Mindfulness-based therapy for insomnia for older adults with sleep difficulties: a randomized clinical trial.

OBJECTIVE: Poor sleep is a modifiable risk factor for multiple disorders. Frontline treatments (e.g. cognitive-behavioral therapy for insomnia) have limitations, prompting a search for alternative approaches. Here, we compare manualized Mindfulness-Based Therapy for Insomnia (MBTI) with a Sleep Hygiene, Education, and Exercise Program (SHEEP) in improving subjective and objective sleep outcomes in older adults. METHODS: We conducted a single-site, parallel-arm trial, with blinded assessments collected at baseline, post-intervention and 6-months follow-up. We randomized 127 participants aged 50-80, with a Pittsburgh Sleep Quality Index (PSQI) score ⩾5, to either MBTI (n = 65) or SHEEP (n = 62), both 2 hr weekly group sessions lasting 8 weeks. Primary outcomes included PSQI and Insomnia Severity Index, and actigraphy- and polysomnography-measured sleep onset latency (SOL) and wake after sleep onset (WASO). RESULTS: Intention-to-treat analysis showed reductions in insomnia severity in both groups [MBTI: Cohen's effect size d = -1.27, 95% confidence interval (CI) -1.61 to -0.89; SHEEP: d = -0.69, 95% CI -0.96 to -0.43], with significantly greater improvement in MBTI. Sleep quality improved equivalently in both groups (MBTI: d = -1.19; SHEEP: d = -1.02). No significant interaction effects were observed in objective sleep measures. However, only MBTI had reduced WASOactigraphy (MBTI: d = -0.30; SHEEP: d = 0.02), SOLactigraphy (MBTI: d = -0.25; SHEEP: d = -0.09), and WASOPSG (MBTI: d = -0.26; SHEEP (d = -0.18). There was no change in SOLPSG. No participants withdrew because of adverse effects. CONCLUSIONS: MBTI is effective at improving subjective and objective sleep quality in older adults, and could be a valid alternative for persons who have failed or do not have access to standard frontline therapies.

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue.

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.

Modulating rest-break length induces differential recruitment of automatic and controlled attentional processes upon task reengagement.

Rest breaks are commonly administered as a countermeasure to reduce on-the-job fatigue, both physical and mental. However, this practice makes the assumption that recovery from fatigue, as measured by the reversal of performance declines, is the sole effect of taking a break on behavior. Here, through administering rest breaks of differing lengths in between blocks of a mentally demanding symbol decoding task, we show that this assumption may not be strictly true. First, we replicate previous work by showing that taking a longer break leads to two correlated effects: greater immediate rebound in performance, and greater subsequent time-on-task decline. Using fMRI, we reveal that time-on-task in this paradigm is associated with increasing recruitment of fronto-parietal areas associated with top-down control, and decreasing deactivation in the default-mode network. Finally, by analyzing individual differences, we reveal a potential neural basis for our behavioral observation: greater recovery following long breaks is associated with greater activity in the putamen, an area associated with the automatic generation of motor responses, followed by greater activity in left middle frontal gyrus by the end of those task periods. Taken together, this suggests a shift in the implicit engagement of automatic and controlled attentional processing following longer breaks. This shift may be undesirable or detrimental in real-world situations where maintaining a stable level of attention over time is necessary.

Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach.

The purpose of the study is to examine the effect of subliminal priming in terms of the perception of images influenced by words with positive, negative, and neutral emotional content, through electroencephalograms (EEGs). Participants were instructed to rate how much they like the stimuli images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words that exhibit positive, negative, and neutral connotations with respect to the images. Simultaneously, the EEGs were recorded. Statistical tests such as repeated measures ANOVAs and two-tailed paired-samples t-tests were performed to measure significant differences in the likability ratings among the three prime affect types; the results showed a strong shift in the likeness judgment for the images in the positively primed condition compared to the other two. The acquired EEGs were examined to assess the difference in brain activity associated with the three different conditions. The consistent results obtained confirmed the overall priming effect on participants' explicit ratings. In addition, machine learning algorithms such as support vector machines (SVMs), and AdaBoost classifiers were applied to infer the prime affect type from the ERPs. The highest classification rates of 95.0% and 70.0% obtained respectively for average-trial binary classifier and average-trial multi-class further emphasize that the ERPs encode information about the different kinds of primes.