Karim Jerbi

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

This study investigates how LSD alters brain dynamics using MEG and machine learning, finding increased peak frequencies, reduced oscillatory power, and changes in spectral complexity.

Task-optimized neural networks reveal distinct contributions of specialized and broader visual learning to neural representations of face familiarity

The paper uses task-optimized neural networks to compare how face and object recognition models align with neural activity in visual cortex during face familiarity processing.

SMA beta bursts reveal distinct mechanisms of anticipatory motor control in children

The paper investigates anticipatory postural control in children using magnetoencephalography, revealing adult-like neural mechanisms and an additional compensatory mechanism for postural stabilization.

Critical dynamics in spontaneous EEG predict perturbational complexity in disorders of consciousness with measurable evoked responses

The paper shows that resting-state EEG criticality features predict perturbational complexity index (PCI) in disorders of consciousness, but only in specific patient subgroups.

LSD Relaxes Structural Constraints on Brain Dynamics: A Connectome Harmonic MEG Study

This study uses MEG and connectome harmonics to show LSD reduces structure-function coupling in low-frequency brain activity, linking this to ego dissolution and altered network dynamics.

Shaped by meaning, weighted by reliability: New insights into multisensory integration

This study investigates how semantic context and sensory reliability influence multisensory integration in decision-making, revealing two distinct pathways for combining sensory information.

Behavioral Imitation with Artificial Neural Networks Leads to Personalized Models of Brain Dynamics During Videogame Play

This paper uses imitation learning with ANNs to model individual brain dynamics during videogame play, finding that subject-specific models better predict brain activity than generic ones.

Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use

The paper investigates how intrinsic neural oscillations relate to verbal learning performance and encoding strategies using resting-state magnetoencephalography and the California Verbal Learning Test.

Identifying Executive Function Profiles in Children's Design Fluency: A Clustering Study

This study uses clustering to identify executive function profiles in children's design fluency tasks, finding that overall task measures better distinguish profiles than minute-by-minute data.

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

This study shows caffeine increases brain complexity and shifts neural dynamics towards criticality during sleep, with age-dependent effects observed in REM sleep.

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

MEEGNet is a Python library for applying convolutional neural networks to MEG data, achieving high accuracy in classification tasks while offering interpretability tools.

Hitting the right pitch: Cortical tracking of speech fundamental frequency in auditory and somatomotor regions

This study investigates how brain rhythms track speech fundamental frequency (F0) across different speech rates using magnetoencephalography, finding coupling in auditory and somatomotor regions.

Meditation induces shifts in neural oscillations, braincomplexity and critical dynamics: Novel insights fromMEG

This study examines how meditation affects neural oscillations, complexity, and critical dynamics using MEG in expert monks, finding increased complexity and reduced gamma-band long-range correlations during meditation.

Structure-function coupling and decoupling during movie-watching and resting-state: Novel insights bridging EEG and structural imaging

This paper quantifies structure-function coupling during movie-watching and resting-state using EEG and diffusion imaging, revealing how brain anatomy shapes electrophysiological dynamics.

Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis

The paper uses EEG, machine learning, and multivariate statistics to identify neural features predicting hypnotic susceptibility, finding that aperiodic EEG activity at rest is the strongest predictor.

Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia

The paper investigates how criticality in resting-state EEG correlates with perturbational complexity and consciousness during anesthesia, finding that unconscious states show reduced criticality.

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

This study finds that the optimal regularization parameter for MEG connectivity estimation is smaller than that for source imaging and provides open-source code for analysis.

Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study

This study uses intracerebral EEG to show that motor cortex activity encodes hand movement direction during both planning and execution, with different frequency features involved in each phase.

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

This paper evaluates the impact of class imbalance on ML performance in neuroscience, showing that Accuracy is misleading and recommending Balanced Accuracy and Random Forest with stratified cross-validation.

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness

This study shows that the aperiodic EEG component better reflects consciousness levels in disorders of consciousness than oscillatory components, and its changes under anesthesia correlate with recovery outcomes.

Rhythmic information sampling in the brain during visual recognition

This study investigates rhythmic sampling of visual information in the brain during recognition tasks using MEG, finding oscillatory processing in early visual areas across stimulus time.

Differential and overlapping effects between exogenous and endogenous attention shape perceptual facilitation during visual processing

This study investigates how exogenous and endogenous attention differentially and overlap in shaping perceptual facilitation during visual processing using EEG and machine learning.

Distinct trajectories in low-dimensional neural oscillation state space track dynamic decision-making in humans

This study tracks dynamic decision-making in humans using low-dimensional neural oscillation states from MEG data, showing distinct roles for alpha and beta bands in choice commitment and motor execution.

Altered brain criticality in Schizophrenia: New insights from MEG

This study examines altered criticality properties in schizophrenia patients using MEG data, finding differences in self-similarity and multifractality gradients compared to healthy controls.

Neural oscillations track natural but not artificial fast speech: Novel insights from speech-brain coupling using MEG

The paper investigates how neural oscillations track natural versus artificial fast speech using MEG, finding that natural fast speech elicits stronger cortico-acoustic coupling than artificially accelerated speech.

Tensorpac : an open-source Python toolbox for tensor-based Phase-Amplitude Couplingmeasurement in electrophysiological brain signals

Tensorpac provides an open-source Python toolbox for efficient and comprehensive phase-amplitude coupling analysis in electrophysiological brain signals.

Decoding the neural dynamics of oculomotor decision making in humans

This study uses intracerebral EEG recordings in humans to show that sustained high gamma activity in fronto-parietal cortex distinguishes free choice from instructed saccades during a delayed oculomotor decision task.

NeuroPycon: An open-source Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

NeuroPycon provides a Python toolkit for reproducible brain connectivity analysis by integrating neuroimaging tools into a unified framework with parameter files for replication.

Disentangling presentation and processing times in the brain

This study uses a reverse correlation paradigm to show that visual information processed at different times during a fixation affects neural responses and behavior, with some differences linked to the N170 ERP component.

Estimation of task-based modulations in functional connectivity with MEG: a comparison of methods

This study compares two methods for estimating task-based modulations in functional connectivity using MEG, finding that computing differences in correlation coefficients (DCC) outperforms semipartial canonical correlation analysis (SP-CCA).