A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

Localization of the epileptogenic network from scalp EEG using a patient-specific whole-brain model

Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion's removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies.

Predictive routing emerges from self-supervised stochastic neural plasticity

Neurophysiology studies propose that predictive coding is implemented via alpha/beta (8-30 Hz) rhythms preparing specific pathways to process predicted inputs. This leads to a state of relative inhibition, reducing feedforward gamma (40-90 Hz) rhythms and spiking for predictable inputs. This is called predictive routing model. It is unclear which circuit mechanisms implement this push-pull interaction between alpha/beta and gamma rhythms. To explore how predictive routing is implemented, we developed a self-supervised learning algorithm we call generalized Stochastic Delta Rule (gSDR). Development of this algorithm was necessary because manual tuning of parameters (frequently used in computational modeling) is inefficient to search through a non-linear parameter space that leads to emergence of neuronal rhythms. We used gSDR to train biophysical neural circuits and validated the algorithm on simple objectives. Then we applied gSDR to model observed neurophysiology. We asked the model to reproduce a shift from baseline oscillatory dynamics (~<20Hz) to stimulus induced gamma (~40-90Hz) dynamics recorded in the macaque visual cortex. This gamma oscillation during stimulation emerged by self-modulation of synaptic weights via gSDR. We further showed that the gamma-beta push-pull interactions implied by predictive routing could emerge via stochastic modulation of the local circuitry as well as top-down modulatory inputs to a network. To summarize, gSDR succeeded in training biophysical neural circuits to satisfy a series of neuronal objectives. This revealed the inhibitory neuron mechanisms underlying the gamma-beta push-pull dynamics that are observed during predictive processing tasks in systems and cognitive neuroscience.

Excitatory-inhibitory homeostasis and bifurcation control in the Wilson-Cowan model of cortical dynamics

Although the primary function of excitatory-inhibitory (E-I) homeostasis is the maintenance of mean firing rates, the conjugation of multiple homeostatic mechanisms is thought to be pivotal to ensuring edge-of-bifurcation dynamics in cortical circuits. However, computational studies on E-I homeostasis have focused solely on the plasticity of inhibition, neglecting the impact of different modes of E-I homeostasis on cortical dynamics. Therefore, we investigate how the diverse mechanisms of E-I homeostasis employed by cortical networks shape oscillations and edge-of-bifurcation dynamics. Using the Wilson-Cowan model, we explore how distinct modes of E-I homeostasis maintain stable firing rates in models with varying levels of input and how it affects circuit dynamics. Our results confirm that E-I homeostasis can be leveraged to control edge-of-bifurcation dynamics and that some modes of homeostasis maintain mean firing rates under higher levels of input by modulating the distance to the bifurcation. Additionally, relying on multiple modes of homeostasis ensures stable activity while keeping oscillation frequencies within a physiological range. Our findings tie relevant features of cortical networks, such as E-I balance, the generation of gamma oscillations, and edge-of-bifurcation dynamics, under the framework of firing-rate homeostasis, providing a mechanistic explanation for the heterogeneity in the distance to the bifurcation found across cortical areas. In addition, we reveal the functional benefits of relying upon different homeostatic mechanisms, providing a robust method to regulate network dynamics with minimal perturbation to the generation of gamma rhythms and explaining the correlation between inhibition and gamma frequencies found in cortical networks.

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors-including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.

Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity

Predictive coding is a fundamental function of the cortex. The predictive routing model proposes a neurophysiological implementation for predictive coding. Predictions are fed back from the deep-layer cortex via alpha/beta (8 to 30 Hz) oscillations. They inhibit the gamma (40 to 100 Hz) and spiking that feed sensory inputs forward. Unpredicted inputs arrive in circuits unprepared by alpha/beta, resulting in enhanced gamma and spiking. To test the predictive routing model and its role in consciousness, we collected data from intracranial recordings of macaque monkeys during passive presentation of auditory oddballs before and after propofol-mediated loss of consciousness (LOC). In line with the predictive routing model, alpha/beta oscillations in the awake state served to inhibit the processing of predictable stimuli. Propofol-mediated LOC eliminated alpha/beta modulation by a predictable stimulus in the sensory cortex and alpha/beta coherence between sensory and frontal areas. As a result, oddball stimuli evoked enhanced gamma power, late period (>200 ms from stimulus onset) spiking, and superficial layer sinks in the sensory cortex. LOC also resulted in diminished decodability of pattern-level prediction error signals in the higher-order cortex. Therefore, the auditory cortex was in a disinhibited state during propofol-mediated LOC. However, despite these enhanced feedforward responses in the auditory cortex, there was a loss of differential spiking to oddballs in the higher-order cortex. This may be a consequence of a loss of within-area and interareal spike-field coupling in the alpha/beta and gamma frequency bands. These results provide strong constraints for current theories of consciousness.

