Biology subjects

Schabus, M.

Publications and source records attributed to Schabus, M..

14 recordsLinked to original sources

bioRxiv2026

Talking Brains: Neonatal Aperiodic Activity in Resting State EEG Relates to Later Communicative Abilities

Communicative abilities acquired over the first year of life are foundational for language development. Understanding the neural mechanisms underlying these abilities can explain individual differences and improve predictions of later language outcomes. While prior research on neural markers of language development has focused predominantly on oscillatory neural activity, the underlying aperiodic component of the neural signal may provide further insights into cortical maturation by indirectly reflecting variation in neural excitation/inhibition (E/I) balance. In neurodevelopmental conditions, both reduced and elevated aperiodic activity are linked to atypical development, and initial evidence from at-risk cohorts also relates aperiodic activity to language outcome. Moving beyond clinical populations, the present study examined whether aperiodic activity during neonatal resting-state EEG relates to 12-month communicative abilities in typically developing infants. Aperiodic offset and exponent were extracted from resting-state EEG recorded two weeks after birth in n = 50 neonates using the FOOOF algorithm. At 12 months, communicative abilities were assessed with the parent-report CSBS-DP Infant-Toddler Checklist. Quadratic regression analyses showed an inverted U-shaped relationship between the neonatal aperiodic exponent and communicative abilities: both lower and higher exponent values were associated with poorer outcomes, with intermediate values linked to better abilities. No robust association was found for the offset. These findings suggest that aperiodic activity captures variability in early communicative development, with an optimum at intermediate levels, consistent with a balanced E/I state. The scope of aperiodic brain activity as a developmental marker may thus extend beyond clinical populations to predicting communicative development in healthy infants.

developmental biology↗

bioRxiv2026

From Variability to Synchrony: Non-linear Development of Auditory Neural Responses During the First Year of Life

In humans, the first year of life is characterized by rapid developmental changes, including substantial brain maturation. As a result, neural responses to auditory stimuli undergo marked changes during this period. In this study, we followed 69 infants across their first year of life and recorded high-density electroencephalography (hdEEG) at 2 weeks, 6 months, and 12 months postpartum. Infants were presented with pure beep tones to examine the development of neural responses to auditory stimulation. We analysed event-related potentials (ERPs), inter-trial phase coherence (ITPC), and time-frequency (TF) responses to the beep tones and controlled for arousal state during stimulus presentation. We found that with increasing age, neural responses became more pronounced and showed reduced trial-to-trial variability. Phase synchronization increased from 2 weeks to later developmental stages in a broad low-frequency range (0 to 11 Hz), indicating improved temporal alignment of brain responses over time. However, phase synchronization decreased from 6 to 12 months, suggesting a developmental transition towards more differentiated brain activity. Taken together, these findings demonstrate that auditory maturation during the first year of life follows a non-linear trajectory driven by dynamic changes in neural synchronization, reflecting the progressive refinement of functional neural circuits. Our results thus provide a critical benchmark for understanding the neural dynamics underlying sensory development during this period.

developmental biology↗

bioRxiv2025

All Memories Are Equal at Night: Sleep Protects Without Preference for Strength

Sleep benefits memory, but whether this benefit depends on initial memory strength remains heavily debated. We asked whether sleep preferentially stabilizes weakly versus strongly encoded declarative memories, and whether classical sleep oscillations contribute to this effect. Ninety healthy adults (n=30/group) learned 80 unrelated word pairs; half presented twice (weak, S-), and half three times (strong, S+). Participants were tested either immediately ([~]40 min post-learning), after 9 hours containing nocturnal sleep monitored with polysomnography, or after a comparable daytime waking period. Memory performance was assessed using cued recall accuracy and reaction times (RT). Strongly encoded items (S+) were recalled better than weakly encoded items (S-) across all groups. Overall memory performance was lowest after wakefulness, whereas performance after sleep was slightly higher than immediate recall. Crucially, wakefulness selectively impaired the consolidation of weak items, with the WAKE group forgetting disproportionately more S- than S+ items. In contrast, the SLEEP group retained weak and strong items similarly, yielding performance comparable to immediate recall. RT analyses showed generally slower responses for weak items, particularly after wakefulness, while response times in the sleep and immediate recall groups did not differ. Analyses of classical sleep features, such as slow oscillations density, sleep spindle intensity, and their coupling, revealed no significant associations with overnight retention. Correlations between these oscillations and memory performance were present both during the adaptation and the learning nights, suggesting trait-like, rather than state-dependent relationships. Overall, our findings indicate that sleep protects declarative memories irrespective of initial encoding strength.

