Biology subjects

Dresler, M.

Publications and source records attributed to Dresler, M..

36 records · Page 2Linked to original sources

bioRxiv2023

Fractal cycles of sleep: a new aperiodic activity-based definition of sleep cycles

Nocturnal human sleep consists of 4 - 6 ninety-minute cycles defined as episodes of non-rapid eye movement (non-REM) sleep followed by an episode of REM sleep. While sleep cycles are considered fundamental components of sleep, their functional significance largely remains unclear. One of the reasons for a lack of research progress in this field is the absence of a data-driven definition of sleep cycles. Here, we proposed to base such a definition on fractal (aperiodic) neural activity, a well-established marker of arousal and sleep stages. We explored temporal dynamics of fractal activity during nocturnal sleep using electroencephalography. Based on the observed pattern of fractal fluctuations, we introduced a new concept of fractal activity-based cycles of sleep or "fractal cycles" for short, defined as a time interval during which fractal activity descends from its local maximum to its local minimum and then leads back to the next local maximum. Next, we assessed correlations between fractal and classical (i.e., non-REM - REM) sleep cycle durations. We also studied cycles with skipped REM sleep, i.e., the cycles where the REM phase is expected to appear except that it does not, being replaced by lightening of sleep. Regarding the sample, we examined fractal cycles in healthy adults (age range: 18 - 75 years, n = 205) as well as in children and adolescents (range: 8 - 17 years, n = 21), the group characterized by deeper sleep and a higher frequency of cycles with skipped REM sleep. Further, we studied fractal cycles in major depressive disorder (n = 111), the condition characterized by altered REM sleep (in addition to its clinical symptoms). We found that fractal and classical cycle durations (89 {+/-} 34 min vs 90 {+/-} 25 min) correlated positively (r = 0.5, p < 0.001). Cycle-to-cycle overnight dynamics showed an inverted U-shape of both fractal and classical cycle durations and a gradual decrease in absolute amplitudes of the fractal descents and ascents from early to late cycles. In adults, the fractal cycle duration and participants age correlated negatively (r = -0.2, p = 0.006). Children and adolescents had shorter fractal cycles compared to young adults (76 {+/-} 34 vs 94 {+/-} 32 min, p < 0.001). The fractal cycle algorithm detected cycles with skipped REM sleep in 91 - 98% of cases. Medicated patients with depression showed longer fractal cycles compared to their own unmedicated state (107 {+/-} 51 min vs 92 {+/-} 38 min, p < 0.001) and age-matched controls (104 {+/-} 49 vs 88 {+/-} 31 min, p < 0.001). In conclusion, fractal cycles are an objective, quantifiable, continuous and biologically plausible way to display sleep neural activity and its cycles. They are useful in healthy adult and pediatric populations as well as in patients with major depressive disorder. Fractal cycles should be extensively studied to advance theoretical research on sleep structure. Highlights- Fractal activity-based cycles of sleep or "fractal cycles" for short is a new concept based on cyclic changes in fractal (aperiodic) neural activity during sleep. - Durations of fractal and classical cycles correlate, and both show an inverted U-shape when seen from early to late cycles. - The fractal cycle algorithm is effective in detecting cycles with skipped REM sleep. - Older healthy adults shower shorter fractal - but not classical - cycle durations. - Fractal cycle duration is shorter in children and adolescents compared to young adults. - In major depressive disorder, antidepressant medication is associated with longer fractal cycles.

neuroscience↗

bioRxiv2023

Sustained polyphasic sleep restriction abolishes human growth hormone release

Voluntary sleep restriction is a common phenomenon in industrialized societies aiming to increase time spent awake and thus productivity. We explored how restricting sleep to a radically polyphasic schedule affects neural, cognitive, and endocrine characteristics. Ten young healthy participants were restricted to one 30-min nap opportunity at the end of every 4 hours (i.e., 6 sleep episodes per 24 hours) without any extended core sleep window, which resulted in a cumulative sleep amount of just 2 hours per day (i.e., [~]20 min per bout). All but one participant terminated this schedule during the first three weeks. The remaining participant (a 25-year-old male) succeeded to adhere to a polyphasic schedule for 5 weeks with no apparent impairments in cognitive and psychiatric measures except for psychomotor vigilance. While in-blood cortisol or melatonin release pattern and amounts were unaltered by the polyphasic as compared to monophasic sleep, growth hormone seemed almost entirely abolished (>95% decrease), with the residual release showing a considerably changed polyphasic secretional pattern. While coarse sleep structure appeared intact during polyphasic sleep, REM sleep showed decreased oscillatory and increased aperiodic EEG activity compared to monophasic sleep. Considering the decreased vigilance, abolished growth hormone release, and neurophysiological changes observed, it is doubtful that radically polyphasic sleep schedules can subserve the different functions of sleep to a sufficient degree.

