Individualised prediction of resilience and vulnerability to sleep loss using EEG features

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects into sleep-loss phenotypic groups, we extracted two EEG features from the first sleep cycle (median duration: 1.6 h), slow-wave activity (SWA) power and SWA rise rate, from four channels during the baseline nights. Using these data, we developed two sets of logistic regression classifiers (resilient versus not-resilient and vulnerable versus not-vulnerable) to predict the probability of sleep-loss resilience or vulnerability, respectively, and evaluated model performance using test datasets not used in model development. Consistently, the most predictive features came from the left cerebral hemisphere. For the resilient versus not-resilient classifiers, we obtained an average testing performance of 0.68 for the area under the receiver operating characteristic curve, 0.72 for accuracy, 0.50 for sensitivity, 0.84 for specificity, 0.61 for positive predictive value, and 3.59 for likelihood ratio. We obtained similar performance for the vulnerable versus not-vulnerable classifiers. These results indicate that logistic regression classifiers based on SWA power and SWA rise rate from routine night sleep can largely predict an individual's sleep-loss phenotype.

Electroencephalographic markers from routine sleep discriminate individuals who are vulnerable or resilient to sleep loss

Sleep loss impairs cognition; however, individuals differ in their response to sleep loss. Current methods to identify an individual's vulnerability to sleep loss involve time-consuming sleep-loss challenges and neurobehavioural tests. Here, we sought to identify electroencephalographic markers of sleep-loss vulnerability obtained from routine night sleep. We retrospectively analysed four studies in which 50 healthy young adults (21 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects as resilient or vulnerable to sleep loss, we extracted three electroencephalographic features from four channels during the baseline nights, evaluated the discriminatory power of these features using the first two studies (discovery), and assessed reproducibility of the results using the remaining two studies (reproducibility). In the discovery analysis, we found that, compared to resilient subjects, vulnerable subjects exhibited: (1) higher slow-wave activity power in channel O1 (p < 0.0042, corrected for multiple comparisons) and in channels O2 and C3 (p < 0.05, uncorrected); (2) higher slow-wave activity rise rate in channels O1 and O2 (p < 0.05, uncorrected); and (3) lower sleep spindle frequency in channels C3 and C4 (p < 0.05, uncorrected). Our reproducibility analysis confirmed the discovery results on slow-wave activity power and slow-wave activity rise rate, and for these two electroencephalographic features we observed consistent group-difference trends across all four channels in both analyses. The higher slow-wave activity power and slow-wave activity rise rate in vulnerable individuals suggest that they have a persistently higher sleep pressure under normal rested conditions.

Distributed processing for value-based choice by prelimbic circuits targeting anterior-posterior dorsal striatal subregions in male mice

Fronto-striatal circuits have been implicated in cognitive control of behavioral output for social and appetitive rewards. The functional diversity of prefrontal cortical populations is strongly dependent on their synaptic targets, with control of motor output mediated by connectivity to dorsal striatum. Despite evidence for functional diversity along the anterior-posterior striatal axis, it is unclear how distinct fronto-striatal sub-circuits support value-based choice. Here we found segregated prefrontal populations defined by anterior/posterior dorsomedial striatal target. During a feedback-based 2-alternative choice task, single-photon imaging revealed circuit-specific representations of task-relevant information with prelimbic neurons targeting anterior DMS (PL::A-DMS) robustly modulated during choices and negative outcomes, while prelimbic neurons targeting posterior DMS (PL::P-DMS) encoded internal representations of value and positive outcomes contingent on prior choice. Consistent with this distributed coding, optogenetic inhibition of PL::A-DMS circuits strongly impacted choice monitoring and responses to negative outcomes while inhibition of PL::P-DMS impaired task engagement and strategies following positive outcomes. Together our data uncover PL populations engaged in distributed processing for value-based choice.

Fear conditioning potentiates the hippocampal CA1 commissural pathway in vivo and increases awake phase sleep

The hippocampus is essential for spatial learning and memory. To assess learning we used contextual fear conditioning (cFC), where animals learn to associate a place with aversive events like foot‐shocks. Candidate memory mechanisms for cFC are long‐term potentiation (LTP) and long‐term depression (LTD), but there is little direct evidence of them operating in the hippocampus in vivo following cFC. Also, little is known about the behavioral state changes induced by cFC. To address these issues, we recorded local field potentials in freely behaving mice by stimulating in the left dorsal CA1 region and recording in the right dorsal CA1 region. Synaptic strength in the commissural pathway was monitored by measuring field excitatory postsynaptic potentials (fEPSPs) before and after cFC. After cFC, the commissural pathway's synaptic strength was potentiated. Although recordings occurred during the wake phase of the light/dark cycle, the mice slept more in the post‐conditioning period than in the pre‐conditioning period. Relative to awake periods, in non‐rapid eye movement (NREM) sleep the fEPSPs were larger in both pre‐ and post‐conditioning periods. We also found a significant negative correlation between the animal's speed and fEPSP size. Therefore, to avoid confounds in the fEFSP potentiation estimates, we controlled for speed‐related and sleep‐related fEPSP changes and still found that cFC induced long‐term potentiation, but no significant long‐term depression. Synaptic strength changes were not found in the control group that simply explored the fear‐conditioning chamber, indicating that exploration of the novel place did not produce the measurable effects caused by cFC. These results show that following cFC, the CA1 commissural pathway is potentiated, likely contributing to the functional integration of the left and right hippocampi in fear memory consolidation. In addition, the cFC paradigm produces significant changes in an animal's behavioral state, which are observable as proximal changes in sleep patterns.