neuroscience↗

bioRxiv2025

Fetal Dehydroepiandrosterone from Hair Samples at Birth Predicts Language Development

ObjectiveSex hormones testosterone and estradiol have been related to childrens language development. Expanding the focus on dehydroepiandrosterone (DHEA), which has not yet been considered as a biological marker of language ability, may provide novel insights, as emerging evidence suggests that fetal DHEA plays a critical role in the organization of the neonatal brain, potentially shaping later language development. The present study investigated whether fetal DHEA, compared to fetal testosterone, is associated with infant language development. Design and methodsDHEA and testosterone concentrations were measured in newborn hair strands (n = 63) collected two weeks after birth, capturing fetal long-term hormone secretions during the third trimester of pregnancy. At six months of age, childrens language abilities were assessed using the German version of the Bayley Scales of Infant Development. Results. Linear regression analysis revealed fetal DHEA levels to be significantly associated with language abilities at six months of age, with lower DHEA levels corresponding to higher language scores. Control analyses assessing general cognitive abilities showed no association of fetal DHEA levels with infant cognitive function. Testosterone levels were not associated with language. ConclusionsThe current study identifies fetal DHEA levels extracted from newborn hair samples as a potential biological factor influencing infant language development.

developmental biology↗

bioRxiv2024

FEATURE-SPECIFIC ANTICIPATORY PROCESSING FADES DURING HUMAN SLEEP

Imagine you are listening to a familiar song on the radio. As the melody and rhythm unfold, you can often anticipate the next note or beat, even before it plays. This ability demonstrates the brains capacity to extract statistical regularities from sensory input and to generate predictions about future sensory events. It is considered automatic, requiring no conscious effort or attentional resources (1-4). But to what extent does this predictive ability operate when our attention is greatly reduced, such as during sleep? Experimental findings from animal and human studies reveal a complex picture of how the brain engages in predictive processing during sleep (5-13). Although evidence suggests that the brain differentially reacts to unexpected stimuli and rhythmic music (5,7,13), there is a notable disruption in feedback processing, which is essential for generating accurate predictions of upcoming stimuli (10). Here, for the first time, we examine the brains ability during sleep to predict or pre-activate low-level features of expected stimuli before presentation. We use sequences of predictable or unpredictable/random tones in a passive-listening paradigm while recording simultaneous electroencephalography (EEG) and magnetoencephalography (MEG) during wakefulness and sleep. We found that during wakefulness, N1 sleep and N2 sleep, subtle changes in tone frequencies elicit unique/distinct neural activations. However, these activations are less distinct and less sustained during sleep than during wakefulness. Critically, replicating previous work in wakefulness (4), we find evidence that neural activations specific to the anticipated tone occur before its presentation. Extending previous findings, we show that such predictive neural patterns fade as individuals fall into sleep. In BriefThe extent to which predictive processing takes place in sleep is yet to be determined. Using a passive-listening EEG/MEG paradigm, Topalidis et al. show that auditory representations in sleep are brief and unstable, easily overwritten by subsequent inputs, which possibly hinders the tracking and extraction of sensory associations. HighlightsO_LIParticipants passively listened to random and predictable sequences of tones during both wakefulness and sleep, without being made aware of the underlying pattern. C_LIO_LIThe brain reta C_LIO_LIins the ability to process basic low-level features during sleep. C_LIO_LIWhile these feature-specific responses are preserved during sleep, they are less distinct and sustained than in wakefulness. C_LIO_LIUnlike in wakefulness, during sleep, the brain does not predict or anticipate upcoming sounds, despite continuing to process basic auditory information. C_LI

neuroscience↗

bioRxiv2024

Non-guided, Mobile, CBT-I-based Sleep Training in War-torn Ukraine: A Feasibility Study

