Seasonal variation of spontaneous blink rate and beta EEG activity

Seasonality of brain function: role in psychiatric disorders

Seasonality patterns are reported in various psychiatric disorders. The current paper summarizes findings on brain adaptations associated with seasonal changes, factors that contribute to individual differences and their implications for psychiatric disorders. Changes in circadian rhythms are likely to prominently mediate these seasonal effects since light strongly entrains the internal clock modifying brain function. Inability of circadian rhythms to accommodate to seasonal changes might increase the risk for mood and behavior problems as well as worse clinical outcomes in psychiatric disorders. Understanding the mechanisms that account for inter-individual variations in seasonality is relevant to the development of individualized prevention and treatment for psychiatric disorders. Despite promising findings, seasonal effects are still understudied and only controlled as a covariate in most brain research. Rigorous neuroimaging studies with thoughtful experimental designs, powered sample sizes and high temporal resolution alongside deep characterization of the environment are needed to better understand the seasonal adaptions of the human brain as a function of age, sex, and geographic latitude and to investigate the mechanisms underlying the alterations in seasonal adaptation in psychiatric disorders.

Infradian Rhythms in Cerebrovascular Oxygenation and Blood Volume in Humans at Rest: A 5-Year Study

BACKGROUND

All parameters of human physiology show chronobiological variability. While circadian (cycle length ~ 24 h) rhythms of the neuronal, hemodynamic and metabolic aspects of human brain activity are increasingly being explored, infradian (cycle length > 24 h) rhythms are largely unexplored.

AIM

We investigated if cerebrovascular oxygen saturation (StO2) and blood volume ([tHb]) values measured over many years in many subjects during resting show infradian rhythmicity.

SUBJECTS AND METHODS

 Absolute StO2 and [tHb] values (median over a 5 min resting-phase while sitting) were measured in 220 healthy subjects (age: 24.7 ± 3.6 years, 87 males, 133 females) 2-4 times on different days over the right and left frontal lobe (FL) and occipital lobe (OL) by employing frequency-domain NIRS as part of different systemic physiology augmented functional near-infrared spectroscopy, SPA-fNIRS, studies. The data set consisted of 708 single measurements performed over a timespan of 5 years (2017-2021). General additive models (GAM) and cosinor modelling were used to analyze the data.

RESULTS

 The GAM analysis revealed (i) a non-linear trend in the StO2 and [tHb] values over the 5-year span, (ii) a circannual (cycle length ~ 12 months) rhythm in StO2 at the FL (amplitude (A): 3.4%, acrophase (φ): June) and OL (A: 1.5%, φ: May) as well as in [tHb] at the OL (A: 1.2 μM, bathyphase (θ): June), and (iii) a circasemiannual (cycle length ~ 6 months) rhythm in [tHb] at the FL (A: 2.7 μM, φ: March and September, respectively). Furthermore, the circannual oscillations of StO2 (at the FL) and [tHb] (at the OL) were statistically significantly correlated with the day length, outdoor temperature, humidity and air pressure.

DISCUSSION AND CONCLUSION

 We conclude that absolute values of StO2 and [tHb] show chronobiological variability on the group-level with a long-term nonlinear trend as well as circannual/circasemiannual rhythmicity. These rhythms need to be taken into account when defining reference values for StO2 and [tHb] and may correlate with the variability of cerebrovascular disease incidents over the year.

