a-protocol-for-uncovering-neural-mechanisms-neurotherapeutic-effects

A Protocol For Uncovering Neural Mechanisms Of Neurotherapeutic Effects On Electroencephalography Using The Human Neocortical Neurosolver

Electroencephalography (EEG) and electrophysiological methods provide millisecond-resolution biomarkers for central nervous system disorders and are ...

Brown University

The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and complex cells in early visual cortex. However, the computational relevance of oscillatory dynamics experimentally observed in the visual system are typically not considered in artificial neural networks (ANNs). Computational models of neocortical dynamics, on the other hand, rarely take inspiration from computer vision. Here, we combine methods from computational neuroscience and machine learning to implement multiplexing in a simple ANN using oscillatory dynamics. We first trained the network to classify individually presented letters. Post-training, we added temporal dynamics to the hidden layer, introducing refraction in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, indicating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activate sequentially, ordered along the phase of the inhibitory oscillations. Our model implements the idea that inhibitory oscillations segregate competing inputs in time. The results of our simulations pave the way for applications in deeper network architectures and more complicated machine learning problems.

Oscillations in an artificial neural network convert competing inputs into a temporal code.

When exposed to rhythmic stimulation, the human brain displays rhythmic activity across sensory modalities and regions. Given the ubiquity of this phenomenon, how sensory rhythms are transformed into neural rhythms remains surprisingly inconclusive. An influential model posits that endogenous oscillations entrain to external rhythms, thereby encoding environmental dynamics and shaping perception. However, research on neural entrainment faces multiple challenges, from ambiguous definitions to methodological difficulties when endogenous oscillations need to be identified and disentangled from other stimulus-related mechanisms that can lead to similar phase-locked responses. Yet, recent years have seen novel approaches to overcome these challenges, including computational modeling, insights from dynamical systems theory, sophisticated stimulus designs, and study of neuropsychological impairments. This review outlines key challenges in neural entrainment research, delineates state-of-the-art approaches, and integrates findings from human and animal neurophysiology to provide a broad perspective on the usefulness, validity, and constraints of oscillatory models in brain-environment interaction.

Challenges and Approaches in the Study of Neural Entrainment.

Visual attention paradigms have revealed that neural excitability in higher-order visual areas is modulated according to a priority map guiding attention towards task-relevant locations. Neural activity in early visual regions, however, has been argued to be modulated based on bottom-up salience. Here, we combined Magnetoencephalography (MEG) and Rapid Invisible Frequency Tagging (RIFT) in a classic visual search paradigm to study feature-guidance in early human visual cortex. Our results demonstrate evidence for both target boosting and distractor suppression when the participants were informed about the task-relevant and -irrelevant colour (guided search) compared to when they were not (unguided search). These results conceptually replicated using both a magnitude-squared coherence approach and a General Linear Model based on a single-trial measure of the RIFT response. The present findings reveal that feature-guidance in visual search affects neuronal excitability as early as primary visual cortex, possibly contributing to a priority-map-based mechanism.

Guided visual search is associated with target boosting and distractor suppression in early visual cortex.

Visual search models have long emphasised that task-relevant items must be prioritized for optimal performance. While it is known that search efficiency also benefits from active distractor inhibition, the underlying neuronal mechanisms are debated. Neuronal alpha oscillations (7-14 Hz) have been associated with functional inhibition of cortical excitability, as well as distractor suppression in spatial attention and visual working memory tasks. We therefore hypothesised that alpha oscillations similarly support the deselection of distractors in visual search. Using Magnetoencephalography (MEG), we here show that high alpha power before the onset of a complex search display is associated with faster search performance. Crucially, we used a General Linear Model (GLM) approach to control for confounds between alpha power and task duration, ruling out that this result was merely driven by practice effects paired with increased fatigue over time. In addition to spontaneous oscillatory activity, we quantified the cortical excitability to colours of the search stimuli based on Rapid Invisible Frequency Tagging (RIFT) responses. In contrast to our initial hypothesis, increased pre-search alpha power did not correlate with the RIFT response, providing no direct evidence for feature-specific inhibition of distracting stimuli by alpha. Our findings challenge the traditional view of alpha oscillations reducing visual processing, showing instead that increased occipital alpha power can enhance performance in a visual task. We propose that the increase in alpha power may reflect increased top-down control supporting visual search.

Higher baseline alpha power is associated with faster responses in visual search.

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Electroencephalography (EEG) and electrophysiology methods provide millisecond resolution biomarkers for central nervous system disorders and are used to assess treatment-related effects. However, lack of understanding about the neural mechanisms generating such biomarkers impedes the development of diagnostics and therapeutics based on these signals. The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling software that connects localized EEG biomarkers to their multi-scale neural generators. This protocol demonstrates a hypothesis-driven workflow using HNN to test possible neural mechanisms of neurotherapy-induced EEG biomarkers by optimizing parameters to achieve a fit between simulated and empirical current source waveforms. Corresponding multi-scale cell- and circuit-level activity can then be visualized and quantified, providing validation targets for model predictions in follow up empirical studies. An example is provided which shows how to examine the generating mechanisms of the early event-related potential (ERP) components of an auditory evoked response (P1, N1 and P2) and to assess changes following neural circuit modification due to neurotherapeutic administration. This protocol demonstration enables scientists to design simulation experiments to develop testable predictions on how EEG biomarkers reflect neural circuit mechanisms of example therapeutics. A similar protocol can be applied to study disease mechanisms or other therapies.

Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver.

A longstanding debate in consciousness research concerns whether subjective perceptual experiences arise primarily from activity in sensory cortices or rely critically on inferences made in higher-order brain regions. We address this question using a compelling visual illusion (perceptual filling-in) that isolates neural processes underlying transitions from veridical to illusory conscious experience. Using whole-brain magnetoencephalographic imaging and rapid invisible frequency tagging, we tracked cortical dynamics during filling-in and assessed their modulation by microsaccadic eye movements, which are known to delay the illusion. We found that transitions in conscious perception involved two dissociable mechanisms: (i) boundary fading in visual cortex, reflected by increased excitability and reduced alpha-band activity, consistent with a shift in excitation-inhibition balance, and (ii) higher-order perceptual monitoring processes involving motor cortex, indexed by decreased high-alpha and beta-band activity. Microsaccades selectively reset both processes. These findings support a hierarchical framework in which visual and motor systems jointly shape transitions in conscious visual experience.

Article	Year
Oscillations in an artificial neural network convert competing inputs into a temporal code. PLoS computational biology\| PubMed ID: 39259769	2024
Challenges and Approaches in the Study of Neural Entrainment. The Journal of neuroscience : the official journal of the Society for Neuroscience\| PubMed ID: 39358026	2024
Guided visual search is associated with target boosting and distractor suppression in early visual cortex. Communications biology\| PubMed ID: 40500285	2025
Higher baseline alpha power is associated with faster responses in visual search. bioRxiv : the preprint server for biology\| PubMed ID: 40909545	2025
Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex? Nature neuroscience\| PubMed ID: 41387576	2026
Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver. bioRxiv : the preprint server for biology\| PubMed ID: 42039506	2026
Hierarchical brain dynamics supporting visual perceptual transitions. Science advances\| PubMed ID: 42102217	2026

Katharina Duecker

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