ObjectivesTo study whether a mobile, unguided Cognitive Behavior Therapy-based Intervention for Sleep Disturbance, Sleep2 is feasible, acceptable, and reduces mental health/sleep disturbance symptoms among the Ukrainian population during the ongoing war. MethodsA single-arm, open-label, uncontrolled pre-post evaluation study was conducted with 487 registered participants: 283 started, out of which 95 completed without an ambulatory heart rate (HR) sensor and 65 with. Assessments were conducted using online questionnaires and continuous objective measurements via HR sensors. Key outcome measures included sleep disturbance, insomnia, fear of sleep, anxiety, depression, PTSD, perceived stress, and somatic symptoms. ResultsEngagement with the program was robust, achieving an 80.72% compliance rate, alongside high levels of feasibility and acceptance. Participants reported significant pre- post reductions in the severity of sleep disturbance (by 22.60%), insomnia (by 35.08%), fear of sleep (by 32.43%), anxiety (by 27.72%), depression (by 28.67%), PTSD (by 32.41%), somatic symptoms (by 24.52%), and perceived stress (by 17.90%), all with medium to high effect sizes. Objective sleep measurements showed a slight reduction in sleep onset latency. ConclusionThe Sleep2Ukraine program demonstrated high feasibility and acceptance, with significant improvements in subjective sleep and mental health measures among participants. These findings demonstrate the potential of scalable mobile-based CBT-I interventions in war-torn regions with or without the instrument, based on the heart rate assessment.

neuroscience↗

bioRxiv2024

Maternal characteristics and their relation to early mother-child interaction and cognitive development in toddlers

Early mother-infant interaction is believed to have a significant impact on the social, cognitive, and emotional development of children. These interactions are not only influenced by child and contextual factors but also by the mothers personality traits and strain. In this study, we investigated the relation between maternal factors such as personality, depressive symptoms, or experiencing of emotions, with (i) childrens early cognitive development and (ii) interaction patterns in a sample of 116 mother-child dyads (mean child age = 18.63 months {+/-} 6.42). Maternal factors were assessed using standardized questionnaires, childrens cognitive development was measured using the Bayley Scales of Infant and Toddler Development, and interaction patterns were evaluated using the CARE-Index. The study found that children of mothers who scored higher in agreeableness performed better in cognitive assessments. Additionally, our analysis revealed statistical trends indicating that mothers with higher levels of conscientiousness tended to be less unresponsive in the interaction with their infants, while those with higher levels of neuroticism were more likely to exhibit compulsive behavior in their toddlers. Additionally, there was a trend indicating that maternal depression was associated with increased maternal controlling behavior towards toddlers. Finally, mothers who placed significant importance on their bodily signals to assess their overall well-being had higher scores in the quality of interaction with their child. Overall, these findings show the intricate relation between maternal behavior and state with dyadic interaction quality. This should underline that optimal infant development is only possible if mothers are well supported especially if in need due to various burdens such as depressive symptoms.

