Electroencephalographic Measures Research Articles

A Phase 1 Assessment of the Safety, Tolerability, Pharmacokinetics and Pharmacodynamics of (2R,6R)-Hydroxynorketamine in Healthy Volunteers.

(R,S)-Ketamine (ketamine) is a dissociative anesthetic that also possesses analgesic and antidepressant activity. Undesirable dissociative side effects and misuse potential limit expanded use of ketamine in several mental health disorders despite promising clinical activity and intensifying medical need. (2R,6R)-Hydroxynorketamine (RR-HNK) is a metabolite of ketamine that lacks anesthetic and dissociative activity but maintains antidepressant and analgesic activity in multiple preclinical models. To enable future assessments in selected human indications, we report the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of RR-HNK in a Phase 1 study in healthy volunteers (NCT04711005). A six-level single-ascending dose (SAD) (0.1-4 mg/kg) and a two-level multiple ascending dose (MAD) (1 and 2 mg/kg) study was performed using a 40-minute IV administration emulating the common practice for ketamine administration for depression. Safety assessments showed RR-HNK possessed a minimal adverse event profile and no serious adverse events at all doses examined. Evaluations of dissociation and sedation demonstrated that RR-HNK did not possess anesthetic or dissociative characteristics in the doses examined. RR-HNK PK parameters were measured in both the SAD and MAD studies and exhibited dose-proportional increases in exposure. Quantitative electroencephalography (EEG) measurements collected as a PD parameter based on preclinical findings and ketamine's established effect on gamma-power oscillations demonstrated increases of gamma power in some participants at the lower/mid-range doses examined. Cerebrospinal fluid examination confirmed RR-HNK exposure within the central nervous system (CNS). Collectively, these data demonstrate RR-HNK is well tolerated with an acceptable PK profile and promising PD outcomes to support the progression into Phase 2.

The Application of EEG Machine Learning in Schizophrenia

Electroencephalographic (EEG) measurements are widely employed in medical and research fields. This review shows the application of EEG machine learning in diagnosing and detecting schizophrenia (SZ), as well as gives the brief background, some diagnostic methods and treatment of SZ with EEG, the latest research findings due to the time of publication, and the advantages and disadvantages of EEG compared to other diagnoses.

Impact of Experimentally Induced Pain on Logical Reasoning and Underlying Attention-Related Psychophysiological Mechanisms

Background. Pain is known to negatively impact attention, but its influence on more complex cognitive abilities, such as logical reasoning, remains inconsistent. This may be due to compensatory mechanisms (e.g., investing additional resources), which might not be detectable at the behavioral level but can be observed through psychophysiological measures. In this study, we investigated whether experimentally induced pain affects logical reasoning and underlying attentional mechanisms, using both behavioral and electroencephalographic (EEG) measures. Methods. A total of 98 female participants were divided into a pain-free control group (N = 47) and a pain group (N = 51). Both groups completed the Advanced Progressive Matrices (APM) task, with EEG recordings capturing task-related power (TRP) changes in the upper alpha frequency band (10–12 Hz). We used a mixed design where all participants completed half of the APM task in a pain-free state (control condition); the second half was completed under pain induction by the pain group but not the pain-free group (experimental condition). Results. Logical reasoning performance, as measured by APM scores and response times, declined during the experimental condition, compared to the control condition for both groups, indicating that the second part of the APM was more difficult than the first part. However, no significant differences were found between the pain and pain-free groups, suggesting that pain did not impair cognitive performance at the behavioral level. In contrast, EEG measures revealed significant differences in upper alpha band power, particularly at fronto-central sites. In the pain group, the decrease in TRP during the experimental condition was significantly smaller compared to both the control condition and the pain-free group. Conclusions. Pain did not impair task performance at the behavioral level but reduced attentional resources, as reflected by changes in upper alpha band activity. This underscores the importance of incorporating more sensitive psychophysiological measures alongside behavioral measures to better understand the impact of pain on cognitive processes.

Nonictal electroencephalographic measures for the diagnosis of functional seizures.

Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians. The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age = 34.76 ± 10.55 years, 14 males) and 29 subjects with epilepsy (mean age = 38.95 ± 13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier. Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.

Electrical brain networks before and after transcranial pulsed shockwave stimulation in Alzheimer's patients.