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

Mendoza-Halliday, Major et al., 2024 ("The Paper")1 advocates a local field potential (LFP)-based approach to functional identification of cortical layers during "laminar" (simultaneous recordings from all cortical layers) multielectrode recordings in nonhuman primates (NHPs). The Paper describes a "ubiquitous spectrolaminar motif" in the primate neocortex: 1) 75-150 Hz power peaks in the supragranular layers, 2) 10-19 Hz power peaks in the infragranular layers and 3) the crossing point of their laminar power gradients identifies Layer 4 (L4). Identification of L4 is critical in general, but especially for The Paper as the "motif" discovery is couched within a framework whose central hypothesis is that gamma activity originates in the supragranular layers and reflects feedforward activity, while alpha-beta activity originates in the infragranular layers and reflects feedback activity. In an impressive scientific effort, The Paper analyzed laminar data from 14 cortical areas in 2 prior macaque studies and compared them to marmoset, mouse, and human data to further bolster the canonical nature of the motif. Identification of such canonical principles of brain operation is clearly a topic of broad scientific interest. Similarly, a reliable online method for L4 identification would be of broad scientific value for the rapidly increasing use of laminar recordings using numerous evolving technologies. Despite The Paper's strengths, and its potential for scientific impact, a series of concerns that are fundamental to the analysis and interpretation of laminar activity profile data in general, and local field potential (LFP) signals in particular, led us to question its conclusions. We thus evaluated the generality of The Paper's methods and findings using new sets of data comprised of stimulus-evoked laminar response profiles from primary and higher-order auditory cortices (A1 and belt cortex), and primary visual cortex (V1). The rationale for using these areas as a test bed for new methods is that their laminar anatomy and physiology have already been extensively characterized by prior studies, and there is general agreement across laboratories on key matters like L4 identification. Our analyses indicate that The Paper's findings do not generalize well to any of these cortical areas. In particular, we find The Paper's methods for L4 identification to be unreliable. Moreover, both methodological and statistical concerns, outlined below and in the supplement, question the stated prevalence of the motif in The Paper's published dataset. After summarizing our findings and related broader concerns, we briefly critique the evidence from biophysical modeling studies cited to support The Paper's conclusions. While our findings are at odds with the proposition of a ubiquitous spectrolaminar motif in the primate neocortex, The Paper already has, and will continue to spark debate and further experimentation. Hopefully this countervailing presentation will lead to robust collegial efforts to define optimal strategies for applying laminar recording methods in future studies.

Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy

OBJECTIVE

The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy.

METHODS

We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome.

RESULTS

For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves.

CONCLUSIONS

The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy.

SIGNIFICANCE

These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.

The laminar organization of cell types in macaque cortex and its relationship to neuronal oscillations

