Biology subjects

Mirjalili, S.

Publications and source records attributed to Mirjalili, S..

8 recordsLinked to original sources

bioRxiv2026

Individual differences in post-encoding sleep continuity predict context memory accuracy and supporting ERPs in younger and older adults

Post-encoding sleep plays an essential role in episodic memory consolidation. Much of the existing literature on sleep and memory relies on deprivation paradigms or laboratory-controlled sleep. Relatively few studies have examined how naturalistic post-encoding sleep relates to memory retrieval and its supporting neural activity, or whether age-related impairments in this sleep are linked to those in episodic memory. In the present study, we used actigraphy and electroencephalography to examine how post-encoding sleep quality relates to context memory performance and retrieval-related ERPs supporting performance in younger and older adults. Participants encoded object-scene pairs and were tested on matching and mismatching pairs after a 96-hour sleep-filled delay. We found that greater post-encoding sleep continuity predicted better delayed context memory performance for mismatching pairs across age groups. Post-encoding sleep continuity was also associated with larger ERP differences between context hits and misses for context-matching pairs, for ERP effects associated with post-retrieval monitoring operations across age groups. Together, these findings suggest that more continuous, naturalistic post-encoding sleep facilitates episodic memory performance and neural mechanisms supporting episodic memory retrieval across adult age.

neuroscience↗

bioRxiv2026

Age Alters the Relationship between Post-Encoding Sleep Quality and Context Memory Neural Reinstatement

Sleep supports episodic memory consolidation, yet it remains unclear how naturalistic post-encoding sleep quality relates to the neural reinstatement of episodic representations across adulthood. The present study examined whether sleep discontinuity during the retention interval predicted delayed context memory and encoding-retrieval similarity (ERS) of EEG in younger and older adults. Participants completed an object-scene context memory task with immediate and delayed retrieval, while EEG was recorded during encoding and retrieval. Actigraphy was used to measure sleep across the post-encoding retention period, and principal component analysis identified sleep discontinuity and sleep time components. Behavioral results showed that greater post-encoding sleep discontinuity, but not sleep time, was associated with poorer delayed memory accuracy for mismatching object-context pairs across age. ERS analyses further showed that greater sleep discontinuity was associated with reduced ERS for correctly rejected mismatching pairs across frontal and posterior spatiotemporal clusters. Age moderated sleep-ERS associations: greater sleep discontinuity was generally related to lower ERS in younger adults, whereas some spatiotemporal clusters showed positive associations in older adults, potentially reflecting compensatory or effortful retrieval-related processing in poorer sleepers. Together, these findings suggest that sleep continuity during the post-encoding retention interval is important for preserving high-fidelity episodic representations needed for later context discrimination. More broadly, the results demonstrate that naturalistic sleep fragmentation is linked to both behavioral memory outcomes and neural reinstatement across adults.

neuroscience↗

bioRxiv2025

The Role of Sleep Duration and Age in Neural Reactivation and Episodic Memory Performance Across the Adult Lifespan

Sleep is essential for episodic memory consolidation, involving the reactivation of neural patterns during encoding and retrieval. This study examined how sleep duration influences encoding-retrieval similarity (ERS) and memory performance across the adult lifespan. Fifty-five adults (18-79 years) participated in a seven-day protocol, combining actigraphy-based sleep monitoring with EEG and memory assessments during a paired associate learning task. Significant correlations between sleep duration and ERS were observed, with clusters showing positive and negative associations across brain regions. In match context trials, ERS in the left frontal region correlated with sleep duration, and age moderated these effects. In mismatch context trials, positive correlations were identified in the left posterior, left frontal, and right posterior regions. These results highlight the role of sleep in modulating neural reactivation patterns during memory retrieval and emphasize the influence of age. The study advances our understanding of how sleep variability impacts memory consolidation and provides a basis for exploring targeted sleep interventions. Clinical RelevanceThis study highlights the impact of habitual sleep variability on neural reactivation patterns and memory retrieval across the adult lifespan. The findings emphasize the moderating role of age and suggest that improving sleep duration may enhance episodic memory performance. These insights could guide clinicians in developing personalized sleep interventions to support cognitive health and mitigate age-related memory decline.

neuroscience↗

bioRxiv2025

Hippocampal transformations occur along dimensions of memory interference

The role of the hippocampus in resolving memory interference has been greatly elucidated by considering the relationship between the similarity of visual stimuli (input) and corresponding similarity of hippocampal activity patterns (output). However, these input-output functions can take surprisingly different forms. Here, we reconcile seemingly conflicting findings by considering the possibility that the hippocampus prioritizes different dimensions of visual similarity across different stages of learning. First, we generated a set of natural scene images from two visual categories and rigorously characterized visual similarity using a wide array of methods: neural networks, artificial intelligence models, and human perceptual and memory decisions. We then identified two orthogonal dimensions of visual similarity that each predicted memory interference, but that did so at distinct stages of learning. Using high-resolution fMRI, we then tested for dimension-specific input-output functions within the hippocampus. Within CA3 and dentate gyrus (CA3/DG), we show that dimensions of visual similarity were inverted (repulsion) at stages of learning when they contributed to memory interference. In contrast, CA3/DG preserved similarity when dimensions did not contribute to interference. These findings reveal that hippocampal representations of visually-similar stimuli are highly dynamic and critically depend on the dimensions of similarity that currently contribute to memory interference.