Alzheimer's disease (AD) is a neurodegenerative disorder that dramatically affects cognitive abilities and represents the most common cause of dementia. Currently, pharmacological interventions represent the main treatment to deal with the symptoms of AD; however, alternative approaches are readily sought. Transcranial pulse stimulation (TPS) is an emerging non-invasive neuromodulation technique that uses short, repetitive shockwaves with the potential to provide a wide range of vascular, metabolic, and neurotrophic changes and that has recently been shown to improve cognitive abilities in AD. This exploratory study aims to gain insight into the neurophysiological effect of one session of TPS in AD as reflected in electroencephalographic measures, e.g., spectral power, coherence, Tsallis entropy (TE), and cross-frequency coupling (cfc). We document changes in power (frontal and occipital), coherence (frontal, occipital and temporal), and TE (temporal and frontal) as well as changes in cfc (parietal-frontal, parietal-temporal, frontal-temporal). Our results emphasize the role of electroencephalographic measures as prospective markers for the neurophysiological effect of TPS.

Alterations in aperiodic and periodic EEG activity in young children with Down syndrome

Down syndrome (DS) is the most common cause of intellectual disability, yet little is known about the neurobiological pathways leading to cognitive impairments. Electroencephalographic (EEG) measures are commonly used to study neurodevelopmental disorders, but few studies have focused on young children with DS. Here we assess resting state EEG data collected from toddlers/preschoolers with DS (n = 29, age 13–48 months old) and compare their aperiodic and periodic EEG features with both age-matched (n = 29) and developmental-matched (n = 58) comparison groups. DS participants exhibited significantly reduced aperiodic slope, increased periodic theta power, and decreased alpha peak amplitude. A majority of DS participants displayed a prominent peak in the theta range, whereas a theta peak was not present in age-matched participants. Overall, similar findings were also observed when comparing DS and developmental-matched groups, suggesting that EEG differences are not explained by delayed cognitive ability.

Electroencephalography Measurements in Awake Marmosets Listening to Conspecific Vocalizations.

Vocal communication plays a crucial role in the social interactions of primates, particularly in survival and social organization. Humans have developed a unique and advanced vocal communication strategy in the form of language. To study the evolution of human language, it is necessary to investigate the neural mechanisms underlying vocal processing in humans, as well as to understand how brain mechanisms have evolved by comparing them with those in nonhuman primates. Herein, we developed a method to noninvasively measure the electroencephalography (EEG) of awake nonhuman primates. This recording method allows for long-term studies without harming the animals, and, importantly, allows us to directly compare nonhuman primate EEG data with human data, providing insights into the evolution of human language. In the current study, we used the scalp EEG recording method to investigate brain activity in response to species-specific vocalizations in marmosets. This study provides novel insights by using scalp EEG to capture widespread neural representations in marmosets during vocal perception, filling gaps in existing knowledge.

Characterization of Cochlear Implant Artifact and Removal Based on Multi-Channel Wiener Filter in Unilateral Child Patients.

Cochlear implants (CI) allow deaf patients to improve language perception and improving their emotional valence assessment. Electroencephalographic (EEG) measures were employed so far to improve CI programming reliability and to evaluate listening effort in auditory tasks, which are particularly useful in conditions when subjective evaluations are scarcely appliable or reliable. Unfortunately, the presence of CI on the scalp introduces an electrical artifact coupled to EEG signals that masks physiological features recorded by electrodes close to the site of implant. Currently, methods for CI artifact removal have been developed for very specific EEG montages or protocols, while others require many scalp electrodes. In this study, we propose a method based on the Multi-channel Wiener filter (MWF) to overcome those shortcomings. Nine children with unilateral CI and nine age-matched normal hearing children (control) participated in the study. EEG data were acquired on a relatively low number of electrodes (n = 16) during resting condition and during an auditory task. The obtained results obtained allowed to characterize CI artifact on the affected electrode and to significantly reduce, if not remove it through MWF filtering. Moreover, the results indicate, by comparing the two sample populations, that the EEG data loss is minimal in CI users after filtering, and that data maintain EEG physiological characteristics.

EF‐CorrCA: A multi‐modal EEG‐fNIRS subject independent model to assess speech quality on brain activity using correlated component analysis

AbstractAn investigation on the effect of mental activity in quality perception is presented using simultaneous measurement of electroencephalography (EEG) and functional near‐infrared spectroscopy (fNIRS), in a subject‐independent approach. Building a subject‐independent model is a harder problem due to noise and high EEG variability between individuals, correlated components analysis (CorrCA) have been proposed to extract significant correlated components for a single subject that experiences multiple identical trials; this is done by identifying spatio‐temporal patterns of activity that are well preserved across trials. The aim is to build a model based on neurophysiological data to assess text‐to‐speech quality. In order to build a subject independent model, we extended the use of CorrCA such that it can be applied to the subject independent model. The authors used two preprocessing steps, namely the subject dependent and the stimulus dependent preprocessing. The second preprocessing used the denoising source separation (DSS) to remove noise/artefact that are subject specific. The discrete convolution is used for data fusion and the support vector machine for regression. With the proposed model, the fusion of EEG and fNIRS performs better than single modality. Using our defined regression accuracy metrics, the authors obtained accuracy of 81.346% for overall impression, 83.28% for valence and 89.714% for arousal. The model compete the baseline that is subject dependent.