neuroscience↗

bioRxiv2023

Divergent associations of slow-wave sleep vs. REM sleep with plasma amyloid-beta

BackgroundRecent evidence shows that during slow-wave sleep (SWS), the brain is cleared from potentially toxic metabolites, such as the amyloid-beta protein. Poor sleep or elevated cortisol levels can worsen amyloid-beta clearance, potentially leading to the formation of amyloid plaques, a neuropathological hallmark of Alzheimers disease. Here, we explore how nocturnal neural and endocrine activity affects amyloid-beta fluctuations in the peripheral blood as a reflection of cerebral clearance. MethodsSimultaneous polysomnography and all-night blood sampling were acquired in 60 healthy volunteers aged 20-68 years old. Nocturnal plasma concentrations of two amyloid-beta species (amyloid-beta-40 and amyloid-beta-42), cortisol, and growth hormone were assessed every 20 minutes from 23:00-7:00. Amyloid-beta fluctuations were modeled with sleep stages, (non)-oscillatory power, and hormones as predictors while controlling for age and multiple comparisons. Time lags between the predictors and amyloid-beta ranged from 20 to 120min. FindingsThe amyloid-beta-40 and amyloid-beta-42 levels correlated positively with growth hormone concentrations, SWS proportion, slow-wave (0.3-4Hz) oscillatory and high-band (30-48Hz) non-oscillatory power, but negatively with cortisol concentrations and rapid eye movement sleep (REM) proportion measured 40-100min before (all t-values>|3|, p-values<0.003). Older participants showed higher amyloid-beta-40 levels. InterpretationSlow-wave oscillations are associated with higher plasma amyloid-beta levels, reflecting their contribution to cerebral amyloid-beta clearance across the blood-brain barrier. REM sleep is related to decreased amyloid-beta plasma levels; however, this link may reflect passive aftereffects of SWS and not REMs effects per se. Strong associations between cortisol, growth hormone, and amyloid-beta presumably reflect the sleep-regulating role of the corresponding releasing hormones. A positive association between age and amyloid-beta-40 may indicate that peripheral clearance becomes less efficient with age. Our study provides important insights into the specificity of different sleep features effects on brain clearance and suggests that cortisol nocturnal fluctuations may serve as a new marker of clearance efficiency.

neuroscience↗

bioRxiv2023

Comparing manual and automatic artifact detection in sleep EEG recordings

All sleep EEG recordings can be contaminated by artifacts. Both visual and automatic methods have been developed to mark such erroneous segments of EEG data. Here we systematically explore the effect of artifacts on the sleep EEG power spectrum density (PSD), and we compare gold-standard visual detections to a simple automatic detector using Hjorth parameters to identify artifacts. We find that most distortions in the all-night average PSD occur because of a small minority of highly anomalous artifacts, which mainly affect the beta and gamma frequency ranges and NREM delta. Visual and automatic detections only show moderate agreement in which data segments are artefactual. However, the resulting all-night average PSD is highly similar across all methods, and PSDs calculated with all methods successfully recover the known correlations of PSD with age and sex. No parameter settings of the automatic detector clearly outperformed others. Additionally, we show that accurate average PSD estimates can be recovered from just a fraction of available data epochs. Our results suggest that artifacts represent a minor and easily solvable problem in sleep EEG recordings. Most visually identified artifacts do not seriously distort estimates of mid-frequency activity in the sleep EEG spectrum, and distortions to low and high frequencies can be eliminated using a simple automatic detection method nearly as well as with visual detections. These findings show that the visual inspection of EEG data is not necessary to eliminate the effects of artifacts, which is encouraging for the expected performance of automatic preprocessing in large sleep EEG databases.

