Biology subjects

Nelli, S. M.

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

4 recordsLinked to original sources

bioRxiv2026

Stress Recovery Through Mindful Breathing: Convergent NeuralSignatures in Around-Ear and Scalp EEG

Mindfulness-based interventions effectively reduce stress and anxiety, yet the neural mechanisms underlying contemplative practices and their optimal implementation parameters remain poorly understood. A critical barrier to real-world application is the absence of validated minimally invasive neural recording technologies. Here, we simultaneously recorded full-coverage scalp and around-ear EEG during a 2x2x2 factorial design manipulating (1) cognitive state (mental arithmetic stress vs. passive viewing), (2) recovery strategy (mindful breathing meditation vs. mind-wandering), and (3) sensory context (eyes open vs. eyes closed). Mental arithmetic robustly elevated subjective stress and modulated canonical oscillatory patterns: increased midline frontal theta power (3-7 Hz), suppressed posterior alpha power (10-12 Hz), and enhanced posterior beta and gamma power (25-48 Hz). All rest conditions reduced subjective stress following stress induction, with eyes-closed mindful breathing producing maximal reduction. Critically, mindful breathing differentially modulated temporal beta and gamma power in a context-dependent manner, with effects determined by prior cognitive state and eye position. Eyes-closed meditation maximally suppressed gamma power within 20 seconds following arithmetic stress, whereas eyes-open meditation alone was sufficient for gamma suppression following passive viewing. Around-ear electrodes detected these stress and meditation signatures with comparable fidelity to scalp recordings. These findings reveal that mindful breathing engages rapid, context-dependent neural regulation mechanisms and establish that wearable EEG can reliably capture these dynamics, enabling real-world stress monitoring and mindfulness guidance. Impact StatementMindfulness-based interventions reduce stress, yet their neural mechanisms and optimal implementation remain unclear, partly due to limited real-world neural measurement tools. Using simultaneous scalp and around-ear EEG, we show that mindful breathing rapidly and context-dependently regulates stress-related brain activity via changes in high-frequency EEG oscillations (i.e., gamma band activity), with effects shaped by prior cognitive state and eye condition. Importantly, around-ear EEG captured these neural signatures with fidelity comparable to scalp recordings, enabling wearable neurotechnology for real-world stress monitoring and personalized mindfulness guidance.

neuroscience↗

bioRxiv2026

Training constrains neural routes to knowledge assembly

A hallmark of human intelligence is the ability to rapidly restructure existing knowledge when new information reveals unexpected connections, a capacity termed knowledge assembly. This cognitive flexibility distinguishes human learning from artificial systems, which catastrophically forget when acquiring new relationships. Understanding the mechanisms underlying flexible knowledge reorganization thus has implications for continual learning in both humans and algorithms. Here, using electroencephalography we show that successful knowledge assembly depends on temporally orchestrated reactivation of prior neural representations. Critically, training schedules bias learners toward different representational strategies: blocked training promotes compressed certainty-weighted codes, while interleaved training yields high-dimensional factorized representations. Vanilla recurrent networks failed to develop human-like certainty geometries despite identical training, revealing missing computational principles in current artificial systems. These findings demonstrate that cognitive flexibility emerges through creative reuse of learned representations, with training history constraining available neural routes to reorganization.

neuroscience↗

bioRxiv2021

Neural knowledge assembly in humans and deep networks

Human understanding of the world can change rapidly when new information comes to light, such as when a plot twist occurs in a work of fiction. This flexible "knowledge assembly" requires few-shot reorganisation of neural codes for relations among objects and events. However, existing computational theories are largely silent about how this could occur. Here, participants learned a transitive ordering among novel objects within two distinct contexts, before exposure to new knowledge that revealed how they were linked. BOLD signals in dorsal frontoparietal cortical areas revealed that objects were rapidly and dramatically rearranged on the neural manifold after minimal exposure to linking information. We then adapt stochastic online gradient descent to permit similar rapid knowledge assembly in a neural network model.

neuroscience↗

bioRxiv2020

Neural state space alignment for magnitude generalization in humans and recurrent networks

A prerequisite for intelligent behaviour is to understand how stimuli are related and to generalise this knowledge across contexts. Generalisation can be challenging when relational patterns are shared across contexts but exist on different physical scales. Here, we studied neural representations in humans and recurrent neural networks performing a magnitude comparison task, for which it was advantageous to generalise concepts of "more" or "less" between contexts. Using multivariate analysis of human brain signals and of neural network hidden unit activity, we observed that both systems developed parallel neural "number lines" for each context. In both model systems, these number state spaces were aligned in a way that explicitly facilitated generalisation of relational concepts (more and less). These findings suggest a previously overlooked role for neural normalisation in supporting transfer of a simple form of abstract relational knowledge (magnitude) in humans and machine learning systems.

neuroscience↗