Investigating Whether a Combination of Electro-Encephalography and Gene Expression Profiling Can Predict the Risk of Chronic Pain: A Protocol for an Observational Prospective Cohort Study.

Despite most episodes of low back pain (LBP) being short-lasting, some transition into persistent long-lasting problems. Hence, the need for a deeper understanding of the physiological mechanisms of this is pertinent. Therefore, the aims of the present study are (1) to map pain-induced changes in brain activity and blood gene expression associated with persistent LBP, and (2) to explore whether these brain and gene expression signatures show promise as predictive biomarkers for the development of persistent LBP. The participants will be allocated into three different pain groups (no pain, mild short-lasting, or moderate long-term). One in-person visit, where two blood samples will be collected and sent for RNA sequencing, along with resting 64-channel electro-encephalography measurements before, during, and after a cold pressor test, will be conducted. Thereafter, follow-up questionnaires will be distributed at 2 weeks, 3 months, and 6 months. Recruitment will start during the second quarter of 2024, with expected completion by the last quarter of 2024. The results are expected to provide insight into the relationship between central nervous system activity, gene expression profiles, and LBP. If successful, this study has the potential to provide physiological indicators that are sensitive to the transition from mild, short-term LBP to more problematic, long-term LBP.

Predicting brain edema and outcomes after thrombectomy in stroke: Frontal delta/alpha ratio as an optimal quantitative EEG index

ObjectiveWe aimed to determine whether quantitative electroencephalography (QEEG) measures have predictive value for cerebral edema (CED) and clinical outcomes in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion who underwent mechanical thrombectomy (MT). MethodsA total of 105 patients with AIS in the anterior circulation were enrolled in this prospective study. The occurrence and severity of CED were assessed through computed tomography conducted 24 h after MT. Clinical outcomes were evaluated based on early neurological deterioration (END) and 3-month functional status, as measured by the modified Rankin scale (mRS). Electroencephalography (EEG) recordings were performed 24 h after MT, and QEEG indices were calculated from the standard 16 electrodes and 2 frontal channels (F3-C3, F4-C4). The delta/alpha ratio (DAR), the (delta + theta) / (alpha + beta) ratio (DTABR), and relative delta power were averaged over all electrodes (global) and the F3-C3 and F4-C4 channels (frontal). The predictive effect and value of QEEG indices for CED and clinical outcomes were assessed using ordinal and logistic regression models, as well as receiver operating characteristic (ROC) curves. ResultsSignificantly, both global and frontal DAR were found to be associated with the severity of CED, END, and poor functional outcomes at 90 days, while global and frontal DTABR and relative delta power were not associated with outcomes. In ROC analysis, the best predictive effect was observed in frontal DAR, with an area under the curve of approximately 0.80. It exhibited approximately 75% sensitivity and 71% specificity for radiological and clinical outcomes when a threshold of 3.3 was used. ConclusionsQEEG techniques may be considered an efficient bedside monitoring method for assessing treatment efficacy, identifying patients at higher risk of severe CED and END, and predicting long-term functional outcomes. SignificanceQEEG can help identify patients at risk of severe neurological complications that can impact long-term functional recovery in AIS patients who underwent MT.

Altered Cortical Activity during a Finger Tap in People with Stroke.

This study describes electroencephalography (EEG) measurements during a simple finger movement in people with stroke to understand how temporal patterns of cortical activation and network connectivity align with prolonged muscle contraction at the end of a task. We investigated changes in the EEG temporal patterns in the beta band (13-26Hz) of people with chronic stroke (N = 10, 7F/3M) and controls (N = 10, 7F/3M), during and after a cued movement of the index finger. We quantified the change in beta band EEG power relative to baseline as activation at each electrode and the change in task-based phase-locking value (tbPLV) and beta band task-based coherence (tbCoh) relative to baseline coherence as connectivity between EEG electrodes. Finger movements were associated with a decrease in beta power (event related desynchronization (ERD)) followed by an increase in beta power (event related resynchronization (ERS)). The ERS in the post task period was lower in the stroke group (7%), compared to controls (44%) (p < 0.001) and the transition from ERD to ERS was delayed in the stroke group (1.43s) compared to controls (0.90s) in the C3 electrode (p = 0.007). In the same post movement period, the stroke group maintained a heightened tbPLV (p = 0.030 for time to baseline of the C3:Fz electrode pair) and did not show the decrease in connectivity in electrode pair C3:Fz that was observed in controls (tbPLV: p = 0.006; tbCoh: p = 0.023). Our results suggest that delays in cortical deactivation patterns following movement coupled with changes in the time course of connectivity between the sensorimotor and frontal cortices in the stroke group might explain clinical observations of prolonged muscle activation in people with stroke. This prolonged activation might be attributed to the combination of cortical reorganization and changes to sensory feedback post-stroke.