neuroscience↗

bioRxiv2023

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross- validated regularized regression to link sleep EEG features to cognitive performance in cross- sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics accounted for more covariance between sleep EEG and cognition than health variables, and consequently reduced this association by a greater degree, but even with the strictest covariate sets a statistically significant association was present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r=0.283), with the strongest effect ascribed to spindle- frequency activity. This association becomes weaker after adjusting for demographic (r=0.186) and health variables (r=0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

neuroscience↗

bioRxiv2022

Scale-free and oscillatory spectral measures of sleep stages in humans

Power spectra of sleep electroencephalograms (EEG) comprise two main components: a decaying power-law corresponding to the aperiodic neural background activity, and spectral peaks present due to neural oscillations.Traditional band-based spectral methods ignore this fundamental structure of the EEG spectra and thus are susceptible to misrepresenting the underlying phenomena. A fitting method that attempts to separate and parameterize the aperiodic and periodic spectral components called fitting oscillations & one over f (FOOOF) was applied to a set of annotated whole-night sleep EEG recordings of 251 subjects from a wide age range (4-69 years). Most of the extracted parameters exhibited sleep stage sensitivity; significant main effects and interactions of sleep stage, age, sex, and brain region were found. The spectral slope (describing the steepness of the aperiodic component) showed especially large and consistent variability between sleep stages (and low variability between subjects), making it a candidate indicator of sleep states. The limitations and arisen problems of the FOOOF method are also discussed, possible solutions for some of them are suggested.

bioinformatics↗

bioRxiv2021

The sleep EEG envelope: a novel, neuronal firing-based human biomarker

Sleep EEG reflects instantaneous voltage differences relative to a reference, while its spectrum reflects the degree to which it is comprised of oscillations at various frequencies. In contrast, the envelope of the sleep EEG reflects the instantaneous amplitude of oscillations at specific frequencies, and its spectrum reflects the rhythmicity of the occurrence of these oscillations. The ordinary sleep EEG and its spectrum have been extensively studied and its individual stability and relationship to various demographic characteristics, psychological traits and pathologies is well known. In contrast, the envelope spectrum has not been extensively studied before. In two studies, we explored the generating mechanisms and utility of studying the envelope of the sleep EEG. First, we used human invasive data from cortex-penetrating microelectrodes and subdural grids to demonstrate that the sleep EEG envelope spectrum reflects local neuronal firing. Second, we used a large database of healthy volunteers to demonstrate that the scalp EEG envelope spectrum is highly stable within individuals, especially in NREM sleep, and that it is affected by age and sex. Multivariate models based on a learning algorithm could predict both age (r=0.6) and sex (r=0.5) with considerable accuracy from the EEG envelope spectrum. With age, oscillations characteristically shifted from a 4-5 second rhythm to higher rhythms. The envelope spectrum was not associated with general cognitive ability (IQ). Our results demonstrate that the sleep envelope spectrum is a promising, neuronal firing-based biomarker of various demographic and disease-related phenotypes.

neuroscience↗

bioRxiv2021

Spindle - slow oscillation coupling correlates with memory performance and connectivity changes in a hippocampal network after sleep

After experiences are encoded, post-encoding reactivations during sleep have been proposed to mediate long-term memory consolidation. Spindle-slow oscillation coupling during NREM sleep is a candidate mechanism through which a hippocampal-cortical dialogue may strengthen a newly formed memory engram. Here, we investigated the role of fast spindle- and slow spindle-slow oscillation coupling in the consolidation of spatial memory in humans with a virtual water maze task involving allocentric and egocentric learning strategies. Furthermore, we analyzed how resting-state functional connectivity evolved across learning, consolidation, and retrieval of this task using a data-driven approach. Our results show task-related connectivity changes in the executive control network, the default mode network, and the hippocampal network at post-task rest. The hippocampal network could further be divided into two subnetworks of which only one showed modulation by sleep. Decreased functional connectivity in this subnetwork was associated with higher spindle-slow oscillation coupling power, which was also related to better memory performance at test. Overall, this study contributes to a more holistic understanding of the functional resting-state networks and the mechanisms during sleep associated to spatial memory consolidation.