Identification of clinical and psychosocial characteristics associated with perinatal depression in the south Indian population

BACKGROUND

Longitudinal perinatal depression (PND) data is sparsely available in the Indian population. We have employed Edinburgh Postnatal Depression Scale (EPDS) to assess the prevalence and identify characteristics associated with PND in the south Indian population. PND was assessed longitudinally using EPDS scores with traditional cut-off approach as well as a novel method of latent class mixture modeling (LCMM). The LCMM method, to the best of our knowledge, has been used for the first time in the Indian population.

METHODS

Three hundred and forty seven women, predominantly from economically-weaker sections of rural and urban South India were longitudinally assessed for antenatal depression (AD) and postnatal depression (PD) using EPDS cutoff-scores ≥13 and ≥10, respectively. Uni/multivariable analyses were used to identify PND associated characteristics. LCMM was then implemented, followed by risk characteristics identification.

RESULTS

PND prevalence from traditional approach was 24.50 % (12.68 % AD; 18.16% PD). Characteristics associated with PND were urban-site and recent adverse life events. Irregular menstrual history and chronic health issues were associated with AD and PD, respectively. Three distinct PND trajectories were observed from LCMM-analysis: low-risk (76.08%), medium-risk (19.89%) and high-risk (4.04%). Urban-site, recent adverse life events, irregular menstrual history and pregnancy complications were associated with medium-risk/high-risk trajectories.

LIMITATIONS

EPDS is a screening tool and not a diagnostic tool for depression. Since the study population included women from economically-weaker sections, the results need verification in other socio-economic groups.

CONCLUSIONS

Both the traditional cut-off-based approach and LCMM provided very similar conclusions regarding the prevalence of PND and characteristics associated with it. Higher PND prevalence was observed in urban women compared to rural women. In low-income countries, identifying risk characteristics associated with PND is a critical component in designing prevention strategies for PND related conditions because of the limited access to mental health resources.

Faster processing of moving compared with flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion

When the brain has determined the position of a moving object, because of anatomical and processing delays the object will have already moved to a new location. Given the statistical regularities present in natural motion, the brain may have acquired compensatory mechanisms to minimize the mismatch between the perceived and real positions of moving objects. A well-known visual illusion-the flash lag effect-points toward such a possibility. Although many psychophysical models have been suggested to explain this illusion, their predictions have not been tested at the neural level, particularly in a species of animal known to perceive the illusion. To this end, we recorded neural responses to flashed and moving bars from primary visual cortex (V1) of awake, fixating macaque monkeys. We found that the response latency to moving bars of varying speed, motion direction, and luminance was shorter than that to flashes, in a manner that is consistent with psychophysical results. At the level of V1, our results support the differential latency model positing that flashed and moving bars have different latencies. As we found a neural correlate of the illusion in passively fixating monkeys, our results also suggest that judging the instantaneous position of the moving bar at the time of flash-as required by the postdiction/motion-biasing model-may not be necessary for observing a neural correlate of the illusion. Our results also suggest that the brain may have evolved mechanisms to process moving stimuli faster and closer to real time compared with briefly appearing stationary stimuli. NEW & NOTEWORTHY We report several observations in awake macaque V1 that provide support for the differential latency model of the flash lag illusion. We find that the equal latency of flash and moving stimuli as assumed by motion integration/postdiction models does not hold in V1. We show that in macaque V1, motion processing latency depends on stimulus luminance, speed and motion direction in a manner consistent with several psychophysical properties of the flash lag illusion.

Dopaminergic and cholinergic learning mechanisms in nicotine addiction

Nicotine addiction drives tobacco use by one billion people worldwide, causing nearly six million deaths a year. Nicotine binds to nicotinic acetylcholine receptors that are normally activated by the endogenous neurotransmitter acetylcholine. The widespread expression of nicotinic receptors throughout the nervous system accounts for the diverse physiological effects triggered by nicotine. A crucial influence of nicotine is on the synaptic mechanisms underlying learning that contribute to the addiction process. Here, we focus on the acquisition phase of smoking addiction and review animal model studies on how nicotine modifies dopaminergic and cholinergic signaling in key nodes of the reinforcement circuitry: ventral tegmental area, nucleus accumbens (NAc), amygdala, and hippocampus. Capitalizing on mechanisms that subserve natural rewards, nicotine activates midbrain dopamine neurons directly and indirectly, and nicotine causes dopamine release in very broad target areas throughout the brain, including the NAc, amygdala, and hippocampus. In addition, nicotine orchestrates local changes within those target structures, alters the release of virtually all major neurotransmitters, and primes the nervous system to the influence of other addictive drugs. Hence, understanding how nicotine affects the circuitry for synaptic plasticity and learning may aid in developing reasoned therapies to treat nicotine addiction.

Macaque monkeys perceive the flash lag illusion

Transmission of neural signals in the brain takes time due to the slow biological mechanisms that mediate it. During such delays, the position of moving objects can change substantially. The brain could use statistical regularities in the natural world to compensate neural delays and represent moving stimuli closer to real time. This possibility has been explored in the context of the flash lag illusion, where a briefly flashed stimulus in alignment with a moving one appears to lag behind the moving stimulus. Despite numerous psychophysical studies, the neural mechanisms underlying the flash lag illusion remain poorly understood, partly because it has never been studied electrophysiologically in behaving animals. Macaques are a prime model for such studies, but it is unknown if they perceive the illusion. By training monkeys to report their percepts unbiased by reward, we show that they indeed perceive the illusion qualitatively similar to humans. Importantly, the magnitude of the illusion is smaller in monkeys than in humans, but it increases linearly with the speed of the moving stimulus in both species. These results provide further evidence for the similarity of sensory information processing in macaques and humans and pave the way for detailed neurophysiological investigations of the flash lag illusion in behaving macaques.