The canonical microcircuit (CMC) has been hypothesized to be the fundamental unit of information processing in cortex. Each CMC unit is thought to be an interconnected column of neurons with specific connections between excitatory and inhibitory neurons across layers. Recently, we identified a conserved spectrolaminar motif of oscillatory activity across the primate cortex that may be the physiological consequence of the CMC. The spectrolaminar motif consists of local field potential (LFP) gamma-band power (40-150 Hz) peaking in superficial layers 2 and 3 and alpha/beta-band power (8-30 Hz) peaking in deep layers 5 and 6. Here, we investigate whether specific conserved cell types may produce the spectrolaminar motif. We collected laminar histological and electrophysiological data in 11 distinct cortical areas spanning the visual hierarchy: V1, V2, V3, V4, TEO, MT, MST, LIP, 8A/FEF, PMD, and LPFC (area 46), and anatomical data in DP and 7A. We stained representative slices for the three main inhibitory subtypes, Parvalbumin (PV), Calbindin (CB), and Calretinin (CR) positive neurons, as well as pyramidal cells marked with Neurogranin (NRGN). We found a conserved laminar structure of PV, CB, CR, and pyramidal cells. We also found a consistent relationship between the laminar distribution of inhibitory subtypes with power in the local field potential. PV interneuron density positively correlated with gamma (40-150 Hz) power. CR and CB density negatively correlated with alpha (8-12 Hz) and beta (13-30 Hz) oscillations. The conserved, layer-specific pattern of inhibition and excitation across layers is therefore likely the anatomical substrate of the spectrolaminar motif.

Significance Statement

Neuronal oscillations emerge as an interplay between excitatory and inhibitory neurons and underlie cognitive functions and conscious states. These oscillations have distinct expression patterns across cortical layers. Does cellular anatomy enable these oscillations to emerge in specific cortical layers? We present a comprehensive analysis of the laminar distribution of the three main inhibitory cell types in primate cortex (Parvalbumin, Calbindin, and Calretinin positive) and excitatory pyramidal cells. We found a canonical relationship between the laminar anatomy and electrophysiology in 11 distinct primate areas spanning from primary visual to prefrontal cortex. The laminar anatomy explained the expression patterns of neuronal oscillations in different frequencies. Our work provides insight into the cortex-wide cellular mechanisms that generate neuronal oscillations in primates.

A ubiquitous spectrolaminar motif of local field potential power across the primate cortex

The mammalian cerebral cortex is anatomically organized into a six-layer motif. It is currently unknown whether a corresponding laminar motif of neuronal activity patterns exists across the cortex. Here we report such a motif in the power of local field potentials (LFPs). Using laminar probes, we recorded LFPs from 14 cortical areas across the cortical hierarchy in five macaque monkeys. The laminar locations of recordings were histologically identified by electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Laminar recordings from additional species showed that the spectrolaminar pattern is highly preserved among primates-macaque, marmoset and human-but more dissimilar in mouse. Our results suggest the existence of a canonical layer-based and frequency-based mechanism for cortical computation.

Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity

Predictive coding is a fundamental function of the cortex. The predictive routing model proposes a neurophysiological implementation for predictive coding. Predictions are fed back from deep-layer cortex via alpha/beta (8-30Hz) oscillations. They inhibit the gamma (40-100Hz) and spiking that feed sensory inputs forward. Unpredicted inputs arrive in circuits unprepared by alpha/beta, resulting in enhanced gamma and spiking. To test the predictive routing model and its role in consciousness, we collected data from intracranial recordings of macaque monkeys during passive presentation of auditory oddballs (e.g., AAAAB) before and after propofol-mediated loss of consciousness (LOC). In line with the predictive routing model, alpha/beta oscillations in the awake state served to inhibit the processing of predictable stimuli. Propofol-mediated LOC eliminated alpha/beta modulation by a predictable stimulus in sensory cortex and alpha/beta coherence between sensory and frontal areas. As a result, oddball stimuli evoked enhanced gamma power, late (> 200 ms from stimulus onset) period spiking, and superficial layer sinks in sensory cortex. Therefore, auditory cortex was in a disinhibited state during propofol-mediated LOC. However, despite these enhanced feedforward responses in auditory cortex, there was a loss of differential spiking to oddballs in higher order cortex. This may be a consequence of a loss of within-area and inter-area spike-field coupling in the alpha/beta and gamma frequency bands. These results provide strong constraints for current theories of consciousness.