EEG-responses to mood induction interact with seasonality and age

The EEG is suggested as a potential diagnostic and prognostic biomarker for seasonal affective disorder (SAD). As a pre-clinical form of SAD, seasonality is operationalized as seasonal variation in mood, appetite, weight, sleep, energy, and socializing. Importantly, both EEG biomarkers and seasonality interact with age. Inducing sad mood to assess cognitive vulnerability was suggested to improve the predictive value of summer assessments for winter depression. However, no EEG studies have been conducted on induced sad mood in relation to seasonality, and no studies so far have controlled for age. We recorded EEG and calculated bandpower in 114 participants during rest and during induced sad mood in summer. Participants were grouped by age and based on a seasonality score as obtained with the seasonal pattern assessment questionnaire (SPAQ). Participants with high seasonality scores showed significantly larger changes in EEG power from rest to sad mood induction, specifically in the alpha frequency range (p = 0.027), compared to participants with low seasonality scores. Furthermore, seasonality interacted significantly with age (p < 0.001), with lower activity in individuals with high seasonality scores that were older than 50 years but the opposite pattern in individuals up to 50 years. Effects of sad mood induction on brain activity are related to seasonality and can therefore be consider as potential predicting biomarkers for SAD. Future studies should control for age as a confounding factor, and more studies are needed to elaborate on the characteristics of EEG biomarkers in participants above 50 years.

Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample

Induced by decreasing light, people affected by seasonal mood fluctuations may suffer from low energy, have low interest in activities, experience changes in weight, insomnia, difficulties in concentration, depression, and suicidal thoughts. Few studies have been conducted in search for biological predictors of seasonal mood fluctuations in the brain, such as EEG oscillations. A sample of 64 participants was examined with questionnaires and electroencephalography in summer. In winter, a follow-up survey was recorded and participants were grouped into those with at least mild (N = 18) and at least moderate (N = 11) mood decline and those without self-reported depressive symptoms both in summer and in winter (N = 46). A support vector machine was trained to predict mood decline by either EEG biomarkers alone, questionnaire data from baseline alone, or a combination of the two. Leave-one-out-cross validation with lasso regularization was used with logistic regression to fit a model. The accuracy for classification for at least mild/moderate mood decline was 77/82% for questionnaire data, 72/82% for EEG alone, and 81/86% for EEG combined with questionnaire data. Self-report data was more conclusive than EEG biomarkers recorded in summer for prediction of worsening of depressive symptoms in winter but it is advantageous to combine EEG with psychological assessment to boost predictive performance.

Spontaneous eye blink rate mediates the relationship between sleepiness and impulsivity to negative stimuli

Previous studies have demonstrated that sleep deprivation results in a negativity bias, especially in the context of impaired response inhibition. In the present study we investigated spontaneous eye blink rate (EBR), a correlate of dopamine function, as a mediator of the relationship between subjective sleepiness and impulsivity toward negative stimuli on a Go/NoGo task. Participants rated their sleepiness on a number of measures including the Epworth Sleepiness Scale (ESS), the Karolinska Sleepiness Scale (KSS) and subscales of the Chronic Sleep Reduction Questionnaire (CSRQ). The findings revealed that EBR mediated the relationship between sleepiness as measured by the Karolinska Sleepiness Scale (KSS) and commission errors on negatively valanced stimuli. These findings suggest that reduced inhibition in responding to negative stimuli can be found as a function of subjective sleepiness and that changes in dopamine function may be one contributing factor explaining this relationship.

Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG

OBJECTIVE

Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study.

METHODS

The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG^™ SubQ), and recorded from 23 to 230 days (median 42 days), with a median data capture rate of 75% (17.9 hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models.

RESULTS

sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods.

SIGNIFICANCE

The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.

Intra-subject consistency of spontaneous eye blink rate in young women across the menstrual cycle

The spontaneous eye blink rate (EBR) has been linked to different cognitive processes and neurobiological factors. It has also been proposed as a putative index for striatal dopaminergic function. While estradiol is well-known to increase dopamine levels through multiple mechanisms, no study up to date has investigated whether the EBR changes across the menstrual cycle. This question is imperative however, as women have sometimes been excluded from studies using the EBR due to potential effects of their hormonal profile. Fifty-four women were tested for spontaneous EBR at rest in three different phases of their menstrual cycle: during menses (low progesterone and estradiol), in the pre-ovulatory phase (when estradiol levels peak and progesterone is still low), and during the luteal phase (high progesterone and estradiol). No significant differences were observed across the menstrual cycle and Bayes factors show strong support for the null hypothesis. Instead, we observed high intra-individual consistency of the EBR in our female sample. Accordingly, we strongly encourage including female participants in EBR studies, regardless of their cycle phase.