Examining attentional control deficits in adolescents with test anxiety: An evidential synthesis using self-report, behavioral, and resting-state EEG measures

Attentional control theory suggests that test anxiety hinders individuals' attentional control, aiding our understanding of how test anxiety may impair cognitive function. However, various methods used to assess attentional control have yielded inconsistent findings. Moreover, past studies, especially on adolescents, that examine the distinct impacts of worry and the emotional components of test anxiety on individuals' attentional control capacity are scarce. This study, using self-report, behavioral, and resting-state EEG measures, explores how worry and emotionality, impact attentional control in adolescents. It enhances our understanding of the link between test anxiety and cognitive function. Referring to the effect size from prior studies, a total of 42 adolescents took part in the study. We used the Test Anxiety Inventory, due to it can assess worry and emotionality components. We employed three widely-utilized measures of attentional control: the Attentional Control Scale (ACS), the Go/Nogo task, and resting-state electroencephalography measures (alpha oscillation and the theta/beta power ratio). Both worry and emotionality components were significantly and negatively correlated with the ACS scores. Unlike worry, emotionality demonstrated a significant positive correlation with response times for the Go trials and alpha power in the parietal cortex. These results suggest that, emotionality, but not worry, is highly correlated with attentional control deficits in adolescents. This study underscores the significance of distinguishing between the components of test anxiety, which aids in comprehending the negative impacts of test anxiety on adolescents' academic performance.

Blocked versus interleaved: How range contexts modulate time perception and its EEG signatures.

Accurate time perception is a crucial element in a wide range of cognitive tasks, including decision-making, memory, and motor control. One commonly observed phenomenon is that when given a range of time intervals to consider, people's estimates often cluster around the midpoint of those intervals. Previous studies have suggested that the range of these intervals can also influence our judgments, but the neural mechanisms behind this "range effect" are not yet understood. We used both behavioral tests and electroencephalographic (EEG) measures to understand how the range of sample time intervals affects the accuracy of people's subsequent time estimates. Study participants were exposed to two different setups: In the "blocked-range" (BR) session, short and long intervals were presented in separate blocks, whereas in the "interleaved-range" (IR) session, intervals of various lengths were presented randomly. Our findings indicated that the BR context led to more accurate time estimates compared to the IR context. In terms of EEG data, the BR context resulted in quicker buildup of contingent negative variation (CNV), which also reached higher amplitude levels and dissolved more rapidly during the encoding stage. We also observed an enhanced amplitude in the offset P2 component of the EEG signal. Overall, our results suggest that the variability in time intervals, as defined by their range, influences the neural processes that underlie time estimation.

Daily associations between salivary cortisol and electroencephalographic-assessed sleep: a 15-day intensive longitudinal study.

Current evidence suggests that cortisol levels are bi-directionally associated with sleep. However, the daily, naturalistic cortisol-sleep associations remain unclear, as current evidence is mostly cross-sectional. This study tested whether pre-sleep cortisol predicts sleep duration and quality, and whether these sleep parameters predict the following day's diurnal cortisol slope using a 15-day intensive longitudinal design with electroencephalographic measures and saliva sampling. Ninety-five young adults (Mage = 20.48 ± 1.59 years) provided saliva samples at awakening and pre-sleep over 14 consecutive days, providing 2345 samples (85% viable). The Z-Machine Insight + was used to record over 900 nights of total sleep time (TST) and sleep efficiency (SE). Multilevel models tested these data at the between- and within-person levels. Higher pre-sleep cortisol predicted shorter TST (p < .001) and lower SE (p < .001) at the within-person level. Individuals with shorter average TST (p = .007) or lower average SE (p < .001) had flatter diurnal cortisol slopes, compared to those with longer average TST or higher average SE. Follow-up analyses showed that individuals with shorter average TST (vs. longer average TST) had higher pre-sleep cortisol levels (p = .01). Our findings provide evidence that pre-sleep cortisol is associated with sleep duration and quality at the within-individual level. Furthermore, individuals with short or poor sleep had flatter diurnal cortisol slopes. Although the effect sizes are small, these findings show the naturalistic associations between sleep and cortisol in a relatively healthy sample. These findings suggest that sleep maintains the regulation of the stress-response system, which is protective against mental and physical disorders.