developmental biology↗

bioRxiv2024

Neural speech tracking in newborns: prenatal learning and contributing factors

IntroductionEarly language development in infants is being increasingly studied, though only recently with direct measurements of brain activity rather than with behavioral or physiological measurements. In the current study, we use electroencephalographic (EEG) recordings of 2-week-old infants to look for signs of prenatal learning and to investigate newborns abilities to process language. We also look at the influence of prenatal stress factors and at the predictive value of the newborns language processing abilities for later language development. MethodsSixty pregnant women played a rhyme to their abdomen twice a day from the 34th week of pregnancy until birth, to familiarize the fetus with the rhyme. At around 2 weeks after delivery (mean age 16 days), the newborns were exposed to the familiar rhyme as well as to an unfamiliar one while their EEG was recorded. Additionally, three manipulations of the familiar rhyme were played: (1) low-pass filtered, (2) with changed rhythm, and (3) inverted and played backwards. The data was analyzed to see how well the infant brain signal followed the speech envelope in each condition. Accounting for the heterogenous approach used for neural speech tracking in the literature, we used four methods, namely: (1) coherence, (2) Hilbert coherence, (3) temporal response functions (TRF), and (4) mutual information (MI). The maternal prenatal depression was evaluated with Edinburgh Prenatal Depression Score and the chronic fetal stress was measured from the hair cortisol levels of the 2 week-olds. The language development at 6 months of age was evaluated with the Bayley Scales. Results and discussionOverall, the results indicate the presence of prenatal learning, with the unfamiliar rhyme eliciting stronger cortical tracking (higher coherence and MI) than the familiar rhyme, which suggests stronger brain-to-speech coupling for the unfamiliar rhyme, perhaps deriving from more effort to process the unexpected stimulus. However, the original version of the familiar rhyme proved to be the easiest to track compared to the language- and rhythm-manipulations, (higher MI for the original rhyme than the language manipulation and higher coherence and mTRF correlation coefficients for the original rhyme than the rhythm manipulation). This indicates language discrimination and a prosodic-based learning of the familiar rhyme. Furthermore, there is an indication of phonotactic sensitivity at this young age, with less tracking (lower Hilbert coherence and lower mTRF correlation coefficients) of the low-pass filtered rhyme than the original version, indicating that the phonological cues erased by the filtering were important for the newborns ability to follow the rhyme. Furthermore, the mothers depression scores positively correlated with the infants tracking ability for the familiar rhyme. This suggests that a slightly lower mood was more stimulative for the fetal language development. The chronic fetal stress levels, however, were negatively correlated with the cortical tracking abilities. Importantly, the newborns cortical tracking was positively correlated with the infants language development at 6 months of age, underlining the predictive value of the early assessment of language processing. ConclusionPrenatal learning is well established, but evidence including (healthy) brain data in the first weeks of life is scarce. The current study shows that newborns can discriminate between a familiar and unfamiliar rhyme, while also highlighting the role of prosody in early language processing, and bringing new evidence of their sensitivity to phonotactic cues in auditory stimuli. Furthermore, the newborns ability to track a rhyme is correlated with their language development at 6 months. The newborns cortical tracking of the familiar rhyme is further increased by moderately low maternal mood, but decreased by fetal stress. Future studies with similar fine-grained linguistic designs but of older infants should teach us the timeline of what exactly is learned prenatally and at very early age in respect to language.

developmental biology↗

bioRxiv2024

The Temporal Dynamics of Aperiodic Neural Activity Track Changes in Sleep Architecture

The aperiodic (1/f-like) component of electrophysiological data - whereby power systematically decreases with increasing frequency, as quantified by the aperiodic exponent - has been shown to differentiate sleep stages. Earlier work, however, has typically focused on measuring the aperiodic exponent across a narrow frequency range. In this work, we sought to further investigate aperiodic activity during sleep by extending these analyses across broader frequency ranges and considering alternate model definitions. This included measuring knees in the aperiodic component, which reflect bends in the power spectrum, indicating a change in the exponent. We also sought to evaluate the temporal dynamics of aperiodic activity during sleep. To do so, we analyzed data from two sources: intracranial EEG (iEEG) from 106 epilepsy patients and high-density EEG from 17 healthy individuals, and measured aperiodic activity, explicitly comparing different frequency ranges and model forms. In doing so, we find that fitting broadband aperiodic models and incorporating a knee feature effectively captures sleep-stage-dependent differences in aperiodic activity as well as temporal dynamics that relate to sleep stage transitions and responses to external stimuli. In particular, the knee parameter shows stage-specific variation, suggesting an interpretation of varying timescales across sleep stages. These results demonstrate that examining broader frequency ranges with the more complex aperiodic models reveals novel insights and interpretations for understanding aperiodic neural activity during sleep.