neuroscience↗

bioRxiv2024

Investigating the Impact of Habitual Sleep Quality on Episodic Memory Performance: An EEG-Based Representational Similarity Analysis

Sleep is crucial for episodic memory consolidation, yet the impact of habitual sleep quality on memory performance remains underexplored. This study investigates the relationship between sleep quality and episodic memory retrieval using EEG-based representational similarity analysis (RSA). Thirty-six participants wore wrist accelerometers for one week to capture habitual sleep patterns, including total sleep time and restlessness. Memory performance was assessed through a paired associate learning task, with EEG data recorded during encoding and retrieval phases. RSA was applied to EEG oscillatory power across time-frequency windows to examine the neural similarity between encoding and retrieval. The results showed both positive and negative correlations between sleep metric and memory performance, with sleep restlessness being linked to both increases and decreases in neural similarity across specific clusters. These findings emphasize the important role of sleep quality in shaping the neural processes underlying episodic memory retrieval, indicating a strong connection between sleep patterns and memory function.

neuroscience↗

bioRxiv2024

Nanoparticle-Supported, Rapid, Digital Quantification of Neutralizing Antibodies Against SARS-CoV-2 Variants

The measurement of neutralizing immune responses to viral infection is essential, given the heterogeneity of human immunity and the emergence of new virus strains. However, neutralizing antibody (nAb) assays often require high-level biosafety containment, sophisticated instrumentation, and long detection times. Here, as a proof-of-principle, we designed a nanoparticle-supported, rapid, electronic detection (NasRED) assay to assess the neutralizing potency of monoclonal antibodies (mAbs) against SARS-CoV-2. The gold nanoparticles (AuNPs) coated with human angiotensin-converting enzyme 2 (ACE2) protein as nAb potency reporters were mixed with the mAbs to be tested, as well as streptavidin-conjugated multivalent spike (S) protein or their receptor binding domains (RBD). High-affinity and ACE2-competitive nAbs alter the S (or RBD)-to-ACE2 binding level and modulate AuNP cluster formation and precipitation. The amount of free-floating AuNP reporters is quantified by a semiconductor-based readout system that measures the AuNPs optical extinction, producing nAb signals that can differentiate SARS-CoV-2 variants (Wuhan-Hu-1, Gamma, and Omicron). The modular design nature, short assay time (less than 30 minutes), and portable and inexpensive readout system make this NasRED-nAb assay applicable to measuring vaccine potency, immune responses to infection, and the efficacy of antibody-based therapies.

immunology↗

bioRxiv2024

Nanoparticle-Supported, Rapid, and Electronic Detection of SARS-CoV-2 Antibodies and Antigens at Attomolar Level

Major challenges remain to precisely detect low-abundance proteins from diverse biofluids in a rapid and cost-effective manner. Here we present a gold nanoparticle (AuNP)-supported, rapid electronic detection (NasRED) platform with sub-femtomolar sensitivity and high specificity. Surface-functionalized AuNPs act as multivalent detectors to recognize target antigens and antibodies through high-affinity binding, subsequently forming aggregates precipitated in a microcentrifuge tube and producing a solution color change. The optical extinction of residual floating AuNPs is digitized using a customized circuitry incorporating inexpensive optoelectronic elements and feedback mechanisms for stabilized readout. Uniquely, NasRED introduces active fluidic forces through engineered centrifugation and vortex agitation, effectively promoting protein detection at low concentrations and accelerating signal generation. Using SARS-CoV-2 as a demonstration, NasRED enables detection of both antibodies and antigens from a small sample volume (6 {micro}L), distinguishes the viral antigens from those of human coronaviruses, and delivers test results in a short time (as fast as <15 min). The limits of detection (LoDs) for antibody detection are approximately 49 aM (7 fg/mL) in phosphate-buffered saline, or >3,000 times more sensitive than Enzyme-Linked Immunosorbent Assay (ELISA), [~]76 aM (11 fg/mL) in human pooled serum and in the femtomolar range in diluted whole blood. For nucleocapsid protein detection, NasRED LoDs are [~]190 aM (10 fg/mL) in human saliva and [~]2 fM (100 fg/mL) in nasal fluid. Unlike laboratory-based ELISA platforms, NasRED is a one-pot, in-solution assay that eliminates the needs for washing, labeling, expensive instrumentation or highly trained operators. With low reagent costs and a compact system footprint, this modular digital platform is well-suited for accurate, near-patient diagnosis and screening of a wide range of infectious and chronic diseases.

microbiology↗

bioRxiv2024

More than the sum of its parts: investigating episodic memory as a multidimensional cognitive process

Why do we remember some events but forget others? Previous studies attempting to decode successful vs. unsuccessful brain states to investigate this question have met with limited success, potentially due, in part, to assessing episodic memory as a unidimensional process, despite evidence that multiple domains contribute to episodic encoding. Using a novel machine learning algorithm known as "transfer learning", we leveraged visual perception, sustained attention, and selective attention brain states to better predict episodic memory performance from trial-to-trial encoding electroencephalography (EEG) activity. We found that this multidimensional treatment of memory decoding improved prediction performance compared to traditional, unidimensional, methods, with each cognitive domain explaining unique variance in decoding of successful encoding-related neural activity. Importantly, this approach could be applied to cognitive domains outside of memory. Overall, this study provides critical insight into the underlying reasons why some events are remembered while others are not.

neuroscience↗