Investigating heart rate variability measures during pregnancy as predictors of postpartum depression and anxiety: an exploratory study.

Perinatal affective disorders are common, but standard screening measures reliant on subjective self-reports might not be sufficient to identify pregnant women at-risk for developing postpartum depression and anxiety. Lower heart rate variability (HRV) has been shown to be associated with affective disorders. The current exploratory study aimed to evaluate the predictive utility of late pregnancy HRV measurements of postpartum affective symptoms. A subset of participants from the BASIC study (Uppsala, Sweden) took part in a sub-study at pregnancy week 38 where HRV was measured before and after a mild stressor (n = 122). Outcome measures were 6-week postpartum depression and anxiety symptoms as quantified by the Edinburgh Postnatal Depression Scale (EPDS) and the Beck Anxiety Inventory (BAI). In total, 112 women were included in a depression outcome analysis and 106 women were included in an anxiety outcome analysis. Group comparisons indicated that lower pregnancy HRV was associated with depressive or anxious symptomatology at 6 weeks postpartum. Elastic net logistic regression analyses indicated that HRV indices alone were not predictive of postpartum depression or anxiety outcomes, but HRV indices were selected as predictors in a combined model with background and pregnancy variables. ROC curves for the combined models gave an area under the curve (AUC) of 0.93 for the depression outcome and an AUC of 0.83 for the anxiety outcome. HRV indices predictive of postpartum depression generally differed from those predictive of postpartum anxiety. HRV indices did not significantly improve prediction models comprised of psychological measures only in women with pregnancy depression or anxiety.

Oscillatory and non-oscillatory features of the magnetoencephalic sensorimotor rhythm in Parkinson's disease.

Parkinson's disease (PD) is associated with changes in neural activity in the sensorimotor alpha and beta bands. Using magnetoencephalography (MEG), we investigated the role of spontaneous neuronal activity within the somatosensory cortex in a large cohort of early- to mid-stage PD patients (N = 78) on Parkinsonian medication and age- and sex-matched healthy controls (N = 60) using source reconstructed resting-state MEG. We quantified features of the time series data in terms of oscillatory alpha power and central alpha frequency, beta power and central beta frequency, and 1/f broadband characteristics using power spectral density. Furthermore, we characterised transient oscillatory burst events in the mu-beta band time-domain signals. We examined the relationship between these signal features and the patients' disease state, symptom severity, age, sex, and cortical thickness. PD patients and healthy controls differed on PSD broadband characteristics, with PD patients showing a steeper 1/f exponential slope and higher 1/f offset. PD patients further showed a steeper age-related decrease in the burst rate. Out of all the signal features of the sensorimotor activity, the burst rate was associated with increased severity of bradykinesia, whereas the burst duration was associated with axial symptoms. Our study shows that general non-oscillatory features (broadband 1/f exponent and offset) of the sensorimotor signals are related to disease state and oscillatory burst rate scales with symptom severity in PD.

Electroencephalography findings in menstrually-related mood disorders: A critical review.

The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being luteal-phase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.

The Swedish National Facility for Magnetoencephalography Parkinson's disease dataset.

Parkinson's disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is an ongoing topic of investigation how the disease-related changes in brain function manifest in PD relate to clinical symptoms. We present The Swedish National Facility for Magnetoencephalography Parkinson's Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson's disease and contribute to diagnosis and treatment. The dataset contains whole-head magnetoencephalographic (MEG) recordings from 66 well-characterised PD patients on their regular dose of dopamine replacement therapy and 68 age- and sex-matched healthy controls. NatMEG-PD contains three-minute eyes-closed resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores. MEG data is rich in nature and can be used to explore numerous functional features. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.

Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol.

