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Ameen, M. S.

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

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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↗

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↗

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↗