neuroscience↗

bioRxiv2021

Circadian modulation of human sleep spindle frequency

Homeostatic and circadian processes play a pivotal role in determining sleep structure, timing and quality. In sharp contrast with the wide accessibility of the EEG index of sleep homeostasis, an electrophysological measure of the circadian modulation of sleep is still non-available. Evidence suggests that sleep spindle frequencies decelerate during biological night. In order to test the feasibility of measuring this marker in common polysomnographic protocols, the Budapest-Munich database of sleep records (N = 251 healthy subjects, 122 females, age range: 4-69 years), as well as an afternoon nap sleep record database (N = 112 healthy subjects, 30 females, age range: 18-30 years) were analysed by the Individual Adjustment Method of sleep spindle analysis. Slow and fast sleep spindle frequencies were characterized by U-shaped overnight dynamics, with highest values in the first and the fourth-to-fifth sleep cycle and the lowest values in the middle of the sleeping period (cycles 2-3). Age-related attenuation of sleep spindle deceleration was evident. Estimated phases of the nadirs in sleep spindle frequencies were advanced in children as compared to other age groups. Additionally, nap sleep spindles were faster than night sleep spindles (0.57 and 0.39 Hz difference for slow and fast types, respectively). The fine frequency resolution analysis of sleep spindles is a feasible method of measuring the assumed circadian modulation of sleep. Moreover, age-related attenuation of circadian sleep modulation might be measurable by assessing the overnight dynamics in sleep spindle frequency. Phase of the minimal sleep spindle frequency is a putative biomarker of chronotype.

neuroscience↗

bioRxiv2021

Non-REM sleep in major depressive disorder

Disturbed sleep is a key symptom in major depressive disorder (MDD). REM sleep alterations are well described in the current fliterature, but little is known about non-REM sleep alterations. Additionally, sleep disturbances relate to a variety of cognitive symptoms in MDD, but which features of non-REM sleep EEG contribute to this, remains unknown. We comprehensively analyzed non-REM sleep EEG features in three independently collected datasets (N=284). These included MDD patients with a broad age range, varying duration and severity of depression, unmedicated or medicated, age- and gender-matched to healthy controls. We explored changes in sleep architecture including sleep stages and cycles, spectral power, sleep spindles, slow waves (SW), and SW-spindle coupling. Next, we analyzed the association of these sleep features with acute measures of depression severity and overnight consolidation of procedural memory. Overall, no major systematic alterations in non-REM sleep architecture were found in patients compared to controls. For the microstructure of non-REM sleep, we observed a higher spindle amplitude in unmedicated patients compared to controls, and after the start of antidepressant medication longer SWs with lower amplitude and a more dispersed SW-spindle coupling. In addition, long-term, but not short-term medication seemed to lower spindle density. Overnight procedural memory consolidation was impaired in medicated patients and associated with lower sleep spindle density. Our results suggest that alterations in non-REM sleep EEG might be more subtle than previously reported. We discuss these findings in the context of antidepressant medication intake and age. Statement of SignificanceDepression affects large and diverse populations worldwide, including their sleep. Most sleep is non-REM sleep, which is vital to cognitive function, including memory. How non-REM is affected during a depression or medical treatment remains poorly investigated. We classified non-REM sleep of depressive patients against healthy controls in unprecedented analysis detail and confidence using the largest dataset published so far while also test sleep alterations associations with impaired memory. Surprisingly, severe depression alone did not alter sleep. We observed severe non-REM sleep alterations only worsening under patient medication, which ultimately coincided with 24-hour memory impairments. Though causal influences of medication on sleep in depressive patients remains to be investigated, this cautions common clinical practice in long-term treatment with antidepressants.

neuroscience↗

bioRxiv2020

Durable memories and efficient neural coding through mnemonic training using the method of loci

Mnemonic techniques, such as the method of loci, can powerfully boost memory. Here, we compared memory athletes ranked among the worlds top 50 in memory sports to mnemonics-naive controls. In a second study, participants completed a six-weeks memory training, working memory training, or no intervention. Behaviorally, memory training enhanced durable, longer-lasting memories. fMRI during encoding and recognition revealed task-based activation decreases in lateral prefrontal, as well as in parahippocampal and retrosplenial cortices in both memory athletes and participants after memory training, partly associated with better performance after four months. This was complemented by hippocampal-neocortical coupling during consolidation, which was stronger the more durable memories participants formed. Our findings are the first to demonstrate that mnemonic training boosts durable memory formation via decreased task-based activation and increased consolidation thereafter. This is in line with conceptual accounts of neural efficiency and highlights a complex interplay of neural processes critical for extraordinary memory.

neuroscience↗

bioRxiv2020

A new set of composite, non-redundant electroencephalogram measures of non-rapid eye movement sleep based on the power law scaling of the Fourier spectrum