INTRODUCTION: Perinatal complications, such as perinatal depression and preterm birth, are major causes of morbidity and mortality for the mother and the child. Prediction of high risk can allow for early delivery of existing interventions for prevention. This ongoing study aims to use digital phenotyping data from the Mom2B smartphone application to develop models to predict women at high risk for mental and somatic complications. METHODS AND ANALYSIS: All Swedish-speaking women over 18 years, who are either pregnant or within 3 months postpartum are eligible to participate by downloading the Mom2B smartphone app. We aim to recruit at least 5000 participants with completed outcome measures. Throughout the pregnancy and within the first year postpartum, both active and passive data are collected via the app in an effort to establish a participant's digital phenotype. Active data collection consists of surveys related to participant background information, mental and physical health, lifestyle, and social circumstances, as well as voice recordings. Participants' general smartphone activity, geographical movement patterns, social media activity and cognitive patterns can be estimated through passive data collection from smartphone sensors and activity logs. The outcomes will be measured using surveys, such as the Edinburgh Postnatal Depression Scale, and through linkage to national registers, from where information on registered clinical diagnoses and received care, including prescribed medication, can be obtained. Advanced machine learning and deep learning techniques will be applied to these multimodal data in order to develop accurate algorithms for the prediction of perinatal depression and preterm birth. In this way, earlier intervention may be possible. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Swedish Ethical Review Authority (dnr: 2019/01170, with amendments), and the project fully fulfils the General Data Protection Regulation (GDPR) requirements. All participants provide consent to participate and can withdraw their participation at any time. Results from this project will be disseminated in international peer-reviewed journals and presented in relevant conferences.

Changes in emotion processing in early Parkinson's disease reflect disease progression.

OBJECTIVE: Parkinson's disease (PD) is a neurodegenerative disorder which can substantially affect nonmotor functions related to emotional processing. We aimed to examine the underlying differences in emotional processing in PD by comparing how early-stage PD patients recognize, rate, and react to facial, bodily, and vocal emotional stimuli to that of healthy controls (HC). METHOD: We compared emotion recognition, emotional rating bias, and emotional response range between a PD patient group (n = 33) and a HC group (n = 29). Pearson's correlations were conducted to evaluate the relationship between emotion processing measures and clinical outcome measures in each group. RESULTS: PD patients showed an enhanced emotion processing as compared to HC. They were overall more accurate than HC's at identifying correct emotions and furthermore showed an increase in emotional ratings and reactions to both positive and negative stimuli that scaled with increased symptom severity, thereby yielding significant correlations between clinical outcomes and emotional range in the PD patient group. CONCLUSION: Our results suggest that alterations in emotional processing reflect disease progression in early PD. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Neural correlates of impaired response inhibition in the antisaccade task in Parkinson's disease.

Deficits in response inhibition are a central feature of the highly prevalent dysexecutive syndrome found in Parkinson's disease (PD). Such deficits are related to a range of common clinically relevant symptoms including cognitive impairment as well as impulsive and compulsive behaviors. In this study, we explored the cortical dynamics underlying response inhibition during the mental preparation for the antisaccade task by recording magnetoencephalography (MEG) and eye-movements in 21 non-demented patients with early to mid-stage Parkinson's disease and 21 age-matched healthy control participants (HC). During the pre-stimulus preparatory period for antisaccades we observed: Taken together, the results indicate that alterations in pre-stimulus prefrontal alpha and beta activity hinder proactive response inhibition and in turn result in higher error rates and prolonged response latencies in PD.

Dopamine and fear memory formation in the human amygdala.

Learning which environmental cues that predict danger is crucial for survival and accomplished through Pavlovian fear conditioning. In humans and rodents alike, fear conditioning is amygdala-dependent and rests on similar neurocircuitry. Rodent studies have implicated a causative role for dopamine in the amygdala during fear memory formation, but the role of dopamine in aversive learning in humans is unclear. Here, we show dopamine release in the amygdala and striatum during fear learning in humans. Using simultaneous positron emission tomography and functional magnetic resonance imaging, we demonstrate that the amount of dopamine release is linked to strength of conditioned fear responses and linearly coupled to learning-induced activity in the amygdala. Thus, like in rodents, formation of amygdala-dependent fear memories in humans seems to be facilitated by endogenous dopamine release, supporting an evolutionary conserved neurochemical mechanism for aversive memory formation.