Biology subjects

Hakkak Moghadam Torbati, A.

Publications and source records attributed to Hakkak Moghadam Torbati, A..

4 recordsLinked to original sources

bioRxiv2026

Neuronal selectivity and geometric alignment in the human hippocampus support abstract generalization

Abstract representations allow the brain to extract shared structure across different experiences and generalize knowledge beyond individual situations. Although previous studies have shown that representational geometry plays a critical role in supporting abstraction, it remains unclear how the composition of neuronal populations gives rise to such generalizable representations. Here, we investigated how neuronal selectivity shapes the emergence of abstract representations by combining a controlled computational model with analyses of human hippocampal single-neuron recordings. We first manipulated the composition of artificial neural populations to test whether increasing task-related information alone is sufficient to improve cross-context generalization. Although increasing stimulus- and response-selective neurons enhanced encoding strength, it did not improve generalization across contexts. In contrast, introducing category-selective neurons increased cross-context generalization, demonstrating that the type of information represented by a population is critical for abstraction. Analyses of human hippocampal neurons revealed a similar principle: category-like and identity-like neurons produced comparable increases in stimulus encoding, but category-like neurons produced substantially stronger improvements in cross-context generalization. Further analyses showed that category-like neurons influenced abstraction by reshaping population geometry. Specifically, category-axis alignment across contexts, rather than the strength of category-related separation, was the geometric property most strongly associated with generalization. Mediation analysis further indicated that category-like neurons contribute to abstraction primarily through their ability to increase geometric alignment across contexts. Together, these findings reveal a population-level mechanism linking neuronal selectivity to abstract computation and suggest that flexible generalization depends not simply on increasing neural information, but on organizing information into geometries that preserve task-relevant relationships across changing conditions.

neuroscience↗

bioRxiv2026

Temporal structure of postural sway reveals altered cortical postural coupling in aging and stroke: insights from nonlinear dynamics and state-space analysis

BackgroundBalance maintenance in humans is not only a mechanical process, but it also relies on continuous interactions between cortical activity and body dynamics. Alterations in postural sway are commonly observed in aging and stroke and are frequently used to assess balance impairment. However, similar balance deficits do not necessarily reflect similar underlying sensorimotor control mechanisms. Therefore, investigating brain-body interactions and their relationship to characteristics of postural behavior may provide deeper insights into the neural processes underlying balance dysfunction in these populations. ObjectiveTo determine whether brain-body coupling is associated with characteristics of postural behavior captured by the temporal organization of postural fluctuations beyond conventional magnitude-based measures of postural sway, and whether these relationships differ between stroke survivors, healthy older adults, and young adults. MethodsEEG and center-of-pressure (CoP) signals were recorded simultaneously in stroke survivors (n = 12), healthy older adults (n = 18), and young controls (n = 17) during quiet standing under 4 different manipulated sensory conditions. Sway-based corticokinematic coherence (CKC) as well as linear and nonlinear features (sample entropy, SE; fractal dimension, FD) of CoP were extracted. Linear mixed-effects model assessed associations between features and CKC, and model performance was compared using Akaike Information Criterion. Multidimensional state vectors were constructed from CKC, linear and nonlinear CoP features, and Euclidean distances between consecutive states in the standardized feature space were computed to quantify condition-dependent transitions in brain-body control organization. ResultsNonlinear features showed significant, group- and feature-dependent associations with CKC in the mediolateral direction, driven by significant SE and FD effects in the stroke group and an SE effect in the older group, while no significant associations were observed in the young group. Including nonlinear features in baseline models containing only linear CoP features significantly improved model fit. CKC alone showed low classification performance (AUC 50 to 65), whereas combining CKC with linear and nonlinear features improved group discrimination (AUC up to 0.86). State-space transition analysis revealed larger condition-dependent transitions in stroke participants compared with healthy older adults, particularly going from eyes open to eyes closed when standing on foam. ConclusionBrain-body coupling during standing may be understood more comprehensively by factoring in the temporal structure of fluctuations rather than their amplitude alone. These findings support the use of nonlinear dynamical features, combined with CKC, as potential markers of balance impairment.

neuroscience↗

bioRxiv2026

Assessment of sensorimotor cortical beta oscillations from peripheral electromyography and force recordings

The beta oscillations of the human primary sensorimotor cortex (SM1) play a crucial role in regulating motor and cognitive behavior in health and disease. However, their assessment relies on costly and complex neuroimaging techniques, limiting scalability and translational applications. We present a novel method for assessing beta oscillations from easily obtained peripheral electromyography and force recordings. We show that movement-induced modulations in SM1 beta oscillations can be assessed from the electromyography or force recordings of a contracted contralateral hand muscle. We demonstrate the fidelity of this method in young and elderly healthy participants and in Parkinsons disease patients. We also demonstrate that the resting-state SM1 beta interhemispheric coupling can be assessed from an interhand coupling between the electromyography or force of contracted homologous hand muscles. This methodology enables scalable and cost-effective investigations of beta oscillations for all fields of human neuroscience and for the development of accessible disease/therapeutic markers.

neuroscience↗

bioRxiv2025

Designing a Model to Detect Beta Burst in EEG Using Nonlinear Dynamic Features Based on Machine Learning

BackgroundBeta bursts are brief, transient increases in beta-band (13-30 Hz) EEG activity that play a key role in motor control, particularly in processes like movement initiation and inhibition. While most existing methods detect these bursts using simple amplitude thresholds, they often ignore variability in burst duration and task context. More advanced techniques exist but are computationally demanding, often opaque, and dependent on large datasets and strong modeling assumptions. ObjectivesThis study aims to develop an automated, machine learning-based approach to classify beta bursts using nonlinear dynamic features, considering both burst duration and task condition. MethodEEG data were collected from 26 healthy, right-handed participants during three motor tasks: (1) a right-hand isometric pinch grip at 10% maximum voluntary contraction (MVC), (2) rhythmic right-hand finger opening-closing in response to auditory cues (3-4 seconds), and (3) the same right-hand opening-closing task and concurrent with a steady left-hand isometric pinch grip at 10% MVC. Beta bursts were extracted from the left motor cortex, categorized by duration (short, medium, long), and time-locked to task events. From each burst, four nonlinear features, Fractal Dimension (FD), Wavelet Entropy (WE), Sample Entropy (SE), and Nonlinear Energy Operator (NEO) were calculated to train machine learning (ML) models. ResultsStatistical tests and feature selection revealed that FD, SE, and WE varied significantly with burst duration and task type, while NEO was more limited in sensitivity. ML models trained on these features achieved up to 91.1% validation and 85.7% test accuracy, especially when distinguishing bursts of different durations within the same task. ConclusionsThese findings suggest that beta bursts reflect structured, task-specific neural dynamics rather than random fluctuations. By using nonlinear features and ML, we introduce a scalable, interpretable framework for burst classification that outperforms traditional methods. This approach advances the understanding of transient beta activity and holds promise for real-time neural decoding in neuroscience and neurotechnology.

neuroscience↗