neuroscience↗

bioRxiv2021

The brain tunes to unfamiliar voices during sleeping

The brain continues to respond selectively to environmental stimuli even during sleep. However, the functional role of such responses, and whether they reflect information processing or rather sensory inhibition is not fully understood. Here, we presented 17 human sleepers (14 females) with their own name and two unfamiliar first names, spoken by either a familiar voice (FV) or an unfamiliar voice (UFV), while recording polysomnography during a full nights sleep. We detected K-complexes, sleep spindles, and micro-arousals, and then assessed event-related potentials, oscillatory power as well as intertrial phase synchronization in response to the different stimuli presented during non-rapid eye movement (NREM) sleep. We show that UFVs evoke more K-complexes and micro-arousals than FVs. When both stimuli evoke a K-complex, we observed larger evoked potentials, higher oscillatory power in the high beta (>16Hz) frequency range, and stronger time-locking in the delta band (1-4 Hz) in response to UFVs relative to FVs. Crucially, these differences in brain responses disappear when no K-complexes are evoked by the auditory stimuli. Our findings highlight discrepancies in brain responses to auditory stimuli based on their relevance to the sleeper and propose a key role for K-complexes in the modulation of sensory processing during sleep. We argue that such content-specific, dynamic reactivity to external sensory information enables the brain to enter a sentinel processing mode in which it engages in the many important processes that are ongoing during sleep while still maintaining the ability to process vital information in the surrounding. Significance statementPrevious research has shown that sensory processing continues during sleep. Here, we studied the capacity of the sleeping brain to extract and process relevant sensory information. We presented sleepers with their own names and unfamiliar names spoken by either a familiar (FV) or an unfamiliar voice (UFV). During non-rapid eye movement (NREM) sleep, UFVs elicited more K-complexes and micro-arousals than FVs. By contrasting stimuli which evoked K-complexes, we demonstrate that UFVs triggered larger evoked potentials, stronger time-locking in the delta (1-4Hz) band, and higher oscillatory power (>16Hz) relative to FVs. These differences in brain responses disappeared when no K-complexes were evoked. Our results suggest a pivotal role for K-complexes in the selective processing of relevant information during NREM sleep.

neuroscience↗

bioRxiv2021

Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults

Previously, we demonstrated that precise temporal coordination between slow oscillations (SO) and sleep spindles indexes declarative memory network development (Hahn et al., 2020). However, it is unclear whether these findings in the declarative memory domain also apply in the motor memory domain. Here, we compared adolescents and adults learning juggling, a real-life gross-motor task. We found that improved task proficiency after sleep lead to an attenuation of the learning curve, suggesting a dynamic juggling learning process. We employed individualized cross-frequency coupling analyses to reduce inter and intra-group variability of oscillatory features. Advancing our previous findings, we identified a more precise SO-spindle coupling in adults compared to adolescents. Importantly, coupling precision over motor areas predicted overnight changes in task proficiency and learning curve, indicating that SO-spindle coupling is sensitive to the dynamic motor learning process. Our results provide first evidence that regionally specific precisely coupled sleep oscillations support gross-motor learning.

neuroscience↗

bioRxiv2019

Actigraphy in brain-injured patients - A valid measurement for assessing circadian rhythms?

BackgroundActigraphy has received increasing attention in classifying rest-activity cycles. However, in patients with disorders of consciousness (DOC), actigraphy data may be considerably confounded by passive movements, such as nursing activities and therapies. Consequently, this study verified whether circadian rhythmicity is (still) visible in actigraphy data from patients with DOC after correcting for passive movements. MethodsWrist actigraphy was recorded over 7-8 consecutive days in patients with DOC (diagnosed with unresponsive wakefulness syndrome [UWS; n=19] and [exit] minimally conscious state [MCS/EMCS; n=11]). Presence and actions of clinical and research staff as well as visitors were indicated using a tablet in the patients room. Following removal and interpolation of passive movements, non-parametric rank-based tests were computed to identify differences between circadian parameters of uncorrected and corrected actigraphy data. ResultsUncorrected actigraphy data overestimated the interdaily stability and intradaily variability of patients activity and underestimated the deviation from a circadian 24h rhythm. Only 5/30 (17%) patients deviated more than 1h from 24h in the uncorrected data, whereas this was the case for 17/30 (57%) patients in the corrected data. When contrasting diagnoses based on the corrected dataset, stronger circadian rhythms and higher activity levels were observed in MCS/EMCS as compared to UWS patients. Day-to-night differences in activity were evident for both patient groups. ConclusionOur findings indicate that uncorrected actigraphy data overestimates the circadian rhythmicity of patients activity, as nursing activities, therapies, and visits by relatives follow a circadian pattern itself. Therefore, we suggest correcting actigraphy data from patients with reduced mobility.