Mothers' and fathers' neural responses toward gender-stereotype violations by their own children.

Gender stereotypes facilitate people's processing of social information by providing assumptions about expected behaviors and preferences. When gendered expectations are violated, people often respond negatively, both on a behavioral and neural level. Little is known about the impact of family kinship on the behavioral and neural reactions to gender-stereotype violations. Therefore, we examined whether parents show different responses when gender stereotypes are violated by their own children vs unknown children. The sample comprised 74 Dutch families with a father (Mage = 37.54), mother (Mage = 35.83), son, and daughter aged 3-6 years. Electroencephalography measurements were obtained while parents viewed pictures of their own and unknown children paired with toy or problem behavior words that violated or confirmed gender stereotypes. In half of the trials, parents evaluated the appropriateness of toy-gender and behavior-gender combinations. Parents showed stronger late positive potential amplitudes toward gender stereotype-violating behaviors by own children compared to unknown children. Moreover, parents' P1 responses toward gender stereotype-violating child behaviors were stronger for boys than for girls and for parents who evaluated gender-stereotype violations as less appropriate than gender-stereotype confirmations. These findings indicated that gender-stereotype violations by parents' own children are particularly salient and viewed as less appropriate than gender-stereotype confirmations.

Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls

Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated.

Interhemispheric differences of electroencephalography signal characteristics in different sleep stages

ObjectiveThe current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages. MethodsType II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages. ResultsThe results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 μv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages. ConclusionsTechnical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.

Differential effects of the stress peptides PACAP and CRF on sleep architecture in mice

Stress produces profound effects on behavior, including persistent alterations in sleep patterns. Here we examined the effects of two prototypical stress peptides, pituitary adenylate cyclase-activating polypeptide (PACAP) and corticotropin-releasing factor (CRF), on sleep architecture and other translationally-relevant endpoints. Male and female mice were implanted with subcutaneous transmitters enabling continuous measurement of electroencephalography (EEG) and electromyography (EMG), as well as body temperature and locomotor activity, without tethering that restricts free movement, body posture, or head orientation during sleep. At baseline, females spent more time awake (AW) and less time in slow wave sleep (SWS) than males. Mice then received intracerebral infusions of PACAP or CRF at doses producing equivalent increases in anxiety-like behavior. The effects of PACAP on sleep architecture were similar in both sexes and resembled those reported in male mice after chronic stress exposure. Compared to vehicle infusions, PACAP infusions decreased time in AW, increased time in SWS, and increased rapid eye movement sleep (REM) time and bouts on the day following treatment. In addition, PACAP effects on REM time remained detectable a week after treatment. PACAP infusions also reduced body temperature and locomotor activity. Under the same experimental conditions, CRF infusions had minimal effects on sleep architecture in either sex, causing only transient increases in SWS during the dark phase, with no effects on temperature or activity. These findings suggest that PACAP and CRF have fundamentally different effects on sleep-related metrics and provide new insights into the mechanisms by which stress disrupts sleep.

Frontal Alpha Asymmetry and Its Modulation by Monoaminergic Neurotransmitters in Depression.

Frontal alpha asymmetry (FAA) is an electroencephalography (EEG) measure that quantifies trait-like left versus right hemisphere lateralization in alpha power. Increased FAA indicates relatively greater left than right frontal cortex activation and is associated with enhanced reward-related approach behaviors rather than avoidance or withdrawal. Studies dating back several decades have often suggested that having greater FAA supports enhanced positive affect and protection against major depressive disorder (MDD), whereas having greater right frontal activation (i.e., reduced FAA) is associated with negative affect and risk for MDD. While this hypothesis is widely known, a number of other studies instead have found increased FAA in MDD, or evidence that either leftward or rightward bias in FAA is associated with depression. Here we briefly review the literature on leftward or rightward lateralization in FAA in MDD, and find much evidence that MDD is not always characterized by reduced FAA. We also review the limited literature on FAA and monoaminergic neurotransmitter systems, including pharmacologic agents that act on them. Studies of serotonin in particular provide genetic and pharmacologic evidence for modulation of FAA, where some of these data may suggest that serotonin reduces FAA. In a synthesis of the collective literature on FAA and the monoamines, we suggest that serotonin and norepinephrine may differentially affect FAA, with serotonin tending to promote right frontal activation and norepinephrine biased toward left frontal activation. These putative differences in frontal lateralization may influence MDD phenotypes or potential subtypes of the disorder, and suggest pharmacologic treatment strategies.