure, impaired glucose regulation, poorer physical fitness, and lower mood. Yet, it remains unclear if and how preterm birth is associated with circadian preference. The aim of this study was to assess this association across the whole gestation range, using both objective and subjective measurements of circadian preference.

Circadian preference was measured among 594 young adults (mean age 24.3 years, SD 1.3) from two cohorts: the ESTER study and the Arvo Ylppö Longitudinal Study. We compared 83 participants born early preterm (<34 weeks) and 165 late preterm (34 to <37 weeks) with those born at term (≥37 weeks, n = 346). We also compared very low birth weight (VLBW, <1500 g) participants with term-born controls. We obtained objective sleep data with actigraphs that were worn for a mean period of 6.8 (SD 1.4) nights. Our primary outcome was sleep midpoint during weekdays and weekend. The sleep midpoint is the half-way time between falling asleep and waking up, and it represents sleep timing. We also investigated subjective chronotype with the Morningness–Eveningness Questionnaire (MEQ) in 688 (n = 138/221/329) ESTER participants. The MEQ consists of 19 questions, which estimates the respondent to be of a “morning”, “evening,” or “intermediate” chronotype, based on the Morningness–Eveningness Score (MES). We analyzed the data from the actigraphs and the MES with three linear regression models, and analyzed distribution of the chronotype class with Pearson χ2.

There were no consistent differences across the study groups in sleep midpoint. As compared with those born at term, the mean differences in minutes:seconds and 95% confidence intervals for the sleep midpoint were: early preterm weekdays 11:47 (?8:34 to 32:08), early preterm weekend 4:14 (?19:45 to 28:13), late preterm weekdays ?10:28 (?26:16 to 5:21), and late preterm weekend ?1:29 (?20:36 to 17:37). There was no difference in sleep timing between VLBW-participants and controls either. The distribution of chronotype in the MEQ among all participants was 12.4% morningness, 65.4% intermediate, and 22.2% eveningness. The distribution of the subjective chronotype class did not differ between the three gestational age groups (p = 0.98). The linear regression models did not show any influence of gestational age group or VLBW status on the MES (all p > 0.5).

We found no consistent differences between adults born early or late preterm and those born at term in circadian preference. The earlier circadian preference previously observed in those born smallest is unlikely to extend across the whole range of preterm birth.     

Automated fluorescent in situ hybridization (FISH) analysis of t(9;22)(q34;q11) in interphase nuclei.

BACKGROUND: For chronic myeloid leukemia, the FISH detection of t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes is an alternative method to bone marrow karyotyping for monitoring treatment. With automation, several drawbacks of manual analysis may be circumvented. In this article, the capabilities of a commercially available automated image acquisition and analysis system were determined by detecting t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes. METHODS: Three peripheral blood samples of normal adults, 21 samples of CML patients, and one sample of a t(9;22)(q34;q11) positive cell-line were used. RESULTS: Single nuclei with correctly detected signals amounted to 99.6% of nuclei analyzed after exclusion of overlapping nuclei and nuclei with incorrect signal detection. A cut-off value of 0.84 mum was defined to discriminate between translocation positive and negative nuclei based on the shortest distance between signals. Using this value, the false positive rate of the automated analysis for negative samples was 7.0%, whereas that of the manual analysis was 5.8%. Automated and manual results showed strong correlation (R(2) = 0.985), the mean difference of results was only 3.7%. CONCLUSIONS: A reliable and objective automated analysis of large numbers of cells is possible, avoiding interobserver variability and producing statistically more accurate results than manual evaluation.