neuroscience↗

bioRxiv2019

On the development of sleep states in the first weeks of life

Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. In the present study we utilized a machine learning approach and investigated how EEG complexity and sleep stages evolve during the very first weeks of life. We analyzed 42 full-term infants which were recorded twice (at week two and five after birth) with full polysomnography. For sleep classification EEG signal complexity was estimated using multi-scale permutation entropy and fed into a machine learning classifier. Interestingly the babys brain signal complexity (and spectral power) revealed huge developmental changes in sleep in the first 5 weeks of life, and were restricted to NREM (\"quiet\") and REM (\"active sleep\") states with little to no changes in state wake. Data demonstrate that our classifier performs well over chance (i.e., >33% for 3-class classification) and reaches almost human scoring accuracy (60% at week-2, 73% at week-5). Altogether, these results demonstrate that characteristics of newborn sleep develop rapidly in the first weeks of life and can be efficiently identified by means of machine learning techniques.\n\nAuthor summaryThe organization of newborn sleep differs from adult sleep, and its ongoing maturation over time corresponds with cortical development. However, sleep scoring in this population is challenging given frequent artifacts in polysomnography (PSG) and absence of established staging criteria which results in low inter-scorer reliability. To investigate changes in the early brain activity, we analyzed large sample of newborn data at week 2 and 5 after birth. First we evaluated sleep that was previously scored visually, in terms of both entropy and oscillatory power. Next we accessed the performance of automatic sleep scoring based on machine learning compared with conventional, manual scoring. We observed clear developmental changes on the brain-level in the first 5 weeks of life in human newborns. These changes were limited to \"quiet\" NREM and \"active\" REM sleep. Also our classifier data demonstrated that we can classify well above chance and similar to human scorers using multi-scale permutation entropy (and just 6 EEG and 5 physiological channels).

neuroscience↗

bioRxiv2017

Standing Sentinel during Sleep: Continued Evaluation of Environmental Stimuli in the Absence of Consciousness

While it is a well-established finding that subjects own names (SON) or familiar voices are salient during wakefulness, we here investigated processing of environmental stimuli during sleep including deep N3 and REM sleep. Besides the effects of sleep depth we investigated how sleep-specific EEG patterns (i.e. sleep spindles and slow oscillations [SOs]) relate to stimulus processing. Using 256-channel EEG we studied processing of auditory stimuli by means of event-related oscillatory responses (de-/ synchronisation, ERD/ERS) and potentials (ERPs) in N = 17 healthy sleepers. We varied stimulus salience by manipulating subjective (SON vs. unfamiliar name) and paralinguistic emotional relevance (familiar vs. unfamiliar voice, FV/UFV). Results reveal that evaluation of voice familiarity continues during all NREM sleep stages and even REM sleep suggesting a sentinel processing mode of the human brain in the absence of wake-like consciousness. Especially UFV stimuli elicit larger responses in a 1-15 Hz range suggesting they continue being salient. Beyond this, we find that sleep spindles and the negative slope of SOs attenuate information processing. However, unlike previously suggested they do not uniformly inhibit information processing, but inhibition seems to be scaled to stimulus salience.\n\nFundingCB is supported by the Konrad-Adenauer-Stiftung e.V. and CB, MW and DPJH are supported by a grant from the Austrian Science Fund FWF (Y-777). CB, RdG, MW and DPJH are also supported by the Doctoral College \"Imaging the Mind\" (FWF; W1233-G17).

neuroscience↗