Infralow frequencies and ultradian rhythms

A non-parametric model: free analysis of actigraphic recordings of acute insomnia patients

Both parametric and non-parametric approaches to time-series analysis have advantages and drawbacks. Parametric methods, although powerful and widely used, can yield inconsistent results due to the oversimplification of the observed phenomena. They require the setting of arbitrary constants for their creation and refinement, and, although these constants relate to assumptions about the observed systems, it can lead to erroneous results when treating a very complex problem with a sizable list of unknowns. Their non-parametric counterparts, instead, are more widely applicable but present a higher detrimental sensitivity to noise and low density in the data. For the case of approximately periodic phenomena, such as human actigraphic time series, parametric methods are widely used and concepts such as acrophase are key in chronobiology, especially when studying healthy and diseased human populations. In this work, we present a non-parametric method of analysis of actigraphic time series from insomniac patients and healthy age-matched controls. The method is fully data-driven, reproduces previous results in the context of activity offset delay and, crucially, extends the concept of acrophase not only to circadian but also for ultradian spectral components.

Sleepy Psychotherapists: How Clinicians' Biological Factors May Affect the Conduct of Psychotherapy

OBJECTIVE

Numerous therapist variables and cognitive biases can affect the quality of the therapeutic alliance and the conduct and outcomes of psychotherapy. This article aims to examine factors that potentially affect clinician performance, including chronobiological variables of clinicians and patients.

METHODS

The author reviewed literature pertaining to biological influences on human cognitive performance and considered how these factors may apply to the practice of psychotherapy.

RESULTS

Biological factors potentially affecting the conduct and quality of psychotherapy were identified. These factors include decision fatigue, hunger, sleep deficit, shift work, and several chronobiological issues related to circadian rhythms and episodic ultradian rhythms. In addition, misaligned scheduling of psychotherapy sessions in relation to therapist and patient evening-morning chronotypes may impede the effectiveness of psychotherapy.

CONCLUSIONS

The practice of psychotherapy is cognitively demanding, requiring that clinicians remain constantly alert and in command of their executive functions. Decreases in clinician alertness resulting from homeostatic depletion, chronobiologically misaligned schedules, and illness-associated factors may reduce the quality and benefit of psychotherapy sessions. Mitigation strategies are available. Investigations of these factors are needed.

Brain rhythm attractor breakdown in Alzheimer's disease: Functional and pathologic implications

This perspective binds emerging evidence on the bidirectional relationship between Alzheimer's disease (AD) and sleep disorders through a model of brain rhythm attractor breakdown. This approach explains behavioral-cognitive changes in AD across the sleep-wake cycle and supports a causal association between early brainstem tau pathology and subsequent cortical amyloid β accumulation. Specifically, early tau dysregulation within brainstem-hypothalamic nuclei leads to breakdown of sleep-wake attractor networks, with patients displaying an attenuated range of behavioral and electrophysiological activity patterns, a "twilight zone" of constant activity between deep rest and full alertness. This constant cortical activity promotes activity-dependent amyloid β accumulation in brain areas that modulate their activity across sleep-wake states, especially the medial prefrontal cortex. In addition, the accompanying breakdown of hippocampal-medial prefrontal cortex interplay across sleep stages could explain deficient memory consolidation through dysregulation of synaptic plasticity. Clinical implications include the potential therapeutic benefit of attractor consolidation (e.g., slow-wave sleep enhancers) in delaying AD progression.

Predicting epileptic seizures in advance

Epilepsy is the second most common neurological disorder, affecting 0.6-0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance.