Biology subjects

Ameen, M. S.

Publications and source records attributed to Ameen, M. S..

6 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↗

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

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↗

bioRxiv2023

Motor adaptation is facilitated by sleep-associated modulation of beta oscillations

Motor adaptation reflects the ability of the brains sensorimotor system to flexibly deal with environmental changes to generate effective motor behaviour. Whether sleep contributes to the consolidation of motor adaptation remains controversial. In this study, we investigated the impact of sleep on motor adaptation and its neurophysiological correlates in a novel motor adaptation task that leverages a highly automatized motor skill, i.e., typing. We hypothesized that sleep-associated memory consolidation would benefit motor adaptation and induce modulations in task-related beta band (13-30Hz) activity during adaptation. Healthy young male experts in typing on the regular computer keyboard were trained to type on a vertically mirrored keyboard while brain activity was recorded using electroencephalography (EEG). Typing performance was assessed either after a full night of sleep with polysomnography or a similar period of daytime wakefulness. Results showed improved motor adaptation performance after nocturnal sleep but not after daytime wakefulness, and decreased beta power (a) during mirrored typing as compared to regular typing, and (b) in the post-sleep vs. the pre-sleep mirrored typing sessions. Furthermore, the slope of the EEG signal, a measure of aperiodic brain activity, decreased during mirrored as compared to regular typing. Changes in the EEG spectral slope from pre- to post-sleep mirrored typing sessions were correlated with changes in task performance. Finally, increased fast sleep spindle density (13-15Hz) during the night following motor adaptation training was predictive of successful motor adaptation. These findings suggest that post-training sleep modulates neural activity mechanisms supporting adaptive motor functions.

neuroscience↗