Significance statement

Neurophysiology studies have found alpha/beta oscillations (8-30Hz), gamma oscillations (40-100Hz), and spiking activity during cognition. Alpha/beta power has an inverse relationship with gamma power/spiking. This inverse relationship suggests that gamma/spiking are under the inhibitory control of alpha/beta. The predictive routing model hypothesizes that alpha/beta oscillations selectively inhibit (and thereby control) cortical activity that is predictable. We tested whether this inhibitory control is a signature of consciousness. We used multi-area neurophysiology recordings in monkeys presented with tone sequences that varied in predictability. We recorded brain activity as the anesthetic propofol was administered to manipulate consciousness. Compared to conscious processing, propofol-mediated unconsciousness disrupted alpha/beta inhibitory control during predictive processing. This led to a disinhibition of gamma/spiking, consistent with the predictive routing model.

Physiological modeling of the BOLD signal and implications for effective connectivity: A primer

In this primer, I provide an overview of the physiological processes that contribute to the observed BOLD signal (i.e., the generative biophysical model), including their time course properties within the framework of the physiologically-informed dynamic causal modeling (P-DCM). The BOLD signal is primarily determined by the change in paramagnetic deoxygenated hemoglobin, which results from combination of changes in oxygen metabolism, and cerebral blood flow and volume. Specifically, the physiological origin of the so-called BOLD signal "transients" will be discussed, including the initial overshoot, steady-state activation and the post-stimulus undershoot. I argue that incorrect physiological assumptions in the generative model of the BOLD signal can lead to incorrect inferences pertaining to both local neuronal activity and effective connectivity between brain regions. In addition, I introduce the recent laminar BOLD signal model, which extends P-DCM to cortical depths-resolved BOLD signals, allowing for laminar neuronal activity to be determined using high-resolution fMRI data.

Spatiotemporal dynamics across visual cortical laminae support a predictive coding framework for interpreting mismatch responses

Context modulates neocortical processing of sensory data. Unexpected visual stimuli elicit large responses in primary visual cortex (V1)-a phenomenon known as deviance detection (DD) at the neural level, or "mismatch negativity" (MMN) when measured with EEG. It remains unclear how visual DD/MMN signals emerge across cortical layers, in temporal relation to the onset of deviant stimuli, and with respect to brain oscillations. Here we employed a visual "oddball" sequence-a classic paradigm for studying aberrant DD/MMN in neuropsychiatric populations-and recorded local field potentials in V1 of awake mice with 16-channel multielectrode arrays. Multiunit activity and current source density profiles showed that although basic adaptation to redundant stimuli was present early (50 ms) in layer 4 responses, DD emerged later (150-230 ms) in supragranular layers (L2/3). This DD signal coincided with increased delta/theta (2-7 Hz) and high-gamma (70-80 Hz) oscillations in L2/3 and decreased beta oscillations (26-36 Hz) in L1. These results clarify the neocortical dynamics elicited during an oddball paradigm at a microcircuit level. They are consistent with a predictive coding framework, which posits that predictive suppression is present in cortical feed-back circuits, which synapse in L1, whereas "prediction errors" engage cortical feed-forward processing streams, which emanate from L2/3.

Spatiotemporal dynamics across visual cortical laminae support a predictive coding framework for interpreting mismatch responses

Context modulates neocortical processing of sensory data. Unexpected visual stimuli elicit large responses in primary visual cortex (V1) -- a phenomenon known as deviance detection (DD) at the neural level, or "mismatch negativity" (MMN) when measured with EEG. It remains unclear how visual DD/MMN signals emerge across cortical layers, in temporal relation to the onset of deviant stimuli, and with respect to brain oscillations. Here we employed a visual "oddball" sequence - a classic paradigm for studying aberrant DD/MMN in neuropsychiatric populations - and recorded local field potentials in V1 of awake mice with 16-channel multielectrode arrays. Multiunit activity and current source density profiles showed that while basic adaptation to redundant stimuli was present early (50ms) in layer 4 responses, DD emerged later (150-230ms) in supragranular layers (L2/3). This DD signal coincided with increased delta/theta (2-7Hz) and high-gamma (70-80Hz) oscillations in L2/3 and decreased beta oscillations (26-36hz) in L1. These results clarify the neocortical dynamics elicited during an oddball paradigm at a microcircuit level. They are consistent with a predictive coding framework, which posits that predictive suppression is present in cortical feed-back circuits, which synapse in L1, while "prediction errors" engage cortical feed-forward processing streams, which emanate from L2/3.