A novel method for deriving composite, non-redundant measures of non-rapid eye movement (NREM) sleep electroencephalogram (EEG) is developed on the basis of the power law scaling of the Fourier spectra. Measures derived are the spectral intercept, the slope (spectral exponent), as well as the maximal whitened spectral peak amplitude and frequency in the sleep spindle range. As a proof of concept, we apply these measures on a large sleep EEG dataset (N = 175; 81 females; age range: 17-60 years) with previously demonstrated effects of age, sex and intelligence. As predicted, aging is associated with decreased overall spectral slopes (increased exponents) and whitened spectral peak amplitudes in the spindle frequency range. In addition, age associates with decreased sleep spindle spectral peak frequencies in the frontal region. Women were characterized by higher spectral intercepts and higher spectral peak frequencies in the sleep spindle range. No sex differences in whitened spectral peak amplitudes of the sleep spindle range were found. Intelligence correlated positively with whitened spectral peak amplitudes of the spindle frequency range in women, but not in men. Last, age-related increases in spectral exponents did not differ in subjects with average and high intelligence. Our findings replicate and complete previous reports in the literature, indicating that the number of variables describing NREM sleep EEG can be effectively reduced in order to overcome redundancy and Type I statistical errors in future electrophysiological studies of sleep. Author summaryGiven the tight reciprocal relationship between sleep and wakefulness, the objective description of the complex neural activity patterns characterizing human sleep is of utmost importance in understanding the several facets of brain function, like sex differences, aging and cognitive abilities. Current approaches are either exclusively based on visual impressions expressed in graded levels of sleep depth (W, N1, N2, N3, REM), whereas computerized quantitative methods provide an almost infinite number of potential metrics, suffering from significant redundancy and arbitrariness. Our current approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) are characterized by coloured noise-like properties. That is, the contribution of different frequencies to the power spectrum of the signal are best described by power law functions with negative exponents. In addition, we assume, that stages N2-N3 are further characterized by additional non-random (non-noise like, sinusoidal) activity patterns, which are emerging at specific frequencies, called sleep spindles (9-18 Hz). By relying on these assumptions we were able to effectively reduce 191 spectral measures to 4: (1) the spectral intercept reflecting the overall amplitude of the signal, (2) the spectral slope reflecting the constant ratio of low over high frequency power, (3) the frequency of the maximal sleep spindle activity and (4) the amplitude of the sleep spindle spectral peak. These 4 measures were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.

neuroscience↗

bioRxiv2019

Sleep time, social jetlag and intelligence: biology or work timing?

Sleep-wake patterns show substantial biological determination, but they are also subject to individual choice and societal pressure. Some evidence suggests that high IQ is associated with later sleep patterns. However, t is therefore unclear whether the relationship between IQ and later sleep is due to biological or social effects, such as timing and flexibility of working hours. We investigated the association between habitual sleep timing during work days and work-free days, working time and intelligence in a sample of 1,172 adults. We found no difference in chronotype, and the later sleep timing of high-IQ individuals on work days was fully accounted for by later work start times. Our results indicate that later sleep timing in those with higher IQs is not due to physiological differences, but rather due to later or more flexible work schedules. Later working times and the resulting lower social jetlag may be one of the reasons why higher IQ is associated with lower prospective morbidity and mortality. Statement of significanceSome evidence shows that higher intelligence is associated with sleep characteristics, but it is unclear if this is because of biological or social mechanisms. We provide evidence for a social mechanism. We found that high IQ individuals indeed sleep later, but only on working days, and this difference is fully accounted for by later work timing. Our evidence is consistent with a view that highly intelligent individuals sleep later because they can afford to, consequently experience lower social jetlag, and this may partially account for better health outcomes.

physiology↗

bioRxiv2019

Medial prefrontal decoupling from the default mode network benefits memory

In the last few years the involvement of the medial prefrontal cortex (mPFC) in memory processing has received increased attention. It is centrally involved when we use prior knowledge (schemas) to improve learning of new material. With the mPFC also being one of the core hubs of the default mode network (DMN) and the DMNs role in memory retrieval, we decided to investigate whether the mPFC in a schema paradigm acts independently of the DMN. We tested this with data from a cross-sectional developmental study. During retrieval of schema items, the mPFC decoupled from the DMN with the degree of decoupling predicting memory performance. This finding suggests that a demand specific reconfiguration of the DMN supports schema memory. Additionally, we found that in the control condition, which relied on episodic memory, activity in the parahippocampal gyrus was positively related to memory performance. We interpret these results as a demand specific network reconfiguration of the DMN: a decoupling of the mPFC to support schema memory and a decoupling of the parahippocampal gyrus facilitating episodic memory. This supports the notion of dynamic reconfiguration of brain networks in response to task demands in the sense of process specific alliances.

