Biology subjects

Harel, Y.

Publications and source records attributed to Harel, Y..

6 recordsLinked to original sources

bioRxiv2026

LSD Relaxes Structural Constraints on Brain Dynamics and Default Mode Decoupling Tracks Ego Dissolution

Psychedelics profoundly alter conscious experience, yet how they reshape the relationship between brain anatomy and electrophysiological dynamics remains unclear. Here we use source-localized magnetoencephalography mapped onto connectome harmonics to quantify structure-function coupling in humans under lysergic acid diethylamide (LSD) and placebo. LSD induces a robust decoupling of low-frequency (theta, alpha and beta) activity from anatomical constraints, indicating a global loosening of structure-aligned large-scale dynamics. High-frequency gamma activity shows selective reorganization rather than uniform disruption. Decoupling within core default-mode network regions predicts ego dissolution intensity across individuals, linking frequency-selective DMN reorganization to subjective loss of self. Functional decoding further reveals system-specific rebalancing: visual and attentional systems preferentially decouple while auditory networks exhibit strengthened coupling. Together, these findings provide electrophysiological evidence that psychedelic states emerge from a frequency-dependent relaxation of structural constraints on brain activity and identify default-mode reorganization as a neural correlate of ego dissolution. These results offer a mechanistic framework for understanding how LSD may exert therapeutic effects by transiently relaxing rigid structural constraints and enhancing dynamical flexibility within networks involved in self-related processing.

neuroscience↗

bioRxiv2026

LSD Reconfigures Cortical Dynamics Through Faster Brain Rhythms and Increased Fractal Dimension

Lysergic acid diethylamide (LSD) profoundly alters conscious experience, yet the electrophysiological mechanisms by which it reshapes neural dynamics remain incompletely understood. A hallmark of psychedelic states is widespread cortical desynchronization, typically inferred from reductions in spectral power, but whether such effects reflect genuine weakening of neural oscillations or are confounded by shifts in oscillatory peak frequencies remains unresolved. Here, we address this gap by combining source-resolved magnetoencephalography (MEG), spectral parameterization, temporal complexity metrics, and interpretable machine learning in an LSD versus placebo design, with and without music. We show that LSD induces robust, spatially structured increases in alpha and beta peak frequencies alongside genuine attenuation of oscillatory power, with these effects displaying partly dissociable cortical patterns. Beyond rhythmic activity, LSD is associated with flattening of the aperiodic 1/f spectral slope and increased neural signal fractality and complexity, preferentially affecting sensory, language, emotion, and imagery-related networks while sparing motor cortex. Machine-learning analyses further identify peak-frequency shifts, aperiodic parameters, and complexity measures as key discriminators of the psychedelic state. Music does not robustly amplify these neural signatures and instead shows a trend toward attenuation. Together, these findings provide a comprehensive electrophysiological account of how LSD reorganizes large-scale human brain dynamics and highlight features that may differentiate its neural signature from that of other psychedelics.

neuroscience↗

bioRxiv2025

MEEGNet: An open source python library for the application of convolutional neural networks to MEG

Artificial Neural Networks (ANNs) are rapidly gaining traction in neuroscience, proving invaluable for decoding and modeling brain signals from techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Although these networks are beginning to find applications in magnetoencephalography (MEG), their use in this domain is still in the early stages. Here, we introduces MEEGNet, a novel Python library paired with an intuitive convolutional neural network (CNN) architecture designed primarily for MEG data, yet adaptable to EEG signals. The MEEGNet model was trained and cross-validated using MEG data from 643 participants across four classification tasks, including auditory and visual stimulus classification and age prediction. Our model achieves competitive performance across all tasks, with a notable balance of accuracy and efficiency--for instance, reaching 92.70% test accuracy in an auditory vs. visual classification task while maintaining shorter training times than other architectures. The MEEGNet pipeline also integrates latent space visualization tools, adapted for MEG and EEG data. These include saliency maps and Grad-CAM methods, which enhance the interpretability of ANN-based classification and help address the black-box critique of such models. Importantly, the MEEGNet library is designed for extensibility, allowing the neuroscience and machine learning communities to add functionalities and ANN models. By prioritizing usability, transparency, and interpretability, MEEGNet empowers MEG- and EEG-based research with a user-friendly and modular framework.

neuroscience↗

bioRxiv2024

Mapping the developmental structure of stereotyped and individual-unique behavioral spaces in C. elegans

Developmental patterns of behavior are variably organized in time and among different individuals. However, long-term behavioral diversity was previously studied using pre-defined behavioral parameters, representing a limited fraction of the full individuality structure. Here, we continuously extracted [~]1.2 billion body postures of [~]2,200 single C. elegans individuals throughout their full development time, to create a complete developmental atlas of stereotyped and individual-unique behavioral spaces. Unsupervised inference of low-dimensional movement modes of each single individual revealed a dynamic developmental trajectory of stereotyped behavioral spaces and exposed unique behavioral trajectories of individuals that deviate from the stereotyped patterns. Moreover, classification of behavioral spaces within tens of neuromodulatory- and environmentally-perturbed populations efficiently uncovered plasticity in the temporal structures of stereotyped behavior and individuality. These results present a comprehensive atlas of continuous behavioral dynamics across development time and a general framework for unsupervised dissection of shared and unique developmental signatures of behavior.

neuroscience↗

bioRxiv2022

Early-life experience reorganizes neuromodulatory regulation of stage-specific behavioral responses and individuality types during development

Early-life experiences may promote stereotyped behavioral alterations that are dynamic across development time, but also behavioral responses that are variable among individuals, even when initially exposed to the same stimulus. Here, by utilizing longitudinal monitoring of C. elegans individuals throughout development we show that behavioral effects of early-life starvation are exposed during early and late developmental stages and buffered during intermediate stages of development. We further found that both dopamine and serotonin shape the discontinuous behavioral responses by opposite and temporally segregated functions across development time. While dopamine buffers behavioral responses during intermediate developmental stages, serotonin promotes behavioral sensitivity to stress during early and late stages. Interestingly, unsupervised analysis of individual biases across development uncovered multiple individuality types that coexist within stressed and unstressed populations and further identified experience-dependent effects on their composition. These results provide insight into the complex temporal regulation of behavioral plasticity across developmental timescales, structuring shared and unique individual responses to early-life experiences.

neuroscience↗

bioRxiv2022

Class imbalance should not throw you off balance: Choosing classifiers and performance metrics for brain decoding with imbalanced data

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG) and magnetoencephalography (MEG). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.

neuroscience↗