Biology subjects

Riera, J. J.

Publications and source records attributed to Riera, J. J..

3 recordsLinked to original sources

bioRxiv2022

Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates

Event-related potentials (ERP) are among the most widely measured indices for studying human cognition. While their timing and magnitude provide valuable insights, their usefulness is limited by our understanding of their neural generators at the circuit level. Inverse source localization offers insights into such generators, but their solutions are not unique. To address this problem, scientists have assumed the source space generating such signals comprises a set of discrete equivalent current dipoles, representing the activity of small cortical regions. Based on this notion, theoretical studies have employed forward modeling of scalp potentials to understand how changes in circuit-level dynamics translate into macroscopic ERPs. However, experimental validation is lacking because it requires in vivo measurements of intracranial brain sources. Laminar local field potentials (LFP) offer a mechanism for estimating intracranial current sources. Yet, a theoretical link between LFPs and intracranial brain sources is missing. Here, we present a forward modeling approach for estimating mesoscopic intracranial brain sources from LFPs and predict their contribution to macroscopic ERPs. We evaluate the accuracy of this LFP-based representation of brain sources utilizing synthetic laminar neurophysiological measurements and then demonstrate the power of the approach in vivo to clarify the source of a representative cognitive ERP component. To that end, LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. We show that area V4 generates dipoles through layer-specific transsynaptic currents that biophysically recapitulate the ERP component through the detailed forward modeling. The constraints imposed on EEG production by this method also revealed an important dissociation between computational and biophysical contributors. As such, this approach represents an important bridge from the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp. HighlightsO_LICognitive EEG production was accurately modeled from empirically measured cortical activity in awake macaques. C_LIO_LIV4 laminar activity plausibly generates the attention-related signal indexed by the EEG. C_LIO_LIModels demonstrate the importance of biophysical geometry in cognitive EEG production. C_LI

neuroscience↗

bioRxiv2020

A Minimal Biophysical Model of Neocortical Pyramidal Cells: Implications for Frontal Cortex Microcircuitry and Field Potential Generation

Ca2+ spikes initiated in the apical dendrites of layer-5 pyramidal cells (PC) underlie nonlinear dynamic changes in the gain of cellular response, which is critical for top-down cognitive control. Detailed models with several compartments and dozens of ionic channels have been proposed to account for this Ca2+ spike-dependent gain with its associated critical frequency. However, current models do not account for all known Ca2+-dependent features. Previous attempts to include more features have required increasing complexity, limiting their interpretability and utility for studying large population dynamics. We present a minimal 2-compartment biophysical model, overcoming these limitations. In our model, a basal-dendritic/somatic compartment included typical Na+ and K+ conductances, while an apical-dendritic/trunk compartment included persistent Na+, hyperpolarization-activated cation (Ih), slow inactivation K+, muscarinic K+, and Ca2+ L-type. The model replicated the Ca2+ spike morphology and its critical frequency plus three other defining features of layer-5 PC synaptic integration: linear frequency-current relationships, backpropagation-activated Ca2+ spike firing, and a shift in the critical frequency by blocking Ih. Simulating 1,000 synchronized layer-5 PCs, we reproduced the current source density patterns evoked by Ca2+-spikes both with and without Ih current. Thus, a 2-compartment model with five non-classic ionic currents in the apical-dendrites reproduces all features of these neurons. We discuss the utility of this minimal model to study the microcircuitry of agranular areas of the frontal lobe involved in cognitive control and responsible for event-related potentials such as the error-related negativity. Significance StatementA tractable model of layer-5 pyramidal cells replicates all known features crucial for distal synaptic integration in these neurons. This minimal model enables new multi-scale investigations of microcircuit functions with associated current flows measured by intracranial local field potentials. It thus establishes a foundation for the future computational evaluation of scalp electroencephalogram signatures imprinted by Ca2+ spikes in pyramidal cells, a phenomenon underlying many brain cognitive processes.

neuroscience↗

bioRxiv2018

Identification negative BOLD responses using windkessel models

Alongside positive BOLD responses (PBR), a variety of negative BOLD responses (NBR) with distinct underlying mechanisms also occur. We identify five mechanisms of NBR: i) local/lateral/contralateral inhibition (LCI), ii) neuronal disruption of network activity (NDA), iii) altered balance of neuro-metabolic/vascular couplings (ANC), iv) arterial blood stealing (ABS), and v) venous blood backpressure (VBB). Detecting and classifying these mechanisms from BOLD signals is pivotal in understanding normal/pathological brain functions. This requires models and parameters with anatomical/functional interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a windkessel model with viscoelastic compliance as well as dynamics of both neuronal and tissue/blood O2 to investigate the generation, detection, classification and interpretation of the BOLD hemodynamic response functions (HRF) of above mechanisms. Firstly, we evaluated the use of the general linear model to detect simulated NBRs. Secondly, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying a new HRF. Crossvalidation indicates NDA and ANC can accurately be classified solely from fMRI BOLD signals; while LCI, ABS and VBB might require additional imaging modalities. Thirdly, we demonstrated that estimators of the model parameters determinant in the NBRs formation are accurate, and precise to certain resolutions. Finally, we successfully applied our detection/classification/estimation methodology to EEG-fMRI data in a clinical situation where several of these mechanisms could coexist. We believe that the proper identification and interpretation of NBR mechanisms have important clinical and cognitive implications in fMRI studies.

neuroscience↗