neuroscience↗

bioRxiv2019

Differences in strategic abilities but not associative processes explain memory development

Childrens learning capabilities change while growing up. One framework that describes the cognitive and neural development of childrens growing learning abilities is the two-component model. It distinguishes processes that integrate separate features into a coherent memory representation (associative component) and executive abilities, such as elaboration, evaluation and monitoring, that support memory processing (strategic component). In an fMRI study using an object-location association paradigm, we investigated how the two components influence memory performance across development. We tested children (10-12 yrs., n=31), late adolescents (18 yrs., n=29) and adults (25+ yrs., n=30) of either sex. For studying the associative component, we also probed how the utilisation of prior knowledge (schemas) facilitates memory across age groups. Children had overall lower retrieval performance, while adolescents and adults did not differ from each other. All groups benefitted from schemas, but this effect did not differ between groups. Performance differences between groups were associated with deactivation of the dorsal medial prefrontal cortex (dmPFC), which in turn was linked to executive functioning. These patterns were stronger in adolescents and adults and seemed absent in children. This pattern of results suggests the childrens executive system, the strategic component, is not as mature and thus cannot facilitate memory performance in the same way as in adolescents/adults. In contrast, we did not find age-related differences in the associative component; with activity in the angular gyrus predicting memory performance systematically across groups. Overall our results suggest that differences of executive rather than associative abilities explain memory differences between children, adolescents and adults.

neuroscience↗

bioRxiv2019

The influence of transcranial magnetic stimulation of the medial prefrontal cortex on emotional memory schemas

Memory bias for negative information is a critical characteristic of major depression, but the underlying neural mechanisms are largely unknown. The recently revived concept of memory schemas may shed new light on memory bias in depression: negative schemas might enhance the encoding and consolidation of negative experiences, thereby contributing to the genesis and perpetuation of depressive pathology. To investigate this relationship, we aimed to transiently perturb processing in the medial prefrontal cortex (mPFC), a core region involved in schema memory, using neuronavigated transcranial magnetic stimulation (TMS) targeting the mPFC. Forty healthy volunteers first underwent a negative mood induction to activate negative schema processing after which they received either active inhibitory (N = 20) or control (N = 20) stimulation to the mPFC. Then, all participants performed the encoding of an emotional false memory task. Recall and recognition performance was tested the following morning. Polysomnographic data was recorded continuously during the night before and after encoding. Secondary measures included sleep and mood questionnaires. We observed a significantly lower number of false recognition of negative critical lures following mPFC perturbation compared to the control group, whereas no differences in veridical memory performance were observed. These findings were supported by reaction time data. No relation between REM sleep and (false) emotional memory performance was observed. These findings support previous causal evidence for a role of the mPFC in schema memory processing and further suggest a role of the mPFC in memory bias.

neuroscience↗

bioRxiv2018

Individual slow wave morphology is a marker of ageing

Slow wave activity is a hallmark of deep NREM sleep. Scalp slow wave morphology is stereotypical, it is highly correlated with the synchronized onset and cessation of cortical neuronal firing measured from the surface or depth of the cortex, strongly affected by ageing, and these changes are causally associated with age-related cognitive decline. We investigated how normal ageing affects the individual morphology of the slow wave, and whether these changes are captured by the summary slow wave parameters generally used in the literature. We recorded full-night polysomnography in 159 subjects (age 17-69 years) and automatically detected slow waves using six different detection methods to ensure methodological robustness. We established individual slow morphologies at 501 data points for each subject and also calculated the individual average slow wave amplitude, average ascending and descending slope steepness and the total number of slow waves (gross parameters). Using LASSO penalized regression we found that fine-grained slow wave morphology is associated with age beyond gross parameters, with young subjects having faster slow wave polarity reversals, suggesting a more efficient initiation and termination of slow wave down- and upstates. Our results demonstrate the superiority of the high-resolution slow wave morphology as a biomarker of ageing, and highlights state transitions as promising targets of restorative stimulation-based interventions.

neuroscience↗

bioRxiv2017

2D:4D and spatial abilities: From rats to humans

Variance in spatial abilities are thought to be determined by in utero levels of testosterone and oestrogen, measurable in adults by the length ratio of the 2nd and 4th digit (2D:4D). We confirmed the relationship between 2D:4D and spatial performance using rats in two different tasks (paired-associate task and watermaze) and replicated this in humans. We further clarified anatomical and functional brain correlates of the association between 2D:4D and spatial performance in humans.

neuroscience↗